A novel finding of this study was the initial increased risk for addiction-related disorders that decreased with time, thus eroding war fighter performance in a military population. Moreover, these results suggest that mild TBI is distinguished from moderate to severe TBI in terms of timing of the risk, indicating that there is a need for screening and prevention of addiction-related disorders in mild TBI. Screening may be warranted in military troops as well as civilians at both short- and long-term milestones following mild TBI.
Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual's genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.Keywords: combinatorial algorithms; data science; graph theoretical techniques; health disparities research; heterogeneous data analysis; high performance computing; public health exposome; relevance networks; scalable computation BackgroundResearch on health disparities in racial/ethnic and disadvantaged groups has received increased attention in recent years. More and better data, new analysis techniques, and focused research programs have helped increase our understanding of issues and potential solutions. One such program, initiated by the authors of this exposition, is the National Health Disparities Research Center of Excellence (HDRCOE), begun over a decade ago at Meharry Medical College in Nashville, Tennessee. Supported by the National Institute on Minority Health and Health Disparities, the HDRCOE is an inter-institutional, inter-disciplinary center designed to study the complex relationships between human factors, community context and macro social forces that may lead to health disparities.A newly established center, with which this paper's authors are also intrinsically affiliated, is the Research Center on Health Disparities, Equity, and the Exposome (RCHDEE) at the University of Tennessee Health Science Center in Memphis, Tennessee. The RCHDEE is a collaboration that brings together academic and community partners to conduct health disparities research in an effort to promote healthy neighborhoods and help eliminate disparities.The public health exposome model [1] identifies enviro...
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.
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