Evidence of quality of life for hospitalised patients with COVID-19: a scoping review

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Background and introduction

Coronavirus disease 2019 (COVID-19) is a new disease caused by the SARS-CoV-2 virus, first discovered in Wuhan, China, in December 2019,1 which has caused global disease, suffering and disruption. Most people infected with the virus will have mild or moderate respiratory or viral symptoms; however, some become seriously ill and require hospital-based treatment. Individuals who develop severe illness due to COVID-19 tend to be older people with comorbidities such as cardiovascular disease, chronic respiratory disease, diabetes and cancer. 2

As well as studying COVID-19 symptoms, risk factors, management and mortality, it is important to know how COVID-19 affects quality of life (QOL). Knowing the impact on QOL gives a holistic picture of the burden of COVID-19 on the patient experience. It is also an important factor in knowing how to guide care to support patients’ needs. Furthermore, vast resources have been spent on combating COVID-19, with estimates of over US$7 billion in research and development funding being made available in the first 9 months of the pandemic. 3 QOL data underpin examinations of how cost-effective interventions that aim to tackle COVID-19 are, by reducing the risks of hospitalisation, intensive care unit (ICU) admission, ventilation, etc., and/or by aiding recovery.

There has been research focusing on the QOL of people with COVID-19 following discharge from hospital, with a systematic review including 21 studies. 4 There have also been studies on how Long COVID-19 affects QOL, with reviews summarising the findings. 5,6 However, we are not aware of any summaries of findings for QOL for patients hospitalised with COVID-19, and this study seeks to fill this gap by providing an overview of research in this area. QOL for hospitalised patients with COVID-19 is important to study alongside post-hospitalised/long COVID-19 patients, as they are the people most impacted during the acute phase of the disease. Thanks to the delivery of large, rapid randomised controlled trials,7,8 there is now much more knowledge about how best to prevent and treat COVID-19, including widespread vaccination,9,10 and the distribution of disease severity in hospitals has consequently changed over time. As novel diagnostics and therapeutics continue to emerge, it is important to understand the evolving burden of disease and impact on QOL for individuals hospitalised with COVID-19.

Quality of life is typically measured using questionnaires, ideally completed by the individual with the disease in question, typically including generic questions which capture the individual’s physical, psychological and social capabilities. Another reason it is important to examine the research on QOL for patients hospitalised with COVID-19 is that collecting data on patient-reported outcome measures (PROMs) in hospitals during the peak of the pandemic presented a logistical challenge, with the protection of patients and staff rightly taking priority.

Aims and objectives

The aim of this paper is to provide an overview of studies reporting QOL data for individuals who are hospitalised with COVID-19, and the methods they used. This will reveal the best currently available evidence on COVID-19 inpatients’ QOL. It will also give an overview of how QOL research progressed throughout the course of the pandemic, given the challenges involved. Finally, the review will highlight where knowledge gaps exist in relation to QOL for people hospitalised with COVID-19, as well as providing recommendations for research practice during future health crises.

Methods

Search terms were developed with reference to Arber et al. 11 by two authors, one a health economist (EW) and one an information specialist (NK). The search terms centred around the concepts of hospital patients, COVID-19 and health utility measures, including quality-adjusted life-years (QALYs), disability-adjusted life-years (DALYs) and survey measures such as EQ-5D, the Short Form (36) Health Survey (SF-36) and the Health Utilities Index (HUI). A full list of search strategies is included in the Appendix. The following databases were searched in May 2022, with searches re-run in December, from database inception to 7 December 2022: EMBASE (Classic and Ovid), Ovid MEDLINE^®, Scopus and Web of Science (Core Collection, SCI-EXPANDED 1900+, SSCI 1900+, A&HCI 1975+, CPCI-S 1900+, CPCI-SSH 1900+, ESCI 2015). We did not apply limits for language or publication date to the search. The search was peer-reviewed by a second information specialist using the PRESS checklist. 12

Search results were stored and deduplicated in an EndNote library. Titles and abstracts were initially screened for inclusion in full-text review, after which the selected full texts were screened for inclusion in the data extraction process. Screening was done by one health economist author (EW).

The inclusion criteria were:

• reporting on original research

• English language full text available

• includes quantitative data specifically for inpatients with COVID-19.

Pre-prints and conference abstracts were included, but editorials, letters and commentaries were not. Likewise, review papers, protocols, secondary analyses and animal and laboratory studies were excluded. Studies only reporting results for pooled samples of COVID-19 inpatients and non-COVID-19 inpatients were not included. Studies were included if they used published utility values for conditions other than COVID-19, provided these utilities were used to represent QOL for COVID-19 inpatients within the context of that study. So, for example, cost–utility analyses of interventions for COVID-19 inpatients were eligible for inclusion, even if the utility values were taken from patients with other conditions.

