{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:52:22Z","timestamp":1774572742902,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100005292","name":"Brigham and Women's Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005292","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis Care &amp;amp; Research"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Despite increasing overall health care spending over the past several decades, little is known about long\u2010term 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\u2010driven approach to classify and predict spending patterns in patients with gout.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using insurance claims data from 2017\u20132019, we used group\u2010based trajectory modeling to classify patients ages 40\u2009years or older diagnosed with gout and treated with urate\u2010lowering therapy (ULT) by their total health care spending over 2\u2009years. We assessed the ability to predict membership in each spending group using logistic and generalized boosted regression with split\u2010sample validation. Models were estimated using different sets of predictors and evaluated using C statistics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In 57,980 patients, the mean\u2009\u00b1\u2009SD age was 71.0\u2009\u00b1\u200910.5\u2009years, and 17,194 patients (29.7%) were female. The best\u2010fitting 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\u2010spending group, the most influential predictors were greater gout medication adherence and diabetes melllitus diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We identified distinct long\u2010term 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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acr.25008","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T07:13:51Z","timestamp":1661843631000},"page":"1300-1310","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using <scp>Data\u2010Driven<\/scp> Approaches to Classify and Predict Health Care Spending in Patients With Gout Using <scp>Urate\u2010Lowering<\/scp> Therapy"],"prefix":"10.1002","volume":"75","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4940-4140","authenticated-orcid":false,"given":"Julie C.","family":"Lauffenburger","sequence":"first","affiliation":[{"name":"Brigham and Women's Hospital and Harvard Medical School  Boston 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