Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator

This paper describes a modeling framework for estimating the acute effects of personal exposure to ambient air pollution in a time series design. First, a spatial hierarchical model is used to relate Census tract-level daily ambient concentrations and simulated exposures for a subset of the study period. The complete exposure time series is then imputed for risk estimation. Modeling exposure via a statistical model reduces the computational burden associated with simulating personal exposures considerably. This allows us to consider personal exposures at a finer spatial resolution to improve exposure assessment and for a longer study period. The proposed approach is applied to an analysis of fine particulate matter of <2.5 mm in aerodynamic diameter (PM2.5) and daily mortality in the New York City metropolitan area during the period 2001-2005. Personal PM2.5 exposures were simulated from the Stochastic Human Exposure and Dose Simulation. Accounting for exposure uncertainty, the authors estimated a 2.32% (95% posterior interval: 0.68, 3.94) increase in mortality per a 10 mu g/m(3) increase in personal exposure to PM2.5 from outdoor sources on the previous day. The corresponding estimates per a 10 mu g/m(3) increase in PM2.5 ambient concentration was 1.13% (95% confidence interval: 0.27, 2.00). The risks of mortality associated with PM2.5 were also higher during the summer months.