Primary Care Physician
Response to a Mental Health
Carve-Out: An Economic
Analysis
Ashley Aull Dunham
Jennifer L. Troyer
William P. Brandon
UNCUNC-Charlotte
Carve-Outs
„ Exclude
specific services from prepaid
health plans
„ “Specialists” manage benefits
„ Distinct budgets, provider networks
and incentive arrangements
„ Effect on Primary Care Physicians?
Incentive Structures
Project Background
„
Mecklenburg County (Experimental)
„
„ n=3497
„
Mandatory Medicaid HMO enrollment in mid-1990s
„ Mental Health, Dental Care and Prescription Drug
Carve-Out – absolved HMO of both responsibility
and risk
„
„
New Hanover County (Control)
„
„
„ n=969
„
Mental Health FFS and
Primary Care FFS (Control)
Primary Care Capitation with Mental Health
Carve-Out (Experimental)
„
Traditional FFS
„
Data Available
„
„
Encounter forms not reliable
Claims data for antidepressant prescriptions by
all providers and visits provided by mental
health professionals coded for depression
“Traditional” incentive to increase business volume
Primary care will refer mental health only when timeconsuming or problem cases that prevent wealth
maximization
Clear incentive to move mental health out of primary care
Does this conflict with the patient’s best interest?
Methods – DID Models
„
Yit = β0 + β1Countyi + β2Phaseint + β3Postt +
β4(Countyi)(Phaseint) + β5(Countyi)(Postt) + εi
Claims/month submitted by mental health providers
coded for depression
„ Antidepressant prescription claims/month
„ Antidepressant prescription claims/month
submitted by mental health providers
„ Antidepressant prescription claims/month
submitted by non-mental health providers
„
„
Research considers the effect of a
mandatory Medicaid mental health
carve-out (that precludes
reimbursement for mental health in
primary care) on depression treatment
for a sample of Medicaid recipients.
1
Effects of the Mental Health CarveCarve-Out
(DID Models)
Methods – Logit Models
„
Pr(Yit=1) = β0 + β1Countyi + β2Carveoutit +
β3(Countyi)(Carveoutit) + β4Racei + β5Genderi +
β6Agegrpi + β7Categoryi + β8Timeoni +εi
Probability that a mental health provider prescribed
antidepressants (as opposed to all other providers)
„ Probability of antidepressant claims in a sample of
all drug claims
„
Mental
Health
Visits
County
-1.96645*
-1.43068**
Claims
Prescribed by
Mental Health
Providers
0.86485
Phasein
.74444
2.92664*
3.79998*
-.87334
Post
-1.20064**
6.41282*
5.35710*
1.05572**
(County)(Phasein) -0.15785
-2.14449**
-3.75920*
1.54743**
(County)(Post)
-3.24974*
-3.69659*
.37957
County
Carve-out
(County)(Carve-out)
Race
Gender
Agegrp
Category
Timeon
*p < .05
**p<.10
Pseudo R2=.1082
n=41,477
Drug Claim Being an
Antidepressant
.5297924**
.4082136
-.1327179
1.333435*
-.2423945
1.687171*
-.792415
-.0048483
No significant change in prescriptions for
antidepressants and no change in probability
that prescription came from a mental health
provider
„
„
„
Significant increase in mental health claims for
depression (supports theory of wealth maximization)
„
„
Increase in referrals may have only been for severe
depression – in the patient’
patient’s best interest
Decrease in antidepressant claims from mental health
providers and no change in antidepressant claims from
nonnon-mental health providers
„
Primary care providers continued to treat for depression by
prescribing (free good) – allows wealth maximization while
continuing to serve the patient’
patient’s best interest
Conclusions
„
„
Decreased likelihood that mental health providers
used antidepressants
No additional barriers to getting antidepressants
relative to all other drugs by eliminating primary
care reimbursement for mental health
-2.22826*
Results – DID Models
Results – Logit Models
„
2.31707*
Claims Prescribed
by Non-Mental
Health Providers
*p < .05
**p<.10
Effects of a Mental Health CarveCarve-Out
(Logit Models)
Pseudo R2=.2121
n=861
Antidepressant Prescribed by a
Mental Health Provider
.8583447
.6562034
-1.112025
1.756301*
-1.238316**
2.634148*
-1.130781
.0251983*
Total
Antidepressant
Claims
Physician’
Physician’s utility function defined by many factors,
including wealth maximization and their obligation to
serve as a perfect agent
Data suggests their obligation to serve as advocate was
more powerful than their need to maximize wealth
„
„
„
Removed from reimbursement arrangements and small
portion of their patient population
Less sensitive to reimbursement changes
Implementation of capitation with the Medicaid
population did not cause uniform change in behavior
2