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 separate 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 underutilization 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 The views expressed in this paper are those of the authors and do not necessarily reflect the views or conclusions of the National Research Council, the Institute of Medicine, the study panel, or the sponsor.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 225
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

OCR for page 225
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).

OCR for page 225
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.

OCR for page 225
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).

OCR for page 225
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.

OCR for page 225
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

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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

OCR for page 225
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

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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

OCR for page 225
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.

OCR for page 225
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.

OCR for page 225
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).

OCR for page 225
264 MEDICAL CARE ECONOMIC RISK REFERENCES Agency for Healthcare Research and Quality. (2008, April). MEPS HC-092: 1996-2004 Risk Adjustment Scores Public Use File. Available: http://www.meps.ahrq.gov/mepsweb/ data_stats/download_data_files_detail.jsp?cboPufNumber=HC-092. Banthin, J.S. (2011). Measuring Medical Care Risk. Powerpoint presentation at the meeting of the National Research Council/Institute of Medicine Panel on Measuring Medical Care Economic Risk in Conjunction with the Supplemental Poverty Measure Committee on National Statistics, February 16, Washington, DC. Banthin, J.S., and Bernard, D.M. (2006). Changes in financial burdens for health care: Na- tional estimates for the population younger than 65 years, 1996-2003. Journal of the American Medical Association 296(22):2,712-2,719. Centers for Medicare & Medicaid Services, Office of the Actuary. (2011). National Health Expenditure projections 2010-2020, Forecast Summary and Selected Tables. Available: http://www.cms.gov/NationalHealthExpendData/downloads/proj2010.pdf. Congressional Budget Office. (2008, January). Technological Change and the Growth of Health Care Spending. Available: http://www.cbo.gov/ftpdocs/89xx/doc8947/01-31- TechHealth.pdf. Doyle, P. (1997). Who Is at Risk?: Designing a Medical Care Risk Index. Working Paper. Washington, DC: U.S. Census Bureau. Available: http://www.census.gov/hhes/povmeas/ publications/medical/doyle2.html. Doyle, P., Beauregard, K., and Lamas, E. (1993). Health Benefits and Poverty: An Analysis Based on the National Medical Expenditure Survey. Center for Intramural Research, Agency for Healthcare Policy and Reseach, U.S. Department of Health and Human Services, Washington, DC. Dror, D.M., and Vaté, M. (2002). To Insure or Not to Insure: Reflections on the Limits of Insurability. In D.M. Dror and A.S. Preker (Eds.), Social Reinsurance: A New Approach to Sustainable Community Health Financing (pp. 125-152). Washington, DC: The World Bank and the International Labour Office. Ellis, R.P., and McGuire, T.G. (2007). Predictability and predictiveness in health care spending. Journal of Health Economics 26(1):25-48. Farley, P.J. (1985). Who are the underinsured? Milbank Quarterly 63(3):476-504. Handel, B.R. (2010, January 26). Adverse Selection and Switching Costs in Health Insurance Markets: When Nudging Hurts. NBER Working Paper No. 17459. Cambridge, MA: National Bureau of Economic Research. Health Connector: Health Insurance for Massachusetts Residents. (2010). Health Care Reform: Key Decisions: Available: https://www.mahealthconnector.org/portal/site/connector/menu item.9ccd4bd144d4e8b2dbef6f47d7468a0c/. Interagency Technical Working Group on the Development of a Supplemental Poverty Mea- sure (2010). Observations from the Interagency Technical Working Group. Available: http://www.census.gov/hhes/www/poverty/SPM_TWGObservations.pdf. Kalinosky, E., and Kohler, B. (2005, February). Treatment of Medical Care Expenditures in Poverty Measurement: The National Academy of Sciences Panel Proposal. Available: http://www.irp.wisc.edu/research/method/kalinkohl.htm. Klugman, S.A., Panjer, H.H., and Willmot, G.E. (2008). Loss Models: From Data to Decisions. Hoboken, NJ: John Wiley & Sons. Kronick, R., Gilmer, T., Dreyfus, T., and Lee, L. (2000). Improving health-based payment for Medicaid beneficiaries: CDPS. Health Care Financing Review 21(3):29-64. Moon, M. (1993). Incorporating Health Issues in the Measurement of Poverty. Unpublished paper prepared for the Panel on Poverty and Family Assistance, Committee on National Statistics, National Research Council, Washington, DC.

