Time-Series Analyses of Air Pollution and Mortality in the United States: A Subsampling Approach

Background: Hierarchical Bayesian methods have been used in previous papers to estimate national mean effects of air pollutants on daily deaths in time-series analyses. Objectives: We obtained maximum likelihood estimates of the common national effects of the criteria pollutants on mortality based on time-series data from ≤ 108 metropolitan areas in the United States. Methods: We used a subsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for common national effects of the criteria pollutants, as measured by the percentage increase in daily mortality associated with a unit increase in daily 24-hr mean pollutant concentration on the previous day, while controlling for weather and temporal trends. We considered five pollutants [PM(10), ozone (O(3)), carbon monoxide (CO), nitrogen dioxide (NO(2)), and sulfur dioxide (SO(2))] in single- and multipollutant analyses. Flexible ambient concentration–response models for the pollutant effects were considered as well. We performed limited sensitivity analyses with different degrees of freedom for time trends. Results: In single-pollutant models, we observed significant associations of daily deaths with all pollutants. The O(3) coefficient was highly sensitive to the degree of smoothing of time trends. Among the gases, SO(2) and NO(2) were most strongly associated with mortality. The flexible ambient concentration–response curve for O(3) showed evidence of nonlinearity and a threshold at about 30 ppb. Conclusions: Differences between the results of our analyses and those reported from using the Bayesian approach suggest that estimates of the quantitative impact of pollutants depend on the choice of statistical approach, although results are not directly comparable because they are based on different data. In addition, the estimate of the O(3)-mortality coefficient depends on the amount of smoothing of time trends.