A National Prediction Model for PM2.5 Component Exposures and Measurement Error-Corrected Health Effect Inference.
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 meas...
| Publicado en: | Environmental Health Perspectives Vol. 121; no. 9; pp. 1017 - 1026 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | case study equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
National Institute of Environmental Health Sciences
Sep2013
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104218949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104218949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00916765 3B5 jtl: Environmental Health Perspectives issn: 00916765 maglogo: N pubinfo: dt: Sep2013 vid: 121 iid: 9 pid: 56539 pub: National Institute of Environmental Health Sciences place: Research Triangle Park, North Carolina artinfo: ui: 104218949 90084334 10.1289/ehp.1206010 104218949 ppf: 1017 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A National Prediction Model for PM2.5 Component Exposures and Measurement Error-Corrected Health Effect Inference. aug: au: Bergen, Silas Sheppard, Lianne Sampson, Paul D. Sun-Young Kim Richards, Mark Vedal, Sverre Kaufman, Joel D. Szpiro, Adam A. affil: Department of Biostatistics, University of Washington, Seattle, Washington, USA sug: subj: Particulate Matter Environmental Exposure Models, Statistical Air Pollution Human Carbon Silicon Sulfur Cross Sectional Studies Carotid Arteries Pathology Descriptive Statistics Confidence Intervals United States Male Female Middle Age Aged Questionnaires Funding Source Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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 PM2.55 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. pubtype: Academic Journal doctype: case study equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|