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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement 2 Medical Care Accounts and Health Accounts: Structure and Data1 In Chapter 1, we identified and distinguished two useful—and nonconflicting—types of health-related economic accounts: one measures the output of the medical care sector and the inputs that contribute toward its production, and the other tracks population health and the factors affecting it. The first account, the medical care account, includes the usual economic inputs—capital, labor, materials, and so forth—plus certain nonmarket elements such as time spent in caring for the ill and household production of medical services. In the second account, the health account, inputs include medical care (the output of the first account), plus nonmedical health-determining factors including, but not limited to, the environment, diet, and health-affecting behaviors and time spent in health-affecting activities. The two accounts are complementary because the first feeds information into the second and because their information is relevant to different types of questions. In addition, we distinguish from these two accounts and recommend, in Chapter 6, a data system for research on the determinants of health. This is not an account, as that term is normally used in the national accounts literature, but rather a research database. The rationale for recommending the research database on the determinants of health over a full-fledged health account is presented in section 2.2.3. 2.1. LINKS TO ECONOMIC ACCOUNTS Both the medical care account and the health account have similarities with the well-known National Income and Product Accounts (NIPAs) produced by the 1 Portions of this chapter are drawn from Triplett (2011).
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement Bureau of Economic Analysis (BEA). The NIPAs present gross domestic product (GDP), the measure of the nation’s overall market output, as well as its major components (consumption, investment, government spending, and net exports). The NIPAs provide essential information about economic growth, trends in the mix of goods and services produced and purchased, international trade balances, and the course of business cycles. The NIPA are used extensively in economic decision making. For example, the Federal Reserve Board and executive branch agencies monitor various indicators in the accounts for purposes of carrying out monetary and fiscal policy. Because the NIPAs are a data-integrating system, they can show shortcomings, lacunae, and inconsistencies in the outputs of other statistical agencies, so they also play a central organizing role in economic measurement. Many excellent sources of information about the NIPAs exist so in this report we do not go into further detail about their construction and attributes.2 BEA includes in the NIPAs a set of industry accounts, which distribute GDP among major industries and sectors of the economy. They show industry output and also input usage. Medical care provider industries (hospitals and so forth) are included in the BEA industry accounts. In addition, one can extract from GDP components estimates of total spending on medical care and on the major elements of medical care—for example, household and government spending on hospital care. Total spending on medical care is larger than the total output of the medical care–providing industries in the industry accounts, largely because direct purchases of pharmaceuticals and medical devices by households cannot be allocated to the conventional medical care provider industries (hospitals and so forth—see below for additional discussion). It has long been recognized that GDP is a measure of output, not a measure of welfare. Moreover, the NIPAs are organized around market output—that is, activities in which money changes hands or that are sufficiently similar to market activities that market data can be used to make an imputation (the most important imputation is the one for the value of owner-occupied housing). Nomarket activities, such as unpaid time spent caring for ill persons and personal investments in one’s own health, are not included in GDP. To measure the costs and benefits of such activities, alternative accounts—ones that include elements outside the limits of the present NIPA system—have been proposed. The medical care account we describe is predominantly, though not exclusively, a market-output type of account. The health account is largely nonmarket in character. 2.2. MEDICAL CARE AND HEALTH ACCOUNTS CONTRASTED Any economic account incorporates an economic framework. The two accounts we consider in this report are not exceptions. The medical care account 2 The BEA website provides a wealth of information on the methodologies, content, and scope of the NIPA. For example, http://bea.gov/national/pdf/NIPA_primer.pdf provides a good introduction to the accounts.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement is constructed around the relation between the output of medical care services and the inputs that are used to produce these services. The inputs are conventionally notated as KLEMS: capital services (K); labor services—the vector of all labor inputs, from surgeons to janitors (L); energy (E); intermediate or purchased materials, which, in medical care–providing industries, includes pharmaceuticals used in hospitals and clinics (M); and purchased services (S). That is (2.1) where all the variables, including output, should be understood as vectors, the elements of which, however, are frequently aggregated for analysis. Labor services may include, in addition to market labor, uncompensated time spent in the care of others; and capital services include those of intangible capital, such as research and development (R&D), in addition to the usual physical plant and equipment elements. In section 2.4, we discuss the inputs to the medical care account; in section 2.5, we turn to the output. A health account, similarly, records the relationships between an output—in this case, a measure of health, which is multidimensional—and the inputs that produce it (or, alternatively, the determinants of population health). Table 2-1 lists some of the determinants of health. Thus, in parallel with equation 2.1, there is an equation for the “production” of health: (2.2) Among the inputs in equation 2.2 include medical services (the output in equation 2.1), including those originating from nonmarket sources, particularly households’ TABLE 2-1 Inputs and Outputs of Health, Market and Nonmarket Inputs Outputs Medical care Current health status Time invested in individual’s own health Survival this year Other consumption items Longevity Research and development Quality of life Quality of environment Risk factors for future health Genes, past history, culture, social capital, preferences, war, age of population
