Helicobacter pylori infections in Ethiopia; prevalence and associated factors: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Helicobacter pylori (H.pylori) infections are prevalent and recognized as major cause of gastrointestinal diseases in Ethiopia. However, Studies conducted on the prevalence, risk factors and other clinical forms of H.pylori on different population and geographical areas are reporting conflicting results. Therefore, this review was conducted to estimate the pooled prevalence of H.pylori infections and associated factors in Ethiopia. Methods PubMed, Embase, Google scholar, and Ethiopian Universities’ repositories were searched following the Preferred Items for Systematic review and Meta-analysis (PRISMA) guideline. The quality of included studies was assessed using the Newcastle-Ottawa Scale in meta-analysis. Heterogeneity between studies was assessed using Cochrane Q test and I2 test statistics based on the random effects model. Comprehensive meta-analysis (CMA 2.0) and Review Manager (RevMan 5.3) were employed to compute the pooled prevalence and summary odds ratios of factors associated with of H.pylori infection. Results Thirty seven studies with a total of 18,890 participants were eligible and included in the analysis. The overall pooled prevalence of H.pylori infection was 52.2% (95% CI: 45.8–58.6). In the subgroup analysis by region, the highest prevalence was found in Somalia (71%; 95% CI: 32.5–92.6) and the lowest prevalence was reported in Oromia (39.9%; 95% CI: 17.3–67.7). Absence of hand washing after toilet (OR = 1.8, 95% CI; 1.19–2.72), alcohol consumption (OR = 1.34, 95% CI; 1.03–1.74) and gastrointestinal (GI) symptoms (OR = 2.23, 95% CI; 1.59–3.14) were associated with H.pylori infection. The trend of H.pylori infection showed a decreasing pattern overtime from 1990 to 2017 in the meta-regression analysis. Conclusion The prevalence of H.pylori infection remains high; more than half of Ethiopians were infected. Although the trend of infection showed a decreasing pattern; appropriate use of eradication therapy, health education primarily to improve knowledge and awareness on the transmission dynamics of the bacteria, behavioral changes, adequate sanitation, population screening and diagnosis using multiple tests are required to reduce H.pylori infections. Recognizing the bacteria as a priority issue and designing gastric cancer screening policies are also recommended.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".