OCR for page 225
CONCEPTUAL FRAMEWORK FOR MEASURING MCER 265 Mullahy, J. (2009). Econometric modeling of health care costs and expenditures: A survey of analytical issues and related policy considerations. Medical Care 47(Supp. 1):S104-S108. National Research Council. (1995). Measuring Poverty: A New Approach. C.F. Citro and R. Michael (Eds.). Panel on Poverty and Family Assistance: Concepts, Information Needs, and Measurement Methods. Committee on National Statistics, Commission on Behav- ioral and Social Sciences and Education. Washington, DC: National Academy Press. Nyman, J.A. (2004). Is “moral hazard” inefficient? The policy implications of a new theory. Health Affairs 23(5):194-199. Pope, G.C., Kautter, J., Ellis, R.P., Ash, A.A., Ayanian, J.Z., Iezzoni, L.I, Ingber, M.J., Levy. J.M., and Robst, J. (2004). Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financing Review 25(4):119-141. Provencher, A. (2011, March). Unit of Analysis for Poverty Measurement: A Comparison of the Supplemental Poverty Measure and the Official Poverty Measure. (SEHSD Work- ing Paper No. 2010-14), Social, Economic and Housing Division, U.S. Census Bureau. Available: http://www.census.gov/hhes/povmeas/methodology/supplemental/research/ SPM_UnitofAnalysis_Provencher.pdf. Short, K. (2011, June 30). The Supplemental Poverty Measure: Examining the Incidence and Depth of Poverty in the U.S. Taking Account of Taxes and Transfers. 86th An- nual Conference of the Western Economic Association International Session. Available: http://www.census.gov/hhes/povmeas/methodology/supplemental/research/WEA2011. kshort.071911_2.rev.pdf. Short, P.F., and Banthin, J.S. (1995). New estimates of the underinsured younger than 65 years. Journal of the American Medical Association 274(16):1,302-1,306. Smeeding, T.M. (1982, March). Alternative Methods for Valuing Selected In-Kind Transfer Benefits and Measuring Their Effect on Poverty. Technical Paper No. 50. U.S. Census Bureau. Washington, DC: U.S. Department of Commerce. Available: http://www.census. gov/hhes/www/poverty/publications/tp-50.pdf. Smeeding, T.M. (2006). Poor people in rich nations: The United States in comparative perspec- tive. Journal of Economic Perspectives 20(1):69-90. U.S. Census Bureau. (2010). Response to May 2010 Federal Register Notice comments. Avai- ����� lable: http://www.census.gov/hhes/www/poverty/methods/spm_fedregister.html. van de Ven, W.P.M.M., and Ellis, R.P. (2000). Risk adjustment in competitive health plan markets. In A.J. Culyer and J.P. Newhouse (Eds.), Handbook of Health Economics (pp. 755-845). Amsterdam: Elsevier. Winkelman, R., and Mehmud, S. (2007, April 20). A Comparative Analysis of Claims-based Tools for Risk Assessment. Society of Actuaries. Available: http://www.soa.org/files/pdf/ risk-assessmentc.pdf. Wolfe, B. (1998). Incorporating health care needs into a measure of poverty: An exploratory proposal. Focus 19(2):29-30. Available: http://www.irp.wisc.edu/publications/focus/pdfs/ foc192.pdf. World Health Organization. (2011). Health Topics: Risk Factors. Available: http://www.who. int/topics/risk_factors/en/.

OCR for page 225