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Despite increasing overall healthcare spending over the past several decades, little is known about long-term patterns of spending among US patients with gout. Current approaches to assessing spending typically focus on composite measures or patients agnostic to disease state; in contrast, examining spending using longitudinal measures may better discriminate patients and target interventions to those in need. We used a data-driven approach to classifying and predicting spending patterns in patients with gout.Using insurance claims data from 2017-2019, we used group-based trajectory modeling to classify patients aged ≥40 years diagnosed with gout and treated with urate-lowering therapy (ULT) by their total healthcare spending over 2 years. We assessed the ability to predict membership in each spending group using logistic and generalized boosted regression with split-sample validation. Models were estimated using different sets of predictors and evaluated using C-statistics.In 57,980 patients, mean age was 71.0 (SD:10.5) years, and 17,194 (29.7%) were female. The best-fitting model included the following groups: minimal-spending (13.2%), moderate-spending (37.4%), and high-spending (49.4%). The ability to predict groups was high overall (e.g., boosted C-statistics with all predictors: minimal-spending [0.89], moderate-spending [0.78], and high-spending [0.90]). While average adherence was relatively high in the population, for the high-spending group, the most influential predictors were greater gout medication adherence and diabetes diagnosis.We identified distinct long-term healthcare spending patterns in patients with gout using ULT with high accuracy. Several clinical predictors could be key areas for intervention, such as gout medication use or diabetes. |
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