A Regression Approach for Estimating Multiday Adverse Health Effects of PM 10 When Daily PM 10 Data Are Unavailable
AbstractThe authors propose a regression-based approach for obtaining multiday estimates of the adverse health effects of ambient particulate matter less than 10 μm in diameter (PM 10 ) when daily PM 10 time-series data are unavailable. This situation is common in the United States, because most US cities take PM 10 measurements every 6 days. Current evidence suggests that adverse effects of PM 10 are not concentrated on a single day but rather are spread out over multiple days, so the unavailability of daily PM 10 data presents a problem for the estimation of these effects. The proposed model estimates weights that are used to construct a linear combination of single-lag PM 10 effect estimates obtained from the available PM 10 data. It is shown that this new approach provides estimates of the effect of PM 10 on mortality that have less bias and mean squared error than currently available methods. Application of this method to the US cities contained in the National Morbidity, Mortality, and Air Pollution Study database produces an estimated national average effect of PM 10 on nonaccidental mortality in persons over age 65 years, corresponding to a 0.32% increase per 10-μg/m 3 increment in PM 10 . The estimated effects for cardiorespiratory mortality and other mortality are 0.34% and 0.22%, respectively.