title: Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment.
abstract: BACKGROUND
  With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns.
  
  
  OBJECTIVE
  To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors.
  
  
  METHODS
  We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011.
  
  
  METHODS
  Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method.
  
  
  RESULTS
  Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82).
  
  
  CONCLUSIONS
  Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.
authors.affiliation: *Division of Pharmacoepidemiology and Pharmacoeconomics †The Center for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA ‡CVS Health, Woonsocket, RI §Data Science, Aetna, Hartford, CT.
authors.forename: Julie C
authors.initials: JC
authors.lastname: Lauffenburger
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Franklin
authors.forename: Alexis A
authors.initials: AA
authors.lastname: Krumme
authors.forename: William H
authors.initials: WH
authors.lastname: Shrank
authors.forename: Troyen A
authors.initials: TA
authors.lastname: Brennan
authors.forename: Olga S
authors.initials: OS
authors.lastname: Matlin
authors.forename: Claire M
authors.initials: CM
authors.lastname: Spettell
authors.forename: Gregory
authors.initials: G
authors.lastname: Brill
authors.forename: Niteesh K
authors.initials: NK
authors.lastname: Choudhry
mesh_terms: D000328:Adult; D003363:Cost Control / Q000379:methods*; D011307:Drug Prescriptions / Q000191:economics* / Q000706:statistics & numerical data; D005260:Female; D017048:Health Care Costs / Q000639:trends*; D005102:Health Expenditures / Q000639:trends*; D006801:Humans; D007348:Insurance, Health / Q000191:economics* / Q000706:statistics & numerical data; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment / Q000379:methods