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ORIGINAL ARTICLE
8 (
1
); 11-18
doi:
10.21106/ijma.264

Decision Making Autonomy and Maternal Healthcare Utilization among Nigerian Women

Department of Liberal Studies, College of Administrative and Business Studies, Niger State Polytechnic, Bida Campus, Niger State, Nigeria
Department of Mathematics and Statistics, College of Science and Technology, Niger State Polytechnic, Zungeru Campus, Niger State, Nigeria
Department of Nursing Services, Comprehensive Health Centre/Federal Medical Centre, Zungeru, Niger State, Nigeria
Corresponding author email: philiobas@yahoo.com
Licence

This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background and Objectives:

Low assess to ante-natal care (ANC) services continue to pose a major public health challenge leading to high maternal mortality rates in developing countries. Non-utilization of ANC services among about a quarter of Nigerian women of reproductive age remains a major concern in the actualization of Sustainable Development Goals. Considering the complexity of healthcare utilization in Nigeria, the relationship between a particular health care utilization pattern and women autonomy has not been fully examined. This study examines the patterns of women autonomy and their relationships with ANC utilization in Nigeria.

Methods:

This was a cross-sectional analysis of the 2013 nationally representative data from the Nigerian Demographic and Health Survey (NDHS). Factor analysis/score were used to construct women autonomy index, while chi-square and logistic regression were used to establish the relationships between the response and exposure variables.

Results:

There is a strong relationship between women decision making autonomy status and ANC services among Nigeria women. The odds of utilizing ANC services among women with more decision making autonomy were significantly 3.79 higher than among women with low decision-making autonomy. The use of ANC increases as age, education and wealth status of respondents increase.

Conclusions and Global Health Implications:

These results indicate that women autonomy is undoubtedly a major determinant of ANC utilization in Nigeria.

Keywords

Autonomy
Antenatal Care
Logistic Regression Analysis
Factor analysis

1. Introduction

1.1 Background of the study

Poor maternal health care utilization is of great concern in developing countries leading to high maternal and child mortality. Globally, maternal mortality is very high with about 830 women dying every day from pregnancy or childbirth related.1 In 2010, World Health Organization (WHO) reported that 358,000 maternal mortalities occurred globally, with more than 354,000 occurring in developing countries and about 200,000 deaths in sub-Saharan African countries.2 This has, however, dropped in 2015 to 303,000 women death globally.3,1

Studies have established that maternal mortality and morbidity are inversely proportional to health care services utilization. That is, when healthcare service utilization is low, this will translate into high maternal and child mortalities. Also, where reproductive healthcare utilization is high, maternal and child mortalities would be low.4 One of the most common indicators of a woman’s health and reproductive behavior is the state of her antenatal care utilization rate.5

The prevalence of non-utilization of healthcare services among several Nigerian women of reproductive age remains a major concern to all stakeholders in the actualization of Sustainable Development Goals (SDGs) by year 2030. It’s been reported that more than 33% of Nigerian pregnant women do not use antenatal care (ANC) service during pregnancy.4 For instance, the WHO’s World Health Statistics reported that in 2015 only 61% of Nigerian pregnant women had attended ANC at least once during their pregnancy period and only 51% met the WHO standard of a minimum of 4 visits.6

Women autonomy as collected in the Nigeria Demographic and Health Survey (NDHS) is defined as the extent to which women are independent on finances, level of participation in family decision making on matters pertaining to her health and that of the household, and freedom to visit outside of her matrimonial home without having to obtain permission.7 Recent studies have reported mixed conclusions on the relationship between women’s autonomy and reproductive healthcare utilization. In some Asian countries, studies found association between women autonomy and use of reproductive healthcare services.8,9 On the other hand,10 one Nepal study concluded that women’s participation in decision making was not associated with the use of prenatal and postnatal care, while another in another study, Deo et al11 found that autonomy is associated with ANC services in Eastern Nepal. However, some studies in Nigeria and sub-Saharan Africa (SSA) found that women autonomy is associated with maternal healthcare services.12,13,14

There are a number of other studies that have investigated the factors responsible for poor maternal healthcare services utilization and many of these studies used the classical regression analysis. For instance, a study conducted by Bamiwuye et al15 used composite score to obtain an overall index for autonomy by adding the dichotomous variables in all the situations for which data were available to have a minimum score of ‘0’ and maximum of ’4’ with higher scores meaning ‘more autonomy’. These were further dichotomized into ‘0’ (less autonomy) and ‘1’ (more autonomy). However, to our knowledge, studies adopting factor analysis are not available. Factor analysis (or principal component factor) is a more robust method that captures the totality of the constituents including their interactions by examining the correlation matrix to capture the underlying factors that explain substantial amount of variations.

