Skip to main content

Advertisement

Log in

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

  • Original Article
  • Published:
International Archives of Occupational and Environmental Health Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • Cernichiari E, Toribara TY, Liang L et al (1995) The biological monitoring of mercury in the Seychelles study. Neurotoxicology 16:613–628

    PubMed  CAS  Google Scholar 

  • Clarkson TW (2002) The three modern faces of mercury. Environ Health Perspect 110(Suppl 1):11–23

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Glantz SA (2002) A primer of biostatistics. McGraw-Hill, New York

    Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • National Research Council (2000) Toxicological effects of methylmercury. National Academy Press, Washington, DC

    Google Scholar 

  • Perneger TV (1998) What’s wrong with Bonferroni adjustments. BMJ 316:1236–1238

    PubMed  CAS  Google Scholar 

  • Rothman KJ (1986) Modern epidemiology. Little Brown, Boston

    Google Scholar 

  • Rothman KJ (1990) No adjustments are needed for multiple comparisons. Epidemiology 1:43–46

    Article  PubMed  CAS  Google Scholar 

  • Savitz DA, Olshan AF (1995) Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol 142:904–908

    PubMed  CAS  Google Scholar 

  • 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

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Shamlaye C, Davidson PW, Myers GJ (2004) The Seychelles child development study: two decades of collaboration. SMDJ Seychelles Med Dent J 7:92–99

    Google Scholar 

  • Stokes ME, Davis CS, Koch GG (2000) Categorical data analysis using the SAS system. SAS Institute, Inc., Cary

    Google Scholar 

  • Thompson JR (1998) Invited commentary: Re: Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol 147:801–806

    PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  Google Scholar 

  • WHO (1990) Environmental health criteria 101 methylmercury. World Health Organization, Geneva

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Edwin van Wijngaarden.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00420-009-0402-0

Keywords

Navigation