Epidemiologic studies on acute effects of air pollution have generally been limited to larger cities, leaving questions about rural populations behind. Recently, we had developed a spatiotemporal model to predict daily PM 2.5 level at a 1 km 2 using satellite aerosol optical depth (AOD) data. Based on the results from the model, we applied a case-crossover study to evaluate the acute effect of PM 2.5 on mortality in North Carolina, South Carolina, and Georgia between 2007 and 2011. Mortality data were acquired from the Departments of Public Health in the States and modeled PM 2.5 exposures were assigned to the zip code of residence of each decedent. We performed various stratified analyses by age, sex, race, education, cause of death, residence, and environmental protection agency (EPA) standards. We also compared results of analyses using our modeled PM 2.5 levels and those imputed daily from the nearest monitoring station. 848,270 non-accidental death records were analyzed and we found each 10 μg/m 3 increase in PM 2.5 (mean lag 0 and lag 1) was associated with a 1.56% (1.19 and 1.94) increase in daily deaths. Cardiovascular disease (2.32%, 1.57-3.07) showed the highest effect estimate. Blacks (2.19%, 1.43-2.96) and persons with education ≤8 year (3.13%, 2.08-4.19) were the most vulnerable populations. The effect of PM 2.5 on mortality still exists in zip code areas that meet the PM 2.5 EPA annual standard (2.06%, 1.97-2.15). The effect of PM 2.5 below both EPA daily and annual standards was 2.08% (95% confidence interval=1.99-2.17). Our results showed more power and suggested that the PM 2.5 effects on rural populations have been underestimated due to selection bias and information bias. We have demonstrated that our AOD-based exposure models can be successfully applied to epidemiologic studies. This will add new study populations in rural areas, and will confer more generalizability to conclusions from such studies.
|Number of pages||7|
|Journal||Journal of Exposure Science and Environmental Epidemiology|
|State||Published - 1 Mar 2016|
- case-crossover study
- criteria pollutants
- exposure modeling