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Conceptual Framework for Measuring
Medical Care Economic Risk1
Sarah Meier and Barbara Wolfe
University of Wisconsin–Madison
INTRODUCTION AND BACKGROUND
This paper focuses on how to incorporate medical care economic risk
(MCER) into a measure of poverty. We consider the advantages of a sepa-
rate index versus incorporating medical risk into a single index of poverty;
we address the appropriate unit of observation, arguing that medical risk
is best measured at the individual level and then aggregated; we argue for
the need to go beyond average expenditures, because risk at its core refers
to expenditures in the tail; we discuss the issue of over- and underutiliza-
tion and how to incorporate insurance coverage into resources. We briefly
discuss data needs, focus on methodology and argue for a prospective
measure. In the end, our goal is to improve the measurement of poverty,
because, without the inclusion of medical care needs, poverty measurement
will be increasingly inaccurate.
Purpose of a Poverty Measure
Why a measure of poverty? It tells how the nation (or other unit of
organization) is doing in terms of deprivation. It serves as a way to both
measure success in avoiding deprivation and the effectiveness of public poli-
1 he views expressed in this paper are those of the authors and do not necessarily reflect the
T
views or conclusions of the National Research Council, the Institute of Medicine, the study
panel, or the sponsor.
225
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226 MEDICAL CARE ECONOMIC RISK
cies (and private ones) in influencing deprivation. It provides one measure
of economic well-being. A poverty measure can be absolute or relative.
The United States uses an absolute standard that has not changed since
originally designed. In most other developed countries, a relative measure
is used, such as 40 to 60 percent of median income. Here we focus only
on a measure that focuses on economic or material well-being. Some argue
for a broader measure that encompasses other aspects of deprivation, such
as exclusion. This might be particularly useful when focusing on health,
as persons with certain chronic conditions or disabilities might in fact face
more isolation. Nevertheless, that is not the focus of this paper. Our task
here is to address an already complex issue: how to capture medical risk
for purposes of more accurately capturing deprivation.
A measure of poverty serves to identify those in need of assistance by
helping set up eligibility standards for programs targeted at those with
insufficient resources. It serves as motivation to design policies to reduce
deprivation. And it serves as a potential measure of the effectiveness of pub-
lic policies in alleviating deprivation. It allows comparison across groups
in the population defined by age, family structure, race/ethnicity, health
or disability status, and geography; and it can provide information on the
dynamics of deprivation or poverty by providing trends over time.
Review of Current Poverty Measure and Related Core Issues
The current poverty measure has two components: a set of poverty
thresholds or lines specific to family size and a definition of family income
to be compared with the thresholds. These thresholds have been the fed-
eral government’s official statistical measure of poverty since 1969. They
originated with the work of Mollie Orshansky, who based her thresholds
on multiplying the cost of a minimum adequate diet for families of various
sizes and then multiplying this value by a factor of three. The minimum
adequate diet is based on the U.S. Department of Agriculture’s Economy
Food Plan; the factor of three was based on a 1955 survey by that depart-
ment. The thresholds are updated annually, so that the real value of the
thresholds has remained unchanged since 1963.2 A family’s before-tax
money income is compared with these thresholds to calculate whether or
not its income is above or below the poverty threshold. The official poverty
rate is calculated using the March Current Population Survey (CPS). It is
calculated for the nation as a whole, for subgroups of the population, and
for geographical areas. It is used to determine eligibility for needs-based
public-sector programs.
2
Although the real value has remained the same, relative to median family income, the
threshold has fallen from 48 percent of family income in 1963 to 28 percent in 2005
(Smeeding, 2006).
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 227
In 1992, the National Research Council (NRC) convened a study
panel at the request of Congress to conduct a comprehensive examination
of poverty measurement in this country. The study panel released its re-
port, Measuring Poverty: A New Approach, in 1995. In 2004, the NRC’s
Committee on National Statistics held a follow-up workshop to review the
panel’s recommendations and to consider alternative poverty measures that
would be regularly reported. The issue of how to handle health care needs
and expenditures was one of the issues addressed by both efforts.
Both the 1995 consensus report and the 2004 workshop participants
came to the following conclusion: The core problem with the official pov-
erty measure is that it does not provide an accurate picture of the extent
of economic poverty, the trend in economic poverty, or differences among
population subgroups or geographic areas.
The current measure does not reflect core consumption needs (food,
clothing, shelter, health care) in the threshold or adequately capture
economic resources, because it measures only pretax monetary income.
Nor does it capture true differences in costs by different family sizes and
c
omposition—so-called economies of scale or equivalence scale issues. It
does not take geographic differences in prices into account (e.g., heating
and cooling needs). With respect to medical care needs and insurance cover-
age, the current measure does not take into account
· he extent of medical care costs and the variation in these costs
T
across the population that reflect real differences in rates of illness
and disability,
· Differences in medical care coverage (health insurance),
· Rising costs of that insurance and required copayments, nor
· ising health care costs as a share of both family budgets and the
R
economy more generally.
