title: Impact of sociodemographic case mix on the HEDIS measures of health plan quality.
abstract: BACKGROUND
  The widely used Health Plan Employer Data and Information Set (HEDIS) measures may be affected by differences among plans in sociodemographic characteristics of members.
  
  
  OBJECTIVE
  The objective of this study was to estimate effects of geographically linked patient sociodemographic characteristics on differential performance within and among plans on HEDIS measures.
  
  
  METHODS
  Using logistic regression, we modeled associations between age, sex, and residential area characteristics of health plan members and results on HEDIS measures. We then calculated the impact of adjusting for these associations on plan-level measures.
  
  
  METHODS
  This study included 92,232 commercially insured members with individual-level HEDIS data and an additional 20,615 members whose geographic distribution was provided.
  
  
  METHODS
  This study used 7 measures of screening and preventive services.
  
  
  RESULTS
  Performance was negatively associated with percent receiving public assistance in the local area (6 of 7 measures), percent black (5 measures), and percent Hispanic (2 measures) and positively associated with percent college educated (6 measures), percent urban (2 measures), and percent Asian (1 measure) after controlling for plan and product type. These effects were generally consistent across plans. When measures were adjusted for these characteristics, rates for most plans changed by less than 5 percentage points. The largest change in the difference between plans ranged from 1.5% for retinal exams for people with diabetes to 20.2% for immunization of adolescents.
  
  
  CONCLUSIONS
  Performance on quality indicators for individual members is associated with sociodemographic context. Adjustment has little impact on the measured performance of most plans but a substantial impact on a few. Further study with more plans is required to determine the appropriateness and feasibility of adjustment.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts 02115, USA. zaslavsk@hcp.med.harvard.edu
authors.forename: A M
authors.initials: AM
authors.lastname: Zaslavsky
authors.forename: J N
authors.initials: JN
authors.lastname: Hochheimer
authors.forename: E C
authors.initials: EC
authors.lastname: Schneider
authors.forename: P D
authors.initials: PD
authors.lastname: Cleary
authors.forename: J J
authors.initials: JJ
authors.lastname: Seidman
authors.forename: E A
authors.initials: EA
authors.lastname: McGlynn
authors.forename: J W
authors.initials: JW
authors.lastname: Thompson
authors.forename: C
authors.initials: C
authors.lastname: Sennett
authors.forename: A M
authors.initials: AM
authors.lastname: Epstein
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003953:Diagnosis-Related Groups; D005260:Female; D006264:Health Benefit Plans, Employee / Q000592:standards* / Q000706:statistics & numerical data; D006279:Health Maintenance Organizations / Q000592:standards*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011247:Pregnancy; D011314:Preventive Health Services / Q000592:standards; D011785:Quality Assurance, Health Care / Q000379:methods*; D019984:Quality Indicators, Health Care; D012959:Socioeconomic Factors; D014481:United States