Understanding Emerging Digital Health Disparities

Inequities in digital health should be interpreted in the context of inequities more generally. Social determinants (upstream or 'structural' causes of inequity) include poverty, poor living conditions, racism and discrimination, exposure to crime and adverse childhood experiences; they exert their effects through multiple, mutually reinforcing mechanisms. Social determinants may become built into technologies and technology-supported pathways (e.g., as algorithmic biases), leading to entrenchment of inequities - but there is also the potential proactively to harness technologies to reveal inequities and contribute to mitigating them. Bourdieu's theory of capital, in which privileged individuals are seen as amassing and exchanging various forms of capital (economic, cultural, symbolic) has been extended to embrace 'digital capital' (devices, technical knowledge and social connections on which to draw when using technologies). Crenshaw's intersectionality theory cautions against using single-axis categories of disadvantage ('race', 'gender') since individual identity, shaped by multiple influences, is unique and singular; designing for inclusivity should embrace diversity but avoid stereotyping. We discuss various approaches to increase equity in digital health, including fairer AI design, development of 'upstream' digital interventions, human intermediaries such as digital navigators, participatory research and design, and personas. We conclude with a call to action to design and implement theory-informed initiatives that address social determinants, engage marginalised communities, and prospectively guard against bias and potential intervention-generated inequalities.