Together, these deficiencies mean that important public policies, such as
the Supplemental Nutrition Assistance Program (SNAP), housing vouchers,
publicly provided health insurance, and changes in taxes, are not captured.
Beyond these deficiencies, the official measure does not reflect the changing
standard of living of most Americans. Thus, rather than a comprehensive
measure of economic well-being, the official poverty measure is a very
narrow concept that is not influenced by real changes in public policy or
changes in the relative prices of core consumption items.
2011 Release of Supplemental Poverty Measure
The Supplemental Poverty Measure (SPM) is designed to provide an
improved understanding of economic well-being in the United States and
to measure the influence of public policies on the low-income population.
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228 MEDICAL CARE ECONOMIC RISK
It is not expected to replace the official poverty line. (Replacing the official
poverty line raises issues of equity across groups currently eligible for fed-
eral needs-based programs as well as issues of a politically sensitive nature,
such as official responsibility for an increase in measured poverty that might
occur with an improvement in measurement.)
The SPM is still in a research stage, even though it was initially included
in the president’s fiscal year 2011 budget, which would have allowed the
measure to become operational. The resource side of this measure is to
include not only money income but also in-kind benefits (e.g., SNAP; the
Special Supplemental Nutrition Program for Women, Infants, and Children
[WIC]; free and reduced-price school lunches; housing subsidies; home
energy assistance) minus taxes (or adding tax credits), and subtracting
out work expenses and out-of-pocket medical expenses. It uses the three-
parameter equivalence scale proposed by the 1995 NRC panel3 and is to
adjust for differences in the cost of shelter across geographical areas. The
threshold is to be set at the 33rd percentile of the food, clothing, shelter,
and utility needs for all families with two children.4
The medical out-of-pocket (MOOP) expenditures are to be based
on questions added to the CPS Annual Social and Economic Supplement
(ASEC). In these questions respondents are to report expenditures on medi-
cal care insurance premiums and fees that the family paid out-of-pocket,
including prescription drugs and provider copayments. According to Short
(2011:8), these expenditures are particularly large for children and the
elderly: there is preliminary evidence that subtracting MOOP from income
increases the SPM poverty rate for the elderly by approximately 7 percent-
age points. This increase is an indication of the (increasing) importance
of medical expenditures in this country and their importance in a correct
calculation of poverty.
INCORPORATING MEDICAL CARE NEED INTO
THE MEASUREMENT OF POVERTY
Insufficient treatment of medical care need (and resulting expenditures)
in the poverty measure has increasingly challenged its validity over time.
Although the poverty measure arguably did not capture the full importance
of the relationship between medical need and poverty in the early decades
of its use, the sheer growth of medical care expenditures as a proportion
of domestic spending has probably exacerbated the real effect of this prob-
3
The three-parameter scale is equal to (adults + α * children)β where α varies between 0.5
and 0.8 and β varies between 0.6 and 0.7; in the SPM, β is set equal to 0.7, and α = 0.5 for
two-parent families but 0.8 for the first child in a single-parent family.
4
This description of the SPM is from Short (2011).
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 229
lem on the measurement of poverty outcomes. Spending on medical care
increased from 5 percent of gross domestic product in 1965 to roughly
17.6 percent in 2010; moreover, it is projected to increase to 19.8 percent
of gross domestic product by 2020.5
In the section that follows, we review recent changes in methodology
that strengthen the capacity of measurement to capture the real effect of
medical expenditures on poverty. Although change in the treatment of
medical expenditures under the SPM is an important component of this
process, it is not the only relevant step. We suggest that recognition of the
need for an index that captures the extent of medical care economic risk
faced by members of society is an important step forward in documenting
the full relationship between medical care need and poverty.
Treatment of Medical Care Need in the Supplemental Poverty Measure
The challenge in poverty measurement with respect to medical care
need has not been to identify the problem, but rather to determine the best
methods to resolve it. Experts have long recognized the need to improve
measures of medical need and (medical) resource availability (Smeeding,
1982); however, the actual assignment of an individual’s poverty status on
the basis of these measures introduces a number of conceptual and technical
considerations that are not easily resolved. These include6
· The nonfungible nature of medical benefits: incorporating a non-
fungible benefit into the resource component of the poverty mea-
sure poses a technical challenge. Specifically, assignment of benefit
values for insurance holding to the resource component of the mea-
sure would incorrectly treat unused benefits as disposable income.
· Large variation in medical need: given the large variation in medi-
cal need across the U.S. population, a large number of thresholds
would be required to adequately capture that variation and the
subsequent poverty effects for those with insufficient resources.
