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

Anne Kershenbaum,Michael Langston,Robert Levine,Gary Rogers,Arnold Saxton,Lisaann Gittner,Tonny Oyana,Paul Juarez,Barbara Kilbourne,Suzanne Baktash,Patricia Matthews-Juarez

doi:10.3390/ijerph111212346

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.

Highlights

US infant mortality rates (IMRs) are generally higher than European rates and preterm birth is often identified as one of the main explanations for the high US IMR [1]
The aims of this study are to investigate the variation in early preterm birth rates across counties, identify social-ecological and environmental factors which account for this variation, and identify counties with unusually high and low preterm birth rates that can be investigated in greater detail to explain disparate outcomes
Preterm birth rates were available by race, many of the explanatory variables were not, so this analysis was performed at the total county population level

Summary

Introduction

US infant mortality rates (IMRs) are generally higher than European rates and preterm birth is often identified as one of the main explanations for the high US IMR [1]. Preterm birth rates are higher in non-Hispanic black women in the US than non-Hispanic white and Hispanic women Black/white racial disparities in preterm birth rates have been found to be associated with a range of social and economic deprivation factors [6] including poverty, disability and low education levels. Preterm birth rates are high even among highly educated black women [7], and have remained higher than the white population over generations [8]. Other risk factors which have been associated with black/white preterm birth rate disparities include exposure to fine particulate matter [9], sexually transmitted infections, nutritional status, access to medical care, stress, and intergenerational effects [10]

Objectives

Methods

Results

Discussion

Conclusion