Chronic comorbid conditions are important predictors of primary care outcomes, provide context for clinical decisions, and are potential complications of diseases and treatments. Comorbidity indices and multimorbidity categorization strategies based on administrative claims data enumerate diagnostic codes in easily modifiable lists, but usually have inflexible temporal requirements, such as requiring two claims greater than 30 days apart, or three claims in three quarters. Table structures and claims data search algorithms were developed to support flexible temporal constraints. Tables of disease categories allow subgroups with different numbers of events, different times between similar claims, variable periods of interest, and specified diagnostic code substitutability. The strategy was tested on five years of private insurance claims from 2.2 million working age adults. The contrast between rarely recorded, high prevalence diagnoses (smoking and obesity) and frequently recorded but not necessarily chronic diagnoses (musculoskeletal complaints) demonstrated the advantage of flexible temporal criteria.
['Walton Sumner', 'Dustin Stwalley', 'Phillip V. Asaro', 'Michael Hagen', 'Margaret A. Olsen']
Adding flexible temporal constraints to identify chronic comorbid conditions in ambulatory claims data.