Estimating Error in Using Residential Outdoor PM2.5 Concentrations as Proxies for Personal Exposures: A Meta-analysis

BACKGROUND Studies examining the health effects of particulate matter <or= 2.5 microm in aerodynamic diameter (PM2.5) commonly use ambient PM2.5 concentrations measured at distal monitoring sites as proxies for personal exposure and assume spatial homogeneity of ambient PM2.5. An alternative proxy-the residential outdoor PM2.5 concentration measured adjacent to participant homes-has few advantages under this assumption. OBJECTIVES We systematically reviewed the correlation between residential outdoor PM2.5 and personal PM2.5 (-rj) as a means of comparing the magnitude and sources of measurement error associated with their use as exposure surrogates. METHODS We searched seven electronic reference databases for studies of the within-participant residential outdoor-personal PM2.5 correlation. RESULTS The search identified 567 candidate studies, nine of which were abstracted in duplicate, that were published between 1996 and 2008. They represented 329 nonsmoking participants 6-93 years of age in eight U.S. cities, among whom -rj was estimated (median, 0.53; range, 0.25-0.79) based on a median of seven residential outdoor-personal PM2.5 pairs per participant. We found modest evidence of publication bias (symmetric funnel plot; pBegg = 0.4; pEgger = 0.2); however, we identified evidence of heterogeneity (Cochran's Q-test p = 0.05). Of the 20 characteristics examined, earlier study midpoints, eastern longitudes, older mean age, higher outdoor temperatures, and lower personal-residential outdoor PM2.5 differences were associated with increased within-participant residential outdoor-personal PM2.5 correlations. CONCLUSIONS These findings were similar to those from a contemporaneous meta-analysis that examined ambient-personal PM2.5 correlations (rj = median, 0.54; range, 0.09-0.83). Collectively, the meta-analyses suggest that residential outdoor-personal and ambient-personal PM2.5 correlations merit greater consideration when evaluating the potential for bias in studies of PM2.5-mediated health effects.