Armed conflict and treatment interruptions: A systematic review and meta-analysis in Amhara, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Armed conflicts significantly disrupt healthcare systems, leading to infrastructure destruction, shortages of medical supplies, and reduced access to essential health services. The Amhara region has experienced prolonged conflict, raising concerns about its impact on healthcare delivery. Understanding the extent of these disruptions is crucial for informing policy responses and humanitarian interventions. OBJECTIVES: This systematic review and meta-analysis aimed to assess the impact of armed conflict on healthcare delivery in the Amhara region. STUDY DESIGN: Systematic review and meta-analysis. METHODS: Conducted between June 1 and July 10, 2024, this meta-analysis followed PRISMA guidelines. A comprehensive search was performed across PubMed/MEDLINE, EMBASE, CINAHL, Google Scholar, ScienceDirect, and the Cochrane Library. Eligible studies included English-language observational studies and grey literature addressing healthcare disruptions, infrastructure damage, and health crises. Data were analyzed using STATA Version 14, and study quality was assessed using a modified Newcastle-Ottawa Scale. RESULTS: Twelve studies, encompassing 12,037,279 participants, were included. The pooled prevalence of health impacts was 76.71 % (95 % CI: 76.63-76.78). The conflict rendered 60 % of healthcare facilities nonfunctional, disrupted medical supplies for 70 % of the population, and reduced service availability by 80 %. Chronic disease management, mental health services, maternal care, and immunization programs experienced significant declines. Subgroup analyses indicated a higher prevalence of health impacts in studies published after 2022 (70.72 %) compared to those published before 2022 (28.35 %). CONCLUSION: Armed conflict in the Amhara region has severely disrupted healthcare services, leading to facility closures, medical supply shortages, and significant declines in essential health services. Urgent interventions are required to restore healthcare infrastructure and services.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".