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Objectives: The current study aimed to determine the magnitude of home delivery and its associated factors in East Africa using data from the Demographic and Health Survey. Methods: We pooled data from the Demographic and Health Survey of the 11 East African countries and included a total weighted sample of 126,107 women in the study. The generalized linear mixed model was fitted to identify factors associated with home delivery. Variables with adjusted odds ratio with a 95% confidence interval, and p value < 0.05 in the final generalized linear mixed model were reported to declare significantly associated factors with home delivery. Result: The weighted prevalence of home delivery was 23.68% (95% confidence interval: [23.45, 23.92]) among women in East African countries. Home delivery was highest in Ethiopia (72.5%) whereas, it was lowest in Mozambique (2.8%). In generalized linear mixed model, respondent’s age group, marital status, educational status, place of residence, living country, wealth index, media exposure, and number of children ever born were shown significant association with the home delivery in the East African countries, Conclusion: Home delivery varied between countries in the East African zone. Home delivery was significantly increased among women aged 20–34 years, higher number of ever born children, rural residence, never married, or formerly married participants. On the contrary, home delivery decreased with higher educational level, media exposure, and higher wealth index. Wide-range interventions to reduce home delivery should focus on addressing inequities associated with maternal education, family wealth, increased access to the media, and narrowing the gap between rural and urban areas, poor and rich families, and married and unmarried mothers.
We conducted a cross-sectional pooled analysis based on DHS conducted in the 11 East African countries (including Burundi, Comoros, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe) from 2012 to 2017. The DHS is considered as the main data source as it was designed to provide population and health indicators at the national and regional levels. The data collection period was varying but includes the data of 5 years prior to the survey. This further data analysis was carried out between January and February 2021. Based on updated country income classifications for the World Bank 2020 fiscal year, Burundi, Ethiopia, Malawi, Mozambique, Rwanda, Tanzania, and Uganda are low-income countries, while Comoros, Kenya, Zambia, and Zimbabwe are LMICs. 12 Data were obtained from the DHS measure program on the website www.measuredhs.com after we submitted concept notes about the project. We pooled the most recent DHS data from the 11 countries of East African countries. There are 20 countries in the Regions of East Africa according to World Health Organization (WHO) classification. In history, only 13 of these countries had DHS data. For this study, 11 countries were included 13 (Figure 1). Schematic presentation of the countries sampled from East Africa for the pooled analysis of home delivery. The DHS used two stages of stratified sampling technique to select the study participants. In the first stage, the Enumeration Areas (EAs) were randomly selected. In the second stage, households were selected. We pooled data from DHS from the 11 East African countries and included a total weighted sample of 126,107 women who had a history of delivering children in the last 5 years prior to the survey day in the study. The DHS program adopts standardized methods that involve uniform questionnaires, manuals, and field procedures to gather information that is comparable between countries around the world. It is the representative household surveys that capture data from a wide range of monitoring and impact evaluation indicators in the area of population, health, and nutrition with face-to-face interviews of women aged 15–49 years. Each country’s survey consists of different data sets, including men, women, children, birth, and household data sets. Detailed survey methodology and sampling methods used in gathering the data have been reported elsewhere. 14 For this study, we used the Individual Record Data Set (KR file) which contained information on eligible women aged 15–49 years in each country. The outcome variable of this study was a home delivery. The response variable was generated from the question asked to women who gave birth within 5 years preceding the survey question. The response was dichotomized as a home delivery and institutional delivery (if delivered at any type of health institutions). Home delivery includes the option given in the survey question termed home of respondents and home and others’ home. Health institutions include government hospitals, health centers, health posts, private clinics, or private hospitals. If women deliver at home, we coded “1,” otherwise coded “0.” Country, age, marital status, educational level, place of residence, wealth index, sex of head of household, age of head of household, media exposure, and total children ever born were included as independent variables in this study The variables were extracted using the KR file. We use STATA software version 16.0 to clean, recode, and analyze the pooled data. After joining the extracted data from the 11 East African countries, we weighted the data using the individual sample weight of the women (v005) and strata (v021). The proportion of home delivery was described and presented using a pie chart. The DHS data had a hierarchical structure as women were nested within a cluster, and clusters within the country. Hence, the data violate the independence of the observation, as the women may share similar characteristics within the cluster (and/or country). This implies that there is a need to consider the variability between clusters by using generalized linear mixed models (GLMMs). The odds ratio test, the intra-cluster correlation coefficient (ICC), the median odds ratio (MOR), and the proportional change in variance (PCV) were calculated to measure the variation between clusters. The ICC quantifies the proportion of the total observed difference in home delivery attributable to cluster variations (degree of heterogeneity). On the contrary, MOR was used to quantify the variation or heterogeneity in home delivery between clusters. Therefore, MOR is defined as the median value of the odds ratio between the high odds of the cluster and the lower odds of the cluster when selecting two clusters/EAs randomly. Finally, PCV measures the total variation in home delivery attributed to factors at the individual and community levels in the final model compared to the null model. The detail description and formulas for ICC, 15 MOR, 16 and PCV 16 are described elsewhere. The null model, individual level, cluster level, and factors of both cluster and individual level were fitted. Model comparison was made based on the deviation likelihood ratio (2LLR) since the models were nested. Finally, a GLMM (family (binomial) link (logit)) with factors both at individual and cluster level was selected. Variables with a p value < 0.2 in the bivariable analysis for individual and community factors were fitted into the multivariable model. Variables with adjusted odds ratio (AOR) with 95% confidence interval (CI), and p value < 0.05 in the final GLMM were reported to declare significantly associated factors with home delivery.
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