Using Data-Driven Approaches to Classify and Predict Health Care Spending in Patients With Gout Using Urate-Lowering Therapy.

Abstract

Despite increasing overall health care 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 classify and predict spending patterns in patients with gout. Using insurance claims data from 2017-2019, we used group-based trajectory modeling to classify patients ages 40 years or older diagnosed with gout and treated with urate-lowering therapy (ULT) by their total health care 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, the mean ± SD age was 71.0 ± 10.5 years, and 17,194 patients (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]). Although average adherence was relatively high in the population, for the high-spending group, the most influential predictors were greater gout medication adherence and diabetes melllitus diagnosis. We identified distinct long-term health care 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 melllitus.