Addressing digital exclusion requires a deeper, intersectional understanding of health inequities

Many individuals lack the means to access and use health technologies. Some lack the skills or motivation to use them due to issues of trust or confidence. Often, these same populations are already marginalised and experience a multitude of factors causing disadvantage, such as ageism, sexism and racism. These same populations frequently have higher prevalence of chronic conditions such as obesity and diabetes, and worse health outcomes. The resulting ‘digital exclusion’ acts to further entrench existing inequities in health and access to health services.

In order to address this, we need to understand the barriers people face, and to do this well we need to look more deeply at the intersection of different factors. A ‘one size fits all’ approach won’t adequately address health inequities. Broad categories such as ‘elderly’ hide wide variations in both needs and the barriers experienced. Different factors have complex interactions, creating significant barriers within some marginalised populations. Most of the research into digital health has taken a relatively limited approach, examining singular factors of the digital exclusion, such as ‘ethnicity’ or ‘socio-economic’ status.

Addressing digital health equity requires a different approach that involves a deeper examination of the underlying root causes of inequities, appreciating the complexities, interrelatedness and the mutually-enforcing nature of factors. Many factors are experienced by some communities from within a cauldron of structural bias and social-economic disadvantage that shapes individual identities, experiences and opportunities both across and within different populations. This ultimately influences digital exclusion and, consequently, inequities in technology use and health.

A more encompassing understanding of the drivers of inequity is now needed to inform the design of effective mitigations. Adopting this approach will enable the complexity of relationships between factors within different populations to be better examined, understood, and addressed.