Unpicking the inequalities in the elective backlogs in England

As part of new research project that explores how NHS providers and integrated care systems (ICSs) are approaching inclusive recovery, we have found that, in 2022, people in the most deprived areas were twice as likely to be waiting more than a year for elective care compared to people in the least deprived areas. This paper explores three big questions health and care leaders should be asking themselves and their teams about inequalities in their elective backlog: how are we measuring inequalities and why, do we know why inequalities exist, and how will we know if things are improving?

Health systems in England are currently grappling with multiple pressures, including elective backlogs, and widening health inequalities. In 2020, NHS England asked NHS organisations to co-ordinate their approaches to these two challenges by ‘restoring NHS services inclusively’. The hope was that this would reduce disparities in access to elective care and improve health outcomes for disadvantaged patients.

Analysis from The King’s Fund showed that in 2021 there were inequalities in elective backlogs between the most and least deprived areas of England. One year on from the publication of the plan for tackling elective backlogs of care and the specific ask from the Secretary of State for NHS organisations to look at their waiting lists by factors such as deprivation, we wanted to know if there had been any improvement in these trends (see Table 1).

Many national and local health and care leaders are still exploring how to measure and understand inequalities in their waiting lists data. Below, we also set out some questions leaders should be asking about their waiting list data. We focus on deprivation, as the data is widely available publicly, but it is equally important that health systems identify and understand inequalities by other protected characteristics such as ethnicity and disability.

How have the inequalities in the elective backlog changed since 2021?

Our original analysis in 2021 looked at both the number of people waiting more than a year for elective care (waiting times) and the increases in total size of elective backlogs (waiting lists), based on NHS England’s 2020/21 referral-to-treatment data (see methodology box). The analysis showed a clear deprivation trend in elective waiting lists and elective waiting times, with the most deprived areas seeing faster growth in waiting lists and longer waits.

Since then, the total elective waiting list has continued to grow, albeit at a slower rate than previously, and the number of patients waiting more than one year for treatment has increased by a third

We used the same methodology to analyse the elective waiting list up toto see if the relationship between deprivation and elective waiting lists had changed over time.

Our new analysis of waiting times finds people in the most deprived areas were twice (2.1 times) as likely to experience a wait of more than one year compared to people in the least deprived areas. The data shows that 9 per cent of patients on waiting lists in the most deprived areas of England had been waiting a year or more for treatment, compared to 4 per cent of those in the least deprived areas. This is similar to our previous analysis that found a 1.8 times difference between the most and least deprived areas.

We also looked at the growth of waiting lists between August 2021 to August 2022 and found that there was not a clear correlation between deprivation and increasing waiting lists. This is a change from the growth between April 2020 and July 2021, where there was a clear deprivation trend. The change is most noticeable in the most deprived quintile, as this went from having the fastest growth in the year up to 2021, to the slowest growth in the year up to 2022.

Taken together, these results show that, while the growth of waiting lists in the most deprived areas has slowed, people in most deprived areas are still twice as likely to be on that waiting list for more than a year.

Table 1 shows how these results compare to our original analysis.

How should we interpret these national trends?

These results show that at a national level there are still inequalities for the longest waiters in the most deprived areas of England. This is an important finding considering the number of people waiting more than one year has grown since the elective recovery plan was published in 2021. Even if the volume of one-year waits does start to fall, there is no guarantee that the disparity between people waiting in the most and least deprived areas will also reduce without targeted intervention.

The story of waiting list growth is more complicated. It is difficult to interpret the difference between the original and the updated findings and even more difficult to verify any interpretations with the data available. Possible interpretations include the following.

• The change could be a sign of unmet need in the most deprived areas. It is possible that demand for elective care in the most deprived areas has continued to grow from 2021 to 2022, but this demand has not translated into referrals on to the elective waiting list. This could be due to people not coming forward for treatment; people being unable to access GP services for referrals; or a change in how GPs refer patients.

• The change could be a sign that providers in the most deprived areas are recovering their services faster than those in less deprived areas. There are many reasons why this may be happening. One contributing factor could be that some providers in most deprived areas received extra support from NHS England through its tiered support system, although many providers in less deprived areas also received this support.

• An alternative interpretation is that we are still seeing the impact of the Covid-19 pandemic unfold variably across England. It could be that Covid-19 had the most impact on services in more deprived areas in the first year of the pandemic (eg, because there were higher infection rates in these areas), whereas services in less deprived areas experienced the impact of Covid-19 over a longer timeframe (eg, because they have populations with a higher proportion of older people who are still at risk from infection).

What questions should leaders be asking about inequalities in waiting lists data?

Our analysis suggests that there is still work to do to understand inequalities in elective waiting lists. Nationally there is still variation by deprivation that needs unpicking and monitoring. Hidden behind national data there is local variation, which means local systems need to do their own research into inequalities in their waiting list. The 2022/23 operational planning guidance asked NHS boards to disaggregate their performance data by deprivation and ethnicity, but initial analysis of trust board papers suggests not all boards are doing this yet.

Based on learnings from our own analysis, the following section outlines three big questions for NHS leaders to ask of their local waiting list data when approaching analysis of inequalities in waiting lists.

• How are we measuring inequalities and why?

• Do we know why inequalities exist?

