Global prevalence of, and risk factors for, gastro-oesophageal reflux symptoms: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Gastro-oesophageal reflux symptoms are common in the community, but there has been no definitive systematic review and meta-analysis of data from all studies to estimate their global prevalence, or potential risk factors for them. DESIGN: Medline, Embase and Embase Classic were searched (until September 2016) to identify population-based studies that reported the prevalence of gastro-oesophageal reflux symptoms in adults (≥15 years); gastro-oesophageal reflux was defined using symptom-based criteria or questionnaires. The prevalence was extracted for all studies, and according to the criteria used to define it. Pooled prevalence, according to study location and certain other characteristics, OR and 95% CIs were calculated. RESULTS: Of the 14 132 citations evaluated, 102 reported the prevalence of gastro-oesophageal reflux symptoms in 108 separate study populations, containing 460 984 subjects. Prevalence varied according to country (from 2.5% in China to 51.2% in Greece) and criteria used to define gastro-oesophageal reflux symptoms. When only studies using a weekly frequency of heart burn or regurgitation to define presence were considered, pooled prevalence was 13.3% (95% CI 12.0% to 14.6%). Prevalence was higher in subjects ≥50 years (OR 1.32; 95% CI 1.12 to 1.54), smokers (OR 1.26; 95% CI 1.04 to 1.52), non-steroidal anti-inflammatory drug (NSAID)/aspirin users (OR 1.44; 95% CI 1.10 to 1.88) and obese individuals (OR 1.73; 95% CI 1.46 to 2.06). CONCLUSIONS: The prevalence of gastro-oesophageal reflux symptoms varied strikingly among countries, even when similar definitions were used to define their presence. Prevalence was significantly higher in subjects ≥50 years, smokers, NSAID users and obese individuals, although these associations were modest.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.079 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".