· Sufficiency of resources: whether an individual has sufficient insur-
ance against the risk of medical care need, and whether an individ-
5
The 1965 estimate is reported by the Congressional Budget Office (2008:3); the forecast for
2010 and projections to 2020 are from the CMS, Office of the Actuary (2011:1).
6
We summarize these issues briefly; the reader is directed to Moon (1993) and National
Research Council (1995) for detailed review of these and other measurement considerations.
As well, we wish to credit participants in the study panel’s September 2011 workshop for
drawing greater attention to the conceptualization of both the medical care burden and medi-
cal care risk constructs. The distinction between these two constructs and the treatment of
their relationship to poverty have important conceptual and methodological implications for
the development of the MCER index.
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230 MEDICAL CARE ECONOMIC RISK
ual had sufficient resources to provide for observed medical need
(ex post) are two different questions. Similarly, a retrospective mea-
sure of medical care need is different from a measure of medical
care need that an individual might experience over a future period.
A measure taken retrospectively is a measure of experienced bur-
den, whereas the latter measure must necessarily incorporate some
consideration of the uncertainty surrounding future consumption
needs. Thus, methodology aside, one must resolve the question of
whether it is conceptually correct to assign poverty in the case of
an uncertain outcome (e.g., medical risk).
The 1995 NRC study panel served an important role in moving pov-
erty research from recognition of these problems toward identification of
actionable solutions. Specifically, in its 1995 report, the panel advocated
the development of a two-index approach to poverty measurement. The
first index would exclude medical care needs from the thresholds and medi-
cal care benefits from resources. Meanwhile, subtraction of medical care
expenditures (premiums and out-of-pocket spending) from the measure of
family resources would, to some degree, capture the influence of medical
circumstances on a family’s available resources (Recommendation 4.2). In
its current form, the SPM adopts this recommendation.
In addition, the 1995 panel’s Recommendation 4.3 called for formation
of a new measure (the second index) to quantify the economic impact of
medical care risk (National Research Council, 1995:225):
Appropriate agencies should work to develop one or more “medical care
risk” indexes that measure the economic risk to families and individuals of
having no or inadequate health insurance coverage. However, such indexes
should be kept separate from the measure of economic poverty.
The effect of this two-index approach on resolving these technical and
conceptual challenges can be understood as follows:
· The fungibility problem is resolved by considering the value of
medical benefits in a separate index.
· Observed expenditures are a proxy for the economic burden a fam-
ily experiences because of medical need (notably, variability of this
measure is not limited by technical considerations).
· Under the first index, poverty is not assigned on this basis of a risk-
based, or uncertain, outcome. The conceptual treatment of medical
risk is left to the second index.
Although this paper focuses on the conceptual and practical develop-
ment of the second index, recognition of the contents and purpose of the
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 231
original and supplemental poverty measures is not inconsequential to this
task. Specifically, we stress that a measure of medical care (economic) risk
does not capture poverty as it is traditionally defined. In both the original
and the supplemental measures, poverty is understood conceptually as a
static outcome. Rather, a measure of MCER is rooted in the conceptual
understanding that the relationship between poverty and health is in fact
dynamic.
WHY IS A MEASURE OF MCER NEEDED?
In the section that follows, we address the value of designing a formal
measure to document the relationship between medical care risk and pov-
erty. Four arguments are presented below.
Four Arguments
Reducing Health Expenditure Risk Is an Important Component of Elimi-
nating Poverty: The suggestion that poverty and health are dynamically
related alludes to the old question: Does poor health cause poverty, or
does poverty cause poor health? Irrespective of the assignment of cause
and effect in this relationship, research in the field of poverty suggests that
breaking this cycle is crucial to moving individuals and communities out
of poverty. In practice, the consideration of medical care out-of-pocket
expenditures under the SPM reflects the measurement of medical care eco-
nomic burden and its point-in-time impact on poverty. We suggest that an
important aspect of poverty policy is not only to minimize the number of
individuals in poverty, but also to minimize the risk of transitioning into
poverty. The SPM is a static measure that cannot capture this effect. In con-
trast, a measure of MCER can assess the effectiveness of policies designed
to meet this objective.
Prospective Assessment of Health Need Results in Misclassification of
Poverty Status: There is an important difference between medical care need
and most other basic needs considered under the poverty measure. In most
cases, the core consumption needs of similarly structured families do not
exhibit substantial variability. When this is the case, it is reasonable to es-
timate the amount of resources a family might require to maintain a basic
standard of living. In the case of medical care, a high degree of variability
in actual need over the course of a year and across years occurs. Thus,
although one might be able to assign an estimate of expected expenditure
to members of a given group (e.g., risk class), this value can be a very
poor representation of the actual experience of any one individual in the
group.