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement provision of care. Nonmedical inputs include time spent by people investing in their own health, consumption and lifestyle variables, R&D, and the quality of the environment. Genetic endowments also play a role, especially for explaining health differences across groups and perhaps internationally. Clearly, identifying all the variables that determine health is a broad and multidisciplinary research area. Research on this task is under way from many sources, but the medical knowledge that would permit making a complete list is a long way off. Nevertheless, much is known. That much also remains to be understood does not preclude starting on the task of accounting for health and its determinants. Even among the variables that clearly belong in equations 2.1 and 2.2, some ambiguities arise. For example, individuals who are ill may generate time demands on relatives for managing their financial affairs; although this is a cost of their illness that is borne by others, it is not clear that it would be put into either account.3 Individuals may also spend time investing in the health of relatives and family members by, for example, preparing more healthful meals instead of relying on processed foods or participating in programs to encourage more exercise by other family members. Although such investments are similar to the time most directly relevant to the health account, spreading the net too broadly complicates measurement problems and, in the end, some boundary must be established on the inclusion of time. Some consumption items may contribute positively to health while others, such as tobacco and some well-liked items in the typical diet, contribute negatively in the long run. As Grossman (1972) pointed out, abstention from things like tobacco and fatty foods are like investments, in the sense that abstaining from consuming them reduces utility today for the sake of benefits in the future (see also Philipson and Posner, 2008). The R&D that appears in the health account is not the R&D that is capitalized in the medical care account. R&D in the medical care account consists of, for example, development of new pharmaceuticals or new medical procedures;4 R&D in the health account includes research that demonstrates the effects of smoking cessation, of healthy diets, or of exercise on health. In the familiar paradigm, R&D in the medical care account augments the inputs to medical care; the additional, and different, R&D in the health account augments the nonmedical determinants of health. Maintaining this distinction empirically is not easy. The output in equation 2.2—the level of health—includes both length and quality of life, as discussed in detail in Chapter 5. It can be implemented as quality-adjusted life expectancy (QALE) and expressed either in years or dollars (Murphy and Topel, 2006). 3 “Burden-of-disease” studies typically do not include these costs. 4 However, see the caveat in section 2.4.7.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement 2.2.1. Productivity Measurement and the Two Accounts Differences between the medical care and the health accounts can be illustrated by their potential uses in productivity analysis. We do not mean to imply that the accounts are useful only for productivity analysis, or even that this is their primary purpose. Productivity measurement has the virtue that it makes use of all the data in an economic account and therefore throws relationships into stark relief. A full or complete productivity measure is the ratio of outputs to all inputs. Productivity growth is the ratio of changes.5 Thus, the medical care accounting relation in equation 2.1 implies (2.3) Equation 2.3 is conventionally estimated as a ratio of index numbers6 and is known as multifactor productivity (MFP) growth.7 Productivity growth is often interpreted as a measure of efficiency change. Thus, equation 2.3 represents the growth in efficiency in the use of resources to produce medical care services. Significantly, Triplett and Bosworth (2004) reported that MFP growth in medical care services (more precisely, North American Industry Classification System [NAICS] sector 62) between 1987 and 2001 was negative, at a rate of about 1 percent per year.8 They attributed the improbable negative productivity growth in the sector to data inadequacies in the measurement of medical care output and also to mismeasurement of several inputs, particularly the high-tech portions of medical equipment. We discuss some of these measurement problems in this chapter and others in Chapter 4. Measurement inadequacies in the medical care account will affect the health account estimates because the output of medical services (from the medical care account) enters the health account as an input. This, among other reasons, demonstrates why one cannot create an account for health without giving major attention to improving the medical care account. 5 It can also be thought of as the rate of growth in the production function f(•), or its time derivative. 6 The form of the function implies an index number formula; the Tornqvist index and the Fisher index have valuable theoretical properties (Caves, Christensen, and Diewert, 1982). 