Do State Parity Laws
Differentially Impact Low
Income or High Need Groups?
Colleen L. Barry, Ph.D.
Susan H. Busch, Ph.D.
Yale School of Medicine
June 2006
We gratefully acknowledge support from RWJ HCFO program (grant no. 56465)
Motivation
• Private insurance is more limited for mental
health than for general medical care
• Advocates view benefit limits as discriminatory
• Economic Explanations:
- Moral hazard: health plan incentive to control consumer demand
for services
- Selection: health plan incentive to compete to avoid ‘bad risks’
• Parity laws aim to address disparity by requiring
equivalent coverage
- 37 states have enacted parity laws
- Federal regulation limited (1996)
2
Previous Research – State Laws
• Prior studies found no change in use in response
to state parity (Sturm, 2000; Sturm and Bao, 2004)
• Puzzling finding -- at least three explanations:
1) Truly no effect of state parity laws on use -- the same
people receive the same services post parity
2) Failure to account for ERISA exemption
3) A re-allocation, such that the net effect is no change in
service use
• If parity impacts important subgroups, but has little
impact overall, may still be sound policy
3
Research Objectives
1) Do state parity laws increase utilization for the
population not exempt under ERISA?
2) Do state parity laws increase mental health care
utilization for low income individuals?
3) Do state parity laws increase mental health care
utilization for individuals with poor mental
health?
4
1) Why does ERISA matter?
• Key explanatory variable - whether the state had
a parity law in effect
• Employers who self insure exempt from state
mandates
– Concern ERISA exemption leads to attenuation bias
• Limitation not addressed in prior parity research
• We don’t know whether employer self-insured,
but do know employer size
– Our approach: Impute ‘probability subject to parity’
based on employer size, state & year using MEPS-IC
5
2) Why study low income individuals?
• Evidence for general health care → low
income may be more responsive to cost
sharing (RAND HIE)
• Low income groups may be at higher risk
of not receiving services
6
3) Why study individuals in poor
mental health?
• Many state policies target SMI
• Improved access to care is likely to have
greatest impact on health for those with a
need for services
• Some misallocation of services
7
Allocation of Services
• Although prior research indicates that parity does
not affect use, allocation may change (i.e., the
same people may not be getting services)
• Pre-parity relied on demand side constraints
• Post-parity may put greater reliance on supplyside constraints (e.g., UR) – managed care
• If managed care better at allocating services, we
expect those in poor mental health to be more
likely to receive services post-parity
8
Misallocation of mental/addictive disorders
and service for adults
% of population
receiving mental
health services in
one year (15%)
% of population with mental/addictive
disorders in one year (29%)
20%
6%
9%
Diagnosis and no treatment (20%)
9
From: Mental Health, A Report of the Surgeon General (1999)
Data
• National Survey of America’s Families
• Large nationally representative sample (3
cross sections - 1997, 1999, 2002)
• Focus on 13 states, 5 of which passed
parity legislation between 97-02
• Limit sample to adults with private
insurance coverage all of past year
10
Measures
• Outcome measure
– Any mental health service use (0/1)
• Parity measure
– Impute ‘probability subject to parity’ from employer size, state
and year, based on MEPS-IC data
• Mental health status measure
– Five-item scale from MOS (Wells, 1996)
– Assesses anxiety, depression, loss of behavior/emotional control
and psychological well being in the past month on Likert scale
– Sum and use cutoff common in literature of 67
– Cannot be tied directly to clinical diagnosis
• Income measure
– Categorize as low income (<200 % FPL) or non-low income
(>200 % FPL)
11
Methods
• State and year fixed effect models
– State f.e. control for time invariant state
characteristics
– Year f.e. control for secular trends in treatment
• Logistic Regression
• Examine effects on:
1) low income and
2) those in poor mental health
– Interact parity variable with indicators of above
12
Impact of ERISA exemption:
Estimates of percent
covered under parity laws, by year
% parity
(0/1)
Probability of
parity (PP)
1997
4.6 %
1999
8.3 %
2002
43.8 %
2.1
4.7
26.7
13
Effect of parity law on ‘any visit’
Any mental
health visit
(parity=0/1)
Parity
.086
(.098)
Any mental
health visit
(parity=PP);
Emp size>50
.280
(.186)
N
28747
16994
Control variables include education, race, ethnicity, gender, age,
emp size, income, mental health status, and state and year fixed effects
14
Effect of parity laws on
low income individuals
(1)
Any MH
visit
(2)
Any MH visit
.280
(.186)
.194
(.185)
1.286***
(.392)
Poor MH
1.253***
(.099)
1.253***
(.099)
1.201***
(.278)
Low income
-.465***
(.168)
-.586***
(.204)
Parity (PP)
PP*Low income
(3)
Any MH visit
(Sample limited to
low income)
.797*
(.450)
15
Predicted probabilities of any
MH use
Low income
Non low income
No parity
Parity
4.6 %
11.2 %
7.9
9.4
16
Effect of parity laws by
mental health care need
Parity (PP)
Poor MH
Low income
PP*Poor MH
(1)
(2)
Any MH visit Any MH visit
(3)
Any MH visit
(Sample limited
to low income)
(Sample limited to
non-low income)
1.296**
(.524)
1.205***
(.325)
.203
(.172)
.272***
(.112)
-.027
(.695)
-.095
(.671)
.287**
(.144)
1.257***
(.113)
-.465***
(.169)
-.033
(.519)
17
Predicted Probabilities
Low Income:
Poor MH
Good MH
Non-Low Income:
Poor MH
Good MH
No Parity
Parity
11.5 %
3.6
24.4 %
9.5
18.8
6.3
20.7
7.7
18
Limitations & Next Steps
• No direct measure of ‘self-insured’
• No measure of clinical diagnosis
• We measure mental health status at a
point in time → service use may impact
mental health status
Next Steps
• Examine additional measures of service
use → number of visits
19
Conclusions
• Parity does increase use for the low income
• For our outcome, no re-allocation of services to
those with greater need
• ERISA exemption and other characteristics of
state laws reduce share of state population
subject to parity by about half
– Suggests an important role of federal policy given
limited reach of state laws
20
Policy Implications
• We see more elastic demand for low income,
suggesting moral hazard more problematic
• Increases are similar for those w/ and w/out need
– Ideally, would like to see increases only among those
w/need
– Perhaps not big concern given very low utilization rates
• Other studies shows parity does not increase total
costs & implication of this research is that parity is
welfare enhancing → win-win
21
The end
22
Descriptive results: Full Sample
Low income
Percent with
any visit
5.3 %
Non-low income
7.5 %
Sample limited to those with
poor mental health
Percent with
any visit
Low income
11.7 %
Non-low income
19.6 %
23
Employer-sponsored private
insurance limits (2002)
• Historically MH coverage has been more limited
than general health coverage
Inpatient day limits
Outpatient visit limit
Higher cost sharing
Small firm
Large firm
58 %
66 %
17 %
67 %
78 %
24 %
24