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Background: Stillbirth is the most frequently reported adverse pregnancy outcome worldwide, which imposes significant psychological and economic consequences to mothers and affected families. East African countries account for one-third of the 2.6 million stillbirths globally. Though stillbirth is a common public health problem in East African countries, there is limited evidence on the pooled prevalence and associated factors of stillbirth in East Africa. Therefore, this study aimed to investigate the prevalence of stillbirth and its associated factors in East Africa. Methods: This study was based on the most recent Demographic and Health Surveys (DHSs) of 12 East African countries. A total weighted sample of 138,800 reproductive-age women who gave birth during the study period were included in this study. The prevalence of stillbirth with the 95% Confidence Interval (CI) was reported using a forest plot. A mixed-effect binary logistic regression analysis was done to identify significantly associated factors of stillbirth. Since the DHS data has hierarchical nature, the presence of clustering effect was assessed using the Likelihood Ratio (LR) test, and Intra-cluster Correlation Coefficient (ICC), and deviance were used for model comparison. Variables with a p-value of less than 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable mixed-effect binary logistic regression analysis, the Adjusted Odds Ratio (AOR) with 95% CI were reported to declare the strength and significance of the association. Results: The prevalence of stillbirth in East Africa was 0.86% (95% CI: 0.82, 0.91) ranged from 0.39% in Kenya to 2.28% in Burundi. In the mixed-effect analysis; country, women aged 25–34 years (AOR = 1.27, 95% CI: 1.11, 1.45), women aged ≥ 35 years (AOR = 1.19, 95% CI: 1.01, 1.44), poor household wealth (AOR = 1.07, 95% CI: 1.02, 1.23), women who didn’t have media exposure (AOR = 1.11, 95% CI: 1.01, 1.25), divorced/widowed/separated marital status (AOR = 2.99, 95% CI: 2.04, 4.39), caesarean delivery (AOR = 1.81, 95% CI: 1.52, 2.15), preceding birth interval < 24 months (AOR = 1.15, 95% CI: 1.06, 1.24), women attained secondary education or above (AOR = 0.68, 95% CI: 0.56, 0.81) and preceding birth interval ≥ 49 months (AOR = 1.45, 95% CI: 1.28, 1.65) were significantly associated with stillbirth. Conclusions: Stillbirth remains a major public health problem in East Africa, which varied significantly across countries. These findings highlight the weak health care system of East African countries. Preceding birth interval, county, maternal education media exposure, household wealth status, marital status, and mode of delivery were significantly associated with stillbirth. Therefore, public health programs enhancing maternal education, media access, and optimizing birth spacing should be designed to reduce the incidence of stillbirth.
The data source for this study was the Demographic and Health Survey (DHS) data of 12 East countries (Burundi, Ethiopia, Comoros, Uganda, Rwanda, Tanzania, Mozambique, Madagascar, Zimbabwe, Kenya, Zambia, and Malawi). The DHS is a nationally representative survey that collects data on basic health indicators like mortality, morbidity, family planning service utilization, fertility, maternal and child health services (vaccination). The data of each country was derived from the measure DHS program. Each country's DHS survey consists of different datasets including men, women, children, birth, and household datasets; for this study, we used the Birth Record dataset (BR file). In the BR file, all births after 7 months of gestation in the last five years preceding the survey were interviewed. The datasets of 12 East African countries were appended together to determine the pooled prevalence of stillbirth and associated factors in East Africa. The DHS employed a two-stage stratified sampling technique to select the study participants. In the first stage, Enumeration Areas (EAs) were randomly selected while in the second stage households were selected. We pooled 12 DHS surveys done in the 12 East African countries, and a total weighted sample of 138,800 births after 7 months of gestation were included in the study (Table (Table11). Countries year of survey and sample size The 2016 EDHS asked women to report any pregnancy loss that occurred in the last five years preceding the survey. The duration of pregnancy was reported for every pregnancy separately which did not result in a live birth. Pregnancy losses occurring after seven completed months of gestation were considered as stillbirth (28). The response variable for this study was the occurrence of stillbirth among mothers of childbearing age (15–49 years). The response variable for the ith mother was represented by a random variable Yi with two possible values coded as 1 and 0. So, the response variable of the ith mother Yi was measured as a dichotomous variable with possible values Yi = 1, if ith mother had experienced stillbirth and Yi = 0 if the mother had a live birth. Socio-demographic and economic variables, maternal obstetric, and health service-related variables were included as independent variables. Socio-demographic and economic variables considered were residence (recoded as rural and urban), country, maternal education status (recoded as no education, primary education, and secondary education and above), husband education status (recoded as no education, primary education, secondary education and above), maternal age (recoded as 15–24 years, 25–34 years and 35–49 years), maternal occupation (recoded as no and yes), household wealth status (recoded as poor, middle and rich), marital status (recoded as single, married, and divorced/widowed/separated), and media exposure (recoded as no and yes). The maternal obstetric and health service-related variables included were parity (recoded as one, two to four, and five and above), place of delivery (home and health facility), mode of delivery (recorded as vaginal, and caesarean delivery), covered by health insurance (recoded as no and yes), number of ANC visit (recoded as no ANC visit, 1–3 ANC visit and ≥ 4 ANC visit) and preceding birth interval (recoded as less than 24 months, 25–48 months and ≥ 49 months). We pooled the DHS data of 12 East African countries together after extracting the variables based on literature. Before any statistical analysis was conducted, the data were weighted using sampling weight, primary sampling unit, and strata to restore the representativeness of the survey and take sampling design when calculating standard errors and reliable estimates. "Svy set" STATA command was used for the descriptive analysis to take into account the complex survey design. Cross tabulations and summary statistics were done using STATA version 14 software. The pooled prevalence of stillbirth with the 95% Confidence Interval (CI) was reported using a forest plot. The DHS data had a hierarchical nature, this could violate the independence of observations and equal variance assumption of the traditional logistic regression model. Hence, women are nested within a cluster, we expect that women within the same cluster are more likely to be related to each other than women in another cluster. This implies that there is a need to take into account the between cluster variability by using advanced models. Therefore, for the associated factors, we used the mixed-effect logistic regression model. The presence of clustering effect was assessed using Intra-class Correlation Coefficient (ICC), and Likelihood Ratio (LR) test. Deviance (-2LLR) was used for model comparison since the models were nested. Accordingly, a mixed effect logistic regression model (both fixed and random effect) was the best-fitted model since it had the lowest deviance value. Variables with a p-value < 0.2 in the bi-variable analysis were considered in the multivariable mixed-effect logistic regression model. Adjusted Odds Ratios (AOR) with a 95% Confidence Interval (CI) and p-value ≤ 0.05 in the multivariable model were used to declare significant factors associated with stillbirth. Ethical approval and participant consent were not necessary for this particular study since the study was a secondary data analysis based on the publicly available DHS data from the MEASURE DHS program. We requested the data from the MEASURE DHS Program and permission was granted to download and use the data for this study from http://www.dhsprogram.com. There are no names of individuals or household addresses in the data files.
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