title: The utility of extended longitudinal profiles in predicting future health care expenditures.
abstract: BACKGROUND
  Health care spending is highly concentrated. Prediction models that accurately identify the characteristics of individuals most likely to incur high levels of health expenditures in a subsequent year are important analytical and statistical tools.
  
  
  OBJECTIVE
  This study examined the capacity of alternative models to predict the likelihood of incurring high levels of medical expenditures in a subsequent year. This effort also evaluated the utility of an additional year of longitudinal information.
  
  
  METHODS
  A nationally representative sample from the Medical Expenditure Panel Survey (MEPS).
  
  
  METHODS
  The MEPS longitudinal data are used to examine the persistence of high expenditures during a 2-year period. With the unique linkage of the MEPS to the National Health Interview Survey, the utility of an additional year of data also was examined. Resultant models were evaluated in terms of sensitivity, specificity, and predictive capacity.
  
  
  RESULTS
  Only modest marginal gains in discrimination capacity were realized from the use of extended longitudinal profiles from the National Health Interview Survey, relative to information on prior year characteristics.
  
  
  CONCLUSIONS
  Our results highlight the continuing concentration of health care expenditures during the period 1996 to 2002 and reveal some attenuation in magnitude in the tail of this distribution over time. Further, our results provide evidence of the utility of probabilistic models as prediction tools to identify individuals likely to incur high levels of expenditures in future years. Predictive capacity does not suffer when restricted to a single year of prior information.
authors.affiliation: Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland 20850, USA. scohen@ahrq.gov
authors.forename: Steven B
authors.initials: SB
authors.lastname: Cohen
authors.forename: Trena
authors.initials: T
authors.lastname: Ezzati-Rice
authors.forename: William
authors.initials: W
authors.lastname: Yu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D017048:Health Care Costs / Q000706:statistics & numerical data / Q000639:trends*; D019538:Health Care Surveys; D005102:Health Expenditures / Q000706:statistics & numerical data / Q000639:trends*; D006297:Health Services Accessibility / Q000191:economics / Q000639:trends; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007348:Insurance, Health / Q000191:economics / Q000706:statistics & numerical data / Q000639:trends*; D016015:Logistic Models; D008137:Longitudinal Studies; D008875:Middle Aged; D017059:Models, Econometric; D020380:Needs Assessment / Q000191:economics / Q000706:statistics & numerical data*; D016016:Proportional Hazards Models; D014481:United States