1.2 Aims of the study

The aim of this study therefore was to determine the patterns of women decision making autonomy and establish how it relates with maternal healthcare utilization in Nigeria using factor analysis, factor score, and logistic regression with and without adjusting for confounding variables.

2. Methods

The 2013 NDHS datasets was used for this analysis. NDHS is a nationally representative survey carried out for Nigeria by MEASURE DHS in collaboration with National Population Commission (NPC).7 The survey has comparable questions on women decision making autonomy and number of ANC visits. For women decision making autonomy, data in 2013 NDHS was collected on the bases of their participation in three dimensions on issues concerning (i) their own health care, (ii) making major household purchases, and (iii) visits to family or relatives without having to take permission.8 For ANC visits, data showed a range of visits from 0 to 36 times during the period of pregnancy with at least 4 visits considered for this study as having attended adequate ANC visits in accordance with WHO standard as at the time of the survey without prejudice to the recent WHO recommendation of a minimum of 8 visits.16,17 In addition, for this study, weights were constructed to correct for imbalance in sampling to ensure national representativeness resulting in a weighted sample of 27,829 women.

2.1 Study variables

The outcome variable (Dependent Variable) is the number of times a woman attended ANC during the last pregnancy. This was categorized as ‘0’ if she attended less than 4 times and ‘1’ if she attended at least 4 times. The principal independent variable (predictor variable) is the decision making status of the woman and was dichotomized as ‘0’ representing ‘low autonomy’ and ‘1’ representing ‘more autonomy’. Other confounding variables of interest examined include: age of respondent and age at 1st marriage, where classified in group as (15-24, 25-34 and 35+), others were marital status (never in union, formerly in union and currently in union); number of children ever born (0-4, 5+) and number children living (0, 1-2, 3-4, 5+), the household wealth status classified as ‘low’ or ‘high’, education status (no education, primary education and secondary education and above); place of residence (whether rural or urban); and region (North East, North West, North Central, South East, South West and South South).

2.2 Statistical analysis

Four levels of analyses procedures were adopted:

  • Level one, at the univariate level, percentage frequency distribution of the study sample was used to show the distribution of respondents by their characteristics and ANC prevalence.

  • Level two, to construct autonomy status, factor analysis/score were used while considering the three dimensions as dichotomous where ‘1’ means participating and ‘0’ not participating. In view of this, we extracted the commonalities, the proportion of the variance explained, the loading of the variables on the factor, the factor score coefficients for each variable and the test of model appropriateness.18

  • Level three, to establish the relationship of the independent variable (decision making autonomy index derived from factor analysis) with maternal health care utilization, we used chi square at 5% level of significance.

  • Level four, logistic regression analysis was used to establish the likelihood effects of principal variable (unadjusted) and (adjusted for confounding factors) with ANC visits. Stata14SE was used for the computation.19

2.3 Ethical approval

Being a secondary data survey, the ethical permission to use the data for this study was obtained from Opinion Research Corporation (ORC) Macro International, Incorporated, Calverton, USA with its approval for survey already approved by Ethics Committee of ORC Macro Inc. and by the National Ethics Committee of Federal Ministry of Health, Nigeria.7

3. Results

3.1. Factor analysis of autonomy status

Using principal component factor of factor analysis, one factor was extracted accounting for 81% of the total variance. The Bartlett test of sphericity for decision making autonomy among Nigerian women was highly significant (p<0.000) with chi-square of 44793 indicating homogeneity of variance by the decision making patterns.

The following equation was used to construct the autonomy score (AS):

The resulting values which lie between 0 and 1.1 were rescaled and multiplied by 100 to give values between zero and 100. This was finally dichotomized into 50/50,5,15 resulting into autonomy index used for the analysis classified as ‘0’ low autonomy and as ‘1’ more autonomy as presented below in Table 1a and 1b.

Table 1a Results of the constructing the autonomy index
Decision on Factor loading Factor score Communalities
Finance (F) 0.92 0,44 0.84
Health (H) 0.90 0.36 0.81
Visits (V) 0.88 0.30 0.78
% of Variance 81
KMO 0.736
Bartlett Test of Sphericity: *P-Value=0.000 Chi-Sq. 44793
Number of Observation. 27274
Table 1b Level of autonomy
Description N %
Low autonomy 16,568 59.53
More autonomy 11,262 40.47

This puts the prevalence rate of low decision making autonomy among Nigerian women to 60% (table 1b).

