Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error

Abstract

In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse particulate matter (PM) with aerodynamic diameter between 2.5 and 10 μm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In this paper, we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved population average exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 US counties between the period 1999 and 2005, we find a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.

Fig. 1.

59 US counties with population greater than 200 000, at least 2 pairs of collocated PM₁₀ and PM_2.5 monitors, and at least 210 daily PM_{10 − 2.5} measurements over the period 1999–2005 (Honolulu, HI and Anchorage, AL not shown). 1

Fig. 2.

Correlations of monitor-level daily PM time series calculated between pairs of PM_2.5 or PM_{10 − 2.5} monitoring locations in the same county and plotted versus the distance between monitor pair. For PM_2.5, we used all available monitors without restricting to those with a collocated PM₁₀ monitor.

Fig. 3.

Posterior distributions of the average exposure to outdoor PM_{10 − 2.5} concentration on July 17, 2000 in Harris County, TX. The vertical line is placed at the 10% TM estimate. The solid and dotted lines represent 4 different PM_{10 − 2.5} posterior distributions obtained from ME modeling.

Fig. 4.

County-specific ME SD (σ₁^2,c and σ₂^2,c in (2.4) for PM_{10 − 2.5} (black) and PM_2.5 (gray) plotted versus log county land area (cubic kilometer) for 59 counties. Each bullet denotes the posterior mean and the vertical line indicates the 95% posterior interval.

Fig. 5.

Upper panels: scatter plot of county-specific standardized health effect estimates for PM_{10 − 2.5}, , comparing 2 approaches: (1) including ME modeling with monitor-specific weighted error variance (WME) versus (2) using TM as PM_{10 − 2.5} exposure. Lower panels: scatter plot of county-specific standardized health-effect estimates for PM_{10 − 2.5}, , using exposure derived from WME comparing Bayesian risk estimation versus regression calibration.

Fig. 6.

Percent increase in emergency hospital admissions rates for cardiovascular and respiratory diseases per 10 μg/m³ increase in same-day particulate matter concentration. Exposure measures for PM_2.5 and PM_{10 − 2.5} are derived using either TM, ME modeling with constant error variance across monitors (ME) or monitor-specific weighted error variance (WME).