Portal de Periódicos da CAPES

Effects of the COVID-19 pandemic on NHS England waiting times for elective hospital care: a modelling study

2024; Elsevier BV; Volume: 403; Issue: 10423 Linguagem: Inglês

10.1016/s0140-6736(23)02744-7

1474-547X

Syed Ahmar Shah, Chris Robertson, Aziz Sheikh,

Global Health Care Issues

The COVID-19 pandemic led to unprecedented health-care disruption across the UK.1Shah SA Brophy S Kennedy J et al.Impact of first UK COVID-19 lockdown on hospital admissions: interrupted time series study of 32 million people.eClinicalMedicine. 2022; 49: 101462Summary Full Text Full Text PDF PubMed Scopus (20) Google Scholar In England, the number of patient referrals waiting to be treated in hospital was more than 7·2 million at the end of October, 2022.2Harwood-Baynes M NHS waiting list hits record high of 7·2 million people—as almost third of patients wait four hours in A&E.https://news.sky.com/story/nhs-waiting-list-hits-record-high-of-7-2-million-people-as-almost-third-of-patients-wait-four-hours-in-a-e-12763943Date: 2022Date accessed: December 10, 2022Google Scholar In response, the UK Government set up an elective recovery taskforce (ERT) in December, 2022, to help NHS England tackle this backlog.3Department of Health and Social Care Barclay S Government turbocharges efforts to tackle COVID-19 backlogs.https://www.gov.uk/government/news/government-turbocharges-efforts-to-tackle-covid-backlogsDate: 2022Date accessed: December 9, 2022Google Scholar NHS England's recovery plan aims to increase capacity by up to 30%, compared with pre-pandemic levels, over 3 years through a range of measures, including extra staff and increased use of the independent sector.4NHS England Delivery plan for tackling the COVID-19 backlog of elective care.https://www.england.nhs.uk/coronavirus/delivering-plan-for-tackling-the-covid-19-backlog-of-elective-care/Date: 2022Date accessed: December 3, 2022Google Scholar NHS England estimates that more than 10 million patients who might otherwise have come forward for treatment did not do so during the pandemic.4NHS England Delivery plan for tackling the COVID-19 backlog of elective care.https://www.england.nhs.uk/coronavirus/delivering-plan-for-tackling-the-covid-19-backlog-of-elective-care/Date: 2022Date accessed: December 3, 2022Google Scholar There is, however, considerable uncertainty in how many of these missing people might actually come forward and also in the increase in capacity that could realistically be delivered. This uncertainty consequently is a challenge for the ERT when assessing the effects of the pandemic on the hospital elective waiting list and, importantly, deciding what increase in capacity is needed to reduce the backlog. We sought to understand these key knowledge gaps by investigating the effects of the pandemic on the waiting list for elective hospital care and by developing projections under different possible capacity-increase scenarios. Our aim was to estimate the extent of health-care disruption during the COVID-19 pandemic to aid decision making regarding the necessary capacity increases that are required to address the ensuing backlog. We investigated the total number of pending referrals at the end of each month using the referral to treatment time (RTT) metric, defined as a patient's waiting time since referral until seen by a consultant-led service for elective treatment.5NHS England Referral to treatment (RTT) waiting times.https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/Date: 2022Date accessed: December 11, 2022Google Scholar We used the latest available data (April 1, 2011, to Oct 31, 2022) that report the monthly total who were waiting (ie, incomplete RTT pathways) and who were seen by a consultant (ie, completed RTT pathways) for England,5NHS England Referral to treatment (RTT) waiting times.https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/Date: 2022Date accessed: December 11, 2022Google Scholar stratified by specialty, hospital, and region. Each pathway relates to an individual referral rather than an individual patient and a patient can have multiple RTT pathways.5NHS England Referral to treatment (RTT) waiting times.https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/Date: 2022Date accessed: December 11, 2022Google Scholar Our appendix provides a conceptual diagram and detailed description of our modelling approach to estimate the total number of patient referrals under different scenarios (appendix pp 1–10). Briefly, our analysis included a pre-pandemic period (Jan 1, 2012, to Feb 29, 2020), the pandemic period (March 1, 2020, to Oct 31, 2022), and a projection period (Nov 1, 2022, to Oct 31, 2025; appendix pp 1–2). We first fitted a first-order polynomial function to the pre-pandemic period to establish trends in the monthly number of pending patient referrals and in the monthly system capacity (ie, the number of completed RTT pathways). From these trends, we estimated the counterfactuals (ie, the numbers in the period March 1, 2020, to Oct 31, 2022, if the pandemic had not happened; appendix pp 2–3). Using these estimates, we computed the total decrease in completed RTTs during the pandemic and then the total number of missed referrals due to pandemic-related health-care disruption (ie, the missing patient referrals; appendix pp 3–5). Finally, we made projections of the total number of patients waiting under four scenarios: 5%, 10%, 20%, and 30% increase in system capacity during the next 3 years. For projections, we used a model based on vector autoregression with exogenous series (appendix pp 5–10).6Wei WWS Multivariate time series analysis and applications. John Wiley & Sons, Hoboken, NJ, USA2019Crossref Scopus (73) Google Scholar. This model assumes that the total number waiting at the end of a given month is a function of the total number waiting at the end of previous months, the monthly demand (affected by the expected number of missing people who might eventually seek health care), and the monthly treated (ie, the system capacity). In the model, the total number waiting is the response variable (the autoregressive component) and the remaining variables (monthly demand and monthly treated) are exogenous variables. Analysis was done in MATLAB version R2022b. At the end of January, 2012, there were 2·4 million pending referrals. This number gradually increased by about 275 000 per year (approximately 23 000 per month) until the end of February, 2020, when the total number waiting was 4·6 million. The waiting list increased by about 2·2 million in the 8 years before the pandemic (Jan 1, 2012, to Feb 29, 2020). However, during the first 32 months of the pandemic, the waiting list increased by 2·6 million to 7·2 million by end of October, 2022 (appendix p 2). There were 1·1 million completed RTT pathways during January, 2012. System capacity gradually increased thereafter until the end of February, 2020, with a gradual increase of about 46 000 per year (approximately 3800 per month). At its peak during October, 2019, there were about 1·6 million completed RTT pathways. However, during the initial phase of the pandemic, there was severe disruption, with the lowest completed RTT pathways of 60 000 during May, 2020. Although completed monthly RTTs have gradually increased since then, they are still far short of pre-pandemic levels (appendix p 4). Between Jan 1, 2017, and Oct 31, 2022, the number of referrals that were resolved within 18 weeks was consistently below the NHS service target of 92% across most specialties (appendix pp 10–13).5NHS England Referral to treatment (RTT) waiting times.https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/Date: 2022Date accessed: December 11, 2022Google Scholar The 92% target has not been attained in any specialty during the pandemic (appendix pp 10–13) and by October, 2022, less than 70% of referrals were resolved within 18 weeks across all specialties, except geriatric medicine at 85% (appendix p 13). Our models suggest an estimated 10·2 million (95% CI 4·4–15·9) missing referrals from the beginning of the COVID-19 pandemic to Oct 31, 2022. A subset of people associated with the missing referrals might not seek health care anymore due to various factors, such as being treated in private care or death, so we consequently simulated a range of scenarios (ie, 25–75% of missing seeking health care). Assuming a linear capacity increase in terms of completed pathways during 3 years (November, 2022, to October, 2025), the system capacity should be increased by more than 10% to reverse the increasing trend in waiting lists. Even with a 30% increase in system capacity, several years would be needed to clear the backlog (figure). A key limitation of our study is that the projections depend on the assumptions of monthly baseline demand and the proportion of people associated with the missing referrals seeking health care and system capacity; different assumptions will lead to different projections. However, we have included several sensitivity analyses to add robustness to our findings. Another limitation is that the modelling was based on aggregated data, with no ability to track any individuals who might have skewed the results during the pandemic (eg, multiple presentations or unexpected deaths). Our model does not explicitly account for any effect on demand due to population growth or effect on system capacity due to other disruptions, such as NHS staff strikes. The processed data and accompanying code are publicly available for anyone to reproduce the study findings and generate new projections under different sets of assumptions. In summary, the NHS waiting list for elective treatment increased between Jan 1, 2012, and the start of the COVID-19 pandemic, suggesting a gradual service decline. The waiting list then substantially increased during the pandemic, but this substantial increase is likely to represent a substantial underestimation of the backlog because of the anticipated large numbers of people who have still not come forward for care. Even if the ambitious target of 30% increase in capacity is achieved during the next 3 years, several years (beyond the end of 2025) will be needed for the backlog to clear. Our study emphasises the need to improve health-care system resilience to ensure that the effects of any future emergencies on the provision of routine care are minimised.7El Akoum M Dhami S Thompson D Sheikh A Lessons in resilience: what COVID-19 taught us about preparing for the crises to come.BMJ. 2022; 378: o2351Crossref PubMed Scopus (1) Google Scholar AS conceived the analysis and oversaw all aspects of the study. SAS, with help from AS and CR, designed the study. SAS conducted the analysis and wrote the first draft of the manuscript. SAS and CR directly accessed and verified the underlying data. All authors edited the manuscript, had full access to all the data in this Correspondence, and had final responsibility for the decision to submit for publication. SAS acknowledges support from the University of Edinburgh's Chancellor's Fellowship scheme. AS is an adviser to several Scottish and UK Government COVID-19 advisory groups. CR declares no competing interests. The original data were downloaded from NHS England (https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/). This data file is also available at https://github.com/syedahmar/HealthCareDisruption-Elective under the Open Government Licence version 3 (https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). The processed data and the accompanying code are publicly available at https://github.com/syedahmar/HealthCareDisruption-Elective. NHS Research Ethics approval was not required as the data are aggregated and publicly available. Download .pdf (1.78 MB) Help with pdf files Supplementary appendix