About the Journal

Abstract

One year after the birth of the International Journal of MCH and AIDS (IJMA), we continue to share the passion to document, and shine the light on the myriads of global health issues that debilitate developing countries.

Although the focus of IJMA is on the social determinants of health and disease as well as on the disparities in the burden of communicable and non-communicable diseases affecting infants, children, women, adults, and families in developing countries, we would like to encourage our fellow researchers and policy makers in both the developing and developed countries to consider submitting work that examines cross-national variations in heath and social inequalities.

Such a global focus allows us to identify and understand social, structural, developmental, and health policy determinants underlying health inequalities between nations.

Global assessment of health and socioeconomic patterns reaffirms the role of broader societal-level factors such as human development, gender inequality, gross national product, income inequality, and healthcare infrastructure as the fundamental determinants of health inequalities between nations.

This is also confirmed by our analysis of the WHO data that shows a strong negative association between levels of human development and infant and maternal mortality rates.

Focusing on socioeconomic, demographic, and geographical inequalities within a developing country, on the other hand, should give us a sense of how big the problem of health inequity is within its own borders.

Such an assessment, then, could lead to development of policy solutions to tackle health inequalities that are unique to that country.

Key Words:

International Journal of MCH and AIDS • IJMA •Social determinants • Non-communicable diseases • Communicable diseases • Health Disparities • Human development • Social inequality.

Almost one year ago, we were on a lunchtime walk in the outskirts of the nation’s capital, Washington, DC. Amidst the ruffles of dry leaves, the birds chirruped. Some lawn mowers vibrated as they chewed grasses in this usually quiet neighborhood. As is the unwritten ‘rule’ for our walks, we joked, we talked about our current projects, our planned work, and the opportunities and the challenges for the future. On this rather chilly day, our discussion somehow veered into how we both can utilize our successful careers to benefit our global ancestry. We had spent our early years in developing countries: Gopal in India, Asia and Romuladus in Nigeria, Africa. As this discussion unveiled, we saw a synchronous passion to give back not only to Asia and Africa but also to the entire developing countries. A few projects came up. After weighing the pros and cons, the idea of the International Journal of MCH and AIDS (IJMA) was preeminent. We agreed that IJMA was a fertile ground to cultivate a global intellectual coalition to highlight the issues in the hinterlands of developing countries and to offer a platform to offer practical policy suggestions to address these issues. Above all, IJMA’s idea went beyond the usual talk about “giving back.” The idea was laden with action.

One year after this lunchtime walk and the birth of IJMA, we continue to share the passion to document, and shine the light on the myriads of global health issues that debilitate developing countries. Although the focus of IJMA is on the social determinants of health and disease as well as on the disparities in the burden of communicable and non-communicable diseases affecting infants, children, women, adults, and families in developing countries, we would like to encourage our fellow researchers and policy makers in both the developing and developed countries to consider submitting work that examines cross-national variations in heath and social inequalities. Such a global focus allows us to identify and understand social, structural, developmental, and health policy determinants underlying health inequalities between nations ^{[1, 2]}. Global assessment of health and socioeconomic patterns reaffirms the role of broader societal-level factors such as human development, gender inequality, gross national product, income inequality, and healthcare infrastructure as the fundamental determinants of health inequalities between nations. This is also confirmed by our analysis of the WHO data in Figure 1 that shows a strong negative association between levels of human development and infant and maternal mortality rates (γ > -0.85) ^[3].

Focusing on socioeconomic, demographic, and geographical inequalities within a developing country, on the other hand, should give us a sense of how big the problem of health inequity is within its own borders. Such an assessment, then, could lead to development of policy solutions to tackle health inequalities that are unique to that country [2].

Figure 1. Relationship between Human Development Index and Infant and Maternal Mortality Rates, 167 Countries, 2008-2010

Source: World Health Statistics 2011 and Human Development Report 2011.

The papers in this inaugural issue—among the best you can get in any journal of its ilk—bail us out. In their commentary, Hairston and experts from the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) make the case for the integration of maternal, sexual, and reproductive health services as a foundation for eliminating pediatric HIV in developing countries ^[4]. While the newly-launched UN Global Plan Towards Elimination of New HIV Infections renews the momentum around the elimination of pediatric HIV, it presents a unique opportunity to place women at its center by linking research, policies, and programs to sexual reproductive and maternal health, writes the experts. They call on stakeholders to utilize the synergies between HIV and sexual and reproductive health programs to address the diverse health needs of women and in eventually eliminating pediatric HIV worldwide ^[4]. As the leading non-profit organization in the field of MCH and HIV/AIDS, a substantive presence in developing countries, and an A rating from US CharityWatch, no other organization understands HIV/AIDS and MCH in developing countries better than EGPAF. And their counsel must be taken seriously.

In their article on global inequalities in cervical cancer, Singh and colleagues look at global disparities in cervical cancer, which is not only a major public health problem in many low- and middle-income countries but is also the number one cancer site and a major killer of women in reproductive ages in East and Sub-Saharan Africa and South Asia ^[5]. They go on to show that inequalities in cervical cancer rates among 184 countries are strongly linked to disparities in human development, social inequality, and living standards and argue for improvements in women’s social status and wider availability of preventive cancer screening services as the primary means to achieving reductions in cervical cancer rates ^[5].

Any comparison of China and India stirs tremendous passion, interest, and debate among academics, policy makers, and social and political commentators around the world. In their article, which appears to be the first of its kind, Singh and Liu present a powerful analysis of how health, socioeconomic, and development patterns have changed in India and China during the past six decades ^[6]. According to the authors, although the two countries started out on a similar footing in the late 1940s, China appears to be doing better than India on several key health measures 60 years later. Because of more rapid improvements in health among the Chinese, the health gap between India and China, particularly in infant, child and maternal mortality and life expectancy, is wider now than ever before ^[6].

Child mortality rates continue to be the highest in the WHO African Region and South-East Asia Region ^[3]. Neonatal mortality, deaths during the first 28 days of life, accounts for 40% of all deaths among children under 5 years old globally, with Tanzania’s rate of 34 per 1,000 live births being among the highest in Africa ^[3]. The article by Ajaari and colleagues examines the impact of place of delivery on neonatal mortality in rural Tanzania using a longitudinal population-based database, showing nearly twice the risk of neonatal mortality among births delivered outside health facilities compared with those delivered in health facilities attended by trained medical staff ^[7]. Despite a marked improvement in child survival over time, India’s current under-five mortality rate of 66 per 1,000 live births remains higher than the global average ^[3]. In terms of the absolute burden, India accounts for nearly one-fourth of all under-5 deaths globally ^[3]. Given the significance of child mortality as a major public health problem in India, Mani and colleagues determine the effects of proximate or programmable determinants on under-five mortality in rural India by applying Cox-frailty and other hazard regression models to the data from the National Family Health Survey. Their analysis identifies maternal age, place of delivery, parity and birth spacing, infant’s birth weight, and breastfeeding as significant determinants of under-five mortality ^[8].

Undernutrition among children is a major public health problem in many developing countries and the problem of underweight and stunting (low height-for-age) is particularly acute in Africa and Asia ^[3]. Using primary data collected for 405 school-aged children in Akwa Ibon State of Nigeria, Opara and colleagues examine the impact of intestinal parasitic infection on nutritional status of children ^[9]. According to their study, more than two-thirds of the Nigerian children in the sample were infected with at least one intestinal parasite, with the risk of stunting, wasting, and underweight being higher among children infected with intestinal parasites ^[9].

In their mixed-method paper, Macherera and colleagues make some stunning revelations ^[10]. Caregivers and guardians have become the newest but most intricate hindrance to access to antiretroviral therapy (ART) for children living HIV/AIDS in rural Zimbabwe. Using data and information from focus groups conducted in a rural clinic and community, the authors demonstrate that while children growing up with HIV/AIDS continue to deal with stigma, discrimination, and rejection, they are embroiled with an unwinnable war within as their guardians are now using their ART medications to take care of themselves first. This paper highlights the urgent need to provide cross-generational access to ART especially in developing countries where targeting one group, like children, neglects the needs of other groups ^[10].

IJMA is founded on a pedestal of rigor and transparency. We conduct blind peer-reviews and select papers based on peer assessment of the merit of each contribution to advancing the field of MCH and HIV/AIDS both regionally and worldwide. With a robust editorial board reflecting the best and finest drawn from the six World Health Organization regions, our editorial board members are key to setting global health research agenda for our journal. Manuscripts by our editors pass through the highest level of our blind peer-review process conducted by experts in the field and from outside the board or the regions of the authors. We thank our friends and colleagues who have joined this coalition. From Oman to Nigeria, from India to Australia, we hope that the IJMA idea will outlive each and every one of us. We hope that IJMA will lay the foundation for highlighting and addressing global health challenges of developing countries by developing country scientists and policy makers. Happy reading!

References

1. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
2. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
3. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
4. Hairston AF, Bobrow EA, Pitter CS. Towards the elimination of pediatric HIV: Enhancing maternal, sexual, and reproductive health services. International Journal of MCH and AIDS. 2012; 1(1):6-13.
5. Singh GK, Azuine RE, Siahpush M. Global health inequalities in cervical cancer incidence and mortality are linked to deprivation, low socioeconomic status, and human development. International Journal of MCH and AIDS. 2012; 1(1):14-27.
6. Singh GK, Liu J. Health improvements have been more rapid and widespread in China than in India: A comparative analysis of health and socioeconomic trends from 1960 to 2011. International Journal of MCH and AIDS. 2012; 1(1):28-45.
7. Ajaari J, Masanja H, Abokyi SA, Owusu-Agyei S. Impact of place of delivery on neonatal mortality in rural Tanzania. International Journal of MCH and AIDS. 2012; 1(1):46-55.
8. Mani K, Dwivedi SN, Pandey RM. Determinants of under-five mortality in Rural Empowered Action Group States in India: An application of Cox frailty model. International Journal of MCH and AIDS. 2012; 1(1):56-67.
9. Opara KN, Udoidung NI, Opara DC, Okon OE, Edosomwan UE, Udoh AJ. The impact of intestinal parasitic infections on the nutritional status of rural and urban school-aged children in Nigeria. International Journal of MCH and AIDS. 2012; 1(1):68-75.
10. Macherera M, Moyo L, Ncube M, Gumby A. Social, cultural, and environmental challenges faced by children on antiretroviral therapy in Zimbabwe: A mixed-method study. International Journal of MCH and AIDS. 2012; 1(1):76-83.

Abstract

Almost 10 years ago, the United Nations adopted a comprehensive, four-pronged approach for the prevention of mother-to-child transmission of HIV (PMTCT). Despite all four prongs being central to the elimination of pediatric HIV, and the health of the mother being critical to reaching this goal, PMTCT programs have historically focused more attention on preventing HIV transmission from mother to child (prong 3) than on preventing HIV in women of reproductive age (prong 1) and preventing unintended pregnancies in women living with HIV (prong 2). In this commentary, experts from the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) argue that within the context of efforts to eliminate pediatric HIV, there are many ways to keep women living with HIV alive and at the center of the response to the global epidemic. One of the ways to do this is to enhance maternal and sexual and reproductive health (SRH) services. Within the elimination agenda, integration and linkages between PMTCT and comprehensive SRH services can keep mothers alive and at the center of the response. The commentary highlights some of the foundation’s global health work supporting, evaluating and enhancing maternal and SRH services provided to women living with HIV and proposes concrete actions for donors, researchers, policy makers and program implementers to further enhance maternal and SRH services within the context of PMTCT. If keeping women living with HIV is an integral component of the elimination of pediatric HIV agenda, maternal and SRH research, policies and programs need to be strengthened within the context of PMTCT. Donor funding and priorities for PMTCT also need to be more supportive of primary prevention of HIV infection among women of childbearing age and preventing unintended pregnancies among women living with HIV.

Key Words:

HIV/AIDS • Elimination of pediatric HIV • PMTCT • Reproductive and sexual health • Maternal health

Introduction

In 2002, the United Nations (UN) adopted a four-pronged approach to the prevention of mother-to-child transmission of HIV (PMTCT). These four prongs represent the cornerstones of comprehensive PMTCT service delivery: primary prevention of HIV infection among women of childbearing age (prong 1), preventing unintended pregnancies among women living with HIV (prong 2), preventing HIV transmission from a woman living with HIV to her infant (prong 3), and treatment, care, and support to women living with HIV, their children, and families (prong 4) ^[1]. Despite all four prongs being central to the elimination of pediatric HIV, and the health of women being important to reaching this goal, PMTCT programs have historically focused more attention on identifying HIV infection among pregnant women to prevent HIV transmission from mother to child (prong 3) than on preventing HIV in women of reproductive age (prong 1) and preventing unintended pregnancies in women living with HIV (prong 2) ^[2-4]. Figure 1 shows estimates of new HIV infections among children in 25 countries from 2009 until 2015 based on scenarios in which different combinations of prongs 1 through 3 are used. ^[5] These estimates show a projected 79% reduction in new infections by 2015 using a combination of prongs 1, 2, and 3 and limiting breastfeeding to 12 months, compared with a 60% reduction using prong 3 alone. The estimates shown in Figure 1 highlight how important prongs 1 and 2 are in reaching the elimination of pediatric HIV and the estimated impact of increasing antiretroviral coverage for women living with HIV.

Figure 1. New HIV Infections Among Children in 25 Countries 2009-2015 By PMTCT Prongs ^[5-6]

Source: Mahy et al. ^[5] and The Inter-Agency Task Team for Prevention and Treatment of HIV Infection in Pregnant Women Mothers and Their Children ^[6].

Global Plan Towards Eliminating New HIV Infections

There has been a concerted effort over the past few years to integrate and link (see Box 1 for definitions) maternal and child health (MCH) and sexual and reproductive health (SRH) services with PMTCT services to more comprehensively address all four prongs and increase the focus on women’s health. At the UN Special Session on HIV in 2011 (UNGASS 2011), world leaders launched the Global Plan Towards the Elimination of New HIV Infections Among Children by 2015 and Keeping their Mothers Alive (the Global Plan), which outlines how countries can ultimately reach the goal of eliminating pediatric HIV ^[7]. The Global Plan not only recognizes the importance of maternal health in its title, but the first of the four overarching principles of the Global Plan is to keep women living with HIV at the center of the response. The Global Plan describes this as including but not limited to; mothers and children having access to optimal HIV prevention and treatment regimens based on the latest guidelines, women living with HIV having access to family planning services and commodities as well as the meaningful participation of women in developing and implementing programs to tackle the barriers to services and to work as partners in providing care and address HIV and gender-related discrimination that impedes service access and uptake as well as client retention. There are also defined targets of reducing maternal deaths by 50%, reducing HIV incidence in women aged 15-49 by 50%, and reducing unmet need for family planning among women living with HIV to zero ^[7]. The Preventing Unintended Pregnancies Strategic Framework has been developed by the Inter-Agency Task Team for Prevention and Treatment of HIV Infection in Pregnant Mothers and Their Children (IATT) in support of national efforts to translate the Global Plan into action ^[6]. Donors are also calling for HIV services to be integrated into SRH services, and encouraging HIV implementers to play active roles in evaluating, coordinating, and leveraging support for broader maternal health service delivery capacity ^[8]. This shift is applauded, but in order to keep mothers living with HIV at the center of the response, there needs to be an increased focus on maternal health and SRH within the context of PMTCT.

In this commentary we recognize that, within the context of efforts to eliminate pediatric HIV, there are many ways to keep women living with HIV alive and at the center of the response and argue that one of the ways to do this is to enhance maternal and sexual and reproductive health (SRH) services. We describe that, within the elimination agenda, integration and linkages between PMTCT and comprehensive SRH services can keep mothers alive (as shown in Figure 2 below) and at the center of the response. We also highlight some of the work that the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) is supporting and evaluating to enhance maternal and SRH services provided to women living with HIV. We conclude by proposing concrete actions for donors, researchers, policy makers and program implementers to further enhance maternal and SRH services within the context of PMTCT.

Figure 2. PMTCT Prongs and Integration and Linkages to SRH Services’ Contribution to the Elimination of Pediatric HIV and Keeping Mothers Alive

Background

Maternal Health Must be the Center of Eliminating Pediatric HIV

Promoting maternal health among women living with HIV is central to keeping HIV-positive mothers living with HIV alive and at the center of the agenda to eliminate pediatric HIV. Just 58 countries contribute to 91% of the global burden of maternal mortality; 38 of these countries are in Africa ^[9] and 17 of these countries are among the 22 focus countries identified in the Global Plan as having the highest estimated numbers of pregnant women living with HIV ^[7]. In fact, the vast majority of HIV-associated maternal deaths (86%-92%) occur in sub-Saharan Africa and more recent assessments of trends in maternal mortality indicate that HIV has become a leading cause of death during pregnancy and the postpartum period in countries with a high HIV prevalence ^[10]. The association between skilled attendance at birth and reduced maternal mortality, coupled with strategies a skilled birth attendant (SBA) can use to reduce both the incidence and severity of complications during labor and delivery constitute sufficient grounds to recommend that an SBA be present for every birth ^[11]. However, many births in resource-limited settings, including settings with a high burden of HIV, are not attended by an SBA. It is estimated that there will be between 130 and 180 million non-SBA births in South Asia and sub-Saharan Africa between 2011 and 2015, and that 90% of these will occur in rural areas ^[12].

Family planning addresses needs of women within the context of PMTCT because it allows women and men to attain their desired number of children and determine the spacing of pregnancies ^[13]. Although many HIV-positive women and couples wish to manage their reproductive options, evidence suggests that unmet need for family planning may be higher among HIV-positive women than the general population ^[14]. Studies in Côte d’Ivoire, Rwanda, South Africa, and Uganda have found levels of unintended pregnancies among HIV-positive women ranging from 51% to 91%. ^[15-18]. Halperin and colleagues conducted an analysis of data from the 2008 UNAIDS report on the AIDS epidemic that suggests family planning is cost-effective for HIV prevention as well as for preventing pregnancies among women living with HIV ^[2]. The direct health benefits of meeting the need for both family planning and maternal and newborn health services (i.e., a continuum of care to help couples plan their pregnancies and provide timely antenatal, delivery, and postpartum services, including urgent care for complications that arise among women and newborns) would lead to health system improvements that could provide lifesaving care to women and their newborns while at the same time strengthening services for other urgent medical needs [^19].

Linking Reproductive Age Women to Sexual and Reproductive Health Services in Developing Countries

Linking HIV-positive women of reproductive age in resource-limited settings and their partners to SRH services is not only an important part of keeping mothers at the center of the agenda to eliminate pediatric HIV, but is also key to ensuring that women remain alive and healthy. A systematic review conducted by WHO and several other agencies in 2009 found that bi-directional linkages between SRH- and HIV-related policies and programs can lead to better access among people living with HIV to SRH services that are tailored to their needs, reduction of HIV-related stigma and discrimination, and enhanced program effectiveness and efficiency ^[20]. SRH linkages that are particularly relevant to the health needs of women within the context of PMTCT include linkages to syphilis and cervical cancer screening. In addition to the morbidity and mortality associated with syphilis and congenital syphilis, co-infection of syphilis and HIV among pregnant women is a significant risk factor for vertical HIV transmission ^{[4, 7]}. It should also be noted that 83% of new cases of cervical cancer and 85% of deaths from the disease occur in resource-limited settings ^[21]. A study in Kenya found that young women with cervical cancer were more often HIV infected than women with fibroids of the same age group ^[22].

EGPAF’S Commitment to Maternal and Sexual and Reproductive Health

A variety of organizations and institutions have committed to, and are engaged in addressing maternal health and SRH within the context of PMTCT. It should however be noted that one of the key challenges related to increasing focus on maternal health and comprehensive SRH services within the elimination of pediatric HIV agenda is that implementing partners working in these different areas may have difficulty integrating their programs because US-funded reproductive health and HIV projects and programs are often separated, with programs using very different results and reporting mechanisms.

EGPAF seeks to prevent pediatric HIV infection and to eliminate pediatric HIV through research, advocacy, and prevention and treatment programs. As such, EGPAF is committed to promoting integration and linkage of SRH and HIV services in all the countries that it supports (see figure 3 below), and to promoting these approaches through its close partnerships with ministries of health and a variety of national and international public health organizations and institutions. EGPAF:

1. Favors policies that affirm that all women and men, regardless of HIV status, have the right to determine the number and spacing of their children;
2. Favors increasing access to prevention, screening, and treatment of sexually transmitted infections (STIs) as part of a comprehensive package of reproductive health services;
3. Favors increasing access to high-quality, safe, and appropriate obstetric care and services to improve maternal and child health and survival; and
4. Favors increasing the availability of high-quality, safe, and comprehensive care for all women accessing services, and places a special emphasis on ensuring that women living with HIV receive the antenatal care (ANC) and HIV services they need for their own health and the health of their children.

The positions summarized here were developed by members of the Foundation’s technical advisory group on MCH/HIV linkages and integration (the M-TAG), which was made up of Foundation country and global staff. These positions are intended to guide a consistent Foundation-wide approach and framework for addressing this issue and do not supersede the national policies and guidelines of the countries in which the Foundation works.

Figure 3. Countries where EGPAF Works in Sub-Saharan Africa

A variety of country-specific efforts point to EGPAF’s growing involvement in this important area. EGPAF, with support from PEPFAR (The US President’s Emergency Plan for AIDS Relief) and various other donors, works in a variety of countries to support increased facility-based deliveries assisted by Skilled Birth Attendants (SBAs), earlier and more frequent antenatal care visits, primary prevention of HIV among pregnant women, provision of family planning (FP) at all points across the continuum of care, and provision of HIV care and treatment (including long-term antiretroviral therapy) for HIV-positive women. Some specific examples of how EGPAF links women to maternal health and SRH services are highlighted below.

In close collaboration with ministries of health in Tanzania, Cameroon and Lesotho, EGPAF is supporting the scale-up of cervical cancer screening and the roll-out of the human papillomavirus (HPV) vaccine though funding from USAID (in Tanzania and Lesotho) and CDC (in Cameroon). In Lesotho, EGPAF is working with the Ministry of Health and Social Welfare (MOHSW) to establish a center of excellence for women living with HIV at the Senkatana Center in Maseru. The center offers screening for cervical cancer alongside HIV care and treatment services. EGPAF will be training health care workers and students in the screening and prevention of cervical cancer. EGPAF and other partners have also worked with the MOHSW in developing national HPV guidelines to support the national roll-out of the vaccine.

With support from the World Health Organization (WHO) Special Program for Research and Training in Tropical Diseases’ Sexually Transmitted Disease Diagnostic Initiative and the London School of Hygiene and Tropical Medicine, EGPAF conducted a study in 2009/2010 that found that introducing rapid syphilis testing for pregnant women at antenatal clinics (ANC) sites offering PMTCT services in Zambia and Uganda is feasible, accepted by health-care providers, and cost effective. The findings of the study have informed guideline development, training of health care workers, and the roll-out of these services in both Zambia and Uganda. ^[23-24]

EGPAF’s Integration of HIV and MCH Services in Developing Countries

EGPAF has a number of on-going studies exploring integration of HIV and MCH services. In Tanzania, the United Nation’s Population Fund (UNFPA) has supported EGPAF to implement and evaluate an FP/HIV integration model in Tanzania’s Shinyanga Region. The study looks at the co-location of FP (i.e., screening, counseling, and commodities) and HIV care and treatment services at local Care and Treatment Centers (CTC). In Kenya, the Gates Foundation supported EGPAF to conduct an observational prospective cohort study that compared infant follow-up results, when services for HIV-exposed children were provided within Maternal and Child Health (MCH) clinics or in specialized HIV Comprehensive Care Clinics (CCC), in two District hospitals in Kenya. [25] HIV services integrated in MCH in one district hospital led to better follow-up of HIV-exposed infants than in a similar hospital with services provided separately in a CCC.

Increasing Focus on Maternal And Sexual Reproductive Health Within the Pediatric HIV Elimination Agenda

Those involved with HIV, MCH, and SRH program implementation at a variety of levels can take concrete actions to enhance maternal and SRH in the context of PMTCT. Recommendations for various stakeholders positioned to lead these efforts are as follows:

Donors

1. Increase funding for development, evaluation and scale-up of program models aimed at linkage and integration of FP, SRH, MCH, and HIV services.
2. Ensure that funding for PMTCT programs supports comprehensive health services for pregnant women and linkages to SRH, FP and MCH services.

Researchers

1. • Evaluate the effectiveness, efficiency, and impact of integrated programs ^[4].
2. • Adopt the recommendations contained in Pregnancy Intentions of HIV-Positive Women: Forwarding the Research Agenda— a report issued by the Harvard School of Public Health Program on International Health and Human Rights following a March 2010 conference and symposium—with a specific focus on the following recommendations^[26]:
   1. Conduct additional studies to assess HIV-positive women’s contraceptive needs, including identifying barriers to acquiring and using various forms of contraception to ensure availability and acceptability
   2. Conduct studies comparing the appropriateness and effectiveness of various service delivery models, including those that integrate HIV care with SRH services.

Policy Makers

1. Ensure that strategies and activities addressing the first two prongs of PMTCT are included (with appropriate resource allocations) in the development and implementation of national plans and strategies for the elimination of pediatric HIV.
2. Ensure that ministries of health, donors, and implementing partners support and adopt the Preventing Unintended Pregnancies Strategic Framework that supports the Global Plan ^[27].

Program Implementers

1. Incorporate evidence-informed approaches to maternal and SRH service linkages within PMTCT programs. A review of the evidence by Gay and colleagues found that successful and promising interventions to improve the SRH and rights of women living with HIV include:
   1. providing contraceptives and family planning counseling as part of HIV services;
   2. ensuring early postpartum visits provide family planning and HIV information and services;
   3. providing youth-friendly services;
   4. providing information and skills building support to reduce unprotected sex;
   5. supporting disclosure to increase safer sexual behavior;
   6. integrating cervical cancer screening and treatment into HIV care;
   7. promoting condom use for dual protection against pregnancy and HIV to make condoms more acceptable; and
   8. providing antiretrovirals and counseling to increase HIV prevention behavior ^[28].
2. Through the development of robust indicators, rigorously monitor and evaluate integrated programs during all phases of implementation to improve current and future programs.
3. Ensure that key reproductive and maternal services (such as voluntary family planning, including preconception planning; prevention and management of gender-based violence; and STI management) are integrated with HIV services at the national, sub-national, and facility level to the largest extent possible.

Conclusions

The launch of the Global Plan and renewed momentum around eliminating pediatric HIV is a unique opportunity to ensure that women remain healthy and alive within efforts to eliminate pediatric HIV. This will require that PMTCT research, policies, and programs are linked with SRH and maternal health. To realize the full impact of such changes, implementing partners must work together to explore and make use of synergies between HIV and SRH programs to address the diverse health needs of women and their partners and eventually realize the goal of eliminating pediatric HIV.

The positions summarized in this commentary were developed by members of the Foundation’s technical advisory group on MCH/HIV linkages and integration (the M-TAG), which was made up of Foundation country and global staff. These positions are intended to guide a consistent Foundation-wide approach and framework for addressing this issue and do not supersede the national policies and guidelines of the countries in which the Foundation works.

References

1. WHO, UNICEF. Guidance on global scale-up of the prevention of mother to child transmission of HIV: Towards universal access for women, infants, and young children and eliminating HIV and AIDS among children. Geneva: 2007.
2. Halperin DT, Stover J, Reynolds HW. Benefits and costs of expanding access to family planning programs to women living with HIV. AIDS. 2009; 23 Suppl 1:S123-30.
3. Hladik W, Stover J, Esiru G, Harper M, Tappero J. The contribution of family planning towards the prevention of vertical HIV transmission in Uganda. PLoS One. 2009; 4(11):e7691.
4. Wilcher R, Cates W. Reproductive choices for women with HIV. Bulletin of the World Health Organization. 2009; 87(11):833-839.
5. Mahy M, Stover J, Kiragu K, Hayashi C, Akwara P, Luo C, Stanecki K, Ekpini R, Shaffer N. What will it take to achieve virtual elimination of mother-to-child transmission of HIV? An assessment of current progress and future needs. Sexually Transmitted Infections. 2010; 86 Suppl 2:ii48-55.
6. The Inter-Agency Task Team for Prevention and Treatemtnt of HIV Infection in Pregnant Women Mothers and Their Children. Preventing HIV and Unintended Pregnancies: Strategic Framework 2011-2015; 2011. Available from: http://www.unfpa.org/webdav/site/global/shared/documents/publications/2012/PreventingHIV_UnintendedPregnancies_SF2011_2015.pdf.
7. UNAIDS. Global Plan Towards Elimination of New HIV Infections Among Children by 2015 and Keeping Mothers Alive; 2011 [cited 2011 June]. Available from: < http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2011/20110609_JC2137_Global-Plan-Elimination-HIV-Children_en.pdf.
8. PEPFAR. Technical Considerations Provided by PEPFAR Technical Working Groups for the FY 2010 Country Operational Plan; 2009. Available from: http://www.pepfar.gov/documents/organization/169737.pdf.
9. UNFPA. The State of the World’s Midwifery 2011; May 14, 2012. Available from: http://www.unfpa.org/sowmy/resources/docs/main_report/en_SOWMR_Full.pdf.
10. Rosen JE, de Zoysa I, Dehne K, Mangiaterra V, Abdool-Karim Q. Understanding Methods for Estimating HIV-Associated Maternal Mortality. Journal of Pregnancy. 2012; 2012:958262.
11. Board on Global Health. Improving Birth Outcomes: Meeting the Challenge in the Developing World. Bale JR, Stoll BJ, Lucas AO, Committee on Improving Birth Outcomes, editors. Washington DC: National Academies Press; 2003.
12. Crowe S, Utley M, Costello A, Pagel C. How many births in sub-Saharan Africa and South Asia will not be attended by a skilled birth attendant between 2011 and 2015? BMC Pregnancy and Childbirth. 2012; 12:4–12.
13. WHO. Family planning: fact sheet. Geneva: World Health Organization; 2011 [May 14, 2012]. Available from: http://www.who.int/mediacentre/factsheets/fs351/en/index.html.
14. Wilcher R, Cates W, Jr. Reaching the underserved: family planning for women with HIV. Studies in Family Planning. 2010; 41(2):125-128.
15. Bangendanye L. Pregnancy, pregnancy desires, and contraceptive use among HIV-infected women: Findings from a survey of women enrolled in PMTCT in Rwanda. Third Annual Pediatric Conference on the Infected and Affected by HIV and AIDS; Kigali, Rwanda; 2007.
16. Desgrees-Du-Lou A, Msellati P, Viho I, Yao A, Yapi D, Kassi P, Welffens-Ekra C, Mandelbrot L, Dabis F; Ditrame Study Group. Contraceptive use, protected sexual intercourse and incidence of pregnancies among African HIV-infected women. DITRAME ANRS 049 Project, Abidjan 1995-2000. International Journal of STD and AIDS. 2002 Jul; 13(7):462-8.
17. Homsy J, Bunnell R, Moore D, King R, Malamba S, Nakityo R, Glidden D, Tappero J, Mermin J. Reproductive intentions and outcomes among women on antiretroviral therapy in rural Uganda: a prospective cohort study. PLoS One. 2009; 4(1):e4149. Epub2009 Jan 8.
18. Rochat TJ, Richter LM, Doll HA, Buthelezi NP, Tomkins A, Stein A. Depression among pregnant rural South African women undergoing HIV testing. Journal of the American Medical Association. 2006; 295(12):1376-1378.
19. Singh S, Darroch JE, Ashford LA, Vlassoff M. Adding It Up: The Costs and Benefits of Investing in Family Planning and Maternal and Newborn Health. 2009.
20. WHO, UNFPA, IPPF, UNAIDS, UCSF. Sexual & Reproductive Health and HIV Linkages: Evidence Review and Recommendations; 2009. Available from: http://www.unaids.org/en/media/unaids/contentassets/dataimport/pub/agenda/2009/2009_linkages_evidence_review_en.pdf.
21. Anorlu RI. Cervical cancer: the Sub-Saharan African perspective. Reproductive Health Matters. 2008; 16(32):41-9.
22. Gichangi PB, Bwayo J, Estambale B, De Vuyst H, Ojwang S, Rogo K, Abwao H, Temmerman M. Impact of HIV infection on invasive cervical cancer in Kenyan women. AIDS. 2003;Sep 5;17(13):1963-1968.
23. Bitarakwate E, Strasser S, Sripipatana T, Chintu N, Shelley K, Musana O, et al. Introduction of Rapid Syphilis Testing within Prevention of Mother-to-Child Transmission of HIV Programs in Uganda and Zambia: A field acceptability and feasibility study. 6th IAS Conference on HIV Pathogenesis, Treatment and Prevention; Rome, Italy; 2011.
24. Strasser S, Bitarakwate E, Gill M, Hoffman HJ, Musana O, Phiri A, Shelley KD, Sripipatana T, Ncube AT, Chintu N. Introduction of Rapid Syphilis Testing within Prevention of Mother-to-Child Transmission of HIV Programs in Uganda and Zambia: A field acceptability and feasibility study. Journal of Acquired Immune Deficiency Syndrome 2012 Jul 19. [Epub ahead of print].
25. Ong’ech JO, Hoffman HJ, Kose J, Audo M, Matu L, Savosnick P, Guay L. Provision of Services and Care for HIV-Exposed Infants: A Comparison of Maternal and Child Health Clinic and HIV Comprehensive Care Clinic Models.Journal of Acquired Immune Deficiency Syndrome, 2012 Sep 1;61(1):83-89.
26. Harvard School of Public Health. The Pregnancy Intentions of HIV-Positive Women: Forwarding the Research Agenda – Conference Report. Pregnancy Intentions of HIV-Positive Women; Boston, MA; May 17-19, 2010.
27. Collins L, Doupe A. Preventing HIV and unintended pregnancies: strategic framework 2011–2015; 2011. Available from:

    http://www.ippf.org/NR/rdonlyres/9C6DE8AA-1C16-48FB-B212-A64FE3A0F2BF/5932/PreventingHIVandunintendedpregnanciesstrategicfram.pdf.

28. Gay J, Hardee K, Croce-Galis M, Hall C. What works to meet the sexual and reproductive health needs of women living with HIV/AIDS. Journal of the International AIDS Society. 2011; 14:56.

Abstract

Objectives

This study examined global inequalities in cervical cancer incidence and mortality rates as a function of cross-national variations in the Human Development Index (HDI), socioeconomic factors, Gender Inequality Index (GII), and healthcare expenditure.

Methods

Age-adjusted incidence and mortality rates were calculated for women in 184 countries using the 2008 GLOBOCAN database, and incidence and mortality trends were analyzed using the WHO cancer mortality database. Log-linear regression was used to model annual trends, while OLS and Poisson regression models were used to estimate the impact of socioeconomic and human development factors on incidence and mortality rates.

Results

Cervical cancer incidence and mortality rates varied widely, with many African countries such as Guinea, Zambia, Comoros, Tanzania, and Malawi having at least 10-to-20-fold higher rates than several West Asian, Middle East, and European countries, including Iran, Saudi Arabia, Syria, Egypt, and Switzerland. HDI, GII, poverty rate, health expenditure per capita, urbanization, and literacy rate were all significantly related to cervical cancer incidence and mortality, with HDI and poverty rate each explaining >52% of the global variance in mortality. Both incidence and mortality rates increased in relation to lower human development and higher gender inequality levels. A 0.2 unit increase in HDI was associated with a 20% decrease in cervical cancer risk and a 33% decrease in cervical cancer mortality risk. The risk of a cervical cancer diagnosis increased by 24% and of cervical cancer death by 42% for a 0.2 unit increase in GII. Higher health expenditure levels were independently associated with decreased incidence and mortality risks.

Conclusions and Public Health Implications:

Global inequalities in cervical cancer are clearly linked to disparities in human development, social inequality, and living standards. Reductions in cervical cancer rates are achievable by reducing inequalities in socioeconomic conditions, availability of preventive health services, and women’s social status.

Key Words:

Cervical cancer • Incidence • Mortality • Global inequality • Human development • Gender inequality • Social inequality • Poverty • Literacy • GNI per capita.

Introduction

Cervical cancer is the third most common cancer in women after breast and colorectal cancers and is one of the leading causes of cancer death among women in the world^[1] . In 2008, approximately 530,000 women were diagnosed with invasive cervical cancer worldwide and 275,000 women died from it ^{[1, 2]}. Cervical cancer is the top cancer site for women in most East African and South Asian countries both in terms of incidence and mortality ^{[1, 2]}. Indeed, developing countries as a whole experience a disproportionate share of the disease burden, accounting for 86% of all cervical cancer cases and 88% of all cervical cancer deaths worldwide ^[1-3]. While cervical cancer rates have declined markedly in industrialized countries over the past several decades, the rates have declined at a much slower pace in the developing world and, for many developing countries, the rates have actually been increasing ^{[1, 2]}. The latest data from the World Health Organization (WHO) show marked disparities in cervical cancer incidence and mortality rates across countries ^{[1, 2]}.

A number of population-based studies from the United States and other industrialized countries have shown marked socioeconomic gradients in cervical cancer incidence and mortality, with those in more deprived groups or lower socioeconomic strata having 2-3 fold higher risk of cervical cancer than their affluent counterparts ^[4-9]. A few case-control studies in Asia, Africa, and South America also indicate substantially higher risks of cervical cancer among women in lower social class groups ^[9]. To our knowledge, the extent to which disparities in cervical cancer incidence and mortality rates between nations arise due to global differences in social inequality and human development factors has not yet been studied. Given the evidence of a strong association between socioeconomic status (SES) and cervical cancer from within-country studies, we should expect differences in education, socioeconomic conditions, material living standards, and health services to account for a substantial portion of global inequalities in cervical cancer incidence and mortality.

Assessing the impact of socioeconomic conditions and human development is important because they represent underlying causes of health inequalities both within and between nations ^{[10, 11]}. Improvements in the broader social determinants such as education, income distribution, gender inequality, labor force participation, employment, and health care have been suggested as central to reducing population health inequalities ^{[10, 12]}. International comparisons can highlight the extent of disease burden in specific countries and important cross-national differences and similarities in socioeconomic conditions, urbanization patterns, prevalence of cancer risk factors such as smoking and human papillomavirus (HPV) infection, and availability and use of cervical cancer screening programs ^{[2, 4]}. Analysis of global inequalities is also important in that cervical cancer tends to affect younger women (aged <45 years) more than the other major cancers, resulting in relatively high years-of-life lost, particularly among women in the developing world ^[2]. Moreover, cervical cancer rates appear to be rising among younger women in many developing as well as developed countries ^[13].

The major purpose of our paper is to quantify the links between inequalities in human development and socioeconomic conditions and global disparities in cervical cancer incidence and mortality. Specifically, we link socioeconomic, demographic, and human development indicators for various countries with the most recent international cervical cancer data to examine global patterns of cervical cancer incidence and mortality. In addition, we document the extent of global disparities in cervical cancer by identifying regions and countries in which women are at high risk of morbidity and mortality from this disease.

Methods

To analyze global inequalities in cervical cancer incidence and mortality, we computed age-adjusted incidence and mortality rates for women in 184 countries using the 2008 GLOBOCAN database ^[1]. The GLOBOCAN database, developed by the International Agency for Research on Cancer, provides contemporary estimates of the incidence of, mortality and prevalence from major type of cancers, at national level, for 184 countries of the world ^[1]. Details of the cancer database are provided elsewhere ^{[1, 2]}. Annual trends in incidence and mortality rates were analyzed for selected countries using the WHO cancer incidence and mortality databases^{[14, 15]}.

The data on the Human Development Index (HDI) and Gender Inequality Index (GII) were taken from the 2011 Human Development Report ^[16], while those on Gross National Income (GNI) per capita, international poverty rate (deprivation level measuring the proportion of population living in extreme poverty), literacy rate, urbanization, and health expenditure came from the 2011 World Health Statistics Report ^[17]. HDI, developed by the United Nations Development Programme, is a composite index of social and economic development and combines indicators of life expectancy, educational attainment, and GNI per capita. HDI varies between 0 and 1, with 0 indicating the lowest level and 1 representing the highest level of development ^[16]. GII is also a composite index that reflects women’s relative social disadvantage in three dimensions – reproductive health, empowerment, and the labor market ^[16]. GII combines 5 indicators, maternal mortality ratios, adolescent fertility rate, educational attainment, parliamentary representation by each sex, and female labor force participation ^[16].

We used bivariate and multivariate ordinary least squares (OLS) regression models to estimate the impact of HDI, GII, and other socioeconomic factors on age-adjusted cervical cancer incidence and mortality rates. We used Poisson regression to model age- and country-specific incidence cases, deaths, and population estimates as a function of socioeconomic and human development factors [12, 18]. For Poisson modeling, we used three broad age-groups: <45, 45-64, and 65+ years. The OLS models were estimated by the SAS REG procedure ^[19], while Poisson models were estimated by the SAS GENMOD procedure ^[20].

Since HDI and GII both include education and economic components, these composite indices are highly correlated with the other individual predictors used in the study, such as literacy rate, per capita income, poverty, and urbanization. Because of estimation problems due to high multicollinearity, multiple regression models containing either HDI or GII and education and income indicators were not estimated. Instead, the net impact of health expenditure per capita was examined in multivariate models that included HDI or GII.

Log-linear regression models were used to estimate annual rates of change in cervical cancer incidence and mortality trends for selected countries^{[7, 12, 18]}. Specifically, during the 1983-2002 or 1983-2008 period, the logarithm of the incidence or mortality rates respectively were modeled as a linear function of time (calendar year), which yielded annual exponential rates of change in incidence or mortality rates ^{[7, 12, 18]}.

Results

Global Inequalities in Cervical Cancer Incidence and Mortality Rates

In 2008, cervical cancer incidence and mortality rates were higher in Eastern, Western, and Sub-Saharan regions of Africa and South Asia and lower in Western Asia, Western Europe, North America, and Australia/New Zealand (Figure 1). Overall, women in developing countries had two-fold higher cervical cancer incidence rates and three-fold higher mortality rates than their counterparts from developed countries.

Figure 1. International Variations in Age-Adjusted Cervical Cancer Incidence and Mortality Rates per 100,000 World Standard Population, 2008

Source: WHO, International Agency for Research on Cancer, GLOBOCAN, 2008.
Long-term trends in cervical cancer incidence and mortality vary across countries (Figures 2 and 3). While incidence rates have generally decreased for most countries, the rates have remained stable at fairly high levels for certain countries such as Thailand (data not shown). During 1983-2002, although incidence rates decreased at 4.3%, 3.1%, and 1.7% per year in Brazil, India (Chennai), and the Philippines, respectively, they remained at much higher levels than those for Hong Kong and Shanghai, China (Figure 2). Mortality trends have also not been uniform across countries. During 1983-2008, while mortality rates in Hong Kong and Singapore decreased at about 4% per year, the rates have either decreased at a much slower pace for Brazil and Mexico or increased for some countries such as Cuba, Venezuela (Figure 3), Thailand, and the Philippines (trend not shown).

In 2008, cervical cancer rates varied widely across individual countries, with many African countries such as Guinea, Zambia, Comoros, Tanzania, and Malawi having at least 10-to-20 fold higher incidence and mortality rates than such countries as Iran, Saudi Arabia, Syria, Egypt, and Switzerland (Table 1). Cross-national disparities in cervical cancer mortality rates (as measured by the coefficient of variation and range) were greater than those in incidence rates. Guinea had the highest age-adjusted incidence rate of 56.3 per 100,000 women, 35 times greater than the rate of 1.6 for Egypt. Guinea also had the highest age-adjusted mortality rate of 41.7 per 100,000 women, 52 times higher than the rate of 0.8 for Syria (Table 1).

Modeling the Impact of HDI, GII, and Socioeconomic Factors
HDI, GII, international poverty rate, health expenditure per capita, urbanization, and literacy rate were all significantly related to cancer incidence and mortality, with HDI and poverty rate each explaining >52% of the global variance in mortality (Table 2 and Figure 4). Both incidence and mortality rates increased in relation to lower levels of human development and higher levels of gender inequality. In bivariate models, a 0.2 unit increase in HDI was on average associated with an 8.7 point decrease in incidence rates and a 7.8 point decrease in mortality rates. A 0.2 unit increase in HDI was associated with a 20% decrease in the risk of cervical cancer incidence and a 33% decrease in the risk of cervical cancer mortality (Table 2).

Cervical cancer incidence and mortality rates increased by 7.1 and 5.9 points, respectively, for every 0.2 unit increase in GII. The risk of a cervical cancer diagnosis increased by 24% and of cervical cancer death by 42% for a 0.2 unit increase in GII (Table 2). GII explained approximately 24% and 33% of the global variance in incidence and mortality rates, respectively.

Deprivation levels or poverty rates were a strong predictor of cross-national variations in cervical cancer incidence and mortality. A 10-percentage point increase in poverty rates was expected to result in an 8% higher risk of incidence and a 14% higher risk of death from cervical cancer. A $5,000 increase in GNI per capita was associated with an 8% lower incidence risk and a 14% lower mortality risk. Incidence risks decreased by 11% and mortality risks by 21% for every 20-percentage point increase in literacy rate. Incidence and mortality rates and risks decreased at increasing levels of urbanization (Table 2).

In multivariate models, higher health expenditure levels were independently associated with decreased incidence and mortality risks. Even after adjusting for the effects of HDI or GII, for every $1,000 increase in healthcare expenditure per capita, there was at least a 7% decrease in the risk of cervical cancer (Table 2). The corresponding net decrease in cervical cancer mortality risk was 8-11% for a similar increase in health expenditure per capita.

Figure 2. Trend in Cervical Cancer Incidence Rates in Selected Countries, 1983-2002

Log-Linear Regression Trend Models of Cervical Cancer Incidence, 1983-2002

Source: WHO, International Agency for Research on Cancer, Cancer Incidence in Five Continents Annual Dataset.

Figure 3. Trend in Cervical Cancer Mortality Rates in Selected Countries, 1983-2008

Log-Linear Regression Trend Models of Cervical Cancer Mortality, 1983-2008

Source: WHO, International Agency for Research on Cancer, Cancer Mortality Database.

Table 1.Age-adjusted Cervical Cancer Incidence and Mortality Rates per 100,000 World Standard Population, 2008

Source: WHO, International Agency for Research on Cancer (IARC), GLOBOCAN 2008.
Table 2. Ordinary Least Squares (OLS) and Poisson Regression Models Showing the Effects of Human Development Index, Gender Inequality Index, and Socioeconomic and Health Care Factors on Age-Adjusted Cervical Cancer Incidence and Mortality Rates, 2008 (N = 184 Countries)

Notes: b=unstandardized regression coefficient; β =standardized regression coefficient; R²=percentage variance explained.

1. β is also equal to the correlation coefficient in bivariate OLS regression models
2. a Increase in incidence/mortality rates or risks associated with a 0.2 unit increase in HDI
3. b Increase in incidence/mortality rates or risks associated with a 0.2 unit increase in the Gender Inequality Index
4. c Increase in incidence/mortality rates or risks associated with a 20-percentage point increase in the adult literacy rate
5. c Increase in incidence/mortality rates or risks associated with a 20-percentage point increase in the adult literacy rate
6. e Increase in incidence/mortality rates or risks associated with a 10-percentage point increase in the poverty rate
7. f Increase in incidence/mortality rates or risks associated with a 10-percentage point increase in the urban population
8. g Increase in incidence/mortality rates or risks associated with a $1,000 increase in health expenditure per capita

Figure 4. Observed and Fitted Plots Showing the Impact of Human Development Index (HDI) and Poverty on Age-Adjusted Cervical Cancer Mortality Rates per 100,000 World Standard Population, 2008

Discussion

In this study, by using the latest global sociodemographic, health, and cancer statistics, we have identified countries at high risk of cervical cancer morbidity and mortality and have examined the impact of socioeconomic and human development factors on cross-national variations in cervical cancer incidence and mortality rates. High rates of incidence and mortality observed for many low- and middle-income countries indicate cervical cancer to be a major public health problem in the developing world ^[2]. In terms of absolute numbers, Indian women bear the greatest burden of the disease as more than a quarter of new cases and cervical cancer deaths in the world occur in India alone ^{[1, 2]}.

We have estimated the magnitude of cervical cancer disparities between countries and attempted to explain these disparities in terms of the effects of major societal determinants. It is important to note that social inequalities in cervical cancer are quite marked within individual countries as well ^{[4, 6, 8, 9]}. As mentioned earlier, this pattern holds for both developed and developing countries ^{[4, 8, 9]}. However, population-based studies of social inequalities are rare for the developing countries that have the greatest disease burden; more studies are needed in these countries to shed light on the magnitude and causes of social inequalities in cervical cancer incidence and mortality.

The findings of our study are consistent with two earlier studies that examined global patterns in cervical cancer rates using the 2008 GLOBOCAN database ^{[2, 3]}. These studies examined disparities in incidence and mortality rates across countries and various regions of the world in much detail ^{[2, 3]}. However, unlike our study, no efforts were made in these studies to link cervical cancer disparities with global patterns of human development and socioeconomic conditions.

Global inequalities in cervical cancer mortality are substantially larger than those in cervical cancer incidence, which indicate the prominent role of cross-national disparities in patient survival rates, the extent of disease at diagnosis, and the impact of differential access to health services and cancer treatment ^[4-8]. Cancer survival varies greatly across countries, with cancer patients in Europe, North America, Australia and New Zealand having higher survival rates than their counterparts in developing countries [21]. A recent study showed wide variation in cervical cancer survival rates among African, Asian, Caribbean, and Central American countries ^[22]. The 5-year age-standardized relative survival rate for cervical cancer ranged from a low of 19% in Uganda and 23% in Gambia to a high of 76% in Seoul, South Korea and 77% in Hong Kong, China ^[22]. The current 5-year relative survival rate is 69% for US women ^[23]. Survival inequalities exist within individual countries as well. For example, the 5-year survival rate for cervical cancer patients in Bhopal, India was 31%, as compared with 60% for women in Chennai, India ^[22].

Lower cancer survival and higher mortality rates partly result from higher rates of late-stage cancer diagnosis among women in developing countries, which is largely due to the lack of effective cervical cancer screening programs in most developing countries ^{[2, 4, 24]}. About 81% of cervical cancer patients in Singapore are diagnosed at an early, localized stage, compared with only 7% in Chennai, India, 33% in Costa Rica, 35% in Manila, Philippines, and 53% in Cuba ^[24]. The high rate of early-stage diagnosis in Singapore is higher than the rate for many industrialized countries, including the United States, where only 52% of invasive cervical cancers in 2008 were diagnosed at localized stage ^{[7, 23]}.

