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IMPLICATIONS OF SELECTING ALTERNATIVE EXPOSURE METRICS IN ANALYZING THE RELATIONSHIPS BETWEEN PM AND ACUTE MORTALITY AND MORBIDITY IN PHILADELPHIA
Citation:
Burke, J M., K. Ito, G A. Norris, A H. Ozkaynak, AND W Wilson. IMPLICATIONS OF SELECTING ALTERNATIVE EXPOSURE METRICS IN ANALYZING THE RELATIONSHIPS BETWEEN PM AND ACUTE MORTALITY AND MORBIDITY IN PHILADELPHIA. Presented at International Society of Exposure Analysis 2002 Conference, Vancouver, Canada, August 11-15, 2002.
Impact/Purpose:
The primary objective of this research is to improve current PM population exposure models to more accurately predict exposures for the general population and susceptible sub-populations. Through model improvements, a better understanding of the major factors controlling exposure to PM will be achieved. Specific objectives of this research are to:
- predict total personal exposure to PM10 and PM2.5 for the general and for susceptible sub-populations residing in different urban environments
- estimate the contribution of ambient PM to predicted total PM exposures
- determine what factors are of primary importance in determining PM exposures, including an analysis of the effects of time spent in various microenvironments and the importance of spatial variability in ambient PM concentrations
- determine what factors contribute the greatest uncertainty to model predictions and make recommendations for measurement and modeling studies to reduce these uncertainties
- predict daily and annual average exposures using single or multi-day time-activity diaries
- incorporate state-of-the-art dosimetric models of the lung into PM population exposure and dose models
- evaluate models against measured data from PM panel and other exposure measurement studies
- develop exposure and dose metrics applicable to acute and chronic environmental epidemiology studies
Description:
The US EPA National Exposure Research Laboratory (NERL) has developed a population exposure model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model. The SHEDS-PM model estimates the population distribution of PM exposures by randomly sampling from distributions of ambient PM concentrations and exposure factors to estimate the distributions of daily-averaged total PM exposures. Daily-averaged total PM exposure for each simulated individual is determined from time-weighted PM concentrations for the various microenvironments (e.g., indoors at home, in vehicles, outdoors, etc.) the individual spent time in. Output from the SHEDS-PM model also includes the contributions to total PM exposure from PM of ambient origin and from indoor sources of PM.
The SHEDS-PM model was applied to the population of Philadelphia, PA, using PM2.5 mass measurements collected daily at one site over a three year period (May 1992 to Sept. 1995). SHEDS-PM predicted total PM2.5 exposures, ambient-origin PM2.5 exposures, and non-ambient (indoor source) PM2.5 exposures were used in an epidemiological analysis and the results were compared with associations obtained using the daily ambient PM2.5 measurement data. Daily cardiovascular mortality, total mortality, cardiovascular hospital admissions, and respiratory hospital admissions were counted for the Philadelphia metropolitan statistical area (PMSA). A Poisson generalized additive model was used to regress the health outcomes on each of the SHEDS-PM predicted PM2.5 exposures (total, ambient-origin, and non-ambient), adjusting for seasonal cycles, day-of-week, major holidays, and weather effects. Distributed lags of 0 through 6 days for the SHEDS-PM predicted exposures were computed, and the sum of the coefficients and standard error (using variance/covariance matrix of lagged coefficients) were computed.
The SHEDS-PM model predictions of ambient-origin PM2.5 exposures produced similar associations (magnitude and significance) with health effects to those obtained using the ambient PM2.5 measurement data. No significant relationships were observed for SHEDS-PM predictions of non-ambient PM2.5 exposures. However, associations with health effects for the total PM2.5 exposures predicted by SHEDS-PM varied depending on the demographic or housing covariates (e.g., data on residential HVAC system characteristics) selected or the distribution statistic used (e.g., average, median, 90th percentile) in the analyses. These results indicate that the SHEDS-PM model can provide alternative exposure metrics that are useful for enhancing the interpretation of epidemiological associations between PM2.5 and health effects.
This work has been wholly funded by the United States Environmental Protection Agency under cooperative agreement number CR827358 to New York University School of Medicine. It has been subjected to Agency Review and approved for publication.