Grantee Research Project Results

Long- and short-term exposure to PM_2.5 and mortality: using novel exposure models.

Citation:

Kloog I, Ridgway B, Koutrakis P, Coull BA, Schwartz JD. Long-and short-term exposure to PM_2.5 and mortality:using novel exposure models. Epidemiology 2013;24(4):555-561.

Abstract:

BACKGROUND: Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM_2.5 exposures on population mortality in Massachusetts, United States, for the years 2000-2008. METHODS: All deaths were geocoded. We performed two separate analyses: a time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM_2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metrics: regional 10 × 10 km PM_2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases). RESULTS: For short-term exposure, we found that for every 10-µg/m³ increase in PM_2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval [CI] = 2.0-3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-µg/m³ increase in long-term PM_2.5 exposure of 1.6 (CI = 1.5-1.8) for particle-related diseases. Local PM_2.5 had an OR of 1.4 (CI = 1.3-1.5), which was independent of and additive to the grid cell effect. CONCLUSIONS: We have developed a novel PM_2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a selective sample from urban locations.