Three authors (EW, DH, BS) used a data extraction form to extract information on the studies’ methods and key results. The data extraction form is provided as Report Supplementary Material 1.

Extracted data were analysed using narrative synthesis.

Results

Figure 1 gives a PRISMA diagram of the search results and paper identification. The final search identified 2222 records with 1040 unique records after deduplication. There were 107 records selected for full-text review, out of which 35 studies were selected for data extraction. Table 1 summarises the individual studies, with all extracted data available as Report Supplementary Material 1.

FIGURE 1.

PRISMA diagram.

Figure 2 illustrates which geographical contexts the studies were set in. Four out of six inhabited continents were represented, with no studies coming from Oceania or South America. The most common country was the USA, with just over a quarter of all studies (N = 9).

FIGURE 2.

Which country studies took place in.

Figure 3 shows which study designs were chosen. The most common design was economic evaluation (N = 13) followed by cross-sectional (N = 10). Figure 3 also illustrates what approaches were taken for economic evaluation. Most (N = 8) used a decision tree, with almost half (N = 6) using a Markov model. 48 These modelling approaches were often combined, with a decision tree representing the hospitalisation phase, followed by a Markov process representing the rest of a patient’s life post discharge, with transitions between ill health, recovery and death. In two studies21,46 a value of information analysis was performed.

FIGURE 3.

(a) Frequency of using study design; (b) frequency that economic evaluation studies used different modelling approaches.

Table 2 gives details about the various QOL measures employed by studies and Figure 4 illustrates how frequently they were used. Most (N = 13) used published utility values, all of which were economic evaluations. Four studies27,37,41,46 used values for influenza to represent mild/moderate COVID-19 and values for patients with Clostridioides difficile infection to represent more severe illness. Two other studies used values for influenza to represent less severe COVID-19 cases and values for either influenza H1N1 (swine flu)19 or pneumonia16 to represent more severe cases. Kelton et al. 30 used values for patients with C. difficile infection and Sheinson et al. 43 used utility values from people with severe acute respiratory syndrome (SARS). Dijk et al. 21 used published HUI values for SARS for patients hospitalised with COVID-19, and published EQ-5D-3L values to represent QOL in post-ICU and post-hospitalisation patients. Two studies reported DALYs for patients using published disability weights from the Global Burden of Disease (GBD) study. 49 One used weights for severe lower respiratory tract infection to represent severe COVID-19 inpatients, and pneumonia to represent critical inpatients;18 the other used severe respiratory tract infection for severe patients, but weights for ICU admission for critical patients. 28 In four studies, it was unclear what conditions some or all published utility values came from. 30,32,38,43 A single study25 used a nine-member expert panel to estimate COVID-19 disability weights for calculating DALYs.

FIGURE 4.

Frequency of using different quality-of-life measures. BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; CDI, Child Depression Inventory; CPTS-RI, Child Post-Traumatic Stress Reaction Index; DALY, disability-adjusted life-year; ESS, Epworth Sleepiness Scale; GAD-7, Generalised Anxiety Disorder-7; HADS, Hospital Anxiety and Depression Scale; HTQ, Harvard Trauma Questionnaire; ISI, Insomnia Severity Index; mBDS, modified Borg Dyspnea Scale; MFI-20, Multidimensional Fatigue Inventory; mMRC, modified Medical Research Council Dyspnea Scale; PHQ-9, Patient Health Questionnaire-9; PSQI, Pittsburgh Sleep Quality Index; PSS-10, Perceived Stress Scale; SAS, Self-rating Anxiety Scale; SCARED, Screen for Child Anxiety Related Disorders; SDS, Self-rating Depression Scale; SF-36, Short Form-36; SIM-C, Short Inventory of Mindfulness Capability; SSS-8, 8-item Somatic Symptom Scale; WHOQOL-OLD, World Health Organization Quality of Life Instrument – Older Adults Module.

