Inter-district and Wealth-related Inequalities in Maternal and Child Health Service Coverage and Child Mortality within Addis Ababa City
Bibliographic record
Abstract
In sub-Saharan Africa, urban areas generally have better access to and use of maternal, newborn, and child health (MNCH) services than rural areas, but previous research indicates that there are significant intra-urban disparities. This study aims to investigate temporal trends and geographic differences in maternal, newborn, and child health service utilization between Addis Ababa's poorest and richest districts and households. A World Bank district-based poverty index was used to classify districts into the top 60% (non-poor) and bottom 40% (poor), and wealth index data from the Ethiopian Demographic and Health Survey (EDHS) was used to classify households into the top 60% (non-poor) and bottom 40% (poor). Essential maternal, newborn, and child health service coverage was estimated from routine health facility data for 2019-2021, and five rounds of the EDHS (2000-2019) were used to estimate child mortality. The results showed that service coverage was substantially higher in the top 60% than in the bottom 40% of districts. Coverage of four antenatal care visits, skill birth attendance, and postnatal care all exceeded 90% in the non-poor districts but only ranged from 54 to 67% in the poor districts. Inter-district inequalities were less pronounced for childhood vaccinations, with over 90% coverage levels across all districts. Inter-district inequalities in mortality rates were considerable. The neonatal mortality rate was nearly twice as high in the bottom 40% of households' as in the top 60% of households. Similarly, the under-5 mortality rate was three times higher in the bottom 40% compared to the top 60% of households. The substantial inequalities in MNCH service utilization and child mortality in Addis Ababa highlight the need for greater focus on the city's women and children living in the poorest households and districts in maternal, newborn, and child health programs.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".