Estimating short-term PM effects accounting for surrogate exposure measurements from ambient monitors

Abstract

We propose a framework for modeling short-term pollutant exposure effects of ambient source personal exposures using ambient monitoring data. We begin by outlining a model for the health effects of air pollutants on individuals given true pollutant exposure. This is aggregated to form a group-level model of pollutant effects. The aggregation preserves the individual-level interpretation of the exposure effect. However, only surrogate exposure data are available from ambient monitors. We therefore also consider the exposure distribution and measurement characteristics of the pollutant data. We combine these with the disease model of interest and discuss estimation of the exposure effect in the presence of the additional modeling. While the framework includes spatial variation of exposure, we simplify the implementation to ignore the spatial component. We examine the approach in simulations and apply it to asthma hospital admissions and particulate matter data from greater Seattle. The results indicate that adjustment for measurement error alone, without inclusion of spatial variation in ambient PM or fraction of ambient air exposure, does not alter the conclusions from the simpler ecologic time series regression analyses typically reported in the literature. Copyright © 2000 John Wiley & Sons, Ltd.