Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 187, Issue 12, Pages 2698-2704Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwy170
Keywords
air pollution; generalized Poisson model; negative binomial model; overdispersion; time-series analysis
Categories
Funding
- Environmental Protection Agency [R834799]
- National Institutes of Health [R01ES027892, UL1TR000454]
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Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Use of the Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating associations between daily air pollution concentrations and aggregated counts of adverse health events throughout a geographical region. We examined how the assumption of constant overdispersion plays a role in estimation of air pollution effects by comparing estimates derived from the standard approach with those estimated from covariate-dependent Bayesian generalized Poisson and negative binomial models that accounted for potential time-varying overdispersion. Through simulation studies, we found that while there was negligible bias in effect estimates, the standard quasi-Poisson approach can result in a larger standard error when the constant overdispersion assumption is violated. This was also observed in a time-series study of daily emergency department visits for respiratory diseases and ozone concentration in Atlanta, Georgia (1999-2009). Allowing for covariate-dependent overdispersion resulted in a reduction in the ozone effect standard error, while the ozone-associated relative risk remained robust to different model specifications. Our findings suggest that improved characterization of overdispersion in time-series modeling can result inmore precise health effect estimates in studies of short-term environmental exposures.
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