Twelve studies17,22,24,26,29,33,34,36,42,44,45,47 used 12 different mental health-related measures: the Patient Health Questionnaire-9 (PHQ-9; N = 3), the Hospital and Anxiety Depression Scale (HADS; N = 2), Generalised Anxiety Disorder-7 (GAD-7; N = 2), the Beck Anxiety Inventory (BAI; N = 1), the Beck Depression Inventory (BDI; N = 1), the Child Depression Inventory (CDI, N = 1), the Child Post-Traumatic Stress Reaction Index (CPTS-RI; N = 1), the Harvard Trauma Questionnaire (HTQ; N = 1), the Perceived Stress Scale (PSS-10; N = 1), the Screen for Child Anxiety Related Disorders (SCARED; N = 1), the Self-Rating Anxiety Scale (SAS; N = 1) and the Self-Rating Depression Scale (SDS; N = 1). Twelve studies used one or more sleep-related QOL measures,13,17,23,26,29,33-36,42,44,47 with the most popular being the Pittsburgh Sleep Quality Index (PSQI; N = 8).

Considering generic health measures, four studies>15,22,36,39 used the Short Form (36) Health Survey (SF-36) and five studies20,24,31,40,47 used EQ-5D, of which two used EQ-5D-3L and three used EQ-5D-5L. Two studies using EQ-5D assessed inpatient rehabilitation programmes24,31 and one looked at patients seen 7 days after undergoing surgery for a proximal femur fracture. 40 Thus, although they qualify for inclusion in this review due to reporting QOL data for inpatients with COVID-19, they did not collect data from patients in the acute phase of the disease. The remaining study20 collected only data from those aged over 80.

Figure 5 shows how many COVID-19 inpatients were analysed by each study. The median number of participants was 97.5 and the mean was 1096.25. The mean was far higher due to two outliers, He et al. 25 and Kairu et al.,28 who included 2702 and 20,836 patients, respectively. These numbers were achieved by reporting DALYs calculated using years of life lost from routine data and disability weights from either published values or a nine-member expert panel.

FIGURE 5.

Analysis sample sizes of COVID-19 inpatients.

Figure 6 shows a timeline of when studies collected data and publication dates (where only the month of data collection start/stop is given, we assumed collection started on the first and/or ended on the last day of the relevant month). The earliest data collection started in February 202033 and seven other studies also began collection in the first half of 2020,13,23–26,29,34 with the majority using sleep and/or mental health-related measures. The first study to be published was Cleary et al.,18 who used published utility values. The first study to be published which reported data collected from COVID-19 inpatients was Akinci et al.,13 who used PSQI and HADS.

FIGURE 6.

Timeline of data collection and study publication dates.

For studies where it was clear what mode was used for data collection, two used paper surveys,25,44 six collected responses online,17,26,34,39,42,45 three collected responses via phone36,39,42 and one used in-person interviews. 23,36

In general, studies found that being an inpatient with COVID-19 negatively affected QOL and, in particular, many studies found a negative impact on mental health13,17,29,34,42,44 and/or sleep quality. 13,17,23,26,29,33,34,42,44 However, many studies did not formally include a comparator group of those without COVID-19, making it difficult to isolate the causal effect of the disease. This is particularly problematic with sleep quality and anxiety where hospitalisation for any condition may be expected to have an impact. A differential impact on some groups was found by several studies, in particular older inpatients,15,20,29 women20,25 and those with comorbidities. 20 The four studies examining inpatient rehabilitation programmes22,24,31,47 all showed a significant post-intervention improvement in QOL compared to baseline.

Discussion

The results show that collecting data on COVID-19 inpatients’ QOL was feasible, even in the early stages of the pandemic. Given the disruption that the pandemic caused to healthcare systems, as well as wider society, this is an encouraging finding for QOL research.

It is positive that many studies were from low- or middle-income countries. However, sample sizes, especially for studies collecting data directly from patients, were often relatively low, with a median of under 100. In addition, many studies were cross-sectional. While this design provides much useful information, it is difficult to get a picture of how QOL has varied over the course of the COVID-19 pandemic.

A large fraction of the included studies used sleep and/or mental health-related survey instruments. However, there was a lack of consensus on what survey measures to use, with a large number employed. Some variation was clearly necessary, for example using child-specific instruments for studies in children, but the wide variety of surveys used hinders comparison of results across different studies.

While sleep and mental health are undoubtedly important topics, there is a lack of data on the overall QOL impact of being hospitalised with COVID-19. For example, only five studies used the EQ-5D measure commonly used in other areas of health research. Only one study targeted the general population of people hospitalised with COVID-19, with the others targeting specific sub-populations (older people,20 those undergoing surgery40 or people in rehabilitation programmes24,31).

The paucity of general QOL data for people hospitalised in the acute phase of the disease meant that economic evaluations used published utility values of disability weights for people with other conditions. This represents a serious knowledge gap, especially given the demonstrated feasibility of collecting such data. Research to fill this gap would be useful, not just for economic evaluation, but also to see what the impact of being hospitalised with COVID-19 is on people’s QOL, and what aspects of health are most affected.

