Estimation of health effects of prenatal methylmercury exposure using structural equation modelsReportar como inadecuado

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Environmental Health

, 1:2

First Online: 14 October 2002Received: 12 June 2002Accepted: 14 October 2002DOI: 10.1186-1476-069X-1-2

Cite this article as: Budtz-Jørgensen, E., Keiding, N., Grandjean, P. et al. Environ Health 2002 1: 2. doi:10.1186-1476-069X-1-2


BackgroundObservational 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.

ResultsStructural 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.

ConclusionsThe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1476-069X-1-2 contains supplementary material, which is available to authorized users.

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Autor: Esben Budtz-Jørgensen - Niels Keiding - Philippe Grandjean - Pal Weihe


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