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232 MEDICAL CARE ECONOMIC RISK
As previous researchers have noted (e.g., Moon, 1993; National Re-
search Council, 1995), this can lead to misclassification if the poverty mea-
sure relies on this estimate as a valid (prospective or retrospective) indicator
of health need. A particular example is the use of poverty guidelines to deter-
mine eligibility for means-tested programs. If program eligibility is assessed
annually or even monthly and one applies a prospective estimate of medical
need, then real variability in need (relative to the predicted value assigned at
the beginning of the assessment period) will result in misclassification.
Importantly, in the case of medical expenditures, the difference between
expected and observed need can be quite large if an individual does not
have insurance. In the case of prospective assessment, a measure of medical
care economic risk can help to identify those who might end up in poverty
due to medical expenses. Recognition of this risk might be a relevant de-
terminant in how to structure and apply programmatic interventions under
poverty policy.
Reduction of Health Care Need Is a Public Objective and the Design of
Public Policy in the United States: The presence and scope of U.S. public
insurance programs, as well as the tax treatment of employer-sponsored
health benefits, demonstrate an existing public interest in supporting the
well-being of those who experience medical need. Subsidies directed at
eliminating health care need might take the form of a prospective ar-
rangement (e.g., premium subsidies) or they might take the form of direct
payment for services. Although safety net mechanisms serve a crucial role
in the U.S. health system, the dominant U.S. policy model is to promote
prospective arrangements.
If U.S. policy views subsidized risk protection (e.g., insurance) as a
“first best” solution to tackling the health and financial consequences of
medical need, then absence of a formal method to quantify MCER and to
assess the effectiveness of subsidies directed toward reducing this risk is
problematic. In the absence of such a measure, it is difficult to objectively
evaluate the effectiveness of current policies or to evaluate the need for and
potential impact of policy change.
Public Insurance Programs and Subsidies Toward Purchasing Cover-
age Have an Economic Cost and an Economic Benefit: We consider two
sources of value arising from health insurance.7 First, we note the tradi-
tional argument that a risk-averse individual purchases insurance because
7
Insurance may have an additional value that we do not explicitly consider here: insurance
coverage may increase consumption of preventive services that may decrease the risk of high
medical expenditures. An example would be early detection of certain cancers or treatable
heart conditions.
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 233
of the utility gain resulting from movement out of uncertainty and into a
state of certainty (or reduced uncertainty) with respect to wealth. Second,
we highlight the work of Nyman (2004), who argues that, in part, the value
of health insurance arises from its transfer of “income” from the healthy
to the ill.
Moral hazard occurs if the benefits of insurance enable a sick individual
to consume more services than he or she would have consumed in the
absence of insurance. However, Nyman suggests that, in some instances,
it is possible for this moral hazard to be efficient. It is welfare-increasing
if, with a direct transfer of money to cover the cost of the service (instead
of service coverage), the individual (whose resource set is expanded by the
transfer) is now willing to pay more for this service than the dollars trans-
ferred to cover the actual cost. Given this argument, Nyman suggests that
the provision of premium subsidies may increase social welfare, particularly
if society is altruistic (and benefits when individuals in medical need receive
access to services).
Applying these concepts to our context, let us take the case of two
individuals, each living at 101 percent of the family poverty level, both
of whom incurred no medical care expenditures over the past year. At the
start of the previous year, before the outcome of no health expenditures is
realized, one of these individuals is handed insurance coverage for which
the premium is fully subsidized. All other things equal, were these two
individuals equally well off over the past year? We suggest that the answer
to this question is no.
First, if both individuals were risk-averse, then the individual holding
the insurance policy experienced a gain in utility from the reduction of
uncertainty. Second, in the event of illness, this insurance policy essentially
extends the (medical care specific) resources available to the covered in-
dividual. Thus, the individual holding the insurance policy has not only
gained protection against the risk of losing present wealth but also gained
protection against the risk of incurring an expense (or forgoing a needed
service) that he or she cannot reasonably afford or repay in the first place.
Finally, we note that, in the case of Medicare and Medicaid (and even
employer-sponsored insurance), public dollars subsidize the cost of cover-
age. In the case that an individual with subsidized coverage becomes ill,
these dollars have partially financed the pool that extends the availability of
resources (perhaps beyond his or her current wealth) to cover medical care
expenses. We suggest that it is relevant to consider the cost of these public
subsidies, as well as the value of this insurance holding when evaluating
poverty (and health) policy.
