Identification of candidate categories of the International Classification of Functioning Disability and Health (ICF) for a Generic ICF Core Setbased on regression modelling

Abstract

Abstract Background The International Classification of Functioning, Disability and Health (ICF) is the framework developed by WHO to describe functioning and disability at both the individual and population levels. While condition-specific ICF Core Sets are useful, a Generic ICF Core Set is needed to describe and compare problems in functioning across health conditions. Methods The aims of the multi-centre, cross-sectional study presented here were: a) to propose a method to select ICF categories when a large amount of ICF-based data have to be handled, and b) to identify candidate ICF categories for a Generic ICF Core Set by examining their explanatory power in relation to item one of the SF-36. The data were collected from 1039 patients using the ICF checklist, the SF-36 and a Comorbidity Questionnaire. ICF categories to be entered in an initial regression model were selected following systematic steps in accordance with the ICF structure. Based on an initial regression model, additional models were designed by systematically substituting the ICF categories included in it with ICF categories with which they were highly correlated. Results Fourteen different regression models were performed. The variance the performed models account for ranged from 22.27% to 24.0%. The ICF category that explained the highest amount of variance in all the models was sensation of pain. In total, thirteen candidate ICF categories for a Generic ICF Core Set were proposed. Conclusion The selection strategy based on the ICF structure and the examination of the best possible alternative models does not provide a final answer about which ICF categories must be considered, but leads to a selection of suitable candidates which needs further consideration and comparison with the results of other selection strategies in developing a Generic ICF Core Set.