Abstract
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.
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References
Ananth CV, Kleinbaum DG (1997) Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol 26:1323–1333. doi:10.1093/ije/26.6.1323
Axelrad DA, Bellinger DC, Ryan LM et al (2007) Dose-response relationship of prenatal mercury exposure and IQ: an integrative analysis of epidemiologic data. Environ Health Perspect 115:609–615
Berry DA, Hochberg Y (1999) Bayesian perspectives on multiple comparisons. J Stat Plan Inference 82:215–227. doi:10.1016/S0378-3758(99)00044-0
Budtz-Jorgensen E, Grandjean P, Keiding N et al (2000) Benchmark dose calculations of methylmercury-associated neurobehavioural deficits. Toxicol Lett 112/113:193–199. doi:10.1016/S0378-4274(99)00283-0
Budtz-Jorgensen E, Keiding N, Grandjean P (2001) Benchmark dose calculation from epidemiological data. Biometrics 57:698–706. doi:10.1111/j.0006-341X.2001.00698.x
Budtz-Jørgensen E, Keiding N, Grandjean P, Weihe P (2002) Estimation of health effects of prenatal methylmercury exposure using structural equation models. Environ Health 1(1):2
Cernichiari E, Toribara TY, Liang L et al (1995) The biological monitoring of mercury in the Seychelles study. Neurotoxicology 16:613–628
Clarkson TW (2002) The three modern faces of mercury. Environ Health Perspect 110(Suppl 1):11–23
Cohen JT, Bellinger DC, Shaywitz BA (2005) A quantitative analysis of prenatal methyl mercury exposure and cognitive development. Am J Prev Med 29:353–365. doi:10.1016/j.amepre.2005.06.007
Counter SA, Buchanan LH (2004) Mercury exposure in children: a review. Toxicol Appl Pharmacol 198:209–230. doi:10.1016/j.taap.2003.11.032
Crump KS, Kjellstrom T, Shipp AM et al (1998) Influence of prenatal mercury exposure upon scholastic and psychological test performance: benchmark analysis of a New Zealand cohort. Risk Anal 18:701–713. doi:10.1023/B:RIAN.0000005917.52151.e6
Crump KS, Van Landingham C, Shamlaye C et al (2000) Benchmark concentrations for methylmercury obtained from the Seychelles child development study. Environ Health Perspect 108:257–263. doi:10.2307/3454443
Davidson PW, Myers GJ, Cox C et al (1998) Effects of prenatal and postnatal methylmercury exposure from fish consumption on neurodevelopment: outcomes at 66 months of age in the Seychelles child development study. JAMA 280:701–707. doi:10.1001/jama.280.8.701
Efron B, Tibshirani R, Storey JD et al (2001) Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 96:1151–1160. doi:10.1198/016214501753382129
Gelman A, Tuerlinckx F (2000) Type S error rates for classical and Bayesian single and multiple comparison procedures. Comput Stat 15:373–390. doi:10.1007/s001800000040
Glantz SA (2002) A primer of biostatistics. McGraw-Hill, New York
Grandjean P, Weihe P, White RF et al (1997) Cognitive deficit in 7-year-old children with prenatal exposure to methylmercury. Neurotoxicol Teratol 19:417–428. doi:10.1016/S0892-0362(97)00097-4
McDowell MA, Dillon CF, Osterloh J et al (2004) Hair mercury levels in U.S. children and women of childbearing age: reference range data from NHANES 1999–2000. Environ Health Perspect 112:1165–1171
Myers GJ, Davidson PW, Cox C et al (2003) Prenatal methylmercury exposure from ocean fish consumption in the Seychelles child development study. Lancet 361:1686–1692. doi:10.1016/S0140-6736(03)13371-5
National Research Council (2000) Toxicological effects of methylmercury. National Academy Press, Washington, DC
Perneger TV (1998) What’s wrong with Bonferroni adjustments. BMJ 316:1236–1238
Rothman KJ (1986) Modern epidemiology. Little Brown, Boston
Rothman KJ (1990) No adjustments are needed for multiple comparisons. Epidemiology 1:43–46
Savitz DA, Olshan AF (1995) Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol 142:904–908
Savitz DA, Olshan AF (1998) Describing data requires no adjustment for multiple comparisons: a reply from Savitz and Olshan. Am J Epidemiol 147:813–814 discussion 815
Scott SC, Goldberg MS, Mayo NE (1997) Statistical assessment of ordinal outcomes in comparative studies. J Clin Epidemiol 50:45–55. doi:10.1016/S0895-4356(96)00312-5
Shamlaye C, Davidson PW, Myers GJ (2004) The Seychelles child development study: two decades of collaboration. SMDJ Seychelles Med Dent J 7:92–99
Stokes ME, Davis CS, Koch GG (2000) Categorical data analysis using the SAS system. SAS Institute, Inc., Cary
Thompson JR (1998) Invited commentary: Re: Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol 147:801–806
Thurston SW, Ruppert D, Davidson PW (2009) Bayesian models for multiple outcomes nested in domains. Biometrics
van Wijngaarden E, Hertz-Picciotto I (2004) A simple approach to performing quantitative cancer risk assessment using published results from occupational epidemiology studies. Sci Total Environ 332:81–87. doi:10.1016/j.scitotenv.2004.04.005
van Wijngaarden E, Beck C, Shamlaye CF et al (2006) Benchmark concentrations for methyl mercury obtained from the 9-year follow-up of the Seychelles child development study. Neurotoxicology 27:702–709. doi:10.1016/j.neuro.2006.05.016
Veazie PJ (2006) When to combine hypotheses and adjust for multiple tests. Health Serv Res 41:804–818. doi:10.1111/j.1475-6773.2006.00512.x
WHO (1990) Environmental health criteria 101 methylmercury. World Health Organization, Geneva
Acknowledgments
This research was supported by Grants 2R01-ES008442-05; R01-ES10219; R01-ES08442 and ES-01247 from the US National Institutes of Health; 1 UL1 RR024160-02 from the National Center for Research Resources; the Food and Drug Administration; US Department of Health and Human Services, and by the Ministry of Health, Republic of Seychelles.
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van Wijngaarden, E., Myers, G.J., Thurston, S.W. et al. Interpreting epidemiological evidence in the presence of multiple endpoints: an alternative analytic approach using the 9-year follow-up of the Seychelles child development study. Int Arch Occup Environ Health 82, 1031–1041 (2009). https://doi.org/10.1007/s00420-009-0402-0
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DOI: https://doi.org/10.1007/s00420-009-0402-0