The 1995 NRC study panel considered multiple approaches to incorpo-
rating medical care need into the measurement of poverty (see, e.g., Moon,
1993; National Research Council, 1995). Although the panel considered
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234 MEDICAL CARE ECONOMIC RISK
the merits of designing a single measure of poverty that could reflect the
importance of medical care needs, it ultimately advocated a two-index
approach to achieving this objective. We argue that this split approach is
superior to a combined approach; it allows the capture of both the medical
care burden and medical care risk perspectives in poverty measurement.
In making this argument, we note the loss of simplicity offered by a single
measure and recognize that, for policy purposes, the need for simplicity
may dominate the wish for greater accuracy. Even in this view, we still favor
the calculation of a separate medical care economic risk index to capture
current and changing medical risk as a separate and important indicator of
well-being and deprivation.
The Importance of Moving Forward in the Design of an MCER Index
Throughout this paper, we argue that current methodologies fall short
of sufficiently recognizing the relationship between medical need and pov-
erty. Although the SPM makes important strides in this direction, capturing
the full dynamic of this relationship requires a measure of MCER. Although
MCER is distinct from a measure of realized economic burden, it is an im-
portant (and we believe necessary) complement to the information captured
in the SPM. The renewed focus on this topic coincides with a number of
important public policy actions that demonstrate the relevance of the issues
addressed in the current study panel’s workshop.
Specifically, the use of measures of affordability and medical risk under
the Affordable Care Act (ACA) demonstrates the relevance of these concepts
in popular policy dialogue, calling attention to the need for standardized
conceptualization and measurement of these constructs. The impending
release of the SPM similarly demonstrates policy interest in expanding the
robustness of poverty assessment.
Entitlement reform, beyond that instituted in the ACA, is increas-
ingly a focal point of policy debate. The potential for substantive reform
of the Medicare and Medicaid programs introduces new uncertainties
regarding access to and the extensiveness of medical risk protection in the
United States. Growth in national medical spending and changing trends
in underlying population morbidity will inevitably require difficult policy
choices. As it moves forward, the United States is in great need of open
and informed dialogue concerning the value of medical spending and the
public role in medical risk protection. The creation of a standardized
MCER measure can provide the general population and policy makers
with a baseline from which to understand and engage in difficult policy
choices.
Although the most basic application of the suggested risk index in-
cludes descriptive reporting of population burden and distribution of
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 235
MCER, valuable extensions of this application are possible. Examples of
feasible applications include (1) assessing and monitoring the effectiveness
of public programs at achieving medical risk protection and (2) directing
policies to reduce and prevent poverty and its health consequences. A well-
formed measure should reflect the importance of coverage access, coverage
take-up, and coverage structure in mitigating the economic effects of medi-
cal circumstances.
In the sections that follow, we identify considerations that are central
in the development of this measure, review previous measurement sug-
gestions, and outline a basic framework for moving forward. There is
no very simple way to capture medical risk: insurance coverage differs,
new treatments and hence expenditures continuously change, there is both
u
nder- and overusage, and there is a trade-off between detail and accuracy
and feasibility of approach.
CRITERIA FOR DEVELOPMENT OF AN MCER INDEX
This section identifies a number of primary design factors that must be
addressed during the development of the MCER index. In addressing each
of these issues, we suggest criteria that developers might introduce as they
contemplate the appropriate structure of the index. We begin with a review
of relevant design criteria outlined in the 1995 NRC panel report, followed
by an overview of Doyle’s (1997) criteria for index development. In the final
section, we expand on some of these previous discussions, highlighting ad-
ditional design components that require substantive panel focus.
Design Recommendations from the 1995 NRC Panel
Criteria specified in the 1995 NRC panel report include that the index
reflect prospective assessment of medical risk and that the index produce
a family-level measure of MCER.8 Given that risk is a notion typically
quantified and applied in an ex ante or prospective context, we suggest that
the MCER index be designed as a risk-based assessment of the potential
economic impact of medical need.
To clarify this assertion, we address the concept of risk as it relates
to health. Dror and Vaté (2002:125) define health risk as “any situation
in which the health status of an individual—or group of individuals—is
exposed to possible deterioration.” Notably, this delineates a circumstance
in which the eventual outcome experienced by an individual or group is
not known with certainty. We interpret the call for a risk-based index to
imply that MCER development should focus on the possible health-related
8
See Doyle (1997:Section A) for an overview of the panel’s treatment of these issues.
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256 MEDICAL CARE ECONOMIC RISK
Assuming Independence of Family Member Claims:
· As is done by Handel, for each individual, simulate draws from
his or her assigned distribution of losses. Apply his or her unique
insurance characteristics to form an insurance-adjusted distribution
of out-of-pocket expenditures.
· Identify the family unit and (assuming independence) aggregate
individual distributions of out-of-pocket losses to the family level.
In the case in which members of the family unit hold a group cov-
erage offering with group-level provisions (family out-of-pocket
maximum), apply these provisions in the process of aggregating to
the family level.
