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Int. J. Environ. Res. Public Health 2014, 11(10), 10419-10443; doi:10.3390/ijerph111010419

Scalable Combinatorial Tools for Health Disparities Research

1
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
2
Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA
3
National Institute for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
4
Department of Public Health, University of Tennessee, Knoxville, TN 37996, USA
5
Department of Epidemiology, Emory University, Atlanta, GA 30322, USA
6
Department of Animal Science, Institute of Agriculture, University of Tennessee, Knoxville, TN 37996, USA
7
Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA
8
Department of Global Environmental Health Sciences, Tulane University, New Orleans, LA 70112, USA
9
Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA
*
Author to whom correspondence should be addressed.
Received: 1 August 2014 / Revised: 30 September 2014 / Accepted: 1 October 2014 / Published: 10 October 2014
(This article belongs to the Special Issue Eliminating Health Disparities to Achieve Health Equity)
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Abstract

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject. View Full-Text
Keywords: combinatorial algorithms; data science; graph theoretical techniques; health disparities research; heterogeneous data analysis; high performance computing; public health exposome; relevance networks; scalable computation combinatorial algorithms; data science; graph theoretical techniques; health disparities research; heterogeneous data analysis; high performance computing; public health exposome; relevance networks; scalable computation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Langston, M.A.; Levine, R.S.; Kilbourne, B.J.; Rogers, G.L., Jr.; Kershenbaum, A.D.; Baktash, S.H.; Coughlin, S.S.; Saxton, A.M.; Agboto, V.K.; Hood, D.B.; Litchveld, M.Y.; Oyana, T.J.; Matthews-Juarez, P.; Juarez, P.D. Scalable Combinatorial Tools for Health Disparities Research. Int. J. Environ. Res. Public Health 2014, 11, 10419-10443.

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