What is new?
Key findingsA new risk adjustment index was derived for use in health-related quality of life (HRQL) studies.
What this adds to what is known?The new HRQL comorbidity index (HRQL-CI) provides better predictive ability for HRQL measures than the commonly used Charlson-CI.
What is the implication, what should change now?Researchers have a comorbidity risk adjustment index to control for the potential influence of other illnesses when studying the effect of specific diseases or other risk factors associated with HRQL measures, which outperforms commonly used Charlson-CI.
Comorbidity is the existence or occurrence of any distinct additional disease or diseases during the clinical course of a patient who has an index disease under study. A comorbidity index (CI) is a weighted measure that, when conducting statistical analyses, will control for the potential influence of those illness on an outcome of interest [1], [2]. A relatively uncomplicated method of controlling for comorbidity is to use the simple count of illnesses [3]. Diagnosis methods often use data available from a medical record derived from hospital stays. These are known as discharge-based CIs [4], [5], [6], [7], [8], [9], [10]. A common diagnosis-based method was derived by Charlson et al. [11]. This method was initially developed as a chart-based weighted index that provides a simple and valid method for estimating the risk of death associated with comorbid illness, taking into account both the number and seriousness of comorbid illnesses. Other versions of the Charlson-CI use data derived from electronic claims data, electronic hospital records, and patient self-report [4], [12].
CIs are used in health services research to control for confounding in observational studies and for risk adjustment in studies of health care quality [13], [14], [15], [16]. Comorbidity affects mortality [17], [18], [19], [20], [21], health resource utilization [22], [23], [24], admission and readmission to hospital [20], [22], [25], and health-related quality of life (HRQL) or functional status [26], [27], [28]. Without adequate measures to adjust for intervening comorbidity differences, valid comparisons of health status and HRQL outcomes in population studies cannot be made [15]. Large nationally representative data sets, such as the National Health and Nutrition Examination Survey and the Medical Expenditure Panel Survey (MEPS) include data derived from health status or HRQL instruments. These measures have and will continue to be used as primary or secondary outcome variables in analyses. It is therefore of interest to control for the presence of comorbidity when using these measures to study HRQL attributed to specific illnesses.
HRQL refers to the physical, emotional, and social impact of disease and related treatments and is distinct from physiologic measures of disease [29], [30]. Generally, HRQL decreases with increasing comorbidity [31], [32], [33], [34], [35]. Two types of questionnaires are used to measure HRQL, general and disease- or intervention-specific. General measures assess concepts that are relevant to a wide range of people, including ability to function in everyday life and emotional well-being [36]. They are not specific to any age, disease, or treatment group and are designed to be broadly applied across different populations to allow for comparisons across many conditions [37], [38], [39], [40].
Researchers have validated measures of comorbidity by how well they predict mortality, health resource use, and expenditures, either as a predictor themselves or to adjust for the contributing effects of other diseases when studying the association of a specific illness. Comparatively little research has been conducted validating existing CIs with HRQL or health status as the primary outcome variable. Moreover, few studies have been conducted to develop and validate HRQL- or health status-specific CI.
The purpose of this study was to derive and validate a CI using diseases that have the greatest association with HRQL. The secondary goal was to compare the results of explanatory models that use the new index derived specifically for HRQL with a CI originally derived to predict mortality and health care resource use, the Charlson-CI using MEPS database.