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Mapping the obesity problems scale to the SF-6D: results based on the Scandinavian Obesity Surgery Registry (SOReg)

Author

Listed:
  • Sun Sun

    (Umeå University
    Karolinska Instiutet)

  • Erik Stenberg

    (Örebro University)

  • Yang Cao

    (Örebro University)

  • Lars Lindholm

    (Umeå University)

  • Klas-Göran Salén

    (Umeå University)

  • Karl A. Franklin

    (Umeå University)

  • Nan Luo

    (National University of Singapore)

Abstract

Background Obesity Problem Scale (OP) is a widely applied instrument for obesity, however currently calculation of health utility based on OP is not feasible as it is not a preference-based measure. Using data from the Scandinavian Obesity Surgery Registry (SOReg), we sought to develop a mapping algorithm to estimate SF-6D utility from OP. Furthermore, to test whether the mapping algorithm is robust to the effect of surgery. Method The source data SOReg (n = 36 706) contains both OP and SF-36, collected at pre-surgery and at 1, 2 and 5 years post-surgery. The Ordinary Least Square (OLS), beta-regression and Tobit regression were used to predict the SF-6D utility for different time points respectively. Besides the main effect model, different combinations of patient characteristics (age, sex, Body Mass Index, obesity-related comorbidities) were tested. Both internal validation (split-sample validation) and validation with testing the mapping algorithm on a dataset from other time points were carried out. A multi-stage model selection process was used, accessing model consistency, parsimony, goodness-of-fit and predictive accuracy. Models with the best performance were selected as the final mapping algorithms. Results The final mapping algorithms were based on OP summary score using OLS models, for pre- and post-surgery respectively. Mapping algorithms with different combinations of patients’ characteristics were presented, to satisfy the user with a different need. Conclusion This study makes available algorithms enabling crosswalk from the Obesity Problem Scale to the SF-6D utility. Different mapping algorithms are recommended for the mapping of pre- and post-operative data.

Suggested Citation

  • Sun Sun & Erik Stenberg & Yang Cao & Lars Lindholm & Klas-Göran Salén & Karl A. Franklin & Nan Luo, 2023. "Mapping the obesity problems scale to the SF-6D: results based on the Scandinavian Obesity Surgery Registry (SOReg)," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(2), pages 279-292, March.
    • Handle: RePEc:spr:eujhec:v:24:y:2023:i:2:d:10.1007_s10198-022-01473-7
    DOI: 10.1007/s10198-022-01473-7
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    References listed on IDEAS

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    1. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    2. Clara Mukuria & Donna Rowen & Sue Harnan & Andrew Rawdin & Ruth Wong & Roberta Ara & John Brazier, 2019. "An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values," Applied Health Economics and Health Policy, Springer, vol. 17(3), pages 295-313, June.
    3. Brazier, John & Roberts, Jennifer & Deverill, Mark, 2002. "The estimation of a preference-based measure of health from the SF-36," Journal of Health Economics, Elsevier, vol. 21(2), pages 271-292, March.
    4. Jeff Round & Annie Hawton, 2017. "Statistical Alchemy: Conceptual Validity and Mapping to Generate Health State Utility Values," PharmacoEconomics - Open, Springer, vol. 1(4), pages 233-239, December.
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