3.2. Distribution of participant’s characteristics

As show in table 2, the mean age of respondents was 28.86 years with standard deviation of 9.68 years. More than 90% of women with age at first marriage between 15 and 24 years participated in the survey. The prevalence of ANC visits meeting the WHO standard (4 times and above during pregnancy) among Nigerian women was slightly above 52%. Over 11,000 of the women in the survey do not have any living child and 22% have more than 4 children. The table also reveals that about three quarters of the women participants are currently in union with about 5% formerly in union, but now no longer in union. An equal number of women who have never had a child also had five or more children.

Table 2 Social demographic characteristics of study participants
Variables Number Percentage
ANC visit status
Did not meet WHO standard(<4) 9,464 47.5
Met WHO standard (>3) 10,457 52.5
Level of autonomy
Low autonomy 16,568 59.53
More autonomy 11,262 40.47
Age at first marriage
15–24 years 26,762 90.4
25–34 years 2,728 9.2
35 years+ 130 0.4
Number of living children
0 11,750 30.2
1–2 9,737 25.0
3–4 8,876 22.8
5+ 8,584 22.0
Union or marital status
Never in union 9,325 23.9
Currently in union 2,7830 71.5
Formerly in union 1,793 4.6
Total children ever born
0–4 children 27541 70.7
5+children 11,406 29.3
Region
North central 5572 14.3
North east 5766 14.8
North west 11876 30.5
South east 4476 11.5
South South 4942 12.7
South west 6314 16.2
Place of residence
Urban 16414 42.1
Rural 22533 57.9
Level of education
No education 14723 37.8
Primary education 6733 17.3
Secondary+ 17485 11.9
Age in group
15-24 years 14576 37.4
25–34 years 12611 32.4
35 years+ 11760 30.2

Table 3 shows that ANC visits at the WHO standard varied with a number of the background variables of participants. Other variables that were significantly associated with ANC include: age in group, number of children ever born, household wealth index, number of children living, level of educational attainment, etc. The proportion of women whose age at first marriage was 35+ years (86%) that met WHO ANC standard was more than those for any other age group. ANC utilization was significantly lower among the rural women (38.9%) than among the urban women (77.6%). ANC utilization significantly increased among women with higher education from 2699 with no formal education to 5377 for those who have completed secondary education and above.

Table 3 Relationship between participant’s characteristics and ANC visit
Variables ANC Visits Chi-Square (P-Value)
Less than 4 More than 3
Level of autonomy
Low autonomy 7,084 4,704 1744.67
More autonomy 2,019 5,085 (0.000) ***
Wealth status
Poor 8281 4693 3919.67
Not poor 1183 5764 (0.000) ***
Age at first marriage
15–24 years 9071 8782 717.79
25–34 years 2523 1333 (0.000) ***
35 years+ 6 38
Total children ever born
0–4 Children 5393 7064 3394
5+Children 4071 3394 (0.000) ***
Place of residence
Urban 1567 5420 2677.92
Rural 7897 5037 (0.000) ***
Level of education
No education 6963 2699 4871.34
Primary education 1414 2381 (0.000) ***
Secondary+ 1088 5377
Age in group
15-24 years 2854 2376 142.54
25–34 years 4163 5194 (0.000)***
35 years+ 2448 2887

3.3 Logistic regression analysis

3.3.1 Unadjusted odds ratio (UOR)

Table 4 shows the logistic regression odds ratio of individual estimates (unadjusted) of the ANC utilization among Nigerian women by the principal variable (decision making autonomy). The odds of ANC utilization increase by a woman’s level of decision making autonomy. The unadjusted odds of women with more autonomy are significantly 3.8 times more likely to complete at least 4 ANC services than those women with low autonomy.

Table 4 Logistic regression analysis for ANC Visits and autonomy status
  Variables UOR CI P-Value
Autonomy status
Low autonomy 1.000
More autonomy 3.792 (3.39 4.24) 0.000***

3.3.2. Adjusted odds ratio(AOR)

After adjusting for several confounding factors, the odds of completing 4 ANC visits for women with ‘more autonomy’ dropped significantly from 3.79 times to 1.24 times for women with ‘low autonomy’ (table 5). The odds of attending at least 4 ANC increased significantly as the age in group, educational level, and wealth status increased.

