Burden of Childhood Diarrhea and Its Associated Factors in Ethiopia: A Review of Observational Studies
Bibliographic record
Abstract
Objectives: This systematic review and meta-analysis aimed to: i) determine the pooled prevalence of acute diarrhea; and ii) synthesize and summarize current evidence on factors of acute diarrheal illnesses among under-five children in Ethiopia. Methods: A comprehensive systematic search was conducted in PubMed, SCOPUS, HINARI, Science Direct, Google Scholar, Global Index Medicus, Directory of Open Access Journals (DOAJ), and the Cochrane Library. This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The methodological quality of each included article was assessed using the Joanna Briggs Institute (JBI) quality assessment tool for cross-sectional and case-control studies. A random-effect meta-analysis model was used to estimate the pooled prevalence of diarrheal illnesses. Heterogeneity and publication bias were assessed using I 2 test statistics and Egger’s test, respectively. The statistical analysis was done using STATA™ software version 14. Results: Fifty-three studies covering over 27,458 under-five children who met the inclusion criteria were included. The pooled prevalence of diarrhea among under-five children in Ethiopia was found to be 20.8% (95% CI: 18.69–22.84, n = 44, I 2 = 94.9%, p < 0.001). Our analysis revealed a higher prevalence of childhood diarrhea in age groups of 12–23 months 25.42% (95%CI: 21.50–29.35, I 2 = 89.4%, p < 0.001). In general, the evidence suggests that diarrheal risk factors could include: i) child level determinants (child’s age 0–23 months, not being vaccinated against rotavirus, lack of exclusive breastfeeding, and being an under-nourished child); ii) parental level determinants {mothers poor handwashing practices [pooled odds ratio (OR) = 3.05; 95% CI:2.08–4.54] and a history of maternal recent diarrhea (pooled OR = 3.19, 95%CI: 1.94–5.25)}; and iii) Water, Sanitation and Hygiene (WASH) determinants [lack of toilet facility (pooled OR = 1.56, 95%CI: 1.05–2.33)], lack handwashing facility (pooled OR = 4.16, 95%CI: 2.49–6.95) and not treating drinking water (pooled OR = 2.28, 95% CI: 1.50–3.46). Conclusion: In Ethiopia, the prevalence of diarrhea among children under the age of five remains high and is still a public health problem. The contributing factors to acute diarrheal illnesses were child, parental, and WASH factors. A continued focus on improving access to WASH facilities, along with enhancing maternal hygiene behavior will accelerate reductions in diarrheal disease burden in Ethiopia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".