The key role of data in solving the health inequalities commissioning conundrum

“The availability of good medical care tends to vary inversely with the need for it in the population served.” —   Julian Tudor Hart, 1971

Andi Orlowski, Director of the Health Economics Unit and Non-executive director and lead for collaboration and clinical engagement at the Association of Professional Healthcare Analysts (AphA), discusses the value of data-led decision-making in addressing health inequalities.

It has been 50 years since Julian Tudor Hart published his article The Inverse Care Law,1 and while many things have changed, it seems that much has not.

In 2010, the Marmot Review ‘Fair Society Healthy Lives’, which looked at the most effective evidence-based strategies for reducing health inequalities in England, concluded: “Inequalities are a matter of life and death, of health and sickness, of well-being and misery. The fact that in England today people from different socio-economic groups experience avoidable differences…is, quite simply, unfair and unacceptable…However, the scale of these dramatic health inequalities… are not inevitable and can be prevented. Putting them right is a matter of social justice”.2

Crushingly, in a 2020 update, Marmot et al reported that not only did health inequalities persist, life expectancy has stalled across England, particularly in the most deprived areas.3

Health inequalities cannot be fixed by the NHS alone.

Medical care accounts for only 10 to 20 per cent of modifiable contributors to population health.4 These social determinants of health (SDH) ? the conditions in which people are born, grow, live, work and age ? also contribute. Individual health behaviours contribute around 30 per cent of modifiable factors, socioeconomic factors roughly 40 per cent, and physical environment around 10 per cent.

This means that to close the health inequalities gap, changes need to come not just from within the NHS, but from a broad range of population-serving agencies, including the social education and even judicial sectors.

The NHS Long Term Plan aims to meet population needs by looking not only at healthcare but also to improve preventive healthcare through integrated care systems (ICSs), working with individuals, communities, government and businesses. This approach improves understanding about who needs the most help and where to direct resources effectively. However, we need the correct tools to find and interpret the information available to us.

Data analysis will play a key role in solving the commissioning conundrum when it comes to reducing health inequalities. However, with most of the routinely collected data reflecting the care of those people who are already most engaged with health services, how can we reach those who are not?

Here are a few data analysis options that have earned their place in the suite of materials commissioners and providers can use when planning care to reduce inequalities – all of which however fall short in some way in targeting resources where they are truly needed.

Let’s look at where the demand should be highest.

In England, it can cost three times more to look after someone aged 75 than another aged 30. The population is growing and ageing. Costs will rise.

However, allocating resources by population size and age group to target the older population would do little to address underlying health inequality. That’s because those who are living to this older age tend to already have better health outcomes, as they are wealthier and spend more of their lives free from disease than those who live with higher social deprivation.

If older age is not the best marker, how about hospitals utilisation?

Hospital resource use is also a potentially poor proxy for access to care. Roemer’s law, which states that “every built bed is a filled bed”, shows systems simply adjust supply (e.g. how sick you need to be to get into a bed) according to demand for services. So, when system leaders decide to give large hospitals (which tend to serve more affluent populations) more resources, because they are always full, they are in fact potentially just driving further health inequalities

If not hospital utilisation, how about a more encompassing measure? Is it best to look to where all the ‘sick people’ are?

One option is to fund the areas where people are already using resources, places with large disease registers. However, given the relationship between demand and supply, the benefit of care use as a proxy is also questionable.

The number of diagnoses (and the people subsequently addressing their health issues) tends to be higher in areas where the number of doctors per capita is higher – i.e. usually areas that are more likely to be affluent, with quality care that is used more,6 so less-sick patients are obtaining more care than the potentially ‘more-sick’, once again invoking the Inverse Care Law.

Of course, there is also the risk of ‘Moral hazard’, or the concept of individuals (patients and healthcare professionals) altering their behaviour, when others (the NHS or insurance companies) are responsible for the risk, can lure resource allocators into the trap of driving inequalities.

So, if not demand or supply, then what?

As sick people tend to use more resources and eventually die of their sicknesses, understanding the variation in mortality could better represent a population’s need.

Mortality data is also routinely collected and hard to ‘game’. It provides good averages against which to compare data from a specific region. Adjustments can be made to even out differences and calculate objective results, such as standardised mortality ratios (SMRs) or comparative mortality factor (CMF), which will indicate the size of the difference from the average. Pushing resources to regions where more than the average deaths occur could lessen inequality.

Should we start by addressing the social determinants of health (SDH)?

