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Background: Abortion is one of the top five causes of maternal mortality in low and middle-income countries. It is associated with a complication related to pregnancy and childbirth. Despite this, there was limited evidence on the prevalence and associated factors of abortion in East African countries. Therefore, this study aimed to investigate the prevalence and associated factors of abortion among reproductive-aged women in East African countries. Methods: The Demographic and Health Surveys (DHS) data of 12 East African countries was used. A total weighted sample of 431,518 reproductive-age women was included in the analysis. Due to the hierarchical nature of the DHS data, a multilevel binary logistic regression model was applied. Both crude and Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was calculated for potential associated factors of abortion in East Africa. In the final model, variables with a p value < 0.05 were declared as statistically significant factors of abortion. Results: Around 5.96% (95%CI: 4.69, 7.22) of reproductive-aged women in East Africa had a history of abortion. Alcohol use, tobacco or cigarette smoking, being single, poorer wealth index, currently working, traditional family planning methods, and media exposure were associated with a higher risk of abortion. However, higher parity, having optimum birth intervals, and modern contraceptive uses were associated with lower odds of abortion. Conclusions: The prevalence of abortion among reproductive-aged women in East Africa was high. Abortion was affected by various socio-economic and obstetrical factors. Therefore, it is better to consider the high-risk groups during the intervention to prevent the burdens associated with abortion.
We used the most recent Demographic and Health Survey (DHS) data of 12 East African countries conducted from 2008 to 2018 to determine the magnitudes and associated factors of abortion in East Africa. The DHS surveys are routinely collected every five years across low-and middle-income countries using structured, pretested, and validated questionnaires. The DHS surveys follow the same standard procedure sampling, questionnaires, data collection, and coding, making multi-country analysis possible. The DHS survey employs a stratified two-stage cluster sampling technique. In the first stage, clusters/enumeration areas (EAs) were randomly selected from the sampling frame (i.e., they are usually developed from the available latest national census). In the second stage, systematic sampling was employed on households listed in each cluster or EA. Interviews were conducted in selected households with target populations (women aged 15–49 and men aged 15–64). All reproductive-aged women who gave birth in the five years preceding the most recent DHS of 12-east African countries were included in this study. However, a woman with missing data on the outcome variable (abortion) was excluded from the study. This includes women are infertile, sexually inactive and did not have pregnancy history. Any missing data at any outcome variable was treated by applying various missing data management techniques according to the instruction of the guide to DHS statistics [31]. A total weighted sample of 431,518 reproductive-age women was included (Table (Table11). Countries, sample size, and survey year of Demographic and Health Surveys included in the analysis for 12 East African countries The outcome variable for this study was abortion among the reproductive-aged, which was derived from the DHS question, "have you ever had a terminated pregnancy.” It was dichotomized as “Yes” if a woman had experienced abortion, either spontaneous or induced (termination of pregnancy before seven completed months of pregnancy), and “No” if a woman hadn't experienced abortion. The independent variables of the study includes community level variables such as residence (urban and rural) and distance to health facility ( not big problem and a big problem), and individual level variables like maternal age (less than 20, 20–34 and greater or equal to 35), education status (no formal education, primary, secondary and higher), marital status (single, married, divorced, widowed and separated), wealth index (poorest, poorer, middle, richer and richest) which was calculated by principal component analysis for urban and rural areas separately based on their asset, currently working (yes and no), mass media (reproductive aged women were considered as exposed to mass media when they watch either television or radio at least once per wee k otherwise considered as not exposed), smoking (yes and no), preceding birth interval (less than 24 months/not optimum and greater or equal to 24 months/optimum), alcohol use (yes and no), contraceptive use (non- user, modern and traditional (when the participant uses either abstinence from intercourse, withdrawal method or calendar method)) and parity (less than 5 births and greater than or equal to 5 births). The variables of the study were extracted, cleaned, and recoded using STATA version 14. The data were weighted using sampling weight during any statistical analysis to adjust for unequal probability of selection due to the sampling design used in DHS data. Hence, the representativeness of the survey results was ensured. A two-level multivariable binary logistic regression analysis was used to estimate the effect of explanatory variables on abortion. The data has two levels with a group of J EAs and within-group j (j = 1, 2…, J), a random sample nj of level-one units (reproductive-aged woman). The response variable is denoted by; So, appropriate inferences and conclusions from this data require proper modeling techniques like multilevel modeling, which contain variables measured at different levels of the hierarchy, to account for the nested effect [32]. Four models were fitted for the data. The first model was an empty model without any explanatory variables to calculate the extent of cluster variation in abortion. Variations between clusters (EAs) were assessed by computing Intra-class Correlation Coefficient (ICC), Proportional Change in Variance (PCV), and Median Odds Ratio (MOR). The ICC is the proportion of variance explained by the grouping structure in the population. Whereas PCV measures the total variation attributed to individual and community level factors in the multilevel model as compared to the null model [33]. The MOR is also defined as the median value of the odds ratio between the cluster at high risk and the cluster at lower risk of abortion when randomly picking out two clusters (EAs). The second model was adjusted with community-level variables only; the third model was adjusted for individual-level variables only, while the fourth was fitted with both individual and community-level variables. These four models were compared by using deviance (-2LLR), and the model with the lowest deviance was selected as the best-fitted model for the data. Variables with a p-value ≤ 0.2 in the bi-variable analysis were considered for the multivariable analysis. In the multivariable multilevel binary logistic model, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) of the best-fitted model was reported to identify the associated factors of abortion. The statistical significance for the final model was set at p < 0.05. This study is a secondary data analysis from the DHS data of 12 East African countries (Burundi, Ethiopia, Kenya, Comoros, Madagascar, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe), so it does not require ethical approval. For conducting this study, online registration and request for measure DHS were conducted. The dataset was downloaded from DHS online archive (http://www.dhsprogram.com) after getting approval to access the data.
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