The latest date on which any included study was open for data collection was September 2021, although results from some studies which collected data at later time points may not yet be available. COVID-19 has evolved during the course of the pandemic and widespread vaccination has also reduced the probability of serious illness for those infected. 73–75 Several studies have looked at how QOL evolved for the general population76,77 or for specific groups such as carers. 78–80 However, the large number of survey measures used and the fact that generic measures were in a minority makes it difficult to track how the pandemic’s evolution has impacted QOL for COVID-19 inpatients.

Several studies examined a group of COVID-19 inpatients, but without also measuring QOL for a comparison group of individuals without the condition. This made it difficult to quantify the impact of being hospitalised with COVID-19, since there was no counterfactual to measure a decrement against. Several other studies had a cross-sectional design, which is a common approach and can give valuable insight. However, a longitudinal design can give additional information, albeit usually with greater resource costs.

A strength of our paper is that we jointly developed a search strategy involving those with health economic experience, but also information specialist expertise. We also searched a wide range of databases. This approach maximised our chances of finding all relevant studies.

While our systematic searches were an advantage, it is a weakness of this study that it is not a systematic review, and no protocol was registered in advance. This may potentially have introduced some bias into our findings. There were limitations to the conclusions that could be drawn from the external data. For example, it was not possible to synthesise data from the included studies to find values representing QOL for COVID-19 inpatients on a full health = 1, dead = 0 scale. Many included studies used published values for people with other conditions. Those that did survey patients used a wide range of measures that were difficult or impossible to directly compare, and often comparison groups were not included. There was also a wide range of settings studied, from ICU to inpatient rehabilitation. A final limitation to the extracted data is that it was often difficult to distinguish between people hospitalised due to COVID-19 and those hospitalised for another reason who had also acquired a COVID-19 infection (although given the time frame of most studies, patients were likely to mostly consist of the former group). Yet it is also a strength of our study that examining the above limitations has allowed us to make recommendations both for future COVID-19 and QOL research and for methodological approaches to measuring the QOL impact of future health crises.

Conclusion

We conclude by summarising some recommendations for COVID-19-related research, as well as for research during future health crises. On the former topic, we recommend that such data be collected longitudinally, so that the QOL impact for patients hospitalised with COVID-19 can be assessed as the disease progresses throughout its acute phase. Several economic evaluations of COVID-19 treatments exist which use utility values from patients with other conditions. The cost-effectiveness of such treatments should be reassessed using data collected from COVID-19 patients.

In future health crises, researchers should be aware of early opportunities to collect QOL data from hospitalised patients, given the feasibility demonstrated during COVID-19. There should also be an effort on behalf of the research community to co-ordinate as much as possible, to enhance comparability of results between studies and to ensure that important knowledge gaps do not arise.

Additional information

List of abbreviations

BAI

Beck Anxiety Inventory

BDI

Beck Depression Inventory

CDI

Child Depression Inventory

COVID-19

coronavirus disease 2019

CPTS-RI

Child Post-Traumatic Stress Reaction Index

DALY

disability-adjusted life-year

EQ-5D

EuroQol-5 Dimensions

EQ-5D-3L

EuroQol-5 Dimensions, three-level version

GAD

generalised anxiety disorder

GBD

Global Burden of Disease

HADS

Hospital and Anxiety Depression Scale

HTQ

Harvard Trauma Questionnaire

HUI

Health Utilities Index

ICU

intensive care unit

PHQ-9

Patient Health Questionnaire-9

PROM

patient-reported outcome measure

PSQI

Pittsburgh Sleep Quality Index

PSS

Perceived Stress Scale

QALY

quality-adjusted life-year

QOL

quality of life

SAS

Self-rating Anxiety Scale

SCARED

Screen for Child Anxiety Related Disorders

SDS

Self-rating Depression Scale

Notes

1. Full extracted data

Supplementary material can be found on the NIHR Journals Library report page (https://doi.org/10.3310/ATPR4281).

Supplementary material has been provided by the authors to support the report and any files provided at submission will have been seen by peer reviewers, but not extensively reviewed. Any supplementary material provided at a later stage in the process may not have been peer reviewed.

