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

backgroundObesity 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.

methodThe 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.

resultsThe 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.

conclusionThis 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.