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

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. PMID: 30099479 [PubMed - as supplied by publisher]
Source: Am J Epidemiol - Category: Epidemiology Authors: Tags: Am J Epidemiol Source Type: research