A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

OBJECTIVES: The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. METHODS: Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. RESULTS: While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. CONCLUSIONS: Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. POLICY IMPLICATIONS: An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy.

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

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.

Scalable combinatorial tools for health disparities research.

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.

Risk for addiction-related disorders following mild traumatic brain injury in a large cohort of active-duty U.S. airmen.

OBJECTIVE: Military personnel are at increased risk for traumatic brain injury (TBI) from combat and noncombat exposures. The sequelae of moderate to severe TBI are well described, but little is known regarding long-term performance decrements associated with mild TBI. Furthermore, while alcohol and drug use are well known to increase risk for TBI, little is known regarding the reverse pattern. The authors sought to assess possible associations between mild TBI and addiction-related disorders in active-duty U.S. military personnel. METHOD: A historical prospective study was conducted using electronically recorded demographic, medical, and military data for more than a half million active-duty U.S. Air Force service members. Cases were identified by ICD-9-CM codes considered by an expert panel to be indicative of mild TBI. Outcomes included ICD-9-CM diagnoses of selected addiction-related disorders. Cox proportional hazards modeling was used to calculate hazard ratios while controlling for varying lengths of follow-up and potential confounding variables. RESULTS: Airmen with mild TBI were at increased risk for certain addiction-related disorders compared with a similarly injured non-mild TBI comparison group. Hazards for alcohol dependence, nicotine dependence, and nondependent abuse of drugs or alcohol were significantly elevated, with a consistent decrease over time. CONCLUSIONS: 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.