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

Esben Budtz-Jørgensen; Niels Keiding; Philippe Grandjean; Pal Weihe

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

Esben Budtz-Jørgensen, Niels Keiding, Philippe Grandjean, Pal Weihe

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

97 Citationer (Scopus)

Abstract

Udgivelsesdato: 2002-Oct-14

Originalsprog	Engelsk
Tidsskrift	Environmental health
Vol/bind	1
Udgave nummer	1
Sider (fra-til)	2
ISSN	1476-069X
Status	Udgivet - 2002

Bibliografisk note

Keywords: Biological Markers; Child; Cohort Studies; Denmark; Developmental Disabilities; Female; Fetal Blood; Fish Products; Humans; Likelihood Functions; Maternal Exposure; Maternal-Fetal Exchange; Mercury Poisoning, Nervous System; Methylmercury Compounds; Multivariate Analysis; Nervous System; Neuropsychological Tests; Pregnancy; Risk Assessment

Citationsformater

@article{be372ed09eaa11debc73000ea68e967b,

title = "Estimation of health effects of prenatal methylmercury exposure using structural equation models",

abstract = "BACKGROUND: 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.",

author = "Esben Budtz-J{\o}rgensen and Niels Keiding and Philippe Grandjean and Pal Weihe",

note = "Keywords: Biological Markers; Child; Cohort Studies; Denmark; Developmental Disabilities; Female; Fetal Blood; Fish Products; Humans; Likelihood Functions; Maternal Exposure; Maternal-Fetal Exchange; Mercury Poisoning, Nervous System; Methylmercury Compounds; Multivariate Analysis; Nervous System; Neuropsychological Tests; Pregnancy; Risk Assessment",

year = "2002",

language = "English",

volume = "1",

pages = "2",

journal = "Environmental Health",

issn = "1476-069X",

publisher = "BioMed Central Ltd.",

number = "1",

}

TY - JOUR

T1 - Estimation of health effects of prenatal methylmercury exposure using structural equation models

AU - Budtz-Jørgensen, Esben

AU - Keiding, Niels

AU - Grandjean, Philippe

AU - Weihe, Pal

N1 - Keywords: Biological Markers; Child; Cohort Studies; Denmark; Developmental Disabilities; Female; Fetal Blood; Fish Products; Humans; Likelihood Functions; Maternal Exposure; Maternal-Fetal Exchange; Mercury Poisoning, Nervous System; Methylmercury Compounds; Multivariate Analysis; Nervous System; Neuropsychological Tests; Pregnancy; Risk Assessment

PY - 2002

Y1 - 2002

N2 - BACKGROUND: 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.

AB - BACKGROUND: 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.

M3 - Journal article

C2 - 12513702

VL - 1

SP - 2

JO - Environmental Health

JF - Environmental Health

SN - 1476-069X

IS - 1

ER -