Coverage and equity of maternal and newborn health care in rural Nigeria, Ethiopia and India
Bibliographic record
Abstract
BACKGROUND: Despite progress toward meeting the Sustainable Development Goals, a large burden of maternal and neonatal mortality persists for the most vulnerable people in rural areas. We assessed coverage, coverage change and inequity for 8 maternal and newborn health care indicators in parts of rural Nigeria, Ethiopia and India. METHODS: We examined coverage changes and inequity in 2012 and 2015 in 3 high-burden populations where multiple actors were attempting to improve outcomes. We conducted cluster-based household surveys using a structured questionnaire to collect 8 priority indicators, disaggregated by relative household socioeconomic status. Where there was evidence of a change in coverage between 2012 and 2015, we used binomial regression models to assess whether the change reduced inequity. RESULTS: In 2015, we interviewed women with a birth in the previous 12 months in Gombe, Nigeria (n = 1100 women), Ethiopia (n = 404) and Uttar Pradesh, India (n = 584). Among the 8 indicators, 2 positive coverage changes were observed in each of Gombe and Uttar Pradesh, and 5 in Ethiopia. Coverage improvements occurred equally for all socioeconomic groups, with little improvement in inequity. For example, in Ethiopia, coverage of facility delivery almost tripled, increasing from 15% (95% confidence interval [CI] 9%–25%) to 43% (95% CI 33%–54%). This change was similar across socioeconomic groups (p = 0.2). By 2015, the poorest women had about the same facility delivery coverage as the least poor women had had in 2012 (32% and 36%, respectively), but coverage for the least poor had increased to 60%. INTERPRETATION: Although coverage increased equitably because of various community-based interventions, underlying inequities persisted. Action is needed to address the needs of the most vulnerable women, particularly those living in the most rural areas.
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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 | 0.001 |
| 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".