Health-Related Quality of Life in Long COVID: Mapping the Condition-Specific C19-YRSm Measure Onto the EQ-5D-5L

Adam B Smith,1 Darren C Greenwood,1,2 Paul Williams,3 Joseph Kwon,4 Stavros Petrou,4 Mike Horton,5 Thomas Osborne,5 Ruairidh Milne,6 Manoj Sivan5,7,8 On behalf of LOCOMOTION Consortium1Leeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, United Kingdom; 2Leeds Institute for Data Analytics, University of Leeds, Leeds, UK; 3COVID Assessment and Rehabilitation Service, Hertfordshire Community NHS Trust, Welwyn Garden City, UK; 4Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; 5Academic Department of Rehabilitation Medicine, Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK; 6Person with Long COVID; Public Health, University of Southampton, Southampton, UK; 7COVID Rehabilitation Service, Leeds Community Healthcare NHS Trust, Leeds, UK; 8National Demonstration Centre of Rehabilitation Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UKCorrespondence: Adam B Smith, Leeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, United Kingdom, Email [email protected]: Long COVID (LC) is a clinical syndrome with persistent, fluctuating symptoms subsequent to COVID-19 infection. LC has significant detrimental effects on health-related quality of life (HRQoL), activities of daily living (ADL), and work productivity. Condition-specific patient-reported outcome measures (PROMs), such as the modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) do not provide the health utility data required for cost-utility analyses of LC interventions. The aim of this study was to derive a mapping algorithm for the C19-YRSm to enable health utilities to be generated from this PROM.Methods: Data were collected from a large study evaluating LC services in the UK. A total of 1434 people with LC had completed both the C19-YRSm and the EQ-5D. Correlation and linear regression analyses were applied to determine items from the C19-YRSm and covariates for inclusion in the algorithm. Model fit, mean differences across the range of EQ-5D-3L utility scores, and Bland-Altman plots were evaluated. Responsiveness (standardised response mean; SRM) of the mapped utilities was investigated on a subset of participants with repeat assessments.Results: There was a strong level of association between 8 items and one domain on the C19-YRSm with the EQ-5D single-item dimensions. Model fit was good (R2 = 0.7). The mean difference between observed and mapped scores was < 0.10 for the range from 0 to 1 indicating good targeting for positive values of the EQ-5D-3L. The SRM for the mapped EQ-5D-3L was 0.37 compared to 0.17 for the observed utility scores, suggesting the mapped EQ-5D-3L is more responsive to change.Conclusion: A simple, responsive, and robust mapping algorithm was developed to generate enable EQ-5D-3L health utilities from the C19-YRSm. This will facilitate economic evaluations of LC interventions, treatment, and management, as well as further helping to describe and characterise patients with LC irrespective of any treatment and interventions.Keywords: Post-COVID Syndrome, mapping, EQ-5D-5L, C19-YRSm, health utilities