| Statistical Methods for Linking Health, Exposure, and Hazards Frances Jean Mather,1 LuAnn Ellis White,2 Elizabeth Cullen Langlois,2 Charles Franklin Shorter,2 Christopher Martin Swalm,3 Jeffrey George Shaffer,1 and William Ralph Hartley2 1Department of Biostatistics, Academic Information Systems, 2Department of Environmental Health Sciences, Center for Applied Environmental Public Health, and 3Academic Information Systems, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA Abstract
The Environmental Public Health Tracking Network (EPHTN) proposes to link environmental hazards and exposures to health outcomes. Statistical methods used in case-control and cohort studies to link health outcomes to individual exposure estimates are well developed. However, reliable exposure estimates for many contaminants are not available at the individual level. In these cases, exposure/hazard data are often aggregated over a geographic area, and ecologic models are used to relate health outcome and exposure/hazard. Ecologic models are not without limitations in interpretation. EPHTN data are characteristic of much information currently being collected—they are multivariate, with many predictors and response variables, often aggregated over geographic regions (small and large) and correlated in space and/or time. The methods to model trends in space and time, handle correlation structures in the data, estimate effects, test hypotheses, and predict future outcomes are relatively new and without extensive application in environmental public health. In this article we outline a tiered approach to data analysis for EPHTN and review the use of standard methods for relating exposure/hazards, disease mapping and clustering techniques, Bayesian approaches, Markov chain Monte Carlo methods for estimation of posterior parameters, and geostatistical methods. The advantages and limitations of these methods are discussed. Key words: Bayesian modeling, data linkage, exposure, GIS, hazards, health outcome data, statistical methods. Environ Health Perspect 112:1440-1445 (2004) . doi:10.1289/ehp.7145 available via http://dx.doi.org/ [Online 3 August 2004]
This article is part of the mini-monograph "National Environmental Public Health Tracking," which is sponsored by the Centers for Disease Control and Prevention (CDC) . Address correspondence to F.J. Mather, Department of Biostatistics, SL 18, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., New Orleans, LA 70112 USA. Telephone: (504) 988-7329. Fax: (504) 988-1706. E-mail: mather@tulane.edu We thank our reviewers for their thoughtful suggestions. Any errors are our responsibility. This work was supported by the CDC Center of Excellence for Environmental Public Health Tracking grant U50/CCU622412. This article was supported by an environmental public health tracking cooperative agreement from CDC. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. The authors declare they have no competing financial interests. Received 1 April 2004 ; accepted 3 August 2004.
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