Scalable combinatorial tools for health disparities research

Abstract

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.

Figure 1

A confluence of resources and technologies.

Figure 2

A sample graph fragment.

Figure 3

A simplified toolchain for health disparities data analysis.

Figure 4

Unsupervised Bayesian network of latent factors derived from paracliques computed in prematurity analysis.

Figure 5

Coarsened graphs based on mortality among black/African-American men, in two sets of 39 counties each, where age-adjusted (25–64 years) 1999–2010 all-cause black mortality is (a) lower than the corresponding average for white men and (b) highest overall.

Figure 6

Coarsened graphs based on lung cancer mortality, preselected for males and high disease incidence, where (a) rates are for blacks/African-Americans and (b) rates are for whites.