We already know that the NHS can’t solve health inequalities on its own. Because of that pesky 80 per cent of other defining contributors to population health, we need a tool, or more likely a suite of tools, to objectively measure differences in the SDH.7,8

We tend to use the Index of Multiple Deprivation (IMD), which compares and monitors changes in income, employment, health and disability, education, skills training, crime, barriers to housing and services, and living environments across England. Should variation in IMD be used to help prioritise the allocation of resources within an ICS?

Could it also be appropriate to assess how people feel? It is increasingly recognised as a crucial wellbeing factor and the Personal Well-Being Index (PWI) can provide relevant information.

ICSs provide an amazing opportunity to enable the processing of regional data from multiple sources right now, and we call on all societal stakeholders to participate. The most advanced ICSs are already using the potential of data analytics to gain a better understanding of the and the social determinants and health needs of their local populations. They are unlocking the power of population health analytics to improve the quality, efficiency and equity of care that is being delivered.

How can we allocate resources to meet our populations needs?

Programme budgeting marginal analysis (PBMA) challenges us to think about what matters and then measure it, not just think about what we can count. It’s something that we have been working hard to further develop and are excited to use with our clients in addressing health inequalities.

It involves measuring the success of outcomes related to previous budget allocations and appraising the added benefits and costs of future investments (or the lost benefits and lower costs of a proposed disinvestment). By bringing together the assessment of cost and benefits, this measure can unite the aspirations of both clinicians and managers in delivering the best care and outcomes in the most cost-efficient, effective ways.

PBMA allows us to compare a handyman tacking down a carpet, a medicine use review, sight assessment, grab rails, drugs and surgery alongside prevention of falls and allocate in the most efficient way.

To achieve the best possible value, outcomes must be improved or resources reduced without compromising either. Conceptualising value in the PBMA way provides a single uniting goal and a common language in which both financial managers and clinicians can speak.

Driving new approaches

Using PBMA, SMR and IMD scores isn’t revolutionary, at the Health Economics Unit we’ve been using them for a while. However, perhaps with a renewed focus on inequalities, 50 years since Tudor-Hart showed us the issues, and with the ICSs collaborating beyond just health, there is now a new time for us to focus on these tools to and address the ‘unfair and unacceptable’ differences.

Is your ICS using different measurement tools or new and innovative data analysis techniques? Please do let us know if you are having similar discussions and if you’d like to share your thoughts on how we can solve the commissioning conundrum and make health inequalities a thing of the past.

Get in touch 

•  www.twitter.com/economics_unit
• www.dhirubhai.net/company/health-economics-unit
• [email protected] 

References

1. Tudor Hart J. The inverse care law. Lancet 1971: 1: 405?12.
2. Marmot M, Allen J, Goldblatt P, et al. Fair society, healthy lives: the Marmot review. Strategic review of health inequalities in England post-2010. London: Institute of Health Equity, 2010.
3. Marmot M, Allen J, Boyce T, Goldblatt P, Morrison J. Health equality in England: The Marmot review 10 years on. London: Institute of Health Equity, 2020.
4. Hood CM, Gennuso KP, Swain GR, Catlin BB. County health rankings: relationships between determinant factors and health outcomes. Am J Prev Med 2016; 50: 129?135. https://doi.org/10.1016/j.amepre.2015.08.024.
5. WHO. What are the social determinants of health? 2012. https://www.who.int/social_determinants/sdh_definition/en/ (accessed Jan 14, 2021).
6. Asaria M, Doran T, Cookson R. The costs of inequality: whole-population modelling study of lifetime inpatient hospital costs in the English National Health Service by level of neighbourhood deprivation. J Epidemiol Community Health 2016; 0: 1–7. DOI:10.1136/jech-2016-207447.
7. The Health Foundation. A health index for England: opportunities and challenges. Responding to the government’s prevention green paper. Oct 3, 2019. https://www.health.org.uk/publications/long-reads/a-health-index-for-england-opportunities-and-challenges?gclid=CjwKCAiAu8SABhAxEiwAsodSZLuabxZlF8C7QMyp9VE5TJ-1M6jUf_3gmZWpZPatV8sUVnv6Xfy6SRoCyY0QAvD_BwE (accessed Feb 1, 2021).
8. Elias RR, Jutte DP, Moore A. Exploring consensus across sectors for measuring the social determinants of health. SSM Popul Health 2019;7:100395. DOI: 10.1016/j.ssmph.2019.100395.