Exploration of preterm birth rates using the public health exposome database and computational analysis methods.

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.

authors

Kershenbaum AD,Langston MA,Levine RS,Saxton AM,Oyana TJ,Kilbourne BJ,Rogers GL,Gittner LS,Baktash SH,Matthews-Juarez P,Juarez PD

doi

10.3390/ijerph111212346

subject

Has Abstract

pub_date

2014-11-28 00:00:00

pages

12346-66

issue

12

eissn

1661-7827

issn

1660-4601

pii

ijerph111212346

journal_volume

11

pub_type

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