Quantifying the relationship between daily changes in air pollution and
short-term effects on mortality and hospitalisations is a complex task with
many steps. Air pollution data contains missing values and outliers.
Uncertainty in exposures should be reflected in uncertainty in effect sizes.
Inference must account for the multitude of factors unrelated to air
pollution that can influence mortality. Exposure-response effects are
non-linear. Analyses across multiple cities and regions must take into
account possible city-level variation in effects.

This talk will describe ongoing work undertaken in collaboration with from Health
Canada to estimate health effects from pollution in 50 Canadian cities. A
key feature of the project is the case-crossover model, a form of partial
likelihood which adjusts for most changes in time using control days.