Equity of maternal and child health services in Afghanistan: a spatiotemporal analysis of national survey datasets
Bibliographic record
Abstract
INTRODUCTION: Afghanistan's healthcare system faces geopolitical instability and inequities in maternal and child health (MCH) services, particularly associated with a temporary collapse in funding in 2021. We analysed coverage levels and spatiotemporal trends in sociodemographic inequalities in the country using data from the 2010/2011 and 2022/2023 Afghan Multiple Indicator Cluster Surveys. METHODS: The study's primary outcome was an adapted Composite Coverage Index (CCI) that combined seven essential MCH interventions with corresponding inequality measures, evaluated using Slope Indices of Inequality. These interventions included antenatal care, skilled birth attendance (SBA), Bacillus-Calmette-Guérin (BCG), diphtheria-pertussis-tetanus, and measles vaccination and treatment for suspected pneumonia and diarrhoea. Inequalities were analysed across wealth, education and urban/rural status at both national and provincial levels. RESULTS: The analysis revealed persistent socioeconomic inequalities across all strata, with the most significant economic disparities observed in SBA and the largest educational disparities in vaccine coverage in 2022/2023. Nationally, the CCI increased by 4.2% from 42.1% (95% CI 40.3% to 44%) in 2010/2011 to 46.2% (95% CI 44.6% to 47.9%) in 2022/2023. Despite a slight increase between the two studies, severe regional disparities are masked, particularly in the eastern and southern regions, where coverage across multiple interventions significantly declined. The provinces of Nooristan and Urozgan significantly lost coverage, while Daykundi and Nimroz recorded increases in coverage and equity. CONCLUSION: The findings underscore the persistence of substantial inequalities in Afghanistan, with severe consequences for already vulnerable populations facing multiple hardships. The findings highlight ways in which geopolitical instability affects healthcare equity. Increasing disparities threaten to exacerbate existing challenges in accessing essential healthcare services, particularly for those of lower socioeconomic status. Urgent, targeted interventions are necessary to address these inequities, the impacts associated with funding cuts and gender marginalisation, and to mitigate their detrimental impact on Afghan women and children.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".