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Estimation of health effects of prenatal methylmercury exposure using structural equation models

Authors
  • Budtz-Jørgensen, Esben1, 2
  • Keiding, Niels1
  • Grandjean, Philippe2, 3
  • Weihe, Pal2, 4
  • 1 University of Copenhagen, Department of Biostatistics, Blegdamsvej 3, Copenhagen, DK-2200, Denmark , Copenhagen (Denmark)
  • 2 University of Southern Denmark, Institute of Public Health, Winslowparken 17, Odense C, DK-5000, Denmark , Odense C (Denmark)
  • 3 Boston University Schools of Medicine and Public Health, Departments of Environmental Health and Neurology, Boston, MA, 02118, USA , Boston (United States)
  • 4 Faroese Hospital System, Torshavn, FR-100, Faroe Islands , Torshavn (Faroe Islands)
Type
Published Article
Journal
Environmental Health
Publisher
BioMed Central
Publication Date
Oct 14, 2002
Volume
1
Issue
1
Identifiers
DOI: 10.1186/1476-069X-1-2
Source
Springer Nature
Keywords
License
Yellow

Abstract

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.

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