Across the UK, someone born in one of the most deprived areas has a life expectancy up to 10 years shorter than someone born in the most affluent areas, and will spend a greater proportion of that life lived in poorer health. Addressing these systematic differences in health across population groups is a global priority, identified in the UN Sustainable Development Goals. The fundamental cause of health inequalities across groups (be that differences across social, demographic or ethnic), is not clinical care, but rather the underlying social determinants of health. These range from income to education, the work people do, and homes and environments they live in. For example, people born in areas where poverty is higher may have reduced access to good housing, healthy food, transport, and secure employment opportunities - all of which impact health. 

“Health is dictated mostly by its social determinants, not health care systems” (Marmot, 2020) 

Whilst innovation in data science and AI is transforming the prediction, early detection and management of health across molecular genomics of disease, risk and personalized medicine, the systematic application to others drivers of health inequality is somewhat slower to progress. 

Can AI help to address the “equity gap” in health? 

The application of AI and data science has the potential to improve our understanding of the complex and causal relationships and interdependencies, across the social determinants of health. Better understanding the key drivers helps to target action on the most important factors. Using AI and data science to inform the design of more effective policies or interventions, and to develop counterfactuals to help understand what works. There are many challenges in terms of innovative methodologies, wealth of data needed, and ensuring responsible and ethical approach. 

Given the World Health Organization estimates that the social determinants of health account for between 30-55% of health outcomes, the Social Determinants DSxHE Community is rising to this challenge. Applying AI and data science innovation to the fundamental drivers of health equity.