Global Perspective of Legionella Infection in Community-Acquired Pneumonia: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Legionnaires’ disease (LD) (Legionella) is a common cause of community-acquired pneumonia (CAP) in those requiring hospitalization. Geographical variation in the importance of Legionella species as an aetiologic agent of CAP is poorly understood. We performed a systematic review and meta-analysis of population-based observational studies that reported the proportion of Legionella infection in patients with CAP (1 January 1990 to 31 May 2020). Using five electronic databases, articles were identified, appraised and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. Univariate and multivariate meta-regression analyses were conducted using study design, WHO region, study quality and healthcare setting as the explanatory variables. We reviewed 2778 studies, of which 219 were included in the meta-analysis. The mean incidence of CAP was 46.7/100,000 population (95% CI: 46.6–46.8). The mean proportion of Legionella as the causative agent for CAP was 4.6% (95% CI: 4.4 to 4.7). Consequently, the mean Legionella incidence rate was 2.8/100,000 population (95% CI: 2.7–2.9). There was significant heterogeneity across all studies I2 = 99.27% (p < 0.0001). After outliers were removed, there was a decrease in the heterogeneity (I2 = 43.53%). Legionella contribution to CAP has a global distribution. Although the rates appear highest in high income countries in temperate regions, there are insufficient studies from low- and middle-income countries to draw conclusions about the rates in these regions. Nevertheless, this study provides an estimate of the mean incidence of Legionella infection in CAP, which could be used to estimate the regional and global burden of LD to support efforts to reduce the impact of this infection as well as to fill important knowledge gaps.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.056 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".