7 Sometimes it is called total factor productivity (TFP). TFP and MFP are synonyms. The term MFP was introduced in a report by the Panel to Review Productivity Statistics (National Research Council, 1979) to avoid the implication that equations such as (2.3) have necessarily enumerated all the inputs—an alternative interpretation of productivity change is that it reflects inputs that have not been accounted for fully. 8 Harper et al. (2008) also report negative MFP growth in medical care.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement The health account also implies a productivity measure, derived from equation 2.2: (2.4) where h (•) is short for the right-hand side of equation 2.2. In this case, productivity change can be interpreted as a measure of efficiency in the use of all of society’s resources that affect the population’s health status. Computing MFP in the production of health requires, as it does in any MFP measurement, measuring all the inputs (or as many of them as can be identified) and finding an appropriate way to summarize them, analogous to the index number formula for inputs in equation 2.3. Representing the health function may be complicated because of the heterogeneity in the units in which the input variables are measured. Partial productivity ratios may also be calculated. For example, measures of labor productivity are common. Based on equation 2.1, labor productivity (LP) growth in the medical care sector9 can be expressed as: (2.5) Similarly, one may also be interested in the productivity of the resources used in the medical care sector in the improvement in health. Indeed, that is one of the most pressing policy issues of the day. Using equations 2.2 and 2.3, this measure of productivity growth (which we designate Ω) can be expressed as: (2.6) where ∂ designates partial derivatives of the variables in equation 2.2. As with any partial derivative, the value of Ω will depend on the values of the other variables in the equation. Thus, the productivity contribution of medical services to improved health will depend on the value of other health inputs such as diet and the environment. Because the contribution of medical care to health depends on other health-determining factors, simple comparisons of changes in medical care and changes in health are seldom meaningful. That is (2.7) One often hears such statements as the following: The U.S. medical care system must not be efficient or productive because the nation does not have the high- 9 Triplett and Bosworth (2004, p. 263) reported that LP in the medical care services sector had negative growth from 1987 to 1995, but after 1995 it turned positive. They attributed the sign change to data improvements in the measure of hospital output in the second period, which demonstrates how measurement issues impact the analysis of medical care.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement est health level in the world, even though it has the highest per capita spending on health care. This statement is fallacious logic because the impact of medical care expenditure on a nation’s health cannot be established without considering nonmedical determinants of health. For example, benefits resulting from expenditures on cholesterol-reducing statin drugs may simply be offsetting greater than average obesity rates for the United States, so there may be no net gain in health relative to a society that is faring better in terms of related social determinants of health. Philipson and Posner (2008) suggest the inverse relationship between obesity and income is a consequence of a fall in the price of consuming calories and a rise in the cost of exercise (once a by-product of manual labor), which higher income groups can better afford: the medical system partly offsets the negative health impacts of obesity.10 These examples demonstrate that the relationship between a nation’s medical expenditures and its health is not a straightforward function of its per capita expenditures on medical care. Measuring the nonmedical determinants of health, though essential, is very difficult, which is why assessing the impact of medical care on health is also very difficult. Separating the influences of medical and nonmedical determinants of health is why estimating an accounting for health would be so valuable. 2.2.2. Strategies for Going Forward A solid case can be made for beginning with and emphasizing the medical care account. For policy purposes, the most pressing needs are to measure properly medical care expenditures and outputs, to improve measures of medical inflation, and to determine what part of increasing medical care costs are attributable to increases in medical services, as opposed to price change. In addition, accurate expenditure and output data on medical care, developed and presented by a detailed cost-of-disease metric, are essential for a “health” account. Finally, in terms of feasibility, much data on medical care goods and services already exist; the challenge is to improve these data and to array them using a more useful organizing principle. In contrast, it will be difficult to collect data on the full range of behaviors and activities that affect population health. Nevertheless, nonmedical inputs to health matter greatly (McKeown, 1976; Mokyr, 1997); one cannot understand changes in health, and health differences between countries and population groups, without considering the nonmedical inputs. It is essential to begin to collect and maintain data on nonmedical and nonmarket inputs to health and to organize them into an analytic framework. Even if it is not currently possible to measure all the nonmedical determinants of health precisely, it is important to think through the measurement and conceptual issues. 10 Michaud et al. (2009) and Lakdawalla, Goldman, and Shang (2005) provide empirical evidence that as much as 30 percent of the growth in spending on medical care in the United States can be linked to increased rates of obesity in the population.