Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study

COVID, Hospital admission, Elective surgery equity ALL Metrics - Views Downloads How to cite this article Reeves K, Remsing S, Kariuki M et al. Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study [version 1; peer review: 1 approved with reservations]. NIHR Open Res 2026, 6:9 (https://doi.org/10.3310/nihropenres.14150.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Research Article [version 1; peer review: 1 approved with reservations] Katharine Reeves1, Sandra Remsing1, Mary Kariuki https://orcid.org/0009-0004-3281-4436 1, [...] Felicity Evison1, Peter Chilton2, Paul Bird1, Samuel I Watson2, Richard Lilford https://orcid.org/0000-0002-0634-984X 2Katharine Reeves1, Sandra Remsing1, [...] Mary Kariuki https://orcid.org/0009-0004-3281-4436 1, Felicity Evison1, Peter Chilton2, Paul Bird1, Samuel I Watson2, Richard Lilford https://orcid.org/0000-0002-0634-984X 2 PUBLISHED 14 Jan 2026 Author details Author details 1 Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England, B15 2TH, UK 2 Institute of Applied Health Research, University of Birmingham, Birmingham, England, B15 2TT, UK 2 Institute of Applied Health Research, University of Birmingham, Birmingham, England, B15 2TT, UK Katharine Reeves Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing Sandra Remsing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Mary Kariuki Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Felicity Evison Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Peter Chilton Roles: Project Administration, Writing – Review & Editing Roles: Project Administration, Writing – Review & Editing Paul Bird Roles: Writing – Review & Editing Roles: Writing – Review & Editing Samuel I Watson Roles: Formal Analysis, Writing – Review & Editing Roles: Formal Analysis, Writing – Review & Editing Richard Lilford Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS To describe trends in elective surgery volumes over the COVID-19 pandemic and to compare post-COVID with pre-COVID volumes with respect to three hypotheses: 1) higher priority operations would be favoured over operations designated a lower priority; 2) the independent sector would take on a greater proportion of NHS-funded surgeries; 3) the proportion of cases switching to the independent providers would be higher for the areas in the lowest deprivation quintile. We extracted data from the Hospital Episodes Statistics (HES) database from 1st April 2015 to 31st December 2024. This database contains all emergency and elective patient admissions, outpatient appointments and A&E attendances funded by the NHS in England. We included spells with a selection of primary operation codes from priority groups 3 and 4, as defined by the “Clinical guide to surgical prioritisation”. We described and illustrated time trends for operations in these groups during the pre-COVID, COVID and post-COVID periods. In the post-COVID epoch, compared to the pre-COVID epoch, we observed: - 1) The number of operations carried out per unit time recovered gradually. - 2) High priority operations increased as a proportion. - 3) The proportion of high priority operations carried out in Independent Sector Healthcare Providers (ISHPs) increased dramatically to almost equal the proportion carried out in NHS owned hospitals. This pattern was not replicated for lower priority operations. - 4) We did not find evidence of discrimination against more deprived IMD quintiles among higher or lower priority operations. Overall, the number of operations has not yet recovered to its pre-COVID level. We found that a proportion of NHS-funded elective surgery shifted towards the independent sector under high demand. We also found that the service discriminated in favour of higher over lower priority but discrimination between people from least deprived vs. most deprived areas was not observed. During the COVID pandemic, we identified a large decrease in elective surgery compared to pre-COVID levels and compared to emergency and cancer operations. We found that elective surgery volumes slowly recovered towards their pre-COVID level, the proportion of operations funded by the NHS but performed in the private sector increased, and the service favoured operations designated as high-priority over low-priority. Clear efforts to improve supply of surgical services did not reach a point where increasing demand could be met, suggesting that increasing use of the independent sector has not overcome overall supply side (capacity) constraints. COVID, Hospital admission, Elective surgery equity Corresponding Author(s) Richard Lilford ([email protected]) Grant information: This work was funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration West Midlands (ARC WM) (NIHR200165). Watson and Lilford are also supported by the Medical Research Council MR/V038591/1, and the NIHR Midlands Patient Safety Research Collaboration (PSRC) (NIHR200165). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Reeves K et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Reeves K, Remsing S, Kariuki M et al. Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study [version 1; peer review: 1 approved with reservations]. NIHR Open Res 2026, 6:9 (https://doi.org/10.3310/nihropenres.14150.1) First published: 14 Jan 2026, 6:9 (https://doi.org/10.3310/nihropenres.14150.1) Latest published: 14 Jan 2026, 6:9 (https://doi.org/10.3310/nihropenres.14150.1) The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Waiting lists for elective surgery increased rapidly in England during and following the COVID pandemic. By the end of March 2021 436,127 patients had been waiting for over 52 weeks from referral for elective treatment, which had only fallen to 309,300 by March 2024. In comparison, prior to COVID in February 2020, this number was 1,8451. Managing the demand for elective surgery is a matter of public concern and political interest. In this paper we studied the supply of elective surgery over the COVID pandemic with particular emphasis on the pre- and post-COVID epochs – in previous work we documented the supply of elective surgical services during the pandemic itself (see pre-print2). Another study found that acute NHS hospital trusts did not fully recover from COVID by 20213. In the current study we used surgical volumes/throughput to shed light on several issues of theoretical and practical interest. First, we simply tracked and described surgical volumes over the two epochs, pre- and post-COVID. Theoretically, we may have expected the supply of operations to increase over time, given the political and clinical imperative to match demand and reduce waiting times and numbers. Second, we looked for evidence that, compared with the pre-COVID epoch, greater priority would be given to certain operation types over other operation types in the post-COVID epoch. Here, we built on the official “Clinical guide to surgical prioritisation”4, which assigns priority levels to surgical procedures. We examined any change in priority between the more urgent elective category (priority 3 – procedures to be performed in less than 3 months) and the less urgent category (priority 4 – procedures to be performed in over 3 months). Thus, we did not examine emergency (priority 1 – procedures to be performed in less than 24/72 hours) and cancer (priority 2 – procedures to be performed in less than 1 month) operations. Unlike cancer operations, no financial incentives are applied to priority 3 and 4 operations. This allowed us to examine the effect of prioritisation in the absence of an extrinsic motivating factor. Theoretical interest here concerned the extent to which the system may have prioritised certain cases, given that there was no penalty or reward for doing so. Third, we examined changes between the pre- and post-COVID epochs with respect to the proportion of NHS-funded operations carried out in publicly owned vs independent facilities. Here we exploited the fact that the NHS funds operations in both sectors. Like, for example, Kaiser Permanente in California, it acts as both a commissioning and a provider organisation with the feature that individual doctors (surgeons and anaesthetists) work in both settings, while nurses and auxiliary workers tend not to. These features enabled us to gain some insights into the relationship between supply and demand – it might be hypothesised, for example, that the number of operations in NHS organisations would remain static, while the proportion done in independent facilities increased, thereby leading to overall increase in volumes. We also examined the ‘sub-hypothesis’ that the proportion of operations in the less urgent category (priority 4) would increase in independent hospitals relative to public hospitals over the two epochs. This would happen if the profit motive, rather than patient need, drove independent provider behaviour. Fourth, we wished to find out whether there would be a change in the proportion of surgeries carried out based on patient characteristics, IMD quintile group and ethnicity, to test the hypothesis that some groups would have been able to negotiate ‘privileged access’. For reasons that will become apparent, we were not able to include ethnic group in this comparison. While our study is limited to England, the above contextual features enable us to examine theoretical insights of potentially wider applicability, for example on the interplay between independent and public provision under a common payer. This paper follows the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines for studies conducted using routinely collected healthcare data5 and we have completed the RECORD statement (see Other Information). We conducted a retrospective, observational, longitudinal study using routinely collected healthcare data to examine the effect of COVID on access to planned NHS-funded surgery. We did this by plotting the monthly number of operations before, during and after the COVID lockdown period and recording the percentages in different IMD quintiles over these three windows. We chose the time windows in line with lockdown dates in England, as recorded on Gov.uk6. The data used for this study were extracted from the Hospital Episodes Statistics (HES) database which contains all emergency and elective patient admissions, outpatient appointments and A&E attendances funded by the NHS in England. All procedures funded by the NHS were