Air Pollution and Mortality

Abstract

We analyzed a national data base of air pollution and mortality for the 88 largest U.S. cities for the period 1987–1994, to estimate relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM 10 ) and the form of the relationship between PM 10 concentration and mortality. To estimate city-specific relative rates of mortality associated with PM 10 , we built log-linear models that included nonparametric adjustments for weather variables and longer term trends. To estimate PM 10 mortality dose-response curves, we modeled the logarithm of the expected value of daily mortality as a function of PM 10 using natural cubic splines with unknown numbers and locations of knots. We also developed spatial models to investigate the heterogeneity of relative mortality rates and of the shapes of PM 10 mortality dose-response curves across cities and geographical regions. To determine whether variability in effect estimates can be explained by city-specific factors, we explored the dependence of relative mortality rates on mean pollution levels, demographic variables, reliability of the pollution data, and specific constituents of particulate matter. We implemented estimation with simulation-based methods, including data augmentation to impute the missing data of the city-specific covariates and the reversible jump Markov chain Monte Carlo (RJMCMC) to sample from the posterior distribution of the parameters in the hierarchical spline model. We found that previous-day PM 10 concentrations were positively associated with total mortality in most the locations, with a .5% increment for a 10 μg/m 3 increase in PM 10 . The effect was strongest in the Northeast region, where the increase in the death rate was twice as high as the average for the other cities. Overall, we found that the pooled concentration-response relationship for the nation was linear.