International Classification of Functioning, Disability and Health (ICF)
International Classification of Functioning, Disability and Health (ICF)
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发表时间：
2004-07
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通讯作者：
Petra Maier
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作者：
Petra Maier
背景：国际功能、残疾和健康分类（ICF）是描述与健康状况相关的功能状态的多用途分类。为了确保实用性，制定了ICF清单，这是ICF的简短形式，仅包含最重要的类别，而不考虑目前的诊断。此外，还制定了ICF综合集，其中包含与特定疾病有关的最重要类别。目的：总体目标是检验ICF检查表的解释力，以解释SF-36的pi得分和mhi得分。具体目的是1)探索ICF类别占SF-36参数方差的百分比，2)确定解释两个SF-36参数方差的ICF类别，3)评估ICF清单中四个组成部分对SF-36参数的重要性。方法：对200例康复中心住院腰痛患者进行横断面分析。《国际功能、残疾和健康分类》属于世卫组织国际分类家族。目前在ICF中包括以下组成部分：1)身体功能2)身体结构3)活动和参与4)环境因素。通过SF-36健康调查（一种测量健康状况的通用工具）评估患者的健康状况。分析的重点是两种综合测量指标身体健康指数得分（PHI-score）和心理健康指数得分（MHI-score）。统计分析分四个步骤进行：在第一步中，使用描述性统计对健康状况的潜在预测变量进行首次选择。对ICF的每个分量进行第2步的回归分析。在步骤3中，将步骤2中四个回归分析中选择的变量整合到一个多元线性回归模型中。第四步对第三步构建的模型进行验证和优化。最后将三个控制变量（性别、年龄和伴发疾病数量）纳入模型。结果：第一个模型对pi得分方差的贡献率为44.6%，F= 16.36 （p< 0.0001）。最重要的预测因素是疼痛感。五个选定的变量中有三个是Activities/Participation，两个变量是Body Functions。所有五个因变量都包含在腰痛患者的ICF综合集中。第二个模型占mhi得分方差的31.1%，F= 10.64 （p< 0.0001）。最重要的预测因素是情感功能。ICF的所有四个组成部分都在模型中表示。四个因变量中的三个也包括在腰痛患者的ICF综合集中。结论：结果强调了ICF综合量表对腰痛患者的有效性。除了一个类别外，所有类别都包括在模型和ICF综合集中。结果受到以下事实的限制：分析只考虑了ICF清单中包含的类别。ICF分类对患者主观健康状况的重要性：背景：国际功能、残疾和健康分类（ICF）是描述与健康状况相关的功能状态的多用途分类。为了确保实用性，制定了ICF清单，这是ICF的简短形式，仅包含最重要的类别，而不考虑目前的诊断。目的：总体目的是检验ICF检查表的解释力，以解释SF-36的phi得分、mhi得分和gh得分。具体目的是1)探索ICF类别占SF-36参数方差的百分比，2)确定解释三个SF-36参数方差大部分的ICF类别，3)评估ICF清单中四个组成部分对SF-36参数的重要性。方法：对1040例康复中心住院患者进行横断面分析。《国际功能、残疾和健康分类》属于世卫组织国际分类家族。目前在ICF中包括以下组成部分：1)身体功能2)身体结构3)活动和参与4)环境因素。通过SF-36健康调查（一种测量健康状况的通用工具）评估患者的健康状况。分析的重点是身体健康指数得分（PHI-score）、心理健康指数得分（MHI-score）和一般健康（Item1, GH-score）。统计分析分四个步骤进行：在第一步中，使用描述性统计对健康状况的潜在预测变量进行首次选择。对ICF的每个分量进行第2步的回归分析。在步骤3中，将步骤2中四个回归分析中选择的变量整合到一个多元线性回归模型中。第四步对第三步构建的模型进行验证和优化。最后将三个控制变量（性别、年龄和伴发疾病数量）纳入模型。结果：体质健康指数总分的回归模型解释了38.6%的方差，F=46.04 （p< 0.0001）。最重要的预测因子是行走类别（R2=16.4%）。该模型包括活动/参与成分的4个变量，功能和环境因素成分各1个变量，以及分析的12种诊断中的2种诊断。确定心理健康指数得分的模型解释了34.5%的方差，F= 51.36 （p< 0.0001）。mhi评分最重要的决定因素是变量抑郁障碍占比（R2=16.5%）。ICF的四个组成部分中的两个在模型中表示，即职能和活动/参与。解释一般健康评分的回归模型占其方差的27.2%，F=25.26 （p< 0.0001）。最重要的预测因素是做家务的类别（R2=11.9%）。该模型包括功能和活动/参与组件的变量。结论：综合测评应注重身体功能（尤其是心理功能和疼痛）和活动/参与（尤其是日常生活活动）。
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.