References

1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497-506.
2. World Health Organization . COVID-19: Vulnerable and High Risk Groups 2023. www.who.int/westernpacific/emergencies/covid-19/information/high-risk-groups (accessed 3 August 2023).
3. Organisation for Economic Co-operation and Development . OECD Science, Technology and Innovation Outlook 2021 2021.
4. Nandasena H, Pathirathna M, Atapattu A, Prasanga P. Quality of life of COVID 19 patients after discharge: systematic review. PLOS ONE 2022;17.
5. Malik P, Patel K, Pinto C, Jaiswal R, Tirupathi R, Pillai S, et al. Post‐acute COVID‐19 syndrome (PCS) and health‐related quality of life (HRQoL): a systematic review and meta‐analysis. J Med Virol 2022;94:253-62.
6. Michelen M, Manoharan L, Elkheir N, Cheng V, Dagens A, Hastie C, et al. Characterising long COVID: a living systematic review. BMJ Glob Health 2021;6.
7. Horby P, Landray M, Haynes R, Juszczak E, Baillie K, Jaki T. Randomised Evaluation of COVID-19 Therapy (RECOVERY): Study Protocol. Oxford: University of Oxford; 2020.
8. Butler CC, Hobbs FR, Gbinigie OA, Rahman NM, Hayward G, Richards DB, et al. Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): an open-label, platform-adaptive randomised controlled trial. Lancet 2022;401:281-93.
9. Watson OJ, Barnsley G, Toor J, Hogan AB, Winskill P, Ghani AC. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect Dis 2022;22:1293-302.
10. World Health Organization . Global COVID-19 Vaccination Strategy in a Changing World, July 2022 Update 2022.
11. Arber M, Garcia S, Veale T, Edwards M, Shaw A, Glanville JM. Performance of Ovid MEDLINE search filters to identify health state utility studies. Int J Technol Assess Health Care 2017;33:472-80.
12. McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 guideline statement. J Clin Epidemiol 2016;75:40-6.
13. Akinci T, Melek Basar H. Relationship between sleep quality and the psychological status of patients hospitalised with COVID-19. Sleep Med 2021;80:167-70. https://doi.org/10.1016/j.sleep.2021.01.034.
14. Bayrak M, Cadirci K. The associations of life quality, depression, and cognitive impairment with mortality in older adults with COVID-19: a prospective, observational study. Acta Clin Belg 2022;77:588-95. https://doi.org/10.1080/17843286.2021.1916687.
15. Bounoua F, Khannous A, Moubachir H, Serhane H. Evaluation of the quality of life in patients treated for COVID-19 at the Hassan II hospital in Agadir using the MOS SF-36 questionnaire. Eur Respir J 2021;58. https://doi.org/10.1183/13993003.congress-2021.PA299.
16. Carta A, Conversano C. Cost utility analysis of Remdesivir and Dexamethasone treatment for hospitalised COVID-19 patients: a hypothetical study. BMC Health Serv Res 2021;21. https://doi.org/10.1186/s12913-021-06998-w.
17. Chakrabarti S. Mental health in hospitalised COVID 19 patients in quarantine during second wave in a south Indian private teaching hospital. J Multidiscip Healthc 2021;14:2777-89. https://doi.org/10.2147/JMDH.S330812.
18. Cleary SM, Wilkinson T, Tamandjou Tchuem CR, Docrat S, Solanki GC. Cost-effectiveness of intensive care for hospitalized COVID-19 patients: experience from South Africa. BMC Health Serv Res 2021;21. https://doi.org/10.1186/s12913-021-06081-4.
19. Congly SE, Varughese RA, Brown CE, Clement FM, Saxinger L. Treatment of moderate to severe respiratory COVID-19: a cost-utility analysis. Sci Rep 2021;11. https://doi.org/10.1038/s41598-021-97259-7.
20. Covino M, Russo A, Salini S, De Matteis G, Simeoni B, Pirone F, et al. Long-term effects of hospitalization for COVID-19 on frailty and quality of life in older adults >=80 years. J. Clin. Med. 2022;11. https://doi.org/10.3390/jcm11195787.
21. Dijk SW, Krijkamp EM, Kunst N, Gross CP, Wong JB, Hunink MGM. Emerging therapies for COVID-19: the value of information from more clinical trials. Value Health 2022;27. https://doi.org/10.1016/j.jval.2022.03.016.
22. Gloeckl R, Leitl D, Jarosch I, Schneeberger T, Nell C, Stenzel N, et al. Benefits of pulmonary rehabilitation in COVID-19: a prospective observational cohort study. ERJ Open Res 2021;7. https://doi.org/10.1183/23120541.00108-2021.
