Using measurement error models to assess effects of prenatal and postnatal methylmercury exposure in the Seychelles Child Development Study

Li Shan Huang, Christopher Cox, Gregory E. Wilding, Gary J. Myers, Philip W. Davidson, Conrad F. Shamlaye, Elsa Cernichiari, Jean Sloane-Reeves, Thomas W. Clarkson

Research output: Contribution to journalArticlepeer-review


Studies of the effects of environmental exposures on human health typically require estimation of both exposure and outcome. Standard methods for the assessment of the association between exposure and outcome include multiple linear regression analysis, which assumes that the outcome variable is observed with error, while the levels of exposure and other explanatory variables are measured with complete accuracy, so that there is no deviation of the measured from the actual value. The term measurement error in this discussion refers to the difference between the actual or true level and the value that is actually observed. In the investigations of the effects of prenatal methylmercury (MeHg) exposure from fish consumption on child development, the only way to obtain a true exposure level (producing the toxic effect) is to ascertain the concentration in fetal brain, which is not possible. As is often the case in studies of environmental exposures, the measured exposure level is a biomarker, such as the average maternal hair level during gestation. Measurement of hair mercury is widely used as a biological indicator for exposure to MeHg and is the only indicator that has been calibrated against the target tissue, the developing brain. Variability between the measured and the true values in explanatory variables in a multiple regression analysis can produce bias, leading to either over or underestimation of regression parameters (slopes). Fortunately, statistical methods known as measurement error models (MEM) are available to account for measurement errors in explanatory variables in multiple regression analysis, and these methods can provide an (either "unbiased" or "bias-corrected") estimate of the unknown outcome/exposure relationship. In this paper, we illustrate MEM analysis by reanalyzing data from the 5.5-year test battery in the Seychelles Child Development Study, a longitudinal study of prenatal exposure to MeHg from maternal consumption of a diet high in fish. The use of the MEM approach was made possible by the existence of independent, calibration data on the magnitude of the variability of the measurement error deviations for the biomarker of prenatal exposure used in this study, the maternal hair level. Our reanalysis indicated that adjustment for measurement errors in explanatory variables had no appreciable effect on the original results.

Original languageEnglish (US)
Pages (from-to)115-122
Number of pages8
JournalEnvironmental research
Issue number2
StatePublished - Oct 2003
Externally publishedYes


  • Child development
  • Fish consumption
  • Interaction effects
  • Measurement error models
  • Methylmercury

ASJC Scopus subject areas

  • Biochemistry
  • Environmental Science(all)

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