included, irrespective of whether they took place in NHS or independent hospitals. We included spells containing one of a selection of primary operation codes from priority groups 3 and 4, listed in Extended Data 17. Operations were chosen on the grounds that they were among the most frequently performed operations in their priority group. Our dataset was divided into three time periods: April 2015 – February 2020 (pre-COVID), March 2020 – July 2021 (COVID) and August 2021 – December 2024 (post-COVID). These time periods were in accordance with the lockdown period recorded by the Government6. We described, across time periods: provider type, sex, Charlson comorbidity score, number of any-cause deaths within 90 days of the operation date, number of emergency readmissions within 90 days of the operation date, median age, median length of stay, ethnicity, IMD quintile and specific operation type. Given that the time periods were of unequal duration, we included the monthly mean number of operations in addition to comparing percentages overall and within groups. We plotted the results across the time periods to allow visual inspection and described the overall trends in operations. We described differences between pre- and post-COVID proportions of surgeries by age, ethnic and IMD quintile group in the Extended Data 2 (Figures A to J)7. We do not test the statistical significance of the difference between proportions because very small differences (less than a percentage point) might be ‘significant’ but provide spurious associations, for example, as a result of equally small changes in the denominator population. Investigators at University Hospitals Birmingham (UHB) had access to pseudonymised, patient-level HES data for the purposes of this study. Data were cleaned using the inclusion criteria listed above and aggregated for reporting purposes. No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for recruitment, design or implementation of the study. No patients were asked to advise on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community. This project was a service evaluation using pseudo-anonymised data, so consent from patients was not required. Ethics approval was not required for this work, and this was confirmed by the University of Birmingham Research Ethics team. The evaluation was registered with the local Clinical Audit Department under the Clinical Audit Registration and Management System (CARMS) number 20835. Access to the dataset was restricted to the designated analyst within University Hospitals Birmingham NHS Foundation Trust. All data were used in accordance with the data sharing agreement with NHS Digital (https://digital.nhs.uk/your-data) and handled in line with the Caldicott Principles and the ethical standards of the Declaration of Helsinki, with suppression of small numbers and sensitive items to minimise disclosure risk. Our method of data selection is illustrated in Figure 1. We included all spells where the primary operation is one of the OPSC-4 codes from priority group 3 or 4 (listed in Extended Data 17) and the operation date is between 1st April 2015 and 31st December 2024. Following this, operations were excluded if: it was a repeat operation for a patient, patient age was not between 18 and 120, admission method was not elective, patient age or sex was unknown or there were inconsistencies e.g. date of death preceded operation date. We summarised our data in Table 1 (priority 3) and Table 2 (priority 4). Total and monthly average number of operations are shown in the first two rows for each period. The number of operations and the percentage of the total are then shown for each period for provider type, sex, Charlson comorbidity score, number of any-cause deaths within 90 days of the operation date, number of emergency readmissions within 90 days of the operation date, median age, median length of stay, ethnicity, IMD quintile and specific operation type. IMD quintiles are calculated using IMD 2015 scores for the Lower Super Output Area (LSOA) 2011 of the patient’s home address. The percentage of priority 3 operations carried out at ISHPs increased from 21% to 32% during COVID and further still to 43% post-COVID. The same pattern was not seen for priority 4 operations, where the percentage carried out at ISHPs remained fairly constant throughout. Changes were also observed in ethnicity groups for priority 3 operations, specifically for White and Unknown groups. The percentage of patients self-reporting as White decreased by 12% from 77% to 68% to 64% over the pre-, during- and post-COVID time periods respectively. Conversely, the percentage of patients self-reporting as Unknown increased by 14% from 15% to 25% to 29% during the same time periods. There was a similar pattern for priority 4 operations, but the changes were smaller. The proportions in the male sex and in the lowest deprivation quintile group changed by only 1 percentage point. We illustrated the total number of operations per month (Figure 2) by organisation type (ISHP and NHS) and overall. The COVID period is shown by the grey, shaded area. For both priority groups, there was a large drop in the number of operations at the start of the COVID period, with