Measuring universal health coverage in reproductive, maternal, newborn and child health: An update of the composite coverage index
Bibliographic record
Abstract
BACKGROUND: Monitoring universal health coverage in reproductive, maternal and child health requires appropriate indicators for assessing coverage and equity. In 2008, the composite coverage index (CCI)-a weighted average of eight indicators reflecting family planning, antenatal and delivery care, immunizations and management of childhood illnesses-was proposed. In 2017, the CCI formula was revised to update the family planning and diarrhea management indicators. We explored the implications of adding new indicators to the CCI. METHODS: We analysed nationally representative surveys to investigate how addition of early breastfeeding initiation (EIBF), tetanus toxoid during pregnancy and post-natal care for babies affected CCI levels and the magnitude of wealth-related inequalities. We used Pearson's correlation coefficient to compare different formulations, and the slope index of inequalities [SII] and concentration index [CIX] to assess absolute and relative inequalities, respectively. RESULTS: 47 national surveys since 2010 had data on the eight variables needed for the original and revised formulations, and on EIBF, tetanus vaccine and postnatal care, related to newborn care. The original CCI showed the highest average value (65.5%), which fell to 56.9% when all 11 indicators were included. Correlation coefficients between pairs of all formulations ranged from 0.93 to 0.99. When analysed separately, 10 indicators showed higher coverage with increasing wealth; the exception was EIBF (SII = -2.1; CIX = -0.5). Inequalities decreased when other indicators were added, especially EIBF-the SII fell from 24.8 pp. to 19.2 pp.; CIX from 7.6 to 6.1. The number of countries with data from two or more surveys since 2010 was 30 for the original and revised formulations and 15 when all the 11 indicators were included. CONCLUSIONS: Given the growing importance of newborn mortality, it would be desirable to include relevant coverage indicators in the CCI, but this would lead a reduction in data availability, and an underestimation of coverage inequalities. We propose that the 2017 version of the revised CCI should continue to be used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".