title: The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients.
abstract: OBJECTIVE
  (1) To determine chronic illness costs for large cohort of primary care patients, (2) to develop prospective model predicting total costs over one year, using demographic and clinical information including widely used comorbidity index.
  
  
  METHODS
  Data including diagnostic, medication, and resource utilization were obtained for 5,861 patients from practice-based computer system over a 1-year period beginning December 1, 1993, for retrospective analysis. Hospital cost data were obtained from hospital cost accounting system.
  
  
  RESULTS
  Average annual per patient cost was $2,655. Older patients and those with Medicare or Medicaid had higher costs. Hospital costs were $1,558, accounting for 58.7% of total costs. In the predictive model, individuals with higher comorbidity incurred exponentially higher annual costs, from $4,317 with comorbidity score of two, to $5,986 with score of three, to $13,326 with scores greater than seven. To use an adapted comorbidity index to predict total yearly costs, four conditions should be added to the index: hypertension, depression, and use of warfarin with a weight of one, skin ulcers/cellulitis, a weight of two.
  
  
  CONCLUSIONS
  The adapted comorbidity index can be used to predict resource utilization. Predictive models may help to identify targets for reducing high costs, by prospectively identifying those at high risk.
authors.affiliation: Division of General Internal Medicine, Weill Cornell Medical College, New York, NY 10065, USA. Electronic address: mecharl@med.cornell.edu.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Charlson
authors.affiliation: Department of Medicine, Yale New Haven Hospital, New Haven, CT 06510, USA.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Charlson
authors.affiliation: Division of General Internal Medicine, Weill Cornell Medical College, New York, NY 10065, USA.
authors.forename: Janey C
authors.initials: JC
authors.lastname: Peterson
authors.affiliation: Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
authors.forename: Spyridon S
authors.initials: SS
authors.lastname: Marinopoulos
authors.affiliation: Department of Mathematics, Central Michigan University, Mount Pleasant, MI 48859 USA.
authors.forename: William M
authors.initials: WM
authors.lastname: Briggs
authors.affiliation: Division of General Internal Medicine, Weill Cornell Medical College, New York, NY 10065, USA.
authors.forename: James P
authors.initials: JP
authors.lastname: Hollenberg
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease / Q000191:economics* / Q000453:epidemiology / Q000628:therapy; D016527:Drug Costs / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006295:Health Resources / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods; D017721:Hospital Costs / Q000706:statistics & numerical data; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D009519:New York City / Q000453:epidemiology; D011320:Primary Health Care / Q000191:economics*; D011379:Prognosis; D055815:Young Adult