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Background: Adolescent pregnancy is a major public health problem both in developed and developing countries with huge consequences to maternal health and pregnancy outcomes. However, there is limited evidence on the prevalence and associated factors of adolescent pregnancy in East Africa. Therefore, this study aimed to investigate the prevalence and associated factors of adolescent pregnancy in Eastern Africa. Method: The most recent Demographic and Health Survey (DHS) datasets of the 12 East African countries were used. A total weighted sample of 17, 234 adolescent girls who ever had sex was included. A multilevel binary logistic regression analysis was fitted to identify the significantly associated factors of adolescent pregnancy. Finally, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported to declare the factors that are significantly associated with adolescent pregnancy. Results: The overall prevalence of adolescent pregnancy in East Africa was 54.6% (95%CI: 53.85, 55.34%). In the multivariable multilevel analysis; being age 18–19 years [AOR = 3.06; 95%CI: 2.83, 3.31], using contraceptive [AOR = 1.41; 95%CI: 1.28, 1.55], being employed girls [AOR = 1.11; 95%CI: 1.03, 1.19], being spouse/head within the family [AOR = 1.62; 95% CI: 1.45, 1.82], and being from higher community level contraceptive utilization [AOR = 1.10; 95%CI:1.02, 1.19] were associated with higher odds of adolescent pregnancy. While adolescent girls attained secondary education and higher [AOR = 0.78; 95%CI: 0.68, 0.91], initiation of sex at age of 15 to 14 years [AOR = 0.69; 95%CI: 0.63, 0.75] and 18 to 19 years [AOR = 0.31; 95%CI: 0.27, 0.35], being unmarried [AOR = 0.25; 95%CI: 0.23, 0.28], having media exposure [AOR = 0.85; 95%CI: 0.78, 0.92], and being girls from rich household [AOR = 0.64; 95%CI: 0.58, 0.71] were associated with lower odds of adolescent pregnancy. Conclusion: This study found that adolescent pregnancy remains a common health care problem in East Africa. Age, contraceptive utilization, marital status, working status, household wealth status, community-level contraceptive utilization, age at initiation of sex, media exposure, educational level and relation to the household head were associated with adolescent pregnancy. Therefore, designing public health interventions targeting higher risk adolescent girls such as those from the poorest household through enhancing maternal education and empowerment is vital to reduce adolescent pregnancy and its complications.
This study was a secondary data analysis based on the datasets from the most recent Demographic and Health Surveys (DHS) conducted in East African countries (Burundi, Ethiopia, Comoros, Uganda, Rwanda, Tanzania, Mozambique, Madagascar, Zimbabwe, Kenya, Zambia, and Malawi). These datasets were appended to determine the prevalence and associated factors of adolescent pregnancy in east Africa. 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. The DHS used two stage stratified sampling technique to select the study participants. Each country’s survey consists of different datasets including men, women, children, birth, and household datasets, and for this study, we used the women’s dataset (individual record (IR) file). In this study, all adolescent girls aged 15–19 years and those who ever had sex (a total weighted sample of 17, 234) were considered for the final analysis. The detailed information on the survey country, the number of adolescents in each country, eligible and actual number of women for each country were provided in Table 1. Survey and sample size characteristics Note: a = Unweighted frequency The outcome variable of this study was “getting pregnant during the age of 15-19 years among adolescents who ever had sex”. A woman was considered as experiencing adolescent/teenage pregnancy if her age was from 15 to 19 and if she had ever been pregnant before or during the survey. We used all girls age 15–19 who had ever experienced sex as our study population. The outcome was derived using the variables; the number of women who have had a birth and the number of women who have not had a birth but are pregnant at the time of interview [14]. The independent variables considered for this study were both individual and community-level variables. The individual-level factors include; the age of respondent, marital status, age at 1st sex, contraceptive use, educational attainment, household wealth status, sex of household head, relation to household head, and access to mass media. The community-level factors were community women education, community poverty, community contraceptive utilization, residence and country. In DHS, except country and residence, all the other variables were collected at the individual level. Therefore, we generate three community-level variables such as community women’s education, community poverty, and community contraceptive utilization by aggregating the individual-level factors at cluster level and categorized as high and low based on the median value (Table 2). Description and measurement of independent variables Data extraction, recoding and analysis were done using STATA version 14 software. The data were weighted before any statistical analysis to restore the representativeness of the data and to get a reliable estimate and standard error. Descriptive statistics were done using frequencies and percentages. Since the DHS data has a hierarchical structure, this violates the independent assumptions of the standard logistic regression model, a multilevel logistic regression analysis was used. Besides, adolescents in the same cluster are more likely to be similar to each other than adolescents from another cluster. This implies that there is a need to take in to account the between cluster variability by using advanced models such as multilevel analysis. The Interclass Correlation Coefficient (ICC) and Median Odds Ratio (MOR) were checked to assess whether there was clustering or not. In this study, four models were fitted; the null model- a model without explanatory variables, model I- a model with individual-level factors, model II- a model with community-level factors, and model III- a model with both individual and community-level factors, simultaneously. Model comparison was done based on deviance (−2LL) and a model with the lowest deviance was selected as the best-fitted model. Both bivariable and multivariable analysis was done using the best-fitted model. At the bivariable analysis variables with a p-value ≤0.2 were considered for multivariable analysis. Finally, variables with a P-value of ≤0.05 in the multivariable analysis were considered a significant factor associated with adolescent pregnancy.
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