Detection of cancer at an early stage may be considered a marker for access to health care and preventive health services, including cervical cancer screening. Screening can reduce rates of both cervical cancer incidence and mortality by detecting precancerous lesions (hence preventing cancer) and detecting invasive cervical cancers at an early stage, thereby increasing patient survival ^{[4, 7, 25]}. Data from the United States and other industrialized countries indicate that rural and socioeconomically disadvantaged women are significantly less likely to receive Pap smear tests than their urban and affluent counterparts ^{[4, 5, 7, 8]}. According to a recent study, only 19% of women in developing countries use cervical cancer screening, compared with 63% in developed countries ^[24]. However, rates of cervical cancer screening vary widely within the developing world, ranging from 1% in Bangladesh to 73% in Brazil [24]. Consistent with inequalities in cervical cancer screening within the developed world, socioeconomic inequalities in countries such as Brazil and China are quite large, and, globally, women in the poorest wealth decile are seven times less likely to receive cervical cancer screening than their rich counterparts (9% versus 64%) ^[24].

Inequalities in cervical cancer partly reflect global disparities in the prevalence of HPV infection, which is the primary cause of cervical cancer ^{[2, 4, 9, 13, 26]}. HPV prevalence tends to be higher in Eastern and Western Africa, Latin America, and South Asia and lower in West Asia and the Mediterranean region ^{[2, 27]}. Globally, HPV prevalence seems to correspond closely with cervical cancer incidence rates (γ = 0.68) ^[2]. Another indicator of high-risk sexual behavior is the HIV prevalence among the adult population, which, according to our analysis (not shown), is fairly highly correlated (γ > 0.40) with both cervical cancer incidence and mortality rates globally.

Conclusions and Public Health Implications

Developing and developed countries differ greatly in their levels of human development and gender inequality ^[16]. Many of the countries with the highest cervical cancer rates such as Guinea, Comoros, Zambia, Malawi, Burundi, and Mozambique also tend to score the lowest on HDI. Countries with low levels of human development also tend to perform poorly with respect to gender equality; women in these societies fare much worse in reproductive health, educational achievements, empowerment, and work force participation than their counterparts in more egalitarian countries ^[16]. As shown previously for the United States and other individual industrialized countries, deprivation, material living conditions, and social inequality are also powerful determinants of cervical cancer incidence and mortality at the global level.

The extent of global inequalities in cervical cancer incidence and mortality, as documented here, contributes substantially to the overall cancer-related health disparities worldwide, since cervical cancer continues to be a leading cancer site among women in many developing countries ^{[1, 2]}. Unfavorable socioeconomic conditions and low levels of human development may hinder a country’s efforts to invest in its health care infrastructure, provision of health services, and in its education sector, which could mean less than optimal resources available for public health improvement and cancer prevention and control efforts. Formulation and implementation of broad societal initiatives are, of course, necessary to address important health and developmental goals, including poverty reduction, larger investments in women’s health and education, expanding economic opportunities for women, and a greater commitment toward gender and social equality in the distribution of power, money, and resources ^{[11, 16, 17]}. Additionally, public health measures such as establishment of cancer prevention and early detection programs through increased cervical cancer screening, public health education programs promoting condom use to reduce risks of sexually transmitted infections such as HPV and HIV, and introduction of affordable HPV tests and vaccination are critical in reducing global cervical cancer disparities, particularly among women in low- and middle-income developing countries ^[2].

Financial Disclosure: None to report. Funding/Support: None. Conflicts of Interest: None.
Acknowledgements: None. The views expressed are the authors’ and not necessarily those of the US Department of Health and Human Services and Global Health and Education Projects, Inc.

References

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. GLOBOCAN 2008, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 10 [Internet]. Lyon, France: International Agency for Research on Cancer; 2010. Available at: http://globocan.iarc.fr. Accessed May 1, 2012.
2. Arbyn M, Castellsagué X, de Sanjosé S, Bruni L, Saraiya M, Bray F, Ferlay J. Worldwide burden of cervical cancer in 2008. Annal of Oncology. 2011; 22(12):2675-86. Epub 2011 Apr 6.
3. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011; 61(2):69-90. Epub 2011 Feb 4. Erratum in: CA: A Cancer Journal for Clinicians. 2011; 61(2):134.
4. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part I – all cancers and lung cancer and part II – colorectal, prostate, breast, and cervical cancers. Journal of Cancer Epidemiology. 2011; 107497:1-27. Epub February 14, 2012.
5. Singh GK, Miller BA, Hankey BF, Edwards BK. Area Socioeconomic Variations in U.S. Cancer Incidence, Mortality, Stage, Treatment, and Survival, 1975-1999. NCI Cancer Surveillance Monograph Series, No. 4. Bethesda, MD: National Cancer Institute; 2003. NIH Publication No. 03-5417.
6. Singh GK, Miller BA, Hankey BF, Edwards BK. Persistent area socioeconomic disparities in U.S. incidence of cervical cancer, mortality, stage, and survival, 1975-2000. Cancer. 2004; 101(5):1051-1057.
7. Singh GK. Rural-urban trends and patterns in cervical cancer mortality, incidence, stage, and survival in the United States, 1950-2008. Journal of Community Health. 2012; 37(1): 217-223.
8. Faggiano F, Partanen T, Kogevinas M, Boffetta P. Socioeconomic differentials in cancer incidence and mortality. IARC Scientific Publications. 1997; 138:65-176.
9. Parikh S, Brennan P, Boffetta P. Meta-analysis of social inequality and the risk of cervical cancer. International Journal of Cancer. 2003; 105(5):687-691.
10. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
11. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
12. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. American Journal of Public Health. 2003; 93(7):1137-1143.
13. Jemal A, Center MM, DeSantis C, Ward EM. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiology, Biomarkers & Prevention. 2010; 19(8):1893-1907.
14. International Agency for Research on Cancer (IARC). Cancer Incidence in Five Continents Annual Dataset. World Health Organization, International Agency for Research on Cancer; 2011. Available at: http://ci5.iarc.fr/CI5plus/ci5plus.htm. Accessed May 1, 2012.
15. International Agency for Research on Cancer (IARC). WHO Cancer Mortality Database. World Health Organization, International Agency for Research on Cancer; 2011. Available at: http://www-dep.iarc.fr/WHOdb/WHOdb.htm. Accessed May 1, 2012.
16. United Nations Development Programme. Human Development Report 2011. New York, NY; 2011.
17. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
18. Singh GK, Siahpush M, Williams SD. Changing urbanization patterns in US lung cancer mortality, 1950-2007. Journal of Community Health. 2012; 37(2):412-420.
19. SAS Institute, Inc. SAS/STAT User’s Guide, Version 9.1: The REG Procedure. Cary, NC: SAS Institute Inc.; 2004.
20. SAS Institute, Inc. SAS/STAT User’s Guide, Version 9.1: The GENMOD Procedure. Cary, NC: SAS Institute Inc.; 2004.
21. Coleman MP, Quaresma M, Berrino F, Lutz JM, De Angelis R, Capocaccia R, Baili P, Rachet B, Gatta G, Hakulinen T, Micheli A, Sant M, Weir HK, Elwood JM, Tsukuma H, Koifman S, E Silva GA, Francisci S, Santaquilani M, Verdecchia A, Storm HH, Young JL; Cancer survival in five continents: a worldwide population-based study (CONCORD). CONCORD Working Group. Lancet Oncology. 2008; 9(8):730-756.
22. Sankaranarayanan R, Swaminathan R, Lucas E. Cancer Survival in Africa, Asia, the Caribbean, and Central America (SurvCan). IARC Scientific Publications, Vol. 162; Lyon, France: International Agency for Research on Cancer; 2011.
23. Howlader N, Noone AM, Krapcho M, Neyman N, Aminou R, Waldron W, Altekruse SF, Kosary CL, Ruhl J, Tatalovich Z, Cho H, Mariotto A, Eisner MP, Lewis DR, Chen HS, Feuer EJ, Cronin KA, Edwards BK (eds). SEER Cancer Statistics Review, 1975-2008, National Cancer Institute. Bethesda, MD, http://us.mg1.mail.yahoo.com/csr/1975_2008/, based on November 2010 SEER data submission, posted to the SEER web site, 2011.
24. Gakidou E, Nordhagen S, Obermeyer Z. Coverage of cervical cancer screening in 57 countries: low average levels and large inequalities. PLoS Medicine. 2008; 5(6):863-868.
25. American Cancer Society. Cancer Facts & Figures 2010. Atlanta, GA: American Cancer Society; 2010.
26. de Sanjose S, Bosch FX, Munoz N, Shah K. Social differences in sexual behaviour and cervical cancer. IARC Scientific Publications. 1997; 138:309-317.
27. Bruni L, Diaz M, Castellsagué X, Ferrer E, Bosch FX, de Sanjosé S. Cervical human papillomavirus prevalence in 5 continents: meta-analysis of 1 million women with normal cytological findings. Journal of Infectious Diseases. 2010; 15;202(12):1789-99. Epub 2010 Nov 10.

Abstract

Objectives

We examined differences between China and India in key health and socioeconomic indicators, including life expectancy, infant and child mortality, non-communicable disease mortality from cancer, cardiovascular diseases (CVD), and diabetes, Human Development Index, Gender Inequality Index, material living conditions, and health expenditure.

Methods

Data on health and social indicators came from various World Health Organization and United Nations databases on global health and development statistics, including the GLOBOCAN cancer database. Mortality trends were modeled by log-linear regression, and differences in rates and relative risks were tested for statistical significance.

Results

Although both countries have made marked improvements, India lags behind China on several key health indicators. Differential rates of mortality decline during 1960-2009 have led to a widening health gap between China and India. In 2009 the infant mortality rate in India was 50 deaths per 1,000 live births, 3 times greater than the rate for China. Sixty-six out of 1,000 Indian children died before reaching their 5th birthday, compared with 19 children in China. China’s life expectancy is 9 years longer than India’s. Life expectancy at birth in India increased from 42 years in 1960 to 65 years in 2009, while life expectancy in China increased from 47 years in 1960 to 74 years in 2009. Major health concerns for China include high rates of stomach, liver, and lung cancer, CVD, and smoking prevalence. Globally, India ranked 90^th and China 102^nd in life satisfaction.

Conclusions and Public Health Implications:

India’s less favorable health profile compared to China is largely attributable to its higher rates of mortality from communicable diseases and maternal and perinatal conditions. Further health gains can be achieved by reducing social inequality, greater investments in human development and health services, and by prevention and control of chronic-disease risks such as hypertension, smoking, obesity, and physical inactivity.

Key Words:

China • India • Health status • Life expectancy • Infant and child mortality • Non-communicable diseases • Cancer • Health risks • Human development • Social inequality.

Introduction

China and India are the two most populous nations in the world. With the populations of 1.35 and 1.21 billion respectively, they jointly account for 37% of the world population ^[1]. China and India gained their independence in the modern era, in 1949 and 1947, respectively. Around the time of their independence, both countries had roughly similar levels of health and socioeconomic development ^[2]. However, according to our analysis that follows, both countries differ greatly in their health and socioeconomic achievements, six decades on.

Because of the remarkable and rapid economic growth of the past two decades, there have been numerous comparisons of economic performance of India and China ^[3-7]. However, to date, few comparisons have been made that provide a comprehensive assessment of how key health, disease, socioeconomic, and human development indicators for these two countries have changed over time ^[5-8]. With the availability of high-quality data from the World Health Organization (WHO) and the United Nations (UN), it is possible to provide a more complete and systematic comparison of China and India on various health and development issues ^{[1, 9-11]}.

In this study, we analyze the extent of disparities between the two nations in key health indicators such as life expectancy, infant mortality, under-five mortality, maternal mortality, cancer, and other chronic diseases, health behaviors, health expenditure, and social and economic factors such as human development, gender inequality, life satisfaction, urbanization, literacy, and income per capita. Additionally, we examine the magnitude of health inequalities within India and China across socioeconomic groups, provinces, and rural and urban areas. Special emphasis is given to the analysis of disparities in cancer rates, detailed data on which are available for both countries. Cancer is also a disease that is expected to account for a greater share of the total disease burden in the future in both China and India as lifestyle factors and consumption patterns begin to mimic those seen in the developed world and aging of the population becomes an important issue in China ^{[10, 12]}.

Simultaneous examination of health and socioeconomic conditions is important because social conditions, including human development, have been shown as fundamental, underlying determinants of health inequalities both within and between nations ^[13-15]. Analysis of existing health and social conditions and changes in these indicators is vital for social planning and public health decision making ^{[1, 6, 9]}. Comparisons between China and India can highlight the extent of disease burden due to specific health conditions, similarities as well as differences in socioeconomic conditions, urbanization patterns, prevalence of health-risk factors, and availability and use of preventive health services ^{[9, 12]}. Analysis of various health and socioeconomic indicators is presented side by side for China and India in the hopes that such information would be readily used by policymakers and researchers in both countries for policy and program formulation, and for facilitating more in-depth examination of specific health and/or development issues.

Methods

Annual trends in life expectancy, infant, child, and maternal mortality rates were analyzed using the 2011 WHO, UN, and the World Bank databases that include current and time-trend data on health and human development variables for all nations, including China and India ^{[1, 9-11]}. Information on these databases can be found elsewhere and is briefly described below ^{[1, 9-11]}. Cancer incidence and mortality data were derived from the 2008 GLOBOCAN database.16-18 The GLOBOCAN database, developed by the International Agency for Research on Cancer, provides contemporary estimates of the incidence of, mortality and prevalence from major type of cancers, at national level, for 184 countries of the world. Details of the GLOBOCAN database are provided elsewhere ^[16-18].

The data on the Human Development Index (HDI), Gender Inequality Index (GII) and life satisfaction were taken from the 2011 Human Development Report ^[1], while those on Gross National Income (GNI) per capita, international poverty rate (the proportion of population living in extreme poverty), literacy rate, urbanization, cause-specific morbidity and morbidity, health behaviors, health expenditure, and health services came from the 2011 World Health Statistics Report, Non-Communicable-Disease country profiles, and the World Bank’s health, nutrition, and population database ^[9-11]. HDI, developed by the UN, is a composite index of social and economic development which combines indicators of life expectancy, educational attainment, and GNI per capita. HDI varies between 0 and 1, with 0 indicating the lowest level and 1 representing the highest level of development ^{[1, 19]}. GII is also a composite index that reflects women’s relative social disadvantage in three dimensions – reproductive health, empowerment, and the labor market ^{[1, 19]}. GII combines 5 indicators, maternal mortality ratios, adolescent fertility rate, educational attainment, parliamentary representation by each sex, and female labor force participation ^{[1, 19]}.

Log-linear regression models were used to estimate annual rates of change in life expectancy, infant mortality, and child mortality trends from 1960 to 2009 ^{[15, 19]}. Specifically, during the 1960-2009 period, the logarithm of the mortality rates or life expectancy were modeled as a linear function of time (calendar year), which yielded annual exponential rates of change in mortality rates or life expectancy ^{[15, 19]}. Differences between China and India in cancer incidence and mortality rates and relative risks were tested for statistical significance at the 0.05 level.

Results

Inequalities in Key Health Measures

In 2009, life expectancy was nine years longer in China than in India (74 versus 65 years) (Figure 1). For both India and China, life expectancy increased substantially during the past 5 decades. Life expectancy at birth in India increased from 42 years in 1960 to 65 years in 2009, while life expectancy in China increased from 47 years in 1960 to 74 years in 2009. Although the rate of increase in life expectancy was similar for the two countries, the absolute increases in life expectancy were larger for China (Figure 2). During 1960-2009, life expectancy increased annually by 0.55 years in China and by 0.47 years in India.

Infant and child mortality rates declined impressively in both India and China between 1960 and 2009. However, disparities between the two nations have widened (Figure 3). In 2009 the infant mortality rate (IMR) of 50 deaths per 1,000 live births in India was three times greater than the rate of 17 in China (Figures 1 and 3). In 1960 the IMR was only 1.3 times higher in India than in China. The average annual rate of decline in infant mortality was faster in China than in India (3.5% versus 2.2%). It is important to note that the IMR in mainland China is still much greater than the rate for Hong Kong and for many developed nations. The 2009 IMR for the US, for example, was 6.4, and, for Hong Kong, Japan, and Singapore, the rate was below 3.9 The disparities in under-5 mortality were also marked, with 66 out of 1,000 Indian children in 2009 dying before reaching their 5th birthday, as compared with 19 children in China (Figure 1). The relative risk of child mortality in India compared to China increased from 1.1 in 1960 to 3.5 in 2009. Child mortality declined at a rate of 4.4% per year in China, compared with an annual rate of 2.5% in India (Figure 3). The rate of low birth weight among newborns in India was 28% [only Mauritania (34%) and Pakistan (32%) have higher rates], compared with only 3% in China (Table 1). China’s low-birth-weight rate was even lower than the overall US rate of 8.2% and 4.2% for Chinese Americans ^[20].

Figure 1. Selected Key Health Indicators for China and India, 2009

Source: World Health Organization, World Health Statistics Report 2011.
Marked differences can also be seen in maternal mortality and cause-specific mortality rates. India’s maternal mortality rate of 230 maternal deaths per 100,000 live births was 6.1 times greater than the rate of 38 in China. Between 1980 and 2008, both China and India were able to reduce maternal mortality rates by 60-65%. Prevalence of tuberculosis was 1.8 times higher and the adult HIV prevalence was 3 times higher in India than in China (Figure 4). There are 2.4 million people living with HIV in India and the annual number of AIDS deaths is 172,000 ^[21]. China, on the other hand, has a lower HIV/AIDS burden, with 740,000 people living with HIV and 26,000 AIDS deaths annually [21].

Figure 2. Trends in Life Expectancy at Birth (Years) in China and India, 1960-2009

Log-Linear Regression Trend Models of Life Expectancy, 1960-2009

Source: World Health Organization and World Bank.

Figure 3. Trends in Infant and Child (Under 5) Mortality Rates in India and China, 1960-2009

Log-Linear Regression Trend Models of Infant and Child Mortality Rates, 1960-2009

Source: World Health Organization and World Bank.

Figure 4. Selected Cause-Specific Mortality and Morbidity Indicators for China and India, 2008

Note: Cause-specific mortality rates are per 100,000 population and are age-adjusted to the world standard population.

Source: World Health Organization, World Health Statistics Report 2011.

Infectious diseases account for 52% of the total years-of-life lost in India compared with 15% in China. The rate of infectious-disease mortality in India was 6.3 times higher than that in China (Figure 4). Even though non-communicable diseases accounted for 65% of the total years-of-life lost in China compared to 35% in India, the absolute rate of mortality from non-communicable diseases was still 13% higher in India than in China (Table 1 and Figure 4).

Overall, cancer incidence and mortality rates were 83-84% higher in China than in India (Table 2). Incidence rates for liver and stomach cancers were 8-12 times higher and those for uterine, lung, colorectal, esophageal, pancreatic, kidney, bladder, and brain cancers were 2-6 times higher in China than in India. Mortality rates from most reproductive cancers were higher in India than in China. Women in India had 2.0, 2.7, and 3.6 times higher rates of breast, ovarian and cervical cancer mortality, respectively, than their Chinese counterparts. Mortality from prostate, lymphoma, and multiple myeloma was also substantially higher in India than in China. Rates of incidence and mortality from oral cancers (lip, oral cavity and pharynx) were 9-14 times higher in India than in China. This is primarily because of high prevalence of bidi smoking, chewing tobacco, and betel quid (paan) in India [18].

Figure 5. Trends in Urbanization and Female Labor Force Participation (Percentage of Total Labor Force) in India and China, 1960-2009

Source: World Health Organization and World Bank.
In terms of childhood cancers, although the overall incidence rate was 10% lower in China than in India, Chinese children had 24% higher overall cancer mortality and approximately two-fold higher brain and leukemia mortality than Indian children (Table 2).

Figure 6. Trends in Human Development Index (HDI) and Gender Inequality Index (GII) for India and China, 1980-2011

Source: United Nations, Human Development Report 2011.
In terms of chronic-disease risk factors, smoking prevalence among Chinese men was 49.3%, two times higher than the prevalence of 25.1% among Indian men (Table 1). Prevalence of physical inactivity, hypertension, obesity/overweight, and raised cholesterol were higher in China, but Indians had a two times higher prevalence of diabetes than Chinese (7.8% versus 4.2%).

Although both China and India spent slightly more than 4% of their GDP on health, health expenditure per capita (i.e., per person) was two times higher in China than in India (Table 1). Moreover, the proportionate government expenditure on health was 2.6 times larger in China than in India. Health care infrastructure (physicians and hospital beds per capita) was also more favorable in China than in India. In terms of access to improved sanitation facilities, rural Indians lag behind their Chinese counterparts (Table 1). Stunting and malnutrition is a major problem among Indian children under age 5 as nearly half of them are stunted and underweight. The prevalence of malnutrition is low among Chinese kids (Table 1).

Inequalities in Socioeconomic Conditions and Human Development

Approximately 42% of Indians live below the international poverty line (<$1 per day), compared with 16% of Chinese. Only half of the female adult population in India has achieved literacy, compared with 91% of Chinese women (Table 1). Both countries have experienced rapid urbanization during the past five decades. In 2009, 44% of the Chinese population and 30% of the Indian population lived in urban areas (Figure 5). Historically, female labor force participation (the proportion of total labor force) has been much lower in India than in China (Figure 5). More than 67% of Chinese women are currently in the workforce, compared with 33% of Indian women (Table 1).

Although India ranks lower than China in human development, improvements in human development have been impressive (Figure 6). Between 1980 and 2011, the HDI score increased from 0.34 to 0.55 for India and from 0.40 to 0.69 in China. Levels of gender inequality remain very high in India, and there has hardly been any improvement in this arena during the past two decades. China, on the other hand, is on par with many western industrialized countries and even exceeds the United States in gender equality ^[1]. In terms of life satisfaction, both Indians and Chinese score in the middle of the 11-point scale (Table 1). Out of 151 countries, India ranked 90th and China 102nd in life satisfaction, with people in Denmark and Canada reporting highest levels of life satisfaction ^[1].

Inequalities within India and China

Health and socioeconomic conditions vary greatly both within India and China, and, consequently, health inequalities within India and China are quite large ^[22-24]. Children in the poorest quintile in India had 3 times higher mortality than their wealthiest counterparts (Table 3). The urban and economically affluent women in India were 2-to-5 times more likely to have births attended by skilled health personnel than their rural and socioeconomically disadvantaged counterparts. Percentage of births attended by skilled health personnel is a measure of health service access and utilization. Life expectancy at birth in India varied from a low of 58 for Chhattisgarh, Madhya Pradesh, and Jharkhand to 74 years in Kerala. State differences in infant mortality were similar, with Madhya Pradesh having the highest infant mortality rate of 61 deaths per 1,000 live births and Kerala the lowest rate of 13 (data not shown) ^[25].

Health inequalities between the Indian states coincide with those in human development and poverty rates. According to the Indian Government estimates, poverty rates in 2009-10 were highest in Bihar (54%) and Chhattisgarh (49%) and lowest in Himachal Pradesh (10%) and Kerala (12%) ^[25]. Kerala ranked the highest on human development (HDI = 0.92) and Madhya Pradesh, Orissa and Bihar ranked the lowest on HDI (0.45-0.49) ^[22].

Table 1. Non-Communicable Disease (NCDs) Mortality Rates, Health-Risk and Health Care Factors, and Social Determinants, 2008-2011

Age-adjusted death rates are per 100,000 population and are age-adjusted to the world standard population.
Source: WHO, World Health Statistics Report 2011 and NCD Country Profiles; United Nations, Human Development Report 2011.

Table 2. Cancer Incidence and Mortality Rates in China and India, 2008

Note. Age-standardized rates, ASR(W), are per 100,000 population and age-adjusted to the world standard population. RR = relative risk. CI = confidence interval.

Source: GLOBOCAN 2008, International Agency for Research on Cancer (IARC); http://globocan.iarc.fr/

Table 3. Health Inequalities within India, 2005-2011

Source: WHO, World Health Statistics Report 2011 and United Nations, Human Development Report 2011.

Health inequalities in China are evident between urban and rural residents and by region or province. In 2005, under-five mortality was 2.4 times higher and maternal mortality 1.8 times higher in rural than in urban areas of China (Table 4). The under-five child mortality rates in Beijing and Qinghai were 5.1 and 35.0 per 1,000 live births, respectively. In 2000, the average life expectancy of China’s urban residents was 75.2 years and that of its rural resident was 69.6 years. Like India, inter-provincial differences in life expectancy in China were very marked ^[23]. Shanghai had the highest life expectancy of 78.1 years, 2 years more than Beijing. Tibet had the lowest life expectancy of 64.4 years, followed by the other two Southwest provinces Yunnan (65.5) and Guizhou (65.9). Provincial patterns in income and human development were similar to those in life expectancy. For example, Tianjin, Shanghai, and Beijing had the highest income per capita, while Tibet, Gansu, Yunnan, and Guizhou had the lowest income per capita. Shanghai and Beijing had the highest HDI scores of 0.91 and 0.89 respectively, while Tibet and Guizhou had the lowest HDI scores of 0.69 and 0.63, respectively ^[23].

Table 4. Health Inequalities within China, 2005

Source: United Nation Development Programme, China Human Development Report 2007-2008.

Discussion

By using contemporary global health and socioeconomic statistics, we have highlighted similarities and differences in health, socioeconomic, and developmental factors between India and China. Because of the faster improvements in mortality in China during the past five decades, the health gap between India and China, particularly in infant, child, and maternal mortality and life expectancy, has widened.

Public health problems and the burden of disease from specific conditions vary greatly between the two nations. While childhood diseases, maternal mortality, cardiovascular disease, diabetes, and cervical and oral cancers are major public health problems in India, high rates of stomach, liver, lung, and esophageal cancers, cardiovascular disease, and smoking are major health concerns in China. More than a quarter of all cervical cancer cases and deaths in the world occur in India alone ^{[16, 19]}. India has the highest number of oral cancer cases and accounts for 39% of oral cancer deaths in the world ^[16]. India also accounts for approximately one-fourth of all under-5 deaths and one-fifth of maternal deaths globally ^{[26, 27]}. China accounts for one-third of all lung cancer cases and deaths, 54% of liver cancer cases and deaths, and 48% of all stomach cancer cases and deaths worldwide ^[16].

Smoking prevalence among Chinese adult men aged ≥15 years is 60%, which is among the highest in the world ^[9]. Men in Russia, Ukraine, Laos, Greece, and Indonesia have higher smoking prevalence. Interestingly, although the adult smoking prevalence is lower in India than in China, Indian teens are twice as likely to smoke as their Chinese counterparts. About 19% of males and 8% of females aged 13-15 in India are smokers, compared with 7% and 4% in China, respectively ^[9]. Thus, the potential exists in India for substantial increases in adult smoking prevalence and premature death from smoking-related diseases.

The two countries share other health and development concerns such as high levels of disparities between socioeconomic groups and between rural and urban areas. As shown here, there is a substantial health and socioeconomic divide, particularly between rural and urban areas and among states, both within India and China.

In the decades ahead, India faces twin challenges of having to reduce deaths from infectious diseases and maternal and perinatal conditions as well as from major chronic diseases such as heart disease, stroke, diabetes, and cancer ^[9]. With expanding industrialization and urbanization levels, people in both India and China are increasingly more likely to adopt lifestyles, dietary, and consumption patterns that are currently prevalent in the West ^[12]. Consequently, rates of obesity, physical inactivity, and smoking (particularly in women) are expected to increase dramatically in the years ahead and chronic diseases are expected to become an even bigger threat to public health in India and China.

Because of the massive size of their population and economy and their growing influence in global matters, the world is paying increasing attention to existing conditions and changes that are occurring in the socioeconomic, political, and public health domains in both China and India. As the two nations make further improvements in their levels of social and economic development, substantial gains in health are expected for both India and China. Despite the marked improvements in health during the past 5 decades, much needs to be done to increase life expectancy and improve maternal and child health. Mainland China’s life expectancy of 74 years is still 8-9 years less than that of Japan, Hong Kong, Singapore, Iceland, and Switzerland and 5 years less than Taiwan’s ^[9]. India’s life expectancy and child survival have to improve a great deal to reach the level seen in many middle- and high-income countries. India’s southern neighbor, Sri Lanka, does significantly better than India in life expectancy, child survival, and maternal mortality ^[9]. One way to improve health at the national level is to reduce inequalities in health and social conditions between population groups and areas both within India and China ^{[6, 9, 14]}.

Conclusions and Public Health Implications

India and China differ greatly in their levels of health, human development, and gender inequality ^{[1, 9]}. China does better than India on several key health measures, with much of the health disparity stemming from higher rates of mortality in India from communicable diseases and maternal and perinatal conditions, which are largely preventable ^[9]. Health and social development often go hand in hand. Our analysis of the WHO data (not shown) indicates that the nations that perform poorly on the human development and gender inequality indices tend to have lower levels of life expectancy and higher infant, child, and maternal mortality (γ > 0.70). Not only does India have lower levels of human development than China, it also does poorly with respect to gender equality. Indian women fare worse in reproductive health, educational achievements, empowerment, and labor force participation than their Chinese counterparts.

Benefits of rapid, sustained economic growth need to be shared more broadly and evenly across population groups, particularly among rural, remote regions in the two countries where the majority of the Indian and Chinese people still live. Unfavorable socioeconomic conditions and low human development levels can be a hindrance for public health improvement, but both India and China need to invest more in their health care infrastructure, provision of health services, and in education and rural sectors. Broad societal initiatives are, of course, needed to address important health and developmental goals, including poverty reduction, larger investments in women’s health and education (particularly in India), expanding economic opportunities for women, and a greater commitment toward gender and social equality in the distribution of power, money, and resources ^{[1, 5-7, 9, 14]}. Both countries can make further health gains by reducing social inequality, greater investments in human development and preventive health services, and by implementing policies related to the prevention and control of chronic-disease risks such as hypertension, smoking, obesity, and physical inactivity.

Financial Disclosure None to report. Funding/Support: None. Conflicts of Interest None.
Acknowledgements: None. The views expressed are the authors’ and not necessarily those of the US Department of Health and Human Services and Global Health and Education Projects.

References

1. United Nations Development Programme. Human Development Report 2011. New York, NY; 2011.
2. Lee R. The demographic transition: three centuries of fundamental change. Journal of Economic Perspectives. 2003; 17(4):167-190.
3. Dougherty S, Valli V. Comparing China and India: an introduction. European Journal of Comparative Economics. 2009; 6(1):53-55.
4. Basu SR. Comparing China and India: is the dividend of economic reforms polarized? European Journal of Comparative Economics. 2009; 6(1):57-99.
5. Sen A. Growth and other concerns. The Hindu. February 14, 2011. http://www.thehindu.com/opinion/op-ed/article1451973.ece?homepage=true. Accessed May 11, 2012.
6. Sen A. Quality of Life: India vs. China. The New York Review of Books. May 12, 2011. http://www.nybooks.com/articles/archives/2011/may/12/quality-life-india-vs-china/?pagination=false. Accessed May 15, 2012.
7. Sen A. India no match for China on social indicators. The Economic Times. January 7, 2012. http://articles.economictimes.indiatimes.com/2012-01-07/news/30601912_1_social-indicators-south-asian-economies-development-indicators. Accessed May 11, 2012.
8. Ma S, Sood N. A Comparison of the Health Systems in China and India. Center for Asia Pacific Policy, Rand Corporation, Santa Monica, California; 2008.
9. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
10. World Health Organization. NCD Country Profiles, India and China, 2011. Geneva, Switzerland; 2011.
11. World Bank. Health, Nutrition, and Population Statistics (HNP Stats). Washington, DC; 2011. Available at: http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS /EXTHEALTHNUTRITIONANDPOPULATION/EXTDATASTATISTICSHNP/EXTHNPSTATS/0,, menuPK:3237172~pagePK:64168427~piPK:64168435~theSitePK:3237118,00.html. Accessed May 15, 2012.
12. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva, Switzerland; 2009.
13. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
14. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
15. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. American Journal of Public Health. 2003; 93(7):1137-1143.
16. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. GLOBOCAN 2008, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 10 [Internet]. Lyon, France: International Agency for Research on Cancer; 2010. Available at: http://globocan.iarc.fr. Accessed May 1, 2012.
17. Arbyn M, Castellsague X, de Sanjose S, et al. Worldwide burden of cervical cancer in 2008. Annals of Oncology. 2011; 22(12):2765-2786.
18. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011; 61(2):69-90.
19. Singh GK, Azuine RE, Siahpush M. Global inequalities in cervical cancer incidence and mortality are linked to deprivation, low socioeconomic status, and human development. International Journal of MCH and AIDS. 2012; 1(1):17-30.
20. Martin JA, Hamilton BE, Ventura SJ, et al. Births: final data for 2009. National Vital Statistics Reports. 2011; 60(1):1-72.
21. UNAIDS. Country Fact Sheets, China and India. Available at: http://aidsinfo.unaids.org. Accessed May 15, 2012.
22. Suryanarayana MH, Agrawal A, Prabhu SK. Inequality-Adjusted Human Development Index for India’s States. New Delhi, India: United Nations Development Programme; 2011.
23. United Nations Development Programme. China Human Development Report, 2007-2008: Basic Public Services Benefitting1.3 Billion Chinese. Beijing, China: UNDP; 2008.
24. Mani K, Dwivedi SN, Pandey RM. Determinants of under-five mortality in Rural Empowered Action Group States in India: An application of Cox frailty model. International Journal of MCH and AIDS. 2012; 1(1):60-72.
25. Government of India, Planning Commission. Databook for DCH, Social Sector – Health and Family Welfare. http://planningcommission.gov.in. Accessed May 15, 2012.
26. Rajaratnam JK, Marcus JR, Flaxman AD, et al. Neonatal, postneonatal, childhood, and under-5 mortality for 187 countries, 1970-2010: a systematic analysis of progress towards Millennium Development Goal 4. Lancet. 2010; 375(9730):1988-2008.
27. World Health Organization. Trends in Maternal Mortality: 1990 to 2008. Geneva, Switzerland; 2010.

Abstract

Objectives

Studies on factors affecting neonatal mortality have rarely considered the impact of place of delivery on neonatal mortality. This study provides epidemiological information regarding the impact of place of delivery on neonatal deaths.

Methods

We analyzed data from the Rufiji Health and Demographic Surveillance System (RHDSS) in Tanzania. A total of 5,124 live births and 166 neonatal deaths were recorded from January 2005 to December 2006. The place of delivery was categorized as either in a health facility or outside, and the neonatal mortality rate (NMR) was calculated as the number of neonatal deaths per 1,000 live births. Univariate and multivariate logistic regression models were used to assess the association between neonatal mortality and place of delivery and other maternal risk factors while adjusting for potential confounders.

Results

Approximately 67% (111) of neonatal deaths occurred during the first week of life. There were more neonatal deaths among deliveries outside health facilities (NMR = 43.4 per 1,000 live births) than among deliveries within health facilities (NMR = 27.0 per 1,000 live births). The overall NMR was 32.4 per 1,000 live births. Mothers who delivered outside a health facility experienced 1.85 times higher odds of experiencing neonatal deaths (adjusted odds ratio = 1.85; 95% confidence interval = 1.33–2.58) than those who delivered in a health facility.

Conclusions and Public Health Implications:

Place of delivery is a significant predictor of neonatal mortality. Pregnant women need to be encouraged to deliver at health facilities and this should be done by intensifying education on where to deliver. Infrastructure, such as emergency transport, to facilitate health facility deliveries also requires urgent attention.

Key Words:

Neonatal mortality • Place of delivery • Tanzania • Socioeconomic status • Maternal age.

Introduction

Reducing neonatal mortality is a major thrust of current international public health policy ^[1]. Place of delivery is an important aspect of reproductive health care. The place of delivery often determines the quality of care received by a mother and infant. It is an important factor in differential risks of neonatal mortality ^[2]. Children delivered at a health facility are likely to experience lower mortality than children delivered at home because such facilities usually provide a sanitary environment and medically correct birth assistance ^[3]. Although almost half of all deliveries in Tanzania take place at home ^{[4, 5]}, studies on neonatal mortality have rarely considered the influence of place of delivery on neonatal mortality in Tanzania.

Greater attention to neonatal deaths could be met through providing epidemiological information regarding the places of neonatal deaths to policy makers and program planning authorities ^[6]. This paper reports on the findings of a study conducted in the Rufiji Health and Demographic Surveillance Site (RHDSS) in rural Tanzania to assess the impact of place of delivery on neonatal mortality.

Methods

Study Area

This study was conducted using data from the RHDSS site in rural Tanzania; Rufiji is one of the six districts of Coast Region in Tanzania about 178 km south of Dar-Es-Salaam. The district has a population of about 226,000 people. The Rufiji demographic surveillance area comprises of 31 villages with a resident population of approximately 93,000 in 18,000 households. The RHDSS monitors households and members within households in cycles or intervals, known as ‘rounds’ of four months each. Members (residents) of the RHDSS are individuals who have resided in the survey area for a period of the previous four months and plan to continue to live there. Rufiji district has 56 health facilities made up of two hospitals (one government and one mission); five government health centres and 48 government dispensaries. A private dispensary based at Kibiti offers mobile clinic services in some parts of the district. About 89% of the population lives within 5 km of a formal health facility.

Study Design

This was an analytical longitudinal study, based on secondary data from the RHDSS data on all neonatal deaths and live births that occurred from January 2005 to December 2006. The place of delivery was classified into two groups: health facility and outside health facility; thus deliveries that occurred at home or on the way to a health facility counted as outside health facility deliveries.

Study Sample

The analyzed sample comprised of all children younger than 28 days born between January 2005 and December 2006 to residents of the Rufiji DSA. A total of 5,124 live births and 166 deaths were registered during the defined study period.

Data

The variables used were selected from five datasets through an internal individual unique ID in the RHDSS database and extracted and combined into a new data set. The following were the key variables of interest:

1. The main outcome variable was neonatal mortality, defined as any death occurring within 28 days of birth and coded using verbal autopsy instrument or death certificate mortality information.
2. The main explanatory variable was place of delivery, which was defined as the place where a birth took place; either in a health facility or outside a health facility.

Other explanatory variables were maternal occupation, marital status, parity, infant’s sex, maternal age at delivery, maternal education, and maternal household socio-economic status (SES)

Maternal Household Socio-Economic Status (SES)

Maternal household SES was constructed by using household characteristics and assets ownership data. The data was transferred from Microsoft visual fox pro professional edition version 5.0 database format into Stata version 10 software with stat transfer. This information was used in the construction of household wealth index using Principal Component Analysis (PCA) in Stata version 10 software. The following variables were used for the PCA analysis; Hoe, matchet, bicycle, vehicle, motorbike, radio, refrigerator, television, clock, sofa, bed, video, mattress, wardrobe, pump, livestock, sewing machine, chicken, bednet, satellite dish, ceiling fan, iron, floor type, wall type, roof type and power or energy source. The assets were combined into a wealth index using weights derived through principal components analysis (PCA).

PCA involves breaking down assets (e.g. radio, bicycle) or household service access (e.g. water, electricity) into categorical or interval variables. The variables are then processed in order to obtain weights and principal components. The Principal Component Analysis Model that was used to construct the wealth index (socio-economic indices) with household characteristics and ownership of assets was based on the model proposed by Filmer and Prichett ^[7] in 2001. This approach uses the PCA which involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.

The model was based on the presence or absence of each asset or the nature of the housing materials .i.e. each asset was dummied with the response, 1 and 0. If the mother had the asset the response was 1, otherwise it was assigned 0. The generated wealth index was used to categorize the households of mothers of the neonates into five socio-economic groups or quintiles; poorest, poorer, poor, less poor, and least poor to arrive at maternal household socio-economic status.

Statistical Analysis

Neonatal mortality rates (NMR) were calculated based on place of delivery (i.e., a health facility delivery or outside health facility delivery) and by dividing the total neonatal deaths that occurred in each place of delivery by the respective total number of live births that occurred in each place of delivery. The NMR was expressed as a rate per 1,000 live births. The NMR was also calculated for the total study population. Univariate and multivariate logistic regression models were used to assess the associations between neonatal mortality and place of delivery while adjusting for potential confounders such as maternal age at delivery, maternal occupation, maternal education, maternal household socio-economic status, parity and marital status. All analyses were done in Stata 10 ^[8].

Limitations of the Study

Information on pregnancy complications or events prior to delivery that may have influenced the risk of newborn deaths was not available. Again, data on prematurity which is a high risk factor for newborn deaths was not available.

The results of this study may not be generalizable to the entire rural Tanzania because quality of health care varies across the country.

Ethical Considerations

The study received ethical approval from the University of the Witwatersrand’s Committee for Research on Human Subjects (Medical) (protocol number M071142) in South Africa and the Institutional Review Board of the Ifakara Health Institute (ethical clearance number: IHI/IRB/No. A 021) in Tanzania. The datasets were anonymized.

Results

Socio-Demographic Characteristics.

A total of 5,124 live births and 166 neonatal deaths were recorded in the Rufiji Health and Demographic Surveillance Area (RHDSA) from January 2005 to December, 2006. There were slightly more boys 2,577 (50.3%) than girls 2,547 (49.7%) born during the study period. There were no significant sex differentials in neonatal deaths 84 (50.6%) females versus 82 (49.4%) males (Table 1). Approximately 67% (111) of neonatal deaths occurred during the first week of life (Table 1). Of the 5,124 births, 3,442 (67.2%) were born in health facilities and 1,682 (32.8%) were born elsewhere (Table 2).

Distribution of Neonatal Mortality Rates by Place of Delivery

Neonatal mortality was significantly higher (43.3 per 1,000 live births) in children born outside health facilities compared to those born in health facilities (27.0 per 1,000 live births) (Table 2).

Maternal Risk Factors Associated with Neonatal Mortality

Because the probability of neonatal death associated with most risk factors was smaller than 0.05, we have used odds and risks of neonatal death interchangeably. Table 3 presents the unadjusted analysis of the association between place of delivery, maternal risk factors and neonatal mortality. The univariate analysis found that mothers who delivered outside a health facility experienced 1.63 times higher odds of experiencing neonatal deaths (unadjusted odds ratio [OR] = 1.63; 95% CI = 1.19–2.23) than mothers who delivered in a health facility (Table 3). Mothers who had no education experienced 0.78 times lower odds of experiencing neonatal deaths than mothers who had secondary education, though this association did not reach significance (unadjusted OR = 0.78; 95% CI = 0.35–1.73). Maternal age was significantly associated with neonatal mortality. Mothers in the age group 20-29 years experienced 0.48 times lower odds of experiencing neonatal deaths than mothers who were under 20 years old (unadjusted OR = 0.48; 95% CI = 0.33–0.71). Mothers aged 30 years and above experienced 0.63 times lower odds of experiencing neonatal deaths than mothers who were under 20 years old (unadjusted OR = 0.63; 95% CI = 0.43–0.92).

Parity was found to be protective against neonatal death since significance was reached. Compared with mothers with a parity of 1-2, mothers with a parity of 3-4 experienced 0.52 times lower odds of experiencing neonatal deaths and mothers who had a parity of 5 and more experienced 0.57 times lower odds of experiencing neonatal death. Maternal marital status, socio-economic status, and maternal occupation were not significantly associated with neonatal mortality.

In multivariate analysis, delivery outside a health facility remained a significant risk factor for neonatal mortality. Mothers who delivered outside a health facility experienced 1.85 times higher odds of experiencing neonatal deaths (adjusted OR = 1.85; 95% CI =1.33–2.58] than those who delivered in a health facility (Table 4). Maternal age, maternal education, maternal marital status, parity, maternal socio-economic status, and maternal occupation were not statistically significantly associated with neonatal mortality.

Table 1.Distribution of Neonatal and Maternal Socio-demographic Characteristics in Rufiji Health and Demographic Surveillance System (HDSS), Tanzania 2005-2006

Table 2. Distribution of Neonatal Mortality Rates by Place of Delivery per 1000 Live Births, Rufiji Health and Demographic Surveillance System (HDSS), Tanzania 2005-2006

NMR*= Neonatal Mortality Rate per 1,000 live births

OHF**= Out-Side Health Facility Deliveries/Births

Table 3. Unadjusted Odds Ratio (OR) Estimates and 95% Confidence Intervals (CI) for Maternal Risk Factors Associated with Neonatal Mortality (Univariate Logistic Regression Analysis)

Table 4. Adjusted Odds ratio (OR) Estimates and 95% Confidence Intervals for Maternal Risk Factors Associated with Neonatal Mortality (Multivariate Logistic Regression Analysis)

* Reference Group

Discussion

The results of this study clearly indicate that delivery outside a health facility is more likely to lead to neonatal death compared with delivery in a health facility; this confirms the role of place of delivery on newborn survival. Place of delivery has consistently been found to be associated with maternal and neonatal outcomes [9,10,11]. Childbirth in a health institution attended to by a trained medical staff reduces maternal and neonatal mortality and morbidity compared to home births [12,13,14]. Most of the studies reported are, however, based on health facility data only which do not demonstrate the actual magnitude of the problem. The community data analyzed here clearly demonstrates this point.

Higher neonatal mortality was found among children born outside health facilities (43.4 versus 27.0 per 1,000 live births), even though health facility deliveries generally have a far greater likelihood of complications likely to result in neonatal death. This is as a result of the health-seeking behaviour where most deliveries will be attended to at home until it becomes complicated; and it is only at this stage that the home delivery attendants will refer to the health facilities. Weak health systems are also likely to account for neonatal deaths among the health facility deliveries.

The overall neonatal mortality in Rufiji district, which was 32.4 per 1,000 live births, is similar to the neonatal mortality rate of 32.0 per 1,000 live births reported by the 2004-05 Tanzania Demography and Health Survey (TDHS). The findings also confirm the average NMR of 33 per 1,000 live births for middle-income and low-income countries where 99% of neonatal deaths occur. The overall NMR is consistent with NMRs reported in other Sub-Saharan African countries such as Uganda (32/1,000), Burkina Faso (31/1,000) and Madagascar (32/1,000) [15]. The NMR of 43.4 per 1,000 for births that occurred outside health facilities is close to England’s NMR of 41 per 1,000 live births in 1905, and the average for Sub-Saharan Africa today [16]. The overall NMR of 32 per 1,000 live births for Rufiji indicates a great improvement in neonatal deaths in a rural Tanzania district, though there is much room for further improvement.

Conclusions and Public Health Implications

The findings from this study lend credence to the vital role that the place of delivery plays in neonatal survival as delivery outside a health facility is a risk factor of neonatal mortality. This finding concurs with the 2005 World Health Report which states that, giving birth in a health facility (not necessary a hospital) with professional staff is safer by far compared to doing so at home [17]. It also conforms to the results of Demographic and Health Survey (DHS) data from 40 countries collected between 1995 and 2003 which reported that more than 50% of neonatal deaths occur after home birth without skilled care attendance [18]. Furthermore, these results are consistent with a study in rural Tanzania which reported that home births without a trained attendant resulted in a three times higher perinatal mortality compared with those in a health facility with trained attendants in rural Tanzania [19]. These findings are in line with those of a study in Papua New Guinea which reported high rate of obstetric complications among apparently normal pregnancies deliveries at home in Papua New Guinea [20]. These findings have important implications for all stakeholders and policy makers in the fight against neonatal mortality in sub-Saharan Africa in general and Tanzania in particular.

The use of longitudinal population-based data is an ideal way of communicating the impact of home deliveries on neonatal mortality. The United Republic of Tanzania’s health system must be strengthened to promote universal facility delivery in order to achieve the Millennium Development Goal 4 and to comply with the National Strategy for Growth and Reduction of Poverty (NSGRP) which aims to reduce infant mortality from 95 infant deaths per 1,000 live births in 2002 to 50 per 1,000 by 2010.

In conclusion, the place of delivery has a significant impact on neonatal survival. The authors therefore recommend the development and implementation of programs that would educate, encourage and support pregnant women to give birth in health facilities.

Conflict of interest: None
Acknowledgements: This paper is published with the permission of the Director and Management of Ifakara Health Institute, for whose support we are very grateful.

The study was funded by Indepth-Network as part of JA’s support by the Indepth-Network for his MSc studies. The funding body had no role in study design, data collection, analysis or interpretation of the data, nor in writing the manuscript or in the decision to submit the manuscript for publication.

References

1. English M., Muhoro A, Aluda M, Were S, Ross A, Peshu NT. Outcome of delivery and cause-Specific mortality and severe morbidity in early infancy: a Kenya district hospital birth cohort. American Journal Tropical Medicine and Hygiene. 2003: 69(2), 228–232.
2. Murthy MSR, Murthy PV, Hari M, Kumar VKR, Rajasekhar K. Place of birth: Why urban women still prefer home deliveries? Journal of Human Ecology. 2007; 21(2): 149-154.
3. Pandey A, Choe MK, Luther NY, Sahu D, Chand J. Infant and Child Mortality in India: National Family Health Survey Subject Reports. Number 11. Indian Institute of Population Sciences, Mumbai; 1998.
4. DHS Macro International, Inc. Tanzania Demographic and Health Survey 2010. Macro International Inc. Calverton, MD; 2010.
5. Walraven GE, Mkanje RJ, van Roosmalen J, van Dongen PW, Dolmans, WM. Assessment of maternal mortality in Tanzania. British Journal of Obstetrics and Gynecology. 1994; 101(5):414–417.
6. Lawn JE, Cousens S, Bhutta ZA. Lancet Neonatal Survival Team. Why are 4 million newborns babies dying each year? Lancet. 2004; 364(9432): 399–401.
7. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data or tears: An application to educational enrolments in states of India. Demography. 2000; 38(1):115-132.
8. StataCorp. College Station, Texas 77845; 2007.
9. Giri K. Discussion. International Journal of Gynecology and Obstetrics. 1995; 50: (supp1 2):S43.
10. Tsu VD. Antenatal screening: its use in assessing obstetric risk factors in Zimbabwe.
11. Journal of Epidemiology and Community Health. 1994; 48(3):297-305.
12. Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Social Science and Medicine. 1994; 38(8): 1091–1110.
13. Howlader AA, Kabir M, Bhuiyan MM. Health-seeking behaviour of mothers and factors affecting infant and child mortality. Demography India. 1999; 28(2):225–238.
14. Luther NY. Mother’s tetanus immunisation is associated not only with lower neonatal mortality but also with lower early childhood mortality. National Family Health Survey Bulletin. 1998; 10:1–4.
15. Jejeebhoy SJ, Rao SR. Unsafe Motherhood: A Review of Reproductive Health. In: Das Gupta M, Chen LC, Krishnan TN, eds. Women’s Health in India: Risk and Vulnerability. Bombay, India: Oxford University Press; 1995.
16. MacFarlane A, Johnsone A, Mugford M., Rennie J, Roberton N. Textbook of Neonatology. 3rd edition, Churchill Livingstone, Cambridge; 1999; 3-33.
17. World Health Organization. The world health report 2005: make every mother and child count. Geneva: WHO; 2005.
18. Lawn JE, Cousens S and Zupan J. 4 million neonatal deaths: when? where? why? Lancet; 2005; 365(9462);891-900.
19. Walraven GE, Mkanje RJ, Roosemalan J, van Dongen PW, Dolmans WM. Perinatal mortality in home births in Rural Tanzania. European Journal Obstetrics and Gynecology and Reproductive. Biology. 1995: 58(2):131-134.
20. Garmer P, Lai D, Baea M. Childbirth in rural areas: maternal deaths, village deliveries and obstetric service use. Papua New Guinea Medical Journal, 1994; 37(3); 166-172.

