Statement of Jessica Banthin, Ph.D.,
Director of Modeling and Simulation,
Center for Financing, Access, and Cost Trends,
Agency for Healthcare Research and Quality (AHRQ),
U.S. Department of Health and Human Services, Before the
Committee on Veterans' Affairs, U.S. House of Representatives
April 29, 2009
Good morning, Mr. Chairman and
Members of the Committee. Thank you for the opportunity to testify before the
Committee on the issue of modeling long term projections. Before beginning the
substance of my remarks, I want to state that the Agency for Healthcare
Research and Quality (AHRQ), an agency of the Department of Health and Human
Services (HHS), has benefited from extensive collaboration with the Department
of Veterans Affairs (VA) in the areas of health services research, patient
safety, and clinical quality of care. We consider the VA an important partner
in improving health care.
I serve as the Director of Modeling
and Simulation in the Center for Financing, Access, and Cost Trends at AHRQ.
At AHRQ, we have extensive experience with working on sophisticated health care
models. For example, we developed a simulation model that estimates the number
of eligible uninsured children in the U.S. and can be used to project
enrollment in Medicaid and the Children's Health Insurance Program (CHIP), and
informs outreach efforts to increase enrollment of eligible children ages 1-4.
We worked closely with actuaries at HHS's Centers for Medicare and Medicaid
Services (CMS) to benchmark national health expenditure estimates.5 In
addition, researchers at AHRQ designed an economic microsimulation model that
predicted consumer choice of health insurance in response to changes in health
insurance offerings.6 The model also projected changes in total health care
spending resulting from the change in insurance offers.
I have had the opportunity to
review the RAND report on the VA Enrollee Health Care Projection Model (EHCPM).7 The EHCPM includes three major components: an enrollment projection
model, a utilization projection model, and a unit cost projection model.
The RAND report draws distinction
between actuarial models that are based on historical trends and economic
models that incorporate behavioral parameters. I have worked with both
actuarial and economic models. I have also worked with models that combine
elements of both approaches. There are caveats to all long-term projection
In my testimony, I will briefly
describe an enrollment model that we have constructed at AHRQ that can be used
to project children's enrollment in Medicaid and CHIP. I will also discuss the
benefits, caveats, and limitations that affect long-term cost and utilization
An Example of Modeling
Medicaid and CHIP Eligibility and Enrollment
In AHRQ's modeling efforts, we
model Medicaid and CHIP enrollment using survey data from our Medical
Expenditure Panel Survey (MEPS) as well as State-specific eligibility rules.
We make use of information on family structure and family income and then apply
State-specific eligibility rules to all sampled children in the MEPS data. We
simulate the eligibility of each child for public coverage through Medicaid or
CHIP. We then compare the simulated eligibility status to the child's reported
insurance status. Many eligible children are enrolled in public coverage, and
our model supports the calculation of take-up rates.
Next, we use output from our
eligibility simulation model to develop economic models that explain why some
children are more likely than others to enroll. These models, as with all
actuarial and economic models, are limited by the available data. We cannot
easily measure the effects of factors that are not observed or measured. Nonetheless,
the enrollment (or take-up) model identifies the factors that have the largest
marginal effects on enrollment. We find, for example, that among children who
are eligible for public coverage age, children's health and disability status
and parents' employment status are strong predictors of enrollment.4 These
models can easily support longer term enrollment projections and are flexible
enough to account for changes that may affect enrollment decisions.
In the aforementioned studies, MEPS
data were used. Data from the American Community Survey (sponsored by the
Bureau of the Census) also measure veteran status. As of 2008, the American
Community Survey is also measuring health insurance status.
Cost and Utilization
The long-term projection of costs
and utilization is very difficult because of the number of factors that affect
use of health care services. Factors include unpredictable changes in both the
demand for and the supply of various services. Technological change can yield
new treatments for medical conditions and improved diagnosis of ailments.
Changes in the prevalence of disease can affect the demand for care. When AHRQ
projects health care expenditures, we refrain from applying complex models and
assumptions and instead apply publicly available projections from census data
(regarding demographic changes) and from CMS (regarding expenditure growth), so
we project expenditures using a more conservative approach that is more aligned
to actuarial methods. AHRQ-projected expenditure data are publicly available,
so modelers can then use these data to develop more complex microsimulation
models that predict the cost changes resulting from various behavioral
parameters and assumptions. These more complex microsimulation models with
behavioral parameters are critical for policy analysis, but their long-term
accuracy in projecting expenditures is very hard to gauge. The advantage of
having extremely detailed information from private claims data on the use of
health care services is that the data project use and costs associated with an
array of specific health care services. Breaking down long-term projections in
this way avoids the need for relying solely on these behavioral parameters.
Issues in Projecting
Enrollment, Utilization, and Costs
Programs such as the VA face
several challenges in projecting utilization and costs for its patient
population when there is limited information on the other non-program sources
of care patients may use. This issue is more pronounced for patients under age
65 without Medicare claims data to examine. To the extent that the VA patient population
is unique and differs in many ways from the commercially insured population,
such data limitations present additional challenges in projecting future
utilization and costs.
It is important to account for
illness severity or morbidity when projecting costs. Morbidity is a strong
predictor of both enrollment and use of services. Morbidity can be measured
with clinical measures but can also be accounted for with some survey-based
measures of patient-reported physical and mental health status, functional
status, and work disability. These patient-reported measures have strong
predictive power in many economic models of demand for services.
In conclusion, I want to emphasize
that there are caveats associated with all long-term projection models, whether
they use actuarial or economic methods. In addition, the accuracy of all
projection models depends critically on the available data. Without sufficient
data there may be areas in the models that rely on best guesses rather than
solid data. As most modelers know, long-term projection models can constantly
be improved and enhanced. This is usually an ongoing process. Nevertheless, the
VA Enrollee Health Care Projection Model is a very sophisticated model that
benefits each year from better information on the current veteran population.
Mr. Chairman, this concludes my
prepared testimony. Thank you, and I would be happy to answer any questions
you may have.
J, Selden T. Children's eligibility and coverage: recent trends and a look ahead. Health Affairs 2007;26(5).
2. Hudson J, Selden T, Banthin J. The impact of SCHIP on insurance coverage of children. Inquiry 2005;42(3):232-54.
3. Selden TM, Hudson JL, Banthin JS. Tracking changes in eligibility and coverage among children,
1996-2002. Health Affairs 2004;23(5):39-50.
4. Selden TM, Banthin JS, Cohen JW. Projecting eligibility and enrollment for the State
Insurance Program. 1999; AHCPR Pub. No. 99-025.
5. Sing M,
Banthin JS, Selden TM, et al. Reconciling medical expenditure estimates from
the MEPS and NHEA, 2002. Health Care Financing Review 2006;28(1):25-40.
D, Selden TM, Moeller JF, Banthin JS. Medical savings accounts: microsimulation
results from a model with adverse selection. Journal of Health Economics 1999;8(2):195-218.
KM, Galasso JP, Eibner C. Review and evaluation of the VA Enrollee Health Care
Projection Model. RAND 2008.
Current as of April 2009
Funding the VA of the Future. Statement of Jessica Banthin, Ph.D., Director of Modeling and Simulation, Center for Financing, Access, and Cost Trends, before the Committee on Veterans' Affairs, U.S. House of Representatives, April 29, 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/jbtest042909.htm