Women, children and adolescents in conflict countries: an assessment of inequalities in intervention coverage and survival
Bibliographic record
Abstract
Introduction: Conflict adversely impacts health and health systems, yet its effect on health inequalities, particularly for women and children, has not been systematically studied. We examined wealth, education and urban/rural residence inequalities for child mortality and essential reproductive, maternal, newborn and child health interventions between conflict and non-conflict low-income and middle-income countries (LMICs). Methods: We carried out a time-series multicountry ecological study using data for 137 LMICs between 1990 and 2017, as defined by the 2019 World Bank classification. The data set covers approximately 3.8 million surveyed mothers (15-49 years) and 1.1 million children under 5 years including newborns (<1 month), young children (1-59 months) and school-aged children and adolescents (5-14 years). Outcomes include annual maternal and child mortality rates and coverage (%) of family planning services, 1+antenatal care visit, skilled attendant at birth (SBA), exclusive breast feeding (0-5 months), early initiation of breast feeding (within 1 hour), neonatal protection against tetanus, newborn postnatal care within 2 days, 3 doses of diphtheria, pertussis and tetanus vaccine, measles vaccination, and careseeking for pneumonia and diarrhoea. Results: Conflict countries had consistently higher maternal and child mortality rates than non-conflict countries since 1990 and these gaps persist despite rates continually declining for both groups. Access to essential reproductive and maternal health services for poorer, less educated and rural-based families was several folds worse in conflict versus non-conflict countries. Conclusions: maternal health and child vaccine interventions are significantly worse in conflict-affected countries. Efforts to protect maternal and child health interventions in conflict settings should target the most disadvantaged families including the poorest, least educated and those living in 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".