Primary Care Physician
Response to a Mental Health
Carve-Out: An Economic
Analysis
Ashley Aull Dunham
Jennifer L. Troyer
William P. Brandon
UNC-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?
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

Traditional FFS
Incentive Structures

Mental Health FFS and
Primary Care FFS (Control)



“Traditional” incentive to increase business volume
Primary care will refer mental health only when timeconsuming or problem cases that prevent wealth
maximization
Primary Care Capitation with Mental Health
Carve-Out (Experimental)


Clear incentive to move mental health out of primary care
Does this conflict with the patient’s best interest?
Data Available



Encounter forms not reliable
Claims data for antidepressant prescriptions by
all providers and visits provided by mental
health professionals coded for depression
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.
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

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

Effects of the Mental Health Carve-Out
(DID Models)
Mental
Health
Visits
Total
Antidepressant
Claims
Claims Prescribed
by Non-Mental
Health Providers
-1.43068**
Claims
Prescribed by
Mental Health
Providers
0.86485
County
-1.96645*
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
*p < .05
**p<.10
2.31707*
-2.22826*
Effects of a Mental Health Carve-Out
(Logit Models)
County
Carve-out
(County)(Carve-out)
Race
Gender
Agegrp
Category
Timeon
*p < .05
**p<.10
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*
Pseudo R2=.1082
n=41,477
Drug Claim Being an
Antidepressant
.5297924**
.4082136
-.1327179
1.333435*
-.2423945
1.687171*
-.792415
-.0048483
Results – DID Models

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’s best interest
Decrease in antidepressant claims from mental health
providers and no change in antidepressant claims from
non-mental health providers

Primary care providers continued to treat for depression by
prescribing (free good) – allows wealth maximization while
continuing to serve the patient’s best interest
Results – Logit Models

No significant change in prescriptions for
antidepressants and no change in probability
that prescription came from a mental health
provider


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
Conclusions


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