Abstract

Objectives

In India there has been a decline in overall under-five mortality, with some states still showing very high mortality rates. It is argued that there is family clustering in mortality among children aged <5 years. We explored the effects of programmable (proximate) determinants on under-five mortality by accounting for family-level clustering and adjusting for background variables using Cox frailty model in rural Empowered Action Group states (EAG) in India and compared results with standard models.

Methods

Analysis included 13,785 live births that occurred five years preceding the National Family Health Survey-3 (2005-06). The Cox frailty model and the traditional Cox proportional hazards models were used.

Results

The Cox frailty model showed that mother’s age at birth, place of delivery, sex of the baby, composite variable of birth order and birth interval, baby size at birth, and breastfeeding were significant determinants of under-five mortality, after adjusting for the familial frailty effect. The hazard ratio was 1.41 (95% CI=1.14–1.75) for children born to mothers aged 12-19 years compared to mothers aged 20-30 years, 1.42 (95% CI=1.12–1.79) for small-sized than average-sized babies at birth, and 102 (95% CI=81–128) for non-breastfed than breastfed babies. Children had significantly lower mortality risks in the richest than poorest wealth quintile. The familial frailty effect was 2.86 in the rural EAG states. The hazard ratios for the determinants in all the three models were similar except the death of a previous child variable in the Cox frailty model, which had the highest R2 and lowest log-likelihood.

Conclusions and Public Health Implications:

While planning for the child survival program in rural EAG states, parental competence which explains the unobserved familial effect needs to be considered along with significant programmable determinants. The frailty models that provide statistically valid estimates of the covariate effects are recommended, when observations are correlated.

Key Words:

Empowered Action Group States • Under-five mortality • National Family Health Survey • Frailty model • Unobserved familial effect • Programmable determinants • India.

Introduction

Reducing under-five mortality is now a global concern. In 2001 as part of the Millennium Development goals (MDG) for health, nations pledged to ensure a two-thirds reduction in under-five mortality between 1990 and 2015 ^[1] and at once a series of articles in Lancet by the Bellagio Study Group described various aspects of child survival ^{[2, 3, 4, 5, 6]}. Although under-five mortality is declining worldwide as a result of socioeconomic development and implementation of child survival interventions, nearly 8.8 million children die every year before their fifth birthday. India alone accounted for 21% of the world’s under-five deaths occurring in 2008 ^[7] owing to its large population. In India, states such as Assam, Arunachal Pradesh, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh, have higher Under-five mortality than the rest of India. The national average for under-five mortality is 74 per 1000 ^[8]. The Ministry of Health and Family Welfare, India, established Empowered Action Group (EAG) in 2001 to have special focus by monitoring and facilitating the attainment of national health goals on some of these states which are demographically lagging behind. The EAG states constitute 45% of the total population of India and also have higher neonatal and infant mortality rates.

In developing countries, efforts have been made during the past three decades to reduce child mortality. Despite socioeconomic development and implementation of child survival interventions, prevailing high mortality may be due to the heterogeneity. This might have considerable implication for reproductive health and child survival programs ^[9]. Studies on determinants of child mortality have mainly used either logistic regression or Cox proportional hazards model assuming that the outcomes are independent. To find more accurate estimates for the determinants of child mortality that has critical implications for resource allocation for improving child survival, sibling structures in child mortality data from demographic surveys have been treated as multivariate failure time data ^{[10, 11, 12, 13]}. As failure time data, many attempts have been made to extend the Cox proportional hazards model. In this context, the variance-corrected Cox model has received much attention ^{[14, 15]}. In the variance-corrected Cox model, regression parameters of the determinants are estimated by ignoring intra-family correlation but adjusted for in the inference procedure; however, it ignores the variation of underlying risk among families. To overcome this, multivariate failure time data are modeled by an unobserved random quantity called frailties ^[16]. These frailties are common to observations from the same cluster and assumed to follow a given statistical distribution, known as multivariate random effects model or Cox frailty model.

In India, studies on child mortality have mainly addressed the role of maternal, socioeconomic and health-related determinants ^{[9, 17, 18]}. These studies were restricted to the analysis of mortality risks in children at individual level and not considered the correlation among children of the same family. We also want to emphasize those determinants which are nearer in time to the outcome and can be modified by program than those which are remote or far apart in time to the outcome of concern. The former covariates are referred to as programmable determinants and the latter as background variables. Therefore, we aimed to identify the programmable determinants of under-five mortality using Cox frailty model to account for sibling-level correlation for providing valid estimates needed for policy-decision making. In order to appreciate the influence of sibling-level correlation over the estimates of the determinants of under-five mortality, the results of Cox frailty model were compared with the Cox proportional hazards model and variance-corrected Cox model.

Methods

Data Sources

The third round of National Family Health Survey-3 (NFHS-3) in India was completed during 2005-06 covering a nationally representative sample of ever married women aged 15-49 years. This survey collected data on fertility, family planning, infant and child mortality, maternal and child health, etc. using a two-stage sample design in rural areas for each state of India. The first stage involved selection of primary sampling units, i.e., villages, with probability proportional to population size and the second stage involved systematic selection of households within each selected village ^[8]. The response rates for household and eligible women identified in the household were 98.5% and 95.5% respectively. The rural data of NFHS-3 for eight EAG states, viz., Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh and Uttarakhand were combined and analyzed to identify the determinants of under-five mortality. In rural EAG states, retrospective maternity history was collected from 24,507 women aged 15-49 years. A total of 14,184 live births occurred within five years preceding the survey and mortality experience of 13,785 children were analyzed in this study. In 399 cases the information was missing on some of the variables used in the analysis. Of these 13,785 live births, 1,068 children died before reaching their fifth birthday.

Study Variables

The primary outcome, under-five mortality, was defined as time to death of a live born baby before his/her fifth birthday. Available potential predictors ^[19] of child survival as summarized in the conceptual framework of Mosley and Chen ^[20] was considered and grouped into programmable (proximate) determinants and background variables.

Programmable determinants included mother’s age at birth, delivery assistance, place of delivery, mode of delivery, combined variable of birth order and birth interval, survival status of previous child, maternal subjective assessment of baby’s size at birth, sex of the baby and ever breastfeeding; and the background variables included region (eight states), religion, caste, mother’s education, mother’s occupation, household wealth index, number of children in the family and desired time for pregnancy.

Analytical Models: Traditional Cox Proportional Hazards Model, Multivariate Cox Variance-Corrected and Frailty Models

The variance-corrected and frailty hazard models are multivariate not only in the usual sense of having multiple predictors, but also in the sense of having multiple responses, that is, responses from more than one child in the family.

Cox Proportional Hazards model:

Mathematically, it is written as

h(t) = h0(t) exp(β zk), t > 0, …………………………… (1)

Where, h0(t) is an unspecified baseline hazard function and β denote the vector of the true regression coefficients for covariates zk, (k=1, 2, …, p). We could obtain an estimator β of β based on the working assumption that the under-five deaths in each family were independent of one another.

Cox variance corrected model:

We supposed that conditional on covariate vector (zik), the marginal hazard function hik(t) for failure time of the kth child in the ith family, (k = 1,2,3,….,Ki; i = 1,2,3,…,n) with the usual proportional hazards form and is given by

hik(t) = h0(t) exp(β zik), t > 0, …………………………… (2)

We could obtain an estimator of β based on the working assumption that the under-five deaths in each family were independent of one another. But the equation (2) assumes that the births are related and hence adjusts for it in the inference, that is, the standard error by means of sandwich-type estimators [13] and so it is called as variance corrected models.

Cox frailty model

For the frailty model, we supposed that conditional on the frailty, vi the hazard function hik(t) for the failure time of the kth children in the ith family (k = 1,2,3,….,Ki; i = 1,2,3,…,n) follows the usual proportional hazards form and is given by:

hik (t) = h0 (t) vi exp(β’zik), t > 0, …………………………… (3)

Where, vi, group-level (family) frailty. These frailties are unobservable, assumed to be independent and identically distributed with unit mean and unknown variance θ. Each family could have different values of random effects and the variability in the vis reflect heterogeneity of risks between families. If the variance of the random effect (frailty) is 0, then children from the same family are independent. The variance of the random effect lies between 0 and α. A larger variance implies greater heterogeneity in frailty across families and greater correlation among children belonging to the same family. The frailty (family) often assumed to follow gamma distribution for the sake of computational convenience and convergence ^{[21, 22, 23]} and this model is expected to yield correct z-ratios, on which researchers rely heavily for their conclusions ^[10].

Equations (2) and (3) reduce to the traditional Cox Proportional Hazards model ^[24], if the responses from each child in the family are assumed to be independent.

Statistical analysis

The complete data for all the EAG states were downloaded from Demographic Health Survey data distribution system website: http://www.measuredhs.com. All the variables were read and coded using Stata 9.0 (College Station, Texas, USA). The under-five mortality rate (U5MR) and its 95% CI with respect to potential determinants influencing under-five mortality was calculated. We identified potential determinants of under-five mortality using three models: the traditional Cox proportional hazards model, the variance-corrected Cox proportional hazards model, and the Cox frailty model. Univariate models were fitted followed by multivariate models. Programmable determinants were adjusted for background factors in the multivariate analysis in all the three models. The model performance was assessed using R-square and log-likelihood. The results were reported as hazard ratio (95% CI). The value of p<0.05 was considered statistically significant. The R-software (version 2.11.1, 2010, The R foundation for Statistical Computing) was used to fit all the models.

Results

The trends in under-five mortality rates by major states in rural India for five years preceding the NFHS-1 (1992-92) and NFHS-3 (2005-06) are given in Table 1. Among the EAG states, no change in under-five mortality was found in Chhattisgarh and the highest decline in mortality (38.1%) was found in Bihar between the two surveys. The percentage decline in under-five mortality in rural India was 31.3 between the two surveys.

The distribution of live births by family is shown in Table 2. More than one third (41%) of the families contributed two or more children to the sample. About 40 percent of the total 13,785 children did not have sibling. A total of 1,068 under-five deaths occurred to 969 (10%) families.

The number of live births and under-five mortality with respect to the background factors and programmable determinants are shown in Table 3. One third of the total live births were from Uttar Pradesh; only 36.4 percent live births belong to scheduled caste (21.3%) and scheduled tribes (15.1 %) and mothers of two-thirds of the live births were illiterate. The under-five mortality (per 1,000 live births) was 86.9 in Uttar Pradesh, 89.9 among scheduled caste mothers, 86.7 among illiterate mothers, 81.7 among families with more than two children, 87.9 in the poorest wealth quintile, 104.2 among children born to mothers’ aged 12-19 years, 139.3 in mothers having previous birth interval of less than two years and parity more than three, 107 among small sized babies, and 143 among children with history of dead sibling, which were having very high under-five mortality than their counterparts.

The results of programmable determinants of under-five deaths adjusting for the background variables using all the three models are given in Table 4. The estimates are exactly the same in Models 1 and 2; only standard errors are corrected in Model 2, and in Model 3, both estimates and standard errors are corrected. The determinants found to be significant in Model 1 were also significant in Model 3 except death of a previous child and in Model 2 except mother’s age at birth. In the frailty model, the mortality hazards for children born to mothers aged 12-19 years at birth were 1.41 (95% CI: 1.14, 1.75) times higher than children born to mothers aged 20-30 years at birth and in the variance-corrected model, the hazard ratio (1.19) for the same variable was not statistically significant. The mortality hazard for the female child has increased from 17% to 22% when unobserved familial effect is taken into account. Small size babies at birth had 42% excess hazard than the average size babies at birth. The mortality hazards for first-born children and fourth-or-higher birth order children with preceding birth interval of less than two years were 2.04 (95% CI: 1.52, 2.73) and 2.42 (95% CI: 1.84, 3.18) times the hazard for second or third birth order children with a longer birth interval (p< 0.001). Infants who were not breastfed had significantly higher hazard of death (HR = 102; 95% CI: 81, 128) than those who were breastfed. The hazard ratio was 44% lower in non-institutional than institutional deliveries.

EAG states as a background variable was significantly associated with under-five mortality. The State, Uttarakhand, was selected as the reference category due to low under-five mortality rates among the EAG states. The hazard ratios were increased in all the EAG states except Jharkhand after adjustment for programmable determinants and other background variables. However, the adjusted hazard ratios were statistically significant for only Madhya Pradesh, Chhattisgarh, Rajasthan, Orissa and Uttar Pradesh. The other background variables such as caste, mother’s education and household wealth index were significantly associated with under-five mortality as shown in Table 4.

Most hazard ratios for the proximate determinants are similar across the three types of models but the most notable finding is the change in the effect of the death of a previous child variable. The multiplicative effect of this variable changes from an 86% excess risk to an 18% reduction in risk (albeit not statistically significant) when unobserved familial effect is taken into account as a gamma frailty. The gamma frailty is 2.86 which means that larger unmeasured familial effect is present and is statistically significant (p<0.001).

In general, the z-statistics (not shown here) are found to be smaller in the random-effects/frailty model than in the traditional Cox and variance-corrected Cox models except for some of the covariates.

The R2 and log likelihood/I-likelihood are preferred for comparing the three models. The Cox frailty model was considered the best model as it had the highest R2 and lowest log likelihood compared to the other two models.

Table 1. Trends in Under-Five Mortality Rates by Major States in Rural India for Five Years Preceding the NFHS-1 (1992-93) and NFHS-3 (2005-06)

*Data represent under-five mortality rates for the complete states

Table 2. Distribution of Live Births by Family

Table 3. The Distribution of Live Births and Under-Five Mortality Rates across Categories of the Background Variables and Programmable Determinants in Rural EAG States for Five Years Preceding the NFHS-3 (2005-06) Survey of India (n = 13,785)

Table 4. Programmable Determinants and Background Variables of Under-five Mortality using Traditional Cox Proportional Hazards, Cox Variance-Corrected and Cox Frailty Models in Rural EAG States for Five Years Preceding the NFHS-3 (2005-06) Survey of India (n = 13,785)

Reference categories: Mother’s age at birth 20-30 y, Institutional Delivery, Delivery assistance by Doctors/ANM/HP, Male children, 2-3 Birth Order & Birth Interval ≥ 2y, Very large & large size of the baby at birth, surviving previous sibling, breastfed children, Uttarakhand state, other caste, Hindu religion, literate mothers, Professional/Clerical/Sales, ≤ 2 children, poorest household wealth index and then for desire for pregnancy; aAdjusted for background factors such as EAG States, religion, caste, mother’s education, mother’s occupation, number of children, wealth index, desire time for pregnancy and other determinants; pb- p-value for multivariate analysis and p<0.05, statistically significant.

Discussion

The primary goal of the study was to assess the determinants of under-five mortality by applying an appropriate model to account for sibling-level correlation and thus provide valid estimates for correct statistical inference needed for policy-decision making. We found that children born in Chhattisgarh had higher risk of dying before age five, followed by children born in Uttar Pradesh and Madhya Pradesh. These states require health interventions that target under-five mortality reduction, particularly in rural areas. Next, mother’s education and wealth index emerge as powerful background covariates of under-five mortality in the EAG states, for the reason that both are known to be associated with better child care practices. Thus, the study urges the policy makers to focus on educating illiterate mothers about the child care; however, policy aiming at improving maternal education and poverty reduction is needed for sustainability.

We know that changes in the z statistics depend on the size of the parameter estimates along with the magnitude of the standard error. In general the z-statistics are smaller in magnitude in frailty model as compared to other models which we also observed in our results, clearly indicating that the sample of correlated observations contains less information than the independent sample. We also observed higher z-statistics for some covariates as observed by Sastry ^[12], for example, mother’s age at birth of 12-19 y (ZModel 3 = 3.13 vs. ZModel 1 = 1.98) in the Cox frailty model than the traditional Cox model.

The assumption of the Cox Proportional Hazards model is likely to be incorrect if we suspect that siblings share environmental or genetic influences beyond explicit covariates included in the model ^[11]. To account for this correlation, if we correct the standard error alone, it might lead to the biased inference, casting doubt especially on the more marginally significant results. The covariate, maternal age 12-19 years at child birth, was found to be marginally significant in the traditional Cox model and was statistically not significant in the variance-corrected Cox model. However, this variable was highly significant in the Cox frailty model which reiterates the importance of simultaneous correction of both parameter estimates and the standard error when analyzing correlated observations.

The next interesting aspect of the paper is estimates of the observed covariate effects. There were remarkably stable in all the three models except survival status of previous child variable. This has been already noted in previous studies ^{[10, 11, 12]} that the positive effect of this variable indeed acts as a proxy in the traditional Cox model. As pointed out by Guo and Rodríguez G ^[11], the hazard ratio of less than one in frailty model suggests that the death of a previous child lowers the risk of the surviving siblings through less competition for family resources or inducing changes in the parental behavior since death is a traumatic event. A non-protective role of institutional deliveries in the present study was found as pointed out by Titaley et al ^[25] and this might be complicated deliveries brought to the institution with three delays ^[26].

Estimation of family influences is difficult in that familial effects other than general socioeconomic status are very difficult to observe. Clustering of deaths in families was explained in rural Punjab ^[9] and in Guatemalan families ^[10] by household’s economic status and mother’s education. We found high variance of unobserved familial effect of 2.86 in the rural area of EAG states even after taking into account all possible cultural and socio-economic variables. This large unobserved heterogeneity at family level could be a result of greater variability in child care practices, health care and mother’s personal abilities ^[18]. Also the female child is more likely to die before reaching age five than the male child which might be related to behavioral and environmental factors ^{[5, 27]}. Thus, parental competence, genetic and other factors like nutritional deficiency, personal illness of the child etc which were not included in the present study might be the explanation for the family frailty in these rural EAG states.

The strengths of this study are the use of nationally representative survey of NFHS-3 (2005-06) data and the application of the Cox frailty model to estimate unbiased parameter estimates for determinants after accounting for familial effect. However, the cross-sectional nature of our study is its main limitation. The study should therefore be interpreted with caution. The variable, breastfeeding, was not considered as a time-dependent covariate due to methodological difficulty of the frailty model.

Conclusions and Public Health Implications

In conclusion, this paper confirms the hypothesis that the risk of under-five death among families is heterogeneous and identifies determinants associated with under-five deaths. Many determinants can be modified by child survival programs to enhance child survival, such as intensive antenatal and delivery care to young pregnant women and women having parity of more than two with preceding birth interval of less than two years; providing ideal nutritional supplement to infants who are small and or very small at the time of birth; improving mother’s child care practices by health education if mother has lost previous child; and reemphasizing exclusive breastfeeding for six months with introduction of complementary feeding at appropriate time. In the setting of correlated observations, the Cox frailty models are recommended for providing statistically valid estimates of the effects of proximate determinants after adjusting for the background variables and unobserved random effects.

Conflicts of Interest: None

Acknowledgements: We acknowledge the National Family Health Survey India, International Institute of Population Sciences, Mumbai, for data collection and providing us data for analysis. We also acknowledge Dr. Padam Singh, Dr. P. P. Talwar, Dr. Bir Singh and Dr. Lalitendu Jagatdeb for their guidance and advice in the work.

References

1. UN, General assembly, 56th session. Road Map towards the implementation of the United Nations millennium declaration: report of the Secretary-General (UN document no. A/56/326). New York: United Nations, 2001.
2. Black RE, Morris SS, Bryce J. Where and why are 10 million children dying every year? Lancet. 2003; 361(9376): 2226-2237.
3. Jones G, Steketee RW, Black RE, Bhutta Z, Morris SS, The Bellagio Child Survival Study Group. How many child deaths can we prevent this year? Lancet. 2003; 362(9377): 65-71.
4. Bryce J, el Arifeen S, Pariyo G, Lanata CF, Gwatkin D, Habicht JP, The Multi-Country Evaluation of IMCI Study Group. Reducing child mortality: can public health deliver? Lancet. 2003; 362(9378): 159-164.
5. Victora CG, Wagstaff A, Schellenberg JA, Gwatkin D, Claeson M, Habicht JP. Applying an equity lens to child health and mortality: more of the same is not enough. Lancet. 2003; 362(9379): 233-241.
6. The Bellagio Study Group on Child Survival. Knowledge into action for child Survival. Lancet. 2003; 362(9380): 323-327.
7. You D, Wardlaw T, Salama P, Jones G. Levels and trends in under-5 mortality, 1990-2008. Lancet. 2010; 375 (9709): 100-103.
8. International Institute for Population Sciences (IIPS) and Macro International. 2007 National Family Health Survey (NFHS-3), 2005-06: India: Volume I. Mumbai: IIPS.
9. Das Gupta M. Socio-economic status and clustering of child deaths in rural Punjab. Population Studies. 1997; 51(2):191-202.
10. Guo G. Use of sibling data to estimate family mortality effects in Guatemala. Demography. 1993; 30 (1): 15-32.
11. Guo G, Rodríguez G. Estimating a multivariate proportional hazards model for clustered data using the EM algorithm with an application to child survival in Guatemala. Journal of the American Statistical Association. 1992; 87(420): 969-976.
12. Sastry N. A nested frailty model for survival data, with an application to the study of child survival in northeast Brazil. Journal of the American Statistical Association. 1997; 92 (438): 426-435.
13. Hung, Wen-Shai, Shu-Hsi Ho, Survival analysis for unobserved heterogeneity on estimated mortality in Taiwan. Economics Bulletin. 2008; 9 (25): 1-10.
14. Spiekerman CF, Lin DY. Marginal regression models for multivariate failure time data. Journal of the American Statistical Association. 1998; 93(443): 1164-1175.
15. Lee EW, Wei LJ, Amato DA. Cox-type regression analysis for large numbers of small groups or correlated failure time observations, survival analysis: state of the art, J. P. Klein and P. K. Goel (eds), Kluwer Academic Publishers, 1992; 237-247.
16. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography. 1979; 16 (3): 439-454.
17. Pandey A, Bhattacharya BN, Sahu D, Sultana R. Components of under-five mortality trends, current stagnation and future forecasting levels. National Commission on Macroeconomics and Health Background papers-Burden of Disease in India. Ministry of Health and Family Welfare, Government of India, 2005.
18. Das Gupta M. Death clustering, mothers’ education and the determinants of child mortality in rural Punjab, India. Population Studies. 1990; 44(3): 489-505.
19. Rutstein SO. Factors associated with trends in infant and child mortality in developing countries during the 1990s. Bulletin of World health Organization. 2000; 78 (10): 1256-1270.
20. Mosley W, Chen L. An analytical framework for the study of child survival in developing countries. Population and Development Review. 1984; 10(Suppl.): 24-45.
21. Klein JP. Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics. 1992; 48(3): 795-806.
22. Parner E. Asymptotic theory for the correlated gamma-frailty model. Annals of Statistics. 1998; 26(1): 183-214.
23. Therneau TM, Grambsch PM. Modeling survival data. Extending the Cox model. Berlin: Springer-Verlag. 2000.
24. Cox DR. Regression models and life tables. Journal of the Royal Statistical Society, Series B. 1972; 74 (2): 187-220.
25. Titaley CR, Dibley MJ, Roberts CL. Type of delivery attendant, place of delivery and risk of early neonatal mortality: analyses of the 1994-2007 Indonesia Demographic and Health Surveys. Health Policy and Planning. 2011. [Epub ahead of print]
26. Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Social Science in Medicine. 1994; 38 (8): 1089-1110.
27. Claeson M, Bos ER, Mawji T, Pathmanathan I. Reducing child mortality in India in the new millennium. Bulletin of the World Health Organization. 2000; 78 (10): 1192-1199.

Abstract

Objectives

Intestinal parasitic infection and undernutrition are still major public health problems in poor and developing countries. The objective of this study was to assess the relationship between intestinal parasitic infection and nutritional status in 405 primary school children from rural and urban areas of Akwa Ibom State, Nigeria.

Methods

This cross-sectional survey in 2009 obtained anthropometric data, height-for-age (HA), weight-for-height (WH) and weight-for-age (WA) Z-scores from each child and fecal samples were also collected and screened for intestinal parasites using standard parasitological protocols.

Results

The prevalence of infection with any intestinal parasite was 67.4%. A total of six intestinal parasites were detected; hookworm (41.7%) had the highest prevalence. The prevalence of intestinal parasites and undernutrition was significantly higher in rural than in urban children (P<0.001). The prevalence of stunting (HAZ < -2), underweight (WAZ < -2) and wasting (WHZ < -2) for rural and urban children were 42.3% vs. 29.7%; underweight 43.2% vs. 29.6% and wasting 10.9% vs. 6.4%, respectively. With respect to nutritional indicators, the infected children had significantly (P<0.05) higher z-scores than the uninfected children. Multivariate logistic regression analysis showed that only Hookworm and Ascaris lumbricoides were each significantly (P<0.05) associated with stunting, wasting, and underweight.

Conclusions and Public Health Implications:

Since intestinal parasitic infections are associated with malnutrition, controlling these parasites could increase the physical development and well-being of the affected children.

Key Words:

Intestinal parasites • malnutrition • children • rural-urban • Nigeria.

Introduction

In developing countries, intestinal parasitism is a major public health problem that is often neglected. In these less developed countries, poor environmental and personal hygiene, poor nutrition, overcrowding and climatic conditions that favor the development and survival of these parasites are some of the factors contributing to the high level of intestinal parasites transmission ^[1-3]. School children carry the heaviest burden of the associated morbidity ^[4], due to their dirty habits of playing or handling of infested soils, eating with soiled hands, unhygienic toilet practices, drinking and eating of contaminated water and food ^[5].

There are documented reports implicating intestinal parasitic infection with poor nutritional status in children of school age ^[6-10]. Amoebiasis, Giardiasis, Acariasis, Hookworm infection, and Trichuriasis are among the most common intestinal parasitic infection worldwide. These infections are associated with decreased child growth, low plasma vitamin A, loss of weight, chronic blood loss, iron deficiency anemia, diarrhea, and stunted growth ^[4,11,12]. Alteration of the normal gastro-intestinal flora by intestinal parasites has been found to be associated with diarrhea, a major cause of childhood morbidity and mortality in developing countries ^[13].

There is a plethora of publications on prevalence and intensity of intestinal parasites in Nigeria, ^[2,3,14]. Surprisingly, there is paucity of information on the impact of these parasites on the nutritional status of their host, especially children. This study therefore aims to fill this gap in knowledge by investigating the association between intestinal parasitic infection and nutritional status of primary school children in rural and urban settings of Akwa Ibom State, Nigeria. The outcome may strengthen the global shift in the control of neglected tropical diseases.

Methods

Study Sites

The study was carried out in two primary schools in Akwa Ibom State, Nigeria. The schools are Methodist Primary School, Mbiabong Ikot Udofia in Ini Local Government Area (LGA), a typical rural setting and Government Primary School, Ikot Ntuen Oku, Uyo in Uyo Local Government Area (an urban setting). The two LGAs are in Akwa Ibom State. The State lies between latitudes 4°33’ and 5°33 North and longitudes 7°35’ and 8°25 East. It is characterized by humid tropical climate with annual rainfall reaching 23,000mm per annum. Temperature regime is uniform with annual values of 20.4°C to 35.7°C. The State has two distinct seasons, the rainy season from May to October, and the dry season from November to April. Pit latrine is common, while open air defecation is freely practiced.

Subjects

The study was carried out between April and November 2009. The study employed stratified random sampling to select the study subjects. For eligibility to participate in the study, a child had to be attending one of the selected schools. Each enrolled child’s date of birth, age, gender, and occupation of parents was obtained from the class teacher’s registers and recorded on a standardized field form. In each selected class, at least 40 pupils from each of the 6 educational grades were chosen at random.

Ethical Clearance

Prior to any enrollment of the children, permission was sought and obtained from the head-teacher of the respective schools and parents and/or guardian of the children. The study protocol was approved by the State Ministry of Health.

Anthropometric Measurement

Anthropometric measurements were carried out by a nutritionist (DCO) in the research team. The subjects were weighed barefooted and in light clothing on a bathroom scale accurate to 0.1kg. The scale was standardized before use with 11kg weight. Height was measured to the nearest 1cm, with a paper stadiometer attached to a vertical wall. Subjects stood barefooted with their scapula, buttocks and heels’ resting against a wall, the neck was held in a natural non-stretched position, and the heels were touching each other. Nutritional status indicators were classified and standardized into sex-specific Z-scores for height-for-age (HAZ), weight-for-height (WHZ) and weight-for-age (WAZ) in EPI Info (version 3.2) relative to the CDC/WHO 1978 reference curves recommended for international use ^[15,16]. Children were classified as stunted, wasted, and underweight if their HAZ, WHZ and WAZ was < – 2 SD respectively.

Parasitological Examination

Each enrolled child was asked to provide a fresh fecal sample in cleaned and dried specimen bottles provided. The pupils were adequately instructed on how to get a little portion of their stool into the bottles. Their class teachers ensured compliance. The samples collected on each occasion were all examined in the University of Uyo Medical Center laboratory without preservation. Each fecal sample was examined as a smear stained with Lugol’s iodine, as a direct wet smear in physiological normal saline and by formol-ether concentration technique ^[17-18]. Diagnosis was based on the identification of helminth ova and protozoan cyst in the sample during microscopic analysis. A child was considered to have a polyparasitic infection if they were found to be positive for more than one species.

Data Analysis

Categorical variables are presented as percentages and continuous variables as means and standard deviation (SD). Univariate analysis was carried out using the χ2 test for proportion. The association between intestinal parasitic infection and nutritional indicators was tested using multivariate logistic regression. All statistical analysis was performed using version 14.0 of the SPSS for windows software package (SPSS Inc., Chicago IL).

Results

A total of 418 children (228 rural and 190 urban) were initially enrolled, the result presented below are the data for 405 children (220 rural and 185 urban) who returned suitable stool specimen after their anthropometric data were collected. There were 100 males and 120 females for rural areas and 105 males and 80 females for urban areas. The subjects were aged between 2.9 and 14 years, with a mean (SD) age of 8.5 (4.7). Table 1 summarizes the prevalence of intestinal parasite in the children. At least one species of intestinal parasite was found in 273 (67.4%) of the children examined. Prevalence of infection was significantly (P<0.001) higher among the rural (80.9%) children than among the urban (51.4%) children. Prevalence of hookworm was higher (55.9% vs. 24.9%) than all other parasites encountered from rural and urban children respectively (P<0.05). The other parasites were A. lumbricoides (30.5% vs. 16.8%); T. trichiura (4.1% vs. 5.4%); G. lamblia (3.2% vs. 2.2%) and E. histolytica (0.9% vs. 4.3%). The prevalence of polyparasitism is further shown in Table 1, one species of intestinal parasite were found in (49.1% vs. 32.4%) for rural and urban children respectively. The subjects co-infected with two species of parasites were (23.6% vs. 14.6%), while 8.2% vs. 4.3% were with 3 or more species of parasite in rural vs. urban children.

The age-specific prevalence of infection in rural children is presented in Table 2. Prevalence tends to increase with age, with the highest infection occurring in the age group of 12–14 years, for Hookworm and Ascaris infection. In urban children, the age group of 3–8 years had the highest infection rate (Table 3). The overall prevalence of nutritional indicators is presented in Table 4, stunting (42.3% vs. 29.7%); underweight (43.2% vs. 29.6%) wasting (10.9% vs. 6.4%) for rural and urban pupils respectively. Rural children were significantly (P<0.001) more malnourished than urban children. Only Hookworm and A. lumbricoides were significantly (P<0.001) associated with low height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). With respect to the nutritional indicators, the infected children had a significantly (P<0.05) higher Z-score than the uninfected children.

Table 1. Prevalence of Intestinal Parasites among Children in Rural and Urban Communities

Table 2. The Age-Specific Prevalence of Intestinal Parasite among 220 Rural children

Table 3. The Age-Specific Prevalence of Intestinal Parasite among 185 Urban Children

Table 4. Summary of Nutritional Indices (Z-scores) of Rural and Urban Children

Discussion

Generally intestinal parasitic infection abounds in developing countries ^[18], with school children carrying the heaviest burden of the associated morbidity ^{[20, 21]}.

The prevalence of 67.4% recorded in this study is consistent with 70%, 70.8%, 62.0% obtained in Kwara, Ogun and Plateau States of Nigeria respectively ^[22-24]. Such high prevalence has been attributed to poor environmental and personal hygiene, shortages of clean potable water and indiscriminate defecation ^[13]. School children from the rural areas (80.9%) were significantly more infected than children from the urban areas (51.4%). This finding corroborates the report of Hurtado et al. ^[25] that a high prevalence of intestinal parasites is consistent with what is found throughout indigenous population in the rural tropical areas. The prevalence obtained for intestinal parasites in the rural areas of the present study is consistent with 82.6% obtained by Ukpai and Ugwu ^[26] in Southern Nigeria. In rural areas, ignorance, unhealthy socio-cultural and religious practices, lack of basic public amenities, poor sanitation, poverty and inadequate access to health care are major predisposing factors to intestinal parasitic infections ^[27]. This study observed the above factors in the rural community where the school is located. The school used in the present study lack adequate toilet facility. There is complete absence of potable water, student obtain their water from nearby stream that might have been contaminated with fecal matter. Evidently, students defecate indiscriminately in the bushes around the school premises as observed with fecal littering, which were likely to contain ova and/or cyst of parasites. The 51.4% prevalence obtained in urban children in this study, though lower than the rural prevalence, is relatively high. The result is similar to the 54.7% and 50.6% recorded by Egwunyenga and Ataikiru ^[2] and Igbinaso et al. ^[28] in Delta and Anambra States of Nigeria respectively. It is possible that the home communities of the urban children are more aware of parasitic infections and have slightly better sanitation and improved personal hygiene than rural communities. Furthermore access to health care is within the reach of the urban children. Despite all these factors, the relatively high prevalence connotes continuous infection, re-infection and transmission of intestinal parasites.

The occurrence of poly parasitism in this study is in line with what is obtained elsewhere in the tropics and subtropics ^{[4, 26, 29-30]}. The commonest was the co-infection of hookworm and A. lumbricoides. No individual had up to four parasites. Hookworm was the most prevalent. The poor fecal disposal system coupled with the fact that most of the children play barefooted might have exposed them to infective stage of the hookworm larvae. The use of excreta as manure commonly practiced by vegetable farmers might also be acting as a veritable source of infection since children and their mothers often go to the farm to tender the vegetables ^[20]. Ukoli ^[31] described warmth, shade, moisture, optimum temperature of 230C to 300C and loose humus soil as suitable environmental conditions for the survival of hookworm egg and larvae. Since these conditions are almost similar to the climatic conditions of the study area, it is possible that the longevity of hookworm eggs and larvae in soil and on vegetation might have been enhancing transmission. Other workers elsewhere have reported a high prevalence of hookworm infection in their studies ^[3,30,32,33]. Hookworm and Ascaris infection increased with age among the rural children, while children aged 3–5 years in the urban area had the highest prevalence for the two parasites. No single age group had the highest prevalence for all the intestinal parasites detected.

This present study recorded a high degree of malnutrition among the children investigated for intestinal parasitic infections. These findings agree with the publication of other investigators ^{[6,8,9,33,34]}. The high rate (>=30%) of stunting and underweight recorded in this study might be due to high prevalence of hookworm, Ascaris and Trichuris. It has been documented by Crompton and Neisheim ^[34] that growth and development during childhood could be diminished by ascariasis, trichiuriasis and hookworm infection. Multivariate analysis showed a significant association between hookworm, Ascaris and anthropometric parameters suggesting that these helminths affect the nutritional status of the studied children. While this present study did not obtain a significant association between nutritional indicators and protozoan parasites (G. lamblia, E. histolytica), similar studies in Tehran and Brazil by Nematian et al. ^[9] and Carvalho Costa et al. ^[8] respectively, recorded a significant association between G. lamblia and nutritional status. According to Assis et al. ^[35] the social, economic and physical environment in which an individual lives are major determinant of the degree of association between intestinal parasites and nutritional status. These factors might be responsible for the difference observed in this study when compared with others elsewhere. Although causes of malnutrition are multifactorial intestinal parasitic infections have been associated with impaired growth ^[9,36,37] and stunting ^[12] in diverse population. There are several mechanisms by which intestinal parasitism may cause or aggravate malnutrition including impaired nutrient absorption resulting from infection and reduced appetite ^[34]. Adult helminth worms residing in the small intestine are in an excellent position to interfere with their host nutrition and can induce damage to the intestinal mucosa that may reduce a person’s ability to extract and absorb nutrient from food ^[38]. Intestinal parasitic infections can cause vomiting, diarrhea, anorexia, abdominal pain and nausea that may result in reduced food intake, thereby further reducing nutrient availability ^[36,39]. The most significant cause of nutritional stress resulting from helminth infection is hookworm associated iron-deficiency anemia. It is documented that light hookworm infections of 20 – 50 adults worms can result in significant iron losses ^[37]. Even mild to moderate intensity helminth infection during childhood have been associated with undernutrition and reduced physical fitness ^[39,40]. All these factors singly or collectively might have contributed to the high degree of malnutrition observed in this study.

Conclusions and Public Health Implications

A weakness of the present study is that no attempt was made to determine biochemical indicators of malnutrition and the intensities of the infection, as a consequence, the actual cause(s) of wasting recorded in the study could not be ascertained. Intestinal parasitism is one of the neglected tropical diseases that remain a common public health problem in most developing countries. With the current global economic meltdown, dietary inadequacies and other forms of nutritional stresses, the harboring of heavy intestinal parasites by children could have adverse health implications. Hence, there is the need for policy makers to intensify health education program in schools. School-based education may be an effective way of reaching the larger population, parents and families of the student and other members of the community. In view of the high prevalence of intestinal parasites recorded in this study, mass chemotherapy will be of tremendous benefit on child growth, development and cognitive abilities.

Intestinal parasitic infections affect childhood development and morbidity in many developing countries. Reducing the prevalence of parasitic infections in school children, may be of immense benefit on child growth, development and educational outcome.

Acknowledgements We express our sincere appreciation to the head-teacher, teachers, parents and pupils of the study schools for their approval and cooperation throughout the duration of the study.

References

1. Vince J. Helminthiasis. In Diseases of Children in the Subtropics and Tropics. (Eds.) Stanfield P, Brueton M, Chan M, Parkin M, Waterston T. pp. 633-648. London, UK: Edward Arnold Ltd; 1991.
2. Egwunyenga OA, Ataikiru DP. Soil-transmitted helminthiasis among school age children in Ethiope East Local Government Area, Delta State, Nigeria. African Journal of Biotechnology. 2005; 4:938-941.
3. Obiukwu MO, Umeanaeto PU, Eneanya CI, Nworgu GO. Prevalence of gastro-intestinal helminth in school children in Mbaukwu, Anambra State, Nigeria. Annals of Tropical Medicine and Parasitology. 2008; 101 (8):705-713.
4. Nematian J, Nematian E. Gholamrezanezhad A, Asgari AA. Prevalence of intestinal parasitic infections and their relation with socio-economic factors and hygiene habits in Tehran primary school students. Acta Tropica. 2004; 92(3):179-186.
5. Nwosu ABC. The Community ecology of soil transmitted helminth infections of human in a hyperendemic area of southern Nigeria. Annals of Tropical Medicine and Parasitology. 1981; 75:197-203.
6. Adekunle L. Intestinal parasites and nutritional status of Nigerian children. African Journal of Biomedical Research. 2002; 5:115-119.
7. Quihui-Cota L, Valencia ME, Crompton DWT, Phillips S, Hagan P, Diaz-Camacho SP, Tejas AT. Prevalence and intensity of intestinal parasitic infections in relation to nutritional status in Mexican School Children. Transaction of the Royal Society of Tropical Medicine and Hygiene. 2004; 98:653-659.
8. Carvalho-Costa FA, Goncalves AQ, Lassance SL, Silva Neto LM, Salmazo CAA, Boi MN. Giardia lamblia and other intestinal parasitic infections and their relationships with nutritional status in children in Brazilian Amazon. Revista do Instituto de Medicina Tropical de Sao Paulo. 2007; 49(3):147-153.
9. Nematian J, Gholamrezanezhad A, Nematian E. Giardiasis and other intestinal parasitic infections in relation to anthropometric indicators of malnutrition: a large-population-based survey of school children in Tehran. Annals of Tropical Medicine and Parasitology. 2008; 102:209-214.
10. Silva RR, Mariano da Silva CA, Pereira CAD, Nicolato RL, Negrao-Correa D, Iamounier JA, Carneiro M. Association between nutritional status, environemtnal and socio-economic factors and Giardia lamblia infections among children aged 6–7 months in Brazil. Transaction of the Royal Society of Tropical Medicine and Hygiene. 2009; 103:512-519.
11. Edungbola LD, Obi AA. A Review of human intestinal parasites in Nigeria: challenges and prospects for integrated control. Nigerian Journal of Parasitology. 1992; 13:27-37.
12. Casapia M, Joseph SA, Nunez C, Rahme E, Gyorkos TW. Parasite risk factors for stunting in grade 5 students in a community of extreme poverty in Peru. International Journal of Parasitolgy. 2006; 36:741-747.
13. Adedoyin MA, Awogun IA, Juergensen T. Prevalence of intestinal parasitoses in relationship to diarrhea among children in Ilorin. West African Journal of Medicine. 1990; 9:83-88.
14. Oninla SO, Owa JA, Onayade AA, Taiwo O. Intestinal helminthiasis among rural and urban schoolchildren in South-western Nigeria. Annals of Tropical Medicine and Parasitology. 2007; 101(8):705-713.
15. Hamill PVV, Drizd TA, Johnson CL, Reed RB, Roche AF, Moore WM. Physical growth: National Centre for Health Statistics percentiles. American Journal of Clinical Nutrition. 1979; 32: 607-629.
16. World Health Organization (WHO). The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. Geneva, Switzerland. World Health Organization; 1986.
17. World Health Organization (WHO). Basic Laboratory Methods in Medical Parasitology. Geneva, Switzerland. World Health Organization; 1991.
18. Cheesbrough M. Medical Laboratory Manual for Tropical Countries. Second edition. London, UK: Butterworth; 1992.
19. World Health Organization (WHO). Public Health Significance of Intestinal Parasitic infections. WHO Bulletin. 1987; 65:575-588.
20. Opara KN, Udoidung NI. Parasitic contamination of leafy vegetable. A function of leaf area index. Global Journal of Pure and Applied Science. 2003; 9:25-29.
21. Itah AY, Opara KN, Atting IA, Udoidung NI. Prevalence of enteropathogens and their association with diarrhea among children of food vendors in Uyo, Nigeria. Mary Slessor Journal of Medicine. 2005; 5:11-21.
22. Awogun IA. The Prevalence of intestinal parasitic infections in children living in Illorin, Kwara state Nigeria. West African Journal of Medicine. 1984; 4(1):16-21.
23. Mafiana CF. Intestinal helminthiasis with particular references to ascariasis among school children in Ilewo-orile Ogun State, Nigeria. Nigerian Journal of Parasitology. 1995; 16:47-53.
24. Azikwe AN. Prevalence and pattern of intestinal infestations in an African University Community. Annals of Tropical Medicine and Parasitology. 1984; 783:333-334.
25. Hurtado M, Lambourne CA, James P, Hill K, Cheman K, Baca K. Human rights, biomedical science and infectious disease among South American indigenous groups. Annual Review of Anthropology. 2005; 34:639-665
26. Ukpai OM, Ugwu CD. The prevalence of gastrointestinal tract parasites in primary school children in Ikwuano Local Government Area, Abia State, Nigeria. Nigerian Journal of Parasitology. 2003; 24:129-136.
27. Suswam EA, Ogbogu VC, Umoh JU, Ogunus RA, Folaranmi DO. Intestinal parasites among school children in Soba and Igabi Local Government Area of Kaduna State, Nigeria. Nigerian Journal of Parasitology. 1992; 13:39-42.
28. Igbinosa IB, Ogbunugu TF. Intestinal helminth parasites in school children in Iragbigi Boripe Local Government Area, Osun State, Nigeria. African Journal of Biomedical Research. 2006; 9:63-65.
29. Enekwechi LE, Azubike CN. Survey of the prevalence of Intestinal parasites in school children of primary school age. West African Journal of Medicine. 1994; 13(4):227-230.
30. Glickman LT, Camara AO, Glickman NW, McCabe GP. Nematode intestinal parasites of children in rural Guinea, Africa: Prevalence and relationship to geophagia. International Journal of Epidemiology. 1999; 28:169-174.
31. Ukoli FMA. Introduction to Parasitology in Tropical Africa. John Wiley and Sons, UK; 1992
32. Asaolu SO, Holland CV, Jegede JO, Frasser NR, Stoddard RC, Crompton DW. The prevalence and intensity of soil-transmitted helminthiasis in rural communities in southern Nigeria. Annals of Tropical Medicine and Parasitology. 1992; 86:279-287.
33. Chandrasena TGAN, De Alvis ACD, De Silva LDR., Morel RP, De Silva NR. Intestinal parasitosis and the nutritional status of Veddah children in Sri Lanka. South-East Asian Journal of Tropical Medicine and Public Health. 2004; 35(2):255-259.
34. Crompton DW, Nesheim MC. Nutritional impact of intestinal helminthiasis during the human life cycle. Annual Review of Nutrition. 2002; 22:35-59.
35. Assis AMO, Prado MS. Reis ML, Reis MG, Pinheiro SMC, Parraga IM, Blanton RE. Childhood stunting in northeast Brazil: the role of Schistosoma mansoni infection and inadequate dietary intake. European Journal of Clinical Nutrition. 2004; 58:1022-1029.
36. Stephenson LS, Lathem MC, Adams EJ, Kinoti SN, Pertet A. Physical fitness, growth and appetite of Kenyan School boys with hookworm, Trichurus trichiura and Ascaris lumbricoides are improved four months after a single dose of albendozole. NI Nutrition. 1993; 123:1036-1046.
37. StoltzFus RJ, Chwaya HM, Tielsch JM, Schulze KJ, Albonico M, Savioli L. Epidemiology of iron deficiency anemia in zanzibari school children: the importance of hookworms. American Journal of Clinical Nutrition. 1997; 65(1):153-159.
38. Tanner S, Leonard WR, McDade T, Garca-Reyes V, Godoy T, Huanca T. Influence of helminth infections on childhood nutritional status in lowland Bolivia, Tsmane. Amazonia Panel study Working Paper 49. http://people.branders.edu/rgodoy/. Accessed June 20, 2009.
39. Stephenson LS, Latham MC, Otensin EA. Malnutrition and parasitic helminth infection. Parasitology. 2000; 121:SUPPL: 23-28.
40. Ezeamama AE, Friedman JF, Olveda RM, Acosta LP, Kurtis JD, Mar V, McGarvey ST. Functional significance of low-intensity polyparasite helminth infections in anemia. Journal of Infectious Diseases. 2005; 192(1):2160-2170.

Abstract

Objectives

Despite the advent of antiretroviral therapy (ART), many children, particularly in the rural communities of Zimbabwe, remain vulnerable. The purpose of this study was to determine the factors and challenges facing children on antiretroviral therapy (ART) in Brunapeg area of Mangwe District, Zimbabwe.

Methods

A mixed-method approach involving interviewer-guided focus group discussions and piloted semi-structured questionnaires was utilized to collect data from different key population groups. The data obtained were analyzed through content coding procedures based on a set of predetermined themes of interest.

Results

A number of challenges emerged as barriers to the success of antiretroviral therapy for children. Primary care givers were less informed about HIV and AIDS issues for people having direct impact on the success of antiretroviral therapy in children whilst some were found to be taking the antiretroviral drugs meant for the children. It also emerged that some primary care givers were either too young or too old to care for the children while others had failed to disclose to the children why they frequently visited the Opportunistic Infections (OI) clinic. Most primary care givers were not the biological parents of the affected children. Other challenges included inadequate access to health services, inadequate food and nutrition and lack of access to clean water, good hygiene and sanitation. The lack of community support and stigma and discrimination affected their school attendance and hospital visits. All these factors contributed to non-adherence to antiretroviral drugs.

Conclusions and Public Health Implications:

Children on ART in rural communities in Zimbabwe remain severely compromised and have unique problems that need multi-intervention strategies both at policy and programmatic levels. Effective mitigating measures must be fully established and implemented in rural communities of developing countries in the fight for universal elimination of HIV/AIDS.

Key Words:

Antiretroviral therapy • Challenges • Children • HIV/AIDS • Mangwe District • Zimbabwe

Introduction

For three decades, HIV and AIDS have affected the lives of many people at individual and family levels through illnesses and deaths of family members and has been a serious challenge medically, financially, and socially ^{[1, 2, 3]}. HIV and AIDS is of considerable and significant impact to children yet the attention given to this population is largely overshadowed by the large scale burden of the epidemic in the adult population, especially in Sub-Saharan African countries ^[2].

HIV and AIDS have caused untold suffering in the rural communities of Sub-Saharan Africa with children being the most affected. About 95% of the infected populations in the world currently live in developing countries, particularly in Sub-Saharan Africa ^[4]. Zimbabwe is one of the Sub-Saharan African countries burdened by high HIV infection prevalence. The country’s population is estimated at about 13 million people with about 1.1 million HIV positive people, of which 151,749 are children below the age of 14 ^[5]. By the year 2010, only 326,241 people in Zimbabwe were estimated to be receiving antiretroviral therapy (ART) ^[4].

Despite the increasing introduction of antiretroviral therapy, many children, particularly in the rural communities of Zimbabwe remain vulnerable. They are impacted by non-adherence ART regimen due to a number of factors. This includes inadequate access to food and nutrition which can be shown by the unbalanced diet and the number of meals they get per day, transportation problems, and long distance commute to hospitals, infrequent visits by the home based care givers (HBCs) and the prevalence of widespread stigma and discrimination ^{[6, 7, 8]}.

Guardianship of children on ART is also crucial since it is directly correlated to the overall care and support provisioned by the primary care givers ^{[6, 7]}. Lack of adequate care and support has been noted in instances where the primary care givers are too old or too young.

A major treatment gap remains since by the end of 2010; many children who were eligible for treatment did not have access to ART. Access to treatment for the children is lower than that for adults since by 2009; only 28% of the registered children received ART whilst coverage for other age groups was 36%. Low ART coverage and access no doubt increases the vulnerability of children to HIV and AIDS ^{[4, 9]}.

Antiretroviral therapy is an integral component in the quest to improve the well-being and health of children living with HIV and AIDS ^[10]. Its success is determined by factors that occur concurrently to the patients’ health and social life during the course of the therapy. Antiretroviral therapy involves continual interaction between health staff and patients through on-going medical check-ups, prescription or drug refills, monitoring and adherence support ^{[6, 7, 11]}.

Research on ART in developing countries is very new and tends to focus primarily on issues of non-adherence yet there are hosts of health, psychological, social and economic challenges which determine the success of ART in children ^{[6, 7, 8, 12, 13]}. The success rate of ART in rural communities of Zimbabwe is low with fewer children covered on therapy whilst those on ART have a poor health a situation worsened by the localities of the communities ^{[14, 15]}.

