The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.

Background: While significant strides have been made in health research, the incorporation of research evidence into healthcare decision-making has been marginal. The purpose of this paper is to provide an overview of how the utility of health services research can be improved through the use of theory. Integrating theory into health services research can improve research methodology and encourage stronger collaboration with decision-makers. Discussion: Recognizing the importance of theory calls for new expectations in the practice of health services research. These include: the formation of interdisciplinary research teams; broadening the training for those who will practice health services research; and supportive organizational conditions that promote collaboration between researchers and decision makers. Further, funding bodies can provide a significant role in guiding and supporting the use of theory in the practice of health services research. Summary: Institutions and researchers should incorporate the use of theory if health services research is to fulfill its potential for improving the delivery of health care.

Objectives. We explored whether the introduction of 3 lifesaving innovations introduced between 1989 and 1996 increased, decreased, or had no effect on disparities in Black–White mortality in the United States through 2006. Methods. Centers for Disease Control and Prevention data were used to assess disease-, age-, gender-, and race-specific changes in mortality after the introduction of highly active anti-retroviral therapy (HAART) for treatment of HIV, surfactants for neonatal respiratory distress syndrome, and Medicare reimbursement of mammography screening for breast cancer. Results. Disparities in Black–White mortality from HIV significantly increased after the introduction of HAART, surfactant therapy, and reimbursement for screening mammography. Between 1989 and 2006, these circumstances may have accounted for an estimated 22 441 potentially avoidable deaths among Blacks. Conclusions. These descriptive data contribute to the formulation of the hypothesis that federal laws promote increased disparities in Black–White mortality by inadvertently favoring Whites with respect to access to lifesaving innovations. Failure of legislation to address known social factors is a plausible explanation, at least in part, for the observed findings. Further research is necessary to test this hypothesis, including analytic epidemiological studies designed a priori to do so.

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.

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 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.

Most major diseases have important social determinants. In this context, classification of disease based on etiologic or anatomic criteria may be neither mutually exclusive nor optimal. Units of analysis comprised large metropolitan central and fringe metropolitan counties with reliable mortality rates – (n = 416). Participants included infants and adults ages 25 to 64 years with selected causes of death (1999 to 2006). Exposures included that residential segregation and race-specific social deprivation variables. Main outcome measures were obtained via principal components analyses with an orthogonal rotation to identify a common factor. To discern whether the common factor was socially mediated, negative binomial multiple regression models were developed for which the dependent variable was the common factor. Results showed that infant deaths, mortality from assault, and malignant neoplasm of the trachea, bronchus and lung formed a common factor for race-gender groups (black/white and men/women). Regression analyses showed statistically significant, positive associations between low socio-economic status for all race-gender groups and this common factor. Between 1999 and 2006, deaths classified as “assault” and “lung cancer”, as well as “infant mortality” formed a socially mediated factor detectable in population but not individual data. Despite limitations related to death certificate data, the results contribute important information to the formulation of several hypotheses: (a) disease classifications based on anatomic or etiologic criteria fail to account for social determinants; (b) social forces produce demographically and possibly geographically distinct population-based disease constellations; and (c) the individual components of population-based disease constellations (e.g., lung cancer) are phenotypically comparable from one population to another but genotypically different, in part, because of socially mediated epigenetic variations. Additional research may produce new taxonomies that unify social determinants with anatomic and/or etiologic determinants. This may lead to improved medical management of individuals and populations.