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Estimating error in using ambient PM2.5 concentrations as proxies for personal exposures: a review.

ABSTRACT: Several methods have been used to account for measurement error inherent in using ambient concentration of particulate matter < 2.5 microm/m(3) (PM(2.5)) as a proxy for personal exposure. Such methods commonly rely on the estimated correlation between ambient and personal PM(2.5) concentrations (r). However, studies of r have not been systematically and quantitatively assessed for publication bias or heterogeneity.We searched 7 electronic reference databases for studies of the within-participant correlation between ambient and personal PM(2.5).We identified 567 candidate studies, 18 (3%) of which met inclusion criteria and were abstracted. The studies were published between 1999 and 2008, representing 619 nonsmoking participants aged 6-93 years in 17 European and North American cities. Correlation coefficients (median 0.54; range 0.09-0.83) were based on a median of 8 ambient-personal PM(2.5) pairs per participant (range 5-20) collected over 27-547 days. Overall, there was little evidence for publication bias (funnel plot symmetry tests: Begg's log-rank test, P 0.9; Egger's regression asymmetry test, P 0.2). However, strong evidence for heterogeneity was noted (Cochran's Q test for heterogeneity, P = 0.001). European locales, eastern longitudes in North America, higher ambient PM(2.5) concentrations, higher relative humidity, and lower between-participant variation in r were associated with increased r.Characteristics of participants, studies, and the environments in which they are conducted may affect the accuracy of ambient PM2.5 as a proxy for personal exposure.

SUBMITTER: Avery CL

PROVIDER: S-EPMC5370079 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Estimating error in using ambient PM2.5 concentrations as proxies for personal exposures: a review.

Avery Christy L CL Mills Katherine T KT Williams Ronald R McGraw Kathleen A KA Poole Charles C Smith Richard L RL Whitsel Eric A EA

Epidemiology (Cambridge, Mass.) 20100301 2

<h4>Background</h4>Several methods have been used to account for measurement error inherent in using ambient concentration of particulate matter < 2.5 microm/m(3) (PM(2.5)) as a proxy for personal exposure. Such methods commonly rely on the estimated correlation between ambient and personal PM(2.5) concentrations (r). However, studies of r have not been systematically and quantitatively assessed for publication bias or heterogeneity.<h4>Methods</h4>We searched 7 electronic reference databases fo ...[more]

PMID: 20087191

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Project description:BackgroundRegional National Weather Service (NWS) heat advisory criteria in New York State (NYS) were based on frequency of heat events estimated by sparse monitoring data. These may not accurately reflect temperatures at which specific health risks occur in large geographic regions. The objectives of the study were to use spatially resolved temperature data to characterize health risks related to summertime heat exposure and estimate the temperatures at which excessive risk of heat-related adverse health occurs in NYS. We also evaluated the need to adjust current heat advisory threshold and messaging based on threshold temperatures of multiple health outcomes.MethodsWe assessed the effect of multi-day lag exposure for maximum near-surface air temperature (Tmax) and maximum Heat Index derived from the gridded National Land Data Assimilation System (NLDAS) reanalysis dataset on emergency department (ED) visits/ hospitalizations for heat stress, dehydration, acute kidney failure (AKF) and cardiovascular diseases (CVD) using a case-crossover analysis during summers of 2008-2012. We assessed effect modification using interaction terms and stratified analysis. Thresholds were estimated using piecewise spline regression.ResultsWe observed an increased risk of heat stress (Risk ratio (RR) = 1.366, 95% confidence interval (CI): 1.347, 1.386) and dehydration (RR = 1.024, 95% CI: 1.021, 1.028) for every 1 °C increase in Tmax on the day of exposure. The highest risk for AKF (RR = 1.017, 95% CI: 1.014, 1.021) and CVD (RR = 1.001, 95% CI: 1.000, 1.002) were at lag 1 and 4 respectively. The increased risk of heat-health effects persists up to 6 days. Rural areas of NYS are at as high a risk of heat-health effects as urban areas. Heat-health risks start increasing at temperatures much lower than the current NWS criteria.ConclusionReanalysis data provide refined exposure-response functions for health research, in areas with sparse monitor observations. Based on this research, rural areas in NYS had similar risk for health effects of heat. Heat advisories in New York City (NYC) had been reviewed and lowered previously. As such, the current NWS heat advisory threshold was lowered for the upstate region of New York and surrounding areas. Enhanced outreach materials were also developed and disseminated to local health departments and the public.

Project description:BackgroundStudies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error.ObjectiveTo illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA).MethodsWe built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures.ResultsOur models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant.ConclusionThe impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.

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Estimating error in using ambient PM2.5 concentrations as proxies for personal exposures: a review.

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Estimating error in using ambient PM2.5 concentrations as proxies for personal exposures: a review.

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