Proton pump inhibitors and the risk of hospitalisation for community-acquired pneumonia: replicated cohort studies with meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Previous observational studies suggest that the use of proton pump inhibitors (PPIs) may increase the risk of hospitalisation for community-acquired pneumonia (HCAP). However, the potential presence of confounding and protopathic biases limits the conclusions that can be drawn from these studies. Our objective was, therefore, to examine the risk of HCAP with PPIs prescribed prophylactically in new users of non-steroidal anti-inflammatory drugs (NSAIDs). DESIGN: We formed eight restricted cohorts of new users of NSAIDs, aged ≥40 years, using a common protocol in eight databases (Alberta, Saskatchewan, Manitoba, Ontario, Quebec, Nova Scotia, US MarketScan and the UK's General Practice Research Database (GPRD)). This specific patient population was studied to minimise bias due to unmeasured confounders. High-dimensional propensity scores were used to estimate site-specific adjusted ORs (aORs) for HCAP at 6 months in PPI patients compared with unexposed patients. Fixed-effects meta-analytic models were used to estimate overall effects across databases. RESULTS: Of the 4,238,504 new users of NSAIDs, 2.3% also started a PPI. The cumulative 6-month incidence of HCAP was 0.17% among patients prescribed PPIs and 0.12% in unexposed patients. After adjustment, PPIs were not associated with an increased risk of HCAP (aOR=1.05; 95% CI 0.89 to 1.25). Histamine-2 receptor antagonists yielded similar results (aOR=0.95, 95% CI 0.75 to 1.21). CONCLUSIONS: Our study does not support the proposition of a pharmacological effect of gastric acid suppressors on the risk of HCAP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.053 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 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".