The Mangwe district in Matabeleland South province of Zimbabwe has a widespread prevalence of HIV and AIDS in addition to highest levels of food insecurity, transportation problems, sanitation and health services challenges ^{[15, 16]}. It is such widespread inequalities, discrimination and poverty in the province which shape and worsen the HIV epidemic in the Brunapeg area of Mangwe district. There are several challenges with direct or indirect impacts on the success of ART in children. This includes the provision of good psychological counseling to the children; access to adequate diet and nutrition; adequate access to health facilities, services and antiretroviral drugs; elimination or minimization of stigmatization and discrimination; and adequate knowledge and use of proper practices by caregivers, i.e. primary care givers and home-based care givers ^{[8, 9, 13, 16, 17, 18]}. Such challenges are profound in rural communities of Zimbabwe ^[14,15]. This paper explores the challenges faced by children on ART in the Brunapeg area of the Mangwe district in Zimbabwe.

Methods

Study Design

This study was conducted in the Brunapeg area of Mangwe District. The area has one diagnosing center for HIV and AIDS located at St. Anne’s Hospital serving a population of approximately 78, 000 people. The hospital has an Opportunistic Infection (OI) clinic which provides HIV Counseling and Testing (HCT), prevention of parent to child transmission (PPTCT), ART and home based care (HBC).

A mixed-method study was designed utilizing both focus group discussions (FGDs) and semi-structured questionnaires to assess the challenges faced by children on ART looking at individual-level and group-level factors. The study also examined the consistency of information acquired through both tools. FGDs give group consensus information while questionnaires assess information from a personal level. The questionnaires were designed for the children and the caregivers and were piloted at Plumtree Hospital area, another hospital in the Mangwe district. For the community leaders, key informant interviews were only employed since they represented different levels of leadership.

Study Population and Sampling

This study focused on different sub-populations in the study district. A multi-stage sampling method was used to identify children (5-15 years) on ART registered at St Annes’ Hospital OI clinic, their corresponding primary care givers, home based care givers registered with St Annes’ Hospital and community leaders in the Brunapeg area.

Utilizing St Annes’ Hospital registry for children on ART, 396 registered children were stratified according to wards. The Brunapeg area has 9 wards and a ward consists of 6-10 villages. Forty (10.1%) children (5-15 years) were purposively selected from all the wards according to their age ensuring that samples comprised of children. Four children were sampled from each of the wards 5, 6, 7, 8 and 9 whilst 5 children were sampled from each of the wards 10, 15, 16, 17 totaling 40 children in all.

The primary care givers were sampled corresponding to the selected 40 children. Among the 460 home based care givers (HBCs) registered in the St Anne’s Hospital HBC program, 40 (11%) were randomly sampled for the study inclusive of those corresponding to the sampled children. It must be noted that unlike primary care givers, in Zimbabwe, HBCs cater for children from different homes.

In sampling the community leaders for the study, all three Chiefs (or village heads) who are overseers of the wards were selected. Out of the seven Headmen, four were selected, out of nine Councilors each corresponding to each of the wards, four were selected and out of 60 Village heads, 10 were selected. These community leaders were the key informants within their respective communities.

Data Collection

The sampled children, primary care givers, HBCs and community leaders were recruited into separate focus groups. Focus groups discussion guides with a set of carefully predetermined questions were used to collect data. The meetings were scheduled at selected outreach points for the wards concerned by making appointments with the chiefs to mobilize other community leaders. The children on ART, primary care givers and HBCs were mobilized through the hospital staff. In addition, questionnaires were distributed to the respondents on the appointed days. The FGDs data was collected through note-taking until all key points and objectives were exhausted. Different FGD guides were designed for different focus groups for relevancy in drawing information. The data collected was analysed by the study team through content coding procedures and categorised based on pre-set themes of interest namely provision of clean water and hygiene, food access, ARV drug access, non-adherence, health services access, knowledge of primary care givers and HBC of HIV/AIDS, school attendance and stigma and discrimination. The questionnaires utilized were designed around these study themes. The data from the FGDs and questionnaires were integrated according to the pre-set themes and objectives to eliminate redundancy.

Results

The problems affecting the success of ART on children in the Brunapeg area of Mangwe district included inadequate access to food and nutrition, lack of access to adequate health services, stigmatization and discrimination and inadequate service provision by the care givers.

Access to Food and Nutrition

The primary care givers and HBCs highlighted the lack of access to adequate food and nutrition (Table 1) as a major challenge affecting the studied children on ART. It was found that the children on ART who were not having adequate access to food and nutrition were not coping well with the therapy whilst some of them ended up begging for food and avoiding the ARV drugs.

Table 1.Number of Meals Received by the Children on ART Per Day According to Primary Care Givers

The study revealed that there was inadequate access to clean water, good hygiene and sanitation, which negatively affected the children on ART. This was worsened by the fact that the Mangwe district is located in drought prone area of Zimbabwe.

Access to Health Services and Provision of Care and Support

Home Base Care (HBC) respondents reported that most primary care givers (Table 2) were not the biological parents of the children (mostly orphaned by HIV/AIDS) on ART. They expressed concerns that the care and support obtained from the foster parents and guardians was not comparable to those that could be given by their biological parents. One primary care giver was quoted by the HBCs to have said to a child, “Your people have arrived, and they want to see how you are taking your things” implying that the HBCs have arrived, they want to assist you in taking your ARV drugs. Stigma is apparently implied in this statement and it translates to lack of ownership of the problem by the caregiver. This also impacted the frequency of taking the children to hospital to obtain health services, a situation also compounded by long distances and transportation problems to hospital in the Brunapeg area.

Table 2. Categories of Primary Care Givers for the Children on ART

Impact of Age of Primary Care Giver on ART

Another challenge was that most primary care givers were either too young or too old to take care for the children on ART such that they had problems adhering to hospital dates and times or monitoring the children on ART during their drug administration (e.g. correct dosages). According to one older lady in the study: “My bones are no longer strong to be moving up and down and I am frequently not feeling well”. This indicates how old age and the individual characteristic of the care giver impacts the adherence of the children to their medication regimen.

It was also apparent that the primary care givers were not sufficiently trained on the issues surrounding ART and counseling for them to adequately support the children that they provide care for. Some primary care givers did not adequately provide care to the children due to the fear of being infected with HIV and AIDS following close interaction with the children living with HIV and AIDS. It was reported that some adult primary care givers took the drugs meant for children on ART for their own use due to the fear of disclosing their own status and going for individual treatments.

Lack of Disclosure of Status

Our study found that 60% of the children studied were not aware of the reasons why they were frequently visiting the health services since their HIV status had not been disclosed to them by their primary care givers. The children believed that they visited the hospital for minor ailments such as flu, headaches, and cough. This implies that some of the children would grow up and become sexually active without knowledge of their HIV status and will thus not reveal that to their partners. According to one of the children who had not formally had a disclosure of status said: “My mum has never told me, but I know”. This causes the children to mistrust their caregivers and may lead to lack of supportive family counseling. Children who became aware of their HIV status feared visiting the hospital due to fear of stigma. This is impacted by that there were no support groups for the children to reduce stigma after disclosure of status.

School Attendance Impacted by Stigma and Discrimination

We found that 70% of the children studied were of school-age yet only 39% of them were attending school (Table 3). This could be attributed to the fact that the children reported stigma and discrimination both at school and community or societal level and felt safer at home since they had been informed that they were HIV positive due to the noticeable signs associated with HIV infection. It was reported that the children had problems at school from other children ranging from verbal to physical abuse. A 14 year-old girl in the study said: “Other children scold me at school, they say I am confused by the pills I am taking”. In addition, the children could have been too unwell to be attending school.

Table 3. School Attendance of the 28 School Eligible Children (70%) on ART

Discrimination and stigmatization is prevalent among these children outside the school settings. Children were not fully involved in social programs whilst those at adolescence stage end up going into relationships without disclosing their status for fear of losing their relationships. This is worsened by lack of knowledge of the existence of programs aimed at canvasing communal support for the children on ART. Another challenge faced by the children on ART was the infrequency of visits by the HBC givers (Table 4). We found that on the average, hospital visits by children on ART supervised by primary care givers were inadequate for close monitoring of the children and ensuring adherence to treatment protocols.

Table 4. Frequency of Visits to Children on ART by HBCs

Another aspect found to compromise the service delivery of HBC givers to children was the verbal abuse they faced and the lack of support from other community members which discouraged them and thereby impacted their service delivery. One HBC recalled a discriminatory comment by a community member, “What you know better is bathing people with AIDS”.

Discussion

Data from different focus group discussions and semi-structured questionnaires indicate that the broad challenges faced by the children on ART can be categorized into food access, health service access and provision of care and support both at household and community levels. These findings are consistent with earlier studies on challenges faced by elderly guardians in sustaining adherence to ART in HIV-infected children and community relations and child-led microfinance ^{[9, 13]}, HIV/AIDS-related stigma and discrimination ^[17] and food insecurity and nutritional barriers to ART ^[16]. Amongst the children (5-15 years), 42% were males whilst 58% were females demonstrating that girls were more affected by HIV and AIDS in the Mangwe district in Zimbabwe.

The primary care givers and HBCs reported food insecurity as a key challenge in the assistance of the children on ART despite the food aid provided by various donors. When providing food aid, the donors issued quantities targeted for children on ART, yet the food is often consumed by the entire family. This is because Mangwe district is in a drought-prone province resulting in low food access and poor nutrition for the children. Poor nutrition compromises the immune system and increases the virulence of HIV whilst also attenuating the efficacy of ARV drugs and magnifying the side effects, ultimately leading to non-adherence ^{[6, 7, 11, 19]}. The lack of access to clean water, hygiene and sanitation increases the susceptibility of the children to opportunistic infections ^{[12, 15]}. Lack of access to clean water and sanitation, inadequate food access, poor nutrition and food insecurity are key barriers to adherence and the success of ART on children in Brunapeg area concurring with the findings by Martin ^[16] who in turn linked this to stigma and discrimination. Lack of access to adequate food forces some children into begging for food in order not to starve to death. This disrupts the ART program as the children are not always at home and end up missing their drugs and even hospital visits. Begging exposes the children to sexual abuse and results in the spread of the virus which would be resistant to some ARV drugs since they would be missing the drugs. The virus could resist the first line treatment leading to the second line drugs which are more expensive ^{[6, 10]}.

According to earlier studies, the quality of life and health of HIV-infected children is also dependent on routine assessments for nutritional status, particularly after the initiation of ART ^[20]. Children on ART also require routine CD4 count tests, liver function tests and monitoring of weight and side effects. These assessments were not consistent in the Mangwe district. Symptomatic HIV-infected children have conditions requiring increased energy and nutrition such as TB, chronic lung disease and other chronic opportunistic infections [OIs] or malignancies. Those who are severely malnourished should be managed for severe acute malnutrition (SAM) and provided with 50–100% additional energy and receive one recommended daily allowance (RDA) of micronutrients daily and high-dose vitamin A ^{[20, 21, 22]}.

Investigators have highlighted that people with HIV/AIDS are stigmatized more than other diseases ^[23]. In this study, stigma and discrimination were found to be predominant amongst the problems faced by the children on ART. This resulted in the children fearing to go to hospital, particularly for those who were aware of their HIV status. This was also attributed to the fact that the OI clinics were centralized and offered separate HIV treatment such that anyone frequently visiting them is implicated to be HIV infected and hence often stigmatized by the community ^[8]. Stigma and discrimination also had an impact on the school attendance of the children who were eligible for school since only 39.3% of these were found to be frequently attending school. This was also due to the fact that stigma and discrimination have been linked and correlated to low service utilization in rural areas ^{[17, 23]}. Social marginalization and discrimination restrict access to health and HIV-related services, food and nutritional support and other assistance programs and hence contributed and led to non-adherence ^{[6, 7, 17, 23]}. Such marginalization and discrimination were attributed ^{[17, 23]} to the lack of public awareness programmes and HIV specific support groups which improve adherence by minimizing stigma and discrimination and increasing communal and social support for the affected children. Studies have shown that expansion and decentralization of health service centers that give aid to the children on ART limits the impacts of social marginalization ^{[6, 7]}.

Conclusions and Public Health Implications

Findings from this study showed that the HBCs and the primary care givers had inadequate knowledge on issues regarding HIV/AIDS and this affected the care they delivered to the children on ART particularly with drug administration, providing adequate care and food and nutrition. Policy states that there should treatment partners trained during pre-ART counseling of caregivers ^{[14, 15]}. The HBCs also faced inadequate availability of HBC kits and lack of support from other community members due to widespread stigma and discrimination against HIV and AIDS patients. This resulted in infrequent visits. In conclusion, the children on ART in the Brunapeg area of the Mangwe district are faced by lack of adequate food and nutrition and by widespread stigma and discrimination, in addition to inadequate support both at household and community levels. These factors severely lower the success of ART program since they have a negative impact on the children’s health and quality of life.

Acknowledgements We would like to express our sincere gratitude to the cutting-edge participants for their contributions and endeavors in this study. In particular, we appreciate the cooperation of the St Anne’s Hospital OI clinic staff throughout the study and for the provision of statistical information on the children on ART. We would also like to thank Ms. Tumelo Nare, a technical assistant at the NUST Medical School for her constructive comments and for editing the manuscript.

References

1. Land H. Introduction. Journal of HIV/AIDS & Social Services. 2010; 9(4):323-326.
2. UNICEF/UNAIDS. Children and AIDS: Fifth Stocktaking Report; 2010.
3. Linsk LN. Thirty Years into the HIV Epidemic: Social Work Perspectives and Prospects. Journal of HIV/AIDS & Social Services. 2011; 10(3):218-229.
4. WHO/UNAIDS/UNICEF. Global HIV/AIDS Response: Epidemic update and health sector progress towards Universal Access 2011; 2011.
5. Joint United Nations program on HIV/AIDS and World Health organization. AIDS Epidemic Update. Geneva, Switzerland: Joint United Nations Program on HIV/AIDS; 2009.
6. Mullen J, Leech S, O’Shea S, Chrystie IL, Du Mont G, Ball C, Sharland M, Cottam F, Zuckerman M, Rice P, Easterbrook P. Antiretroviral drug resistance among HIV-1 infected children failing treatment. Journal of Medical Virology. 2002; 68(3):299-304.
7. Martin S, Elliott-DeSorbo DK, Wolters PL, Toledo-Tamula MA, Roby G, Zeichner S, Wood LV. Patient, caregiver and regimen characteristics associated with adherence to highly active antiretroviral therapy among HIV-infected children and adolescents. The Pediatric Infectious Disease Journal. 2007; 26(1):61-67
8. World Health Organization (WHO). Community home-based care in resource limited setting: a framework for action. Geneva, Switzerland: WHO; 2002.
9. Skovdal M, Campbell C, Madanhire C, Nyamukapa C, Gregson S. Challenges faced by elderly guardians in sustaining the adherence to antiretroviral therapy in HIV-infected children in Zimbabwe. AIDS Care. 2011; 23(8):957-964.
10. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, Wagener MM, Singh N. Adherence to protease inhibitor therapy and outcomes in patients with HIV infections. Annals of Internal Medicine. 2000; 133(1):21-30.
11. Castleman T, Seumo-Fosso E, Cogill B. Food and nutrition implications of antiretroviral therapy in resource limited settings. Technical Note No. 7. Food and Nutrition Technical assistance (FANTA). Academy of Educational Development, Washington DC; 2004.
12. World Health Organization (WHO). Antiretroviral therapy of HIV infection in infants and children in resource-limited settings: towards universal access recommendations for a public health approach; 2006.
13. Skovdal M. Community relations and child-led microfinance: a case study of caregiving in Kenya. AIDS Care. 2010; 22(2):1652-1661.
14. UNAIDS. Investing in Our Future Psychosocial Support for Children Affected by HIV/AIDS: A Case Study in Zimbabwe and the United Republic of Tanzania. UNAIDS case study best practice collection; 2001.
15. Zimbabwe Ministry of Health and Child Welfare. Zimbabwe National HIV and AIDS Estimates. ZMoHCW, Harare; 2009.
16. Martin A, Palar K, Derose PK, Adams J. Food Insecurity and Nutritional Barriers to Antiretroviral Therapy: Lessons from Latin America and the Caribbean. Journal of HIV/AIDS & Social Services. 2011; 10(2):194-214
17. Parker R, Aggleton P, Attawell K, Pulerwitz J, Brown L. HIV/AIDS-related Stigma and Discrimination: A Conceptual Framework and an Agenda for Action. Horizons Program; 2002.
18. Paranthaman K, Kumarasamy N, Bella D, Webster P. Factors influencing adherence to anti-retroviral treatment in children with human immunodeficiency virus in South India – a qualitative study. AIDS Care. 2009; 21(8):1025-1031.
19. Ivers L, Chang Y, Jerome J, Freedberg K. Food assistance is associated with improved body mass index, food security and attendance at clinic in an HIV program in central Haiti: A prospective observational cohort study. AIDS Research and Therapy. 2010; 7(33):1–8.
20. World Health Organization. Food security. 2010. Retrieved from: http://www.who.int/trade/glossary/story028/en/.
21. Marston B, De Cock KM. Multivitamins, nutrition and antiretroviral therapy for HIV disease in Africa. New England Journal of Medicine. 2004; 351(1):78-80.
22. Hsu J, Pencharz PB, Macallan D, Tomkins A. Macronutrients and HIV/AIDS: A review of current evidence. Geneva, Switzerland: World Health Organization, Department of Nutrition for Health and Development; 2005.
23. Brems C, Johnson ME, Warner TD, Roberts LW. Health care providers’ reports of perceived stigma associated with HIV and AIDS in rural and urban communities. Journal of HIV/AIDS & Social Services. 2010; 9(4):356-370.
24. Gahagan J, Rehman L, Barbour L, McWilliam S, The preliminary findings of a study exploring the perceptions of a sample of young heterosexual males regarding HIV prevention education programming in Nova Scotia, Canada. Journal of HIV/AIDS Prevention in Children & Youth. 2007; 8(1):135-153.
25. DiIorio CK, McCarty F, Pluhar E, Set the P.A.C.E.! Results of an HIV prevention intervention for mothers of young children. Journal of HIV/AIDS & Social Services. 2011; 10(3):290-310.
26. Centers for Disease Control and Prevention. HIV transmission rates in the United States; 2010.

Abstract

Objectives

Highly active anti-retroviral therapy (HAART), including protease inhibitors (PI) have led to dramatic improvements in the quality and quantity of life in patients with acquired immunodeficiency syndrome (AIDS). However, a significant number of AIDS patients on HAART develop characteristic changes in body fat redistribution referred to as lipodystrophy syndrome (LDS). Features of LDS include hypertrophy in the neck fat pad (buffalo hump), increased fat in the abdominal region (protease paunch), gynecomastia and loss of fat in the mid-face and extremities.

Methods

The aim of this paper is to review the current knowledge regarding this syndrome. This article reviews the published investigations on biochemical manifestation of HIV lipodystrophy syndrome.

Results

It is estimated that approximately 64% of patients treated with PI will experience this syndrome. Biochemically, these patients have increased triglycerides (Trig), total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C) and extremely low high-density lipoprotein-cholesterol (HDL-C).

Conclusions and Public Health Implications

It is hoped that awareness of this syndrome would aid in early diagnosis and better patient management, possibly leading to a lower incidence of cardiovascular complications among these patients.

Key Words:

HIV Lipodystrophy Syndrome, Highly active anti-retroviral therapy, Nucleoside Reverse Transcriptase Inhibitors, buffalo hump.

Introduction

Lipodystrophy syndrome, a condition associated with metabolic abnormalities and distinct morphological changes has been increasingly reported in HIV-1 infected individuals ^[1]. HIV-LDS in infected patients is now considered a major adverse effect of antiretroviral therapy. Many descriptions of this syndrome are reported in the literature but there exists no universally agreed definition. In general, this condition is characterized by fat loss (lipo-atrophy) in the face, arms, legs, and buttocks; fat gain in the abdomen, over the back of the neck (dorso-cervical fat pad or “buffalo hump”), and in the breast and occasionally isolated lipomata may be present.

HIV-LDS has been associated with both protease inhibitor (PI) and nucleoside analogue therapy, particularly in combination therapy involving both classes of drugs ^{[2, 3]}. Current evidence suggests that HIV-LDS may affect up to half or even more HIV-infected patients receiving antiretroviral therapy ^{[1, 4]}. Thus as the survival rate of HIV positive individuals increases with the introduction of highly active anti-retroviral treatment (HAART), atherosclerotic vascular disease, severe premature coronary artery disease (CAD) and other metabolic diseases could become an important HIV–related complication. Indeed CAD and metabolic disorders have increasingly been reported in patients treated with these medications ^[5]. However, the mechanisms remain largely unknown.

The hallmark of HIV-LDS is a dyslipidemia, a biochemical abnormality of the blood lipid profile that frequently presents before distinctive clinical features of fat redistribution become apparent. To date, no consensus guidelines for treatment of LDS exist. A distinct feature of this condition, body fat changes, could be socially stigmatizing and pose serious problems in treatment compliance and antiretroviral therapy failure. Awareness of HAART complications therefore by all parties concerned, coupled with early diagnosis, could impact positively on HIV prognosis and management. This report aims at describing a typical HIV-LDS case including a review of diverse patho-physiological mechanisms thought to underlie development of this condition in HIV treated patients.

Methods

Studies were identified through a PubMed database search. Case-control and longitudinal studies into clinical and biochemical manifestation of HIV lipodystrophy were selected. Areas covered include data on lipid dysregulation, cytokines, adipokines, proteins, clinical manifestations and management strategies.

HIV-LDS: Clinical Features and Metabolic Changes

Body shape changes

A number of anecdotal reports of increased abdominal girth have been linked with the use of protease inhibitors ^{[6, 7]}. An ongoing Fat Redistribution and Metabolism (FRAM) study ^[8], a prospective, multi-center, cross-sectional investigation of HIV-infected subjects and controls aims to address some of the uncertainties concerning the prevalence, etiology, risk factors and clinical features of HIV-LDS. Preliminary findings to date, mainly from a subgroup of 1200 male subjects and 300 controls suggest a strong association between HIV and lipoatrophy (depletion of subcutaneous fat) but no association between HIV and visceral fat accumulation. It was therefore concluded that lipoatrophy develops independently of fat accumulation and therefore the term ‘fat redistribution’ may be a misnomer. Although HIV infection is well known to cause body wasting usually in advanced disease ^[9], it has not been shown to cause the fat accumulation, breast hypertrophy and buffalo hump of lipodystrophy. Generally speaking, exposure to HAART (in particular PIs) appears to be relevant to the onset of HIV-LDS ^[10]. Thus the variability in clinical manifestations of this syndrome may reflect differences in the underlying pathogenesis.

Lipids

Changes in lipid profile have been the most remarkable biochemical abnormalities in HIV-LDS. The mechanisms predisposing to abnormal lipid profiles in HIV infected individuals are still a matter of debate because HIV infection itself is associated with several metabolic disturbances that may be part of host response to viral infection, decreased HDL-C and LDL-C have been demonstrated in HIV positive men ^[11]. However, these changes usually occur early in HIV infection. Very low density lipoprotein cholesterol (VLDL-C) also increases with immunosuppression as HIV infection becomes manifest. These changes are thought to lead to increased triglyceride levels ^{[11, 12]}. Furthermore, host response to viral infections also causes increases in Interferon-α, which ultimately may cause both increased production and decreased clearance of triglyceride ^{[13, 14]}.

Thus in a cross-sectional study, Carr et al. ^[15], reported that HIV positive patients on PI therapy had higher triglyceride levels (> 100%) and higher cholesterol levels (> 20%) than HIV positive patients not on PI therapy. Increased cholesterol and triglyceride levels have been reported in HIV negative healthy volunteers receiving ritonavir for 2 weeks ^[16], confirming a direct effect of PI treatment on lipid metabolism. Other studies which suggest increases in lipid levels include those of Periad et al. ^[17], which showed increases in mean plasma level of triglyceride (> 100%) and total cholesterol (> 40%) in patients treated with ritonavir compared with a PI naïve group matched for age and body mass index. Sergerer et al. ^[18] reported an average increase in plasma triglyceride by 25% and cholesterol by 15% at 3 months in 148 patients; there were no further significant increases after 3 months. A control group treated only with Nucleoside Reverse Transcriptase Inhibitors (NRTI) had no change in their lipid profiles. Taken together, it is now known that dyslipidemia associated with various stages of HIV/AIDS disease is cytokine mediated whereas dyslipidemias observed in HIV/AIDS treated patients on HAART is likely due to alteration in adipogenesis.

Insulin Resistance

Shikuma et al., ^[19], reported increased fasting insulin and waist-hip ratios in non- wasting patients with AIDS suggesting that such body shape changes were related to HIV infection or to factors associated with immunological dysfunction. Hadigan et al. ^[20] compared metabolic parameters in 75 women with HIV, some of whom were not on PI therapy to 30 weight-matched but younger control women. They noted that fasting insulin was nearly double in HIV patients and independent of PI use. In line with this study, Behrens et al. ^[21] reported impaired glucose tolerance in 24 % of PI naïve patients. Collectively, these studies suggest that HIV itself may cause insulin resistance, which deteriorates with increasing duration of infection.

However, more recent studies suggest that insulin resistance in HIV positive patients is due to antiretroviral therapy and not to HIV infection. Prior to the introduction of HAART, HIV- infected patients were found to have normal or decreased glucose levels and no significant insulin resistance ^{[22, 23]}. While after therapy, hyperglycemia has been reported in several studies ^{[15, 24]} and the US Food and Drug administration have described 83 cases of new onset hyperglycemia or worsening pre-existing diabetes ^[25]. In one study, Carr et al., ^[26], evaluated 116 HIV-infected, otherwise healthy patients receiving one or more PI, 32 HIV–infected PI-naïve patients, and 47 healthy male control subjects. Three recipients had worsening or new diabetes mellitus, and the 64% of PI recipients who developed body composition changes had significantly higher insulin and C-peptide levels than PI naïve patients or controls. Other studies were those of Walli et al. ^[27] who performed intravenous glucose tolerance tests in 67 patients receiving PI- containing therapy, 13 PI-naïve patients and 18 HIV negative controls; 61% of those receiving PI exhibited insulin resistance. In a 5–year cohort analysis in 221 HIV-infected patients, it was found that the incidence of new onset hyperglycemia was 5%. Thus protease inhibitors were independently associated with a five-fold increase in the incidence of hyperglycemia. Taken together, several of the studies cited above suggest that PI therapy may cause insulin resistance. Clinically it has been observed that PI therapy may predispose to glucose intolerance or indeed frank adult onset diabetes in some individuals. Such abnormalities may be more likely if lipodystrophy is present.

Pathogenesis of Lipodystrophy

It has been suggested that the pathogenesis of HIV-LDS may be multifactorial. Possible mechanistic abnormalities in HIV-LDS are described as below:

Inhibition of nuclear receptor complex (PPAR-gamma) and Retinoid X receptor (RXR)

The first attempt at defining the hypothesis for HIV–LDS pathogenesis was put forward by Carr et al. ^[26]. They postulated that protease inhibitors affect adipocyte differentiation by inhibiting the heterodimeric nuclear receptor complex composed of peroxisome proliferator activated receptor gamma (PPAR-gamma) and the retinoid X receptor (RXR). This complex enhances target gene transcription in pre-adipocytes. The catalytic region of HIV-1 protease (to which PI bind) has approximately 60 % homology to regions within cytoplasmic retinoic acid binding protein type 1 (CRABP-1), which enhances the production of cis-9-retinoic acid that is the sole ligand of RXR. Ligand binding to RXR, inhibit adipocyte apoptosis and up-regulate adipocyte differentiation and proliferation. Protease inhibitors may bind to and inhibit CRABP-1, cause decreased production of cis-9-retinoic acid, decreasing RXR-PPAR gamma activity and so reduce differentiation and increase apoptosis of adipocytes. PPAR-gamma is preferentially expressed in peripheral as against central fat so these changes are most marked in peripheral tissues. Although it is plausible from the discussion above that PIs may act as PPAR-gamma antagonists and predispose HIV-infected patients to develop HIV-LDS, this hypothesis has been disputed by Wentworth et al. ^[28], who studied the effects of PIs on human adipocytes in vitro. They found no evidence that PIs acted as PPAR-gamma antagonists suggesting that impaired adipogenesis does underlie PI-associated HIV-LDS but does not directly involve PPAR-gamma and RXR.

Inhibition of sterol regulatory binding element

The sterol regulatory element-binding proteins (SREBPs) are membrane bound transcription factors, which have been proposed to play central role in cellular lipid homeostasis ^[29]. They regulate the transcription of many genes including the LDL receptor gene. SREBPs have been found to be increased in the nuclei of hepatocytes of animals treated with ritonavir ^[30]. Furthermore, it has been suggested that PIs inhibit the protease that degrades SREBPs thereby leading to decreased degradation of apolipoprotein B-100, which will then cause increases in VLDL ^{[31, 32]}.

Upregulation of Pro-inflammatory Cytokines

There is an association between HIV-LDS and levels of pro-inflammatory cytokines. Tumor necrosis factor-alpha (TNF-α) and its receptors are increased in HIV-infected patients ^[33] suggesting that increased concentrations of pro-inflammatory cytokines inhibit the production of acylation-stimulating protein (ASP), a protein which up-regulates the pathways for glucose uptake and fat deposition in adipocytes; they demonstrated an association between lower limb lipoatrophy and subnormal ASP production.

Lipodystrophy and Glucose homeostasis

PI associated diabetes mellitus is similar to type 2 diabetes mellitus. Hyperglycemia is not associated with ketoacidosis and patients respond to oral hypoglycemia treatment ^[34], suggesting that the underlying mechanism is insulin resistance. In studies examining the effect of indinavir on adipocytes, dramatic inhibition of insulin-stimulated glucose up-take was reported ^[35]. Taken together, these results suggest that indinavir directly inhibit GLUT4 (a glucose transporter protein that mediates insulin-stimulated cellular uptake of glucose). Other studies have since supported these findings by demonstrating that indinavir induces muscle insulin resistance ^[36]. Furthermore, increased fasting glucose concentrations and increased secretory response of insulin, pro-insulin and C-peptide to glucose ingestion have been reported in patients treated with PIs ^[21].

Other Clinical signs include Familial combined hyperlipidemia (also called the atherogenic phenotype B) with a constellation of moderately elevated triglyceride (> 150 mg/dL), borderline or moderately decreased HDL-C. It may also manifest as normal or moderately elevated LDL, increased remnants composed of IDL, and small dense LDL ^[37] and increased apolipoprotein B ^[38]. Previously, this was thought to be a familial trait. More recently, it has become apparent that it can also be acquired and expressed as a result of obesity and insulin resistance. This phenotype has been shown to be linked to increases in heart disease ^[38]. The lipid profile in this lipidemia is very similar to that in the lipodystrophy syndrome. Although the exact mechanism leading to the atherogenic phenotype is unknown, it is known that insulin is an important regulator of fatty acid and lipid metabolism ^[39] and that the atherogenic phenotype is largely due to a net over production of VLDL by the liver causing increases in all beta-lipoproteins ^[38].

Mitochondrial toxicity

Madelung’s disease or multiple symmetric lipomatosis (an inherited mitochondrial disease) has clinical features similar to HIV-LDS ^[40]. Thus certain lipodystrophy features are observed in patients’ naïve to PI but treated with NRTIs (Definition) only ^{[41, 42]}. Brinkman et al. ^[43], had suggested that mitochondrial DNA polymerase (the sole enzyme responsible for mitochondrial DNA replication) may be inhibited by NRTIs causing mitochondrial dysfunction. Thus all toxic effects attributed to NTRIs such as peripheral neuropathy, myopathy, pancreatitis and lactic acidosis resemble the clinical syndrome seen in inherited mitochondrial diseases ^[44].

Steroid hormones and lipodystrophy

Changes in steroid hormone levels (particularly glucocorticoids and androgens) have been found in untreated HIV infected patients. Cortisol levels increase in HIV infected men in all stages of infection, whereas androgen levels are elevated early in HIV infection and decrease dramatically in AIDS ^{[45, 46]}.

Management of Lipodystrophy

Treatment of metabolic dysfunction such as lipidemia and glycemia are required. Although, withdrawal of PI therapy for either NNRTI or ABC may improve the metabolic profile regression of lipodystrophy can occur and this may not be an option for all patients ^{[47, 48]}. Despite the lack of consensus treatment guidelines for HIV-LDS, management of lipid and glucose abnormalities should follow current treatment strategies or guidelines for HIV negative patient populations.

Lipid-lowering Agents

Statins and fibrates are commonly used for their lipid-lowering effects. Considerations for drug interactions should help guide the selection of the particular agent used from each class. Newer statins provide greater lipid reductions than the older pravastatin used in earlier studies ^{[49, 50]}. However, there may be greater risk for drug interactions with the newer statins. Fibrates which are more potent than statins in lowering triglyceride and raising HDL-C levels are often needed to reach current guideline targets ^[51].

Glycemia Control

The insulin sensitizing-agent, metformin is commonly used with consistent positive results (e.g. weight neutral or weight loss effects) in clinical trials ^[52]. The use of metformin with some HIV therapies can increase the potential for lactic acidosis ^[53]. Pioglitazone is the only thiazolidinedione that should be considered, however, the benefits are minimal ^[54]. Insulin-like growth factor (IGF), used in extreme insulin resistance syndromes, have demonstrated positive glycemic and cholesterol effects in HIV-LDS ^[55].

Lipoatrophy and fat accumulation

Plastic surgery and fat transplantation are options for HIV-LDS; however, they have no effect on lipid abnormalities or cardiovascular risk reduction and recurrence is common ^[56]. Tesamorelin, a synthetic analogue of human growth hormone-releasing factor, is indicated for the treatment of HIV-LDS. Reduction in visceral adipose tissue is significantly decreased and maintained at 26, and 52 weeks, respectively, with Tesamorelin ^{[57, 58]}.

Conclusions and Public Health Implications

These findings suggest that the metabolic changes associated with the use of HIV PIs, including their adverse effects on triglyceride rich lipoproteins and their associated clinical features may be multi-factorial. Patients receiving HIV PIs should be screened for hyperlipidemias and may be candidates for lipid–lowering therapies that improve endothelial cell function and prevent adverse cardiovascular events. The potential for drug interactions between lipid lowering medications and HIV PIs should also be considered. Clinical decisions regarding initiation and intensification of drug therapies for patients with HIV infection should include their adverse effects on lipids, lipoproteins and cardiovascular function.

Conflicts of Interest: None;
Acknowledgements: DM is supported by Satellite Healthcare, Inc. and Genzyme, Inc.; KI is supported by ACPHS Intramural Funds and the School of Health Sciences, ACPHS.

References

1. Carr A, Samaras K, Burton S, Law M, Freund J, Chisholm DJ, Cooper DA. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS. 1998;12(7):F51-58.
2. Maggi P, Serio G, Epifani G, Fiorentino G, Saracino A, Fico C, Perilli F, Lillo A, Ferraro S, Gargiulo M, Chirianni A, Angarano G, Regina G, Pastore G. Premature lesions of the carotid vessels in HIV-1-infected patients treated with protease inhibitors. AIDS. 2000;14(16):F123-128.
3. Lichtenstein KA, Ward DJ, Moorman AC, Delaney KM, Young B, Palella FJ, Jr., Rhodes PH, Wood KC, Holmberg SD. Clinical assessment of HIV-associated lipodystrophy in an ambulatory population. AIDS. 2001;15(11):1389-1398.
4. Heath KV, Hogg RS, Chan KJ, Harris M, Montessori V, O’Shaughnessy MV, Montanera JS. Lipodystrophy-associated morphological, cholesterol and triglyceride abnormalities in a population-based HIV/AIDS treatment database. AIDS. 2001;15(2):231-239.
5. Gallet B, Pulik M, Genet P, Chedin P, Hiltgen M. Vascular complications associated with use of HIV protease inhibitors. Lancet. 1998;351(9120):1958-1959.
6. Miller KD, Jones E, Yanovski JA, Shankar R, Feuerstein I, Falloon J. Visceral abdominal-fat accumulation associated with use of indinavir. Lancet. 1998;351(9106):871-875.
7. Lo JC, Mulligan K, Tai VW, Algren H, Schambelan M. “Buffalo hump” in men with HIV-1 infection. Lancet. 1998;351(9106):867-870.
8. Grunfeld, C. Basic science and metabolic disturbances. Program and abstracts of the XIV International AIDS Conference:July 7 –12, 2002; Barcelona,Spain. Abstract T 158.
9. Kotler DP, Rosenbaum K, Wang J, Pierson RN. Studies of body composition and fat distribution in HIV-infected and control subjects. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology : Official Publication of the International Retrovirology Association. 1999;20(3):228-237.
10. Reeds DN, Mittendorfer B, Patterson BW, Powderly WG, Yarasheski KE, Klein S. Alterations in lipid kinetics in men with HIV-dyslipidemia. American Journal of Physiology Endocrinology and Metabolism. 2003;285(3):E490-497.
11. Grunfeld C, Pang M, Doerrler W, Shigenaga JK, Jensen P, Feingold KR. Lipids, lipoproteins, triglyceride clearance, and cytokines in human immunodeficiency virus infection and the acquired immunodeficiency syndrome. The Journal of Clinical Endocrinology and Metabolism. 1992;74(5):1045-1052.
12. Fernandez-Miranda C, Pulido F, Carrillo JL, Larumbe S, Gomez Izquierdo T, Ortuno B, Rubio R, del Palacio A. Lipoprotein alterations in patients with HIV infection: relation with cellular and humoral immune markers. Clinica Chimica Acta; International Journal of Clinical Chemistry. 1998;274(1):63-70.
13. Grunfeld C, Kotler DP, Hamadeh R, Tierney A, Wang J, Pierson RN. Hypertriglyceridemia in the acquired immunodeficiency syndrome. The American Journal of Medicine. 1989;86(1):27-31.
14. Hellerstein MK, Grunfeld C, Wu K, Christiansen M, Kaempfer S, Kletke C, Shackleton CH. Increased de novo hepatic lipogenesis in human immunodeficiency virus infection. The Journal of Clinical Endocrinology and Metabolism. 1993;76(3):559-565.
15. Carr A, Samaras K, Thorisdottir A, Kaufmann GR, Chisholm DJ, Cooper DA. Diagnosis, prediction, and natural course of HIV-1 protease-inhibitor-associated lipodystrophy, hyperlipidaemia, and diabetes mellitus: a cohort study. Lancet. 1999;353(9170):2093-2099.
16. Purnell JQ, Zambon A, Knopp RH, Pizzuti DJ, Achari R, Leonard JM, Locke C, Brunzell JD. Effect of ritonavir on lipids and post-heparin lipase activities in normal subjects. AIDS. 2000;14(1):51-57.
17. Periard D, Telenti A, Sudre P, Cheseaux JJ, Halfon P, Reymond MJ, Marcovina SM, Glauser MP, Nicod P, Darioli R, Mooser V. Atherogenic dyslipidemia in HIV-infected individuals treated with protease inhibitors. The Swiss HIV Cohort Study. Circulation. 1999;100(7):700-705.
18. Segerer S, Bogner JR, Walli R, Loch O, Goebel FD. Hyperlipidemia under treatment with proteinase inhibitors. Infection. 1999;27(2):77-81.
19. Shikuma CM, Waslien C, McKeague J, Baker N, Arakaki M, Cui XW, Souza S, Imrie A, Arakaki R. Fasting hyperinsulinemia and increased waist-to-hip ratios in non-wasting individuals with AIDS. AIDS. 1999;13(11):1359-1365.
20. Hadigan C, Miller K, Corcoran C, Anderson E, Basgoz N, Grinspoon S. Fasting hyperinsulinemia and changes in regional body composition in human immunodeficiency virus-infected women. The Journal of Clinical Endocrinology and Metabolism. 1999;84(6):1932-1937.
21. Behrens G, Dejam A, Schmidt H, Balks HJ, Brabant G, Korner T, Stoll M, Schmidt RE. Impaired glucose tolerance, beta cell function and lipid metabolism in HIV patients under treatment with protease inhibitors. AIDS. 1999;13(10):F63-70.
22. Stein TP, Nutinsky C, Condoluci D, Schluter MD, Leskiw MJ. Protein and energy substrate metabolism in AIDS patients. Metabolism: Clinical and Experimental. 1990;39(8):876-881.
23. Hommes MJ, Romijn JA, Endert E, Eeftinck Schattenkerk JK, Sauerwein HP. Insulin sensitivity and insulin clearance in human immunodeficiency virus-infected men. Metabolism: Clinical and Experimental. 1991;40(6):651-656.
24. Montserrat SR, Pena, R., Cervantes, M. Hyperglycemia associated with protease inhibitor therapy. Program and Abstracts of XII Internationmal Conference on AIDS. 1998(Geneva, Switzerland).
25. MM L. Report of diabetes and hyperglycemia in patients receiving protease inhibitors for the treatment of human immunodeficiency virus (HIV). FDA Public Health advisory. June 11; 1997.
26. Carr A, Samaras K, Chisholm DJ, Cooper DA. Pathogenesis of HIV-1-protease inhibitor-associated peripheral lipodystrophy, hyperlipidaemia, and insulin resistance. Lancet. 1998;351(9119):1881-1883.
27. Walli R, Herfort O, Michl GM, Demant T, Jager H, Dieterle C, Bogner JR, Landgraf R, Goebel FD. Treatment with protease inhibitors associated with peripheral insulin resistance and impaired oral glucose tolerance in HIV-1-infected patients. AIDS. 1998;12(15):F167-173.
28. Wentworth JM, Burris TP, Chatterjee VK. HIV protease inhibitors block human preadipocyte differentiation, but not via the PPARgamma/RXR heterodimer. The Journal of Endocrinology. 2000;164(2):R7-R10.
29. Sakai J, Rawson RB. The sterol regulatory element-binding protein pathway: control of lipid homeostasis through regulated intracellular transport. Current Opinion in Lipidology. 2001;12(3):261-266.
30. Riddle TM, Kuhel DG, Woollett LA, Fichtenbaum CJ, Hui DY. HIV protease inhibitor induces fatty acid and sterol biosynthesis in liver and adipose tissues due to the accumulation of activated sterol regulatory element-binding proteins in the nucleus. The Journal of Biological Chemistry. 2001;276(40):37514-37519.
31. Andre P, Groettrup M, Klenerman P, de Giuli R, Booth BL, Jr., Cerundolo V, Bonneville M, Jotereau F, Zinkernagel RM, Lotteau V. An inhibitor of HIV-1 protease modulates proteasome activity, antigen presentation, and T cell responses. Proceedings of the National Academy of Sciences of the United States of America. 1998;95(22):13120-13124.
32. Liang JS, Distler O, Cooper DA, Jamil H, Deckelbaum RJ, Ginsberg HN, Sturley SL. HIV protease inhibitors protect apolipoprotein B from degradation by the proteasome: a potential mechanism for protease inhibitor-induced hyperlipidemia. Nature Medicine. 2001;7(12):1327-1331.
33. Ionescu G HQ, Engelson ES, Johnson JA, Albu JB, Inada Y, Kotler DP. Acylation stimulating protein (ASP) and tumor necrosis factor (TNF) production in subcutaneous adipose tissue of HIV-infected patients with and without lipodystrophy. Poster Exhibition: The XIV International AIDS Conference:. June 7 – 12, 2002:Abstract no. WePeA5794.
34. Dube MP, Johnson DL, Currier JS, Leedom JM. Protease inhibitor-associated hyperglycaemia. Lancet. 1997;350(9079):713-714.
35. Murata H, Hruz PW, Mueckler M. The mechanism of insulin resistance caused by HIV protease inhibitor therapy. The Journal of Biological Chemistry. 2000;275(27):20251-20254.
36. Nolte LA, Yarasheski KE, Kawanaka K, Fisher J, Le N, Holloszy JO. The HIV protease inhibitor indinavir decreases insulin- and contraction-stimulated glucose transport in skeletal muscle. Diabetes. 2001;50(6):1397-1401.
37. Austin MA, King MC, Vranizan KM, Krauss RM. Atherogenic lipoprotein phenotype. A proposed genetic marker for coronary heart disease risk. Circulation. 1990;82(2):495-506.
38. Aguilar Salinas CA, Zamora M, Gomez-Diaz RA, Mehta R, Gomez Perez FJ, Rull JA. Familial combined hyperlipidemia: controversial aspects of its diagnosis and pathogenesis. Seminars in Vascular Medicine. 2004;4(2):203-209.
39. Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. The Journal of Clinical Investigation. 2005;115(5):1111-1119.
40. Enzi G, Inelmen EM, Baritussio A, Dorigo P, Prosdocimi M, Mazzoleni F. Multiple symmetric lipomatosis: a defect in adrenergic-stimulated lipolysis. The Journal of Clinical Investigation. 1977;60(6):1221-1229.
41. Gervasoni C, Ridolfo AL, Trifiro G, Santambrogio S, Norbiato G, Musicco M, Clerici M, Galli M, Moroni M. Redistribution of body fat in HIV-infected women undergoing combined antiretroviral therapy. AIDS. 1999;13(4):465-471.
42. Madge S, Kinloch-de-Loes S, Mercey D, Johnson MA, Weller IV. Lipodystrophy in patients naive to HIV protease inhibitors. AIDS. 1999;13(6):735-737.
43. Brinkman K, Smeitink JA, Romijn JA, Reiss P. Mitochondrial toxicity induced by nucleoside-analogue reverse-transcriptase inhibitors is a key factor in the pathogenesis of antiretroviral-therapy-related lipodystrophy. Lancet. 1999;354(9184):1112-1115.
44. Silvestri G, Ciafaloni E, Santorelli FM, Shanske S, Servidei S, Graf WD, Sumi M, DiMauro S. Clinical features associated with the A–>G transition at nucleotide 8344 of mtDNA (“MERRF mutation”). Neurology. 1993;43(6):1200-1206.
45. Nunez, E.A., Christeff, N. Steroid hormones, cytokines, lipid and metabolic disturbances in HIV infection. The Endocrinology and Metabolism of HIV Infection. Balliere and Tindall. 1994; 803 – 824.
46. Christeff N, Gharakhanian S, Thobie N, Rozenbaum W, Nunez EA. Evidence for changes in adrenal and testicular steroids during HIV infection. Journal of Acquired Immune Deficiency Syndromes. 1992;5(8):841-846.
47. Vigano A, Brambilla P, Cafarelli L, Giacomet V, Borgonovo S, Zamproni I, Zuccotti G, Mora S. Normalization of fat accrual in lipoatrophic, HIV-infected children switched from stavudine to tenofovir and from protease inhibitor to efavirenz. Antiviral Therapy. 2007;12(3):297-302.
48. Ribera E, Paradineiro JC, Curran A, Sauleda S, Garcia-Arumi E, Castella E, Puiggros C, Crespo M, Feijoo M, Diaz M, Del Saz SV, Planas M, Sureda D, Falco V, Ocana I, Pahissa A. Improvements in subcutaneous fat, lipid profile, and parameters of mitochondrial toxicity in patients with peripheral lipoatrophy when stavudine is switched to tenofovir (LIPOTEST study). HIV Clinical Trials. 2008;9(6):407-417.
49. Aslangul E, Assoumou L, Bittar R, Valantin MA, Kalmykova O, Peytavin G, Fievet MH, Boccara F, Bonnefont-Rousselot D, Melchior JC, Giral P, Costagliola D. Rosuvastatin versus pravastatin in dyslipidemic HIV-1-infected patients receiving protease inhibitors: a randomized trial. AIDS. 2010;24(1):77-83.
50. Calza L, Manfredi R, Colangeli V, Pocaterra D, Pavoni M, Chiodo F. Rosuvastatin, pravastatin, and atorvastatin for the treatment of hypercholesterolaemia in HIV-infected patients receiving protease inhibitors. Current HIV Research. 2008;6(6):572-578.
51. Giannarelli C, Klein RS, Badimon JJ. Cardiovascular implications of HIV-induced dyslipidemia. Atherosclerosis. 2011;219(2):384-389.
52. Sheth SH, Larson RJ. The efficacy and safety of insulin-sensitizing drugs in HIV-associated lipodystrophy syndrome: a meta-analysis of randomized trials. BMC Infectious Diseases. 2010;10:183.
53. Kazazis C. A case of lactic acidosis (LA) after administration of tenofovir and metformin in a diabetic patient with recently diagnosed HIV infection. Journal of Renal Care. 2011;37(3):174.
54. Raboud JM, Diong C, Carr A, Grinspoon S, Mulligan K, Sutinen J, Rozenbaum W, Cavalcanti RB, Wand H, Costagliola D, Walmsley S, Glitazone, Lipoatrophy Meta-Analysis Working G. A meta-analysis of six placebo-controlled trials of thiazolidinedione therapy for HIV lipoatrophy. HIV Clinical Trials. 2010;11(1):39-50.
55. Rao MN, Mulligan K, Tai V, Wen MJ, Dyachenko A, Weinberg M, Li X, Lang T, Grunfeld C, Schwarz JM, Schambelan M. Effects of insulin-like growth factor (IGF)-I/IGF-binding protein-3 treatment on glucose metabolism and fat distribution in human immunodeficiency virus-infected patients with abdominal obesity and insulin resistance. The Journal of Clinical Endocrinology and Metabolism. 2010;95(9):4361-4366.
56. Nelson L, Stewart KJ. Plastic surgical options for HIV-associated lipodystrophy. Journal of Plastic, Reconstructive & Aesthetic Surgery : JPRAS. 2008;61(4):359-365.
57. Falutz J, Mamputu JC, Potvin D, Moyle G, Soulban G, Loughrey H, Marsolais C, Turner R, Grinspoon S. Effects of tesamorelin (TH9507), a growth hormone-releasing factor analog, in human immunodeficiency virus-infected patients with excess abdominal fat: a pooled analysis of two multicenter, double-blind placebo-controlled phase 3 trials with safety extension data. The Journal of Clinical Endocrinology and Metabolism. 2010;95(9):4291-4304.
58. Falutz J, Potvin D, Mamputu JC, Assaad H, Zoltowska M, Michaud SE, Berger D, Somero M, Moyle G, Brown S, Martorell C, Turner R, Grinspoon S. Effects of tesamorelin, a growth hormone-releasing factor, in HIV-infected patients with abdominal fat accumulation: a randomized placebo-controlled trial with a safety extension. Journal of Acquired Immune Deficiency Syndromes. 2010;53(3):311-322.

Abstract

One year after the birth of the International Journal of MCH and AIDS (IJMA), we continue to share the passion to document, and shine the light on the myriads of global health issues that debilitate developing countries.

Although the focus of IJMA is on the social determinants of health and disease as well as on the disparities in the burden of communicable and non-communicable diseases affecting infants, children, women, adults, and families in developing countries, we would like to encourage our fellow researchers and policy makers in both the developing and developed countries to consider submitting work that examines cross-national variations in heath and social inequalities.