23. Gunes A, Sensoy B. Sleepiness, insomnia, and sleep quality of hospitalized patients with coronavirus disease-2019: sleep scale evaluation. J Turk Sleep Med 2022;9:11-5. https://doi.org/10.4274/jtsm.galenos.2021.39974.
24. Hayden MC, Limbach M, Schuler M, Merkl S, Schwarzl G, Jakab K, et al. Effectiveness of a three-week inpatient pulmonary rehabilitation program for patients after COVID-19: a prospective observational study. Int J Environ Res Public Health 2021;18. https://doi.org/10.3390/ijerph18179001.
25. He M, Li X, Tan Q, Chen Y, Kong Y, You J, et al. Disease burden from COVID-19 symptoms among inpatients at the temporary military hospitals in Wuhan: a retrospective multicentre cross-sectional study. BMJ Open 2021;11. https://doi.org/10.1136/bmjopen-2021-048822.
26. Huang S, Zhuang W, Wang D, Zha L, Xu X, Li X, et al. Persistent somatic symptom burden and sleep disturbance in patients with COVID-19 during hospitalization and after discharge: a prospective cohort study. Med Sci Monit 2021;27. https://doi.org/10.12659/MSM.930447.
27. Jovanoski N, Kuznik A, Becker U, Hussein M, Briggs A. Cost-effectiveness of casirivimab/imdevimab in patients with COVID-19 in the ambulatory setting. J Manag Care Spec Pharm 2022;28:555-65. https://doi.org/10.18553/jmcp.2022.21469.
28. Kairu A, Were V, Isaaka L, Agweyu A, Aketch S, Barasa E. Modelling the cost-effectiveness of essential and advanced critical care for COVID-19 patients in Kenya. BMJ Glob 2021;6. https://doi.org/10.1136/bmjgh-2021-007168.
29. Karaogullarindan A, Erkan SO, Tuhanioglu B, Kuran G, Gorgulu O. Sleep quality in patients over 65 years of age in the Covid-19 pandemic. Turk Geriatri Dergisi 2021;24:381-90. https://doi.org/10.31086/tjgeri.2021.235.
30. Kelton K, Klein T, Murphy D, Belger M, Hille E, McCollam PL, et al. Cost-effectiveness of combination of Baricitinib and Remdesivir in hospitalized patients with COVID-19 in the United States: a modelling study. Adv Ther 2022;39:562-82. https://doi.org/10.1007/s12325-021-01982-6.
31. Kokhan S, Kolokoltsev M, Vorozheikin A, Gryaznykh А, Romanova E, Guryanov M, et al. Physical rehabilitation of patients with post-COVID syndrome. J Phys Educ Sport 2022;22:2005-11. https://doi.org/10.7752/jpes.2022.09255.
32. Kowal S, Ng CD, Schuldt R, Sheinson D, Cookson R. The impact of funding inpatient treatments for COVID-19 on health equity in the United States: a distributional cost-effectiveness analysis. Value Health 2022;26:216-25. https://doi.org/10.1016/j.jval.2022.08.010.
33. Li J, Zhang YY, Cong XY, Ren SR, XM T. Wu JF 5-min mindfulness audio induction alleviates psychological distress and sleep disorders in patients with COVID-19. World J Clin Cases 2022;10:576-84. https://doi.org/10.12998/wjcc.v10.i2.576.
34. Li X, Cai Q, Jia Z, Liu L, Zhou Y, Zhang B, et al. The correlation between mental health status, sleep quality, and inflammatory markers, virus negative conversion time among patients confirmed with 2019-nCoV during the COVID-19 outbreak in China: an observational study. Medicine 2021;100. https://doi.org/10.1097/MD.0000000000026520.
35. Moretti A, Belfiore A, Bianco M, Liguori S, Paoletta M, Toro G, et al. Functioning issues in inpatients affected by COVID-19-related moderate pulmonary impairment: a real-practice observational study. J Int Med Res 2022;50. https://doi.org/10.1177/03000605221126657.
36. Moseholm E, Midtgaard J, Bollerup S, Apol AD, Olesen OB, Jespersen S, et al. Psychological distress among hospitalized COVID-19 patients in Denmark during the first 12 months of the pandemic. Int J Environ Res Public Health 2022;19. https://doi.org/10.3390/ijerph191610097.
37. Ohsfeldt R, Kelton K, Klein T, Belger M, Mc Collam PL, Spiro T, et al. Cost-effectiveness of Baricitinib compared with standard of care: a modeling study in hospitalized patients with COVID-19 in the United States. Clin Ther 2021;43:1877-93.e4. https://doi.org/10.1016/j.clinthera.2021.09.016.
38. Oksuz E, Malhan S, Gonen MS, Kutlubay Z, Keskindemirci Y, Jarrett J, et al. Cost-effectiveness analysis of Remdesivir treatment in COVID-19 patients requiring low-flow oxygen therapy: payer perspective in Turkey. Adv Ther 2021;38:4935-48. https://doi.org/10.1007/s12325-021-01874-9.