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement 2.2.3. Priorities: A Health Account or a Database on the Determinants of Health? Grossman’s (1972) “production function” approach to the analysis of health and its determinants (incorporated into equation 2.2, above) has become the standard for economists’ thinking about the subject (see Bolin, Jacobson, and Lindgren, 2001). Yet attempts to implement the model empirically are few. The difficulties include the fact that health is multidimensional and—more problematic—that identifying and measuring its determinants are complex tasks. Rosen and Cutler, in an ongoing project, are estimating disease models and combining them with economic data (see Chapter 3; Rosen and Cutler, 2007). Their research is couched within a Grossman-type framework and the related epidemiological perspective, so one can think of it as estimating equation 2.2 on a disease-by-disease basis. They have selected disease categories that account for a large portion of national health care expenditures and are working toward determining the factors—including medical care—that affect changes in mortality and morbidity. A second effort along similar lines for cancer and circulatory diseases in England is reported in Martin, Rice, and Smith (2008). Beyond the Market: Designing Nonmarket Accounts for the United States (National Research Council, 2005) suggests constructing a health account that would provide a welfare-oriented measure as a counterpart to the market-oriented measures of the NIPA. Thus, it would be structured by analogy to the familiar national accounts that record economic activity but would be built around the functional relation and the variables in equation 2.2. Beyond the Market begins by identifying “gaps” in the existing national accounts that arise because their measures of outputs (and inputs) are incomplete—only market inputs and outputs are included. If the goal is to fill gaps in the NIPA coverage and structure, then it seems reasonable to work out an expanded accounting system that is patterned after the traditional national accounts structure. However, assembling the data for an economic welfare account for health goes beyond the requirements for a database for research on health determinants. For example, Beyond the Market lists diet among health determinants, certainly an important consideration. The report of the World Cancer Research Fund/American Institute for Cancer Research (2007) summarizes evidence connecting dietary factors to different types of cancer—consumption of red and processed meats raises the risk of colorectal cancers, and excessive consumption of salt for stomach cancer, while consumption of fresh fruits and vegetables reduces risks of a number of digestive system cancers. A research model for cancers might be designed in which dietary data are employed in conjunction with medical care data to determine the relative impacts of diet and medical care on cancer death and incidence rates. If the objective is to estimate dietary determinants of health, then information on consumption of the foods of interest (data that are readily available) is the main requirement. In the Grossman (1972) model, and from a welfare perspective, it is necessary
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement to compute the net gain (Nordhaus, 2003), which is not the utility from improved health. It is the value of the increment to health, less the loss in utility from abstaining from steak and ham or from eating vegetables one does not like.11 One can conceive of a research project to measure such utility losses and, though more difficult, perhaps include them in an aggregate welfare estimation. It is much less clear, however, how utility losses should be fitted into a welfare-oriented health account patterned on a NIPAs-type economic accounting structure. One possible parallel is with environmental accounting. Some production processes (electricity generation, for example) produce both goods and “bads” (the bad in this case is pollution). In an environmental account, one subtracts the values of the bads from the goods to get a net welfare measure. In the health case, the bads are utility losses from pursuing more healthy lifestyles. The net output (the utility of health gains from changes in diet and lifestyle less the loss of utility from giving up things that give present utility but in the long run are deleterious to health) is the relevant measure for welfare and therefore for the NIPA-analog health account. In principle, both objectives—an economic welfare account and a database for research on health determinants—should be pursued. However, in programs to generate data, choices must be made and priorities established. A database on the determinants of health that includes a measure of health has the most immediate policy value. It seems inevitable that estimating the utility loss from health-promoting lifestyle and dietary changes will compete with resources for moving forward on this work. If so, it is our judgment that the database for research on the determinants of health should receive higher priority. First, it is more immediately useful for a wide range of purposes. Second, information on the determinants of health is necessary for a welfare-type account in any case. Estimating the net gains from changes in lifestyles can follow. Thus, it is not premature to recommend the collection of more information about the determinants of health. It may, however, be premature to recommend that statistical agencies organize health data to accommodate a health account of the welfare-oriented NIPAs type. As was true of the development of national economic accounts in the 1930s, health accounts have not yet evolved very far, but the situation should change as more work on their conceptual underpinnings and practical needs is undertaken. The conflict between competing data needs is reduced the larger that the proportionate contribution of medical care is to the change in health. Historically, medical care was not the main determinant of past health improvements (McKeown, 1976). However, Cutler (2004) contends that medical care has been the main factor over the past half-century, particularly if one adds in the contribution of medical research to the information that has led to changed lifestyles. Even so, he concludes from an informal calculation that the loss of utility from 11 On this point see also Philipson and Posner (2008).
