Background (1)
Is Managed Care Superior to
Traditional Fee-For-Service
among HIV-Infected
Beneficiaries of Medicaid?
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David Zingmond, MD, PhD
UCLA Division of General Internal
Medicine and Health Services Research
June 8, 2004
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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)
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Hypotheses
In California, Medicaid HMOs and enrollment
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HMO enrollment is associated with lower
hospitalization rates.
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Medi- Cal HMO enrollment is associated with
lower antiretroviral medication usage.
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HMO enrollment reduces survival.
policy are implemented on a county- by- county
basis
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Depending upon the county, Medicaid
managed care is mandatory, voluntary, or not
offered.
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Conceptual Model
DISEASE STAGE
COMORBID DISEASE
DEMOGRAPHICS
MEDI-CAL HMO
ENROLLMENT
Methods: Data Sources
Data Source
• ANTIRETROVIRAL THERAPY
• HOSPITALIZATION
• DISEASE PROGRESSION
• MORTALITY
COUNTY POLICY FOR MEDI-CAL HMO
ENROLLMENT OF HIV/AIDS PATIENTS
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Data Measures
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Medi-Cal Eligibility File
Demographics & Enrollment
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Medi-Cal Claims
Antiretroviral Medication Usage
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OSHPD Discharge File
Hospitalizations, SCAH
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Death Stat’l Master File
Time to Death
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AIDS Registry & HIV
Reporting System
Exposure Risk, CD4, Time
since AIDS diagnosis
SCAH - Severity Classification of AIDS Hospitalizations
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Methods: Dependent
Variables
Methods: Cohort Definition
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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.
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Mortality by follow
- up
Disease progression by follow
- up
Hospitalization (or death) by follow
- up
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Use of HAART (at study baseline)
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In sensitivity analyses, we relaxed restrictions
regarding county of residence and of
continuous enrollment.
HAART- Highly Active Antiretroviral Therapy
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Methods: Independent
Variables
Methods:
Regression Analyses (1)
z Baseline HMO enrollment (& home county)
Covariates:
z Demographics
- Age, gender, & race
z Comorbidity
- non
- HIV hospitalizations
z Disease severity - HIV hospitalizations,
CD4*, & SCAH*
z Health Habits
- Exposure risk category*
z Treatment
- Baseline HAART or ARV
Bivariate comparison of dependent and
independent variables by HMO enrollment
z We employed standard multivariate probit
regression model predicting:
Dependent Variable = Function (HMO Enrollment,
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Demographics, Disease Severity, Comorbidity,
Treatment)
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* Only AIDS patient analyses
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Methods:
Regression Analyses (2)
However, this approach may result in biased
estimates if unmeasured severity is correlated
with enrollment and outcomes.
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Results
Solution- Treatment Selection Model
(bivariate probit):
HMO Enrollment = Function (County Plan Type,
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Demographics, Disease Severity/Stage, Comorbidity,
Treatment) + ε
Dependent Variable = Function (HMO Enrollment,
Demographics, Disease Severity/Stage, Comorbidity,
Treatment) + η
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The error terms of the two equations, ε and η, are
modeled as being correlated.
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2
Results: Unadjusted Outcomes
by Disease Stage - HMO vs FFS
Results: Demographics
HMO
2,838
51
N
Male (%)
Race (%)
White
Black
Latino
Age (%)
20-29
30-49
50+
AIDS (%)
37
30
23
13
69
16
45
FFS
15,357
72
**
**
42
30
17
**
6
68
26
52
N
Baseline Treatment (%)
Any ARV
HAART
Death (%)
Hospitalization (%)
** P < 0.01, * P< 0.05
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Results: Impact of HMO
Enrollment on AIDS Patients
Probit
RR
1.14
RR
95% CI
1.07 0.88
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10
6
52
25
13
9
56
**
**
**
**
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Probit
RR
Death
Disease Progression
Death or hospital’n
HAART at baseline*
1.28 0.49
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Discussion (1)
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**
**
** P < 0.01, * P< 0.05
P ρ*
Covariates: age, race, gender, baseline HAART, baseline other ARV,
prior hiv- hospitalization, prior non-hiv- hospitalization, lowest CD4,
exposure category, SCAH.
Pρ* - Chi-square test of rho coefficient different from 0
RR - Relative Risk with 95% CI calculated by bootstrapping with
1000 repetitions.
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69
46
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57
HIV+, No AIDS
HMO
FFS
1,539
7,435
Results: Impact of HMO
Enrollment on HIV+ Patients
D eath
D eath or hospital’n 1.04 0.99 1.09 0.98 0.90 1.07 0.05
H AA RT at baseline 0.80 0.74 0.86 0.90 0.80 1.01 0.01
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AIDS
FFS
7,922
64
36
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60
Bivariate probit
95% CI
1.01 0.88
HMO
1,299
Bivariate probit
95% CI
RR
95% CI
P ρ*
1.01 0.89
1.12
1.21
0.78 1.75 0.17
1.13 0.98
1.31
1.22
0.95 1.54 0.49
0.98 0.93
1.03
0.99
0.89 1.08 0.92
0.79 0.65
0.94
0.87
0.64 1.19 0.51
Covariates: age, race, gender, baseline HAART, baseline other ARV,
prior hiv- hospitalization, prior non-hiv-hospitalization
Pρ* - Chi-square test of rho coefficient different from 0
RR - Relative Risk with 95% CI calculated by bootstrapping with
1000 repetitions.
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Discussion (2)
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
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– Include guaranteed access to medications and
specialist providers
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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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Conclusions and Policy
Implications
Limitations
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Single state
Limited follow
- p
u
No ambulatory care data
HIV+ without AIDS patients had fewer case
mix measures
HMO implementation is heterogeneous and
distributed geographically
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Medicaid HMOs for patients with HIV/AIDS
have similar outcomes as standard FFS
Medicaid.
– Expansion of Medicaid HMOs may be justified if
cost beneficial
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Similar approaches may be used to examine
benefits of managed care models for other
medically needy Medicaid populations.
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