Going with the Flow: Legionellosis Risk in Toronto, Canada Is Strongly Associated with Local Watershed Hydrology
Bibliographic record
Abstract
Legionella species are increasingly recognized as a cause of both healthcare- and community-acquired pneumonia (so-called "Legionnaire's disease"). These pathogens are ubiquitous in the environment, but environmental factors in the occurrence of sporadic legionellosis remain poorly understood. We analyzed all legionellosis cases identified in the Greater Toronto Area of Ontario from 1978 to 2006, and evaluated seasonal and environmental patterns in legionellosis case occurrence by using both negative binomial models and case-crossover analysis. A total of 837 cases were reported during the study period. After adjusting for seasonal effects, changes in the local watershed, rather than weather, were the strongest contributors to legionellosis risk. A 3.6-fold increase (95% confidence interval (CI), 2.4-5.3) in odds of disease was identified with decreasing watershed levels approximately 4 weeks before case-occurrence. We also found a 33% increase (95% CI, 8-64%) in odds of disease with decreasing lake temperature during the same period and a 34% increase (95% CI, 14-57%) with increasing humidity 5 weeks before case-occurrence. We conclude that local watershed ecology influences the risk of legionellosis, notwithstanding the availability of advanced water treatment capacity in Toronto. Enhancement of risk might occur through direct contamination of water sources or via introduction of micronutrients or commensal organisms into residential and hospital water supplies. These observations suggest testable hypotheses for future empiric studies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".