A Novel Maternal and Newborn Health Composite Indicator Using National Health Surveys from Low- and Middle-Income Countries: Validation and Association with Infant Mortality
Bibliographic record
Abstract
OBJECTIVES: To create a composite indicator to measure maternal and newborn health (MNH) interventions using national surveys from low- and middle-income countries (LMICs) and assess its relationship with neonatal and post-neonatal mortality. STUDY DESIGN: Cross-sectional, multi-country study using nationally representative household survey data. METHODS: The Maternal and Newborn Health composite indicator (MNHci) was created based on three essential interventions: four or more antenatal care visits, institutional delivery, and postnatal care for the woman or baby within two days of delivery. One point was assigned per intervention received, ranging from zero to three. We analyzed national distributions and stratified results by wealth and residence. Spearman coefficients were used to assess the correlation between MNHci and neonatal and post-neonatal mortality. Multilevel Poisson regression using DHS data examined the relationship between MNHci and neonatal mortality. RESULTS: MNHci was estimated for 97 LMICs using data from 2010 to 2022. In 29 countries, over 80% of woman-baby dyads received all interventions; in 15 countries, more than 20% received none. Inequalities were evident: 83% of dyads in the wealthiest decile received all interventions compared to 44% in the poorest. Data from 80 countries showed a strong inverse correlation with neonatal (-0.64; 95% CI: -0.77, -0.50) and post-neonatal (-0.67; 95% CI: -0.81, -0.54) mortality. Adjusted Poisson regression indicated a 43% lower neonatal mortality incidence ratio (IR: 0.57; 95% CI: 0.38-0.83) for dyads receiving all interventions compared to none. CONCLUSIONS: The MNHci is a simple, standardized, and meaningful tool for tracking MNH coverage across LMICs, supporting efforts to reduce neonatal mortality and health inequalities.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".