Medicare Managed Care and Primary Care Quality: Examining Racial/Ethnic Effects Across States

On September 16, 2009, Jayasree Basu made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (958 KB) (Plugin Software Help).

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Medicare Managed Care and Primary Care Quality: Examining Racial/Ethnic Effects across States

Jayasree Basu, Ph.D.

AHRQ 2009 Annual Conference

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Background

• Medicare Modernization Act of 2003 sparked renewed interest in Medicare managed care (MMC)
• Medicare spends more each year on beneficiaries enrolled in Medicare Advantage (MA) plans -little evidence to suggest added value worth the extra investment
• Understudied topic: program's effectiveness in reducing racial and ethnic disparities in quality of health care delivery and access

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Study Objective

• To assess the role of MA plans in providing quality primary care compared to Fee For Service (FFS) Medicare in three states (NY, CA, FL) across three racial ethnic groups (White, African American, and Hispanic)
• The performance will be measured in terms of providing better quality primary care, defined as lowering the risk of preventable (or Ambulatory Care Sensitive) hospital admissions

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Hypotheses

• Managed care plans reduce preventable hospitalizations (PH) through care coordination and provision of preventive care
• Relative to FFS, improved care coordination in HMO plans reduce PH for minorities more than whites
• H₁: PHMA < PH FFS
• H₂: PHMA(Minorities|Whites) < PH FFS(Minorities|Whites)

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Patient Selection

• States : NY, CA, FL, Year: 2004
• Hospitalized Medicare FFS and Medicare advantage (MA) plan enrollees (Age 65 and over)
• Patient level data on MA versus FFS enrollment as recorded in the confidential files of discharge database of the three states

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MMC penetration by State and US

All three states had higher penetration than US average in 1994, increasing further by 2000. CA reached 54% penetration rate By 2000.

MMC penetration by State and US

State source: InterStudy

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Data

• Hospital discharge data (HCUP-SID, AHRQ) for elderly Medicare (age 65+), 2004
• Medicare managed care plans available in 2004 were predominately HMO types (96-99%)
• Inpatient discharge data linked to area resource files, US Census, AHA, Interstudy, HRSA
• Multivariate cross sectional framework with patient-level data for each State

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Variables

• Individual patient characteristics:
  • Three Racial ethnic groups
  • • Whites
    • African Americans (AA)
    • Hispanics
  • Type of insurer, age groups, gender, severity of illness, indirect severity indicators, severity*HMO, race*HMO
• Contextual data: socio-demographic conditions and provider characteristics in each Primary Care Service area (PCSA) where patients live

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PCSA

• PCSA is the smallest geographic area validated as a discrete service area for primary care.
• Defined on FFS Medicare patient flows to physician offices, updated frequently by HRSA.
• Since managed care is expected to improve outcomes through better availability of primary and preventive care in the community, an area denominator which more accurately reflects a primary care market is appropriate

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Design

• PH admissions compared with admissions for "marker conditions" for each State in each Racial group

PH

• Sensitive to primary care
• Ex: Severe ENT infections, UTI, COPD, Tuberculosis, Hypertension etc.,

Marker

• Urgent, insensitive to primary care
• Appendicitis with appendectomy, acute MI, gastrointestinal obstruction, fracture of hip/femur

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PH Admissions

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Marker Admissions: The Comparison Group

• Diagnoses for which provision of timely and effective outpatient care is likely to have little impact on the need for hospital admission
• Agreement among practitioners on clinical criteria for admission:

appendicitis with appendectomy
acute myocardial infarction
gastrointestinal obstruction
fracture of hip/femur

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Analysis

• Unit of analysis = patients
• Logistic regression models with odds of PH admission compared to marker admission for MA versus FFS enrollees
• Logistic models by each Racial group and Pooled models
• Multivariate logistic models with multilevel data, adjusting for area-level clustering, by state

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RESULTS Odds Ratios of PH Admissions (relative to Marker): MA VS. FFS enrollees

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RESULTS Odds Ratios of PH Admissions (relative to .Marker): MA VS. FFS enrollees

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% Difference in Odds of PH Admissions: MA versus FFS Enrollees

CA and FL had greater reductions in odds of PH among MA enrollees by racial groups, minorities in particular, relative to FFS
 

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Odds Ratios of PH Admissions versus Marker Admissions: Race*HMO Interactions

*MA enrollment associated with significantly lower PH among
Minorities relative to Whites
 

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Summary

• In all racial groups, MA enrollment was associated with lower risks of PH admissions (versus marker admissions) than FFS enrollment
• Minority MA enrollees had lower risks of PH admissions (versus marker admissions) than white MA enrollees, relative to their FFS counterparts
• CA and FL: Interaction effect in pooled model shows statistically significant reductions in PH rates among minority relative to white MA enrollees

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Conclusion

• MA plans were associated with beneficial impacts in all three states by improving quality primary care and reducing preventable hospitalizations
• The benefit also spilled over to different racial and ethnic subgroups
• In CA and FL, MA enrollment was associated with significant reductions in racial and ethnic differences in preventable hospitalization rates

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Implications

• MA plans (HMO) added value to the quality of primary care to the elderly by racial groups.
• Greater reduction of PH rates among minority subgroups indicates favorable role of MA plans in achieving racial/ethnic equalities.
• Care management provided in Medicare HMOs may have implications for future strategies to reduce racial ethnic gaps and improve quality of primary care.
• The findings may have implications for greater use of preventive care advocated for health reform.
• Future research should evaluate the MMC programs by other plan types using more recent data.