Estimating effects of ambient PM2.5 exposure on health using PM2.5 component measurements and regression calibration

Most air pollution and health studies conducted in recent years have examined how a health outcome is related to pollution concentrations from a fixed outdoor monitor. The pollutant effect estimate in the health model used indicates how ambient pollution concentrations are associated with the health outcome, but not how actual exposure to ambient pollution is related to health. In this article, we propose a method of estimating personal exposures to ambient PM2.5 (particulate matter less than 2.5 μm in diameter) using sulfate, a component of PM2.5 that is derived primarily from ambient sources. We demonstrate how to use regression calibration in conjunction with these derived values to estimate the effects of personal ambient PM2.5 exposure on a continuous health outcome, forced expiratory volume in 1 s (FEV1), using repeated measures data. Through simulation, we show that a confidence interval (CI) for the calibrated estimator based on large sample theory methods has an appropriate coverage rate. In an application using data from our health study involving children with moderate to severe asthma, we found that a 10 μg/m3 increase in PM2.5 was associated with a 2.2% decrease in FEV1 at a 1-day lag of the pollutant (95% CI: 0.0–4.3% decrease). Regressing FEV1 directly on ambient PM2.5 concentrations from a fixed monitor yielded a much weaker estimate of 1.0% (95% CI: 0.0–2.0% decrease). Relatively small amounts of personal monitor data were needed to calibrate the estimate based on fixed outdoor concentrations.