Assuming Correlation of Family Member Claims:
· Identify the family unit and aggregate the parametric loss distri-
butions of unit members to create a multivariate distribution of
family-level losses. If possible, incorporate correlation of family
member claims when forming this distribution (see below for fur-
ther discussion).
· Simulate multivariate draws from the joint distribution of family
losses and apply individual and family-level coverage characteris-
tics to generate out-of-pocket payments for each draw.
· Once this process is completed, focus only on the overall prob-
ability of family-level out-of-pocket expenses for stage three of the
model (once estimated properly, the multivariate properties of this
distribution do not affect the outcomes).
If the correlation coefficient is known in advance and does not vary with
family unit characteristics, then forming the aggregate loss distribution may
be rather straightforward. However, introducing family-level correlations
into the model may prove to be a rather complex task: specifically, correla-
tion of family member claims might depend on the characteristics of each
family unit (e.g., member risk types, number of members).
Estimating these correlations with MEPS data (as Handel has done at
the individual level across claim types) may be infeasible due to the number
of possible member number/risk type combinations and the small number
of observations per family unit type. At best, this may require limiting the
number of risk types (and perhaps family sizes) represented in the model.
With respect to the accuracy of the index, we are not sure that the gains
from introducing correlation of family member claims in this manner would
outweigh the losses from reducing the capacity of the model to distinguish
between different types of family units and individual risks.
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 257
We expect correlation of family member claims for such reasons as
shared physical and social environments, similar genetics, and perhaps simi-
lar behaviors. Assuming independence is certainly problematic for medical
events that are random and occur across the family simultaneously (e.g., an
accident affecting all members). We are less certain that this independence
assumption is problematic in the case of expenditures that result from
managing a chronic condition.
Family members may share a particular factor (genetic/environment)
that results in the presence of a particular chronic condition among mem-
bers, but presence of these conditions is reflected in risk cell assignment.
Acute events related to a condition occur at the individual level and
not across a family. These cells will not reflect base severity or the likeli-
hood and frequency of acute events, which might be similar within families.
Accidents and genetic diseases are likely to be the main causes of posi-
tive covariance. Noting that covariance resulting from these (and other) fac-
tors is already likely in studies by insurers; we suggest further consultation
with insurers on this issue and perhaps empirical testing to determine the
best route forward.26 The likelihood of these (and other) types of positive co-
variance might be adjusted in the risk index after these informed discussions.
Stage Three: Indexing Economic Resources to Family-Level Risk
Under the remaining component of index development, developers must
identify a standard definition of unaffordable premium and out-of-pocket
expenditures, which we refer to as an unaffordability threshold. Previously
suggested by Doyle (1997), we advocate the development of an “inverted
threshold” that reflects “the amount of out-of-pocket expenses you should
be able to afford for medical care,” whereby the threshold “can be estab-
lished for a group as a function of the poverty threshold itself or can be
computed for an individual or family as a function of income or assets.”
Specifically, we suggest that the threshold identify the maximum
percentage of family income allocated toward medical care expenses that
can be considered affordable.27 In determining a family’s ability to pay
26
A straightforward empirical test for covariance is to compare the difference between
expenditures summed across members of a (fully insured) family and expenditures summed
across a collection of individuals whose combined risk profile is equivalent to that of the
comparator family.
27
Doyle (1997) recommends that income definitions utilized under a medical risk index
reflect those adopted under one of the poverty measures. Alternatively, developers may wish
to consider both income and assets when defining appropriate thresholds. Our suggestion
regarding assets is to use an annuitized flow concept to the extent it is feasible to measure
financial assets. Ultimately, however, determining which approach is most appropriate is left
to the study panel.
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258 MEDICAL CARE ECONOMIC RISK
for medical care services, the threshold should consider the amount of
family resources required to cover base needs identified in the SPM or the
original poverty measure. As such, a well-formed index should include the
development of multiple thresholds to reflect other relevant factors, such
as family size and family income.28 As one example, this approach might
be draw from the poverty categories and income percentages introduced
under the ACA to distribute premium and out-of-pocket expenditure
subsidies.29
The procedure of applying affordability thresholds to family-level re-
source and risk information might entail the following steps:
· Assign the appropriate threshold to a family based on family re-
sources and characteristics.
· Combine threshold and family-level income information to deter-
mine the amount of medical expenditures that meets this threshold.
The remaining steps depend on the selected measurement methodology.
In the case of the loss distribution Risk Measure I (probability of exceeding
the affordability threshold) approach, the next steps include
· Subtract premium costs from the assigned threshold.30
· If premium costs exceed this threshold, the family is not “at risk”
of accruing unaffordable expenses. Rather, the family experiences
unaffordable medical care costs (e.g., probability of exceeding af-
fordability threshold = 1).
