Daily Mortality/Morbidity and Air Quality: Using Multivariate Time Series with Seasonally Varying Covariances. Issue 1 (21st January 2022)

• Record Type: Journal Article
• Authors: Huang, Guowen; Brown, Patrick E.; Fu, Sze Hang; Shin, Hwashin Hyun
• Is Part Of: Journal of the Royal Statistical Society. Volume 71:Issue 1(2022)
• Page Start: 148
• Page End: 174
• Publication Date: 2022-01-21
• Subjects: Bayesian inference -- case crossover -- daily mortality -- environmental epidemiology -- time series; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12525
• Languages: English
• ISSNs: 0035-9254

Abstract

Abstract: We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ) and ozone (O 3 ) 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 O 3 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 NO 2 is associated with a 2.13% (0.42%, 3.87%) increase in circulatory morbidity. The health impacts of PM 2.5 are not found to be present once other pollutants are accounted for.