title: Case-mix adjustment of the National CAHPS benchmarking data 1.0: a violation of model assumptions?
abstract: OBJECTIVE
  To compare models for the case-mix adjustment of consumer reports and ratings of health care.
  
  
  METHODS
  The study used the Consumer Assessment of Health Plans (CAHPS) survey 1.0 National CAHPS Benchmarking Database data from 54 commercial and 31 Medicaid health plans from across the United States: 19,541 adults (age > or = 18 years) in commercial plans and 8,813 adults in Medicaid plans responded regarding their own health care, and 9,871 Medicaid adults responded regarding the health care of their minor children.
  
  
  METHODS
  Four case-mix models (no adjustment; self-rated health and age; health, age, and education; and health, age, education, and plan interactions) were compared on 21 ratings and reports regarding health care for three populations (adults in commercial plans, adults in Medicaid plans, and children in Medicaid plans). The magnitude of case-mix adjustments, the effects of adjustments on plan rankings, and the homogeneity of these effects across plans were examined.
  
  
  METHODS
  All ratings and reports were linearly transformed to a possible range of 0 to 100 for comparability.
  
  
  RESULTS
  Case-mix adjusters, especially self-rated health, have substantial effects, but these effects vary substantially from plan to plan, a violation of standard case-mix assumptions.
  
  
  CONCLUSIONS
  Case-mix adjustment of CAHPS data needs to be re-examined, perhaps by using demographically stratified reporting or by developing better measures of response bias.
authors.affiliation: RAND, Santa Monica, CA 90407-2138, USA.
authors.forename: M N
authors.initials: MN
authors.lastname: Elliott
authors.forename: R
authors.initials: R
authors.lastname: Swartz
authors.forename: J
authors.initials: J
authors.lastname: Adams
authors.forename: K L
authors.initials: KL
authors.lastname: Spritzer
authors.forename: R D
authors.initials: RD
authors.lastname: Hays
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D019985:Benchmarking / Q000379:methods*; D015982:Bias; D002648:Child; D002675:Child, Preschool; D003258:Consumer Behavior / Q000706:statistics & numerical data*; D016208:Databases, Factual; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D005260:Female; D006304:Health Status; D006801:Humans; D007223:Infant; D007255:Information Services; D007348:Insurance, Health / Q000592:standards*; D016018:Least-Squares Analysis; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D008875:Middle Aged; D008962:Models, Theoretical; D015999:Multivariate Analysis; D017149:Private Sector / Q000706:statistics & numerical data; D014481:United States