The use of Bayesian inference to inform the surveillance of temperature-related occupational morbidity in Ontario, Canada, 2004–2010
Bibliographic record
Abstract
PURPOSE: To assess the associations of occupational heat and cold-related illnesses presenting in emergency departments in south western Ontario, Canada, with daily meteorological conditions using Bayesian inference. METHODOLOGY: Meteorological and air pollution data for the south western economic region of Ontario were gathered from Environment Canada and the Ministry of Environment. Daily heat and cold-related emergency department visits clinically attributed to work from 2004 to 2010 were tabulated. A novel application of Bayesian inference on a flexible Poisson time series model was undertaken to examine linear and non-linear associations between average, regional meteorological conditions and daily morbidity rates, to adjust for relevant confounders and temporal trends, and to consider potential interactions. RESULTS: Bilinear associations were observed between regional temperatures and morbidities resulting from extreme temperature exposures. The median increase in the daily rate of emergency department visits for heat illness was 75% for each degree above 22°C (posterior 95% credible interval (CI) relative rate=1.56-1.99) in the daily maximum temperature. Below 0°C, rates of occupational cold illness increased by a median of 15% for each degree decrease in the minimum temperature (posterior 95% CI 0.80-0.91); wind speed also had a significant effect. CONCLUSIONS: The observed associations can inform occupational surveillance and injury prevention programming, as well as public health efforts targeting vulnerable populations. Methodologically, the use of Bayesian inference in time series analyses of meteorological exposures is feasible and conducive to providing accurate advice for policy and practice.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".