Wealth-related inequalities in demand for family planning satisfied among married and unmarried adolescent girls and young women in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: The use of modern contraception has increased in much of sub-Saharan Africa (SSA). However, the extent to which changes have occurred across the wealth spectrum among adolescents is not well known. We examine poor-rich gaps in demand for family planning satisfied by modern methods (DFPSm) among sexually active adolescent girls and young women (AGYW) using data from national household surveys. METHODS: We used recent Demographic and Health Surveys and Multiple Indicator Cluster Surveys to describe levels of wealth-related inequalities in DFPSm among sexually active AGYW using an asset index as an indicator of wealth. Further, we used data from countries with more than one survey conducted from 2000 to assess DFPSm trends. We fitted linear models to estimate annual average rate of change (AARC) by country. We fitted random effects regression models to estimate regional AARC in DFPSm. All analysis were stratified by marital status. RESULTS: Overall, there was significant wealth-related disparities in DFPSm in West Africa only (17.8 percentage points (pp)) among married AGYW. The disparities were significant in 5 out of 10 countries in Eastern, 2 out of 6 in Central, and 7 out of 12 in West among married AGYW and in 2 out of 6 in Central and 2 out of 9 in West Africa among unmarried AGYW. Overall, DFPSm among married AGYW increased over time in both poorest (AARC = 1.6%, p < 0.001) and richest (AARC = 1.4%, p < 0.001) households and among unmarried AGYW from poorest households (AARC = 0.8%, p = 0.045). DPFSm increased over time among married and unmarried AGYW from poorest households in Eastern (AARC = 2.4%, p < 0.001) and Southern sub-regions (AARC = 2.1%, p = 0.030) respectively. Rwanda and Liberia had the largest increases in DPFSm among married AGYW from poorest (AARC = 5.2%, p < 0.001) and richest (AARC = 5.3%, p < 0.001) households respectively. There were decreasing DFPSm trends among both married (AARC = - 1.7%, p < 0.001) and unmarried (AARC = - 4.7%, p < 0.001) AGYW from poorest households in Mozambique. CONCLUSION: Despite rapid improvements in DFPSm among married AGYW from the poorest households in many SSA countries there have been only modest reductions in wealth-related inequalities. Significant inequalities remain, especially among married AGYW. DFPSm stalled in most sub-regions among unmarried AGYW.
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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) | 0.002 | 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".