A Bayesian hierarchical distributed lag model for estimating the time course of hospitalization risk associated with particulate matter air pollution

Time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased hospitalizations, typically at a single lag of 0, 1, or 2 days after an air pollution episode. Two important scientific objectives are to better understand how the risk of hospitalization associated with a given day’s air pollution increase is distributed over multiple days in the future and to estimate the cumulative short-term health effect of an air pollution episode over the same multi-day period. We propose a Bayesian hierarchical distributed lag model that integrates information from national health and air pollution databases with prior knowledge of the time course of hospitalization risk after an air pollution episode. This model is applied to air pollution and health data on 6.3 million enrollees of the US Medicare system living in 94 counties covering the years 1999–2002. We obtain estimates of the distributed lag functions relating fine particulate matter pollution to hospitalizations for both ischemic heart disease and acute exacerbation of chronic obstructive pulmonary disease, and we use our model to explore regional variation in the health risks across the US.