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Int. J. Environ. Res. Public Health 2014, 11(12), 12346-12366; doi:10.3390/ijerph111212346

Exploration of Preterm Birth Rates Using the Public Health Exposome Database and Computational Analysis Methods

1
Department of Public Health, University of Tennessee, Knoxville, TN 37996, USA
2
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
3
Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA
4
Department of Animal Science, Institute of Agriculture, University of Tennessee, Knoxville, TN 37996, USA
5
Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA
6
National Institute for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
7
Department of Political Sciences, Texas Tech University, Lubbock, TX 79409, USA
*
Author to whom correspondence should be addressed.
Received: 8 October 2014 / Revised: 12 November 2014 / Accepted: 19 November 2014 / Published: 28 November 2014
(This article belongs to the Special Issue Eliminating Health Disparities to Achieve Health Equity)
View Full-Text   |   Download PDF [866 KB, uploaded 28 November 2014]   |  

Abstract

Recent advances in informatics technology has made it possible to integrate, manipulate, and analyze variables from a wide range of scientific disciplines allowing for the examination of complex social problems such as health disparities. This study used 589 county-level variables to identify and compare geographical variation of high and low preterm birth rates. Data were collected from a number of publically available sources, bringing together natality outcomes with attributes of the natural, built, social, and policy environments. Singleton early premature county birth rate, in counties with population size over 100,000 persons provided the dependent variable. Graph theoretical techniques were used to identify a wide range of predictor variables from various domains, including black proportion, obesity and diabetes, sexually transmitted infection rates, mother’s age, income, marriage rates, pollution and temperature among others. Dense subgraphs (paracliques) representing groups of highly correlated variables were resolved into latent factors, which were then used to build a regression model explaining prematurity (R-squared = 76.7%). Two lists of counties with large positive and large negative residuals, indicating unusual prematurity rates given their circumstances, may serve as a starting point for ways to intervene and reduce health disparities for preterm births. View Full-Text
Keywords: exposome; county rates; data reduction; health disparities; geographical variation; premature birth rates; preterm birth exposome; county rates; data reduction; health disparities; geographical variation; premature birth rates; preterm birth
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

Kershenbaum, A.D.; Langston, M.A.; Levine, R.S.; Saxton, A.M.; Oyana, T.J.; Kilbourne, B.J.; Rogers, G.L.; Gittner, L.S.; Baktash, S.H.; Matthews-Juarez, P.; Juarez, P.D. Exploration of Preterm Birth Rates Using the Public Health Exposome Database and Computational Analysis Methods. Int. J. Environ. Res. Public Health 2014, 11, 12346-12366.

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