Health-related quality of life in Long COVID: Mapping the condition-specific C19-YRSm measure onto the EQ-5D-5L

Abstract

Background: Long Covid (LC) is a clinical syndrome of persistent, fluctuating symptoms subsequent to COVID-19 infection with a prevalence global estimate of many millions of cases. 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), have been developed to capture the impact of LC. However, these do not provide health utility data required for cost-utility analyses of LC interventions. The aim of this study was therefore 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-5L on the same day. The EQ-5D-5L responses were then converted to EQ-5D-3L utility scores. 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 scores (-0.59 to 1), and Bland-Altman plots were used to evaluate the algorithm. Responsiveness (standardised response mean; SRM) of the mapped utilities was also investigated on a subset of participants with repeat assessments (N=85). Results: There was a strong level of association between 8 items and 2 domains on the C19-YRSm with the EQ-5D single-item dimensions. These related to joint pain, muscle pain, anxiety, depression, walking/moving around, personal care, ADL, and social role, as well as Overall Health and Other Symptoms. Model fit was good (R2 = 0.7). The mean difference between the actual and mapped scores was < 0.10 for the range from 0 to 1 indicating a good degree of targeting for positive values of the EQ-5D-3L. The SRM for the mapped EQ-5D-3L health utilities (based on the C19-YRSm) was 0.37 compared to 0.17 for the observed EQ-5D-3L utility scores, suggesting the mapped EQ-5D-3L is more responsive to change. Conclusions: We have developed a simple, responsive, and robust mapping algorithm to enable EQ-5D-3L health utilities to be generated from 10 items of the C19-YRSm. This mapping algorithm will facilitate economic evaluations of interventions, treatment, and management of people with LC, as well as further helping to describe and characterise patients with LC irrespective of any treatment and interventions.