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement changed lifestyles and dietary changes costs around half the gain in utility from increased health. Thus, the net gain remains substantially less than the gross. The arguments laid out in this section provide some of the logic underlying Recommendation 1.1 that BEA should produce an account for medical care. In addition, developing a database for research on the determinants of health, as discussed in Chapters 5 and 6, should receive high priority from a range of players. Components of this database will no doubt continue to be developed by a number of the health-oriented statistical agencies, by academic researchers, and through cooperative research ventures between the two; BEA will also have a role and may provide input about which data elements would be useful for its programs. Even though a welfare account for health of the type advocated in Beyond the Market would also be of great value and should retain the interest of researchers, for the present developing this account will, and probably should, receive lower priority from statistical agencies. 2.3. STRUCTURE AND DATA FOR A MEDICAL CARE ACCOUNT We noted earlier that, in the NIPAs, BEA produces industry accounts that link inputs and outputs, with detail approximately at the sector level of the NAICS. BEA has recently introduced a KLEMS input structure for its accounts for all sectors, including medical care (Moyer, 2008). Thus, the current industry accounts provide a good starting place for producing a medical care production account. This and the following two sections—buttressed by detail in the annex to this chapter—suggest improvements that will create a medical care account with the characteristics needed for analyzing the sector. Chapters 3 and 4 provide additional analysis and recommendations. 2.3.1. Account Boundary Medical Care and Social Services (NAICS Sector 62) Our discussion of boundaries for the medical care accounts begins with the definitions in the NAICS because many of the data that will be used in the account’s construction will generally conform to it. For example, data were collected by NAICS industry definitions for the 2007 U.S. Economic Census and the Producer Price Index (PPI). We begin with NAICS sector 62, which encompasses medical care and social services. Within this category are, at the three-digit subsector level, ambulatory care (621), hospitals (622), nursing homes (623), and social assistance (624). Lower-level industries are nested within the 3-digit levels: ambulatory care (621) contains seven 4-digit industries (offices of physicians, 6211, and medical and diagnostic labs, 6215, are examples of 4-digit industries in ambulatory care), and those 4-digit groupings are in some cases subdivided into 5- and 6-digit
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement industries (for example, NAICS industry 62132 is offices of optometrists, under NAICS 6213, offices of other health practitioners). The level at which a medical care account will be constructed depends on balancing several competing factors. On one hand, more detailed industry-level accounts are preferable because they give more analytically relevant information and minimize aggregation difficulties. The size of the medical care sector is large enough to make subsector (3-digit) level analysis a data priority; also, aggregation conditions favor this approach because the production functions of the separate NAICS 3-digit subsectors seem quite distinct from one another.12 On the other hand, data availability hinders developing accounts at too detailed a level. For example, data on inputs for building an account for the production of optometrists’ services (NAICS 62132) are not currently available. The NAICS 3-digit subsector is also the level at which medical care appears in BEA’s industry accounts programs. Thus, accounts corresponding to equation 2.1 should be constructed for ambulatory care (NAICS 621), hospitals (NAICS 622), and nursing homes (NAICS 623), as well as an account at the sectoral level. The present BEA program also contains an industry account for social assistance, NAICS 624. Although parts of NAICS 624 have some connection to health or health status (for example, NAICS 62412, services for the elderly and persons with disabilities), most of subsector 624 does not. Accordingly, a more useful sectoral account for medical care would omit the data for NAICS subsector 624. The three subsector accounts (NAICS 621, 622, and 623) could then be aggregated to form an account that includes only medical care, that is, NAICS 62 less social services. Alternatively, the NAICS 62 sector account can be estimated directly. In the 2002 Economic Census, the three subsectors accounted for the following proportions of the total receipts of NAICS 62 less social services (U.S. Census Bureau, 2002 Economic Census Geographic Area Series summary statistics, see http://factfinder.census.gov): Ambulatory care (NAICS 621) 44% $488.7 billion Hospitals (NAICS 622) 45% $500.1 billion Nursing homes (NAICS 623) 11% $127.1 billion Total 100% $1,115.9 billion Nursing homes are the smallest subsector; however, with $127 billion of receipts in 2002, it is surely large enough to merit constructing an economic account for it alone (BEA currently combines nursing homes with hospitals). In addition, it is a portion of medical care in which the disease unit of output may 12 Ideal aggregation of producing units demands identical homogeneous production functions across the units—the standard reference is Fisher (1993). In practice, aggregations into industries are chosen in the NAICS so that the producing units have closely similar production processes, so far as possible, and so that dissimilarities in production processes provide the “breaks” that separate one industry from another. Triplett (1990) provided the conceptual bases for the industry structure of the NAICS.