Adolescent first births in East Africa: disaggregating characteristics, trends and determinants

The use of a single national figure fails to capture the complex patterns and inequalities in early childbearing that occur within countries, as well as the differing contexts in which these pregnancies occur. Further disaggregated data that examine patterns and trends for different groups are needed to enable programmes to be focused on those most at risk. This paper describes a comprehensive analysis of adolescent first births using disaggregated data from Demographic and Household surveys (DHS) for three East African countries: Uganda, Kenya and Tanzania.

The study initially produces cross-sectional descriptive data on adolescent motherhood by age (under 16, 16-17 and 18-19 years), marital status, wealth, education, state or region, urban/rural residence and religion. Trends for two or more surveys over a period of 18-23 years are then analysed, and again disaggregated by age, wealth, urban/rural residence and marital status to ascertain which groups within the population have benefited most from reductions in adolescent first birth. In order to adjust for confounding factors we also use multinomial logistic regression to analyse the social and economic determinants of adolescent first birth, with outcomes again divided by age.

In all three countries, a significant proportion of women gave birth before age 16 (7%-12%). Both the bivariate analysis and logistic regression show that adolescent motherhood is strongly associated with poverty and lack of education/literacy, and this relationship is strongest among births within the youngest age group (<16 years). There are also marked differences by region, religion and urban/rural residence. Trends over time show there has been limited progress in reducing adolescent first births overall, with no reductions among the poorest.

Adolescent first births, particularly at the youngest ages, are most common among the poorest and least educated, and progress in reducing rates within this group has not been made over the last few decades. Disaggregating data allows such patterns to be understood, and enables efforts to be better directed where needed.