Collapsing high-end categories of comorbidity may yield misleading results

Adequate control of comorbidity has long been recognized as a critical challenge in clinical epidemiology. Comorbidity scales reduce information about coexistent disease to a single index that is easy to comprehend and statistically efficient. These are the main advantages of an index over incorporating each disease into an analysis as an individual variable. Many study populations have a low prevalence of subjects with high comorbidity scores, so it is common to combine subjects with some score above a threshold into a single open-ended category. This paper examines the impact of collapsing comorbidity scores into these categories. It shows analytically and by synthetic example that collapsing the high-end categories of a comorbidity scale changes the pattern of effect of comorbidity. Furthermore, collapsing the high-end categories biases analyses that control for comorbidity as a confounder or analyze modification of an exposure's effect by comorbidity. Each of these results specific to comorbidity scoring derives from more general epidemiologic principles. The appeal of collapsing categories to facilitate interpretation and statistical analysis may be offset by misleading results. Analysts should assure the uniformity of outcome risk in collapsed categories, informed by judgment and possibly statistical testing, or use analytic methods, such as restriction or spline regression, which can achieve similar goals without sacrificing the validity of results.