Time-Series Analysis of Air Pollution and Health Accounting for Covariate-Dependent Overdispersion

Abstract

Time-series studies are routinely used to estimate associations between adverse health outcomes and short-term exposures to ambient air pollutants. Poisson log-linear model with the assumption of constant overdispersion is the most common approach, particularly when estimating association between daily air pollution concentrations and aggregated counts of adverse health events over a geographical region. We examined how the assumption of constant overdispersion plays a role in air pollution effect estimation by comparing estimates derived from standard approach to 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 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 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 in more precise health effect estimates in studies of short-term environmental exposures.