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

Anne D. Kershenbaum, Michael A. Langston, Robert S. Levine, Arnold M. Saxton, Tonny J. Oyana, Barbara J. Kilbourne, Gary L. Rogers, Lisaann S. Gittner, Suzanne H. Baktash, Patricia Matthews-Juarez and Paul D. Juarez
Additional contact information
Anne D. Kershenbaum: Department of Public Health, University of Tennessee, Knoxville, TN 37996, USA
Michael A. Langston: Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
Robert S. Levine: Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA
Arnold M. Saxton: Department of Animal Science, Institute of Agriculture, University of Tennessee, Knoxville, TN 37996, USA
Tonny J. Oyana: Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA
Barbara J. Kilbourne: Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA
Gary L. Rogers: National Institute for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Lisaann S. Gittner: Department of Political Sciences, Texas Tech University, Lubbock, TX 79409, USA
Suzanne H. Baktash: Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
Patricia Matthews-Juarez: Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA
Paul D. Juarez: Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA

IJERPH, 2014, vol. 11, issue 12, 1-21

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.

Keywords: exposome; county rates; data reduction; health disparities; geographical variation; premature birth rates; preterm birth (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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