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
1
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
2
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.
3
Hypotheses

HMO enrollment is associated with lower
hospitalization rates.

Medi-Cal HMO enrollment is associated with
lower antiretroviral medication usage.

HMO enrollment reduces survival.
4
Conceptual Model
DISEASE STAGE
COMORBID DISEASE
DEMOGRAPHICS
MEDI-CAL HMO
ENROLLMENT
• ANTIRETROVIRAL THERAPY
• HOSPITALIZATION
• DISEASE PROGRESSION
• MORTALITY
COUNTY POLICY FOR MEDI-CAL HMO
ENROLLMENT OF HIV/AIDS PATIENTS
5
Methods: Data Sources

Data Source
Medi-Cal Eligibility File
Data Measures
Demographics & Enrollment

Medi-Cal Claims
Antiretroviral Medication Usage

OSHPD Discharge File
Hospitalizations, SCAH

Death Stat’l Master File
Time to Death

AIDS Registry & HIV
Reporting System
Exposure Risk, CD4, Time
since AIDS diagnosis
SCAH - Severity Classification of AIDS Hospitalizations
6
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.
7
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
8
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
9
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.
10
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.
11
Results
12
Results: Demographics
N
Male (%)
Race (%)
White
Black
Latino
Age (%)
20-29
30-49
50+
AIDS (%)
** P < 0.01, * P< 0.05
HMO
2,838
51
37
30
23
13
69
16
45
FFS
15,357
72
**
**
42
30
17
**
6
68
26
52
13
Results: Unadjusted Outcomes
by Disease Stage - HMO vs FFS
N
Baseline Treatment (%)
Any ARV
HAART
Death (%)
Hospitalization (%)
** P < 0.01, * P< 0.05
AIDS
HMO
FFS
1,299
7,922
64
36
16
60
69
46
18
57
HIV+, No AIDS
HMO
FFS
1,539
7,435
**
**
21
10
6
52
25
13
9
56
**
**
**
**
14
Results: Impact of HMO
Enrollment on AIDS Patients
Probit
RR
95% CI
Bivariate probit
RR
95% CI
P*
1.01 0.88 1.14 1.07 0.88 1.28 0.49
Death
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 0
RR - Relative Risk with 95% CI calculated by bootstrapping with
1000 repetitions.
15
Results: Impact of HMO
Enrollment on HIV+ Patients
Probit
RR
Death
Disease Progression
Death or hospital’n
HAART at baseline*
95% CI
Bivariate probit
RR
95% CI
P*
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
P* - Chi-square test of rho coefficient different from 0
RR - Relative Risk with 95% CI calculated by bootstrapping with
1000 repetitions.
16
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
17
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.
18
Limitations





Single state
Limited follow-up
No ambulatory care data
HIV+ without AIDS patients had fewer casemix measures
HMO implementation is heterogeneous and
distributed geographically
19
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.
20