some recovery part-way through, followed by a second drop before the lockdown restrictions were lifted. The trend here is one of gradual recovery of volumes over three years and nine months but with differences by priority group as we now describe. Extended Data 3 (Figures K to W) shows the numbers of each individual operation over time7. We initially included YAG laser capsulotomy surgery in the priority 4 group, as this is one of the most frequently performed surgeries. However, we chose to exclude it as the increase in operations at ISHPs was so large that it completely changed the overall trend for that group. We included the time trend plot for this operation in Figure X in Extended Data 37. This large increase seemed to be in keeping with a general shift towards ISHPs for ophthalmology treatments, as reported by the Health Foundation8. The number of operations per month for priority 3 (Figure 2, Panel A) was reasonably flat pre-COVID. It decreased during the pandemic, gradually recovering to at least pre-pandemic levels over the subsequent three years and nine months. This pattern is not shown for priority 4 (Figure 2, Panel B) operations, where the number of operations following the COVID period recovers slowly, remaining below the pre-COVID number. There is, thus, clear evidence that the service was prioritising the operations that the NHS had classified as more urgent. This excluded the single relatively new ophthalmic operation (YAG laser), whose use had increased dramatically despite being priority 4 (Extended Data 3, Figure X)7. It can be seen in Figure 2, Panel A that the proportion of priority 3 operations carried out in ISHPs increased dramatically post-COVID to almost equal the proportion carried out in NHS-owned hospitals. This pattern was not replicated for priority 4 operations in Figure 2, Panel B. The change over time by organisation type and by priority level was illustrated further by calculating indexed operations in Figure 3. Operations were indexed against the total for April 2015, the first month in our dataset, which was set to 100 as the baseline. For month X, the indexed value was The plots show the percentage change from the baseline over time. For priority 3 operations in Panel A, the number of operations carried out at NHS hospitals clearly decreased as a percentage of the initial total. The opposite is true for operations at ISHPs, which also dipped during COVID but then increased to more than 200% of the April 2015 baseline figure. For priority 4 operations in Panel B, the number of operations at both NHS hospitals and ISHPs fell as a percentage of the baseline value, with minimal change in favour of ISHPs. The fourth question we aimed to answer was whether there was evidence that the proportion of operations changed in favour of people from the least deprived neighbourhoods. While the point estimate showed a slightly increased proportion for the least deprived group, the magnitude of this difference is very small (half of a percentage point) (Table 3). We chose not to carry out the same analysis for ethnic group because of the large shift in self-categorisation described earlier. We documented changes in trends in operations as per the first aim. Overall, the number of operations did not recover to its pre-COVID level. Given the backlog, and presumably increasing demand, it is not surprising that waiting lists remain high. Waiting lists may underestimate demand since the perception of supply constrains is likely to affect patient willingness to request appointments. Regarding the second aim, we found that changes were in the hypothesised direction. Priority 3 operations were indeed (and increasingly) prioritised over priority 4 operations in comparison to the pre-COVID period. Turning to the third aim, we again confirmed our prior hypothesis: the proportion of operations in the independent sector increased relative to the total of NHS funded procedures. However, our hypothesis that operations overall would increase was disproven. Likewise, we found the opposite of our hypothesis that the independent sector would provide the greatest increase for priority 4 (lower priority) operations. The fourth aim examined possible service discrimination in favour of some groups over others relative to the pre-COVID era. While there was a small effect in this direction, the difference was less than one percentage point, and such a small effect could be accounted for by factors other than discrimination. Despite targeting higher priority procedures and greater use in independent providers, there remains considerable unmet demand. Our findings provide some clues as to what might be achieved at the demand and supply sides of the health economy. Demand cannot be wished away but one option is to ration by priority as advocated by Blom and colleagues with respect to elective orthopaedic surgery9. The change we observed post-COVID in favour of higher priority surgeries was in line with government policy and the principle of making best use of scarce resources under scarcity. This was achieved without a financial incentive. On the assumption that the benefits (QALY gains) are greater for priority 3 than priority 4 surgeries, why not fund