Mapping the World Health Organization Disability Assessment Schedule (WHODAS 2.0) onto SF-6D Using Swedish General Population Data

Anna Philipson (), Lars Hagberg, Liselotte Hermansson, Jan Karlsson, Emma Ohlsson-Nevo and Linda Ryen
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Anna Philipson: Örebro University
Lars Hagberg: Örebro University
Liselotte Hermansson: Örebro University
Jan Karlsson: Örebro University
Emma Ohlsson-Nevo: Örebro University
Linda Ryen: Örebro University

PharmacoEconomics - Open, 2023, vol. 7, issue 5, No 6, 765-776

Abstract: Abstract Background and Objective Mapping algorithms can be used for estimating quality-adjusted life years (QALYs) when studies apply non-preference-based instruments. In this study, we estimate a regression-based algorithm for mapping between the World Health Organization Disability Assessment Schedule (WHODAS 2.0) and the preference-based instrument SF-6D to obtain preference estimates usable in health economic evaluations. This was done separately for the working and non-working populations, as WHODAS 2.0 discriminates between these groups when estimating scores. Methods Using a dataset including 2258 participants from the general Swedish population, we estimated the statistical relationship between SF-6D and WHODAS 2.0. We applied three regression methods, i.e., ordinary least squares (OLS), generalized linear models (GLM), and Tobit, in mapping onto SF-6D from WHODAS 2.0 at the overall-score and domain levels. Root mean squared error (RMSE) and mean absolute error (MAE) were used for validation of the models; R2 was used to assess model fit. Results The best-performing models for both the working and non-working populations were GLM models with RMSE ranging from 0.084 to 0.088, MAE ranging from 0.068 to 0.071, and R2 ranging from 0.503 to 0.608. When mapping from the WHODAS 2.0 overall score, the preferred model also included sex for both the working and non-working populations. When mapping from the WHODAS 2.0 domain level, the preferred model for the working population included the domains mobility, household activities, work/study activities, and sex. For the non-working population, the domain-level model included the domains mobility, household activities, participation, and education. Conclusions It is possible to apply the derived mapping algorithms for health economic evaluations in studies using WHODAS 2.0. As conceptual overlap is incomplete, we recommend using the domain-based algorithms over the overall score. Different algorithms must be applied depending on whether the population is working or non-working, due to the characteristics of WHODAS 2.0.

Date: 2023
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DOI: 10.1007/s41669-023-00425-y

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