PURPOSE: The potential for ill-informed causal inference is a major concern in published longitudinal studies evaluating impaired neurological function in children prenatally exposed to background levels of methyl mercury (MeHg). These studies evaluate a large number of developmental tests. We propose an alternative analysis strategy that reduces the number of comparisons tested in these studies. METHODS: Using data from the 9-year follow-up of 643 children in the Seychelles child development study, we grouped 18 individual endpoints into one overall ordinal outcome variable as well as by developmental domains. Subsequently, ordinal logistic regression analyses were performed. RESULTS: We did not find an association between prenatal MeHg exposure and developmental outcomes at 9 years of age. CONCLUSION: Our proposed framework is more likely to result in a balanced interpretation of a posteriori associations. In addition, this new strategy should facilitate the use of complex epidemiological data in quantitative risk assessment.
PURPOSE: The potential for ill-informed causal inference is a major concern in published longitudinal studies evaluating impaired neurological function in children prenatally exposed to background levels of methyl mercury (MeHg). These studies evaluate a large number of developmental tests. We propose an alternative analysis strategy that reduces the number of comparisons tested in these studies. METHODS: Using data from the 9-year follow-up of 643 children in the Seychelles child development study, we grouped 18 individual endpoints into one overall ordinal outcome variable as well as by developmental domains. Subsequently, ordinal logistic regression analyses were performed. RESULTS: We did not find an association between prenatal MeHg exposure and developmental outcomes at 9 years of age. CONCLUSION: Our proposed framework is more likely to result in a balanced interpretation of a posteriori associations. In addition, this new strategy should facilitate the use of complex epidemiological data in quantitative risk assessment.
Authors: E Cernichiari; T Y Toribara; L Liang; D O Marsh; M W Berlin; G J Myers; C Cox; C F Shamlaye; O Choisy; P Davidson Journal: Neurotoxicology Date: 1995 Impact factor: 4.294
Authors: P Grandjean; P Weihe; R F White; F Debes; S Araki; K Yokoyama; K Murata; N Sørensen; R Dahl; P J Jørgensen Journal: Neurotoxicol Teratol Date: 1997 Nov-Dec Impact factor: 3.763
Authors: Gary J Myers; Philip W Davidson; Christopher Cox; Conrad F Shamlaye; Donna Palumbo; Elsa Cernichiari; Jean Sloane-Reeves; Gregory E Wilding; James Kost; Li-Shan Huang; Thomas W Clarkson Journal: Lancet Date: 2003-05-17 Impact factor: 79.321
Authors: Margaret A McDowell; Charles F Dillon; John Osterloh; P Michael Bolger; Edo Pellizzari; Reshan Fernando; Ruben Montes de Oca; Susan E Schober; Thomas Sinks; Robert L Jones; Kathryn R Mahaffey Journal: Environ Health Perspect Date: 2004-08 Impact factor: 9.031