• How will we know if things are improving?

Our suggestions focus on deprivation but could also be relevant to other types of inequality that go beyond deprivation. NHS England has created some tools to help support some of this analysis (eg, the health care inequalities improvement dashboard). However, currently these tools cannot replace the insights gained from analysis bespoke to a local area. Any local analysis will be heavily reliant on analytical capacity and good-quality data. Our wider project on inclusive backlog recovery hopes to identify how NHS providers and systems can overcome any barriers to carrying out this analysis.

How are we measuring inequalities and why?

There are multiple ways to measure inequalities in waiting lists, and which to use is an important choice. There are different ways to measure waiting lists (eg, growth, size, or length of wait) and different ways to breakdown lists (eg, treatment specialities, or admitted verses outpatient). Our analysis showed that using just two different measures created a mixed picture of inequalities in waiting lists. Therefore, it’s important to use a wide range of measures to understand the dynamics between waiting lists, waiting times and inequalities. Some metrics are suggested in the final chapter of The Strategy Unit’s report on planned care inequalities.

It's also important to choose the right geographical unit of analysis. More granular geographies (eg, patient postcode) will create the most accurate picture of inequalities, but larger geographies (eg, integrated care systems) may sometimes be more appropriate if that’s where responsibility lies for addressing inequalities.

Do we know why inequalities exist?

It is important to understand the cause of any inequalities identified by analysis of national or local waiting lists. Being able to pinpoint the cause of inequalities makes it easier to develop interventions to address them – which is especially important in systems with limited capacity for improvement work. We have identified four hypotheses around why inequalities might exist, although these may not be exhaustive. These are based on the fundamental principles of waiting lists and waiting time management, which were identified in research into strategies to reduce waiting times for elective care.

• Hypothesis one: demand for elective care is higher in more deprived areas.

• Hypothesis two: supply of elective care (eg, elective activity) is lower in more deprived areas.

• Hypothesis three: waiting processes for patients from more deprived areas are slowing down the treatment pathway. Processes, such as clinical reviews of patients, diagnostic appointments and patient choice could be putting patients in more deprived areas at a greater disadvantage. For example, patients in more deprived areas might have less flexibility to attend appointments if they are unable to access transport, time off work or childcare.

• Hypothesis four: trends are not the direct result of deprivation, but of other closely linked external factors. The primary cause of variation in waiting lists and waiting times could be a combination of different factors (eg, geography, the Covid-19 infection rates, funding allocations, workforce availability) and deprivation is a secondary cause.

Analysis from other sources using granular non-publicly available data has already provided evidence in some of these areas. For example, in relation in hypothesis one and two, analysis from The Strategy Unit showed that across four clinical pathways, patients in the most deprived areas are more likely to receive an initial diagnosis from a GP, but are less likely to receive secondary care treatment. Analysis from the Nuffield Trust supports hypothesis two, as it showed that more deprived areas had greater falls in their elective activity over the first year of the Covid-19 pandemic. And survey data from the Office for National Statistics showed that the waiting process has a greater negative impact on people in the most deprived areas, which could contribute to hypothesis three. However, the picture could be different in each local area.

Data analysis can only tell part of the story. If data highlights inequalities, it’s critical to ask what engagement work is being done with patients, the public, local communities, and clinicians. Engagement and collaborative working with these partners will create a better understand of why inequalities exist and, therefore, how to tackle them. For example, what are the specific barriers to accessing care in the local area?

We hope to identify how providers and local systems are currently approaching this task through the case studies in our wider inclusive backlog recovery research project.

How will we know if things are improving?

As our analysis has shown, trends in inequalities can grow and change over time. To monitor progress, it will be important to periodically update both national and local analysis of inequalities in waiting lists. This is an essential part of measuring the success of interventions to tackle inequalities.

It is equally important to know what good looks like and where you are trying to get to. What would a waiting list free of inequalities look like? For example, would there still be some variation in waiting times? And would variation in one part of the pathway (eg, referrals) be acceptable if it evened out later in the pathway (eg, elective activity)? These are questions that require further discussion between everyone involved in tackling inequalities in waiting lists (eg, organisational leaders, clinicians, patients and the public).

Turning insights into action

Identifying and understanding inequalities through analysis and research is only the first step in tackling inequalities in elective waiting lists. The next step is for leaders across the NHS to take action to address inequalities across deprivation and other dimensions such as ethnicity.

At a national level it appears that there are still inequalities for patients in the most deprived areas, as they are more likely to wait more than a year for care (people who wait more than a year for care are sometimes referred to as ‘long waiters’). NHS England’s goal is for there to be no one waiting more than a year by March 2024. But for this to happen, there needs to be particular focus on reducing the high volume of people waiting in the most deprived areas. Achieving this is likely to require targeted support and close monitoring of progress, with national, regional and ICS teams working in partnership.

ICSs and NHS providers also need to tackle inequalities in their local elective waiting lists. NHS England’s guidance has so far been permissive and allowed flexibility in how ICSs approach tackling backlogs inclusively. Our wider inclusive recovery research project is due to be published later this year, and will explore how local systems are interpreting this task and the actions they have taken so far. It will also explore how local systems are being held to account and whether there have been any lessons so far about how to tackle elective backlogs inclusively.