Determinants of Neonatal Mortality in Kenya: Evidence from the Kenya Demographic and Health Survey 2014

2.1. Study Design

The Kenya Demographic and Health Survey (KDHS) is a nationally representative household survey that provides data for monitoring population indicators including population, health and nutrition. This data was collected by the Kenya National Bureau of Statistics in collaboration with the Ministry of Health, National AIDS Control Council (NACC), National Council for Population and Development (NCPD) and Kenya Medical Research Institute (KEMRI). ICF Macro provided technical assistance for the survey through the DHS program. The sample design for the KDHS 2014 was drawn from the master sampling of the fifth National Sample Survey and evaluation program (NASSEP V).3 KDHS is designed as a stratified 2 stage cluster design with the first sampling stage involving a random selection of 1,612 clusters from the NASSEP V spread across the country (995 in the rural areas and 617 in the urban areas). In the second stage, 25 households are randomly selected from each of the already selected enumeration areas (EAs). Data was collected using a structured household and women’s questionnaire administered by an enumerator. The women’s questionnaire collected information including the women’s birth history, childhood mortality, fertility preferences, antenatal and post-natal care, place of delivery, childhood diseases and childhood vaccinations. Conceptual frameworks for analyzing child survival for developing countries have been used by other authors including the Mosley and Chen framework which has been widely used.11,12,13,14 In this study, the conceptual framework based on the Mosley and Chen framework was developed to guide the potential explanatory variables (determinants) to neonatal mortality as shown in Figure 1.

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Figure 1

Conceptual framework for neonatal mortality Source: Authors own work

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2.2. Study Variables

The outcome variable for this study is neonatal deaths as reported by the mothers. It was defined as the death of a neonate between birth and 28 days of life. The variable took a binary form such that neonatal death was a success and coded as= 1 and neonatal survival in the 28 days was a failure and coded as=0. The variable was examined against the explanatory variables categorized into 4 categories i.e., socioeconomic and demographic characteristics, maternal characteristics, child characteristics and health facility characteristics. The explanatory variables included in this study are in Table 1.

2.3. Statistical Analysis

Statistical analysis was done using SPSS V 20.0.16 Descriptive statistics was used to explain the characteristics of the mothers and neonates captured in the study. Frequency distribution tables were used to present the descriptive statistics. Neonatal mortality was computed using the formula defined by Rutstein and Rojas17 in the guide to analyzing demographic health surveys and the results presented in Table 2. Univariate and multivariate analyses were conducted to establish associations between the potential explanatory variables and the study outcome. Logistic regression models were fitted using SPSS regression functions. The weights used for the adjustment of the KDHS data are recommended in the guide to analyzing DHS statistics.17 Odds Ratios (OR) and 95% confidence intervals (CI) were calculated to assess the adjusted risk factors that affected the study outcomes and those with p=0.05 were retained in the final model.

Approval to utilize data for this study was obtained from the DHS program, Rockville Maryland, USA. Approval was also obtained from Egerton University, Njoro, Kenya.

3.1. Neonatal Mortality Characteristics

Table 2 presents a description and distribution of neonatal deaths across the explanatory variables. The study results show that 64.1% of the births occurred in rural areas whereas 35.9% occurred in urban areas. In addition, 11.9% of women had no education, 56.4% of women had primary education, 19.2% of women had secondary level education and 12.4% of the women had tertiary education. Regarding mothers’ age, 46.8% were below 19 years, 50% were between 20-29 years and 2.6% were above 30 years as presented in Table 2.

