Spatial misalignment in time series studies of air pollution and health data.
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude...
| Publicado en: | Biostatistics Vol. 11; no. 4; pp. 720 - 741 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Oct2010
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| 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=104917657&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104917657 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14654644 N58 jtl: Biostatistics issn: 14654644 maglogo: N pubinfo: dt: Oct2010 vid: 11 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104917657 NLM20392805 2010759728 10.1093/biostatistics/kxq017 NLM20392805 PMC3025780 104917657 ppf: 720 ppct: 21 formats: tig: atl: Spatial misalignment in time series studies of air pollution and health data. aug: au: Peng RD Bell ML Peng, Roger D Bell, Michelle L affil: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA sug: subj: Air Pollution Health Status Models, Statistical Air Pollution Adverse Effects Air Pollution Analysis Algorithms Probability Bias (Research) Cardiovascular Diseases Epidemiology Computer Simulation Resource Databases Environmental Exposure Environmental Monitoring Geographic Factors Hospitalization Statistics and Numerical Data Human Linear Regression Particulate Matter Adverse Effects Particulate Matter Analysis Regression Risk Assessment Statistics Time Factors United States Urban Health ab: Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter (PM). One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the PM components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a 2-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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