only the former until the backlog has cleared? These are problems, if not flaws, in such a policy. First, the prioritisation was based on consensus, not a formal decision analysis (cost-utility or cost-benefit) model. Second, the effect of the condition is not uniform across operation categories – severity and preferences differ such that, inevitably, there would be a considerable overlap in capacity to benefit across operation types. An intermediate solution may be to increase the reimbursement differential between the categories – a type of “firm nudge”. Overall, however, we see little opportunity for demand management to solve the problem. We find, first, that the public sector has not retained its throughput and second, the independent sector, while expanding, has not done so to the extent necessary to make good the reduced volumes in the public sector. One might have expected (or hoped) that either: 1) the independent sector would have added to, not substituted for, NHS activity; or 2) the independent sector would have continued to expand, so that overall capacity increased despite falling NHS capacity. Our findings cannot explain the enigmatic finding that neither of these desirable scenarios have occurred, and we must speculate. Regarding the public sector, the problem could lie with a physical limit on human resources. The government has tried to expand capacity by building High Volume Low Complexity (HVLC) units, but it is still possible that capacity is constrained, say by overflow of medical patients in the surgery beds. There is some evidence for this hypothesis in the high number of cancelled operations in the system10. Moreover, a recent study has shown that there is no hospital-level variable that is systematically associated with recovery in surgical volume post-COVID3. An alternative, or additional, explanation is that medical staff are depleted in the public sector because their efforts have been diverted into the independent sector. It would be wrong to categorise all hospital beds as uniform; capacity is divided between general inpatient beds, day case or short stay beds and specialist High Dependency or Intensive Care beds. Much of the elective work workload is delivered through short-stay or day case beds which are often only open from Monday to Friday and are therefore protected from emergency patients being outlined to them which may explain why fewer priority 4 patients have been outsourced to independent providers. It may be that as well as the constraint of overall numbers of beds, the attribution of beds may also not reflect the balance of required activity. The independent sector has taken up the shortfall in operations in the public sector but has not gone further such that overall supply would increase. It is possible that the increased demand has used up whatever spare capacity might have existed in the independent system. What could explain failure to further expand supply? One possible explanation lies in delays in the (notoriously) constrained UK planning system. A perhaps more likely, explanation lies in the workforce. This explanation is supported by the difficulty the government is having in recruiting and retaining its nursing workforce. The NHS reported a 10.3% vacancy rate for nursing in September 202311. This may be the final limiting factor across both sectors, public and independent. However, we should also consider demand which may have been altered by price. The NHS payment scheme specifies unit price for various procedures but allows for different payment mechanisms. In practice, surgery in independent hospitals costs more than those in NHS hospitals due to the NHS’s ability to purchase at scale. As NHS hospitals sub-contract to independent hospitals directly for activity this creates a bottleneck – independent hospitals cannot do many surgeries as cheaply as a NHS hospital, but the NHS hospital is paid a set price wherever it is performed and so cannot afford to pay the independent provider more than it will receive. This, more than workforce or facility constraints, may explain failure to further expand independent provision. By increasing capacity in independent providers, the NHS has created a market in elective surgical care. However, markets need to be managed or shaped. This means that they need to be understood. In our opinion, new policy needs to be based on a much more complete understanding of the existing market with respect to its interactions and bottlenecks. There are always limitations when working with routinely collected healthcare data that are not collected for the purpose of audit, service evaluation or research. There are possible coding errors, and missing data, however, we follow trends over time and there is no strong reason to suspect that data collection accuracy varies over time. We do not carry out statistical analysis since we make numerous observations and our study covers the whole population, not a sample thereof. Our study is based on the highest prevalence operations, and we do not break the operations down into individual types, save for YAG eye surgery. We think these decisions are consistent with the practical and theoretical aims of our study. Our study assumes stable underlying rates, for example for IMD quintile groups, and in the case of ethnic group, in particular, this does not hold. Accessibility of protocol, raw data and programming code Protocol can be shared on request. Raw data and programming code is not available to share due to data sharing agreements with NHS Digital. Extended data is available at: https://osf.io/94uyz/files/q9p7c. OSF Repository: RECORD statement for ‘Elective Surgery Before, During and After the COVID-19 Pandemic in England 2015 – 2024: A Database Study.’ https://osf.io/94uyz/files/pcwn2 Extended data are available under the terms of the CC-BY 4.0 Creative Commons Attribution Only license. Faculty Opinions recommendedReferences - 1. Royal College of Surgeons of England: Clinical guide to surgical prioritisation during the coronavirus pandemic. 2021; (accessed 27-Oct-2025. Reference Source - 2. Remsing S, Reeves K, Evison F, et al.: Elective surgery before, during and after the COVID-19 pandemic in England 2015 – 2022: a database study. medRxiv. 2023; 2023.01.20.23284826. Publisher Full Text - 3. Bosque-Mercader L, Conroy S, Lasserson D, et al.: Resilience of the acute sector in recovery from COVID-19 pressures. Soc Sci Med. 2025; 375: 118062. PubMed Abstract | Publisher Full Text - 4. Federation of Surgical Specialty Associations: Clinical guide to surgical prioritisation in the recovery from the Coronavirus Pandemic. 2020; (accessed 27-Oct-2025. Reference Source - 5. STROBE: Strengthening the reporting of observational studies in epidemiology (STROBE). 2025; (accessed 27-Oct-2025. Reference Source - 6. Council IIA: Independent report. COVID-19 and occupational impacts. 2022. Reference Source - 7. Reeves K, Remsing S, Kariuki M, et al.: Elective Surgery Before, During and After the COVID-19 Pandemic in England 2015 – 2024: A Database Study. Extended Data, 2025. - 8. Coughlan E, Keith J, Gardner T, et al.: Waiting for NHS hospital care: the role of the independent sector in delivering orthopaedic and ophthalmic care. London, UK: The Health Foundation, 2023. Reference Source - 9. Blom AW, Donovan RL, Beswick AD, et al.: Common elective orthopaedic procedures and their clinical effectiveness: umbrella review of level 1 evidence. BMJ. 2021; 374: n1511. PubMed Abstract | Publisher Full Text | Free Full Text - 10. Gillies MA, Wijeysundera DN, Harrison EM: Counting the cost of cancelled surgery: a system wide approach is needed. Br J Anaesth. 2018; 121(4): 691–4. PubMed Abstract | Publisher Full Text | Free Full Text - 11. Digital N: NHS Vacancy statistics England, April 2015 - September 2023, experimental statistics. 2023; (accessed 27-Oct-2025. Reference Source Author details Author details 1 Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England, B15 2TH, UK 2 Institute of Applied Health Research, University of Birmingham, Birmingham, England, B15 2TT, UK 2 Institute of Applied Health Research, University of Birmingham, Birmingham, England, B15 2TT, UK Katharine Reeves Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Visualization, Writing – Review & Editing Sandra Remsing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Mary Kariuki Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Felicity Evison Roles: Data Curation, Formal Analysis, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Writing – Review & Editing Peter Chilton Roles: Project Administration, Writing – Review & Editing Roles: Project Administration, Writing – Review & Editing Paul Bird Roles: Writing – Review & Editing Roles: Writing – Review & Editing Samuel I Watson Roles: Formal Analysis, Writing – Review & Editing Roles: Formal Analysis, Writing – Review & Editing Richard Lilford Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Roles: Conceptualization, Funding Acquisition, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration West Midlands (ARC WM) (NIHR200165). Watson and Lilford are also supported by the Medical Research Council MR/V038591/1, and the NIHR Midlands Patient Safety Research Collaboration (PSRC) (NIHR200165). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright © 2026 Reeves K et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. metrics VIEWS $counts.viewCount downloads Citations CITE how to cite this article Reeves K, Remsing S, Kariuki M et al. Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study [version 1; peer review: 1 approved with reservations]. NIHR Open Res 2026, 6:9 (https://doi.org/10.3310/nihropenres.14150.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 14 Jan 2026 Views 0 How to cite this report: Fowler AJ. Reviewer Report For: Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study [version 1; peer review: 1 approved with reservations]. NIHR Open Res 2026, 6:9 (https://doi.org/10.3310/nihropenres.15397.r39629) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/6-9/v1#referee-response-39629 https://openresearch.nihr.ac.uk/articles/6-9/v1#referee-response-39629 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 20 Mar 2026 Approved with Reservations VIEWS 0 Thank you for asking me to review this study of the Hospital Episode Statistics database which describes how procedures of differing priority varied in the pre-, and post pandemic period.

