How patients' sociodemographic characteristics affect comparisons of competing health plans in California on HEDIS quality measures.

OBJECTIVE To estimate effects of patient sociodemographic characteristics on differential performance within and between plans in a single market area on the HEDIS quality of care measures, widely used for purchasing and accreditation decisions in the United States. DESIGN Using logistic regression, we modeled associations of age, sex, and zip-code-linked sociodemographic characteristics of health plan members with HEDIS measures of screening and preventive services. We calculated the impact of adjusting for these associations on measures of health plan performance. SETTING Twenty-two California health plans provided individual-level HEDIS data and zip codes of residence for up to 2 years. PARTICIPANTS 110 541 commercially insured health plan members. MAIN OUTCOME MEASURES Ten HEDIS quality-of-care measures. RESULTS Performance on quality measures was negatively associated with percent receiving public assistance in the local area (seven out of 10 measures), percent Black (three measures), and percent Hispanic (four measures), and positively associated with percent college educated (six measures), and percent urban (three measures), controlling for plan, while associations with percent Asian were positive for three measures and negative for one (P < 0.05 for six associations, P < 0.01 for four, P < 0.001 for 17). Associations were consistent across plans and over time. Adjustment for these characteristics changed rates for most plans and measures by <5 percentage points. CONCLUSIONS Adjustment for socioeconomic case mix has little impact on the measured performance of most plans in California, but substantially affects a few. The impact of case mix on indicators should be considered when making comparisons of health plan quality.

[1]  S V Subramanian,et al.  Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures--the public health disparities geocoding project. , 2003, American journal of public health.

[2]  P. Romano Should health plan quality measures be adjusted for case mix? , 2000, Medical care.

[3]  A. Zaslavsky,et al.  Adjusting Performance Measures to Ensure Equitable Plan Comparisons , 2001, Health care financing review.

[4]  P. Wingo,et al.  Comparison of mammography and Pap test use from the 1987 and 1992 National Health Interview Surveys: are we closing the gaps? , 1996, American journal of preventive medicine.

[5]  M. Clark,et al.  Breast cancer screening practices among black, Hispanic, and white women: reassessing differences. , 1996, American journal of preventive medicine.

[6]  Sullivan La Health system reform. , 1994, Iowa medicine : journal of the Iowa Medical Society.

[7]  N Segnan,et al.  Socioeconomic status and cancer screening. , 1997, IARC scientific publications.

[8]  A. Zaslavsky,et al.  Using Survey Measures to Assess Risk Selection among Medicare Managed Care Plans , 2002, Inquiry : a journal of medical care organization, provision and financing.

[9]  L. Baker,et al.  Factors associated with women's adherence to mammography screening guidelines. , 1998, Health services research.

[10]  J P Kassirer,et al.  The use and abuse of practice profiles. , 1994, The New England journal of medicine.

[11]  F. Connell,et al.  Adequacy of well-child care and immunizations in US infants born in 1988. , 1994, JAMA.

[12]  A. Epstein,et al.  Performance reports on quality--prototypes, problems, and prospects. , 1995, The New England journal of medicine.

[13]  A M Zaslavsky,et al.  Impact of Sociodemographic Case Mix on the HEDIS Measures of Health Plan Quality , 2000, Medical care.

[14]  A M Epstein,et al.  Rolling down the runway: the challenges ahead for quality report cards. , 1998, JAMA.

[15]  T. Jost,et al.  Health system reform : forward or backward with quality oversight ? , 1994 .

[16]  R. Little Direct Standardization: A Tool for Teaching Linear Models for Unbalanced Data , 1982 .

[17]  N. Krieger Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology. , 1992, American journal of public health.

[18]  A. Zaslavsky,et al.  Adjusting for Patient Characteristics When Analyzing Reports From Patients About Hospital Care , 2001, Medical care.

[19]  M. Thun,et al.  Demographic predictors of mammography and Pap smear screening in US women. , 1993, American journal of public health.

[20]  W. Cumberland,et al.  Immunization status of children of employees in a large corporation. , 1994, JAMA.

[21]  B J McNeil,et al.  Current issues in profiling quality of care. , 1992, Inquiry : a journal of medical care organization, provision and financing.

[22]  E. Perrin,et al.  Eliminating Health Disparities: Measurement and Data Needs , 2004 .

[23]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[24]  L. Kuller,et al.  Demographic, psychosocial, and medical correlates of Pap testing: a literature review. , 1991, American journal of preventive medicine.