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dc.contributor.authorBudtz-Jørgensen, Esben
dc.contributor.authorKeiding, Niels
dc.contributor.authorGrandjean, Philippe
dc.contributor.authorWeihe, Pal
dc.date.accessioned2018-02-06T21:23:01Z
dc.date.issued2002
dc.identifierQuick submit: 2017-09-21T14:24:48-0400
dc.identifier.citationBudtz-Jørgensen, Esben, Niels Keiding, Philippe Grandjean, and Pal Weihe. 2002. “Estimation of Health Effects of Prenatal Methylmercury Exposure Using Structural Equation Models.” Environmental Health 1 (1) (October 14). doi:10.1186/1476-069x-1-2.en_US
dc.identifier.issn1476-069Xen_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34787305
dc.description.abstractBackground Observational studies in epidemiology always involve concerns regarding validity, especially measurement error, confounding, missing data, and other problems that may affect the study outcomes. Widely used standard statistical techniques, such as multiple regression analysis, may to some extent adjust for these shortcomings. However, structural equations may incorporate most of these considerations, thereby providing overall adjusted estimations of associations. This approach was used in a large epidemiological data set from a prospective study of developmental methyl-mercury toxicity. Results Structural equation models were developed for assessment of the association between biomarkers of prenatal mercury exposure and neuropsychological test scores in 7 year old children. Eleven neurobehavioral outcomes were grouped into motor function and verbally mediated function. Adjustment for local dependence and item bias was necessary for a satisfactory fit of the model, but had little impact on the estimated mercury effects. The mercury effect on the two latent neurobehavioral functions was similar to the strongest effects seen for individual test scores of motor function and verbal skills. Adjustment for contaminant exposure to poly chlorinated biphenyls (PCBs) changed the estimates only marginally, but the mercury effect could be reduced to non-significance by assuming a large measurement error for the PCB biomarker. Conclusions The structural equation analysis allows correction for measurement error in exposure variables, incorporation of multiple outcomes and incomplete cases. This approach therefore deserves to be applied more frequently in the analysis of complex epidemiological data sets.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1186/1476-069X-1-2en_US
dash.licenseLAA
dc.titleEstimation of health effects of prenatal methylmercury exposure using structural equation modelsen_US
dc.typeJournal Articleen_US
dc.date.updated2017-09-21T18:24:50Z
dc.description.versionVersion of Recorden_US
dc.relation.journalEnvironmental Healthen_US
dash.depositing.authorGrandjean, Philippe
dc.date.available2002
dc.date.available2018-02-06T21:23:01Z
dc.identifier.doi10.1186/1476-069X-1-2*
dash.contributor.affiliatedWeihe, Pal
dash.contributor.affiliatedGrandjean, Philippe
dc.identifier.orcid0000-0003-4046-9658


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