Jonathan Y Huang, PhD, MPH

For decades now, advocates, academic researchers, and decision makers have highlighted the robust, persistent, and often large differences in chances for good health that are the consequence of material and social (dis)advantage and begin in early life. However, the lack of overall progress in reducing health inequalities or devising novel interventions has spurred a demand for more "consequential" epidemiologic research, taking as a goal policy or practice change. To this end, recent theoretical and methodological developments in causal inference, the application of counterfactual frameworks to define and estimate effects of interventions, have greatly improved researchers' abilities to investigate complex mechanistic questions as they exist in the real world (i.e. outside of controlled experimental settings). This is particularly important for the study of social causation and longitudinal, life course, and intergenerational effects. However, one major criticism is that the application of counterfactual approaches may tend to be conservative in practice, e.g. limited explicitly or de facto to the most plausible existing interventions.

 

In contrast, a concurrent flood of molecular epidemiologic findings across the "-omics" (genomics, transcriptomics, metabolomics, etc.) suggest compelling new mechanisms for how social and material disadvantage "gets under the skin" to produce health inequities. For example, epigenetic modifications due to fetal and early childhood exposures may lead to persistent, potentially pathological, differences in gene function without changes to underlying DNA sequence. Better understanding of such mechanisms and the social exposures that influence them may lead to new modes of intervention. However the complexity of biological systems often lead to focused investigations that defy extrapolation to whole individuals, let alone populations within and across generations.

 

One fundamental challenge in motivating health equity policies is poor understanding of the extent to which later intervention can eliminate observed health disparities that begin in early life. More deliberate integration of epigenetic and other biomarkers of physical functioning into causal inference frameworks may help motivate and inform such policies: Representing both malleable and immutable features of health variation, biomarkers provide not only potential targets for intervention, but also encode the most up-to-date knowledge of when and where interventions may be less effective. This is critical for pushing the boundaries for intervention in a disciplined manner. Correspondingly, causal inference frameworks provide formal structures within which putative mechanisms may be modeled as well as defining the conditions under which potential intervention effects can be identified from observed data.