title: Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey.
abstract: OBJECTIVE
  Uncertainties exist about when and how best to adjust performance measures for case mix. Our aims are to quantify the impact of case-mix adjustment on practice-level scores in a national survey of patient experience, to identify why and when it may be useful to adjust for case mix, and to discuss unresolved policy issues regarding the use of case-mix adjustment in performance measurement in health care.
  
  
  METHODS
  Secondary analysis of the 2009 English General Practice Patient Survey. Responses from 2 163 456 patients registered with 8267 primary care practices. Linear mixed effects models were used with practice included as a random effect and five case-mix variables (gender, age, race/ethnicity, deprivation, and self-reported health) as fixed effects.
  
  
  METHODS
  Primary outcome was the impact of case-mix adjustment on practice-level means (adjusted minus unadjusted) and changes in practice percentile ranks for questions measuring patient experience in three domains of primary care: access; interpersonal care; anticipatory care planning, and overall satisfaction with primary care services.
  
  
  RESULTS
  Depending on the survey measure selected, case-mix adjustment changed the rank of between 0.4% and 29.8% of practices by more than 10 percentile points. Adjusting for case-mix resulted in large increases in score for a small number of practices and small decreases in score for a larger number of practices. Practices with younger patients, more ethnic minority patients and patients living in more socio-economically deprived areas were more likely to gain from case-mix adjustment. Age and race/ethnicity were the most influential adjustors.
  
  
  CONCLUSIONS
  While its effect is modest for most practices, case-mix adjustment corrects significant underestimation of scores for a small proportion of practices serving vulnerable patients and may reduce the risk that providers would 'cream-skim' by not enrolling patients from vulnerable socio-demographic groups.
authors.affiliation: Cambridge Centre for Health Services Research, University of Cambridge, Cambridge, UK.
authors.forename: Charlotte
authors.initials: C
authors.lastname: Paddison
authors.forename: Marc
authors.initials: M
authors.lastname: Elliott
authors.forename: Richard
authors.initials: R
authors.lastname: Parker
authors.forename: Laura
authors.initials: L
authors.lastname: Staetsky
authors.forename: Georgios
authors.initials: G
authors.lastname: Lyratzopoulos
authors.forename: John L
authors.initials: JL
authors.lastname: Campbell
authors.forename: Martin
authors.initials: M
authors.lastname: Roland
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D004739:England; D005260:Female; D058006:General Practice / Q000458:organization & administration*; D006297:Health Services Accessibility / Q000458:organization & administration; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D010347:Patient Care Planning / Q000458:organization & administration; D017060:Patient Satisfaction / Q000706:statistics & numerical data*; D010817:Physician-Patient Relations; D011320:Primary Health Care / Q000458:organization & administration*; D020379:Risk Adjustment / Q000379:methods*; D012737:Sex Factors; D012959:Socioeconomic Factors; D055815:Young Adult