Table 5 Logistic regression analysis for ANC Visits and autonomy status (Adjusted odds ratio)
Odds ratio CI P-Value
Autonomy level
Low autonomy 1.000
More autonomy 1.240 (1.101 1.396) 0.000***
Age in group
15–24 years 1.000
25–34 years 1.151 (0.010 1.311) 0.035*
35+years 1.282 (1.081 1.522) 0.004***
Education level
No education 1.000
Primary education 1.935 (1.662 2.242) 0.000***
Secondary+ 2.959 (2.497 3.506) 0.000***
Place of residence
Urban 1.000
Rural 0.818 (0.681 0.982) 0.032*
Wealth status
Poorest 1.000
Poor 1.892 (1.564 2.289) 0.000***
Middle 3.416 (2.743 4.253) 0.000***
Richer 5.072 (3.961 6.492) 0.000***
Richest 8.260 (6.194 11.016) 0.000***
Region
North central 1.000
North east 0.978 (0.759 1.266) 0.876(NS)
North west 0.649 (0.510 0.825) 0.000***
South south 2.598 (1.917 3.521) 0.000***
South west 0.688 (0.537 0.881) 0.003***
South east 2.856 (1.891 4.313) 0.000***
Children ever born
0 1.000
1–2 1.031 (0.890 1.195) 0.685(NS)
3–4 0.963 (0.857 1.082) 0.523(NS)
5+ 1.000
Age at 1st marriage
15–24 years 1.000
25–34 years 1.238 (1.026 1.494) 0.026*
35+years 2.196 (0.751 6.418) 0.150(NS)

4. Dicussion

4.1 Discussion

We applied factor analysis to construct the Autonomy Index (AI) for the respondents using the principal component factor option. We used the scores (expressed in 10 scales) for the three dimensions of women decision making autonomy (deciding on their own health care; making major household purchases, and visits to family or relatives) as collected in 2013 NDHS. These variables were further converted into dichotomous variables of ‘0’ & ‘1’, where ‘1’ means having the item and ‘0’ not having it.

The kmo of 0.736 measures the adequacy of sample-size of women decision making autonomy indicating that a higher correlation existed between different decision making autonomy statuses to warrant using factor analysis.20 The finding that only about 40% Nigerian women has autonomy in deciding on issues that pertain to her health, large purchases for family and visit outside the home either singly or jointly with her husband is relatively low. Furthermore, the principal exposure variable, women autonomy was found to be significantly associated with ANC utilization in Nigeria. This agrees with similar studies in Nigeria12, 13 and in some Asian countries,8, 9 using different analytical approaches, but the result was at variance with other findings in Nepal.10 The significance of women autonomy as a risk factor for ANC utilization even after adjusting for other confounding factors makes it very relevant especially in resource limited settings like Nigeria.

4.2 Limitations

However, the interpretations of the results from this study are subject to a number of limitations. First, the study is cross sectional and as such causal effects could not be determined. Second, the study did not examine the independent predictor effects of other significant independent variables. Third, in view of the multi ethnic diversities of Nigeria, cultural impediments to ANC were not examined. Fourth, the constituents of women decision status as collected by NDHS were in 3 dimensions. These could have been more to further justify the use of factor analysis. Overall, the strength of the results from the study draws upon the nationally representativeness of the large sample size of the NDHS. Future studies should identify more research gap in the areas of identifying cultural impediments in the use of ANC in Nigeria. For example, interactions of both maternal and paternal variables can be investigated and how much both household and paternal variables can explain the relationships between maternal characteristics and ANC.

5. Conclusion and Global Health Implications

This study used factor analysis to construct autonomy index for women in Nigeria and have demonstrated that it is as good as any other analytical methods.15 Women autonomy is undoubtedly a major determinant of ANC utilization in Nigeria. The result further demonstrated that the usage of ANC increases as age of respondents, education status, wealth index and age at 1st marriage increase. In view of the above, it suggest the need for the implementations of policies and programs that will enhance women autonomy status in the following areas: (1) women empowerment programs that will increase their income generating power especially the rural women, (2) encouraging parents to educate their girl-child rather than forcing them into early child marriage, (3) increasing the focus of men to be more responsive in their wives needs to access ANC during pregnancy and postpartum periods. This is important because paternal attitude has been found to be an important factor that influences many women in seeking quality care,21 and (4) increasing access to quality ANC centers especially in the rural areas.

Acknowledgement

We deeply acknowledge Measure DHS and National Population Commission for the permission granted us to use the data for this study.

Conflicts of Interest: The authors declared no conflicts of interest relevant to this study.

Ethics Approval: Ethical permission to use the data for this study was obtained from Opinion Research Corporation (ORC) Macro International, Incorporated, Calverton, USA; the Nigeria Demographic and Health survey (NDHS) was approved by Ethics Committee of ORC Macro Inc. and by the National Ethics Committee of Federal Ministry of Health, Nigeria.

References

  1. . Maternal mortality-Factsheets. http://www.who.int/en/news-room/fact-sheets/detail/maternal-mortality
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