Table 3 presents the total live births and Neonatal Mortality Rate (NMR) by socio-demographic and economic characteristics, maternal, child and health facility characteristics. We found that NMR was higher in rural areas than in urban areas (NMR:19.6 vs 17.4). Neonates born to poor mothers had a higher NMR compared to mothers from middle-class households (NMR: 20.6 vs 19.2). Neonates with low birth weight had a higher NMR compared to those with normal birth weight (NMR:45.7 vs 9.0). Mothers with a shorter interpregnancy interval < 2 years, had a higher NMR compared though those with interpregnancy intervals >2 years (NMR: 23.6 vs 15.9). Mothers who delivered at home experienced higher NMR compared to those who delivered at health facilities (NMR:19.3 vs 17.9). Mothers who did not attend any ANC at the time of their pregnancy had a higher NMR compared to those who attended between 1-3 visits and those who attended the requisite minimum of 4 ANC visits (NMR: 42.6 vs 14.8 vs 10.7).

3.2. Factors Associated with Neonatal Mortality

Logistic regression analysis was conducted in 2 stages as presented in Table 4. In the first step of the logistic regression model, each of the explanatory variables was entered individually into the regression model. The analysis showed that infants whose mothers had no education were 1.4 times more likely to die in the first 28 days compared to mothers with higher education levels Crude Odds Ratio (cOR)=1.44, 95% CI: 1.21-2.94, p=0.003. First-borns were 1.4 times more likely to die in the first month of life (cOR=1.43, 95% CI: 1.01- 2.08, p=0.043) compared to 4^th order infants. Low birth weight infants (<2.5 kgs) were 3.5 times more likely to die (cOR=3.58, 95% CI:1.47- 8.75, p=0.005) compared to infants with a birth weight >3.5 kgs. Mothers who did not attend ANC clinic were 4 times more likely to experience deaths of their neonates (cOR=4.05, 95% CI:1.72-9.51, p=0.001) compared to mothers attending more than 4 visits. Infants of mothers who attended between 1-3 ANC visits were 1.7 times more likely to die (cOR=1.70, 95% CI: 1.10- 2.63, p=0.017) in the first month of life.

In the second stage logistic regression model, only the variables that were significant at the first analysis were entered in the final model. After adjusting for confounding factors, the mother’s education, birth weight and the number of ANC visits were established to be significant determinants of neonatal mortality. Mothers with no education were 2.2 times more likely to lose their infants in the neonatal period compared to those with higher education levels Adjusted Odds Ratio (aOR=2.20, 95% CI: 1.43-4.15, p=0.049). Low birth weight infants were 3.2 times more likely to die in the neonatal period (aOR=3.21, 95% CI: 1.85-12.08, p=0.006) compared to those with >3.5 kgs at birth. Mothers who attended >4 ANC had a lower risk of experiencing neonatal mortality. Those who only attended between 1-3 ANC visits were three times more likely to lose their infants (aOR=3.35, 95% CI:1.62-8.53, p=0,041 in the first 28 days, compared to those with >4 ANC visits. Similarly, mothers who did not attend any ANC visits were twice as likely to experience neonatal deaths (aOR=2.32, 95% CI: 1.10-4.88, p=0.027) compared to mothers who attended >4 ANC visits.

4.1. Limitations

The strength and weaknesses of this study need to be considered when drawing conclusions and inferences. This study is a nationally representative survey with a 2-stage stratified sampling that presents generally representative data and findings. Limitations of this study should also be considered when drawing conclusions and interpretations of the results. KDHS collects data up to 5 years before the survey and recalls errors are a possibility especially on information related to neonatal mortality. Some variables such as birth weight had missing data and as such should be interpreted with caution. Information on the pregnancy-related complications and other underlying comorbidities that may influence pregnancy outcomes such as pre-existing health conditions before delivery was not available.

4.2. Recommendation for Further Studies

The findings from this study indicate the need to continuously educate the communities on the importance of girls’ education. Whereas the Kenyan government is implementing free primary education, cultural inclinations, and practices such as female genital cutting predispose girls to early marriages, thus pushing them out of school.

The government of Kenya, through the community health strategy, should facilitate continuous sensitizations and referral mechanisms on the uptake and follow-up of prenatal care to at least meet the recommended threshold of a minimum of 4 visits.