Background: 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.