Social determinants of health: the need for data science methods and capacity

Abstract

Social determinants of health (SDoH) include forces, systems, and conditions that shape the environments in which people are born, grow, work, live, and age. Racism and climate change, for example, affect quality of life and other health outcomes through structural factors, including economic policies, social norms, and other factors that shape environments and consequently the behaviours of people. Indeed, mitigating health inequities requires attention to their root causes and adjacent factors. SDoH can have a greater effect on quality of life and other health outcomes than health-care spendings or lifestyle choices alone.1 , 2 Accordingly, we need to inform analytical models (eg, those used to assess the effect of exposures or interventions on health outcomes) to account for, analyse, and implement interventions on SDoH, such as greenspace improvements.3 In this Comment, we highlight three challenges to measuring and analysing SDoH for which data science—a cross-disciplinary set of skills to make judgements and decisions with data by using it responsibly and effectively—can be harnessed. The three challenges listed are briefly introduced and elaborated on, including clear examples of data science approaches to address: data necessary for capturing the exposure of interest at multiple levels appropriately are not always available nor easy to measure; SDoH are distal to individual health outcomes compared to biomedical determinants such as comorbidities; and the distal placement of SDoH in relation to health outcomes results in requires long periods of time to observe their effect (in some cases over decades or generations). The complex interplay of SDoH at individual, community, societal, and policy levels, and how these determinants operate to affect health outcomes is shown in the figure.