Such a global focus allows us to identify and understand social, structural, developmental, and health policy determinants underlying health inequalities between nations.

Global assessment of health and socioeconomic patterns reaffirms the role of broader societal-level factors such as human development, gender inequality, gross national product, income inequality, and healthcare infrastructure as the fundamental determinants of health inequalities between nations.

This is also confirmed by our analysis of the WHO data that shows a strong negative association between levels of human development and infant and maternal mortality rates.

Focusing on socioeconomic, demographic, and geographical inequalities within a developing country, on the other hand, should give us a sense of how big the problem of health inequity is within its own borders.

Such an assessment, then, could lead to development of policy solutions to tackle health inequalities that are unique to that country.

Key Words:

International Journal of MCH and AIDS • IJMA •Social determinants • Non-communicable diseases • Communicable diseases • Health Disparities • Human development • Social inequality.

Almost one year ago, we were on a lunchtime walk in the outskirts of the nation’s capital, Washington, DC. Amidst the ruffles of dry leaves, the birds chirruped. Some lawn mowers vibrated as they chewed grasses in this usually quiet neighborhood. As is the unwritten ‘rule’ for our walks, we joked, we talked about our current projects, our planned work, and the opportunities and the challenges for the future. On this rather chilly day, our discussion somehow veered into how we both can utilize our successful careers to benefit our global ancestry. We had spent our early years in developing countries: Gopal in India, Asia and Romuladus in Nigeria, Africa. As this discussion unveiled, we saw a synchronous passion to give back not only to Asia and Africa but also to the entire developing countries. A few projects came up. After weighing the pros and cons, the idea of the International Journal of MCH and AIDS (IJMA) was preeminent. We agreed that IJMA was a fertile ground to cultivate a global intellectual coalition to highlight the issues in the hinterlands of developing countries and to offer a platform to offer practical policy suggestions to address these issues. Above all, IJMA’s idea went beyond the usual talk about “giving back.” The idea was laden with action.

One year after this lunchtime walk and the birth of IJMA, we continue to share the passion to document, and shine the light on the myriads of global health issues that debilitate developing countries. Although the focus of IJMA is on the social determinants of health and disease as well as on the disparities in the burden of communicable and non-communicable diseases affecting infants, children, women, adults, and families in developing countries, we would like to encourage our fellow researchers and policy makers in both the developing and developed countries to consider submitting work that examines cross-national variations in heath and social inequalities. Such a global focus allows us to identify and understand social, structural, developmental, and health policy determinants underlying health inequalities between nations ^{[1, 2]}. Global assessment of health and socioeconomic patterns reaffirms the role of broader societal-level factors such as human development, gender inequality, gross national product, income inequality, and healthcare infrastructure as the fundamental determinants of health inequalities between nations. This is also confirmed by our analysis of the WHO data in Figure 1 that shows a strong negative association between levels of human development and infant and maternal mortality rates (γ > -0.85) ^[3].

Focusing on socioeconomic, demographic, and geographical inequalities within a developing country, on the other hand, should give us a sense of how big the problem of health inequity is within its own borders. Such an assessment, then, could lead to development of policy solutions to tackle health inequalities that are unique to that country [2].

Figure 1. Relationship between Human Development Index and Infant and Maternal Mortality Rates, 167 Countries, 2008-2010

Source: World Health Statistics 2011 and Human Development Report 2011.

The papers in this inaugural issue—among the best you can get in any journal of its ilk—bail us out. In their commentary, Hairston and experts from the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) make the case for the integration of maternal, sexual, and reproductive health services as a foundation for eliminating pediatric HIV in developing countries ^[4]. While the newly-launched UN Global Plan Towards Elimination of New HIV Infections renews the momentum around the elimination of pediatric HIV, it presents a unique opportunity to place women at its center by linking research, policies, and programs to sexual reproductive and maternal health, writes the experts. They call on stakeholders to utilize the synergies between HIV and sexual and reproductive health programs to address the diverse health needs of women and in eventually eliminating pediatric HIV worldwide ^[4]. As the leading non-profit organization in the field of MCH and HIV/AIDS, a substantive presence in developing countries, and an A rating from US CharityWatch, no other organization understands HIV/AIDS and MCH in developing countries better than EGPAF. And their counsel must be taken seriously.

In their article on global inequalities in cervical cancer, Singh and colleagues look at global disparities in cervical cancer, which is not only a major public health problem in many low- and middle-income countries but is also the number one cancer site and a major killer of women in reproductive ages in East and Sub-Saharan Africa and South Asia ^[5]. They go on to show that inequalities in cervical cancer rates among 184 countries are strongly linked to disparities in human development, social inequality, and living standards and argue for improvements in women’s social status and wider availability of preventive cancer screening services as the primary means to achieving reductions in cervical cancer rates ^[5].

Any comparison of China and India stirs tremendous passion, interest, and debate among academics, policy makers, and social and political commentators around the world. In their article, which appears to be the first of its kind, Singh and Liu present a powerful analysis of how health, socioeconomic, and development patterns have changed in India and China during the past six decades ^[6]. According to the authors, although the two countries started out on a similar footing in the late 1940s, China appears to be doing better than India on several key health measures 60 years later. Because of more rapid improvements in health among the Chinese, the health gap between India and China, particularly in infant, child and maternal mortality and life expectancy, is wider now than ever before ^[6].

Child mortality rates continue to be the highest in the WHO African Region and South-East Asia Region ^[3]. Neonatal mortality, deaths during the first 28 days of life, accounts for 40% of all deaths among children under 5 years old globally, with Tanzania’s rate of 34 per 1,000 live births being among the highest in Africa ^[3]. The article by Ajaari and colleagues examines the impact of place of delivery on neonatal mortality in rural Tanzania using a longitudinal population-based database, showing nearly twice the risk of neonatal mortality among births delivered outside health facilities compared with those delivered in health facilities attended by trained medical staff ^[7]. Despite a marked improvement in child survival over time, India’s current under-five mortality rate of 66 per 1,000 live births remains higher than the global average ^[3]. In terms of the absolute burden, India accounts for nearly one-fourth of all under-5 deaths globally ^[3]. Given the significance of child mortality as a major public health problem in India, Mani and colleagues determine the effects of proximate or programmable determinants on under-five mortality in rural India by applying Cox-frailty and other hazard regression models to the data from the National Family Health Survey. Their analysis identifies maternal age, place of delivery, parity and birth spacing, infant’s birth weight, and breastfeeding as significant determinants of under-five mortality ^[8].

Undernutrition among children is a major public health problem in many developing countries and the problem of underweight and stunting (low height-for-age) is particularly acute in Africa and Asia ^[3]. Using primary data collected for 405 school-aged children in Akwa Ibon State of Nigeria, Opara and colleagues examine the impact of intestinal parasitic infection on nutritional status of children ^[9]. According to their study, more than two-thirds of the Nigerian children in the sample were infected with at least one intestinal parasite, with the risk of stunting, wasting, and underweight being higher among children infected with intestinal parasites ^[9].

In their mixed-method paper, Macherera and colleagues make some stunning revelations ^[10]. Caregivers and guardians have become the newest but most intricate hindrance to access to antiretroviral therapy (ART) for children living HIV/AIDS in rural Zimbabwe. Using data and information from focus groups conducted in a rural clinic and community, the authors demonstrate that while children growing up with HIV/AIDS continue to deal with stigma, discrimination, and rejection, they are embroiled with an unwinnable war within as their guardians are now using their ART medications to take care of themselves first. This paper highlights the urgent need to provide cross-generational access to ART especially in developing countries where targeting one group, like children, neglects the needs of other groups ^[10].

IJMA is founded on a pedestal of rigor and transparency. We conduct blind peer-reviews and select papers based on peer assessment of the merit of each contribution to advancing the field of MCH and HIV/AIDS both regionally and worldwide. With a robust editorial board reflecting the best and finest drawn from the six World Health Organization regions, our editorial board members are key to setting global health research agenda for our journal. Manuscripts by our editors pass through the highest level of our blind peer-review process conducted by experts in the field and from outside the board or the regions of the authors. We thank our friends and colleagues who have joined this coalition. From Oman to Nigeria, from India to Australia, we hope that the IJMA idea will outlive each and every one of us. We hope that IJMA will lay the foundation for highlighting and addressing global health challenges of developing countries by developing country scientists and policy makers. Happy reading!

References

1. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
2. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
3. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
4. Hairston AF, Bobrow EA, Pitter CS. Towards the elimination of pediatric HIV: Enhancing maternal, sexual, and reproductive health services. International Journal of MCH and AIDS. 2012; 1(1):6-13.
5. Singh GK, Azuine RE, Siahpush M. Global health inequalities in cervical cancer incidence and mortality are linked to deprivation, low socioeconomic status, and human development. International Journal of MCH and AIDS. 2012; 1(1):14-27.
6. Singh GK, Liu J. Health improvements have been more rapid and widespread in China than in India: A comparative analysis of health and socioeconomic trends from 1960 to 2011. International Journal of MCH and AIDS. 2012; 1(1):28-45.
7. Ajaari J, Masanja H, Abokyi SA, Owusu-Agyei S. Impact of place of delivery on neonatal mortality in rural Tanzania. International Journal of MCH and AIDS. 2012; 1(1):46-55.
8. Mani K, Dwivedi SN, Pandey RM. Determinants of under-five mortality in Rural Empowered Action Group States in India: An application of Cox frailty model. International Journal of MCH and AIDS. 2012; 1(1):56-67.
9. Opara KN, Udoidung NI, Opara DC, Okon OE, Edosomwan UE, Udoh AJ. The impact of intestinal parasitic infections on the nutritional status of rural and urban school-aged children in Nigeria. International Journal of MCH and AIDS. 2012; 1(1):68-75.
10. Macherera M, Moyo L, Ncube M, Gumby A. Social, cultural, and environmental challenges faced by children on antiretroviral therapy in Zimbabwe: A mixed-method study. International Journal of MCH and AIDS. 2012; 1(1):76-83.

Abstract

Almost 10 years ago, the United Nations adopted a comprehensive, four-pronged approach for the prevention of mother-to-child transmission of HIV (PMTCT). Despite all four prongs being central to the elimination of pediatric HIV, and the health of the mother being critical to reaching this goal, PMTCT programs have historically focused more attention on preventing HIV transmission from mother to child (prong 3) than on preventing HIV in women of reproductive age (prong 1) and preventing unintended pregnancies in women living with HIV (prong 2). In this commentary, experts from the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) argue that within the context of efforts to eliminate pediatric HIV, there are many ways to keep women living with HIV alive and at the center of the response to the global epidemic. One of the ways to do this is to enhance maternal and sexual and reproductive health (SRH) services. Within the elimination agenda, integration and linkages between PMTCT and comprehensive SRH services can keep mothers alive and at the center of the response. The commentary highlights some of the foundation’s global health work supporting, evaluating and enhancing maternal and SRH services provided to women living with HIV and proposes concrete actions for donors, researchers, policy makers and program implementers to further enhance maternal and SRH services within the context of PMTCT. If keeping women living with HIV is an integral component of the elimination of pediatric HIV agenda, maternal and SRH research, policies and programs need to be strengthened within the context of PMTCT. Donor funding and priorities for PMTCT also need to be more supportive of primary prevention of HIV infection among women of childbearing age and preventing unintended pregnancies among women living with HIV.

Key Words:

HIV/AIDS • Elimination of pediatric HIV • PMTCT • Reproductive and sexual health • Maternal health

Introduction

In 2002, the United Nations (UN) adopted a four-pronged approach to the prevention of mother-to-child transmission of HIV (PMTCT). These four prongs represent the cornerstones of comprehensive PMTCT service delivery: primary prevention of HIV infection among women of childbearing age (prong 1), preventing unintended pregnancies among women living with HIV (prong 2), preventing HIV transmission from a woman living with HIV to her infant (prong 3), and treatment, care, and support to women living with HIV, their children, and families (prong 4) ^[1]. Despite all four prongs being central to the elimination of pediatric HIV, and the health of women being important to reaching this goal, PMTCT programs have historically focused more attention on identifying HIV infection among pregnant women to prevent HIV transmission from mother to child (prong 3) than on preventing HIV in women of reproductive age (prong 1) and preventing unintended pregnancies in women living with HIV (prong 2) ^[2-4]. Figure 1 shows estimates of new HIV infections among children in 25 countries from 2009 until 2015 based on scenarios in which different combinations of prongs 1 through 3 are used. ^[5] These estimates show a projected 79% reduction in new infections by 2015 using a combination of prongs 1, 2, and 3 and limiting breastfeeding to 12 months, compared with a 60% reduction using prong 3 alone. The estimates shown in Figure 1 highlight how important prongs 1 and 2 are in reaching the elimination of pediatric HIV and the estimated impact of increasing antiretroviral coverage for women living with HIV.

Figure 1. New HIV Infections Among Children in 25 Countries 2009-2015 By PMTCT Prongs ^[5-6]

Source: Mahy et al. ^[5] and The Inter-Agency Task Team for Prevention and Treatment of HIV Infection in Pregnant Women Mothers and Their Children ^[6].

Global Plan Towards Eliminating New HIV Infections

There has been a concerted effort over the past few years to integrate and link (see Box 1 for definitions) maternal and child health (MCH) and sexual and reproductive health (SRH) services with PMTCT services to more comprehensively address all four prongs and increase the focus on women’s health. At the UN Special Session on HIV in 2011 (UNGASS 2011), world leaders launched the Global Plan Towards the Elimination of New HIV Infections Among Children by 2015 and Keeping their Mothers Alive (the Global Plan), which outlines how countries can ultimately reach the goal of eliminating pediatric HIV ^[7]. The Global Plan not only recognizes the importance of maternal health in its title, but the first of the four overarching principles of the Global Plan is to keep women living with HIV at the center of the response. The Global Plan describes this as including but not limited to; mothers and children having access to optimal HIV prevention and treatment regimens based on the latest guidelines, women living with HIV having access to family planning services and commodities as well as the meaningful participation of women in developing and implementing programs to tackle the barriers to services and to work as partners in providing care and address HIV and gender-related discrimination that impedes service access and uptake as well as client retention. There are also defined targets of reducing maternal deaths by 50%, reducing HIV incidence in women aged 15-49 by 50%, and reducing unmet need for family planning among women living with HIV to zero ^[7]. The Preventing Unintended Pregnancies Strategic Framework has been developed by the Inter-Agency Task Team for Prevention and Treatment of HIV Infection in Pregnant Mothers and Their Children (IATT) in support of national efforts to translate the Global Plan into action ^[6]. Donors are also calling for HIV services to be integrated into SRH services, and encouraging HIV implementers to play active roles in evaluating, coordinating, and leveraging support for broader maternal health service delivery capacity ^[8]. This shift is applauded, but in order to keep mothers living with HIV at the center of the response, there needs to be an increased focus on maternal health and SRH within the context of PMTCT.

In this commentary we recognize that, within the context of efforts to eliminate pediatric HIV, there are many ways to keep women living with HIV alive and at the center of the response and argue that one of the ways to do this is to enhance maternal and sexual and reproductive health (SRH) services. We describe that, within the elimination agenda, integration and linkages between PMTCT and comprehensive SRH services can keep mothers alive (as shown in Figure 2 below) and at the center of the response. We also highlight some of the work that the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) is supporting and evaluating to enhance maternal and SRH services provided to women living with HIV. We conclude by proposing concrete actions for donors, researchers, policy makers and program implementers to further enhance maternal and SRH services within the context of PMTCT.

Figure 2. PMTCT Prongs and Integration and Linkages to SRH Services’ Contribution to the Elimination of Pediatric HIV and Keeping Mothers Alive

Background

Maternal Health Must be the Center of Eliminating Pediatric HIV

Promoting maternal health among women living with HIV is central to keeping HIV-positive mothers living with HIV alive and at the center of the agenda to eliminate pediatric HIV. Just 58 countries contribute to 91% of the global burden of maternal mortality; 38 of these countries are in Africa ^[9] and 17 of these countries are among the 22 focus countries identified in the Global Plan as having the highest estimated numbers of pregnant women living with HIV ^[7]. In fact, the vast majority of HIV-associated maternal deaths (86%-92%) occur in sub-Saharan Africa and more recent assessments of trends in maternal mortality indicate that HIV has become a leading cause of death during pregnancy and the postpartum period in countries with a high HIV prevalence ^[10]. The association between skilled attendance at birth and reduced maternal mortality, coupled with strategies a skilled birth attendant (SBA) can use to reduce both the incidence and severity of complications during labor and delivery constitute sufficient grounds to recommend that an SBA be present for every birth ^[11]. However, many births in resource-limited settings, including settings with a high burden of HIV, are not attended by an SBA. It is estimated that there will be between 130 and 180 million non-SBA births in South Asia and sub-Saharan Africa between 2011 and 2015, and that 90% of these will occur in rural areas ^[12].

Family planning addresses needs of women within the context of PMTCT because it allows women and men to attain their desired number of children and determine the spacing of pregnancies ^[13]. Although many HIV-positive women and couples wish to manage their reproductive options, evidence suggests that unmet need for family planning may be higher among HIV-positive women than the general population ^[14]. Studies in Côte d’Ivoire, Rwanda, South Africa, and Uganda have found levels of unintended pregnancies among HIV-positive women ranging from 51% to 91%. ^[15-18]. Halperin and colleagues conducted an analysis of data from the 2008 UNAIDS report on the AIDS epidemic that suggests family planning is cost-effective for HIV prevention as well as for preventing pregnancies among women living with HIV ^[2]. The direct health benefits of meeting the need for both family planning and maternal and newborn health services (i.e., a continuum of care to help couples plan their pregnancies and provide timely antenatal, delivery, and postpartum services, including urgent care for complications that arise among women and newborns) would lead to health system improvements that could provide lifesaving care to women and their newborns while at the same time strengthening services for other urgent medical needs [^19].

Linking Reproductive Age Women to Sexual and Reproductive Health Services in Developing Countries

Linking HIV-positive women of reproductive age in resource-limited settings and their partners to SRH services is not only an important part of keeping mothers at the center of the agenda to eliminate pediatric HIV, but is also key to ensuring that women remain alive and healthy. A systematic review conducted by WHO and several other agencies in 2009 found that bi-directional linkages between SRH- and HIV-related policies and programs can lead to better access among people living with HIV to SRH services that are tailored to their needs, reduction of HIV-related stigma and discrimination, and enhanced program effectiveness and efficiency ^[20]. SRH linkages that are particularly relevant to the health needs of women within the context of PMTCT include linkages to syphilis and cervical cancer screening. In addition to the morbidity and mortality associated with syphilis and congenital syphilis, co-infection of syphilis and HIV among pregnant women is a significant risk factor for vertical HIV transmission ^{[4, 7]}. It should also be noted that 83% of new cases of cervical cancer and 85% of deaths from the disease occur in resource-limited settings ^[21]. A study in Kenya found that young women with cervical cancer were more often HIV infected than women with fibroids of the same age group ^[22].

EGPAF’S Commitment to Maternal and Sexual and Reproductive Health

A variety of organizations and institutions have committed to, and are engaged in addressing maternal health and SRH within the context of PMTCT. It should however be noted that one of the key challenges related to increasing focus on maternal health and comprehensive SRH services within the elimination of pediatric HIV agenda is that implementing partners working in these different areas may have difficulty integrating their programs because US-funded reproductive health and HIV projects and programs are often separated, with programs using very different results and reporting mechanisms.

EGPAF seeks to prevent pediatric HIV infection and to eliminate pediatric HIV through research, advocacy, and prevention and treatment programs. As such, EGPAF is committed to promoting integration and linkage of SRH and HIV services in all the countries that it supports (see figure 3 below), and to promoting these approaches through its close partnerships with ministries of health and a variety of national and international public health organizations and institutions. EGPAF:

1. Favors policies that affirm that all women and men, regardless of HIV status, have the right to determine the number and spacing of their children;
2. Favors increasing access to prevention, screening, and treatment of sexually transmitted infections (STIs) as part of a comprehensive package of reproductive health services;
3. Favors increasing access to high-quality, safe, and appropriate obstetric care and services to improve maternal and child health and survival; and
4. Favors increasing the availability of high-quality, safe, and comprehensive care for all women accessing services, and places a special emphasis on ensuring that women living with HIV receive the antenatal care (ANC) and HIV services they need for their own health and the health of their children.

The positions summarized here were developed by members of the Foundation’s technical advisory group on MCH/HIV linkages and integration (the M-TAG), which was made up of Foundation country and global staff. These positions are intended to guide a consistent Foundation-wide approach and framework for addressing this issue and do not supersede the national policies and guidelines of the countries in which the Foundation works.

Figure 3. Countries where EGPAF Works in Sub-Saharan Africa

A variety of country-specific efforts point to EGPAF’s growing involvement in this important area. EGPAF, with support from PEPFAR (The US President’s Emergency Plan for AIDS Relief) and various other donors, works in a variety of countries to support increased facility-based deliveries assisted by Skilled Birth Attendants (SBAs), earlier and more frequent antenatal care visits, primary prevention of HIV among pregnant women, provision of family planning (FP) at all points across the continuum of care, and provision of HIV care and treatment (including long-term antiretroviral therapy) for HIV-positive women. Some specific examples of how EGPAF links women to maternal health and SRH services are highlighted below.

In close collaboration with ministries of health in Tanzania, Cameroon and Lesotho, EGPAF is supporting the scale-up of cervical cancer screening and the roll-out of the human papillomavirus (HPV) vaccine though funding from USAID (in Tanzania and Lesotho) and CDC (in Cameroon). In Lesotho, EGPAF is working with the Ministry of Health and Social Welfare (MOHSW) to establish a center of excellence for women living with HIV at the Senkatana Center in Maseru. The center offers screening for cervical cancer alongside HIV care and treatment services. EGPAF will be training health care workers and students in the screening and prevention of cervical cancer. EGPAF and other partners have also worked with the MOHSW in developing national HPV guidelines to support the national roll-out of the vaccine.

With support from the World Health Organization (WHO) Special Program for Research and Training in Tropical Diseases’ Sexually Transmitted Disease Diagnostic Initiative and the London School of Hygiene and Tropical Medicine, EGPAF conducted a study in 2009/2010 that found that introducing rapid syphilis testing for pregnant women at antenatal clinics (ANC) sites offering PMTCT services in Zambia and Uganda is feasible, accepted by health-care providers, and cost effective. The findings of the study have informed guideline development, training of health care workers, and the roll-out of these services in both Zambia and Uganda. ^[23-24]

EGPAF’s Integration of HIV and MCH Services in Developing Countries

EGPAF has a number of on-going studies exploring integration of HIV and MCH services. In Tanzania, the United Nation’s Population Fund (UNFPA) has supported EGPAF to implement and evaluate an FP/HIV integration model in Tanzania’s Shinyanga Region. The study looks at the co-location of FP (i.e., screening, counseling, and commodities) and HIV care and treatment services at local Care and Treatment Centers (CTC). In Kenya, the Gates Foundation supported EGPAF to conduct an observational prospective cohort study that compared infant follow-up results, when services for HIV-exposed children were provided within Maternal and Child Health (MCH) clinics or in specialized HIV Comprehensive Care Clinics (CCC), in two District hospitals in Kenya. [25] HIV services integrated in MCH in one district hospital led to better follow-up of HIV-exposed infants than in a similar hospital with services provided separately in a CCC.

Increasing Focus on Maternal And Sexual Reproductive Health Within the Pediatric HIV Elimination Agenda

Those involved with HIV, MCH, and SRH program implementation at a variety of levels can take concrete actions to enhance maternal and SRH in the context of PMTCT. Recommendations for various stakeholders positioned to lead these efforts are as follows:

Donors

1. Increase funding for development, evaluation and scale-up of program models aimed at linkage and integration of FP, SRH, MCH, and HIV services.
2. Ensure that funding for PMTCT programs supports comprehensive health services for pregnant women and linkages to SRH, FP and MCH services.

Researchers

1. • Evaluate the effectiveness, efficiency, and impact of integrated programs ^[4].
2. • Adopt the recommendations contained in Pregnancy Intentions of HIV-Positive Women: Forwarding the Research Agenda— a report issued by the Harvard School of Public Health Program on International Health and Human Rights following a March 2010 conference and symposium—with a specific focus on the following recommendations^[26]:
   1. Conduct additional studies to assess HIV-positive women’s contraceptive needs, including identifying barriers to acquiring and using various forms of contraception to ensure availability and acceptability
   2. Conduct studies comparing the appropriateness and effectiveness of various service delivery models, including those that integrate HIV care with SRH services.

Policy Makers

1. Ensure that strategies and activities addressing the first two prongs of PMTCT are included (with appropriate resource allocations) in the development and implementation of national plans and strategies for the elimination of pediatric HIV.
2. Ensure that ministries of health, donors, and implementing partners support and adopt the Preventing Unintended Pregnancies Strategic Framework that supports the Global Plan ^[27].

Program Implementers

1. Incorporate evidence-informed approaches to maternal and SRH service linkages within PMTCT programs. A review of the evidence by Gay and colleagues found that successful and promising interventions to improve the SRH and rights of women living with HIV include:
   1. providing contraceptives and family planning counseling as part of HIV services;
   2. ensuring early postpartum visits provide family planning and HIV information and services;
   3. providing youth-friendly services;
   4. providing information and skills building support to reduce unprotected sex;
   5. supporting disclosure to increase safer sexual behavior;
   6. integrating cervical cancer screening and treatment into HIV care;
   7. promoting condom use for dual protection against pregnancy and HIV to make condoms more acceptable; and
   8. providing antiretrovirals and counseling to increase HIV prevention behavior ^[28].
2. Through the development of robust indicators, rigorously monitor and evaluate integrated programs during all phases of implementation to improve current and future programs.
3. Ensure that key reproductive and maternal services (such as voluntary family planning, including preconception planning; prevention and management of gender-based violence; and STI management) are integrated with HIV services at the national, sub-national, and facility level to the largest extent possible.

Conclusions

The launch of the Global Plan and renewed momentum around eliminating pediatric HIV is a unique opportunity to ensure that women remain healthy and alive within efforts to eliminate pediatric HIV. This will require that PMTCT research, policies, and programs are linked with SRH and maternal health. To realize the full impact of such changes, implementing partners must work together to explore and make use of synergies between HIV and SRH programs to address the diverse health needs of women and their partners and eventually realize the goal of eliminating pediatric HIV.

The positions summarized in this commentary were developed by members of the Foundation’s technical advisory group on MCH/HIV linkages and integration (the M-TAG), which was made up of Foundation country and global staff. These positions are intended to guide a consistent Foundation-wide approach and framework for addressing this issue and do not supersede the national policies and guidelines of the countries in which the Foundation works.

References

1. WHO, UNICEF. Guidance on global scale-up of the prevention of mother to child transmission of HIV: Towards universal access for women, infants, and young children and eliminating HIV and AIDS among children. Geneva: 2007.
2. Halperin DT, Stover J, Reynolds HW. Benefits and costs of expanding access to family planning programs to women living with HIV. AIDS. 2009; 23 Suppl 1:S123-30.
3. Hladik W, Stover J, Esiru G, Harper M, Tappero J. The contribution of family planning towards the prevention of vertical HIV transmission in Uganda. PLoS One. 2009; 4(11):e7691.
4. Wilcher R, Cates W. Reproductive choices for women with HIV. Bulletin of the World Health Organization. 2009; 87(11):833-839.
5. Mahy M, Stover J, Kiragu K, Hayashi C, Akwara P, Luo C, Stanecki K, Ekpini R, Shaffer N. What will it take to achieve virtual elimination of mother-to-child transmission of HIV? An assessment of current progress and future needs. Sexually Transmitted Infections. 2010; 86 Suppl 2:ii48-55.
6. The Inter-Agency Task Team for Prevention and Treatemtnt of HIV Infection in Pregnant Women Mothers and Their Children. Preventing HIV and Unintended Pregnancies: Strategic Framework 2011-2015; 2011. Available from: http://www.unfpa.org/webdav/site/global/shared/documents/publications/2012/PreventingHIV_UnintendedPregnancies_SF2011_2015.pdf.
7. UNAIDS. Global Plan Towards Elimination of New HIV Infections Among Children by 2015 and Keeping Mothers Alive; 2011 [cited 2011 June]. Available from: < http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2011/20110609_JC2137_Global-Plan-Elimination-HIV-Children_en.pdf.
8. PEPFAR. Technical Considerations Provided by PEPFAR Technical Working Groups for the FY 2010 Country Operational Plan; 2009. Available from: http://www.pepfar.gov/documents/organization/169737.pdf.
9. UNFPA. The State of the World’s Midwifery 2011; May 14, 2012. Available from: http://www.unfpa.org/sowmy/resources/docs/main_report/en_SOWMR_Full.pdf.
10. Rosen JE, de Zoysa I, Dehne K, Mangiaterra V, Abdool-Karim Q. Understanding Methods for Estimating HIV-Associated Maternal Mortality. Journal of Pregnancy. 2012; 2012:958262.
11. Board on Global Health. Improving Birth Outcomes: Meeting the Challenge in the Developing World. Bale JR, Stoll BJ, Lucas AO, Committee on Improving Birth Outcomes, editors. Washington DC: National Academies Press; 2003.
12. Crowe S, Utley M, Costello A, Pagel C. How many births in sub-Saharan Africa and South Asia will not be attended by a skilled birth attendant between 2011 and 2015? BMC Pregnancy and Childbirth. 2012; 12:4–12.
13. WHO. Family planning: fact sheet. Geneva: World Health Organization; 2011 [May 14, 2012]. Available from: http://www.who.int/mediacentre/factsheets/fs351/en/index.html.
14. Wilcher R, Cates W, Jr. Reaching the underserved: family planning for women with HIV. Studies in Family Planning. 2010; 41(2):125-128.
15. Bangendanye L. Pregnancy, pregnancy desires, and contraceptive use among HIV-infected women: Findings from a survey of women enrolled in PMTCT in Rwanda. Third Annual Pediatric Conference on the Infected and Affected by HIV and AIDS; Kigali, Rwanda; 2007.
16. Desgrees-Du-Lou A, Msellati P, Viho I, Yao A, Yapi D, Kassi P, Welffens-Ekra C, Mandelbrot L, Dabis F; Ditrame Study Group. Contraceptive use, protected sexual intercourse and incidence of pregnancies among African HIV-infected women. DITRAME ANRS 049 Project, Abidjan 1995-2000. International Journal of STD and AIDS. 2002 Jul; 13(7):462-8.
17. Homsy J, Bunnell R, Moore D, King R, Malamba S, Nakityo R, Glidden D, Tappero J, Mermin J. Reproductive intentions and outcomes among women on antiretroviral therapy in rural Uganda: a prospective cohort study. PLoS One. 2009; 4(1):e4149. Epub2009 Jan 8.
18. Rochat TJ, Richter LM, Doll HA, Buthelezi NP, Tomkins A, Stein A. Depression among pregnant rural South African women undergoing HIV testing. Journal of the American Medical Association. 2006; 295(12):1376-1378.
19. Singh S, Darroch JE, Ashford LA, Vlassoff M. Adding It Up: The Costs and Benefits of Investing in Family Planning and Maternal and Newborn Health. 2009.
20. WHO, UNFPA, IPPF, UNAIDS, UCSF. Sexual & Reproductive Health and HIV Linkages: Evidence Review and Recommendations; 2009. Available from: http://www.unaids.org/en/media/unaids/contentassets/dataimport/pub/agenda/2009/2009_linkages_evidence_review_en.pdf.
21. Anorlu RI. Cervical cancer: the Sub-Saharan African perspective. Reproductive Health Matters. 2008; 16(32):41-9.
22. Gichangi PB, Bwayo J, Estambale B, De Vuyst H, Ojwang S, Rogo K, Abwao H, Temmerman M. Impact of HIV infection on invasive cervical cancer in Kenyan women. AIDS. 2003;Sep 5;17(13):1963-1968.
23. Bitarakwate E, Strasser S, Sripipatana T, Chintu N, Shelley K, Musana O, et al. Introduction of Rapid Syphilis Testing within Prevention of Mother-to-Child Transmission of HIV Programs in Uganda and Zambia: A field acceptability and feasibility study. 6th IAS Conference on HIV Pathogenesis, Treatment and Prevention; Rome, Italy; 2011.
24. Strasser S, Bitarakwate E, Gill M, Hoffman HJ, Musana O, Phiri A, Shelley KD, Sripipatana T, Ncube AT, Chintu N. Introduction of Rapid Syphilis Testing within Prevention of Mother-to-Child Transmission of HIV Programs in Uganda and Zambia: A field acceptability and feasibility study. Journal of Acquired Immune Deficiency Syndrome 2012 Jul 19. [Epub ahead of print].
25. Ong’ech JO, Hoffman HJ, Kose J, Audo M, Matu L, Savosnick P, Guay L. Provision of Services and Care for HIV-Exposed Infants: A Comparison of Maternal and Child Health Clinic and HIV Comprehensive Care Clinic Models.Journal of Acquired Immune Deficiency Syndrome, 2012 Sep 1;61(1):83-89.
26. Harvard School of Public Health. The Pregnancy Intentions of HIV-Positive Women: Forwarding the Research Agenda – Conference Report. Pregnancy Intentions of HIV-Positive Women; Boston, MA; May 17-19, 2010.
27. Collins L, Doupe A. Preventing HIV and unintended pregnancies: strategic framework 2011–2015; 2011. Available from:

    http://www.ippf.org/NR/rdonlyres/9C6DE8AA-1C16-48FB-B212-A64FE3A0F2BF/5932/PreventingHIVandunintendedpregnanciesstrategicfram.pdf.

28. Gay J, Hardee K, Croce-Galis M, Hall C. What works to meet the sexual and reproductive health needs of women living with HIV/AIDS. Journal of the International AIDS Society. 2011; 14:56.

Abstract

Objectives

This study examined global inequalities in cervical cancer incidence and mortality rates as a function of cross-national variations in the Human Development Index (HDI), socioeconomic factors, Gender Inequality Index (GII), and healthcare expenditure.

Methods

Age-adjusted incidence and mortality rates were calculated for women in 184 countries using the 2008 GLOBOCAN database, and incidence and mortality trends were analyzed using the WHO cancer mortality database. Log-linear regression was used to model annual trends, while OLS and Poisson regression models were used to estimate the impact of socioeconomic and human development factors on incidence and mortality rates.

Results

Cervical cancer incidence and mortality rates varied widely, with many African countries such as Guinea, Zambia, Comoros, Tanzania, and Malawi having at least 10-to-20-fold higher rates than several West Asian, Middle East, and European countries, including Iran, Saudi Arabia, Syria, Egypt, and Switzerland. HDI, GII, poverty rate, health expenditure per capita, urbanization, and literacy rate were all significantly related to cervical cancer incidence and mortality, with HDI and poverty rate each explaining >52% of the global variance in mortality. Both incidence and mortality rates increased in relation to lower human development and higher gender inequality levels. A 0.2 unit increase in HDI was associated with a 20% decrease in cervical cancer risk and a 33% decrease in cervical cancer mortality risk. The risk of a cervical cancer diagnosis increased by 24% and of cervical cancer death by 42% for a 0.2 unit increase in GII. Higher health expenditure levels were independently associated with decreased incidence and mortality risks.

Conclusions and Public Health Implications:

Global inequalities in cervical cancer are clearly linked to disparities in human development, social inequality, and living standards. Reductions in cervical cancer rates are achievable by reducing inequalities in socioeconomic conditions, availability of preventive health services, and women’s social status.

Key Words:

Cervical cancer • Incidence • Mortality • Global inequality • Human development • Gender inequality • Social inequality • Poverty • Literacy • GNI per capita.

Introduction

Cervical cancer is the third most common cancer in women after breast and colorectal cancers and is one of the leading causes of cancer death among women in the world^[1] . In 2008, approximately 530,000 women were diagnosed with invasive cervical cancer worldwide and 275,000 women died from it ^{[1, 2]}. Cervical cancer is the top cancer site for women in most East African and South Asian countries both in terms of incidence and mortality ^{[1, 2]}. Indeed, developing countries as a whole experience a disproportionate share of the disease burden, accounting for 86% of all cervical cancer cases and 88% of all cervical cancer deaths worldwide ^[1-3]. While cervical cancer rates have declined markedly in industrialized countries over the past several decades, the rates have declined at a much slower pace in the developing world and, for many developing countries, the rates have actually been increasing ^{[1, 2]}. The latest data from the World Health Organization (WHO) show marked disparities in cervical cancer incidence and mortality rates across countries ^{[1, 2]}.

A number of population-based studies from the United States and other industrialized countries have shown marked socioeconomic gradients in cervical cancer incidence and mortality, with those in more deprived groups or lower socioeconomic strata having 2-3 fold higher risk of cervical cancer than their affluent counterparts ^[4-9]. A few case-control studies in Asia, Africa, and South America also indicate substantially higher risks of cervical cancer among women in lower social class groups ^[9]. To our knowledge, the extent to which disparities in cervical cancer incidence and mortality rates between nations arise due to global differences in social inequality and human development factors has not yet been studied. Given the evidence of a strong association between socioeconomic status (SES) and cervical cancer from within-country studies, we should expect differences in education, socioeconomic conditions, material living standards, and health services to account for a substantial portion of global inequalities in cervical cancer incidence and mortality.

Assessing the impact of socioeconomic conditions and human development is important because they represent underlying causes of health inequalities both within and between nations ^{[10, 11]}. Improvements in the broader social determinants such as education, income distribution, gender inequality, labor force participation, employment, and health care have been suggested as central to reducing population health inequalities ^{[10, 12]}. International comparisons can highlight the extent of disease burden in specific countries and important cross-national differences and similarities in socioeconomic conditions, urbanization patterns, prevalence of cancer risk factors such as smoking and human papillomavirus (HPV) infection, and availability and use of cervical cancer screening programs ^{[2, 4]}. Analysis of global inequalities is also important in that cervical cancer tends to affect younger women (aged <45 years) more than the other major cancers, resulting in relatively high years-of-life lost, particularly among women in the developing world ^[2]. Moreover, cervical cancer rates appear to be rising among younger women in many developing as well as developed countries ^[13].

The major purpose of our paper is to quantify the links between inequalities in human development and socioeconomic conditions and global disparities in cervical cancer incidence and mortality. Specifically, we link socioeconomic, demographic, and human development indicators for various countries with the most recent international cervical cancer data to examine global patterns of cervical cancer incidence and mortality. In addition, we document the extent of global disparities in cervical cancer by identifying regions and countries in which women are at high risk of morbidity and mortality from this disease.

Methods

To analyze global inequalities in cervical cancer incidence and mortality, we computed age-adjusted incidence and mortality rates for women in 184 countries using the 2008 GLOBOCAN database ^[1]. The GLOBOCAN database, developed by the International Agency for Research on Cancer, provides contemporary estimates of the incidence of, mortality and prevalence from major type of cancers, at national level, for 184 countries of the world ^[1]. Details of the cancer database are provided elsewhere ^{[1, 2]}. Annual trends in incidence and mortality rates were analyzed for selected countries using the WHO cancer incidence and mortality databases^{[14, 15]}.

The data on the Human Development Index (HDI) and Gender Inequality Index (GII) were taken from the 2011 Human Development Report ^[16], while those on Gross National Income (GNI) per capita, international poverty rate (deprivation level measuring the proportion of population living in extreme poverty), literacy rate, urbanization, and health expenditure came from the 2011 World Health Statistics Report ^[17]. HDI, developed by the United Nations Development Programme, is a composite index of social and economic development and combines indicators of life expectancy, educational attainment, and GNI per capita. HDI varies between 0 and 1, with 0 indicating the lowest level and 1 representing the highest level of development ^[16]. GII is also a composite index that reflects women’s relative social disadvantage in three dimensions – reproductive health, empowerment, and the labor market ^[16]. GII combines 5 indicators, maternal mortality ratios, adolescent fertility rate, educational attainment, parliamentary representation by each sex, and female labor force participation ^[16].

We used bivariate and multivariate ordinary least squares (OLS) regression models to estimate the impact of HDI, GII, and other socioeconomic factors on age-adjusted cervical cancer incidence and mortality rates. We used Poisson regression to model age- and country-specific incidence cases, deaths, and population estimates as a function of socioeconomic and human development factors [12, 18]. For Poisson modeling, we used three broad age-groups: <45, 45-64, and 65+ years. The OLS models were estimated by the SAS REG procedure ^[19], while Poisson models were estimated by the SAS GENMOD procedure ^[20].

Since HDI and GII both include education and economic components, these composite indices are highly correlated with the other individual predictors used in the study, such as literacy rate, per capita income, poverty, and urbanization. Because of estimation problems due to high multicollinearity, multiple regression models containing either HDI or GII and education and income indicators were not estimated. Instead, the net impact of health expenditure per capita was examined in multivariate models that included HDI or GII.

Log-linear regression models were used to estimate annual rates of change in cervical cancer incidence and mortality trends for selected countries^{[7, 12, 18]}. Specifically, during the 1983-2002 or 1983-2008 period, the logarithm of the incidence or mortality rates respectively were modeled as a linear function of time (calendar year), which yielded annual exponential rates of change in incidence or mortality rates ^{[7, 12, 18]}.

Results

Global Inequalities in Cervical Cancer Incidence and Mortality Rates

In 2008, cervical cancer incidence and mortality rates were higher in Eastern, Western, and Sub-Saharan regions of Africa and South Asia and lower in Western Asia, Western Europe, North America, and Australia/New Zealand (Figure 1). Overall, women in developing countries had two-fold higher cervical cancer incidence rates and three-fold higher mortality rates than their counterparts from developed countries.

Figure 1. International Variations in Age-Adjusted Cervical Cancer Incidence and Mortality Rates per 100,000 World Standard Population, 2008

Source: WHO, International Agency for Research on Cancer, GLOBOCAN, 2008.
Long-term trends in cervical cancer incidence and mortality vary across countries (Figures 2 and 3). While incidence rates have generally decreased for most countries, the rates have remained stable at fairly high levels for certain countries such as Thailand (data not shown). During 1983-2002, although incidence rates decreased at 4.3%, 3.1%, and 1.7% per year in Brazil, India (Chennai), and the Philippines, respectively, they remained at much higher levels than those for Hong Kong and Shanghai, China (Figure 2). Mortality trends have also not been uniform across countries. During 1983-2008, while mortality rates in Hong Kong and Singapore decreased at about 4% per year, the rates have either decreased at a much slower pace for Brazil and Mexico or increased for some countries such as Cuba, Venezuela (Figure 3), Thailand, and the Philippines (trend not shown).

In 2008, cervical cancer rates varied widely across individual countries, with many African countries such as Guinea, Zambia, Comoros, Tanzania, and Malawi having at least 10-to-20 fold higher incidence and mortality rates than such countries as Iran, Saudi Arabia, Syria, Egypt, and Switzerland (Table 1). Cross-national disparities in cervical cancer mortality rates (as measured by the coefficient of variation and range) were greater than those in incidence rates. Guinea had the highest age-adjusted incidence rate of 56.3 per 100,000 women, 35 times greater than the rate of 1.6 for Egypt. Guinea also had the highest age-adjusted mortality rate of 41.7 per 100,000 women, 52 times higher than the rate of 0.8 for Syria (Table 1).

Modeling the Impact of HDI, GII, and Socioeconomic Factors
HDI, GII, international poverty rate, health expenditure per capita, urbanization, and literacy rate were all significantly related to cancer incidence and mortality, with HDI and poverty rate each explaining >52% of the global variance in mortality (Table 2 and Figure 4). Both incidence and mortality rates increased in relation to lower levels of human development and higher levels of gender inequality. In bivariate models, a 0.2 unit increase in HDI was on average associated with an 8.7 point decrease in incidence rates and a 7.8 point decrease in mortality rates. A 0.2 unit increase in HDI was associated with a 20% decrease in the risk of cervical cancer incidence and a 33% decrease in the risk of cervical cancer mortality (Table 2).

Cervical cancer incidence and mortality rates increased by 7.1 and 5.9 points, respectively, for every 0.2 unit increase in GII. The risk of a cervical cancer diagnosis increased by 24% and of cervical cancer death by 42% for a 0.2 unit increase in GII (Table 2). GII explained approximately 24% and 33% of the global variance in incidence and mortality rates, respectively.

Deprivation levels or poverty rates were a strong predictor of cross-national variations in cervical cancer incidence and mortality. A 10-percentage point increase in poverty rates was expected to result in an 8% higher risk of incidence and a 14% higher risk of death from cervical cancer. A $5,000 increase in GNI per capita was associated with an 8% lower incidence risk and a 14% lower mortality risk. Incidence risks decreased by 11% and mortality risks by 21% for every 20-percentage point increase in literacy rate. Incidence and mortality rates and risks decreased at increasing levels of urbanization (Table 2).

In multivariate models, higher health expenditure levels were independently associated with decreased incidence and mortality risks. Even after adjusting for the effects of HDI or GII, for every $1,000 increase in healthcare expenditure per capita, there was at least a 7% decrease in the risk of cervical cancer (Table 2). The corresponding net decrease in cervical cancer mortality risk was 8-11% for a similar increase in health expenditure per capita.

Figure 2. Trend in Cervical Cancer Incidence Rates in Selected Countries, 1983-2002

Log-Linear Regression Trend Models of Cervical Cancer Incidence, 1983-2002

Source: WHO, International Agency for Research on Cancer, Cancer Incidence in Five Continents Annual Dataset.

Figure 3. Trend in Cervical Cancer Mortality Rates in Selected Countries, 1983-2008

Log-Linear Regression Trend Models of Cervical Cancer Mortality, 1983-2008

Source: WHO, International Agency for Research on Cancer, Cancer Mortality Database.

Table 1.Age-adjusted Cervical Cancer Incidence and Mortality Rates per 100,000 World Standard Population, 2008

Source: WHO, International Agency for Research on Cancer (IARC), GLOBOCAN 2008.
Table 2. Ordinary Least Squares (OLS) and Poisson Regression Models Showing the Effects of Human Development Index, Gender Inequality Index, and Socioeconomic and Health Care Factors on Age-Adjusted Cervical Cancer Incidence and Mortality Rates, 2008 (N = 184 Countries)

Notes: b=unstandardized regression coefficient; β =standardized regression coefficient; R²=percentage variance explained.

1. β is also equal to the correlation coefficient in bivariate OLS regression models
2. a Increase in incidence/mortality rates or risks associated with a 0.2 unit increase in HDI
3. b Increase in incidence/mortality rates or risks associated with a 0.2 unit increase in the Gender Inequality Index
4. c Increase in incidence/mortality rates or risks associated with a 20-percentage point increase in the adult literacy rate
5. c Increase in incidence/mortality rates or risks associated with a 20-percentage point increase in the adult literacy rate
6. e Increase in incidence/mortality rates or risks associated with a 10-percentage point increase in the poverty rate
7. f Increase in incidence/mortality rates or risks associated with a 10-percentage point increase in the urban population
8. g Increase in incidence/mortality rates or risks associated with a $1,000 increase in health expenditure per capita

Figure 4. Observed and Fitted Plots Showing the Impact of Human Development Index (HDI) and Poverty on Age-Adjusted Cervical Cancer Mortality Rates per 100,000 World Standard Population, 2008

Discussion

In this study, by using the latest global sociodemographic, health, and cancer statistics, we have identified countries at high risk of cervical cancer morbidity and mortality and have examined the impact of socioeconomic and human development factors on cross-national variations in cervical cancer incidence and mortality rates. High rates of incidence and mortality observed for many low- and middle-income countries indicate cervical cancer to be a major public health problem in the developing world ^[2]. In terms of absolute numbers, Indian women bear the greatest burden of the disease as more than a quarter of new cases and cervical cancer deaths in the world occur in India alone ^{[1, 2]}.

We have estimated the magnitude of cervical cancer disparities between countries and attempted to explain these disparities in terms of the effects of major societal determinants. It is important to note that social inequalities in cervical cancer are quite marked within individual countries as well ^{[4, 6, 8, 9]}. As mentioned earlier, this pattern holds for both developed and developing countries ^{[4, 8, 9]}. However, population-based studies of social inequalities are rare for the developing countries that have the greatest disease burden; more studies are needed in these countries to shed light on the magnitude and causes of social inequalities in cervical cancer incidence and mortality.

The findings of our study are consistent with two earlier studies that examined global patterns in cervical cancer rates using the 2008 GLOBOCAN database ^{[2, 3]}. These studies examined disparities in incidence and mortality rates across countries and various regions of the world in much detail ^{[2, 3]}. However, unlike our study, no efforts were made in these studies to link cervical cancer disparities with global patterns of human development and socioeconomic conditions.

Global inequalities in cervical cancer mortality are substantially larger than those in cervical cancer incidence, which indicate the prominent role of cross-national disparities in patient survival rates, the extent of disease at diagnosis, and the impact of differential access to health services and cancer treatment ^[4-8]. Cancer survival varies greatly across countries, with cancer patients in Europe, North America, Australia and New Zealand having higher survival rates than their counterparts in developing countries [21]. A recent study showed wide variation in cervical cancer survival rates among African, Asian, Caribbean, and Central American countries ^[22]. The 5-year age-standardized relative survival rate for cervical cancer ranged from a low of 19% in Uganda and 23% in Gambia to a high of 76% in Seoul, South Korea and 77% in Hong Kong, China ^[22]. The current 5-year relative survival rate is 69% for US women ^[23]. Survival inequalities exist within individual countries as well. For example, the 5-year survival rate for cervical cancer patients in Bhopal, India was 31%, as compared with 60% for women in Chennai, India ^[22].

Lower cancer survival and higher mortality rates partly result from higher rates of late-stage cancer diagnosis among women in developing countries, which is largely due to the lack of effective cervical cancer screening programs in most developing countries ^{[2, 4, 24]}. About 81% of cervical cancer patients in Singapore are diagnosed at an early, localized stage, compared with only 7% in Chennai, India, 33% in Costa Rica, 35% in Manila, Philippines, and 53% in Cuba ^[24]. The high rate of early-stage diagnosis in Singapore is higher than the rate for many industrialized countries, including the United States, where only 52% of invasive cervical cancers in 2008 were diagnosed at localized stage ^{[7, 23]}.

Detection of cancer at an early stage may be considered a marker for access to health care and preventive health services, including cervical cancer screening. Screening can reduce rates of both cervical cancer incidence and mortality by detecting precancerous lesions (hence preventing cancer) and detecting invasive cervical cancers at an early stage, thereby increasing patient survival ^{[4, 7, 25]}. Data from the United States and other industrialized countries indicate that rural and socioeconomically disadvantaged women are significantly less likely to receive Pap smear tests than their urban and affluent counterparts ^{[4, 5, 7, 8]}. According to a recent study, only 19% of women in developing countries use cervical cancer screening, compared with 63% in developed countries ^[24]. However, rates of cervical cancer screening vary widely within the developing world, ranging from 1% in Bangladesh to 73% in Brazil [24]. Consistent with inequalities in cervical cancer screening within the developed world, socioeconomic inequalities in countries such as Brazil and China are quite large, and, globally, women in the poorest wealth decile are seven times less likely to receive cervical cancer screening than their rich counterparts (9% versus 64%) ^[24].

