Daily mortality/morbidity and air quality: Using multivariate time series with seasonally varying covariances

Abstract

We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter ((Formula presented.)), nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) in four cities in Canada. First, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half-Cauchy distributions and the correlations between pollutants vary seasonally. A case-crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase in (Formula presented.) is associated with a 3.88% (95% credible interval [CI], 2.5%, 5.18%) increase in all-cause mortality, a 5.04% (2.84%, 7.43%) increase in circulatory mortality, a 7.87% (2.4%, 12.9%) increase in respiratory mortality, a 0.76% (0.19%, 1.35%) increase in all-cause morbidity and a 6.6% (0.58%, 12.7%) increase in respiratory morbidity. Similarly, every 10 ppb increase in (Formula presented.) is associated with a 2.13% (0.42%, 3.87%) increase in circulatory morbidity. The health impacts of (Formula presented.) are not found to be present once other pollutants are accounted for.