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Maier, Petra (2004): International Classification of Functioning, Disability and Health (ICF): Validation of the ICF Comprehensive Set for Patients with Low Back Pain & Basic Information for a Generic Comprehensive Set. Dissertation, LMU München: Faculty of Medicine
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Abstract

Validation of the ICF Comprehensive Set for Patients with Low Back Pain: Background: The International Classification of Functioning, Disability and Health (ICF) is a multipurpose classification to describe functional states associated with health conditions. To ensure practicability the ICF Checklist was developed, a short form of the ICF which only contains the most important categories irrespective of the present diagnoses. Furthermore ICF Comprehensive Sets were developed which contain the most important categories concerning a specific disease. Objectives: The general objective is to examine the explanatory power of the ICF Checklist in order to explain the PHI-score and the MHI-score of the SF-36. The specific aims are 1) to explore the percentage of variance of the SF-36 parameters accounted for by the ICF categories, 2) to identify the ICF categories which explain most of the variance of the two SF-36 parameters, 3) to assess the importance of the four components of the ICF Checklist for the SF-36 parameters. Methods: Cross sectional analysis of n=200 inpatients of rehabilitation centres suffering from low back pain. The International Classification of Functioning, Disability and Health (ICF) belongs to the WHO family of international classifications. At present in the ICF the following components are included: 1) Body Functions 2) Body Structures 3) Activities and Participations 4) Environmental Factors. Patients’ health status was assessed by the SF-36 Health Survey, a generic instrument to measure health status. Analyses were focused on the two summary measures Physical Health Index Score (PHI-score) and Mental Health Index Score (MHI-score). Statistical Analysis was conducted in four steps: In step 1 a first selection of potential predictor variables of health status was performed by the use of descriptive statistics. Analysis of regression in step 2 was conducted for each component of the ICF. In step 3 the variables selected in the four analyses of regression in step 2 were integrated into one multiple linear regression model. In the fourth step the model constructed in step 3 was verified and optimized. Finally three control variables were included into the model (gender, age and number of concomitant diseases). Results: The first model accounts for 44.6% of the variance of the PHI-score with F= 16.36 (p<.0001). The most important predictor is sensation of pain. Three of the five selected variables are Activities/Participation, two variables are Body Functions. All five dependent variables are included in the ICF Comprehensive Set for patients with low back pain. The second model accounts for 31.1% of the variance of the MHI-score with F= 10.64 (p<.0001). The most important predictor is the category emotional functions. All four components of the ICF are represented in the model. Three of the four dependent variables are also included in the ICF Comprehensive Set for patients with low back pain. Conclusion: The results emphasize the validity of the ICF Comprehensive Set for patients with low back pain. All categories except one are included in both the model and the ICF Comprehensive Set. The results are limited by the fact that the analyses did only account for categories included in the ICF Checklist. The Importance of ICF Categories for Patients’ Subjective Health Status: Background: The International Classification of Functioning, Disability and Health (ICF) is a multipurpose classification to describe functional states associated with health conditions. To ensure practicability the ICF Checklist was developed, a short form of the ICF which only contains the most important categories irrespective of the present diagnoses. Objectives: The general objective is to examine the explanatory power of the ICF Checklist in order to explain the PHI-score, the MHI-score and the GH-score of the SF-36. The specific aims are 1) to explore the percentage of variance of the SF-36 parameters accounted for by the ICF categories, 2) to identify the ICF categories which explain most of the variance of the three SF-36 parameters, 3) to assess the importance of the four components of the ICF Checklist for the SF-36 parameters. Methods: Cross sectional analysis of n=1040 inpatients of rehabilitation centres. The International Classification of Functioning, Disability and Health (ICF) belongs to the WHO family of international classifications. At present in the ICF the following components are included: 1) Body Functions 2) Body Structures 3) Activities and Participation 4) Environmental Factors. Patients’ health status was assessed by the SF-36 Health Survey, a generic instrument to measure health status. Analyses were focused on Physical Health Index Score (PHI-score), Mental Health Index Score (MHI-score) and on General Health (Item1, GH-score). Statistical Analysis was conducted in four steps: In step 1 a first selection of potential predictor variables of health status was performed by the use of descriptive statistics. Analysis of regression in step 2 was conducted for each component of the ICF. In step 3 the variables selected in the four analyses of regression in step 2 were integrated into one multiple linear regression model. In the fourth step the model constructed in step 3 was verified and optimized. Finally three control variables were included into the model (gender, age and number of concomitant diseases). Results: The regression model to explain the Physical Health Index Score in total accounts for 38.6% of its variance with F=46.04 (p<.0001). The most important predictor is the category walking (R2=16.4%). The model includes four variables of the component Activities/Participation, one variable each of the component Functions and Environmental Factors as well as two diagnoses of the twelve diagnoses analyzed. The model to determine the Mental Health Index Score explains 34.5% of its variance with F= 51.36 (p<.0001). The most important determinant of MHI-score is the variable depressive disorder accounting (R2=16.5%). Two of the four components of the ICF are represented in the model, that is Functions and Activities/Participation. The regression model to explain the General Health Score accounts for 27.2% of its variance with F=25.26 (p<.0001). The most important predictor is the category doing housework (R2=11.9%). The model includes variables of the components Functions and Activities/Participation. Conclusion: These results suggest that a generic Comprehensive Set should focus on Body Functions, especially psychological ones and pain, as well as on Activities/Participation, especially activities of every day life.