- Generally more details ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close

- Generally more details ... Continue reading Thank you for asking me to review this study of the Hospital Episode Statistics database which describes how procedures of differing priority varied in the pre-, and post pandemic period.

- Generally more details needed; elective procedures only I think from the plain English summary? adults only? - The COVID-epochs are important to define i.e. dates - Some numbers in the abstract would be helpful, at present just descriptive words are used rather than actual figures. We have previously described a ~30% reduction in general surgical activity, I can't get a sense of what reduction you observed, and the associated recovery rates, stratified by the type of procedure. - The abstract (and indeed main paper) is missing the DARS reference for your data access permissions.

- I think reasonable rationale, and I think the hypothesis around independent sector working for these procedure categories is interesting

- You refer to HES database - most studies of this type would use admitted patient care (APC), did you also look at outpatient care (OPC)? HES ECDS doesn't include any elective care, so obviously not relevant. - The derivation of variables is almost entirely missing - I can see some reference to it in the prior preprint (referred to here), but it may be helpful to reiterate it. - Each hospital spell is made up of multiple episodes, many of the variables referred to in Table 1 of the previous preprint are episode level variables, did you take the first episode in a spell, or the episode in which surgery occurred? - The list of codes maps to the priority areas - but obviously could be performed on an elective or emergency basis, which admission method codes did you use to identify elective care? - Spells are referred to - continuous inpatient spells or provider spells? This is very important if you're capturing hospital readmissions. It looks like it is provider spell based from the preprint- how did you differentiate readmissions from transfers of care? - Presumably, if patients had multiple surgical spells during the study period, they could be included multiple times so this is a study of spells? - IMD - at patient level based on LSOAs? (Reading further through the manuscript this is described in the

section, probably best to have this in the methods) - Charlson comorbidity index - did this use a look back file (e.g. to capture prior diagnoses) or only the index spell? Your comorbidity patterns looks odd - 64% no comorbidities; 8% 1-3 comorbidities and 27% >3! This doesn't quite fit with expected patterns, especially if using Charlson where common things like e.g. hypertension are typically excluded, so a description of how it was constructed would be helpful. - Please provide the DSA reference. - Did study authors have access to the underlying, crude HES data, or was an aggregated report created for them? A copy of the full SQL query to aggregate the report would be helpful if underlying data not accessed.

- Date of death is referred to in the results but not the method - see above comment about Charlson comorbidity index. - Referring to people self reporting ethnicity as unknown isn't quite my understanding of how ethnicity reporting works in HES - but you're quite right that ethnicity in HES is basically uninterpretable given the high level of not-stated/unknown/missing. - Probably worth putting something about cataracts into the discussion, as it's a very high volume important procedure. - Figure 3 - this is presumably indexed at the level of the provider e.g. NHS and ISHP - it looks like the ISHP is doing more NHS work than the NHS if not! Probably worth clarifying this represents a change from baseline compared to 2015 at the level of NHS and ISHP separately - interesting to see some divergence before 2019 anyway - were there policies that were encouraging this? Some sort of precision measure may be useful here - not sure how best to calculate as with these very large numbers often you get very tight confidence intervals. Perhaps contrasting the lowest vs. highest change, or the 25-75% quintile of change? - A common narrative in the NHS is that the private sector likes taking the 'easy' cases - do you have any evidence for this? e.g. is it possible to contrast features of patients treated at ISHP vs. NHS for example, hip replacement to see if they have drastically differing features? - Why group Q1-Q4 together for the purposes of table 3? - If possible, it may be more informative to present LOS as: number with 0 day LOS (i.e. daycase) and then the median/IQR of those staying for one or more nights?

- I appreciate there may not be a direct financial incentive for 3 vs. 4 but I think lots of the category 3 procedures attract a much higher tariff than those listed in 4.... which is an incentive itself. - There are 3 references in the discussion - despite a fair amount of work in this area around surgical recovery following Covid e.g. COVIDSurg work etc. - Potential for miscoding during COVID was probably higher? Which may influence drops in rates of procedures. - Do you have numbers for private care provided that wasn't NHS? One potential explanation here is that given the long waiting lists, far more people have accessed private care directly via. insurance etc, and perhaps that is why the total productivity appears to be flat, but actually there is an extra chunk on top we aren't seeing here. PHIN data may help with this (https://www.phin.org.uk/data/datasheets). - Your points on demand vs. supply and the huge challenges of these are well made. - Has anyone explored code quality in HES submitted by ISHP vs. NHS? - Lock down dates are helpful, but obviously the strain on the NHS probably continued long beyond lock down, certainly on the shop floor things didn't feel normal for a longer period than lockdown.

- Generally more details needed; elective procedures only I think from the plain English summary? adults only? - The COVID-epochs are important to define i.e. dates - Some numbers in the abstract would be helpful, at present just descriptive words are used rather than actual figures. We have previously described a ~30% reduction in general surgical activity, I can't get a sense of what reduction you observed, and the associated recovery rates, stratified by the type of procedure. - The abstract (and indeed main paper) is missing the DARS reference for your data access permissions.

