Estimating error in using residential outdoor PM2.5 concentrations as proxies for personal exposures: a meta-analysis

Abstract

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.

Figure 1

Forest plot for 16 estimates of r̄_j (95% CIs) from nine studies of the within-participant residential outdoor-personal PM_2.5 correlation.

Figure 2

Funnel plot for 16 estimates of the within-participant residential outdoor-personal PM_2.5 correlation.

Figure 3

Galbraith plot with 95% CIs for 16 estimates of the within-participant residential outdoor-personal PM_2.5 correlation.

Figure 4

Unadjusted summary correlations (95% CIs) and differences (95% CIs) by study, participant, and environment characteristics for nine studies examining the within-participant residential outdoor-personal PM_2.5 correlation. Summary correlations represent stratum-specific estimates of r̄. Increases in r̄ per unit change of study, participant, and environment characteristics are provided by r̄ difference estimates. SLP, sea level pressure.

Figure 5

Plot for 16 estimates of the within-participant residential outdoor-personal PM_2.5 correlation (95% CI) versus mean outdoor temperature, including the univariate random-effects meta-regression line.