39. Ouanes S, Al-Amin H, Hussein NB, Khan F, Al Shahrani A, David P, et al. Physical and psychosocial well-being of hospitalized and non-hospitalized patients with COVID-19 compared to the general population in Qatar. Front Psychiat 2021;12. https://doi.org/10.3389/fpsyt.2021.792058.
40. Pass B, Vajna E, Knauf T, Rascher K, Aigner R, Eschbach D, et al. Registry for Geriatric Trauma (ATR-DGU) . COVID-19 and proximal femur fracture in older adults: a lethal combination? An analysis of the Registry for Geriatric Trauma (ATR-DGU). J Am Med Dir Assoc 2022;23:576-80. https://doi.org/10.1016/j.jamda.2021.09.027.
41. Rafia R, Martyn-St James M, Harnan S, Metry A, Hamilton J, Wailoo A. A cost-effectiveness analysis of Remdesivir for the treatment of hospitalized patients with COVID-19 in England and Wales. Value Health 2022;25:761-9. https://doi.org/10.1016/j.jval.2021.12.015.
42. Samushiya MA, Kryzhanovsky SM, Ragimova AA, Berishvili TZ, Chorbinskaya SA, Ivannikova EI. Psychoemotional disorders and sleep impairments in patients with COVID-19. Neurosci Behav Physiol 2022;52:231-5. https://doi.org/10.1007/s11055-022-01229-z.
43. Sheinson D, Dang J, Shah A, Meng Y, Elsea D, Kowal S. A cost-effectiveness framework for COVID-19 treatments for hospitalized patients in the United States. Adv Ther 2021;38:1811-31. https://doi.org/10.1007/s12325-021-01654-5.
44. Tapan OO, Tapan U, Alasan F, Akgul AF, Genc S. Factors that affect sleep quality in hospitalized patients with COVID-19 pneumonia. J Turk Sleep Med 2022;9:51-6. https://doi.org/10.4274/jtsm.galenos.2021.86648.
45. Taskesen B, Kardas O, Yilmaz K. Evaluation of depression, anxiety and posttraumatic stress response levels of children and adolescents treated with COVID-19. Eur J Pediatr 2022;182:567-74. https://doi.org/10.1007/s00431-022-04713-3.
46. Whittington MD, Pearson SD, Rind DM, Campbell JD. The cost-effectiveness of Remdesivir for hospitalized patients with COVID-19. Value Health 2022;25:744-50. https://doi.org/10.1016/j.jval.2021.11.1378.
47. Wimmer C, Egger M, Bergmann J, Huge V, Muller F, Jahn K. Critical COVID-19 disease: clinical course and rehabilitation of neurological deficits. Front Neurol 2022;13. https://doi.org/10.3389/fneur.2022.1012685.
48. Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. PharmacoEcon 1998;13:397-409.
49. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020;396:1204-22.
50. Beck AT, Epstein N, Brown G, Steer R. Beck Anxiety Inventory. J Consult Clin Psychol 1993;56:893-7.
51. Beck AT, Steer RA, Brown GK. Beck Depression Inventory. New York: Harcourt Brace Jovanovich; 1987.
52. Kovacs M. Child Depression Inventory. Person Individ Diff 1992. https://doi.org/10.1037/t19482-000.
53. Steinberg AM, Brymer MJ, Decker KB, Pynoos RS. The University of California at Los Angeles Post-Traumatic Stress Disorder Reaction Index. Curr Psychiatry Rep 2004;6:96-100.
54. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991;14:540-5.
55. Group TE. EuroQol: a new facility for the measurement of health-related quality of life. Health Pol 1990;16:199-208.
56. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006;166:1092-7.
57. Mollica RF, Caspi-Yavin Y, Bollini P, Truong T, Tor S, Lavelle J. The Harvard Trauma Questionnaire: validating a cross-cultural instrument for measuring torture, trauma, and posttraumatic stress disorder in Indochinese refugees. J Nerv Ment Dis 1992;180:111-6.
58. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361-70.
59. Morin CM. Insomnia: Psychological Assessment and Management. New York: Guilford Press; 1993.
60. Muza SR, Silverman MT, Gilmore GC, Hellerstein HK, Kelsen SG. Comparison of scales used to quantitate the sense of effort to breathe in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1990;141:909-13.
61. Fletcher CM, Elmes PC, Fairbairn AS, Wood CH. Significance of respiratory symptoms and the diagnosis of chronic bronchitis in a working population. Br Med J 1959;2:257-66.
62. Smets E, Garssen B, Bonke B, De Haes J. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res 1995;39:315-25.
63. Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann 2002:509-15. https://doi.org/10.3928/0048-5713-20020901-06.
64. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1994;10:385-96. https://doi.org/10.2307/2136404.
65. Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193-21.
66. Birmaher B, Khetarpal S, Brent D, Cully M, Balach L, Kaufman J, et al. The Screen for Child Anxiety Related Emotional Disorders (SCARED): scale construction and psychometric characteristics. J Am Acad Child Adolesc Psychiat 1997;36:545-53.
67. Zung WW. A rating instrument for anxiety disorders. Psychosomatics 1971;12:371-9. https://doi.org/10.1016/S0033-3182(71)71479-0.
68. Zung WW. A self-rating depression scale. Arch Gen Psychiatry 1965;12:63-70.
69. Hays RD, Sherbourne CD, Mazel RM. The rand 36‐item health survey 10. Health Econ 1993;2:217-27.
70. Duan W, Li J. Short Inventory of Mindfulness Capability. Front Psychol 2016. https://psycnet.apa.org/doi/10.1037/t58213-000.
71. Gierk B, Kohlmann S, Kroenke K, Spangenberg L, Zenger M, Brähler E, et al. The Somatic Symptom Scale–8 (SSS–8): a brief measure of somatic symptom burden. JAMA Intern Med 2014;174:399-407.
72. Power M, Quinn K, Schmidt S. WHOQOL-OLD Group . Development of the WHOQOL-old module. Qual Life Res: Int J Qual Life Asp Treat Care Rehabil 2005;14:2197-214.
73. Knoll MD, Wonodi C. Oxford–AstraZeneca COVID-19 vaccine efficacy. Lancet 2021;397:72-4.
74. Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med 2020;383:2603-15.
75. Heath PT, Galiza EP, Baxter DN, Boffito M, Browne D, Burns F, et al. 2019nCoV-302 Study Group . Safety and efficacy of NVX-CoV2373 Covid-19 vaccine. N Engl J Med 2021;385:1172-83.
76. Herrera MS, Elgueta R, Fernández MB, Giacoman C, Leal D, Marshall P, et al. A longitudinal study monitoring the quality of life in a national cohort of older adults in Chile before and during the COVID-19 outbreak. BMC Geriatr 2021;21:1-12.
77. Ishikawa H, Kato M, Kiuchi T. Declines in health literacy and health-related quality of life during the COVID-19 pandemic: a longitudinal study of the Japanese general population. BMC Publ Health 2021;21:1-9.
78. Willner P, Rose J, Stenfert Kroese B, Murphy GH, Langdon PE, Clifford C, et al. Effect of the COVID‐19 pandemic on the mental health of carers of people with intellectual disabilities. J Appl Res Intel Disab 2020;33:1523-33.
79. Mak HW, Bu F, Fancourt D. Mental health and wellbeing amongst people with informal caring responsibilities across different time points during the COVID-19 pandemic: a population-based propensity score matching analysis. Perspectives in Public Health 2023;143:275-84. https://doi.org/10.1177/175791392211049.
80. Whitley E, Reeve K, Benzeval M. Tracking the mental health of home-carers during the first COVID-19 national lockdown: evidence from a nationally representative UK survey. Psychol Med 2021;53:1096-105.
81. AUHE Information Specialists . Checking for Duplicates Guidance 2016.

Appendix

We searched the following academic databases for quantitative measures of quality of life in patients hospitalised with COVID-19 on 6 May 2022, with searches re-run on 7 December 2022:

• EMBASE Classic + EMBASE (Ovid) 1947 to 6 December 2022

• Ovid MEDLINE^® ALL 1946 to 6 December 2022

• Scopus

• Web of Science Core Collection; SCI-EXPANDED 1900+, SSCI 1900+, A&HCI 1975+, CPCI-S 1900+, CPCI-SSH 1900+, and ESCI 2015 + searched simultaneously.

Searches were developed for the concepts: COVID-19, health utility measures and hospital patients. Subject headings and free text words were identified for use in the search concepts by the information specialist and project team members. Further terms were identified and tested from known relevant papers.

For the QOL measures concept, we based our search on the MEDLINE precision maximising health utilities filter (FSF3) designed by Arber et al. 11 with the addition of extra QOL measurement tools. The search was then translated into the other databases. We did not apply limits for language or publication date to the search. The search was peer-reviewed by a second information specialist using the PRESS checklist. 12 Duplicates were removed automatically and manually using Academic Unit of Health Economics, University of Leeds guidance. 81