- I think reasonable rationale, and I think the hypothesis around independent sector working for these procedure categories is interesting

- You refer to HES database - most studies of this type would use admitted patient care (APC), did you also look at outpatient care (OPC)? HES ECDS doesn't include any elective care, so obviously not relevant. - The derivation of variables is almost entirely missing - I can see some reference to it in the prior preprint (referred to here), but it may be helpful to reiterate it. - Each hospital spell is made up of multiple episodes, many of the variables referred to in Table 1 of the previous preprint are episode level variables, did you take the first episode in a spell, or the episode in which surgery occurred? - The list of codes maps to the priority areas - but obviously could be performed on an elective or emergency basis, which admission method codes did you use to identify elective care? - Spells are referred to - continuous inpatient spells or provider spells? This is very important if you're capturing hospital readmissions. It looks like it is provider spell based from the preprint- how did you differentiate readmissions from transfers of care? - Presumably, if patients had multiple surgical spells during the study period, they could be included multiple times so this is a study of spells? - IMD - at patient level based on LSOAs? (Reading further through the manuscript this is described in the

section, probably best to have this in the methods) - Charlson comorbidity index - did this use a look back file (e.g. to capture prior diagnoses) or only the index spell? Your comorbidity patterns looks odd - 64% no comorbidities; 8% 1-3 comorbidities and 27% >3! This doesn't quite fit with expected patterns, especially if using Charlson where common things like e.g. hypertension are typically excluded, so a description of how it was constructed would be helpful. - Please provide the DSA reference. - Did study authors have access to the underlying, crude HES data, or was an aggregated report created for them? A copy of the full SQL query to aggregate the report would be helpful if underlying data not accessed.

- Date of death is referred to in the results but not the method - see above comment about Charlson comorbidity index. - Referring to people self reporting ethnicity as unknown isn't quite my understanding of how ethnicity reporting works in HES - but you're quite right that ethnicity in HES is basically uninterpretable given the high level of not-stated/unknown/missing. - Probably worth putting something about cataracts into the discussion, as it's a very high volume important procedure. - Figure 3 - this is presumably indexed at the level of the provider e.g. NHS and ISHP - it looks like the ISHP is doing more NHS work than the NHS if not! Probably worth clarifying this represents a change from baseline compared to 2015 at the level of NHS and ISHP separately - interesting to see some divergence before 2019 anyway - were there policies that were encouraging this? Some sort of precision measure may be useful here - not sure how best to calculate as with these very large numbers often you get very tight confidence intervals. Perhaps contrasting the lowest vs. highest change, or the 25-75% quintile of change? - A common narrative in the NHS is that the private sector likes taking the 'easy' cases - do you have any evidence for this? e.g. is it possible to contrast features of patients treated at ISHP vs. NHS for example, hip replacement to see if they have drastically differing features? - Why group Q1-Q4 together for the purposes of table 3? - If possible, it may be more informative to present LOS as: number with 0 day LOS (i.e. daycase) and then the median/IQR of those staying for one or more nights?

- I appreciate there may not be a direct financial incentive for 3 vs. 4 but I think lots of the category 3 procedures attract a much higher tariff than those listed in 4.... which is an incentive itself. - There are 3 references in the discussion - despite a fair amount of work in this area around surgical recovery following Covid e.g. COVIDSurg work etc. - Potential for miscoding during COVID was probably higher? Which may influence drops in rates of procedures. - Do you have numbers for private care provided that wasn't NHS? One potential explanation here is that given the long waiting lists, far more people have accessed private care directly via. insurance etc, and perhaps that is why the total productivity appears to be flat, but actually there is an extra chunk on top we aren't seeing here. PHIN data may help with this (https://www.phin.org.uk/data/datasheets). - Your points on demand vs. supply and the huge challenges of these are well made. - Has anyone explored code quality in HES submitted by ISHP vs. NHS? - Lock down dates are helpful, but obviously the strain on the NHS probably continued long beyond lock down, certainly on the shop floor things didn't feel normal for a longer period than lockdown. - Is the work clearly and accurately presented and does it cite the current literature? Partly - Is the study design appropriate and is the work technically sound? Yes - Are sufficient details of methods and analysis provided to allow replication by others? No - If applicable, is the statistical analysis and its interpretation appropriate? Yes - Are all the source data underlying the results available to ensure full reproducibility? Partly - Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: I have published multiple papers using HES data, both aggregated and patient level, I am a clinical academic with a PhD in epidemiology specifically using these data sets in surgical patients. CITE HOW TO CITE THIS REPORT Fowler AJ. Reviewer Report For: Elective surgery before, during and after the COVID-19 pandemic in England 2015 - 2024: A database study [version 1; peer review: 1 approved with reservations]. NIHR Open Res 2026, 6:9 (https://doi.org/10.3310/nihropenres.15397.r39629) The direct URL for this report is: https://openresearch.nihr.ac.uk/articles/6-9/v1#referee-response-39629 https://openresearch.nihr.ac.uk/articles/6-9/v1#referee-response-39629 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. 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