Comparing measures of multimorbidity to predict outcomes in primary care: A cross sectional study

Abstract

Background. An increasing proportion of people are living with multiple health conditions, or 'multimorbidity'. Measures of multimorbidity are useful in studies of interventions in primary care to take account of confounding due to differences in case-mix. Objectives. Assess the predictive validity of commonly used measures of multimorbidity in relation to a health outcome (mortality) and a measure of health service utilization (consultation rate). Methods. We included 95 372 patients registered on 1 April 2005 at 174 English general practices included in the General Practice Research Database. Using regression models we compared the explanatory power of six measures of multimorbidity: count of chronic diseases from the Quality and Outcomes Framework (QOF); Charlson index; count of prescribed drugs; three measures from the John Hopkins ACG software [Expanded Diagnosis Clusters count (EDCs), Adjusted Clinical Groups (ACGs), Resource Utilisation Bands (RUBs)]. Results. A model containing demographics and GP practice alone explained 22% of the uncertainty in consultation rates. The number of prescribed drugs, ACG category, EDC count, RUB category, QOF disease count, or Charlson index increased this to 42%, 37%, 36%, 35%, 30%, and 26%, respectively. Measures of multimorbidity made little difference to the fit of a model predicting 3-year mortality. Nonetheless, Charlson index score was the best performing measure, followed by the number of prescribed drugs. Conclusion. The number of prescribed drugs is the most powerful measure for predicting future consultations and the second most powerful measure for predicting mortality. It may have potential as a simple proxy measure of multimorbidity in primary care. © The Author 2012. Published by Oxford University Press.