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. 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 wh

SUBMITTER: Pan A 

PROVIDER: S-EPMC6269244 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature