ASSSESSING THE FINANCIAL IMPACT OF STATE
HEALTH BENEFIT MANDATES IN CALIFORNIA:
FINDINGS FROM THE CALIFORNIA HEALTH
BENEFITS PROJECT (CHBRP)
Gerald F. Kominski, Ph.D.
P f
Professor,
Department
D
t
t off Health
H lth Services
S i
UCLA School of Public Health
June 30,, 2009
Academy Health
Annual Research Meeting
2009 Academy Health ARM
Objectives
bj
i
off Cost Impact Analyses
l
T
To develop
d
l and
d implement
i l
a model
d l for
f estimating
i
i
financial impacts of proposed health insurance benefit
g
mandates introduced in the California legislature
 Model must produce estimates that are:
 Timely
 Based on California-specific data, where possible, or
data that can be adjusted to reflect California’s
population and market conditions
 Consistent across analyses
2009 Academy Health ARM
Conceptual Approach
 Develop a baseline expenditure and
population model
 Analyze marginal cost impact of new
benefits
2009 Academy Health ARM
Data
 California Employer Health Benefits Survey
 California Health Interview Survey
 Milliman
Milli
Health
H lth Care
C
Cost
C t Guidelines
G id li
 Ad hoc
h surveys off health
h l h plans
l
and
d insurers
i
2009 Academy Health ARM
Baseline Coverage
g Model
Number of people with coverage, by market segment


Segments reflecting which state agency has regulatory authority
•
•


Department
D
t
t off M
Managed
dH
Health
lth Care
C
(DMHC) =>
> HMOs,
HMO POS,
POS and
d
two large PPOs
Department of Insurance (DOI) => remaining PPOs and FFS
Large Group/Small Group/Individual
HDHP vs. non-HDHP within each of the above market segments
E ti t average expenditures
Estimate
dit
(PMPMs)
(PMPM )




