Health inequalities vastly pre-date the pandemic, but evidence demonstrating that a person’s postcode or ethnicity could be a determinant of COVID-19 outcomes has combined with a wider focus on societal inequality – and Levelling Up – to create renewed policy attention.
It is a complex and deep-rooted challenge to reduce health disparities across the country. WA recently produced a The Health Inequalities Policy Map, to explore some of the different policy levers and influencers shaping the landscape: some are explored in more detail here.
The Health and Care Act passed in April, establishing long-awaited NHS system reforms to formalise Integrated Care Systems (ICSs). Included in the legislation was a statutory duty for Integrated Care Boards (ICBs) to reduce health inequalities.
ICSs have responsibility for larger geographical areas than their predecessor CCGs. This means that they may have greater success in taking a population health perspective that can drive improvements to health inequalities, and the introduction of NHS England’s CORE20PLUS5 approach provides a supportive framework for ICSs to refine their thinking.
ICBs bring together health, public health, and other partners to organise provision of care. That should mean that services can be better designed to respond to the needs of the population, creating an ideal environment for supporting communities experiencing health inequalities.
However, there are also likely to be challenges. ICSs are under enormous pressure to address immediate and pressing local challenges, including waiting list backlogs, overstretched emergency care and workforce crunches. Longer-term issues like health inequalities require time, capacity and holistic approaches, and can easily be deprioritised, particularly if there are not incentives in place to drive action.
Digital technologies have the potential to reduce burdens on the NHS workforce through automation and artificial intelligence, freeing up staff resources and establishing uniform processes.
But health tech must be designed so that it does not perpetuate existing inequalities. Much has been discussed about lower access or confidence in digital tools for different cohorts, such as older people or those with lower digital literacy. The newly re-integrated digital teams in NHS England will need a clear focus on how tech can enhance outcomes for all communities, not just the lowest hanging fruit.
There are also more complex barriers that need to be addressed, as demonstrated by an example in skin cancer diagnosis.
The creation of an algorithm to diagnose skin cancer was a significant achievement. However, in the paper announcing the algorithm, the examples of images used in the build process did not show diverse ethnicities[i]. This led to concerns that an automated diagnostic tool could actually lead to worse outcomes for different ethnic cohorts.
With a White Paper on Health Disparities due, a Health Secretary committed to tackling “the disease of disparity”, and a new Office for Health Improvement and Disparities, the Government has shown its commitment to resolving health inequalities. They were elected in 2019 on a manifesto which promised to “Level Up” the country.
But when the Levelling Up White Paper was published earlier this year, it was clear that the economic impact of the pandemic has had a significant impact on the Government’s ability to deliver their agenda, and the subsequent budget further underlined the narrow focus on reinvigorating the economy. As the cost-of-living crisis grows, it’s difficult to imagine that the Health Disparities White Paper will be supported by the kind of long-term funding commitment needed from the very top to make a real difference on the ground.
With many avenues and levers for change, there could be huge potential to make a meaningful difference in health inequalities; but without careful planning to overcome challenges, renewed policy attention and NHS reforms could prove a wasted opportunity.
[i] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118, https://pubmed.ncbi.nlm.nih.gov/28117445/