Estimating the burden of illness caused by domestic waterborne Legionnaires’ disease in Canada: 2015–2019
Bibliographic record
Abstract
Abstract Legionellosis is a disease caused by the bacterium Legionella that most commonly presents as Legionnaires’ disease (LD), a severe form of pneumonia. From 2015 to 2019, an average of 438 LD cases per year were reported in Canada. However, it is believed that the actual number of cases is much higher, since LD may be underdiagnosed and underreported. The purpose of this study was to develop an estimate of the true incidence of illnesses, hospitalizations, and deaths associated with LD in Canada. Values were derived using a stochastic model, based on Canadian surveillance data from 2015 to 2019, which were scaled up to account for underdiagnosis and underreporting. Overall, there were an estimated 1,113 (90% CrI: 737–1,730) illnesses, 1,008 (90% CrI: 271–2,244) hospitalizations, and 34 (90% CrI: 4–86) deaths due to domestically acquired waterborne LD annually in Canada from 2015 to 2019. It was further estimated that only 36% of illnesses and 39% of hospitalizations and deaths were captured in surveillance, and that 22% of illnesses were caused by Legionella serogroups and species other than Legionella pneumophila serogroup 1 (non-Lp1). This study highlights the true burden and areas for improvement in Canada’s surveillance and detection of LD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".