title: Chronic disease score as a predictor of hospitalization.
abstract: BACKGROUND
  The Chronic Disease Score is a risk-adjustment metric based on age, gender, and history of dispensed drugs. We compared four versions of the score for their ability to predict hospitalization among members of eight health maintenance organizations nationwide.
  
  
  METHODS
  The study included 29,247 women age 45 years and older. Logistic regression models were constructed using rank quintile and rank decile indicators for each of four scores as predictors of hospitalization during the year after 1 October 1995. Discrimination and model fit were compared using several model properties including the C statistic and the odds ratio comparing highest with lowest quantiles.
  
  
  RESULTS
  All Chronic Disease Score versions performed similarly, with the version that predicts total healthcare cost, proposed by Clark et al. (Med Care 1995;33:783-795), performing somewhat better than the other three. The overall risk of hospitalization was 12%. Individuals with higher quantile ranks had a higher risk of hospitalization. Among the Chronic Disease Score versions, the risk of hospitalization ranged from 4% for the lowest decile to 27-29% for the highest decile. Odds ratios comparing the highest with the lowest deciles ranged from 8.9 to 10.2.
  
  
  CONCLUSIONS
  The Chronic Disease Score predicts hospitalization and therefore may be a useful indicator of baseline comorbidity for control of confounding.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: Kathleen G
authors.initials: KG
authors.lastname: Putnam
authors.forename: Diana S M
authors.initials: DS
authors.lastname: Buist
authors.forename: Paul
authors.initials: P
authors.lastname: Fishman
authors.forename: Susan E
authors.initials: SE
authors.lastname: Andrade
authors.forename: Myde
authors.initials: M
authors.lastname: Boles
authors.forename: Gary A
authors.initials: GA
authors.lastname: Chase
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Goodman
authors.forename: Jerry H
authors.initials: JH
authors.lastname: Gurwitz
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.forename: K
authors.initials: K
authors.lastname: Arnold Chan
mesh_terms: D000367:Age Factors; D002908:Chronic Disease / Q000453:epidemiology* / Q000628:therapy; D015986:Confounding Factors, Epidemiologic; D011307:Drug Prescriptions / Q000191:economics / Q000706:statistics & numerical data; D015340:Epidemiologic Research Design; D005260:Female; D006279:Health Maintenance Organizations / Q000191:economics / Q000706:statistics & numerical data; D006760:Hospitalization / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D016016:Proportional Hazards Models; D012737:Sex Factors; D014481:United States / Q000453:epidemiology