Global epidemiology of diarrhea among internally displaced populations and refugee camp populations an evidence-based systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diarrhea remains a significant health burden among populations affected by inadequate water and sanitation, particularly in internally displaced persons (IDPs) and refugee camp populations. This systematic review and meta-analysis aim to quantify the global prevalence of diarrhea in these vulnerable groups. The findings will inform targeted interventions and policy decisions to mitigate the health impacts in these settings. METHODS: Systematic searches were conducted from PubMed, Epistemonikos, ScienceDirect, Scopus, Web of Science, and Embase, encompassing studies published up to June 10, 2024. Microsoft Excel 2021 was used for data extraction and STATA version 17 for statistical analyses. Newcastle-Ottawa Scale (NOS) assessed the methodological quality of included studies. The random-effects model was utilized to estimate the pooled prevalence. Publication bias was evaluated through funnel plots and Egger's regression test, while heterogeneity was measured using the I² statistic. The review protocol is registered with PROSPERO (registration number: CRD42024554686). RESULTS: The comprehensive search yielded 23 eligible articles, representing 121,037 samples. The pooled prevalence of diarrhea was 29% (95% CI: 24%, 33%). Subgroup analyses by continents revealed notable regional variations, the pooled prevalence in Asia was 35% (95% CI: 24%, 45%), Africa 28% (95% CI: 21%, 35%), and North America 9% (95% CI: 7%, 11%). Subgroup analysis by subcontinent showed that Southeast Asia with 74% (95% CI: 66%, 81%) and South-central Asia with 54% (95% CI: 44%, 63%) has the highest pooled prevalence from Asia. From Africa, East Africa with 35% (95% CI: 19%, 51%) and Southeastern Africa with 35% (95% CI: 32%, 38%) has highest pooled prevalence. Furthermore, by study setting, the pooled prevalence of diarrhea was 27% (95% CI: 22%, 31%) in IDPs and 32% (95% CI: 22%, 42%) in refugee camps. CONCLUSIONS: The results of this study underscore the significant global burden of diarrhea among IDPs and refugees living in camps. The implementation of robust health programs specifically tailored to IDPs and refugee camps, including regular screening, deworming, and comprehensive care initiatives, is critical. A multifaceted, holistic approach that addresses socio-economic, behavioral, and environmental determinants is essential to reduce the health burden of this vulnerable population.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".