Does the Addition of Functional Status Indicators to Case-Mix Adjustment Indices Improve Prediction of Hospitalization, Institutionalization, and Death in the Elderly?

Abstract

Case-mix adjustment is widely used in health services research to ensure that groups being compared are equivalent on variables predicting outcome. There has been considerable development and testing of comorbidity indices derived from diagnostic codes recorded in administrative databases, but increasingly, the benefit of clinical information and patient reported ratings of health and functional status is being recognized. One type of information that is highly valued but has so far not been captured by administrative health databases is functional status indicators (FSI). The purpose of this study was to estimate the extent to which prediction of health outcomes can be improved on by including information on functional status indicators (FSI). The data for the current study was obtained from a clustered randomized trial evaluating computerized decision support for managing drug therapy in the elderly, conducted from 1997 to 1998. A total of 107 primary care physicians participated in this trial and 6465 of their patients (51%) completed a generic health status measure-the SF-12-before the intervention. C statistics and R were used to compare the predictive value of sociodemographic factors, 2 comorbidity indices, and 11 FSI predictor variables derived from the SF-12 and coded (possible for 8) using the International Classification of Functioning (ICF). Using stepwise logistic regression, FSI, particularly limitation in stair climbing or doing moderate activities like housework, were found to be strong and independent predictors of all outcomes, even after controlling for sociodemographics and comorbidity. This study indicates that FSI provided as robust a prediction of health events as did complex comorbidity indices. Additionally, the ICF coding system provides a mechanism whereby information on FSI could be incorporated into administrative databases through the use of electronic health records that include a health or functional status measure.