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Is Managed Care Superior to Traditional Fee-For-Service

among HIV-Infected Beneficiaries of Medicaid?

David Zingmond, MD, PhDUCLA Division of General Internal

Medicine and Health Services Research

June 8, 2004

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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

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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.

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Hypotheses

HMO enrollment is associated with lower hospitalization rates.

Medi-Cal HMO enrollment is associated with lower antiretroviral medication usage.

HMO enrollment reduces survival.

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Conceptual Model

DISEASE STAGECOMORBID DISEASEDEMOGRAPHICS

MEDI-CAL HMO ENROLLMENT

COUNTY POLICY FOR MEDI-CAL HMO ENROLLMENT OF HIV/AIDS PATIENTS

• ANTIRETROVIRAL THERAPY• HOSPITALIZATION• DISEASE PROGRESSION• MORTALITY

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Methods: Data Sources

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

SCAH - Severity Classification of AIDS Hospitalizations

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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.

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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

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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

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Methods: Regression Analyses (1)

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.

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Methods: Regression Analyses (2)

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.

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Results

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Results: Demographics

HMO FFSN 2,838 15,357Male (%) 51 72 **Race (%) **

White 37 42Black 30 30Latino 23 17

Age (%) **20-29 13 630-49 69 6850+ 16 26

AIDS (%) 45 52

** P < 0.01, * P< 0.05

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Results: Unadjusted Outcomes by Disease Stage - HMO vs FFS

AIDS HIV+, No AIDS

HMO FFS HMO FFS

N 1,299 7,922 1,539 7,435Baseline Treatment (%)

Any ARV 64 69 ** 21 25 **HAART 36 46 ** 10 13 **

Death (%) 16 18 6 9 **Hospitalization (%) 60 57 52 56 **

** P < 0.01, * P< 0.05

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Results: Impact of HMO Enrollment on AIDS Patients

Probit Bivariate probitRR 95% CI RR 95% CI P*

Death 1.01 0.88 1.14 1.07 0.88 1.28 0.49

Death or hospital’n 1.04 0.99 1.09 0.98 0.90 1.07 0.05

HAART at baseline 0.80 0.74 0.86 0.90 0.80 1.01 0.01

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 0RR - Relative Risk with 95% CI calculated by bootstrapping with 1000 repetitions.

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Results: Impact of HMO Enrollment on HIV+ Patients

Covariates: age, race, gender, baseline HAART, baseline other ARV,prior hiv- hospitalization, prior non-hiv-hospitalizationP* - Chi-square test of rho coefficient different from 0RR - Relative Risk with 95% CI calculated by bootstrapping with 1000 repetitions.

Probit Bivariate probitRR 95% CI RR 95% CI P*

Death 1.01 0.89 1.12 1.21 0.78 1.75 0.17

Disease Progression 1.13 0.98 1.31 1.22 0.95 1.54 0.49

Death or hospital’n 0.98 0.93 1.03 0.99 0.89 1.08 0.92

HAART at baseline* 0.79 0.65 0.94 0.87 0.64 1.19 0.51

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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

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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.

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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

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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.