Social determinants of health, data science, and decision-making: Forging a transdisciplinary synthesis

Abstract

Two parallel, intellectual strands that seldom intersect have emerged in our evolving understanding of health and health systems over the past couple of decades. First is the growing appreciation of the role that social and economic factors play in shaping health. These so-called social determinants of health are now widely accepted to be core contributors to the health of populations, as indicated by a substantial body of empirical work. We know, for example, that income and education fundamentally shape health through the life course, that disparities by sex and ethnicity affect health outcomes, that characteristics of place (including the built environment) determine opportunities for health-promoting behavior and access to healthcare resources, and that public policies, market incentives, and commercial forces are inextricably linked with the conditions that ultimately generate health [1][2][3][4]. This has led to recognition of the centrality of these concepts by public health scholars and governmental organizations at all levels. The 2008 report of WHO Commission on Social Determinants of Health, chaired by Michael Marmot, was a landmark study in this area [5]. The United States Department of Health and Human Services has advocated for Public Health 3.0, which urges thinking beyond clinical settings to include complementary perspectives from community institutions to link prevention and treatment with social, economic, and environmental factors that help explain patterns of disease in given populations. Several books have translated these ideas for the general public [6,7], and slowly, the political-and private-sector conversation is also beginning to recognize the role that social determinants play in health.A second strand has followed the emergence of technologies that can collect data that characterize our biology, how we live, and the forces with which we intersect with far greater granularity than was ever possible before. These "big data" approaches (characterized typically by large volumes of data, collected quickly, and varied across levels of human organization [e.g., from the biological to the social]) have paved the way for unprecedented, new opportunities for measuring, analyzing, and documenting individual and population health. In particular, these approaches (enabled by new data-analytic tools from artificial intelligence and machine learning) have deployed new data sources and new technologies that can improve health and improve decision-making at the individual and community level. The potential of big data to improve health is becoming apparent both in the genomic revolution that has created an enormous growth in precision medicine approaches to health and in the digital revolution more broadly. These technological advances have made it possible to obtain more data about aspects of human activity, behavior, and context. Recent developments in genomics, big data, and artificial intelligence offer intriguing prospects for the development of a new discipline of

Integrating geo-linked electronic health records with neighborhood data to identify place-based barriers for diabetes management in a public healthcare system

“…Beyond working with other public health partners in their region, healthcare systems can also leverage EHRs to integrate population health informatics to conduct their own programs and interventions, including those at the structural or neighborhood level. [21,35,36] Population-level data infrastructure within healthcare systems can be a tool for surveillance of health disparities, programmatic decisionmaking, integration of health outcomes and social determinants of health, and evaluation of interventions. [21,23,26,[35][36][37][38] Furthermore, this work is underscored by new reimbursement approaches prioritizing value and incentivizing health plans and systems to more holistically address social and medical needs, reduce healthcare disparities, and build infrastructural and organizational capacity for structural change.…”

“…[21,35,36] Population-level data infrastructure within healthcare systems can be a tool for surveillance of health disparities, programmatic decisionmaking, integration of health outcomes and social determinants of health, and evaluation of interventions. [21,23,26,[35][36][37][38] Furthermore, this work is underscored by new reimbursement approaches prioritizing value and incentivizing health plans and systems to more holistically address social and medical needs, reduce healthcare disparities, and build infrastructural and organizational capacity for structural change. [24,25,27] Limitations.…”

“…[20] Beyond facilitating clinical data linkages and affording nuanced objective measures of health outcomes, healthcare systems themselves are increasingly prioritizing social determinants of health (SDOH) and considering new ways to address upstream and structural factors. [21][22][23] Financial incentive structures and value-based payment models have emphasized population and public health. [24,25] Often, this focus on SDOH has increased the screening and referral for social resources for patient populations, and documenting these actions within electronic health records (EHRs).…”

Integrating Social Determinants in Decision-Making Processes for Health: Insights from Conceptual Frameworks—the 3-D Commission

“…This has been made particularly clear during the ongoing COVID-19 pandemic as socially and economically patterned health outcomes have become manifest worldwide [ 7 ]. Integrating social determinants in decision-making for health is, therefore, more imperative now than ever [ 8 ].…”

Estudio Ecológico de la Pandemia por SARS-Cov2 en el estado de Veracruz, México. 2020; evaluación de medio camino

“…A pesar de la importancia que estos resultados tendrán para la toma de decisiones y la emisión de política pública y sanitaria, se hace necesario llegar hasta el ámbito de los dominios singular y particular, ya que ello será propicio para integrar una radiografía social más amplia, útil para la toma de decisiones. (Cardona, 2016; "Ethnicity and COVID-19: an urgent public health research priority | Elsevier Enhanced Reader," n.d.; Galea, Abdalla, & Sturchio, 2020;Halford, Dixon, Farrell, Malleson, & Tilley, 2020;Ugwa, 2016;Verhagen, Brazel, Dowd, Kashnitsky, & Mills, 2020) En el tránsito de acercamiento al objeto de estudio en esta pandemia, diversas estrategias de investigación exploratorias son útiles y necesarias. Una aproximación ecológica que tome a la población como unidad de estudio y permita hacer una descripción de la situación de salud o exposiciones relevantes, en el caso particular de cuestiones sociales y demográficas, cobran especial relevancia para el establecimiento de futuras investigaciones o la construcción de planes de acción, con la consigna de las limitaciones epistémicas conocidas que estos puedan contener.…”