Insurance premiums
•
Employer and employee shares
State expenditures for publicly insured
Out-of-pocket expenditures,
•
Including
c ud g copayments,
copay e ts, deductibles,
deduct b es, and
a d expenditures
e pe d tu es for
o nono
covered services subject to a mandate
2009 Academy Health ARM
California Population Affected
Privately
P
i t l insured,
i
d group and
d individual*
i di id l*
Publicly insured
 State retirees’ health program (CalPERS)
 Healthy Families (SCHIP)
 Medi-Cal managed care
Total = 21,340,000 in 2009
*Excludes self-insured firms
2009 Academy Health ARM
Population Potentially Affected by
Mandates by Type of Insurance
Mandates,
N = 21,340,000
Healthy Families
715,000
MediCal HMO <65
2,366,000
MediCal HMO 65+
159 000
159,000
CalPERS HMO
820,000
Individual DOI
1,038,000
Individual DMHC
966 000
966,000
Small Group DOI
932,000
Small Group DMHC
2,844,000
L
Large
Group
G
DOI
390 000
390,000
Large Group DMHC
11,100,000
0
2,000,000
4,000,000
6,000,000
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8,000,000
10,000,000
12,000,000
Health Expenditures by
Plan Type
 Estimate PMPM Premiums based on employer
surveys and data from state-financed plans
 Estimate
E i
administration/profit
d i i
i / fi component off
premiums based on surveys and Milliman data
collection
 Model allocation of remaining PMPM health costs
into service categories using Milliman Health Cost
Guidelines and related data
2009 Academy Health ARM
Baseline PMPM Values for Population
Potentially Affected by Mandates,
by Type of Insurance
Healthy Families
$85.19
MediCal HMO <65
$128.80
MediCal HMO 65+
$239.00
CalPERS HMO
$377.95
Individual DOI
$171.57
Individual DMHC
$345 62
$345.62
Small Group DOI
$409.62
Small Group DMHC
$329.95
Large Group DOI
$441.93
Large Group DMHC
$0.00
$
$349.91
$50.00
$100.00
$150.00
$200.00
$250.00
2009 Academy Health ARM
$300.00
$350.00
$400.00
$450.00
$500.00
Core Elements of Cost
Impact Analysis
 Estimate price and utilization, both before and after
mandate, to determine the incremental impact of the
mandate
 Determine the extent of existing coverage for the
mandated
d t d benefit,
b
fit and
d how
h
many individuals
i di id l would
ld be
b
newly covered
 Determine iff there
h
are significant
f
offsets
ff
as a result
l off
expanded coverage
2009 Academy Health ARM
Utilization Impacts
 For most analyses obtain utilization rates from
claims data from large
g employers
p y
that alreadyy
offer coverage for the proposed mandate
 If d
data
t nott available,
il bl determine
d t
i % off population
l ti
that might need the benefit, and estimate the
actual use rate
 Utilization effects are estimated for broad
population categories,
categories rather than at the
individual level
 CHBRP’s approach is an actuarial model, not a
microsimulation model
2009 Academy Health ARM
Existing Coverage
 CHBRP conducts bill-specific, ad hoc surveys of
th 7 largest
the
l
t insurers
i
in
i California
C lif i (representing
(
ti
about 95% of the private market) to determine
extent of current coverage
 Typically, most insurers already cover the
benefits in a proposed mandate
mandate, subject to
medical necessity
 Research bill’s intent to determine if there
examples of insurers denying coverage
2009 Academy Health ARM
Cost Offsets
Will increased utilization of mandated
service decrease other health costs, such
as inpatient care, emergency room, etc?
Cost team relies on literature review by
Effectiveness Team
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Challenges
Impact on covered populations and the
uninsured
Predicting carrier response
Understanding the bill’s intent
Using
g clinical trial effectiveness data to
measure reduction in other costs
Short term vs. long
g term cost impacts
p
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Impact
p
on Covered Populations
p
and the Uninsured
G
Generally
ll assume no changes
h
in
i insured
i
d populations
l i
due
d
to mandate
 Will employers/individuals drop coverage over a 0.1%
cost increase due to a mandate when there is generally a
10% annual increase in premiums?
 However, when a mandate is expected to increase
premiums by 1% or more, CHBRP estimates potential
i
impacts
t on rates
t off uninsurance
i
b
based
d on elasticities
l ti iti off
demand for insurance from the literature
 Chernew, Culter, and Keean 2003; Hadley 2006; Glied and Jack 2003
2009 Academy Health ARM
Predicting Carrier Response
 Often easy:
y carriers have to add coverage
g to all
plans
 More difficult when plans have to provide a
minimum level of coverage if it chooses to cover
at all
 For example, bills for prosthetics and preventive
benefits in high deductible plans
 In these cases
cases, CHBRP presents alternative scenarios
to illustrate the range of plausible responses
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Short Term vs. Long Term
 Primary results focus on a 12-month period
 Certain mandates may have long-term impacts,
such as smoking cessation,
cessation vaccines,
vaccines etc
etc.
 In these cases, CHBRP has presented long-term estimates from
published sources
 Now summarize potential long-term impacts for
everyy bill analysis
y
2009 Academy Health ARM
Estimated Percent Change in Total Expenditures Among
California's
California
s Population Subject to State Mandates,
Mandates 2009
(N = 21,340,000)
0.10%
0.0900%
0 09%
0.09%
0.08%
0.07%
0.06%
0.05%
0.0400%
0.0400%
0.04%
0.03%
0.02%
0.01%
0.0040%
0.0059%
0.0000%
0.0007%
0.0019%
MH Treatment Breast Feeding
Limit Parity
HPV
Vaccination
0.0016%
0.00%
Chemotherapy
Parity
CNMs
2009 Academy Health ARM
DME
Elemental
Formulas
Mammography
Notification
Maternity
Services
Principal Findings from Bills
Analyzed in 2009
 Small marginal impact of mandates
 Usually <0.10% over the past 5 years
 Mostly <0.02% in 2009
 Typically, a high proportion of individuals in the largegroup
g
p market alreadyy have coverage
g for the mandated
benefits, thus mitigating the total cost impact
 Greatest impact
p
tends to be concentrated in the smallgroup and individual (non-group) markets regulated by
DOI, since those policies tend to be less comprehensive in
their benefit design
2009 Academy Health ARM
Conclusions
 Actuarial models are useful for developing
timely estimates of the effects of benefit
mandates
 Cost impacts vary among different market
segments
 It’s
It s important to estimate effects by market segment
segment,
not just the aggregate statewide effects
 Benefit mandates a
are
e not necessa
necessarily
il costl
costly
 But most have not been passed, or if passed, have
not been signed by the Governor
2009 Academy Health ARM