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

International Journal of Environmental Research and Public Health

Anne D. Kershenbaum⁶  Michael A. Langston¹  Robert S. Levine⁴  Arnold M. Saxton²  Tonny J. Oyana³  Barbara J. Kilbourne⁴  Gary L. Rogers⁷  Lisaann S. Gittner⁵  Suzanne H. Baktash¹  Patricia Matthews-Juarez³  Paul D. Juarez³ 

[1] Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA; E-Mails:;Department of Animal Science, Institute of Agriculture, University of Tennessee, Knoxville, TN 37996, USA; E-Mail:;Research Center on Health Disparities, Equity, and the Exposome, University of Tennessee Health Science Center, Memphis, TN 38163, USA; E-Mails:;Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA; E-Mails:;Department of Political Sciences, Texas Tech University, Lubbock, TX 79409, USA; E-Mail:;Department of Public Health, University of Tennessee, Knoxville, TN 37996, USA;National Institute for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; E-Mail:

关键词: exposome;    county rates;    data reduction;    health disparities;    geographical variation;    premature birth rates;    preterm birth;   

DOI  :  10.3390/ijerph111212346

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.