Is socioeconomic inequality in antenatal care coverage widening or reducing between- and within-socioeconomic groups? A case of 19 countries in sub-Saharan Africa
Bibliographic record
Abstract
Maternal health statistics have improved in many countries in sub-Saharan Africa (SSA). Still, progress remains slow in meeting the Sustainable Development Goals (SDG) targets. Accelerating antenatal care (ANC) coverage is critical to improving maternal health outcomes. To progress, countries should understand whether to target reducing health disparities between- or within-socioeconomic groups, as policies for achieving these may differ. This paper develops a framework for decomposing changes in socioeconomic inequalities in health into changes in between- and within-socioeconomic groups using the concentration index, a popular measure for assessing socioeconomic inequalities in health. It begins by noting the challenge in decomposing the concentration index into only between- and within-group components due to the possibility of an overlap created by overlapping distributions of socioeconomic status between groups. Using quantiles of socioeconomic status provides a convenient way to decompose the concentration index so that the overlap component disappears. In characterising the decomposition, a pro-poor shift occurs when socioeconomic inequality is reduced over time, including between- and within-socioeconomic groups, while a pro-rich shift or change occurs conversely. The framework is applied to data from two rounds of the Demographic and Health Survey of 19 countries in SSA conducted about ten years apart in each country. It assessed changes in socioeconomic inequalities in an indicator of at least four antenatal care visits (ANC4+) and the count of ANC visits (ANC intensity). The results show that many countries in SSA witnessed significant pro-poor shifts or reductions in socioeconomic inequalities in ANC coverage because pro-rich inequalities in ANC4+ and ANC intensity become less pro-rich. Changes in between-socioeconomic group inequalities drive the changes in ANC service coverage inequalities in all countries. Thus, policies addressing inequalities between-socioeconomic groups are vital to reducing overall disparities and closing the gap between the rich and the poor, a crucial objective for the SDGs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".