UCLA Division of General Internal Medicine and Health Services Research June 8, 2004

1. Is Managed Care Superior to Traditional Fee-For-Service among HIV-Infected Beneficiaries of Medicaid?David Zingmond, MD, PhD UCLA Division of General Internal Medicine and Health Services Research June 8, 2004

2. Background (1) • Medicaid is the largest payer of healthcare for HIV/AIDS • Annual budget > $4.1B for HIV/AIDS • High costs of treating HIV/AIDS (and other diseases) have led to the adoption of managed care (HMO) in place of traditional fee-for-service (FFS) by Medicaid • Concerns that HMO enrollment might worsen care & outcomes of HIV/AIDS patients

3. Background (2) • In California, Medicaid HMOs and enrollment policy are implemented on a county-by-county basis • Depending upon the county, Medicaid managed care is mandatory, voluntary, or not offered.

4. Hypotheses • HMO enrollment is associated with lower hospitalization rates. • Medi-Cal HMO enrollment is associated with lower antiretroviral medication usage. • HMO enrollment reduces survival.

5. Conceptual Model DISEASE STAGE COMORBID DISEASE DEMOGRAPHICS • ANTIRETROVIRAL THERAPY • HOSPITALIZATION • DISEASE PROGRESSION • MORTALITY MEDI-CAL HMO ENROLLMENT COUNTY POLICY FOR MEDI-CAL HMO ENROLLMENT OF HIV/AIDS PATIENTS

6. Data Source Medi-Cal Eligibility File Medi-Cal Claims OSHPD Discharge File Death Stat’l Master File AIDS Registry & HIV Reporting System Data Measures Demographics & Enrollment Antiretroviral Medication Usage Hospitalizations, SCAH Time to Death Exposure Risk, CD4, Time since AIDS diagnosis Methods: Data Sources SCAH - Severity Classification of AIDS Hospitalizations

7. Methods: Cohort Definition • Identified all adult HIV/AIDS patients enrolled in Medi-Cal in January 1999 (in counties with mandatory or optional HMO enrollment) who were continuously enrolled until 12/2001 or death. • In sensitivity analyses, we relaxed restrictions regarding county of residence and of continuous enrollment.

8. Methods: Dependent Variables • Mortality by follow-up • Disease progression by follow-up • Hospitalization (or death) by follow-up • Use of HAART (at study baseline) HAART - Highly Active Antiretroviral Therapy

9. Methods: Independent Variables • Baseline HMO enrollment (& home county) Covariates: • Demographics - Age, gender, & race • Comorbidity - non-HIV hospitalizations • Disease severity - HIV hospitalizations, CD4*, & SCAH* • Health Habits - Exposure risk category* • Treatment - Baseline HAART or ARV * Only AIDS patient analyses

10. Bivariate comparison of dependent and independent variables by HMO enrollment We employed standard multivariate probit regression model predicting: Dependent Variable= Function(HMO Enrollment, Demographics, Disease Severity, Comorbidity, Treatment) However, this approach may result in biased estimates if unmeasured severity is correlated with enrollment and outcomes. Methods: Regression Analyses (1)

11. Solution - Treatment Selection Model (bivariate probit): HMO Enrollment= Function(County Plan Type, Demographics, Disease Severity/Stage, Comorbidity, Treatment) +  Dependent Variable= Function(HMO Enrollment, Demographics, Disease Severity/Stage, Comorbidity, Treatment) +  The error terms of the two equations,  and , are modeled as being correlated. Methods: Regression Analyses (2)

12. Results

13. Results: Demographics ** P < 0.01, * P< 0.05

14. Results: Unadjusted Outcomes by Disease Stage - HMO vs FFS ** P < 0.01, * P< 0.05

15. Results: Impact of HMO Enrollment on AIDS Patients Covariates: age, race, gender, baseline HAART, baseline other ARV, prior hiv- hospitalization, prior non-hiv- hospitalization, lowest CD4, exposure category, SCAH. P* - Chi-square test of rho coefficient different from 0 RR- Relative Risk with 95% CI calculated by bootstrapping with 1000 repetitions.

16. Results: Impact of HMO Enrollment on HIV+ Patients Covariates: age, race, gender, baseline HAART, baseline other ARV, prior hiv- hospitalization, prior non-hiv-hospitalization P* - Chi-square test of rho coefficient different from 0 RR- Relative Risk with 95% CI calculated by bootstrapping with 1000 repetitions.

17. Discussion (1) • HMO enrollment in California appears to have negligible impact on hospitalization and death. • Despite concerns that HMOs might provide less necessary medications for AIDS patients, analysis results show no difference. • Important treatment guarantees may mediate the effects of plan type on outcomes • Include guaranteed access to medications and specialist providers

18. Discussion (2) • Differences in treatment appear to exist among the HIV+, non-AIDS patients. • Treatment criteria are less stringent for non-AIDS patients. • Disease severity is more varied but less well measured as that for AIDS patients. • Overall, the bivariate probit approach gives greater confidence to standard regression results.

19. Limitations • Single state • Limited follow-up • No ambulatory care data • HIV+ without AIDS patients had fewer case-mix measures • HMO implementation is heterogeneous and distributed geographically

20. Conclusions and Policy Implications • Medicaid HMOs for patients with HIV/AIDS have similar outcomes as standard FFS Medicaid. • Expansion of Medicaid HMOs may be justified if cost beneficial • Similar approaches may be used to examine benefits of managed care models for other medically needy Medicaid populations.