Drug Resistance Patterns of Commonly Used Antibiotics for the Treatment of Helicobacter pylori Infection among South Asian Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: In South Asia, resistance to commonly used antibiotics for the treatment of Helicobacter pylori infection is increasing. Despite this, accurate estimates of overall antibiotic resistance are missing. Thus, this review aims to analyze the resistance rates of commonly used antibiotics for the treatment of H. pylori in South Asia. Methods: The systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. We searched five medical databases for relevant studies from inception to September 2022. A random effect model with a 95% confidence interval (CI) was used to calculate the pooled prevalence of antibiotic resistance. Results: This systematic review and meta-analysis included 23 articles, 6357 patients, 3294 Helicobacter pylori isolates, and 2192 samples for antibiotic resistance. The prevalences of antibiotic resistance to common antibiotics were clarithromycin: 27% (95%CI: 0.17–0.38), metronidazole: 69% (95%CI: 0.62–0.76), tetracycline: 16% (95%CI: 0.06–0.25), amoxicillin: 23% (95%CI: 0.15–0.30), ciprofloxacin: 12% (95%CI: 0.04–0.23), levofloxacin: 34% (95%CI: 0.22–0.47), and furazolidone: 14% (95%CI: 0.06–0.22). Subgroup analysis showed antibiotic resistances were more prevalent in Pakistan, India, and Bangladesh. Furthermore, a ten-year trend analysis showed the increasing resistance prevalence for clarithromycin (21% to 30%), ciprofloxacin (3% to 16%), and tetracycline (5% to 20%) from 2003 to 2022. Conclusion: This meta-analysis showed a high prevalence of resistance among the commonly used antibiotics for H. pylori in South Asian countries. Furthermore, antibiotic resistance has been increasing over the time of 20 years. In order to tackle this situation, a robust surveillance system, and strict adherence to antibiotic stewardship are required.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".