The use of Bayesian inference to inform the surveillance of temperature-related occupational morbidity in Ontario, Canada, 2004-2010.

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.