28
We note that a large family with a lower level of income might be assigned a lower afford-
ability threshold than a similarly low-income but smaller sized family. A larger family will “use
up” a larger proportion of income on other relevant needs (e.g., housing, food, etc.), leaving
fewer resources to allocate toward medical care.
29
We caution that if these categories were adopted directly, any level of medical care expen-
ditures for families above 400 percent of the family poverty level (even catastrophic expenses)
would be considered affordable. Further attention should be directed to this issue if, in fact,
these guidelines are considered for purposes of the MCER index.
30
An alternative approach is to subtract premiums and any other (insurance-adjusted) family
member expenditures that are “known” ahead of time (e.g., the costs of appropriate preventive
care and disease management). In this respect, we assume there is no component of risk in
the realization of these expenses during the next year. Following this approach, these expen-
ditures should be excluded when loss distributions are fit to the claims experience observed in
each risk cell. Although this approach correctly distinguishes between expenditures that are
known with certainty and expenditure risk, we suggest that in practice it is difficult to assign
a correct measure of known expenses prospectively. Nonetheless, in principle, we agree with
commentary from the workshop advocating this type of approach. If developers are able to
incorporate this method into the model, it would improve the accuracy of the MCER measure.
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 259
· If premium costs do not exceed the threshold, the next step is to
determine the amount of out-of-pocket expenditures that (with
these premium costs) would place a family at their respective af-
fordability threshold.
· The final step is to determine the family’s probability of exceeding
this amount of out-of-pocket expenditures using the family-level
adjusted loss distribution.
The method outlined above represents our preferred approach. We be-
lieve this method meets the objectives of MCER development while remain-
ing feasible (assuming there is some capacity to invest in additional data
collection). Using this approach, it is possible to report national-level (and
perhaps state-level) estimates of the number of families at risk of exceeding
an affordability threshold. As well, it is possible to estimate the number of
families who exceed the threshold with premium purchases. This could also
be calculated for subgroups by, for example, race/ethnicity, age, and region.
Reporting might also include information on risk level, such as the number
of families at low/medium/high risk of exceeding this threshold. It would
also be possible to calculate the depth of expected unaffordable expenses
similar to a poverty gap measure (e.g., if families are at risk of experienc-
ing unaffordable expenses, how extreme are these prospects?). Finally, we
note that a family could be assigned a threshold range if it is undesirable
to define one specific level of “unaffordable” expenditures.
In the case that Risk Measure II (expected costs) is selected, the next
steps entail subtracting premium costs from the affordability threshold and
comparing this value with the family’s insurance-adjusted expected expen-
ditures. In addition, we suggest repeating this exercise using something
akin to standard deviation values (if applying the loss model approach).
This measure can be interpreted as the level of expenditures a family might
expect to incur in the next year, with the standard deviations reflecting the
type of expenditure outcomes observed by families who incur a high (and
low) level of expenditures relative to their expected outcome. The easiest
way to do this is to square the difference of the predicted value minus the
actual value; however, this has an ex post aspect to it that is not consistent
with a prospective risk concept.
Finally, in the case that econometric methods are used to estimate
probability of unaffordable expenses, developers will need to identify an
appropriate method of moving from an individual-level risk characteriza-
tion to a family-level expenditure model. Alternatively, the initial set of risk
characteristics might be defined only at the family level. Developers would
need to consider when and how to best introduce coverage adjustments to
expenditures and, similarly, how to treat mixed-coverage families.
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260 MEDICAL CARE ECONOMIC RISK
Treatment of the Uninsured Coverage Eligible
Although the measurement of MCER should reflect current coverage
status, we suggest that developers also consider computing this measure in
the hypothetical case of full take-up among those who are eligible for pub-
lic coverage. This could also include private coverage if there were a data
set that permitted one to know if the firm at which an individual worked
offered employer-sponsored insurance (and the coverage characteristics
of this offer). This secondary measure represents an upper bound on the
potential impact of improved communication and targeted policy measures
to increase take-up under current offerings.
LIMITATIONS AND CONCLUDING POINTS
The choice of data set for MCER reporting introduces some important
trade-offs for developer consideration. The ideal base data set includes
family-level economic variables, insurance characteristics, and an appropri-
ate level of health information. Although MEPS contains the largest pro-
portion of these data, the sampling design does not enable release of basic
statistics at the state level. Other surveys are designed to meet this reporting
objective; however, selection of an alternative data set introduces greater
need to add new questions during base data collection. At a minimum, any
alternative data set should include insurance data and a subset of health
characteristics sufficient to match these data at the cell or adjuster level
to expenditure models developed in MEPS. Alterations to sample design
and the addition of new variables introduce added costs that developers
should consider. It is suggested that developers consider not only the rela-
tive benefits of these choices in the context of MCER development, but also
the relative benefit of survey question additions or sampling expansions in
complementary areas of research.
