Abstract
Comorbidity measures, such as the Charlson Comorbidity Index, are commonly used in risk adjustment models to account for variability in disease burden. This narrative synthesis describes and critiques available comorbidity indices and offers implementation guidance to researchers based on a critical review of existing literature. First, common comorbidity measures are described. Instruments derived using case definitions, grouping of International Classification of Diseases (ICD) codes, and mapping of dispensed medications to chronic conditions are presented. Comorbidity indices that combine diagnostic and medication data are also introduced. No single option consistently outperforms the rest. Next, important considerations when applying a comorbidity index are described. It is crucial to respect temporality and exclude health events that arise after the study index date. Researchers must also weigh the interpretability of using a weighted sum against the flexibility of using a large set of binary variables. When modelling long-term outcomes, there are benefits to applying a one-year look-back window and augmenting data via linkage. For short-term outcomes, certain chronic conditions may exhibit a protective association; however, not all indices capture these relationships. Implementation of these findings will improve the interpretability of comorbidity measures and the quality of future studies.