Inequalities in early marriage, childbearing and sexual debut among adolescents in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Adolescent sexual and reproductive health (ASRH) is a major public health concern in sub-Saharan Africa (SSA). However, inequalities in ASRH have received less attention than many other public health priority areas, in part due to limited data. In this study, we examine inequalities in key ASRH indicators. METHODS: We analyzed national household surveys from 37 countries in SSA, conducted during 1990-2018, to examine trends and inequalities in adolescent behaviors related to early marriage, childbearing and sexual debut among adolescents using data from respondents 15-24 years. Survival analyses were conducted on each survey to obtain estimates for the ASRH indicators. Multilevel linear regression modelling was used to obtain estimates for 2000 and 2015 in four subregions of SSA for all indicators, disaggregated by sex, age, household wealth, urban-rural residence and educational status (primary or less versus secondary or higher education). RESULTS: In 2015, 28% of adolescent girls in SSA were married before age 18, declined at an average annual rate of 1.5% during 2000-2015, while 47% of girls gave birth before age 20, declining at 0.6% per year. Child marriage was rare for boys (2.5%). About 54% and 43% of girls and boys, respectively, had their sexual debut before 18. The declines were greater for the indicators of early adolescence (10-14 years). Large differences in marriage and childbearing were observed between adolescent girls from rural versus urban areas and the poorest versus richest households, with much greater inequalities observed in West and Central Africa where the prevalence was highest. The urban-rural and wealth-related inequalities remained stagnant or widened during 2000-2015, as the decline was relatively slower among rural and the poorest compared to urban and the richest girls. The prevalence of the ASRH indicators did not decline or increase in either education categories. CONCLUSION: Early marriage, childbearing and sexual debut declined in SSA but the 2015 levels were still high, especially in Central and West Africa, and inequalities persisted or became larger. In particular, rural, less educated and poorest adolescent girls continued to face higher ASRH risks and vulnerabilities. Greater attention to disparities in ASRH is needed for better targeting of interventions and monitoring of progress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".