Although we identify a feasible approach to MCER development, a
number of compromises are introduced throughout this framework. Devel-
opment of an operationally feasible index may necessitate that developers
introduce a relatively coarse system of risk classification. Similarly, the final
index might reflect a simplified examination of the impact of insurance
coverage characteristics on family expenditure risk. As a trade-off, this
approach might reduce data collection burden and limit the complexity of
risk modeling and associated challenges. Finally, developers face trade-offs
in the selection of an appropriate method of representing risk. Reliance on
estimates of expected expenditures in the underlying methodology does not
capture the real occurrence of outlier events. In contrast, a measure that
categorizes all families without a stop-loss provision as experiencing MCER
might place too much weight on these tail events. Developers must carefully
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 261
consider the objectives of this index and its implications as they refine the
definition and representation of medical care expenditure risk.
A number of additional relevant yet challenging issues are left unre-
solved in this framework. Developers must reach consensus on standard
definitions of a minimum benefits package and affordability. In addition,
those involved in the development of risk models must identify an ap-
propriate method of adjusting for underutilization of the uninsured or
underinsured in the baseline data source. Finally, we note that the suggested
framework does not distinguish between medical risk that is not modifi-
able and medical risk that can be prevented or reduced through the use of
preventive services or good care management practices. Research in this
direction might identify other important routes to reducing the medical care
economic risk experienced by families.
Although this framework outlines alternative methods of modeling
expenditure risk, developers may identify superior modeling approaches
as they move forward with index design. There is much work to be done
to complete the process of moving from a framework to an operational
MCER index; this framework identifies a conceptual base to build on while
completing this task.
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262 MEDICAL CARE ECONOMIC RISK
ANNEX A
Risk Classification Examples
Risk Cell Model I (survey collected data, claims data not necessary)
Characteristic Categories
Gender × age Female × age
(20-30 categories) Male × age
CMS-HCC age categories are a: 0-34, 45-44, 54-59,
60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+
(Pope et al., 2004)
High-cost morbidity High number of ADLs or extreme obesity
(2 categories) Neither
Pregnancy Yes
(female, age appropriate only) No
(2 categories)
Risk Cell Model II (survey collected data; claims data necessary)
Characteristic Categories
Gender × age Female × age
(20-30 categories) Male × age
CMS-HCC age categories are: 0-34, 45-44, 54-59, 60-64,
65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+ (Pope et
al., 2004)
Diagnosis-based risk level Low-, moderate-, or high-risk score
(3 categories)
High-cost morbidity High number of ADLs or extreme obesity
(2 categories) Neither
NOTE: ADLs = activities of daily living; CMS-HCC = Centers for Medicare & Medicaid
Services-Hierarchal Condition Category.
aThis model is developed for the Medicare population; additional categories for the 0-34
population (e.g., infant, child, and young adult) and perhaps fewer categories in older age
ranges are suggested.
SOURCE: Developed by the authors.
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CONCEPTUAL FRAMEWORK FOR MEASURING MCER 263
ANNEX B
THE MASSACHUSETTS 2010/2011 MINIMUM
CREDITABLE COVERAGE STANDARDS
Coverage for a broad range of medical services. Specifically:
· Ambulatory patient services, including outpatient day surgery and
related anesthesia
· Diagnostic imaging and screening procedures, including X-rays
· Emergency services
· Hospitalization, including at a minimum, inpatient acute care ser-
vices, which are generally provided by an acute care hospital for
covered benefits in accordance with the member’s subscriber cer-
tificate or plan description
· Maternity and newborn care
· Medical/surgical care, including preventative and primary care
· Mental health and substance abuse services
· Prescription drugs
· Radiation therapy and chemotherapy
· Doctor visits for preventive care, without a deductible
· A cap on annual deductibles of $2,000 for an individual and
$4,000 for a family for services received in-network
· For plans with up-front deductibles or coinsurance on core services,
an annual maximum on out-of-pocket spending of no more than
$5,000 for an individual and $10,000 for a family for services
received in-network
· No caps on total benefits for a particular illness or for a single year
· No policy that covers only fixed dollar amount per day or stay in
the hospital, with the patient responsible for all other charges
· For policies that have a separate prescription drug deductible, it
cannot exceed $250 for an individual or $500 for a family for
services received in-network
In 2011, the standards will also include
· No fixed-dollar cap on prescription drug benefits
· Core medical services and a broad range of medical services for any
dependents, if dependents are covered
An exemption is available for people who have a firmly held religious
belief that prevents them from enrolling in a health plan.
SOURCE: Health Connector (2010).
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264 MEDICAL CARE ECONOMIC RISK
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