Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: The epidemiology of Helicobacter pylori infection has changed with improvements in sanitation and methods of eradication. We performed a systematic review and meta-analysis to evaluate changes in the global prevalence of H pylori infection. METHODS: We performed a systematic search of the MEDLINE and EMBASE databases for studies of the prevalence of H pylori infection published from January 1, 1970 through January 1, 2016. We analyzed data based on United Nations geoscheme regions and individual countries. We used a random effects model to calculate pooled prevalence estimates with 95% confidence intervals (CIs), weighted by study size. We extrapolated 2015 prevalence estimates to obtain the estimated number of individuals with H pylori infection. RESULTS: Among 14,006 reports screened, we identified 263 full-text articles on the prevalence of H pylori infection; 184 were included in the final analysis, comprising data from 62 countries. Africa had the highest pooled prevalence of H pylori infection (70.1%; 95% CI, 62.6-77.7), whereas Oceania had the lowest prevalence (24.4%; 95% CI, 18.5-30.4). Among individual countries, the prevalence of H pylori infection varied from as low as 18.9% in Switzerland (95% CI, 13.1-24.7) to 87.7% in Nigeria (95% CI, 83.1-92.2). Based on regional prevalence estimates, there were approximately 4.4 billion individuals with H pylori infection worldwide in 2015. CONCLUSIONS: In a systematic review and meta-analysis to assess the prevalence of H pylori infection worldwide, we observed large amounts of variation among regions-more than half the world's population is infected. These data can be used in development of customized strategies for the global eradication.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".