Impact of conflict on maternal and child health service delivery: a country case study of Afghanistan
Bibliographic record
Abstract
INTRODUCTION: Since decades, the health system of Afghanistan has been in disarray due to ongoing conflict. We aimed to explore the direct effects of conflict on provision of reproductive, maternal, newborn, child and adolescent health and nutrition (RMNCAH&N) services and describe the contextual factors influencing these services. METHOD: We conducted a quantitative analysis of secondary data on RMNCAH&N indicators and undertook a supportive qualitative study to help understand processes and contextual factors. For quantitative analysis, we stratified the various provinces of Afghanistan into minimal-, moderate- and severe conflict categories based on battle-related deaths from Uppsala Conflict Data Program (UCDP) and through accessibility of health services using a Delphi methodology. The coverage of RMNCAH&N indicators across the continuum of care were extracted from the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Survey (MICS). The qualitative data was captured by conducting key informant interviews of multi-sectoral stakeholders working in government, NGOs and UN agencies. RESULTS: Comparison of various provinces based on the severity of conflict through Delphi process showed that the mean coverage of various RMNCAH&N indicators including antenatal care (OR: 0.42, 95%CI: 0.32-0.55), facility delivery (OR: 0.42, 95%CI: 0.32-0.56), skilled birth attendance (OR: 0.43, 95%CI: 0.33-0.57), DPT3 (OR: 0.26, 95% CI: 0.20-0.33) and oral rehydration therapy (OR: 0.37, 95% CI: 0.25-0.55) was significantly lower for severe conflict provinces when compared to minimal conflict provinces. The qualitative analysis identified various factors affecting decision making and service delivery including insecurity, cultural norms, unavailability of workforce, poor monitoring, lack of funds and inconsistent supplies. Other factors include weak stewardship, capacity gap at the central level and poor coordination at national, regional and district level. CONCLUSION: RMNCAH&N service delivery has been significantly hampered by conflict in Afghanistan over the last several years. This has been further compromised by poor infrastructure, weak stewardship and poor capacity and collaboration at all levels. With the potential of peace and conflict resolution in Afghanistan, we would underscore the importance of continued oversight and integrated implementation of sustainable, grass root RMNCAH&N services with a focus on reaching the most marginalized.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".