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.
journal_name
Int J Environ Res Public Healthauthors
Kershenbaum AD,Langston MA,Levine RS,Saxton AM,Oyana TJ,Kilbourne BJ,Rogers GL,Gittner LS,Baktash SH,Matthews-Juarez P,Juarez PDdoi
10.3390/ijerph111212346subject
Has Abstractpub_date
2014-11-28 00:00:00pages
12346-66issue
12eissn
1661-7827issn
1660-4601pii
ijerph111212346journal_volume
11pub_type
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