4.7 Article

Estimating the mortality impacts of particulate matter: What can be learned from between-study variability?

期刊

ENVIRONMENTAL HEALTH PERSPECTIVES
卷 108, 期 2, 页码 109-117

出版社

US DEPT HEALTH HUMAN SCIENCES PUBLIC HEALTH SCIENCE
DOI: 10.2307/3454508

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air pollution; confounding; empirical Bayes; epidemiology; hierarchical linear models; meta-analysis; mortality; particulate matter

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Epidemiologic studies of the link between particulate matter (PM) concentrations and mortality rates have yielded a range of estimates, leading to disagreement about the magnitude of the relationship and the strength of the causal connection. Previous meta-analyses of this literature have provided pooled effect estimates, but have not addressed between-study variability that may be associated,vith analytical models, pollution patterns, and exposed populations. To determine whether study-specific factors can explain some of the variability in the time-series studies on mortality from particulate matter less than or equal to 10 mu m in aerodynamic diameter (PM10), we applied an empirical Bayes meta-analysis. We estimate that mortality rates increase on average by 0.7% per 10 mu g/m(3) increase in PM10 concentrations, with greater effects at sites with higher ratios of particulate matter less than or equal to 2.5 mu m in aerodynamic diameter (PM2.5)/PM10. This finding did not change with the inclusion of a number of potential confounders and effect modifiers, although there is some evidence that PM effects are influenced by climate, housing characteristics, demographics, and the presence of sulfur dioxide and ozone. Although further analysis would be needed to determine which factors causally influence the relationship between PM10 and mortality, these findings can help guide future epidemiologic investigations and policy decisions.

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