Inequalities in cervical cancer partly reflect global disparities in the prevalence of HPV infection, which is the primary cause of cervical cancer ^{[2, 4, 9, 13, 26]}. HPV prevalence tends to be higher in Eastern and Western Africa, Latin America, and South Asia and lower in West Asia and the Mediterranean region ^{[2, 27]}. Globally, HPV prevalence seems to correspond closely with cervical cancer incidence rates (γ = 0.68) ^[2]. Another indicator of high-risk sexual behavior is the HIV prevalence among the adult population, which, according to our analysis (not shown), is fairly highly correlated (γ > 0.40) with both cervical cancer incidence and mortality rates globally.

Conclusions and Public Health Implications

Developing and developed countries differ greatly in their levels of human development and gender inequality ^[16]. Many of the countries with the highest cervical cancer rates such as Guinea, Comoros, Zambia, Malawi, Burundi, and Mozambique also tend to score the lowest on HDI. Countries with low levels of human development also tend to perform poorly with respect to gender equality; women in these societies fare much worse in reproductive health, educational achievements, empowerment, and work force participation than their counterparts in more egalitarian countries ^[16]. As shown previously for the United States and other individual industrialized countries, deprivation, material living conditions, and social inequality are also powerful determinants of cervical cancer incidence and mortality at the global level.

The extent of global inequalities in cervical cancer incidence and mortality, as documented here, contributes substantially to the overall cancer-related health disparities worldwide, since cervical cancer continues to be a leading cancer site among women in many developing countries ^{[1, 2]}. Unfavorable socioeconomic conditions and low levels of human development may hinder a country’s efforts to invest in its health care infrastructure, provision of health services, and in its education sector, which could mean less than optimal resources available for public health improvement and cancer prevention and control efforts. Formulation and implementation of broad societal initiatives are, of course, necessary to address important health and developmental goals, including poverty reduction, larger investments in women’s health and education, expanding economic opportunities for women, and a greater commitment toward gender and social equality in the distribution of power, money, and resources ^{[11, 16, 17]}. Additionally, public health measures such as establishment of cancer prevention and early detection programs through increased cervical cancer screening, public health education programs promoting condom use to reduce risks of sexually transmitted infections such as HPV and HIV, and introduction of affordable HPV tests and vaccination are critical in reducing global cervical cancer disparities, particularly among women in low- and middle-income developing countries ^[2].

Financial Disclosure: None to report. Funding/Support: None. Conflicts of Interest: None.
Acknowledgements: None. The views expressed are the authors’ and not necessarily those of the US Department of Health and Human Services and Global Health and Education Projects, Inc.

References

1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. GLOBOCAN 2008, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 10 [Internet]. Lyon, France: International Agency for Research on Cancer; 2010. Available at: http://globocan.iarc.fr. Accessed May 1, 2012.
2. Arbyn M, Castellsagué X, de Sanjosé S, Bruni L, Saraiya M, Bray F, Ferlay J. Worldwide burden of cervical cancer in 2008. Annal of Oncology. 2011; 22(12):2675-86. Epub 2011 Apr 6.
3. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011; 61(2):69-90. Epub 2011 Feb 4. Erratum in: CA: A Cancer Journal for Clinicians. 2011; 61(2):134.
4. Singh GK, Williams SD, Siahpush M, Mulhollen A. Socioeconomic, rural-urban, and racial inequalities in US cancer mortality: part I – all cancers and lung cancer and part II – colorectal, prostate, breast, and cervical cancers. Journal of Cancer Epidemiology. 2011; 107497:1-27. Epub February 14, 2012.
5. Singh GK, Miller BA, Hankey BF, Edwards BK. Area Socioeconomic Variations in U.S. Cancer Incidence, Mortality, Stage, Treatment, and Survival, 1975-1999. NCI Cancer Surveillance Monograph Series, No. 4. Bethesda, MD: National Cancer Institute; 2003. NIH Publication No. 03-5417.
6. Singh GK, Miller BA, Hankey BF, Edwards BK. Persistent area socioeconomic disparities in U.S. incidence of cervical cancer, mortality, stage, and survival, 1975-2000. Cancer. 2004; 101(5):1051-1057.
7. Singh GK. Rural-urban trends and patterns in cervical cancer mortality, incidence, stage, and survival in the United States, 1950-2008. Journal of Community Health. 2012; 37(1): 217-223.
8. Faggiano F, Partanen T, Kogevinas M, Boffetta P. Socioeconomic differentials in cancer incidence and mortality. IARC Scientific Publications. 1997; 138:65-176.
9. Parikh S, Brennan P, Boffetta P. Meta-analysis of social inequality and the risk of cervical cancer. International Journal of Cancer. 2003; 105(5):687-691.
10. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
11. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
12. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. American Journal of Public Health. 2003; 93(7):1137-1143.
13. Jemal A, Center MM, DeSantis C, Ward EM. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiology, Biomarkers & Prevention. 2010; 19(8):1893-1907.
14. International Agency for Research on Cancer (IARC). Cancer Incidence in Five Continents Annual Dataset. World Health Organization, International Agency for Research on Cancer; 2011. Available at: http://ci5.iarc.fr/CI5plus/ci5plus.htm. Accessed May 1, 2012.
15. International Agency for Research on Cancer (IARC). WHO Cancer Mortality Database. World Health Organization, International Agency for Research on Cancer; 2011. Available at: http://www-dep.iarc.fr/WHOdb/WHOdb.htm. Accessed May 1, 2012.
16. United Nations Development Programme. Human Development Report 2011. New York, NY; 2011.
17. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
18. Singh GK, Siahpush M, Williams SD. Changing urbanization patterns in US lung cancer mortality, 1950-2007. Journal of Community Health. 2012; 37(2):412-420.
19. SAS Institute, Inc. SAS/STAT User’s Guide, Version 9.1: The REG Procedure. Cary, NC: SAS Institute Inc.; 2004.
20. SAS Institute, Inc. SAS/STAT User’s Guide, Version 9.1: The GENMOD Procedure. Cary, NC: SAS Institute Inc.; 2004.
21. Coleman MP, Quaresma M, Berrino F, Lutz JM, De Angelis R, Capocaccia R, Baili P, Rachet B, Gatta G, Hakulinen T, Micheli A, Sant M, Weir HK, Elwood JM, Tsukuma H, Koifman S, E Silva GA, Francisci S, Santaquilani M, Verdecchia A, Storm HH, Young JL; Cancer survival in five continents: a worldwide population-based study (CONCORD). CONCORD Working Group. Lancet Oncology. 2008; 9(8):730-756.
22. Sankaranarayanan R, Swaminathan R, Lucas E. Cancer Survival in Africa, Asia, the Caribbean, and Central America (SurvCan). IARC Scientific Publications, Vol. 162; Lyon, France: International Agency for Research on Cancer; 2011.
23. Howlader N, Noone AM, Krapcho M, Neyman N, Aminou R, Waldron W, Altekruse SF, Kosary CL, Ruhl J, Tatalovich Z, Cho H, Mariotto A, Eisner MP, Lewis DR, Chen HS, Feuer EJ, Cronin KA, Edwards BK (eds). SEER Cancer Statistics Review, 1975-2008, National Cancer Institute. Bethesda, MD, http://us.mg1.mail.yahoo.com/csr/1975_2008/, based on November 2010 SEER data submission, posted to the SEER web site, 2011.
24. Gakidou E, Nordhagen S, Obermeyer Z. Coverage of cervical cancer screening in 57 countries: low average levels and large inequalities. PLoS Medicine. 2008; 5(6):863-868.
25. American Cancer Society. Cancer Facts & Figures 2010. Atlanta, GA: American Cancer Society; 2010.
26. de Sanjose S, Bosch FX, Munoz N, Shah K. Social differences in sexual behaviour and cervical cancer. IARC Scientific Publications. 1997; 138:309-317.
27. Bruni L, Diaz M, Castellsagué X, Ferrer E, Bosch FX, de Sanjosé S. Cervical human papillomavirus prevalence in 5 continents: meta-analysis of 1 million women with normal cytological findings. Journal of Infectious Diseases. 2010; 15;202(12):1789-99. Epub 2010 Nov 10.

Abstract

Objectives

We examined differences between China and India in key health and socioeconomic indicators, including life expectancy, infant and child mortality, non-communicable disease mortality from cancer, cardiovascular diseases (CVD), and diabetes, Human Development Index, Gender Inequality Index, material living conditions, and health expenditure.

Methods

Data on health and social indicators came from various World Health Organization and United Nations databases on global health and development statistics, including the GLOBOCAN cancer database. Mortality trends were modeled by log-linear regression, and differences in rates and relative risks were tested for statistical significance.

Results

Although both countries have made marked improvements, India lags behind China on several key health indicators. Differential rates of mortality decline during 1960-2009 have led to a widening health gap between China and India. In 2009 the infant mortality rate in India was 50 deaths per 1,000 live births, 3 times greater than the rate for China. Sixty-six out of 1,000 Indian children died before reaching their 5th birthday, compared with 19 children in China. China’s life expectancy is 9 years longer than India’s. Life expectancy at birth in India increased from 42 years in 1960 to 65 years in 2009, while life expectancy in China increased from 47 years in 1960 to 74 years in 2009. Major health concerns for China include high rates of stomach, liver, and lung cancer, CVD, and smoking prevalence. Globally, India ranked 90^th and China 102^nd in life satisfaction.

Conclusions and Public Health Implications:

India’s less favorable health profile compared to China is largely attributable to its higher rates of mortality from communicable diseases and maternal and perinatal conditions. Further health gains can be achieved by reducing social inequality, greater investments in human development and health services, and by prevention and control of chronic-disease risks such as hypertension, smoking, obesity, and physical inactivity.

Key Words:

China • India • Health status • Life expectancy • Infant and child mortality • Non-communicable diseases • Cancer • Health risks • Human development • Social inequality.

Introduction

China and India are the two most populous nations in the world. With the populations of 1.35 and 1.21 billion respectively, they jointly account for 37% of the world population ^[1]. China and India gained their independence in the modern era, in 1949 and 1947, respectively. Around the time of their independence, both countries had roughly similar levels of health and socioeconomic development ^[2]. However, according to our analysis that follows, both countries differ greatly in their health and socioeconomic achievements, six decades on.

Because of the remarkable and rapid economic growth of the past two decades, there have been numerous comparisons of economic performance of India and China ^[3-7]. However, to date, few comparisons have been made that provide a comprehensive assessment of how key health, disease, socioeconomic, and human development indicators for these two countries have changed over time ^[5-8]. With the availability of high-quality data from the World Health Organization (WHO) and the United Nations (UN), it is possible to provide a more complete and systematic comparison of China and India on various health and development issues ^{[1, 9-11]}.

In this study, we analyze the extent of disparities between the two nations in key health indicators such as life expectancy, infant mortality, under-five mortality, maternal mortality, cancer, and other chronic diseases, health behaviors, health expenditure, and social and economic factors such as human development, gender inequality, life satisfaction, urbanization, literacy, and income per capita. Additionally, we examine the magnitude of health inequalities within India and China across socioeconomic groups, provinces, and rural and urban areas. Special emphasis is given to the analysis of disparities in cancer rates, detailed data on which are available for both countries. Cancer is also a disease that is expected to account for a greater share of the total disease burden in the future in both China and India as lifestyle factors and consumption patterns begin to mimic those seen in the developed world and aging of the population becomes an important issue in China ^{[10, 12]}.

Simultaneous examination of health and socioeconomic conditions is important because social conditions, including human development, have been shown as fundamental, underlying determinants of health inequalities both within and between nations ^[13-15]. Analysis of existing health and social conditions and changes in these indicators is vital for social planning and public health decision making ^{[1, 6, 9]}. Comparisons between China and India can highlight the extent of disease burden due to specific health conditions, similarities as well as differences in socioeconomic conditions, urbanization patterns, prevalence of health-risk factors, and availability and use of preventive health services ^{[9, 12]}. Analysis of various health and socioeconomic indicators is presented side by side for China and India in the hopes that such information would be readily used by policymakers and researchers in both countries for policy and program formulation, and for facilitating more in-depth examination of specific health and/or development issues.

Methods

Annual trends in life expectancy, infant, child, and maternal mortality rates were analyzed using the 2011 WHO, UN, and the World Bank databases that include current and time-trend data on health and human development variables for all nations, including China and India ^{[1, 9-11]}. Information on these databases can be found elsewhere and is briefly described below ^{[1, 9-11]}. Cancer incidence and mortality data were derived from the 2008 GLOBOCAN database.16-18 The GLOBOCAN database, developed by the International Agency for Research on Cancer, provides contemporary estimates of the incidence of, mortality and prevalence from major type of cancers, at national level, for 184 countries of the world. Details of the GLOBOCAN database are provided elsewhere ^[16-18].

The data on the Human Development Index (HDI), Gender Inequality Index (GII) and life satisfaction were taken from the 2011 Human Development Report ^[1], while those on Gross National Income (GNI) per capita, international poverty rate (the proportion of population living in extreme poverty), literacy rate, urbanization, cause-specific morbidity and morbidity, health behaviors, health expenditure, and health services came from the 2011 World Health Statistics Report, Non-Communicable-Disease country profiles, and the World Bank’s health, nutrition, and population database ^[9-11]. HDI, developed by the UN, is a composite index of social and economic development which combines indicators of life expectancy, educational attainment, and GNI per capita. HDI varies between 0 and 1, with 0 indicating the lowest level and 1 representing the highest level of development ^{[1, 19]}. GII is also a composite index that reflects women’s relative social disadvantage in three dimensions – reproductive health, empowerment, and the labor market ^{[1, 19]}. GII combines 5 indicators, maternal mortality ratios, adolescent fertility rate, educational attainment, parliamentary representation by each sex, and female labor force participation ^{[1, 19]}.

Log-linear regression models were used to estimate annual rates of change in life expectancy, infant mortality, and child mortality trends from 1960 to 2009 ^{[15, 19]}. Specifically, during the 1960-2009 period, the logarithm of the mortality rates or life expectancy were modeled as a linear function of time (calendar year), which yielded annual exponential rates of change in mortality rates or life expectancy ^{[15, 19]}. Differences between China and India in cancer incidence and mortality rates and relative risks were tested for statistical significance at the 0.05 level.

Results

Inequalities in Key Health Measures

In 2009, life expectancy was nine years longer in China than in India (74 versus 65 years) (Figure 1). For both India and China, life expectancy increased substantially during the past 5 decades. Life expectancy at birth in India increased from 42 years in 1960 to 65 years in 2009, while life expectancy in China increased from 47 years in 1960 to 74 years in 2009. Although the rate of increase in life expectancy was similar for the two countries, the absolute increases in life expectancy were larger for China (Figure 2). During 1960-2009, life expectancy increased annually by 0.55 years in China and by 0.47 years in India.

Infant and child mortality rates declined impressively in both India and China between 1960 and 2009. However, disparities between the two nations have widened (Figure 3). In 2009 the infant mortality rate (IMR) of 50 deaths per 1,000 live births in India was three times greater than the rate of 17 in China (Figures 1 and 3). In 1960 the IMR was only 1.3 times higher in India than in China. The average annual rate of decline in infant mortality was faster in China than in India (3.5% versus 2.2%). It is important to note that the IMR in mainland China is still much greater than the rate for Hong Kong and for many developed nations. The 2009 IMR for the US, for example, was 6.4, and, for Hong Kong, Japan, and Singapore, the rate was below 3.9 The disparities in under-5 mortality were also marked, with 66 out of 1,000 Indian children in 2009 dying before reaching their 5th birthday, as compared with 19 children in China (Figure 1). The relative risk of child mortality in India compared to China increased from 1.1 in 1960 to 3.5 in 2009. Child mortality declined at a rate of 4.4% per year in China, compared with an annual rate of 2.5% in India (Figure 3). The rate of low birth weight among newborns in India was 28% [only Mauritania (34%) and Pakistan (32%) have higher rates], compared with only 3% in China (Table 1). China’s low-birth-weight rate was even lower than the overall US rate of 8.2% and 4.2% for Chinese Americans ^[20].

Figure 1. Selected Key Health Indicators for China and India, 2009

Source: World Health Organization, World Health Statistics Report 2011.
Marked differences can also be seen in maternal mortality and cause-specific mortality rates. India’s maternal mortality rate of 230 maternal deaths per 100,000 live births was 6.1 times greater than the rate of 38 in China. Between 1980 and 2008, both China and India were able to reduce maternal mortality rates by 60-65%. Prevalence of tuberculosis was 1.8 times higher and the adult HIV prevalence was 3 times higher in India than in China (Figure 4). There are 2.4 million people living with HIV in India and the annual number of AIDS deaths is 172,000 ^[21]. China, on the other hand, has a lower HIV/AIDS burden, with 740,000 people living with HIV and 26,000 AIDS deaths annually [21].

Figure 2. Trends in Life Expectancy at Birth (Years) in China and India, 1960-2009

Log-Linear Regression Trend Models of Life Expectancy, 1960-2009

Source: World Health Organization and World Bank.

Figure 3. Trends in Infant and Child (Under 5) Mortality Rates in India and China, 1960-2009

Log-Linear Regression Trend Models of Infant and Child Mortality Rates, 1960-2009

Source: World Health Organization and World Bank.

Figure 4. Selected Cause-Specific Mortality and Morbidity Indicators for China and India, 2008

Note: Cause-specific mortality rates are per 100,000 population and are age-adjusted to the world standard population.

Source: World Health Organization, World Health Statistics Report 2011.

Infectious diseases account for 52% of the total years-of-life lost in India compared with 15% in China. The rate of infectious-disease mortality in India was 6.3 times higher than that in China (Figure 4). Even though non-communicable diseases accounted for 65% of the total years-of-life lost in China compared to 35% in India, the absolute rate of mortality from non-communicable diseases was still 13% higher in India than in China (Table 1 and Figure 4).

Overall, cancer incidence and mortality rates were 83-84% higher in China than in India (Table 2). Incidence rates for liver and stomach cancers were 8-12 times higher and those for uterine, lung, colorectal, esophageal, pancreatic, kidney, bladder, and brain cancers were 2-6 times higher in China than in India. Mortality rates from most reproductive cancers were higher in India than in China. Women in India had 2.0, 2.7, and 3.6 times higher rates of breast, ovarian and cervical cancer mortality, respectively, than their Chinese counterparts. Mortality from prostate, lymphoma, and multiple myeloma was also substantially higher in India than in China. Rates of incidence and mortality from oral cancers (lip, oral cavity and pharynx) were 9-14 times higher in India than in China. This is primarily because of high prevalence of bidi smoking, chewing tobacco, and betel quid (paan) in India [18].

Figure 5. Trends in Urbanization and Female Labor Force Participation (Percentage of Total Labor Force) in India and China, 1960-2009

Source: World Health Organization and World Bank.
In terms of childhood cancers, although the overall incidence rate was 10% lower in China than in India, Chinese children had 24% higher overall cancer mortality and approximately two-fold higher brain and leukemia mortality than Indian children (Table 2).

Figure 6. Trends in Human Development Index (HDI) and Gender Inequality Index (GII) for India and China, 1980-2011

Source: United Nations, Human Development Report 2011.
In terms of chronic-disease risk factors, smoking prevalence among Chinese men was 49.3%, two times higher than the prevalence of 25.1% among Indian men (Table 1). Prevalence of physical inactivity, hypertension, obesity/overweight, and raised cholesterol were higher in China, but Indians had a two times higher prevalence of diabetes than Chinese (7.8% versus 4.2%).

Although both China and India spent slightly more than 4% of their GDP on health, health expenditure per capita (i.e., per person) was two times higher in China than in India (Table 1). Moreover, the proportionate government expenditure on health was 2.6 times larger in China than in India. Health care infrastructure (physicians and hospital beds per capita) was also more favorable in China than in India. In terms of access to improved sanitation facilities, rural Indians lag behind their Chinese counterparts (Table 1). Stunting and malnutrition is a major problem among Indian children under age 5 as nearly half of them are stunted and underweight. The prevalence of malnutrition is low among Chinese kids (Table 1).

Inequalities in Socioeconomic Conditions and Human Development

Approximately 42% of Indians live below the international poverty line (<$1 per day), compared with 16% of Chinese. Only half of the female adult population in India has achieved literacy, compared with 91% of Chinese women (Table 1). Both countries have experienced rapid urbanization during the past five decades. In 2009, 44% of the Chinese population and 30% of the Indian population lived in urban areas (Figure 5). Historically, female labor force participation (the proportion of total labor force) has been much lower in India than in China (Figure 5). More than 67% of Chinese women are currently in the workforce, compared with 33% of Indian women (Table 1).

Although India ranks lower than China in human development, improvements in human development have been impressive (Figure 6). Between 1980 and 2011, the HDI score increased from 0.34 to 0.55 for India and from 0.40 to 0.69 in China. Levels of gender inequality remain very high in India, and there has hardly been any improvement in this arena during the past two decades. China, on the other hand, is on par with many western industrialized countries and even exceeds the United States in gender equality ^[1]. In terms of life satisfaction, both Indians and Chinese score in the middle of the 11-point scale (Table 1). Out of 151 countries, India ranked 90th and China 102nd in life satisfaction, with people in Denmark and Canada reporting highest levels of life satisfaction ^[1].

Inequalities within India and China

Health and socioeconomic conditions vary greatly both within India and China, and, consequently, health inequalities within India and China are quite large ^[22-24]. Children in the poorest quintile in India had 3 times higher mortality than their wealthiest counterparts (Table 3). The urban and economically affluent women in India were 2-to-5 times more likely to have births attended by skilled health personnel than their rural and socioeconomically disadvantaged counterparts. Percentage of births attended by skilled health personnel is a measure of health service access and utilization. Life expectancy at birth in India varied from a low of 58 for Chhattisgarh, Madhya Pradesh, and Jharkhand to 74 years in Kerala. State differences in infant mortality were similar, with Madhya Pradesh having the highest infant mortality rate of 61 deaths per 1,000 live births and Kerala the lowest rate of 13 (data not shown) ^[25].

Health inequalities between the Indian states coincide with those in human development and poverty rates. According to the Indian Government estimates, poverty rates in 2009-10 were highest in Bihar (54%) and Chhattisgarh (49%) and lowest in Himachal Pradesh (10%) and Kerala (12%) ^[25]. Kerala ranked the highest on human development (HDI = 0.92) and Madhya Pradesh, Orissa and Bihar ranked the lowest on HDI (0.45-0.49) ^[22].

Table 1. Non-Communicable Disease (NCDs) Mortality Rates, Health-Risk and Health Care Factors, and Social Determinants, 2008-2011

Age-adjusted death rates are per 100,000 population and are age-adjusted to the world standard population.
Source: WHO, World Health Statistics Report 2011 and NCD Country Profiles; United Nations, Human Development Report 2011.

Table 2. Cancer Incidence and Mortality Rates in China and India, 2008

Note. Age-standardized rates, ASR(W), are per 100,000 population and age-adjusted to the world standard population. RR = relative risk. CI = confidence interval.

Source: GLOBOCAN 2008, International Agency for Research on Cancer (IARC); http://globocan.iarc.fr/

Table 3. Health Inequalities within India, 2005-2011

Source: WHO, World Health Statistics Report 2011 and United Nations, Human Development Report 2011.

Health inequalities in China are evident between urban and rural residents and by region or province. In 2005, under-five mortality was 2.4 times higher and maternal mortality 1.8 times higher in rural than in urban areas of China (Table 4). The under-five child mortality rates in Beijing and Qinghai were 5.1 and 35.0 per 1,000 live births, respectively. In 2000, the average life expectancy of China’s urban residents was 75.2 years and that of its rural resident was 69.6 years. Like India, inter-provincial differences in life expectancy in China were very marked ^[23]. Shanghai had the highest life expectancy of 78.1 years, 2 years more than Beijing. Tibet had the lowest life expectancy of 64.4 years, followed by the other two Southwest provinces Yunnan (65.5) and Guizhou (65.9). Provincial patterns in income and human development were similar to those in life expectancy. For example, Tianjin, Shanghai, and Beijing had the highest income per capita, while Tibet, Gansu, Yunnan, and Guizhou had the lowest income per capita. Shanghai and Beijing had the highest HDI scores of 0.91 and 0.89 respectively, while Tibet and Guizhou had the lowest HDI scores of 0.69 and 0.63, respectively ^[23].

Table 4. Health Inequalities within China, 2005

Source: United Nation Development Programme, China Human Development Report 2007-2008.

Discussion

By using contemporary global health and socioeconomic statistics, we have highlighted similarities and differences in health, socioeconomic, and developmental factors between India and China. Because of the faster improvements in mortality in China during the past five decades, the health gap between India and China, particularly in infant, child, and maternal mortality and life expectancy, has widened.

Public health problems and the burden of disease from specific conditions vary greatly between the two nations. While childhood diseases, maternal mortality, cardiovascular disease, diabetes, and cervical and oral cancers are major public health problems in India, high rates of stomach, liver, lung, and esophageal cancers, cardiovascular disease, and smoking are major health concerns in China. More than a quarter of all cervical cancer cases and deaths in the world occur in India alone ^{[16, 19]}. India has the highest number of oral cancer cases and accounts for 39% of oral cancer deaths in the world ^[16]. India also accounts for approximately one-fourth of all under-5 deaths and one-fifth of maternal deaths globally ^{[26, 27]}. China accounts for one-third of all lung cancer cases and deaths, 54% of liver cancer cases and deaths, and 48% of all stomach cancer cases and deaths worldwide ^[16].

Smoking prevalence among Chinese adult men aged ≥15 years is 60%, which is among the highest in the world ^[9]. Men in Russia, Ukraine, Laos, Greece, and Indonesia have higher smoking prevalence. Interestingly, although the adult smoking prevalence is lower in India than in China, Indian teens are twice as likely to smoke as their Chinese counterparts. About 19% of males and 8% of females aged 13-15 in India are smokers, compared with 7% and 4% in China, respectively ^[9]. Thus, the potential exists in India for substantial increases in adult smoking prevalence and premature death from smoking-related diseases.

The two countries share other health and development concerns such as high levels of disparities between socioeconomic groups and between rural and urban areas. As shown here, there is a substantial health and socioeconomic divide, particularly between rural and urban areas and among states, both within India and China.

In the decades ahead, India faces twin challenges of having to reduce deaths from infectious diseases and maternal and perinatal conditions as well as from major chronic diseases such as heart disease, stroke, diabetes, and cancer ^[9]. With expanding industrialization and urbanization levels, people in both India and China are increasingly more likely to adopt lifestyles, dietary, and consumption patterns that are currently prevalent in the West ^[12]. Consequently, rates of obesity, physical inactivity, and smoking (particularly in women) are expected to increase dramatically in the years ahead and chronic diseases are expected to become an even bigger threat to public health in India and China.

Because of the massive size of their population and economy and their growing influence in global matters, the world is paying increasing attention to existing conditions and changes that are occurring in the socioeconomic, political, and public health domains in both China and India. As the two nations make further improvements in their levels of social and economic development, substantial gains in health are expected for both India and China. Despite the marked improvements in health during the past 5 decades, much needs to be done to increase life expectancy and improve maternal and child health. Mainland China’s life expectancy of 74 years is still 8-9 years less than that of Japan, Hong Kong, Singapore, Iceland, and Switzerland and 5 years less than Taiwan’s ^[9]. India’s life expectancy and child survival have to improve a great deal to reach the level seen in many middle- and high-income countries. India’s southern neighbor, Sri Lanka, does significantly better than India in life expectancy, child survival, and maternal mortality ^[9]. One way to improve health at the national level is to reduce inequalities in health and social conditions between population groups and areas both within India and China ^{[6, 9, 14]}.

Conclusions and Public Health Implications

India and China differ greatly in their levels of health, human development, and gender inequality ^{[1, 9]}. China does better than India on several key health measures, with much of the health disparity stemming from higher rates of mortality in India from communicable diseases and maternal and perinatal conditions, which are largely preventable ^[9]. Health and social development often go hand in hand. Our analysis of the WHO data (not shown) indicates that the nations that perform poorly on the human development and gender inequality indices tend to have lower levels of life expectancy and higher infant, child, and maternal mortality (γ > 0.70). Not only does India have lower levels of human development than China, it also does poorly with respect to gender equality. Indian women fare worse in reproductive health, educational achievements, empowerment, and labor force participation than their Chinese counterparts.

Benefits of rapid, sustained economic growth need to be shared more broadly and evenly across population groups, particularly among rural, remote regions in the two countries where the majority of the Indian and Chinese people still live. Unfavorable socioeconomic conditions and low human development levels can be a hindrance for public health improvement, but both India and China need to invest more in their health care infrastructure, provision of health services, and in education and rural sectors. Broad societal initiatives are, of course, needed to address important health and developmental goals, including poverty reduction, larger investments in women’s health and education (particularly in India), expanding economic opportunities for women, and a greater commitment toward gender and social equality in the distribution of power, money, and resources ^{[1, 5-7, 9, 14]}. Both countries can make further health gains by reducing social inequality, greater investments in human development and preventive health services, and by implementing policies related to the prevention and control of chronic-disease risks such as hypertension, smoking, obesity, and physical inactivity.

Financial Disclosure None to report. Funding/Support: None. Conflicts of Interest None.
Acknowledgements: None. The views expressed are the authors’ and not necessarily those of the US Department of Health and Human Services and Global Health and Education Projects.

References

1. United Nations Development Programme. Human Development Report 2011. New York, NY; 2011.
2. Lee R. The demographic transition: three centuries of fundamental change. Journal of Economic Perspectives. 2003; 17(4):167-190.
3. Dougherty S, Valli V. Comparing China and India: an introduction. European Journal of Comparative Economics. 2009; 6(1):53-55.
4. Basu SR. Comparing China and India: is the dividend of economic reforms polarized? European Journal of Comparative Economics. 2009; 6(1):57-99.
5. Sen A. Growth and other concerns. The Hindu. February 14, 2011. http://www.thehindu.com/opinion/op-ed/article1451973.ece?homepage=true. Accessed May 11, 2012.
6. Sen A. Quality of Life: India vs. China. The New York Review of Books. May 12, 2011. http://www.nybooks.com/articles/archives/2011/may/12/quality-life-india-vs-china/?pagination=false. Accessed May 15, 2012.
7. Sen A. India no match for China on social indicators. The Economic Times. January 7, 2012. http://articles.economictimes.indiatimes.com/2012-01-07/news/30601912_1_social-indicators-south-asian-economies-development-indicators. Accessed May 11, 2012.
8. Ma S, Sood N. A Comparison of the Health Systems in China and India. Center for Asia Pacific Policy, Rand Corporation, Santa Monica, California; 2008.
9. World Health Organization. World Health Statistics Report 2011. Geneva, Switzerland; 2011.
10. World Health Organization. NCD Country Profiles, India and China, 2011. Geneva, Switzerland; 2011.
11. World Bank. Health, Nutrition, and Population Statistics (HNP Stats). Washington, DC; 2011. Available at: http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS /EXTHEALTHNUTRITIONANDPOPULATION/EXTDATASTATISTICSHNP/EXTHNPSTATS/0,, menuPK:3237172~pagePK:64168427~piPK:64168435~theSitePK:3237118,00.html. Accessed May 15, 2012.
12. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva, Switzerland; 2009.
13. Wilkinson R, Marmot M, eds. Social Determinants of Health: The Solid Facts. 2nd Edition. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 2003.
14. Rasanathan K. Closing the Gap: Policy into Practice on Social Determinants of Health: Discussion Paper for the World Conference on Social Determinants of Health. Rio de Janeiro, Brazil: World Health Organization; 2011.
15. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. American Journal of Public Health. 2003; 93(7):1137-1143.
16. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. GLOBOCAN 2008, Cancer Incidence and Mortality Worldwide: IARC Cancer Base No. 10 [Internet]. Lyon, France: International Agency for Research on Cancer; 2010. Available at: http://globocan.iarc.fr. Accessed May 1, 2012.
17. Arbyn M, Castellsague X, de Sanjose S, et al. Worldwide burden of cervical cancer in 2008. Annals of Oncology. 2011; 22(12):2765-2786.
18. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011; 61(2):69-90.
19. Singh GK, Azuine RE, Siahpush M. Global inequalities in cervical cancer incidence and mortality are linked to deprivation, low socioeconomic status, and human development. International Journal of MCH and AIDS. 2012; 1(1):17-30.
20. Martin JA, Hamilton BE, Ventura SJ, et al. Births: final data for 2009. National Vital Statistics Reports. 2011; 60(1):1-72.
21. UNAIDS. Country Fact Sheets, China and India. Available at: http://aidsinfo.unaids.org. Accessed May 15, 2012.
22. Suryanarayana MH, Agrawal A, Prabhu SK. Inequality-Adjusted Human Development Index for India’s States. New Delhi, India: United Nations Development Programme; 2011.
23. United Nations Development Programme. China Human Development Report, 2007-2008: Basic Public Services Benefitting1.3 Billion Chinese. Beijing, China: UNDP; 2008.
24. Mani K, Dwivedi SN, Pandey RM. Determinants of under-five mortality in Rural Empowered Action Group States in India: An application of Cox frailty model. International Journal of MCH and AIDS. 2012; 1(1):60-72.
25. Government of India, Planning Commission. Databook for DCH, Social Sector – Health and Family Welfare. http://planningcommission.gov.in. Accessed May 15, 2012.
26. Rajaratnam JK, Marcus JR, Flaxman AD, et al. Neonatal, postneonatal, childhood, and under-5 mortality for 187 countries, 1970-2010: a systematic analysis of progress towards Millennium Development Goal 4. Lancet. 2010; 375(9730):1988-2008.
27. World Health Organization. Trends in Maternal Mortality: 1990 to 2008. Geneva, Switzerland; 2010.

Abstract

Objectives

Studies on factors affecting neonatal mortality have rarely considered the impact of place of delivery on neonatal mortality. This study provides epidemiological information regarding the impact of place of delivery on neonatal deaths.

Methods

We analyzed data from the Rufiji Health and Demographic Surveillance System (RHDSS) in Tanzania. A total of 5,124 live births and 166 neonatal deaths were recorded from January 2005 to December 2006. The place of delivery was categorized as either in a health facility or outside, and the neonatal mortality rate (NMR) was calculated as the number of neonatal deaths per 1,000 live births. Univariate and multivariate logistic regression models were used to assess the association between neonatal mortality and place of delivery and other maternal risk factors while adjusting for potential confounders.

Results

Approximately 67% (111) of neonatal deaths occurred during the first week of life. There were more neonatal deaths among deliveries outside health facilities (NMR = 43.4 per 1,000 live births) than among deliveries within health facilities (NMR = 27.0 per 1,000 live births). The overall NMR was 32.4 per 1,000 live births. Mothers who delivered outside a health facility experienced 1.85 times higher odds of experiencing neonatal deaths (adjusted odds ratio = 1.85; 95% confidence interval = 1.33–2.58) than those who delivered in a health facility.

Conclusions and Public Health Implications:

Place of delivery is a significant predictor of neonatal mortality. Pregnant women need to be encouraged to deliver at health facilities and this should be done by intensifying education on where to deliver. Infrastructure, such as emergency transport, to facilitate health facility deliveries also requires urgent attention.

Key Words:

Neonatal mortality • Place of delivery • Tanzania • Socioeconomic status • Maternal age.

Introduction

Reducing neonatal mortality is a major thrust of current international public health policy ^[1]. Place of delivery is an important aspect of reproductive health care. The place of delivery often determines the quality of care received by a mother and infant. It is an important factor in differential risks of neonatal mortality ^[2]. Children delivered at a health facility are likely to experience lower mortality than children delivered at home because such facilities usually provide a sanitary environment and medically correct birth assistance ^[3]. Although almost half of all deliveries in Tanzania take place at home ^{[4, 5]}, studies on neonatal mortality have rarely considered the influence of place of delivery on neonatal mortality in Tanzania.

Greater attention to neonatal deaths could be met through providing epidemiological information regarding the places of neonatal deaths to policy makers and program planning authorities ^[6]. This paper reports on the findings of a study conducted in the Rufiji Health and Demographic Surveillance Site (RHDSS) in rural Tanzania to assess the impact of place of delivery on neonatal mortality.

Methods

Study Area

This study was conducted using data from the RHDSS site in rural Tanzania; Rufiji is one of the six districts of Coast Region in Tanzania about 178 km south of Dar-Es-Salaam. The district has a population of about 226,000 people. The Rufiji demographic surveillance area comprises of 31 villages with a resident population of approximately 93,000 in 18,000 households. The RHDSS monitors households and members within households in cycles or intervals, known as ‘rounds’ of four months each. Members (residents) of the RHDSS are individuals who have resided in the survey area for a period of the previous four months and plan to continue to live there. Rufiji district has 56 health facilities made up of two hospitals (one government and one mission); five government health centres and 48 government dispensaries. A private dispensary based at Kibiti offers mobile clinic services in some parts of the district. About 89% of the population lives within 5 km of a formal health facility.

Study Design

This was an analytical longitudinal study, based on secondary data from the RHDSS data on all neonatal deaths and live births that occurred from January 2005 to December 2006. The place of delivery was classified into two groups: health facility and outside health facility; thus deliveries that occurred at home or on the way to a health facility counted as outside health facility deliveries.

Study Sample

The analyzed sample comprised of all children younger than 28 days born between January 2005 and December 2006 to residents of the Rufiji DSA. A total of 5,124 live births and 166 deaths were registered during the defined study period.

Data

The variables used were selected from five datasets through an internal individual unique ID in the RHDSS database and extracted and combined into a new data set. The following were the key variables of interest:

1. The main outcome variable was neonatal mortality, defined as any death occurring within 28 days of birth and coded using verbal autopsy instrument or death certificate mortality information.
2. The main explanatory variable was place of delivery, which was defined as the place where a birth took place; either in a health facility or outside a health facility.

Other explanatory variables were maternal occupation, marital status, parity, infant’s sex, maternal age at delivery, maternal education, and maternal household socio-economic status (SES)

Maternal Household Socio-Economic Status (SES)

Maternal household SES was constructed by using household characteristics and assets ownership data. The data was transferred from Microsoft visual fox pro professional edition version 5.0 database format into Stata version 10 software with stat transfer. This information was used in the construction of household wealth index using Principal Component Analysis (PCA) in Stata version 10 software. The following variables were used for the PCA analysis; Hoe, matchet, bicycle, vehicle, motorbike, radio, refrigerator, television, clock, sofa, bed, video, mattress, wardrobe, pump, livestock, sewing machine, chicken, bednet, satellite dish, ceiling fan, iron, floor type, wall type, roof type and power or energy source. The assets were combined into a wealth index using weights derived through principal components analysis (PCA).

PCA involves breaking down assets (e.g. radio, bicycle) or household service access (e.g. water, electricity) into categorical or interval variables. The variables are then processed in order to obtain weights and principal components. The Principal Component Analysis Model that was used to construct the wealth index (socio-economic indices) with household characteristics and ownership of assets was based on the model proposed by Filmer and Prichett ^[7] in 2001. This approach uses the PCA which involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.

The model was based on the presence or absence of each asset or the nature of the housing materials .i.e. each asset was dummied with the response, 1 and 0. If the mother had the asset the response was 1, otherwise it was assigned 0. The generated wealth index was used to categorize the households of mothers of the neonates into five socio-economic groups or quintiles; poorest, poorer, poor, less poor, and least poor to arrive at maternal household socio-economic status.

Statistical Analysis

Neonatal mortality rates (NMR) were calculated based on place of delivery (i.e., a health facility delivery or outside health facility delivery) and by dividing the total neonatal deaths that occurred in each place of delivery by the respective total number of live births that occurred in each place of delivery. The NMR was expressed as a rate per 1,000 live births. The NMR was also calculated for the total study population. Univariate and multivariate logistic regression models were used to assess the associations between neonatal mortality and place of delivery while adjusting for potential confounders such as maternal age at delivery, maternal occupation, maternal education, maternal household socio-economic status, parity and marital status. All analyses were done in Stata 10 ^[8].

Limitations of the Study

Information on pregnancy complications or events prior to delivery that may have influenced the risk of newborn deaths was not available. Again, data on prematurity which is a high risk factor for newborn deaths was not available.

The results of this study may not be generalizable to the entire rural Tanzania because quality of health care varies across the country.

Ethical Considerations

The study received ethical approval from the University of the Witwatersrand’s Committee for Research on Human Subjects (Medical) (protocol number M071142) in South Africa and the Institutional Review Board of the Ifakara Health Institute (ethical clearance number: IHI/IRB/No. A 021) in Tanzania. The datasets were anonymized.

Results

Socio-Demographic Characteristics.

A total of 5,124 live births and 166 neonatal deaths were recorded in the Rufiji Health and Demographic Surveillance Area (RHDSA) from January 2005 to December, 2006. There were slightly more boys 2,577 (50.3%) than girls 2,547 (49.7%) born during the study period. There were no significant sex differentials in neonatal deaths 84 (50.6%) females versus 82 (49.4%) males (Table 1). Approximately 67% (111) of neonatal deaths occurred during the first week of life (Table 1). Of the 5,124 births, 3,442 (67.2%) were born in health facilities and 1,682 (32.8%) were born elsewhere (Table 2).

Distribution of Neonatal Mortality Rates by Place of Delivery

Neonatal mortality was significantly higher (43.3 per 1,000 live births) in children born outside health facilities compared to those born in health facilities (27.0 per 1,000 live births) (Table 2).

Maternal Risk Factors Associated with Neonatal Mortality

Because the probability of neonatal death associated with most risk factors was smaller than 0.05, we have used odds and risks of neonatal death interchangeably. Table 3 presents the unadjusted analysis of the association between place of delivery, maternal risk factors and neonatal mortality. The univariate analysis found that mothers who delivered outside a health facility experienced 1.63 times higher odds of experiencing neonatal deaths (unadjusted odds ratio [OR] = 1.63; 95% CI = 1.19–2.23) than mothers who delivered in a health facility (Table 3). Mothers who had no education experienced 0.78 times lower odds of experiencing neonatal deaths than mothers who had secondary education, though this association did not reach significance (unadjusted OR = 0.78; 95% CI = 0.35–1.73). Maternal age was significantly associated with neonatal mortality. Mothers in the age group 20-29 years experienced 0.48 times lower odds of experiencing neonatal deaths than mothers who were under 20 years old (unadjusted OR = 0.48; 95% CI = 0.33–0.71). Mothers aged 30 years and above experienced 0.63 times lower odds of experiencing neonatal deaths than mothers who were under 20 years old (unadjusted OR = 0.63; 95% CI = 0.43–0.92).

Parity was found to be protective against neonatal death since significance was reached. Compared with mothers with a parity of 1-2, mothers with a parity of 3-4 experienced 0.52 times lower odds of experiencing neonatal deaths and mothers who had a parity of 5 and more experienced 0.57 times lower odds of experiencing neonatal death. Maternal marital status, socio-economic status, and maternal occupation were not significantly associated with neonatal mortality.

In multivariate analysis, delivery outside a health facility remained a significant risk factor for neonatal mortality. Mothers who delivered outside a health facility experienced 1.85 times higher odds of experiencing neonatal deaths (adjusted OR = 1.85; 95% CI =1.33–2.58] than those who delivered in a health facility (Table 4). Maternal age, maternal education, maternal marital status, parity, maternal socio-economic status, and maternal occupation were not statistically significantly associated with neonatal mortality.

Table 1.Distribution of Neonatal and Maternal Socio-demographic Characteristics in Rufiji Health and Demographic Surveillance System (HDSS), Tanzania 2005-2006

Table 2. Distribution of Neonatal Mortality Rates by Place of Delivery per 1000 Live Births, Rufiji Health and Demographic Surveillance System (HDSS), Tanzania 2005-2006

NMR*= Neonatal Mortality Rate per 1,000 live births

OHF**= Out-Side Health Facility Deliveries/Births

Table 3. Unadjusted Odds Ratio (OR) Estimates and 95% Confidence Intervals (CI) for Maternal Risk Factors Associated with Neonatal Mortality (Univariate Logistic Regression Analysis)

Table 4. Adjusted Odds ratio (OR) Estimates and 95% Confidence Intervals for Maternal Risk Factors Associated with Neonatal Mortality (Multivariate Logistic Regression Analysis)

* Reference Group

Discussion

The results of this study clearly indicate that delivery outside a health facility is more likely to lead to neonatal death compared with delivery in a health facility; this confirms the role of place of delivery on newborn survival. Place of delivery has consistently been found to be associated with maternal and neonatal outcomes [9,10,11]. Childbirth in a health institution attended to by a trained medical staff reduces maternal and neonatal mortality and morbidity compared to home births [12,13,14]. Most of the studies reported are, however, based on health facility data only which do not demonstrate the actual magnitude of the problem. The community data analyzed here clearly demonstrates this point.

Higher neonatal mortality was found among children born outside health facilities (43.4 versus 27.0 per 1,000 live births), even though health facility deliveries generally have a far greater likelihood of complications likely to result in neonatal death. This is as a result of the health-seeking behaviour where most deliveries will be attended to at home until it becomes complicated; and it is only at this stage that the home delivery attendants will refer to the health facilities. Weak health systems are also likely to account for neonatal deaths among the health facility deliveries.

The overall neonatal mortality in Rufiji district, which was 32.4 per 1,000 live births, is similar to the neonatal mortality rate of 32.0 per 1,000 live births reported by the 2004-05 Tanzania Demography and Health Survey (TDHS). The findings also confirm the average NMR of 33 per 1,000 live births for middle-income and low-income countries where 99% of neonatal deaths occur. The overall NMR is consistent with NMRs reported in other Sub-Saharan African countries such as Uganda (32/1,000), Burkina Faso (31/1,000) and Madagascar (32/1,000) [15]. The NMR of 43.4 per 1,000 for births that occurred outside health facilities is close to England’s NMR of 41 per 1,000 live births in 1905, and the average for Sub-Saharan Africa today [16]. The overall NMR of 32 per 1,000 live births for Rufiji indicates a great improvement in neonatal deaths in a rural Tanzania district, though there is much room for further improvement.

Conclusions and Public Health Implications

The findings from this study lend credence to the vital role that the place of delivery plays in neonatal survival as delivery outside a health facility is a risk factor of neonatal mortality. This finding concurs with the 2005 World Health Report which states that, giving birth in a health facility (not necessary a hospital) with professional staff is safer by far compared to doing so at home [17]. It also conforms to the results of Demographic and Health Survey (DHS) data from 40 countries collected between 1995 and 2003 which reported that more than 50% of neonatal deaths occur after home birth without skilled care attendance [18]. Furthermore, these results are consistent with a study in rural Tanzania which reported that home births without a trained attendant resulted in a three times higher perinatal mortality compared with those in a health facility with trained attendants in rural Tanzania [19]. These findings are in line with those of a study in Papua New Guinea which reported high rate of obstetric complications among apparently normal pregnancies deliveries at home in Papua New Guinea [20]. These findings have important implications for all stakeholders and policy makers in the fight against neonatal mortality in sub-Saharan Africa in general and Tanzania in particular.

The use of longitudinal population-based data is an ideal way of communicating the impact of home deliveries on neonatal mortality. The United Republic of Tanzania’s health system must be strengthened to promote universal facility delivery in order to achieve the Millennium Development Goal 4 and to comply with the National Strategy for Growth and Reduction of Poverty (NSGRP) which aims to reduce infant mortality from 95 infant deaths per 1,000 live births in 2002 to 50 per 1,000 by 2010.

In conclusion, the place of delivery has a significant impact on neonatal survival. The authors therefore recommend the development and implementation of programs that would educate, encourage and support pregnant women to give birth in health facilities.

Conflict of interest: None
Acknowledgements: This paper is published with the permission of the Director and Management of Ifakara Health Institute, for whose support we are very grateful.

The study was funded by Indepth-Network as part of JA’s support by the Indepth-Network for his MSc studies. The funding body had no role in study design, data collection, analysis or interpretation of the data, nor in writing the manuscript or in the decision to submit the manuscript for publication.

References

1. English M., Muhoro A, Aluda M, Were S, Ross A, Peshu NT. Outcome of delivery and cause-Specific mortality and severe morbidity in early infancy: a Kenya district hospital birth cohort. American Journal Tropical Medicine and Hygiene. 2003: 69(2), 228–232.
2. Murthy MSR, Murthy PV, Hari M, Kumar VKR, Rajasekhar K. Place of birth: Why urban women still prefer home deliveries? Journal of Human Ecology. 2007; 21(2): 149-154.
3. Pandey A, Choe MK, Luther NY, Sahu D, Chand J. Infant and Child Mortality in India: National Family Health Survey Subject Reports. Number 11. Indian Institute of Population Sciences, Mumbai; 1998.
4. DHS Macro International, Inc. Tanzania Demographic and Health Survey 2010. Macro International Inc. Calverton, MD; 2010.
5. Walraven GE, Mkanje RJ, van Roosmalen J, van Dongen PW, Dolmans, WM. Assessment of maternal mortality in Tanzania. British Journal of Obstetrics and Gynecology. 1994; 101(5):414–417.
6. Lawn JE, Cousens S, Bhutta ZA. Lancet Neonatal Survival Team. Why are 4 million newborns babies dying each year? Lancet. 2004; 364(9432): 399–401.
7. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data or tears: An application to educational enrolments in states of India. Demography. 2000; 38(1):115-132.
8. StataCorp. College Station, Texas 77845; 2007.
9. Giri K. Discussion. International Journal of Gynecology and Obstetrics. 1995; 50: (supp1 2):S43.
10. Tsu VD. Antenatal screening: its use in assessing obstetric risk factors in Zimbabwe.
11. Journal of Epidemiology and Community Health. 1994; 48(3):297-305.
12. Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Social Science and Medicine. 1994; 38(8): 1091–1110.
13. Howlader AA, Kabir M, Bhuiyan MM. Health-seeking behaviour of mothers and factors affecting infant and child mortality. Demography India. 1999; 28(2):225–238.
14. Luther NY. Mother’s tetanus immunisation is associated not only with lower neonatal mortality but also with lower early childhood mortality. National Family Health Survey Bulletin. 1998; 10:1–4.
15. Jejeebhoy SJ, Rao SR. Unsafe Motherhood: A Review of Reproductive Health. In: Das Gupta M, Chen LC, Krishnan TN, eds. Women’s Health in India: Risk and Vulnerability. Bombay, India: Oxford University Press; 1995.
16. MacFarlane A, Johnsone A, Mugford M., Rennie J, Roberton N. Textbook of Neonatology. 3rd edition, Churchill Livingstone, Cambridge; 1999; 3-33.
17. World Health Organization. The world health report 2005: make every mother and child count. Geneva: WHO; 2005.
18. Lawn JE, Cousens S and Zupan J. 4 million neonatal deaths: when? where? why? Lancet; 2005; 365(9462);891-900.
19. Walraven GE, Mkanje RJ, Roosemalan J, van Dongen PW, Dolmans WM. Perinatal mortality in home births in Rural Tanzania. European Journal Obstetrics and Gynecology and Reproductive. Biology. 1995: 58(2):131-134.
20. Garmer P, Lai D, Baea M. Childbirth in rural areas: maternal deaths, village deliveries and obstetric service use. Papua New Guinea Medical Journal, 1994; 37(3); 166-172.

