当前位置： SCI文献检索 > ENVIRONMENTAL HEALTH PERSPECTIVES期刊下所有文献 > A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.

A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.

Abstract：

BACKGROUND:Studies 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. OBJECTIVE:To 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). METHODS:We 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. RESULTS:Our 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. CONCLUSION:The 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.

journal_name

Environ Health Perspect

journal_title

Environmental health perspectives

authors

Bergen S,Sheppard L,Sampson PD,Kim SY,Richards M,Vedal S,Kaufman JD,Szpiro AA

doi

10.1289/ehp.1206010

subject

Has Abstract

pub_date

2013-09-01 00:00:00

pages

1017-25

issue

eissn

0091-6765

issn

1552-9924

journal_volume

121

pub_type

杂志文章

在线工具

A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.