Bayesian modeling of air pollution health effects with missing exposure data.
The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing c...
| Publicado en: | American Journal of Epidemiology Vol. 164; no. 1; pp. 69 - 77 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2006
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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=106133029&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106133029 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: Jul2006 vid: 164 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 106133029 2009222429 10.1093/aje/kwj150 NLM16624966 106133029 ppf: 69 ppct: 8 formats: tig: atl: Bayesian modeling of air pollution health effects with missing exposure data. aug: au: Molitor J Molitor N Jerrett M McConnell R Gauderman J Berhane K Thomas D affil: Department of Preventive Medicine, University of Southern California, Los Angeles, CA. sug: subj: Air Pollutants Adverse Effects Air Pollution Adverse Effects Models, Statistical Nitrogen Oxides Respiratory Tract Diseases Epidemiology Air Pollutants Analysis Air Pollution Analysis Bias (Research) California Child Data Analysis Software Data Collection Environmental Monitoring Methods Forced Expiratory Volume Funding Source Probability Regression Respiratory Function Tests Respiratory Tract Diseases Etiology Risk Assessment Risk Factors Vital Capacity Human Child: 6-12 years ab: The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing covariates in an informative way. The authors apply this methodology to study the effect of household-level long-term air pollution exposures on lung function for subjects from the Southern California Children's Health Study pilot project, conducted in the year 2000. Specifically, they propose techniques to examine the long-term effects of nitrogen dioxide (NO(2)) exposure on children's lung function for persons living in 11 southern California communities. The effect of nitrogen dioxide exposure on various measures of lung function was examined, but, similar to many air pollution studies, no completely accurate measure of household-level long-term nitrogen dioxide exposure was available. Rather, community-level nitrogen dioxide was measured continuously over many years, but household-level nitrogen dioxide exposure was measured only during two 2-week periods, one period in the summer and one period in the winter. From these incomplete measures, long-term nitrogen dioxide exposure and its effect on health must be inferred. Results show that the method improves estimates when compared with standard frequentist approaches. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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