Abstract

Objectives

In India there has been a decline in overall under-five mortality, with some states still showing very high mortality rates. It is argued that there is family clustering in mortality among children aged <5 years. We explored the effects of programmable (proximate) determinants on under-five mortality by accounting for family-level clustering and adjusting for background variables using Cox frailty model in rural Empowered Action Group states (EAG) in India and compared results with standard models.

Methods

Analysis included 13,785 live births that occurred five years preceding the National Family Health Survey-3 (2005-06). The Cox frailty model and the traditional Cox proportional hazards models were used.

Results

The Cox frailty model showed that mother’s age at birth, place of delivery, sex of the baby, composite variable of birth order and birth interval, baby size at birth, and breastfeeding were significant determinants of under-five mortality, after adjusting for the familial frailty effect. The hazard ratio was 1.41 (95% CI=1.14–1.75) for children born to mothers aged 12-19 years compared to mothers aged 20-30 years, 1.42 (95% CI=1.12–1.79) for small-sized than average-sized babies at birth, and 102 (95% CI=81–128) for non-breastfed than breastfed babies. Children had significantly lower mortality risks in the richest than poorest wealth quintile. The familial frailty effect was 2.86 in the rural EAG states. The hazard ratios for the determinants in all the three models were similar except the death of a previous child variable in the Cox frailty model, which had the highest R2 and lowest log-likelihood.

Conclusions and Public Health Implications:

While planning for the child survival program in rural EAG states, parental competence which explains the unobserved familial effect needs to be considered along with significant programmable determinants. The frailty models that provide statistically valid estimates of the covariate effects are recommended, when observations are correlated.

Key Words:

Empowered Action Group States • Under-five mortality • National Family Health Survey • Frailty model • Unobserved familial effect • Programmable determinants • India.

Introduction

Reducing under-five mortality is now a global concern. In 2001 as part of the Millennium Development goals (MDG) for health, nations pledged to ensure a two-thirds reduction in under-five mortality between 1990 and 2015 ^[1] and at once a series of articles in Lancet by the Bellagio Study Group described various aspects of child survival ^{[2, 3, 4, 5, 6]}. Although under-five mortality is declining worldwide as a result of socioeconomic development and implementation of child survival interventions, nearly 8.8 million children die every year before their fifth birthday. India alone accounted for 21% of the world’s under-five deaths occurring in 2008 ^[7] owing to its large population. In India, states such as Assam, Arunachal Pradesh, Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh, have higher Under-five mortality than the rest of India. The national average for under-five mortality is 74 per 1000 ^[8]. The Ministry of Health and Family Welfare, India, established Empowered Action Group (EAG) in 2001 to have special focus by monitoring and facilitating the attainment of national health goals on some of these states which are demographically lagging behind. The EAG states constitute 45% of the total population of India and also have higher neonatal and infant mortality rates.

In developing countries, efforts have been made during the past three decades to reduce child mortality. Despite socioeconomic development and implementation of child survival interventions, prevailing high mortality may be due to the heterogeneity. This might have considerable implication for reproductive health and child survival programs ^[9]. Studies on determinants of child mortality have mainly used either logistic regression or Cox proportional hazards model assuming that the outcomes are independent. To find more accurate estimates for the determinants of child mortality that has critical implications for resource allocation for improving child survival, sibling structures in child mortality data from demographic surveys have been treated as multivariate failure time data ^{[10, 11, 12, 13]}. As failure time data, many attempts have been made to extend the Cox proportional hazards model. In this context, the variance-corrected Cox model has received much attention ^{[14, 15]}. In the variance-corrected Cox model, regression parameters of the determinants are estimated by ignoring intra-family correlation but adjusted for in the inference procedure; however, it ignores the variation of underlying risk among families. To overcome this, multivariate failure time data are modeled by an unobserved random quantity called frailties ^[16]. These frailties are common to observations from the same cluster and assumed to follow a given statistical distribution, known as multivariate random effects model or Cox frailty model.

In India, studies on child mortality have mainly addressed the role of maternal, socioeconomic and health-related determinants ^{[9, 17, 18]}. These studies were restricted to the analysis of mortality risks in children at individual level and not considered the correlation among children of the same family. We also want to emphasize those determinants which are nearer in time to the outcome and can be modified by program than those which are remote or far apart in time to the outcome of concern. The former covariates are referred to as programmable determinants and the latter as background variables. Therefore, we aimed to identify the programmable determinants of under-five mortality using Cox frailty model to account for sibling-level correlation for providing valid estimates needed for policy-decision making. In order to appreciate the influence of sibling-level correlation over the estimates of the determinants of under-five mortality, the results of Cox frailty model were compared with the Cox proportional hazards model and variance-corrected Cox model.

Methods

Data Sources

The third round of National Family Health Survey-3 (NFHS-3) in India was completed during 2005-06 covering a nationally representative sample of ever married women aged 15-49 years. This survey collected data on fertility, family planning, infant and child mortality, maternal and child health, etc. using a two-stage sample design in rural areas for each state of India. The first stage involved selection of primary sampling units, i.e., villages, with probability proportional to population size and the second stage involved systematic selection of households within each selected village ^[8]. The response rates for household and eligible women identified in the household were 98.5% and 95.5% respectively. The rural data of NFHS-3 for eight EAG states, viz., Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh and Uttarakhand were combined and analyzed to identify the determinants of under-five mortality. In rural EAG states, retrospective maternity history was collected from 24,507 women aged 15-49 years. A total of 14,184 live births occurred within five years preceding the survey and mortality experience of 13,785 children were analyzed in this study. In 399 cases the information was missing on some of the variables used in the analysis. Of these 13,785 live births, 1,068 children died before reaching their fifth birthday.

Study Variables

The primary outcome, under-five mortality, was defined as time to death of a live born baby before his/her fifth birthday. Available potential predictors ^[19] of child survival as summarized in the conceptual framework of Mosley and Chen ^[20] was considered and grouped into programmable (proximate) determinants and background variables.

Programmable determinants included mother’s age at birth, delivery assistance, place of delivery, mode of delivery, combined variable of birth order and birth interval, survival status of previous child, maternal subjective assessment of baby’s size at birth, sex of the baby and ever breastfeeding; and the background variables included region (eight states), religion, caste, mother’s education, mother’s occupation, household wealth index, number of children in the family and desired time for pregnancy.

Analytical Models: Traditional Cox Proportional Hazards Model, Multivariate Cox Variance-Corrected and Frailty Models

The variance-corrected and frailty hazard models are multivariate not only in the usual sense of having multiple predictors, but also in the sense of having multiple responses, that is, responses from more than one child in the family.

Cox Proportional Hazards model:

Mathematically, it is written as

h(t) = h0(t) exp(β zk), t > 0, …………………………… (1)

Where, h0(t) is an unspecified baseline hazard function and β denote the vector of the true regression coefficients for covariates zk, (k=1, 2, …, p). We could obtain an estimator β of β based on the working assumption that the under-five deaths in each family were independent of one another.

Cox variance corrected model:

We supposed that conditional on covariate vector (zik), the marginal hazard function hik(t) for failure time of the kth child in the ith family, (k = 1,2,3,….,Ki; i = 1,2,3,…,n) with the usual proportional hazards form and is given by

hik(t) = h0(t) exp(β zik), t > 0, …………………………… (2)

We could obtain an estimator of β based on the working assumption that the under-five deaths in each family were independent of one another. But the equation (2) assumes that the births are related and hence adjusts for it in the inference, that is, the standard error by means of sandwich-type estimators [13] and so it is called as variance corrected models.

Cox frailty model

For the frailty model, we supposed that conditional on the frailty, vi the hazard function hik(t) for the failure time of the kth children in the ith family (k = 1,2,3,….,Ki; i = 1,2,3,…,n) follows the usual proportional hazards form and is given by:

hik (t) = h0 (t) vi exp(β’zik), t > 0, …………………………… (3)

Where, vi, group-level (family) frailty. These frailties are unobservable, assumed to be independent and identically distributed with unit mean and unknown variance θ. Each family could have different values of random effects and the variability in the vis reflect heterogeneity of risks between families. If the variance of the random effect (frailty) is 0, then children from the same family are independent. The variance of the random effect lies between 0 and α. A larger variance implies greater heterogeneity in frailty across families and greater correlation among children belonging to the same family. The frailty (family) often assumed to follow gamma distribution for the sake of computational convenience and convergence ^{[21, 22, 23]} and this model is expected to yield correct z-ratios, on which researchers rely heavily for their conclusions ^[10].

Equations (2) and (3) reduce to the traditional Cox Proportional Hazards model ^[24], if the responses from each child in the family are assumed to be independent.

Statistical analysis

The complete data for all the EAG states were downloaded from Demographic Health Survey data distribution system website: http://www.measuredhs.com. All the variables were read and coded using Stata 9.0 (College Station, Texas, USA). The under-five mortality rate (U5MR) and its 95% CI with respect to potential determinants influencing under-five mortality was calculated. We identified potential determinants of under-five mortality using three models: the traditional Cox proportional hazards model, the variance-corrected Cox proportional hazards model, and the Cox frailty model. Univariate models were fitted followed by multivariate models. Programmable determinants were adjusted for background factors in the multivariate analysis in all the three models. The model performance was assessed using R-square and log-likelihood. The results were reported as hazard ratio (95% CI). The value of p<0.05 was considered statistically significant. The R-software (version 2.11.1, 2010, The R foundation for Statistical Computing) was used to fit all the models.

Results

The trends in under-five mortality rates by major states in rural India for five years preceding the NFHS-1 (1992-92) and NFHS-3 (2005-06) are given in Table 1. Among the EAG states, no change in under-five mortality was found in Chhattisgarh and the highest decline in mortality (38.1%) was found in Bihar between the two surveys. The percentage decline in under-five mortality in rural India was 31.3 between the two surveys.

The distribution of live births by family is shown in Table 2. More than one third (41%) of the families contributed two or more children to the sample. About 40 percent of the total 13,785 children did not have sibling. A total of 1,068 under-five deaths occurred to 969 (10%) families.

The number of live births and under-five mortality with respect to the background factors and programmable determinants are shown in Table 3. One third of the total live births were from Uttar Pradesh; only 36.4 percent live births belong to scheduled caste (21.3%) and scheduled tribes (15.1 %) and mothers of two-thirds of the live births were illiterate. The under-five mortality (per 1,000 live births) was 86.9 in Uttar Pradesh, 89.9 among scheduled caste mothers, 86.7 among illiterate mothers, 81.7 among families with more than two children, 87.9 in the poorest wealth quintile, 104.2 among children born to mothers’ aged 12-19 years, 139.3 in mothers having previous birth interval of less than two years and parity more than three, 107 among small sized babies, and 143 among children with history of dead sibling, which were having very high under-five mortality than their counterparts.

The results of programmable determinants of under-five deaths adjusting for the background variables using all the three models are given in Table 4. The estimates are exactly the same in Models 1 and 2; only standard errors are corrected in Model 2, and in Model 3, both estimates and standard errors are corrected. The determinants found to be significant in Model 1 were also significant in Model 3 except death of a previous child and in Model 2 except mother’s age at birth. In the frailty model, the mortality hazards for children born to mothers aged 12-19 years at birth were 1.41 (95% CI: 1.14, 1.75) times higher than children born to mothers aged 20-30 years at birth and in the variance-corrected model, the hazard ratio (1.19) for the same variable was not statistically significant. The mortality hazard for the female child has increased from 17% to 22% when unobserved familial effect is taken into account. Small size babies at birth had 42% excess hazard than the average size babies at birth. The mortality hazards for first-born children and fourth-or-higher birth order children with preceding birth interval of less than two years were 2.04 (95% CI: 1.52, 2.73) and 2.42 (95% CI: 1.84, 3.18) times the hazard for second or third birth order children with a longer birth interval (p< 0.001). Infants who were not breastfed had significantly higher hazard of death (HR = 102; 95% CI: 81, 128) than those who were breastfed. The hazard ratio was 44% lower in non-institutional than institutional deliveries.

EAG states as a background variable was significantly associated with under-five mortality. The State, Uttarakhand, was selected as the reference category due to low under-five mortality rates among the EAG states. The hazard ratios were increased in all the EAG states except Jharkhand after adjustment for programmable determinants and other background variables. However, the adjusted hazard ratios were statistically significant for only Madhya Pradesh, Chhattisgarh, Rajasthan, Orissa and Uttar Pradesh. The other background variables such as caste, mother’s education and household wealth index were significantly associated with under-five mortality as shown in Table 4.

Most hazard ratios for the proximate determinants are similar across the three types of models but the most notable finding is the change in the effect of the death of a previous child variable. The multiplicative effect of this variable changes from an 86% excess risk to an 18% reduction in risk (albeit not statistically significant) when unobserved familial effect is taken into account as a gamma frailty. The gamma frailty is 2.86 which means that larger unmeasured familial effect is present and is statistically significant (p<0.001).

In general, the z-statistics (not shown here) are found to be smaller in the random-effects/frailty model than in the traditional Cox and variance-corrected Cox models except for some of the covariates.

The R2 and log likelihood/I-likelihood are preferred for comparing the three models. The Cox frailty model was considered the best model as it had the highest R2 and lowest log likelihood compared to the other two models.

Table 1. Trends in Under-Five Mortality Rates by Major States in Rural India for Five Years Preceding the NFHS-1 (1992-93) and NFHS-3 (2005-06)

*Data represent under-five mortality rates for the complete states

Table 2. Distribution of Live Births by Family

Table 3. The Distribution of Live Births and Under-Five Mortality Rates across Categories of the Background Variables and Programmable Determinants in Rural EAG States for Five Years Preceding the NFHS-3 (2005-06) Survey of India (n = 13,785)

Table 4. Programmable Determinants and Background Variables of Under-five Mortality using Traditional Cox Proportional Hazards, Cox Variance-Corrected and Cox Frailty Models in Rural EAG States for Five Years Preceding the NFHS-3 (2005-06) Survey of India (n = 13,785)

Reference categories: Mother’s age at birth 20-30 y, Institutional Delivery, Delivery assistance by Doctors/ANM/HP, Male children, 2-3 Birth Order & Birth Interval ≥ 2y, Very large & large size of the baby at birth, surviving previous sibling, breastfed children, Uttarakhand state, other caste, Hindu religion, literate mothers, Professional/Clerical/Sales, ≤ 2 children, poorest household wealth index and then for desire for pregnancy; aAdjusted for background factors such as EAG States, religion, caste, mother’s education, mother’s occupation, number of children, wealth index, desire time for pregnancy and other determinants; pb- p-value for multivariate analysis and p<0.05, statistically significant.

Discussion

The primary goal of the study was to assess the determinants of under-five mortality by applying an appropriate model to account for sibling-level correlation and thus provide valid estimates for correct statistical inference needed for policy-decision making. We found that children born in Chhattisgarh had higher risk of dying before age five, followed by children born in Uttar Pradesh and Madhya Pradesh. These states require health interventions that target under-five mortality reduction, particularly in rural areas. Next, mother’s education and wealth index emerge as powerful background covariates of under-five mortality in the EAG states, for the reason that both are known to be associated with better child care practices. Thus, the study urges the policy makers to focus on educating illiterate mothers about the child care; however, policy aiming at improving maternal education and poverty reduction is needed for sustainability.

We know that changes in the z statistics depend on the size of the parameter estimates along with the magnitude of the standard error. In general the z-statistics are smaller in magnitude in frailty model as compared to other models which we also observed in our results, clearly indicating that the sample of correlated observations contains less information than the independent sample. We also observed higher z-statistics for some covariates as observed by Sastry ^[12], for example, mother’s age at birth of 12-19 y (ZModel 3 = 3.13 vs. ZModel 1 = 1.98) in the Cox frailty model than the traditional Cox model.

The assumption of the Cox Proportional Hazards model is likely to be incorrect if we suspect that siblings share environmental or genetic influences beyond explicit covariates included in the model ^[11]. To account for this correlation, if we correct the standard error alone, it might lead to the biased inference, casting doubt especially on the more marginally significant results. The covariate, maternal age 12-19 years at child birth, was found to be marginally significant in the traditional Cox model and was statistically not significant in the variance-corrected Cox model. However, this variable was highly significant in the Cox frailty model which reiterates the importance of simultaneous correction of both parameter estimates and the standard error when analyzing correlated observations.

The next interesting aspect of the paper is estimates of the observed covariate effects. There were remarkably stable in all the three models except survival status of previous child variable. This has been already noted in previous studies ^{[10, 11, 12]} that the positive effect of this variable indeed acts as a proxy in the traditional Cox model. As pointed out by Guo and Rodríguez G ^[11], the hazard ratio of less than one in frailty model suggests that the death of a previous child lowers the risk of the surviving siblings through less competition for family resources or inducing changes in the parental behavior since death is a traumatic event. A non-protective role of institutional deliveries in the present study was found as pointed out by Titaley et al ^[25] and this might be complicated deliveries brought to the institution with three delays ^[26].

Estimation of family influences is difficult in that familial effects other than general socioeconomic status are very difficult to observe. Clustering of deaths in families was explained in rural Punjab ^[9] and in Guatemalan families ^[10] by household’s economic status and mother’s education. We found high variance of unobserved familial effect of 2.86 in the rural area of EAG states even after taking into account all possible cultural and socio-economic variables. This large unobserved heterogeneity at family level could be a result of greater variability in child care practices, health care and mother’s personal abilities ^[18]. Also the female child is more likely to die before reaching age five than the male child which might be related to behavioral and environmental factors ^{[5, 27]}. Thus, parental competence, genetic and other factors like nutritional deficiency, personal illness of the child etc which were not included in the present study might be the explanation for the family frailty in these rural EAG states.

The strengths of this study are the use of nationally representative survey of NFHS-3 (2005-06) data and the application of the Cox frailty model to estimate unbiased parameter estimates for determinants after accounting for familial effect. However, the cross-sectional nature of our study is its main limitation. The study should therefore be interpreted with caution. The variable, breastfeeding, was not considered as a time-dependent covariate due to methodological difficulty of the frailty model.

Conclusions and Public Health Implications

In conclusion, this paper confirms the hypothesis that the risk of under-five death among families is heterogeneous and identifies determinants associated with under-five deaths. Many determinants can be modified by child survival programs to enhance child survival, such as intensive antenatal and delivery care to young pregnant women and women having parity of more than two with preceding birth interval of less than two years; providing ideal nutritional supplement to infants who are small and or very small at the time of birth; improving mother’s child care practices by health education if mother has lost previous child; and reemphasizing exclusive breastfeeding for six months with introduction of complementary feeding at appropriate time. In the setting of correlated observations, the Cox frailty models are recommended for providing statistically valid estimates of the effects of proximate determinants after adjusting for the background variables and unobserved random effects.

Conflicts of Interest: None

Acknowledgements: We acknowledge the National Family Health Survey India, International Institute of Population Sciences, Mumbai, for data collection and providing us data for analysis. We also acknowledge Dr. Padam Singh, Dr. P. P. Talwar, Dr. Bir Singh and Dr. Lalitendu Jagatdeb for their guidance and advice in the work.

References

1. UN, General assembly, 56th session. Road Map towards the implementation of the United Nations millennium declaration: report of the Secretary-General (UN document no. A/56/326). New York: United Nations, 2001.
2. Black RE, Morris SS, Bryce J. Where and why are 10 million children dying every year? Lancet. 2003; 361(9376): 2226-2237.
3. Jones G, Steketee RW, Black RE, Bhutta Z, Morris SS, The Bellagio Child Survival Study Group. How many child deaths can we prevent this year? Lancet. 2003; 362(9377): 65-71.
4. Bryce J, el Arifeen S, Pariyo G, Lanata CF, Gwatkin D, Habicht JP, The Multi-Country Evaluation of IMCI Study Group. Reducing child mortality: can public health deliver? Lancet. 2003; 362(9378): 159-164.
5. Victora CG, Wagstaff A, Schellenberg JA, Gwatkin D, Claeson M, Habicht JP. Applying an equity lens to child health and mortality: more of the same is not enough. Lancet. 2003; 362(9379): 233-241.
6. The Bellagio Study Group on Child Survival. Knowledge into action for child Survival. Lancet. 2003; 362(9380): 323-327.
7. You D, Wardlaw T, Salama P, Jones G. Levels and trends in under-5 mortality, 1990-2008. Lancet. 2010; 375 (9709): 100-103.
8. International Institute for Population Sciences (IIPS) and Macro International. 2007 National Family Health Survey (NFHS-3), 2005-06: India: Volume I. Mumbai: IIPS.
9. Das Gupta M. Socio-economic status and clustering of child deaths in rural Punjab. Population Studies. 1997; 51(2):191-202.
10. Guo G. Use of sibling data to estimate family mortality effects in Guatemala. Demography. 1993; 30 (1): 15-32.
11. Guo G, Rodríguez G. Estimating a multivariate proportional hazards model for clustered data using the EM algorithm with an application to child survival in Guatemala. Journal of the American Statistical Association. 1992; 87(420): 969-976.
12. Sastry N. A nested frailty model for survival data, with an application to the study of child survival in northeast Brazil. Journal of the American Statistical Association. 1997; 92 (438): 426-435.
13. Hung, Wen-Shai, Shu-Hsi Ho, Survival analysis for unobserved heterogeneity on estimated mortality in Taiwan. Economics Bulletin. 2008; 9 (25): 1-10.
14. Spiekerman CF, Lin DY. Marginal regression models for multivariate failure time data. Journal of the American Statistical Association. 1998; 93(443): 1164-1175.
15. Lee EW, Wei LJ, Amato DA. Cox-type regression analysis for large numbers of small groups or correlated failure time observations, survival analysis: state of the art, J. P. Klein and P. K. Goel (eds), Kluwer Academic Publishers, 1992; 237-247.
16. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography. 1979; 16 (3): 439-454.
17. Pandey A, Bhattacharya BN, Sahu D, Sultana R. Components of under-five mortality trends, current stagnation and future forecasting levels. National Commission on Macroeconomics and Health Background papers-Burden of Disease in India. Ministry of Health and Family Welfare, Government of India, 2005.
18. Das Gupta M. Death clustering, mothers’ education and the determinants of child mortality in rural Punjab, India. Population Studies. 1990; 44(3): 489-505.
19. Rutstein SO. Factors associated with trends in infant and child mortality in developing countries during the 1990s. Bulletin of World health Organization. 2000; 78 (10): 1256-1270.
20. Mosley W, Chen L. An analytical framework for the study of child survival in developing countries. Population and Development Review. 1984; 10(Suppl.): 24-45.
21. Klein JP. Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics. 1992; 48(3): 795-806.
22. Parner E. Asymptotic theory for the correlated gamma-frailty model. Annals of Statistics. 1998; 26(1): 183-214.
23. Therneau TM, Grambsch PM. Modeling survival data. Extending the Cox model. Berlin: Springer-Verlag. 2000.
24. Cox DR. Regression models and life tables. Journal of the Royal Statistical Society, Series B. 1972; 74 (2): 187-220.
25. Titaley CR, Dibley MJ, Roberts CL. Type of delivery attendant, place of delivery and risk of early neonatal mortality: analyses of the 1994-2007 Indonesia Demographic and Health Surveys. Health Policy and Planning. 2011. [Epub ahead of print]
26. Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Social Science in Medicine. 1994; 38 (8): 1089-1110.
27. Claeson M, Bos ER, Mawji T, Pathmanathan I. Reducing child mortality in India in the new millennium. Bulletin of the World Health Organization. 2000; 78 (10): 1192-1199.

Abstract

Objectives

Intestinal parasitic infection and undernutrition are still major public health problems in poor and developing countries. The objective of this study was to assess the relationship between intestinal parasitic infection and nutritional status in 405 primary school children from rural and urban areas of Akwa Ibom State, Nigeria.

Methods

This cross-sectional survey in 2009 obtained anthropometric data, height-for-age (HA), weight-for-height (WH) and weight-for-age (WA) Z-scores from each child and fecal samples were also collected and screened for intestinal parasites using standard parasitological protocols.

Results

The prevalence of infection with any intestinal parasite was 67.4%. A total of six intestinal parasites were detected; hookworm (41.7%) had the highest prevalence. The prevalence of intestinal parasites and undernutrition was significantly higher in rural than in urban children (P<0.001). The prevalence of stunting (HAZ < -2), underweight (WAZ < -2) and wasting (WHZ < -2) for rural and urban children were 42.3% vs. 29.7%; underweight 43.2% vs. 29.6% and wasting 10.9% vs. 6.4%, respectively. With respect to nutritional indicators, the infected children had significantly (P<0.05) higher z-scores than the uninfected children. Multivariate logistic regression analysis showed that only Hookworm and Ascaris lumbricoides were each significantly (P<0.05) associated with stunting, wasting, and underweight.

Conclusions and Public Health Implications:

Since intestinal parasitic infections are associated with malnutrition, controlling these parasites could increase the physical development and well-being of the affected children.

Key Words:

Intestinal parasites • malnutrition • children • rural-urban • Nigeria.

Introduction

In developing countries, intestinal parasitism is a major public health problem that is often neglected. In these less developed countries, poor environmental and personal hygiene, poor nutrition, overcrowding and climatic conditions that favor the development and survival of these parasites are some of the factors contributing to the high level of intestinal parasites transmission ^[1-3]. School children carry the heaviest burden of the associated morbidity ^[4], due to their dirty habits of playing or handling of infested soils, eating with soiled hands, unhygienic toilet practices, drinking and eating of contaminated water and food ^[5].

There are documented reports implicating intestinal parasitic infection with poor nutritional status in children of school age ^[6-10]. Amoebiasis, Giardiasis, Acariasis, Hookworm infection, and Trichuriasis are among the most common intestinal parasitic infection worldwide. These infections are associated with decreased child growth, low plasma vitamin A, loss of weight, chronic blood loss, iron deficiency anemia, diarrhea, and stunted growth ^[4,11,12]. Alteration of the normal gastro-intestinal flora by intestinal parasites has been found to be associated with diarrhea, a major cause of childhood morbidity and mortality in developing countries ^[13].

There is a plethora of publications on prevalence and intensity of intestinal parasites in Nigeria, ^[2,3,14]. Surprisingly, there is paucity of information on the impact of these parasites on the nutritional status of their host, especially children. This study therefore aims to fill this gap in knowledge by investigating the association between intestinal parasitic infection and nutritional status of primary school children in rural and urban settings of Akwa Ibom State, Nigeria. The outcome may strengthen the global shift in the control of neglected tropical diseases.

Methods

Study Sites

The study was carried out in two primary schools in Akwa Ibom State, Nigeria. The schools are Methodist Primary School, Mbiabong Ikot Udofia in Ini Local Government Area (LGA), a typical rural setting and Government Primary School, Ikot Ntuen Oku, Uyo in Uyo Local Government Area (an urban setting). The two LGAs are in Akwa Ibom State. The State lies between latitudes 4°33’ and 5°33 North and longitudes 7°35’ and 8°25 East. It is characterized by humid tropical climate with annual rainfall reaching 23,000mm per annum. Temperature regime is uniform with annual values of 20.4°C to 35.7°C. The State has two distinct seasons, the rainy season from May to October, and the dry season from November to April. Pit latrine is common, while open air defecation is freely practiced.

Subjects

The study was carried out between April and November 2009. The study employed stratified random sampling to select the study subjects. For eligibility to participate in the study, a child had to be attending one of the selected schools. Each enrolled child’s date of birth, age, gender, and occupation of parents was obtained from the class teacher’s registers and recorded on a standardized field form. In each selected class, at least 40 pupils from each of the 6 educational grades were chosen at random.

Ethical Clearance

Prior to any enrollment of the children, permission was sought and obtained from the head-teacher of the respective schools and parents and/or guardian of the children. The study protocol was approved by the State Ministry of Health.

Anthropometric Measurement

Anthropometric measurements were carried out by a nutritionist (DCO) in the research team. The subjects were weighed barefooted and in light clothing on a bathroom scale accurate to 0.1kg. The scale was standardized before use with 11kg weight. Height was measured to the nearest 1cm, with a paper stadiometer attached to a vertical wall. Subjects stood barefooted with their scapula, buttocks and heels’ resting against a wall, the neck was held in a natural non-stretched position, and the heels were touching each other. Nutritional status indicators were classified and standardized into sex-specific Z-scores for height-for-age (HAZ), weight-for-height (WHZ) and weight-for-age (WAZ) in EPI Info (version 3.2) relative to the CDC/WHO 1978 reference curves recommended for international use ^[15,16]. Children were classified as stunted, wasted, and underweight if their HAZ, WHZ and WAZ was < – 2 SD respectively.

Parasitological Examination

Each enrolled child was asked to provide a fresh fecal sample in cleaned and dried specimen bottles provided. The pupils were adequately instructed on how to get a little portion of their stool into the bottles. Their class teachers ensured compliance. The samples collected on each occasion were all examined in the University of Uyo Medical Center laboratory without preservation. Each fecal sample was examined as a smear stained with Lugol’s iodine, as a direct wet smear in physiological normal saline and by formol-ether concentration technique ^[17-18]. Diagnosis was based on the identification of helminth ova and protozoan cyst in the sample during microscopic analysis. A child was considered to have a polyparasitic infection if they were found to be positive for more than one species.

Data Analysis

Categorical variables are presented as percentages and continuous variables as means and standard deviation (SD). Univariate analysis was carried out using the χ2 test for proportion. The association between intestinal parasitic infection and nutritional indicators was tested using multivariate logistic regression. All statistical analysis was performed using version 14.0 of the SPSS for windows software package (SPSS Inc., Chicago IL).

Results

A total of 418 children (228 rural and 190 urban) were initially enrolled, the result presented below are the data for 405 children (220 rural and 185 urban) who returned suitable stool specimen after their anthropometric data were collected. There were 100 males and 120 females for rural areas and 105 males and 80 females for urban areas. The subjects were aged between 2.9 and 14 years, with a mean (SD) age of 8.5 (4.7). Table 1 summarizes the prevalence of intestinal parasite in the children. At least one species of intestinal parasite was found in 273 (67.4%) of the children examined. Prevalence of infection was significantly (P<0.001) higher among the rural (80.9%) children than among the urban (51.4%) children. Prevalence of hookworm was higher (55.9% vs. 24.9%) than all other parasites encountered from rural and urban children respectively (P<0.05). The other parasites were A. lumbricoides (30.5% vs. 16.8%); T. trichiura (4.1% vs. 5.4%); G. lamblia (3.2% vs. 2.2%) and E. histolytica (0.9% vs. 4.3%). The prevalence of polyparasitism is further shown in Table 1, one species of intestinal parasite were found in (49.1% vs. 32.4%) for rural and urban children respectively. The subjects co-infected with two species of parasites were (23.6% vs. 14.6%), while 8.2% vs. 4.3% were with 3 or more species of parasite in rural vs. urban children.

The age-specific prevalence of infection in rural children is presented in Table 2. Prevalence tends to increase with age, with the highest infection occurring in the age group of 12–14 years, for Hookworm and Ascaris infection. In urban children, the age group of 3–8 years had the highest infection rate (Table 3). The overall prevalence of nutritional indicators is presented in Table 4, stunting (42.3% vs. 29.7%); underweight (43.2% vs. 29.6%) wasting (10.9% vs. 6.4%) for rural and urban pupils respectively. Rural children were significantly (P<0.001) more malnourished than urban children. Only Hookworm and A. lumbricoides were significantly (P<0.001) associated with low height-for-age (stunting), weight-for-height (wasting) and weight-for-age (underweight). With respect to the nutritional indicators, the infected children had a significantly (P<0.05) higher Z-score than the uninfected children.

Table 1. Prevalence of Intestinal Parasites among Children in Rural and Urban Communities

Table 2. The Age-Specific Prevalence of Intestinal Parasite among 220 Rural children

Table 3. The Age-Specific Prevalence of Intestinal Parasite among 185 Urban Children

Table 4. Summary of Nutritional Indices (Z-scores) of Rural and Urban Children

Discussion

Generally intestinal parasitic infection abounds in developing countries ^[18], with school children carrying the heaviest burden of the associated morbidity ^{[20, 21]}.

The prevalence of 67.4% recorded in this study is consistent with 70%, 70.8%, 62.0% obtained in Kwara, Ogun and Plateau States of Nigeria respectively ^[22-24]. Such high prevalence has been attributed to poor environmental and personal hygiene, shortages of clean potable water and indiscriminate defecation ^[13]. School children from the rural areas (80.9%) were significantly more infected than children from the urban areas (51.4%). This finding corroborates the report of Hurtado et al. ^[25] that a high prevalence of intestinal parasites is consistent with what is found throughout indigenous population in the rural tropical areas. The prevalence obtained for intestinal parasites in the rural areas of the present study is consistent with 82.6% obtained by Ukpai and Ugwu ^[26] in Southern Nigeria. In rural areas, ignorance, unhealthy socio-cultural and religious practices, lack of basic public amenities, poor sanitation, poverty and inadequate access to health care are major predisposing factors to intestinal parasitic infections ^[27]. This study observed the above factors in the rural community where the school is located. The school used in the present study lack adequate toilet facility. There is complete absence of potable water, student obtain their water from nearby stream that might have been contaminated with fecal matter. Evidently, students defecate indiscriminately in the bushes around the school premises as observed with fecal littering, which were likely to contain ova and/or cyst of parasites. The 51.4% prevalence obtained in urban children in this study, though lower than the rural prevalence, is relatively high. The result is similar to the 54.7% and 50.6% recorded by Egwunyenga and Ataikiru ^[2] and Igbinaso et al. ^[28] in Delta and Anambra States of Nigeria respectively. It is possible that the home communities of the urban children are more aware of parasitic infections and have slightly better sanitation and improved personal hygiene than rural communities. Furthermore access to health care is within the reach of the urban children. Despite all these factors, the relatively high prevalence connotes continuous infection, re-infection and transmission of intestinal parasites.

The occurrence of poly parasitism in this study is in line with what is obtained elsewhere in the tropics and subtropics ^{[4, 26, 29-30]}. The commonest was the co-infection of hookworm and A. lumbricoides. No individual had up to four parasites. Hookworm was the most prevalent. The poor fecal disposal system coupled with the fact that most of the children play barefooted might have exposed them to infective stage of the hookworm larvae. The use of excreta as manure commonly practiced by vegetable farmers might also be acting as a veritable source of infection since children and their mothers often go to the farm to tender the vegetables ^[20]. Ukoli ^[31] described warmth, shade, moisture, optimum temperature of 230C to 300C and loose humus soil as suitable environmental conditions for the survival of hookworm egg and larvae. Since these conditions are almost similar to the climatic conditions of the study area, it is possible that the longevity of hookworm eggs and larvae in soil and on vegetation might have been enhancing transmission. Other workers elsewhere have reported a high prevalence of hookworm infection in their studies ^[3,30,32,33]. Hookworm and Ascaris infection increased with age among the rural children, while children aged 3–5 years in the urban area had the highest prevalence for the two parasites. No single age group had the highest prevalence for all the intestinal parasites detected.

This present study recorded a high degree of malnutrition among the children investigated for intestinal parasitic infections. These findings agree with the publication of other investigators ^{[6,8,9,33,34]}. The high rate (>=30%) of stunting and underweight recorded in this study might be due to high prevalence of hookworm, Ascaris and Trichuris. It has been documented by Crompton and Neisheim ^[34] that growth and development during childhood could be diminished by ascariasis, trichiuriasis and hookworm infection. Multivariate analysis showed a significant association between hookworm, Ascaris and anthropometric parameters suggesting that these helminths affect the nutritional status of the studied children. While this present study did not obtain a significant association between nutritional indicators and protozoan parasites (G. lamblia, E. histolytica), similar studies in Tehran and Brazil by Nematian et al. ^[9] and Carvalho Costa et al. ^[8] respectively, recorded a significant association between G. lamblia and nutritional status. According to Assis et al. ^[35] the social, economic and physical environment in which an individual lives are major determinant of the degree of association between intestinal parasites and nutritional status. These factors might be responsible for the difference observed in this study when compared with others elsewhere. Although causes of malnutrition are multifactorial intestinal parasitic infections have been associated with impaired growth ^[9,36,37] and stunting ^[12] in diverse population. There are several mechanisms by which intestinal parasitism may cause or aggravate malnutrition including impaired nutrient absorption resulting from infection and reduced appetite ^[34]. Adult helminth worms residing in the small intestine are in an excellent position to interfere with their host nutrition and can induce damage to the intestinal mucosa that may reduce a person’s ability to extract and absorb nutrient from food ^[38]. Intestinal parasitic infections can cause vomiting, diarrhea, anorexia, abdominal pain and nausea that may result in reduced food intake, thereby further reducing nutrient availability ^[36,39]. The most significant cause of nutritional stress resulting from helminth infection is hookworm associated iron-deficiency anemia. It is documented that light hookworm infections of 20 – 50 adults worms can result in significant iron losses ^[37]. Even mild to moderate intensity helminth infection during childhood have been associated with undernutrition and reduced physical fitness ^[39,40]. All these factors singly or collectively might have contributed to the high degree of malnutrition observed in this study.

Conclusions and Public Health Implications

A weakness of the present study is that no attempt was made to determine biochemical indicators of malnutrition and the intensities of the infection, as a consequence, the actual cause(s) of wasting recorded in the study could not be ascertained. Intestinal parasitism is one of the neglected tropical diseases that remain a common public health problem in most developing countries. With the current global economic meltdown, dietary inadequacies and other forms of nutritional stresses, the harboring of heavy intestinal parasites by children could have adverse health implications. Hence, there is the need for policy makers to intensify health education program in schools. School-based education may be an effective way of reaching the larger population, parents and families of the student and other members of the community. In view of the high prevalence of intestinal parasites recorded in this study, mass chemotherapy will be of tremendous benefit on child growth, development and cognitive abilities.

Intestinal parasitic infections affect childhood development and morbidity in many developing countries. Reducing the prevalence of parasitic infections in school children, may be of immense benefit on child growth, development and educational outcome.

Acknowledgements We express our sincere appreciation to the head-teacher, teachers, parents and pupils of the study schools for their approval and cooperation throughout the duration of the study.

References

1. Vince J. Helminthiasis. In Diseases of Children in the Subtropics and Tropics. (Eds.) Stanfield P, Brueton M, Chan M, Parkin M, Waterston T. pp. 633-648. London, UK: Edward Arnold Ltd; 1991.
2. Egwunyenga OA, Ataikiru DP. Soil-transmitted helminthiasis among school age children in Ethiope East Local Government Area, Delta State, Nigeria. African Journal of Biotechnology. 2005; 4:938-941.
3. Obiukwu MO, Umeanaeto PU, Eneanya CI, Nworgu GO. Prevalence of gastro-intestinal helminth in school children in Mbaukwu, Anambra State, Nigeria. Annals of Tropical Medicine and Parasitology. 2008; 101 (8):705-713.
4. Nematian J, Nematian E. Gholamrezanezhad A, Asgari AA. Prevalence of intestinal parasitic infections and their relation with socio-economic factors and hygiene habits in Tehran primary school students. Acta Tropica. 2004; 92(3):179-186.
5. Nwosu ABC. The Community ecology of soil transmitted helminth infections of human in a hyperendemic area of southern Nigeria. Annals of Tropical Medicine and Parasitology. 1981; 75:197-203.
6. Adekunle L. Intestinal parasites and nutritional status of Nigerian children. African Journal of Biomedical Research. 2002; 5:115-119.
7. Quihui-Cota L, Valencia ME, Crompton DWT, Phillips S, Hagan P, Diaz-Camacho SP, Tejas AT. Prevalence and intensity of intestinal parasitic infections in relation to nutritional status in Mexican School Children. Transaction of the Royal Society of Tropical Medicine and Hygiene. 2004; 98:653-659.
8. Carvalho-Costa FA, Goncalves AQ, Lassance SL, Silva Neto LM, Salmazo CAA, Boi MN. Giardia lamblia and other intestinal parasitic infections and their relationships with nutritional status in children in Brazilian Amazon. Revista do Instituto de Medicina Tropical de Sao Paulo. 2007; 49(3):147-153.
9. Nematian J, Gholamrezanezhad A, Nematian E. Giardiasis and other intestinal parasitic infections in relation to anthropometric indicators of malnutrition: a large-population-based survey of school children in Tehran. Annals of Tropical Medicine and Parasitology. 2008; 102:209-214.
10. Silva RR, Mariano da Silva CA, Pereira CAD, Nicolato RL, Negrao-Correa D, Iamounier JA, Carneiro M. Association between nutritional status, environemtnal and socio-economic factors and Giardia lamblia infections among children aged 6–7 months in Brazil. Transaction of the Royal Society of Tropical Medicine and Hygiene. 2009; 103:512-519.
11. Edungbola LD, Obi AA. A Review of human intestinal parasites in Nigeria: challenges and prospects for integrated control. Nigerian Journal of Parasitology. 1992; 13:27-37.
12. Casapia M, Joseph SA, Nunez C, Rahme E, Gyorkos TW. Parasite risk factors for stunting in grade 5 students in a community of extreme poverty in Peru. International Journal of Parasitolgy. 2006; 36:741-747.
13. Adedoyin MA, Awogun IA, Juergensen T. Prevalence of intestinal parasitoses in relationship to diarrhea among children in Ilorin. West African Journal of Medicine. 1990; 9:83-88.
14. Oninla SO, Owa JA, Onayade AA, Taiwo O. Intestinal helminthiasis among rural and urban schoolchildren in South-western Nigeria. Annals of Tropical Medicine and Parasitology. 2007; 101(8):705-713.
15. Hamill PVV, Drizd TA, Johnson CL, Reed RB, Roche AF, Moore WM. Physical growth: National Centre for Health Statistics percentiles. American Journal of Clinical Nutrition. 1979; 32: 607-629.
16. World Health Organization (WHO). The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. Geneva, Switzerland. World Health Organization; 1986.
17. World Health Organization (WHO). Basic Laboratory Methods in Medical Parasitology. Geneva, Switzerland. World Health Organization; 1991.
18. Cheesbrough M. Medical Laboratory Manual for Tropical Countries. Second edition. London, UK: Butterworth; 1992.
19. World Health Organization (WHO). Public Health Significance of Intestinal Parasitic infections. WHO Bulletin. 1987; 65:575-588.
20. Opara KN, Udoidung NI. Parasitic contamination of leafy vegetable. A function of leaf area index. Global Journal of Pure and Applied Science. 2003; 9:25-29.
21. Itah AY, Opara KN, Atting IA, Udoidung NI. Prevalence of enteropathogens and their association with diarrhea among children of food vendors in Uyo, Nigeria. Mary Slessor Journal of Medicine. 2005; 5:11-21.
22. Awogun IA. The Prevalence of intestinal parasitic infections in children living in Illorin, Kwara state Nigeria. West African Journal of Medicine. 1984; 4(1):16-21.
23. Mafiana CF. Intestinal helminthiasis with particular references to ascariasis among school children in Ilewo-orile Ogun State, Nigeria. Nigerian Journal of Parasitology. 1995; 16:47-53.
24. Azikwe AN. Prevalence and pattern of intestinal infestations in an African University Community. Annals of Tropical Medicine and Parasitology. 1984; 783:333-334.
25. Hurtado M, Lambourne CA, James P, Hill K, Cheman K, Baca K. Human rights, biomedical science and infectious disease among South American indigenous groups. Annual Review of Anthropology. 2005; 34:639-665
26. Ukpai OM, Ugwu CD. The prevalence of gastrointestinal tract parasites in primary school children in Ikwuano Local Government Area, Abia State, Nigeria. Nigerian Journal of Parasitology. 2003; 24:129-136.
27. Suswam EA, Ogbogu VC, Umoh JU, Ogunus RA, Folaranmi DO. Intestinal parasites among school children in Soba and Igabi Local Government Area of Kaduna State, Nigeria. Nigerian Journal of Parasitology. 1992; 13:39-42.
28. Igbinosa IB, Ogbunugu TF. Intestinal helminth parasites in school children in Iragbigi Boripe Local Government Area, Osun State, Nigeria. African Journal of Biomedical Research. 2006; 9:63-65.
29. Enekwechi LE, Azubike CN. Survey of the prevalence of Intestinal parasites in school children of primary school age. West African Journal of Medicine. 1994; 13(4):227-230.
30. Glickman LT, Camara AO, Glickman NW, McCabe GP. Nematode intestinal parasites of children in rural Guinea, Africa: Prevalence and relationship to geophagia. International Journal of Epidemiology. 1999; 28:169-174.
31. Ukoli FMA. Introduction to Parasitology in Tropical Africa. John Wiley and Sons, UK; 1992
32. Asaolu SO, Holland CV, Jegede JO, Frasser NR, Stoddard RC, Crompton DW. The prevalence and intensity of soil-transmitted helminthiasis in rural communities in southern Nigeria. Annals of Tropical Medicine and Parasitology. 1992; 86:279-287.
33. Chandrasena TGAN, De Alvis ACD, De Silva LDR., Morel RP, De Silva NR. Intestinal parasitosis and the nutritional status of Veddah children in Sri Lanka. South-East Asian Journal of Tropical Medicine and Public Health. 2004; 35(2):255-259.
34. Crompton DW, Nesheim MC. Nutritional impact of intestinal helminthiasis during the human life cycle. Annual Review of Nutrition. 2002; 22:35-59.
35. Assis AMO, Prado MS. Reis ML, Reis MG, Pinheiro SMC, Parraga IM, Blanton RE. Childhood stunting in northeast Brazil: the role of Schistosoma mansoni infection and inadequate dietary intake. European Journal of Clinical Nutrition. 2004; 58:1022-1029.
36. Stephenson LS, Lathem MC, Adams EJ, Kinoti SN, Pertet A. Physical fitness, growth and appetite of Kenyan School boys with hookworm, Trichurus trichiura and Ascaris lumbricoides are improved four months after a single dose of albendozole. NI Nutrition. 1993; 123:1036-1046.
37. StoltzFus RJ, Chwaya HM, Tielsch JM, Schulze KJ, Albonico M, Savioli L. Epidemiology of iron deficiency anemia in zanzibari school children: the importance of hookworms. American Journal of Clinical Nutrition. 1997; 65(1):153-159.
38. Tanner S, Leonard WR, McDade T, Garca-Reyes V, Godoy T, Huanca T. Influence of helminth infections on childhood nutritional status in lowland Bolivia, Tsmane. Amazonia Panel study Working Paper 49. http://people.branders.edu/rgodoy/. Accessed June 20, 2009.
39. Stephenson LS, Latham MC, Otensin EA. Malnutrition and parasitic helminth infection. Parasitology. 2000; 121:SUPPL: 23-28.
40. Ezeamama AE, Friedman JF, Olveda RM, Acosta LP, Kurtis JD, Mar V, McGarvey ST. Functional significance of low-intensity polyparasite helminth infections in anemia. Journal of Infectious Diseases. 2005; 192(1):2160-2170.

Abstract

Objectives

Despite the advent of antiretroviral therapy (ART), many children, particularly in the rural communities of Zimbabwe, remain vulnerable. The purpose of this study was to determine the factors and challenges facing children on antiretroviral therapy (ART) in Brunapeg area of Mangwe District, Zimbabwe.

Methods

A mixed-method approach involving interviewer-guided focus group discussions and piloted semi-structured questionnaires was utilized to collect data from different key population groups. The data obtained were analyzed through content coding procedures based on a set of predetermined themes of interest.

Results

A number of challenges emerged as barriers to the success of antiretroviral therapy for children. Primary care givers were less informed about HIV and AIDS issues for people having direct impact on the success of antiretroviral therapy in children whilst some were found to be taking the antiretroviral drugs meant for the children. It also emerged that some primary care givers were either too young or too old to care for the children while others had failed to disclose to the children why they frequently visited the Opportunistic Infections (OI) clinic. Most primary care givers were not the biological parents of the affected children. Other challenges included inadequate access to health services, inadequate food and nutrition and lack of access to clean water, good hygiene and sanitation. The lack of community support and stigma and discrimination affected their school attendance and hospital visits. All these factors contributed to non-adherence to antiretroviral drugs.

Conclusions and Public Health Implications:

Children on ART in rural communities in Zimbabwe remain severely compromised and have unique problems that need multi-intervention strategies both at policy and programmatic levels. Effective mitigating measures must be fully established and implemented in rural communities of developing countries in the fight for universal elimination of HIV/AIDS.

Key Words:

Antiretroviral therapy • Challenges • Children • HIV/AIDS • Mangwe District • Zimbabwe

Introduction

For three decades, HIV and AIDS have affected the lives of many people at individual and family levels through illnesses and deaths of family members and has been a serious challenge medically, financially, and socially ^{[1, 2, 3]}. HIV and AIDS is of considerable and significant impact to children yet the attention given to this population is largely overshadowed by the large scale burden of the epidemic in the adult population, especially in Sub-Saharan African countries ^[2].

HIV and AIDS have caused untold suffering in the rural communities of Sub-Saharan Africa with children being the most affected. About 95% of the infected populations in the world currently live in developing countries, particularly in Sub-Saharan Africa ^[4]. Zimbabwe is one of the Sub-Saharan African countries burdened by high HIV infection prevalence. The country’s population is estimated at about 13 million people with about 1.1 million HIV positive people, of which 151,749 are children below the age of 14 ^[5]. By the year 2010, only 326,241 people in Zimbabwe were estimated to be receiving antiretroviral therapy (ART) ^[4].

Despite the increasing introduction of antiretroviral therapy, many children, particularly in the rural communities of Zimbabwe remain vulnerable. They are impacted by non-adherence ART regimen due to a number of factors. This includes inadequate access to food and nutrition which can be shown by the unbalanced diet and the number of meals they get per day, transportation problems, and long distance commute to hospitals, infrequent visits by the home based care givers (HBCs) and the prevalence of widespread stigma and discrimination ^{[6, 7, 8]}.

Guardianship of children on ART is also crucial since it is directly correlated to the overall care and support provisioned by the primary care givers ^{[6, 7]}. Lack of adequate care and support has been noted in instances where the primary care givers are too old or too young.

A major treatment gap remains since by the end of 2010; many children who were eligible for treatment did not have access to ART. Access to treatment for the children is lower than that for adults since by 2009; only 28% of the registered children received ART whilst coverage for other age groups was 36%. Low ART coverage and access no doubt increases the vulnerability of children to HIV and AIDS ^{[4, 9]}.

Antiretroviral therapy is an integral component in the quest to improve the well-being and health of children living with HIV and AIDS ^[10]. Its success is determined by factors that occur concurrently to the patients’ health and social life during the course of the therapy. Antiretroviral therapy involves continual interaction between health staff and patients through on-going medical check-ups, prescription or drug refills, monitoring and adherence support ^{[6, 7, 11]}.

