A Novel Approach to Estimate Distributed Lag Model Between Hospital Admissions and Ozone: A Multi-City Time Series Analysis

Abstract

ISEE-0507 Background and Objective: Time series analyses have been extensively used to examine the short-term effects of air pollution on health. Standard methods have been used to eliminate confounding by long term and seasonal trends. Distributed lag models have been used to examine whether the exposure effect persists for some time. This creates two issues; first ozone concentrations are serially correlated over time, leading to oscillations in the effect size from lag to lag. Second, from a causal modeling viewpoint, it is preferable to have the exposure as close as possible to randomly assigned. Haugh and Box proposed filtering exposure to remove all autocorrelation, resulting in random fluctuations in differences from expected exposure over time. We apply this approach to season specific analyses to examine the distributed lag between ozone and cardiovascular and respiratory hospital admissions in 92 US cities for the years 1985–2003, for the months May to September. Methods: We fit a city-specific quasi Poisson regression model controlling for seasonality, temperature, and day of the week. We used ARIMA models to pre-filter the ozone series and its lags. Results: We found a 0.14% increase (95% CI: 0.05–0.23) and a 0.10% increase (95% CI:0.01–0.20) in CVD admissions for 10 ppb increase in the same day 8-hour mean ozone when using the original and pre-filtered ozone respectively. The sum of the six day distributed lag increased from −0.23% (95% CI: −0.46 −0.01) to 0.53% (95% CI: 0.23–0.81) for CVD and from 2.11% (95% CI: 1.67–2.54) to 4.4% (95% CI: 3.8–4.9) for respiratory disease when using the pre-filtered ozone vs. the original series. Conclusions: This study presents a new approach to examine distributed lag models and shows that the collinearity between lags could underestimate the exposure health effects. Funded by: EPA RD83241601 and EPA STAR grant R832752010.