Research on ART in developing countries is very new and tends to focus primarily on issues of non-adherence yet there are hosts of health, psychological, social and economic challenges which determine the success of ART in children ^{[6, 7, 8, 12, 13]}. The success rate of ART in rural communities of Zimbabwe is low with fewer children covered on therapy whilst those on ART have a poor health a situation worsened by the localities of the communities ^{[14, 15]}.

The Mangwe district in Matabeleland South province of Zimbabwe has a widespread prevalence of HIV and AIDS in addition to highest levels of food insecurity, transportation problems, sanitation and health services challenges ^{[15, 16]}. It is such widespread inequalities, discrimination and poverty in the province which shape and worsen the HIV epidemic in the Brunapeg area of Mangwe district. There are several challenges with direct or indirect impacts on the success of ART in children. This includes the provision of good psychological counseling to the children; access to adequate diet and nutrition; adequate access to health facilities, services and antiretroviral drugs; elimination or minimization of stigmatization and discrimination; and adequate knowledge and use of proper practices by caregivers, i.e. primary care givers and home-based care givers ^{[8, 9, 13, 16, 17, 18]}. Such challenges are profound in rural communities of Zimbabwe ^[14,15]. This paper explores the challenges faced by children on ART in the Brunapeg area of the Mangwe district in Zimbabwe.

Methods

Study Design

This study was conducted in the Brunapeg area of Mangwe District. The area has one diagnosing center for HIV and AIDS located at St. Anne’s Hospital serving a population of approximately 78, 000 people. The hospital has an Opportunistic Infection (OI) clinic which provides HIV Counseling and Testing (HCT), prevention of parent to child transmission (PPTCT), ART and home based care (HBC).

A mixed-method study was designed utilizing both focus group discussions (FGDs) and semi-structured questionnaires to assess the challenges faced by children on ART looking at individual-level and group-level factors. The study also examined the consistency of information acquired through both tools. FGDs give group consensus information while questionnaires assess information from a personal level. The questionnaires were designed for the children and the caregivers and were piloted at Plumtree Hospital area, another hospital in the Mangwe district. For the community leaders, key informant interviews were only employed since they represented different levels of leadership.

Study Population and Sampling

This study focused on different sub-populations in the study district. A multi-stage sampling method was used to identify children (5-15 years) on ART registered at St Annes’ Hospital OI clinic, their corresponding primary care givers, home based care givers registered with St Annes’ Hospital and community leaders in the Brunapeg area.

Utilizing St Annes’ Hospital registry for children on ART, 396 registered children were stratified according to wards. The Brunapeg area has 9 wards and a ward consists of 6-10 villages. Forty (10.1%) children (5-15 years) were purposively selected from all the wards according to their age ensuring that samples comprised of children. Four children were sampled from each of the wards 5, 6, 7, 8 and 9 whilst 5 children were sampled from each of the wards 10, 15, 16, 17 totaling 40 children in all.

The primary care givers were sampled corresponding to the selected 40 children. Among the 460 home based care givers (HBCs) registered in the St Anne’s Hospital HBC program, 40 (11%) were randomly sampled for the study inclusive of those corresponding to the sampled children. It must be noted that unlike primary care givers, in Zimbabwe, HBCs cater for children from different homes.

In sampling the community leaders for the study, all three Chiefs (or village heads) who are overseers of the wards were selected. Out of the seven Headmen, four were selected, out of nine Councilors each corresponding to each of the wards, four were selected and out of 60 Village heads, 10 were selected. These community leaders were the key informants within their respective communities.

Data Collection

The sampled children, primary care givers, HBCs and community leaders were recruited into separate focus groups. Focus groups discussion guides with a set of carefully predetermined questions were used to collect data. The meetings were scheduled at selected outreach points for the wards concerned by making appointments with the chiefs to mobilize other community leaders. The children on ART, primary care givers and HBCs were mobilized through the hospital staff. In addition, questionnaires were distributed to the respondents on the appointed days. The FGDs data was collected through note-taking until all key points and objectives were exhausted. Different FGD guides were designed for different focus groups for relevancy in drawing information. The data collected was analysed by the study team through content coding procedures and categorised based on pre-set themes of interest namely provision of clean water and hygiene, food access, ARV drug access, non-adherence, health services access, knowledge of primary care givers and HBC of HIV/AIDS, school attendance and stigma and discrimination. The questionnaires utilized were designed around these study themes. The data from the FGDs and questionnaires were integrated according to the pre-set themes and objectives to eliminate redundancy.

Results

The problems affecting the success of ART on children in the Brunapeg area of Mangwe district included inadequate access to food and nutrition, lack of access to adequate health services, stigmatization and discrimination and inadequate service provision by the care givers.

Access to Food and Nutrition

The primary care givers and HBCs highlighted the lack of access to adequate food and nutrition (Table 1) as a major challenge affecting the studied children on ART. It was found that the children on ART who were not having adequate access to food and nutrition were not coping well with the therapy whilst some of them ended up begging for food and avoiding the ARV drugs.

Table 1.Number of Meals Received by the Children on ART Per Day According to Primary Care Givers

The study revealed that there was inadequate access to clean water, good hygiene and sanitation, which negatively affected the children on ART. This was worsened by the fact that the Mangwe district is located in drought prone area of Zimbabwe.

Access to Health Services and Provision of Care and Support

Home Base Care (HBC) respondents reported that most primary care givers (Table 2) were not the biological parents of the children (mostly orphaned by HIV/AIDS) on ART. They expressed concerns that the care and support obtained from the foster parents and guardians was not comparable to those that could be given by their biological parents. One primary care giver was quoted by the HBCs to have said to a child, “Your people have arrived, and they want to see how you are taking your things” implying that the HBCs have arrived, they want to assist you in taking your ARV drugs. Stigma is apparently implied in this statement and it translates to lack of ownership of the problem by the caregiver. This also impacted the frequency of taking the children to hospital to obtain health services, a situation also compounded by long distances and transportation problems to hospital in the Brunapeg area.

Table 2. Categories of Primary Care Givers for the Children on ART

Impact of Age of Primary Care Giver on ART

Another challenge was that most primary care givers were either too young or too old to take care for the children on ART such that they had problems adhering to hospital dates and times or monitoring the children on ART during their drug administration (e.g. correct dosages). According to one older lady in the study: “My bones are no longer strong to be moving up and down and I am frequently not feeling well”. This indicates how old age and the individual characteristic of the care giver impacts the adherence of the children to their medication regimen.

It was also apparent that the primary care givers were not sufficiently trained on the issues surrounding ART and counseling for them to adequately support the children that they provide care for. Some primary care givers did not adequately provide care to the children due to the fear of being infected with HIV and AIDS following close interaction with the children living with HIV and AIDS. It was reported that some adult primary care givers took the drugs meant for children on ART for their own use due to the fear of disclosing their own status and going for individual treatments.

Lack of Disclosure of Status

Our study found that 60% of the children studied were not aware of the reasons why they were frequently visiting the health services since their HIV status had not been disclosed to them by their primary care givers. The children believed that they visited the hospital for minor ailments such as flu, headaches, and cough. This implies that some of the children would grow up and become sexually active without knowledge of their HIV status and will thus not reveal that to their partners. According to one of the children who had not formally had a disclosure of status said: “My mum has never told me, but I know”. This causes the children to mistrust their caregivers and may lead to lack of supportive family counseling. Children who became aware of their HIV status feared visiting the hospital due to fear of stigma. This is impacted by that there were no support groups for the children to reduce stigma after disclosure of status.

School Attendance Impacted by Stigma and Discrimination

We found that 70% of the children studied were of school-age yet only 39% of them were attending school (Table 3). This could be attributed to the fact that the children reported stigma and discrimination both at school and community or societal level and felt safer at home since they had been informed that they were HIV positive due to the noticeable signs associated with HIV infection. It was reported that the children had problems at school from other children ranging from verbal to physical abuse. A 14 year-old girl in the study said: “Other children scold me at school, they say I am confused by the pills I am taking”. In addition, the children could have been too unwell to be attending school.

Table 3. School Attendance of the 28 School Eligible Children (70%) on ART

Discrimination and stigmatization is prevalent among these children outside the school settings. Children were not fully involved in social programs whilst those at adolescence stage end up going into relationships without disclosing their status for fear of losing their relationships. This is worsened by lack of knowledge of the existence of programs aimed at canvasing communal support for the children on ART. Another challenge faced by the children on ART was the infrequency of visits by the HBC givers (Table 4). We found that on the average, hospital visits by children on ART supervised by primary care givers were inadequate for close monitoring of the children and ensuring adherence to treatment protocols.

Table 4. Frequency of Visits to Children on ART by HBCs

Another aspect found to compromise the service delivery of HBC givers to children was the verbal abuse they faced and the lack of support from other community members which discouraged them and thereby impacted their service delivery. One HBC recalled a discriminatory comment by a community member, “What you know better is bathing people with AIDS”.

Discussion

Data from different focus group discussions and semi-structured questionnaires indicate that the broad challenges faced by the children on ART can be categorized into food access, health service access and provision of care and support both at household and community levels. These findings are consistent with earlier studies on challenges faced by elderly guardians in sustaining adherence to ART in HIV-infected children and community relations and child-led microfinance ^{[9, 13]}, HIV/AIDS-related stigma and discrimination ^[17] and food insecurity and nutritional barriers to ART ^[16]. Amongst the children (5-15 years), 42% were males whilst 58% were females demonstrating that girls were more affected by HIV and AIDS in the Mangwe district in Zimbabwe.

The primary care givers and HBCs reported food insecurity as a key challenge in the assistance of the children on ART despite the food aid provided by various donors. When providing food aid, the donors issued quantities targeted for children on ART, yet the food is often consumed by the entire family. This is because Mangwe district is in a drought-prone province resulting in low food access and poor nutrition for the children. Poor nutrition compromises the immune system and increases the virulence of HIV whilst also attenuating the efficacy of ARV drugs and magnifying the side effects, ultimately leading to non-adherence ^{[6, 7, 11, 19]}. The lack of access to clean water, hygiene and sanitation increases the susceptibility of the children to opportunistic infections ^{[12, 15]}. Lack of access to clean water and sanitation, inadequate food access, poor nutrition and food insecurity are key barriers to adherence and the success of ART on children in Brunapeg area concurring with the findings by Martin ^[16] who in turn linked this to stigma and discrimination. Lack of access to adequate food forces some children into begging for food in order not to starve to death. This disrupts the ART program as the children are not always at home and end up missing their drugs and even hospital visits. Begging exposes the children to sexual abuse and results in the spread of the virus which would be resistant to some ARV drugs since they would be missing the drugs. The virus could resist the first line treatment leading to the second line drugs which are more expensive ^{[6, 10]}.

According to earlier studies, the quality of life and health of HIV-infected children is also dependent on routine assessments for nutritional status, particularly after the initiation of ART ^[20]. Children on ART also require routine CD4 count tests, liver function tests and monitoring of weight and side effects. These assessments were not consistent in the Mangwe district. Symptomatic HIV-infected children have conditions requiring increased energy and nutrition such as TB, chronic lung disease and other chronic opportunistic infections [OIs] or malignancies. Those who are severely malnourished should be managed for severe acute malnutrition (SAM) and provided with 50–100% additional energy and receive one recommended daily allowance (RDA) of micronutrients daily and high-dose vitamin A ^{[20, 21, 22]}.

Investigators have highlighted that people with HIV/AIDS are stigmatized more than other diseases ^[23]. In this study, stigma and discrimination were found to be predominant amongst the problems faced by the children on ART. This resulted in the children fearing to go to hospital, particularly for those who were aware of their HIV status. This was also attributed to the fact that the OI clinics were centralized and offered separate HIV treatment such that anyone frequently visiting them is implicated to be HIV infected and hence often stigmatized by the community ^[8]. Stigma and discrimination also had an impact on the school attendance of the children who were eligible for school since only 39.3% of these were found to be frequently attending school. This was also due to the fact that stigma and discrimination have been linked and correlated to low service utilization in rural areas ^{[17, 23]}. Social marginalization and discrimination restrict access to health and HIV-related services, food and nutritional support and other assistance programs and hence contributed and led to non-adherence ^{[6, 7, 17, 23]}. Such marginalization and discrimination were attributed ^{[17, 23]} to the lack of public awareness programmes and HIV specific support groups which improve adherence by minimizing stigma and discrimination and increasing communal and social support for the affected children. Studies have shown that expansion and decentralization of health service centers that give aid to the children on ART limits the impacts of social marginalization ^{[6, 7]}.

Conclusions and Public Health Implications

Findings from this study showed that the HBCs and the primary care givers had inadequate knowledge on issues regarding HIV/AIDS and this affected the care they delivered to the children on ART particularly with drug administration, providing adequate care and food and nutrition. Policy states that there should treatment partners trained during pre-ART counseling of caregivers ^{[14, 15]}. The HBCs also faced inadequate availability of HBC kits and lack of support from other community members due to widespread stigma and discrimination against HIV and AIDS patients. This resulted in infrequent visits. In conclusion, the children on ART in the Brunapeg area of the Mangwe district are faced by lack of adequate food and nutrition and by widespread stigma and discrimination, in addition to inadequate support both at household and community levels. These factors severely lower the success of ART program since they have a negative impact on the children’s health and quality of life.

Acknowledgements We would like to express our sincere gratitude to the cutting-edge participants for their contributions and endeavors in this study. In particular, we appreciate the cooperation of the St Anne’s Hospital OI clinic staff throughout the study and for the provision of statistical information on the children on ART. We would also like to thank Ms. Tumelo Nare, a technical assistant at the NUST Medical School for her constructive comments and for editing the manuscript.

References

1. Land H. Introduction. Journal of HIV/AIDS & Social Services. 2010; 9(4):323-326.
2. UNICEF/UNAIDS. Children and AIDS: Fifth Stocktaking Report; 2010.
3. Linsk LN. Thirty Years into the HIV Epidemic: Social Work Perspectives and Prospects. Journal of HIV/AIDS & Social Services. 2011; 10(3):218-229.
4. WHO/UNAIDS/UNICEF. Global HIV/AIDS Response: Epidemic update and health sector progress towards Universal Access 2011; 2011.
5. Joint United Nations program on HIV/AIDS and World Health organization. AIDS Epidemic Update. Geneva, Switzerland: Joint United Nations Program on HIV/AIDS; 2009.
6. Mullen J, Leech S, O’Shea S, Chrystie IL, Du Mont G, Ball C, Sharland M, Cottam F, Zuckerman M, Rice P, Easterbrook P. Antiretroviral drug resistance among HIV-1 infected children failing treatment. Journal of Medical Virology. 2002; 68(3):299-304.
7. Martin S, Elliott-DeSorbo DK, Wolters PL, Toledo-Tamula MA, Roby G, Zeichner S, Wood LV. Patient, caregiver and regimen characteristics associated with adherence to highly active antiretroviral therapy among HIV-infected children and adolescents. The Pediatric Infectious Disease Journal. 2007; 26(1):61-67
8. World Health Organization (WHO). Community home-based care in resource limited setting: a framework for action. Geneva, Switzerland: WHO; 2002.
9. Skovdal M, Campbell C, Madanhire C, Nyamukapa C, Gregson S. Challenges faced by elderly guardians in sustaining the adherence to antiretroviral therapy in HIV-infected children in Zimbabwe. AIDS Care. 2011; 23(8):957-964.
10. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, Wagener MM, Singh N. Adherence to protease inhibitor therapy and outcomes in patients with HIV infections. Annals of Internal Medicine. 2000; 133(1):21-30.
11. Castleman T, Seumo-Fosso E, Cogill B. Food and nutrition implications of antiretroviral therapy in resource limited settings. Technical Note No. 7. Food and Nutrition Technical assistance (FANTA). Academy of Educational Development, Washington DC; 2004.
12. World Health Organization (WHO). Antiretroviral therapy of HIV infection in infants and children in resource-limited settings: towards universal access recommendations for a public health approach; 2006.
13. Skovdal M. Community relations and child-led microfinance: a case study of caregiving in Kenya. AIDS Care. 2010; 22(2):1652-1661.
14. UNAIDS. Investing in Our Future Psychosocial Support for Children Affected by HIV/AIDS: A Case Study in Zimbabwe and the United Republic of Tanzania. UNAIDS case study best practice collection; 2001.
15. Zimbabwe Ministry of Health and Child Welfare. Zimbabwe National HIV and AIDS Estimates. ZMoHCW, Harare; 2009.
16. Martin A, Palar K, Derose PK, Adams J. Food Insecurity and Nutritional Barriers to Antiretroviral Therapy: Lessons from Latin America and the Caribbean. Journal of HIV/AIDS & Social Services. 2011; 10(2):194-214
17. Parker R, Aggleton P, Attawell K, Pulerwitz J, Brown L. HIV/AIDS-related Stigma and Discrimination: A Conceptual Framework and an Agenda for Action. Horizons Program; 2002.
18. Paranthaman K, Kumarasamy N, Bella D, Webster P. Factors influencing adherence to anti-retroviral treatment in children with human immunodeficiency virus in South India – a qualitative study. AIDS Care. 2009; 21(8):1025-1031.
19. Ivers L, Chang Y, Jerome J, Freedberg K. Food assistance is associated with improved body mass index, food security and attendance at clinic in an HIV program in central Haiti: A prospective observational cohort study. AIDS Research and Therapy. 2010; 7(33):1–8.
20. World Health Organization. Food security. 2010. Retrieved from: http://www.who.int/trade/glossary/story028/en/.
21. Marston B, De Cock KM. Multivitamins, nutrition and antiretroviral therapy for HIV disease in Africa. New England Journal of Medicine. 2004; 351(1):78-80.
22. Hsu J, Pencharz PB, Macallan D, Tomkins A. Macronutrients and HIV/AIDS: A review of current evidence. Geneva, Switzerland: World Health Organization, Department of Nutrition for Health and Development; 2005.
23. Brems C, Johnson ME, Warner TD, Roberts LW. Health care providers’ reports of perceived stigma associated with HIV and AIDS in rural and urban communities. Journal of HIV/AIDS & Social Services. 2010; 9(4):356-370.
24. Gahagan J, Rehman L, Barbour L, McWilliam S, The preliminary findings of a study exploring the perceptions of a sample of young heterosexual males regarding HIV prevention education programming in Nova Scotia, Canada. Journal of HIV/AIDS Prevention in Children & Youth. 2007; 8(1):135-153.
25. DiIorio CK, McCarty F, Pluhar E, Set the P.A.C.E.! Results of an HIV prevention intervention for mothers of young children. Journal of HIV/AIDS & Social Services. 2011; 10(3):290-310.
26. Centers for Disease Control and Prevention. HIV transmission rates in the United States; 2010.

Abstract

Objectives

Highly active anti-retroviral therapy (HAART), including protease inhibitors (PI) have led to dramatic improvements in the quality and quantity of life in patients with acquired immunodeficiency syndrome (AIDS). However, a significant number of AIDS patients on HAART develop characteristic changes in body fat redistribution referred to as lipodystrophy syndrome (LDS). Features of LDS include hypertrophy in the neck fat pad (buffalo hump), increased fat in the abdominal region (protease paunch), gynecomastia and loss of fat in the mid-face and extremities.

Methods

The aim of this paper is to review the current knowledge regarding this syndrome. This article reviews the published investigations on biochemical manifestation of HIV lipodystrophy syndrome.

Results

It is estimated that approximately 64% of patients treated with PI will experience this syndrome. Biochemically, these patients have increased triglycerides (Trig), total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C) and extremely low high-density lipoprotein-cholesterol (HDL-C).

Conclusions and Public Health Implications

It is hoped that awareness of this syndrome would aid in early diagnosis and better patient management, possibly leading to a lower incidence of cardiovascular complications among these patients.

Key Words:

HIV Lipodystrophy Syndrome, Highly active anti-retroviral therapy, Nucleoside Reverse Transcriptase Inhibitors, buffalo hump.

Introduction

Lipodystrophy syndrome, a condition associated with metabolic abnormalities and distinct morphological changes has been increasingly reported in HIV-1 infected individuals ^[1]. HIV-LDS in infected patients is now considered a major adverse effect of antiretroviral therapy. Many descriptions of this syndrome are reported in the literature but there exists no universally agreed definition. In general, this condition is characterized by fat loss (lipo-atrophy) in the face, arms, legs, and buttocks; fat gain in the abdomen, over the back of the neck (dorso-cervical fat pad or “buffalo hump”), and in the breast and occasionally isolated lipomata may be present.

HIV-LDS has been associated with both protease inhibitor (PI) and nucleoside analogue therapy, particularly in combination therapy involving both classes of drugs ^{[2, 3]}. Current evidence suggests that HIV-LDS may affect up to half or even more HIV-infected patients receiving antiretroviral therapy ^{[1, 4]}. Thus as the survival rate of HIV positive individuals increases with the introduction of highly active anti-retroviral treatment (HAART), atherosclerotic vascular disease, severe premature coronary artery disease (CAD) and other metabolic diseases could become an important HIV–related complication. Indeed CAD and metabolic disorders have increasingly been reported in patients treated with these medications ^[5]. However, the mechanisms remain largely unknown.

The hallmark of HIV-LDS is a dyslipidemia, a biochemical abnormality of the blood lipid profile that frequently presents before distinctive clinical features of fat redistribution become apparent. To date, no consensus guidelines for treatment of LDS exist. A distinct feature of this condition, body fat changes, could be socially stigmatizing and pose serious problems in treatment compliance and antiretroviral therapy failure. Awareness of HAART complications therefore by all parties concerned, coupled with early diagnosis, could impact positively on HIV prognosis and management. This report aims at describing a typical HIV-LDS case including a review of diverse patho-physiological mechanisms thought to underlie development of this condition in HIV treated patients.

Methods

Studies were identified through a PubMed database search. Case-control and longitudinal studies into clinical and biochemical manifestation of HIV lipodystrophy were selected. Areas covered include data on lipid dysregulation, cytokines, adipokines, proteins, clinical manifestations and management strategies.

HIV-LDS: Clinical Features and Metabolic Changes

Body shape changes

A number of anecdotal reports of increased abdominal girth have been linked with the use of protease inhibitors ^{[6, 7]}. An ongoing Fat Redistribution and Metabolism (FRAM) study ^[8], a prospective, multi-center, cross-sectional investigation of HIV-infected subjects and controls aims to address some of the uncertainties concerning the prevalence, etiology, risk factors and clinical features of HIV-LDS. Preliminary findings to date, mainly from a subgroup of 1200 male subjects and 300 controls suggest a strong association between HIV and lipoatrophy (depletion of subcutaneous fat) but no association between HIV and visceral fat accumulation. It was therefore concluded that lipoatrophy develops independently of fat accumulation and therefore the term ‘fat redistribution’ may be a misnomer. Although HIV infection is well known to cause body wasting usually in advanced disease ^[9], it has not been shown to cause the fat accumulation, breast hypertrophy and buffalo hump of lipodystrophy. Generally speaking, exposure to HAART (in particular PIs) appears to be relevant to the onset of HIV-LDS ^[10]. Thus the variability in clinical manifestations of this syndrome may reflect differences in the underlying pathogenesis.

Lipids

Changes in lipid profile have been the most remarkable biochemical abnormalities in HIV-LDS. The mechanisms predisposing to abnormal lipid profiles in HIV infected individuals are still a matter of debate because HIV infection itself is associated with several metabolic disturbances that may be part of host response to viral infection, decreased HDL-C and LDL-C have been demonstrated in HIV positive men ^[11]. However, these changes usually occur early in HIV infection. Very low density lipoprotein cholesterol (VLDL-C) also increases with immunosuppression as HIV infection becomes manifest. These changes are thought to lead to increased triglyceride levels ^{[11, 12]}. Furthermore, host response to viral infections also causes increases in Interferon-α, which ultimately may cause both increased production and decreased clearance of triglyceride ^{[13, 14]}.

Thus in a cross-sectional study, Carr et al. ^[15], reported that HIV positive patients on PI therapy had higher triglyceride levels (> 100%) and higher cholesterol levels (> 20%) than HIV positive patients not on PI therapy. Increased cholesterol and triglyceride levels have been reported in HIV negative healthy volunteers receiving ritonavir for 2 weeks ^[16], confirming a direct effect of PI treatment on lipid metabolism. Other studies which suggest increases in lipid levels include those of Periad et al. ^[17], which showed increases in mean plasma level of triglyceride (> 100%) and total cholesterol (> 40%) in patients treated with ritonavir compared with a PI naïve group matched for age and body mass index. Sergerer et al. ^[18] reported an average increase in plasma triglyceride by 25% and cholesterol by 15% at 3 months in 148 patients; there were no further significant increases after 3 months. A control group treated only with Nucleoside Reverse Transcriptase Inhibitors (NRTI) had no change in their lipid profiles. Taken together, it is now known that dyslipidemia associated with various stages of HIV/AIDS disease is cytokine mediated whereas dyslipidemias observed in HIV/AIDS treated patients on HAART is likely due to alteration in adipogenesis.

Insulin Resistance

Shikuma et al., ^[19], reported increased fasting insulin and waist-hip ratios in non- wasting patients with AIDS suggesting that such body shape changes were related to HIV infection or to factors associated with immunological dysfunction. Hadigan et al. ^[20] compared metabolic parameters in 75 women with HIV, some of whom were not on PI therapy to 30 weight-matched but younger control women. They noted that fasting insulin was nearly double in HIV patients and independent of PI use. In line with this study, Behrens et al. ^[21] reported impaired glucose tolerance in 24 % of PI naïve patients. Collectively, these studies suggest that HIV itself may cause insulin resistance, which deteriorates with increasing duration of infection.

However, more recent studies suggest that insulin resistance in HIV positive patients is due to antiretroviral therapy and not to HIV infection. Prior to the introduction of HAART, HIV- infected patients were found to have normal or decreased glucose levels and no significant insulin resistance ^{[22, 23]}. While after therapy, hyperglycemia has been reported in several studies ^{[15, 24]} and the US Food and Drug administration have described 83 cases of new onset hyperglycemia or worsening pre-existing diabetes ^[25]. In one study, Carr et al., ^[26], evaluated 116 HIV-infected, otherwise healthy patients receiving one or more PI, 32 HIV–infected PI-naïve patients, and 47 healthy male control subjects. Three recipients had worsening or new diabetes mellitus, and the 64% of PI recipients who developed body composition changes had significantly higher insulin and C-peptide levels than PI naïve patients or controls. Other studies were those of Walli et al. ^[27] who performed intravenous glucose tolerance tests in 67 patients receiving PI- containing therapy, 13 PI-naïve patients and 18 HIV negative controls; 61% of those receiving PI exhibited insulin resistance. In a 5–year cohort analysis in 221 HIV-infected patients, it was found that the incidence of new onset hyperglycemia was 5%. Thus protease inhibitors were independently associated with a five-fold increase in the incidence of hyperglycemia. Taken together, several of the studies cited above suggest that PI therapy may cause insulin resistance. Clinically it has been observed that PI therapy may predispose to glucose intolerance or indeed frank adult onset diabetes in some individuals. Such abnormalities may be more likely if lipodystrophy is present.

Pathogenesis of Lipodystrophy

It has been suggested that the pathogenesis of HIV-LDS may be multifactorial. Possible mechanistic abnormalities in HIV-LDS are described as below:

Inhibition of nuclear receptor complex (PPAR-gamma) and Retinoid X receptor (RXR)

The first attempt at defining the hypothesis for HIV–LDS pathogenesis was put forward by Carr et al. ^[26]. They postulated that protease inhibitors affect adipocyte differentiation by inhibiting the heterodimeric nuclear receptor complex composed of peroxisome proliferator activated receptor gamma (PPAR-gamma) and the retinoid X receptor (RXR). This complex enhances target gene transcription in pre-adipocytes. The catalytic region of HIV-1 protease (to which PI bind) has approximately 60 % homology to regions within cytoplasmic retinoic acid binding protein type 1 (CRABP-1), which enhances the production of cis-9-retinoic acid that is the sole ligand of RXR. Ligand binding to RXR, inhibit adipocyte apoptosis and up-regulate adipocyte differentiation and proliferation. Protease inhibitors may bind to and inhibit CRABP-1, cause decreased production of cis-9-retinoic acid, decreasing RXR-PPAR gamma activity and so reduce differentiation and increase apoptosis of adipocytes. PPAR-gamma is preferentially expressed in peripheral as against central fat so these changes are most marked in peripheral tissues. Although it is plausible from the discussion above that PIs may act as PPAR-gamma antagonists and predispose HIV-infected patients to develop HIV-LDS, this hypothesis has been disputed by Wentworth et al. ^[28], who studied the effects of PIs on human adipocytes in vitro. They found no evidence that PIs acted as PPAR-gamma antagonists suggesting that impaired adipogenesis does underlie PI-associated HIV-LDS but does not directly involve PPAR-gamma and RXR.

Inhibition of sterol regulatory binding element

The sterol regulatory element-binding proteins (SREBPs) are membrane bound transcription factors, which have been proposed to play central role in cellular lipid homeostasis ^[29]. They regulate the transcription of many genes including the LDL receptor gene. SREBPs have been found to be increased in the nuclei of hepatocytes of animals treated with ritonavir ^[30]. Furthermore, it has been suggested that PIs inhibit the protease that degrades SREBPs thereby leading to decreased degradation of apolipoprotein B-100, which will then cause increases in VLDL ^{[31, 32]}.

Upregulation of Pro-inflammatory Cytokines

There is an association between HIV-LDS and levels of pro-inflammatory cytokines. Tumor necrosis factor-alpha (TNF-α) and its receptors are increased in HIV-infected patients ^[33] suggesting that increased concentrations of pro-inflammatory cytokines inhibit the production of acylation-stimulating protein (ASP), a protein which up-regulates the pathways for glucose uptake and fat deposition in adipocytes; they demonstrated an association between lower limb lipoatrophy and subnormal ASP production.

Lipodystrophy and Glucose homeostasis

PI associated diabetes mellitus is similar to type 2 diabetes mellitus. Hyperglycemia is not associated with ketoacidosis and patients respond to oral hypoglycemia treatment ^[34], suggesting that the underlying mechanism is insulin resistance. In studies examining the effect of indinavir on adipocytes, dramatic inhibition of insulin-stimulated glucose up-take was reported ^[35]. Taken together, these results suggest that indinavir directly inhibit GLUT4 (a glucose transporter protein that mediates insulin-stimulated cellular uptake of glucose). Other studies have since supported these findings by demonstrating that indinavir induces muscle insulin resistance ^[36]. Furthermore, increased fasting glucose concentrations and increased secretory response of insulin, pro-insulin and C-peptide to glucose ingestion have been reported in patients treated with PIs ^[21].

Other Clinical signs include Familial combined hyperlipidemia (also called the atherogenic phenotype B) with a constellation of moderately elevated triglyceride (> 150 mg/dL), borderline or moderately decreased HDL-C. It may also manifest as normal or moderately elevated LDL, increased remnants composed of IDL, and small dense LDL ^[37] and increased apolipoprotein B ^[38]. Previously, this was thought to be a familial trait. More recently, it has become apparent that it can also be acquired and expressed as a result of obesity and insulin resistance. This phenotype has been shown to be linked to increases in heart disease ^[38]. The lipid profile in this lipidemia is very similar to that in the lipodystrophy syndrome. Although the exact mechanism leading to the atherogenic phenotype is unknown, it is known that insulin is an important regulator of fatty acid and lipid metabolism ^[39] and that the atherogenic phenotype is largely due to a net over production of VLDL by the liver causing increases in all beta-lipoproteins ^[38].

Mitochondrial toxicity

Madelung’s disease or multiple symmetric lipomatosis (an inherited mitochondrial disease) has clinical features similar to HIV-LDS ^[40]. Thus certain lipodystrophy features are observed in patients’ naïve to PI but treated with NRTIs (Definition) only ^{[41, 42]}. Brinkman et al. ^[43], had suggested that mitochondrial DNA polymerase (the sole enzyme responsible for mitochondrial DNA replication) may be inhibited by NRTIs causing mitochondrial dysfunction. Thus all toxic effects attributed to NTRIs such as peripheral neuropathy, myopathy, pancreatitis and lactic acidosis resemble the clinical syndrome seen in inherited mitochondrial diseases ^[44].

Steroid hormones and lipodystrophy

Changes in steroid hormone levels (particularly glucocorticoids and androgens) have been found in untreated HIV infected patients. Cortisol levels increase in HIV infected men in all stages of infection, whereas androgen levels are elevated early in HIV infection and decrease dramatically in AIDS ^{[45, 46]}.

Management of Lipodystrophy

Treatment of metabolic dysfunction such as lipidemia and glycemia are required. Although, withdrawal of PI therapy for either NNRTI or ABC may improve the metabolic profile regression of lipodystrophy can occur and this may not be an option for all patients ^{[47, 48]}. Despite the lack of consensus treatment guidelines for HIV-LDS, management of lipid and glucose abnormalities should follow current treatment strategies or guidelines for HIV negative patient populations.

Lipid-lowering Agents

Statins and fibrates are commonly used for their lipid-lowering effects. Considerations for drug interactions should help guide the selection of the particular agent used from each class. Newer statins provide greater lipid reductions than the older pravastatin used in earlier studies ^{[49, 50]}. However, there may be greater risk for drug interactions with the newer statins. Fibrates which are more potent than statins in lowering triglyceride and raising HDL-C levels are often needed to reach current guideline targets ^[51].

Glycemia Control

The insulin sensitizing-agent, metformin is commonly used with consistent positive results (e.g. weight neutral or weight loss effects) in clinical trials ^[52]. The use of metformin with some HIV therapies can increase the potential for lactic acidosis ^[53]. Pioglitazone is the only thiazolidinedione that should be considered, however, the benefits are minimal ^[54]. Insulin-like growth factor (IGF), used in extreme insulin resistance syndromes, have demonstrated positive glycemic and cholesterol effects in HIV-LDS ^[55].

Lipoatrophy and fat accumulation

Plastic surgery and fat transplantation are options for HIV-LDS; however, they have no effect on lipid abnormalities or cardiovascular risk reduction and recurrence is common ^[56]. Tesamorelin, a synthetic analogue of human growth hormone-releasing factor, is indicated for the treatment of HIV-LDS. Reduction in visceral adipose tissue is significantly decreased and maintained at 26, and 52 weeks, respectively, with Tesamorelin ^{[57, 58]}.

Conclusions and Public Health Implications

These findings suggest that the metabolic changes associated with the use of HIV PIs, including their adverse effects on triglyceride rich lipoproteins and their associated clinical features may be multi-factorial. Patients receiving HIV PIs should be screened for hyperlipidemias and may be candidates for lipid–lowering therapies that improve endothelial cell function and prevent adverse cardiovascular events. The potential for drug interactions between lipid lowering medications and HIV PIs should also be considered. Clinical decisions regarding initiation and intensification of drug therapies for patients with HIV infection should include their adverse effects on lipids, lipoproteins and cardiovascular function.

Conflicts of Interest: None;
Acknowledgements: DM is supported by Satellite Healthcare, Inc. and Genzyme, Inc.; KI is supported by ACPHS Intramural Funds and the School of Health Sciences, ACPHS.

References

1. Carr A, Samaras K, Burton S, Law M, Freund J, Chisholm DJ, Cooper DA. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS. 1998;12(7):F51-58.
2. Maggi P, Serio G, Epifani G, Fiorentino G, Saracino A, Fico C, Perilli F, Lillo A, Ferraro S, Gargiulo M, Chirianni A, Angarano G, Regina G, Pastore G. Premature lesions of the carotid vessels in HIV-1-infected patients treated with protease inhibitors. AIDS. 2000;14(16):F123-128.
3. Lichtenstein KA, Ward DJ, Moorman AC, Delaney KM, Young B, Palella FJ, Jr., Rhodes PH, Wood KC, Holmberg SD. Clinical assessment of HIV-associated lipodystrophy in an ambulatory population. AIDS. 2001;15(11):1389-1398.
4. Heath KV, Hogg RS, Chan KJ, Harris M, Montessori V, O’Shaughnessy MV, Montanera JS. Lipodystrophy-associated morphological, cholesterol and triglyceride abnormalities in a population-based HIV/AIDS treatment database. AIDS. 2001;15(2):231-239.
5. Gallet B, Pulik M, Genet P, Chedin P, Hiltgen M. Vascular complications associated with use of HIV protease inhibitors. Lancet. 1998;351(9120):1958-1959.
6. Miller KD, Jones E, Yanovski JA, Shankar R, Feuerstein I, Falloon J. Visceral abdominal-fat accumulation associated with use of indinavir. Lancet. 1998;351(9106):871-875.
7. Lo JC, Mulligan K, Tai VW, Algren H, Schambelan M. “Buffalo hump” in men with HIV-1 infection. Lancet. 1998;351(9106):867-870.
8. Grunfeld, C. Basic science and metabolic disturbances. Program and abstracts of the XIV International AIDS Conference:July 7 –12, 2002; Barcelona,Spain. Abstract T 158.
9. Kotler DP, Rosenbaum K, Wang J, Pierson RN. Studies of body composition and fat distribution in HIV-infected and control subjects. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology : Official Publication of the International Retrovirology Association. 1999;20(3):228-237.
10. Reeds DN, Mittendorfer B, Patterson BW, Powderly WG, Yarasheski KE, Klein S. Alterations in lipid kinetics in men with HIV-dyslipidemia. American Journal of Physiology Endocrinology and Metabolism. 2003;285(3):E490-497.
11. Grunfeld C, Pang M, Doerrler W, Shigenaga JK, Jensen P, Feingold KR. Lipids, lipoproteins, triglyceride clearance, and cytokines in human immunodeficiency virus infection and the acquired immunodeficiency syndrome. The Journal of Clinical Endocrinology and Metabolism. 1992;74(5):1045-1052.
12. Fernandez-Miranda C, Pulido F, Carrillo JL, Larumbe S, Gomez Izquierdo T, Ortuno B, Rubio R, del Palacio A. Lipoprotein alterations in patients with HIV infection: relation with cellular and humoral immune markers. Clinica Chimica Acta; International Journal of Clinical Chemistry. 1998;274(1):63-70.
13. Grunfeld C, Kotler DP, Hamadeh R, Tierney A, Wang J, Pierson RN. Hypertriglyceridemia in the acquired immunodeficiency syndrome. The American Journal of Medicine. 1989;86(1):27-31.
14. Hellerstein MK, Grunfeld C, Wu K, Christiansen M, Kaempfer S, Kletke C, Shackleton CH. Increased de novo hepatic lipogenesis in human immunodeficiency virus infection. The Journal of Clinical Endocrinology and Metabolism. 1993;76(3):559-565.
15. Carr A, Samaras K, Thorisdottir A, Kaufmann GR, Chisholm DJ, Cooper DA. Diagnosis, prediction, and natural course of HIV-1 protease-inhibitor-associated lipodystrophy, hyperlipidaemia, and diabetes mellitus: a cohort study. Lancet. 1999;353(9170):2093-2099.
16. Purnell JQ, Zambon A, Knopp RH, Pizzuti DJ, Achari R, Leonard JM, Locke C, Brunzell JD. Effect of ritonavir on lipids and post-heparin lipase activities in normal subjects. AIDS. 2000;14(1):51-57.
17. Periard D, Telenti A, Sudre P, Cheseaux JJ, Halfon P, Reymond MJ, Marcovina SM, Glauser MP, Nicod P, Darioli R, Mooser V. Atherogenic dyslipidemia in HIV-infected individuals treated with protease inhibitors. The Swiss HIV Cohort Study. Circulation. 1999;100(7):700-705.
18. Segerer S, Bogner JR, Walli R, Loch O, Goebel FD. Hyperlipidemia under treatment with proteinase inhibitors. Infection. 1999;27(2):77-81.
19. Shikuma CM, Waslien C, McKeague J, Baker N, Arakaki M, Cui XW, Souza S, Imrie A, Arakaki R. Fasting hyperinsulinemia and increased waist-to-hip ratios in non-wasting individuals with AIDS. AIDS. 1999;13(11):1359-1365.
20. Hadigan C, Miller K, Corcoran C, Anderson E, Basgoz N, Grinspoon S. Fasting hyperinsulinemia and changes in regional body composition in human immunodeficiency virus-infected women. The Journal of Clinical Endocrinology and Metabolism. 1999;84(6):1932-1937.
21. Behrens G, Dejam A, Schmidt H, Balks HJ, Brabant G, Korner T, Stoll M, Schmidt RE. Impaired glucose tolerance, beta cell function and lipid metabolism in HIV patients under treatment with protease inhibitors. AIDS. 1999;13(10):F63-70.
22. Stein TP, Nutinsky C, Condoluci D, Schluter MD, Leskiw MJ. Protein and energy substrate metabolism in AIDS patients. Metabolism: Clinical and Experimental. 1990;39(8):876-881.
23. Hommes MJ, Romijn JA, Endert E, Eeftinck Schattenkerk JK, Sauerwein HP. Insulin sensitivity and insulin clearance in human immunodeficiency virus-infected men. Metabolism: Clinical and Experimental. 1991;40(6):651-656.
24. Montserrat SR, Pena, R., Cervantes, M. Hyperglycemia associated with protease inhibitor therapy. Program and Abstracts of XII Internationmal Conference on AIDS. 1998(Geneva, Switzerland).
25. MM L. Report of diabetes and hyperglycemia in patients receiving protease inhibitors for the treatment of human immunodeficiency virus (HIV). FDA Public Health advisory. June 11; 1997.
26. Carr A, Samaras K, Chisholm DJ, Cooper DA. Pathogenesis of HIV-1-protease inhibitor-associated peripheral lipodystrophy, hyperlipidaemia, and insulin resistance. Lancet. 1998;351(9119):1881-1883.
27. Walli R, Herfort O, Michl GM, Demant T, Jager H, Dieterle C, Bogner JR, Landgraf R, Goebel FD. Treatment with protease inhibitors associated with peripheral insulin resistance and impaired oral glucose tolerance in HIV-1-infected patients. AIDS. 1998;12(15):F167-173.
28. Wentworth JM, Burris TP, Chatterjee VK. HIV protease inhibitors block human preadipocyte differentiation, but not via the PPARgamma/RXR heterodimer. The Journal of Endocrinology. 2000;164(2):R7-R10.
29. Sakai J, Rawson RB. The sterol regulatory element-binding protein pathway: control of lipid homeostasis through regulated intracellular transport. Current Opinion in Lipidology. 2001;12(3):261-266.
30. Riddle TM, Kuhel DG, Woollett LA, Fichtenbaum CJ, Hui DY. HIV protease inhibitor induces fatty acid and sterol biosynthesis in liver and adipose tissues due to the accumulation of activated sterol regulatory element-binding proteins in the nucleus. The Journal of Biological Chemistry. 2001;276(40):37514-37519.
31. Andre P, Groettrup M, Klenerman P, de Giuli R, Booth BL, Jr., Cerundolo V, Bonneville M, Jotereau F, Zinkernagel RM, Lotteau V. An inhibitor of HIV-1 protease modulates proteasome activity, antigen presentation, and T cell responses. Proceedings of the National Academy of Sciences of the United States of America. 1998;95(22):13120-13124.
32. Liang JS, Distler O, Cooper DA, Jamil H, Deckelbaum RJ, Ginsberg HN, Sturley SL. HIV protease inhibitors protect apolipoprotein B from degradation by the proteasome: a potential mechanism for protease inhibitor-induced hyperlipidemia. Nature Medicine. 2001;7(12):1327-1331.
33. Ionescu G HQ, Engelson ES, Johnson JA, Albu JB, Inada Y, Kotler DP. Acylation stimulating protein (ASP) and tumor necrosis factor (TNF) production in subcutaneous adipose tissue of HIV-infected patients with and without lipodystrophy. Poster Exhibition: The XIV International AIDS Conference:. June 7 – 12, 2002:Abstract no. WePeA5794.
34. Dube MP, Johnson DL, Currier JS, Leedom JM. Protease inhibitor-associated hyperglycaemia. Lancet. 1997;350(9079):713-714.
35. Murata H, Hruz PW, Mueckler M. The mechanism of insulin resistance caused by HIV protease inhibitor therapy. The Journal of Biological Chemistry. 2000;275(27):20251-20254.
36. Nolte LA, Yarasheski KE, Kawanaka K, Fisher J, Le N, Holloszy JO. The HIV protease inhibitor indinavir decreases insulin- and contraction-stimulated glucose transport in skeletal muscle. Diabetes. 2001;50(6):1397-1401.
37. Austin MA, King MC, Vranizan KM, Krauss RM. Atherogenic lipoprotein phenotype. A proposed genetic marker for coronary heart disease risk. Circulation. 1990;82(2):495-506.
38. Aguilar Salinas CA, Zamora M, Gomez-Diaz RA, Mehta R, Gomez Perez FJ, Rull JA. Familial combined hyperlipidemia: controversial aspects of its diagnosis and pathogenesis. Seminars in Vascular Medicine. 2004;4(2):203-209.
39. Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. The Journal of Clinical Investigation. 2005;115(5):1111-1119.
40. Enzi G, Inelmen EM, Baritussio A, Dorigo P, Prosdocimi M, Mazzoleni F. Multiple symmetric lipomatosis: a defect in adrenergic-stimulated lipolysis. The Journal of Clinical Investigation. 1977;60(6):1221-1229.
41. Gervasoni C, Ridolfo AL, Trifiro G, Santambrogio S, Norbiato G, Musicco M, Clerici M, Galli M, Moroni M. Redistribution of body fat in HIV-infected women undergoing combined antiretroviral therapy. AIDS. 1999;13(4):465-471.
42. Madge S, Kinloch-de-Loes S, Mercey D, Johnson MA, Weller IV. Lipodystrophy in patients naive to HIV protease inhibitors. AIDS. 1999;13(6):735-737.
43. Brinkman K, Smeitink JA, Romijn JA, Reiss P. Mitochondrial toxicity induced by nucleoside-analogue reverse-transcriptase inhibitors is a key factor in the pathogenesis of antiretroviral-therapy-related lipodystrophy. Lancet. 1999;354(9184):1112-1115.
44. Silvestri G, Ciafaloni E, Santorelli FM, Shanske S, Servidei S, Graf WD, Sumi M, DiMauro S. Clinical features associated with the A–>G transition at nucleotide 8344 of mtDNA (“MERRF mutation”). Neurology. 1993;43(6):1200-1206.
45. Nunez, E.A., Christeff, N. Steroid hormones, cytokines, lipid and metabolic disturbances in HIV infection. The Endocrinology and Metabolism of HIV Infection. Balliere and Tindall. 1994; 803 – 824.
46. Christeff N, Gharakhanian S, Thobie N, Rozenbaum W, Nunez EA. Evidence for changes in adrenal and testicular steroids during HIV infection. Journal of Acquired Immune Deficiency Syndromes. 1992;5(8):841-846.
47. Vigano A, Brambilla P, Cafarelli L, Giacomet V, Borgonovo S, Zamproni I, Zuccotti G, Mora S. Normalization of fat accrual in lipoatrophic, HIV-infected children switched from stavudine to tenofovir and from protease inhibitor to efavirenz. Antiviral Therapy. 2007;12(3):297-302.
48. Ribera E, Paradineiro JC, Curran A, Sauleda S, Garcia-Arumi E, Castella E, Puiggros C, Crespo M, Feijoo M, Diaz M, Del Saz SV, Planas M, Sureda D, Falco V, Ocana I, Pahissa A. Improvements in subcutaneous fat, lipid profile, and parameters of mitochondrial toxicity in patients with peripheral lipoatrophy when stavudine is switched to tenofovir (LIPOTEST study). HIV Clinical Trials. 2008;9(6):407-417.
49. Aslangul E, Assoumou L, Bittar R, Valantin MA, Kalmykova O, Peytavin G, Fievet MH, Boccara F, Bonnefont-Rousselot D, Melchior JC, Giral P, Costagliola D. Rosuvastatin versus pravastatin in dyslipidemic HIV-1-infected patients receiving protease inhibitors: a randomized trial. AIDS. 2010;24(1):77-83.
50. Calza L, Manfredi R, Colangeli V, Pocaterra D, Pavoni M, Chiodo F. Rosuvastatin, pravastatin, and atorvastatin for the treatment of hypercholesterolaemia in HIV-infected patients receiving protease inhibitors. Current HIV Research. 2008;6(6):572-578.
51. Giannarelli C, Klein RS, Badimon JJ. Cardiovascular implications of HIV-induced dyslipidemia. Atherosclerosis. 2011;219(2):384-389.
52. Sheth SH, Larson RJ. The efficacy and safety of insulin-sensitizing drugs in HIV-associated lipodystrophy syndrome: a meta-analysis of randomized trials. BMC Infectious Diseases. 2010;10:183.
53. Kazazis C. A case of lactic acidosis (LA) after administration of tenofovir and metformin in a diabetic patient with recently diagnosed HIV infection. Journal of Renal Care. 2011;37(3):174.
54. Raboud JM, Diong C, Carr A, Grinspoon S, Mulligan K, Sutinen J, Rozenbaum W, Cavalcanti RB, Wand H, Costagliola D, Walmsley S, Glitazone, Lipoatrophy Meta-Analysis Working G. A meta-analysis of six placebo-controlled trials of thiazolidinedione therapy for HIV lipoatrophy. HIV Clinical Trials. 2010;11(1):39-50.
55. Rao MN, Mulligan K, Tai V, Wen MJ, Dyachenko A, Weinberg M, Li X, Lang T, Grunfeld C, Schwarz JM, Schambelan M. Effects of insulin-like growth factor (IGF)-I/IGF-binding protein-3 treatment on glucose metabolism and fat distribution in human immunodeficiency virus-infected patients with abdominal obesity and insulin resistance. The Journal of Clinical Endocrinology and Metabolism. 2010;95(9):4361-4366.
56. Nelson L, Stewart KJ. Plastic surgical options for HIV-associated lipodystrophy. Journal of Plastic, Reconstructive & Aesthetic Surgery : JPRAS. 2008;61(4):359-365.
57. Falutz J, Mamputu JC, Potvin D, Moyle G, Soulban G, Loughrey H, Marsolais C, Turner R, Grinspoon S. Effects of tesamorelin (TH9507), a growth hormone-releasing factor analog, in human immunodeficiency virus-infected patients with excess abdominal fat: a pooled analysis of two multicenter, double-blind placebo-controlled phase 3 trials with safety extension data. The Journal of Clinical Endocrinology and Metabolism. 2010;95(9):4291-4304.
58. Falutz J, Potvin D, Mamputu JC, Assaad H, Zoltowska M, Michaud SE, Berger D, Somero M, Moyle G, Brown S, Martorell C, Turner R, Grinspoon S. Effects of tesamorelin, a growth hormone-releasing factor, in HIV-infected patients with abdominal fat accumulation: a randomized placebo-controlled trial with a safety extension. Journal of Acquired Immune Deficiency Syndromes. 2010;53(3):311-322.