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement others are not. Yet quantities and prices of goods and services in these nondisease-specific categories of medical care should be tracked over time. When lab costs appear as inputs to the other medical sectors, they usually do so as costs of a specific ailment; but when lab output is a final product (tests done on behalf of the patient, for example), it is best measured in conventional ways. Prices and quantities of medical labs, in other words, are measured in terms of the tests the labs perform. As well, doctor visits and many medical tests are sought for reassurance that a medical condition does not exist. Those contacts with the medical system contribute to well-being. Some are probably reported in claims records and so forth under the disease that is found not to be present, and there is a heading for “symptoms, signs, and ill-defined conditions” in the ICD. Recommendation 2.11: Although starting with medical care on a disease-by-disease basis is a realistic way to proceed in order to begin accounting for a very significant share of the medical care economy, work should also begin on estimating the costs of, and eventually the health return from, interventions other than treating specific diseases (e.g., management, preventive, diagnostic, screening) and long-term medical services. 2.5.4. A Treatment Index or an Outcomes Index? Equation 2.8 suggests that medical care interventions are valued by their incremental contributions to health—that is, the output of each intervention is its medical outcome measure. If so, why not measure medical outcomes directly, disease-by-disease, and combine them into a weighted measure, rather than forming measures of treatments? That is, why not ignore the intervention entirely and look only at its effect on health? Indeed, Dawson and colleagues (2005) proposed exactly that. Their preferred basic measure of the output of the National Health Service is a weighted index of quality-adjusted life years (QALYs), grouped by disease classifications, in which one QALY is valued at £30,000 (see their equations 12 and 111).28 Moreover, even a treatment-based system requires medical outcome measures. As spelled out in Chapter 4, medical outcomes are needed to adjust the output measure for improvements in treatments. The issue, then, is whether a treatment index should be constructed that is adjusted by medical outcomes, or whether an output index should be constructed that is composed entirely of medical outcomes, QALY or QALE (see Chapter 5), without recourse to counting or valuing treatments. One reason for preferring the medical outcome measure is the general knowl- 28 Garber and Phelps (1997) further substantiate the use of a QALY measure as an indicator of health care output.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement edge that not all treatments are effective. Errors and mistakes, misprescriptions and misdiagnoses (patients still receive antibiotics for viral infections, for example), botched operations, and variance across areas in modes of treatment29 are well known. Some interventions do not make a positive incremental contribution to health. For these cases, bypassing the treatment measure would bring the output measure closer to one that truly measures the incremental contribution to health that the medical care system makes. Similar phenomena occur in other parts of the economy and are not adjusted out of national accounts. Botched and inappropriate car repairs, for example, occur with considerable frequency; sometimes they are corrected by the original repairer so the corrections do not result in new output, but sometimes the customer seeks out a new shop, so that repairing the botched job actually increases GDP. Such “redos” are not subtracted from GDP, even though they hardly contribute to consumers’ welfare, nor is GDP adjusted for defective manufactured products that are also not infrequently produced. But parallelism does not necessarily lead to good measurement practices. Methods for measuring medical output need to be considered on their own merits, apart from other national accounts practices, especially if the medical care account will be some form of alternative or satellite account, as seems likely. Nevertheless, the distinction between output and welfare has a bearing on measurement principles. Particularly when there is to be a health account, in addition to a medical care account, adjusting medical care output is not the only way to handle defective and inappropriate treatments. Whether appropriate or not, treatments are still produced in the medical care sector, and they still use resources in the medical care sector. By that standard (the conventional way of looking at output), they are outputs of the medical care sector. Determining whether or not medical sector output arising from inappropriate treatments contributes to welfare is a task for the compilers of the health account, particularly since they are more likely than national accountants to have the expertise to determine when treatments are not effective. When George Washington was bled as a treatment for pneumonia, his doctors must have thought they were contributing to his health, and the national accounts of his day, had they existed, would have recorded a treatment (or its resource use) in national output. When medical knowledge advanced enough to understand that Washington’s treatment hastened his death, was the accounting revision that the advance in medical knowledge demanded best put into the national accounts measure of medical output or into the health account? The most important thing, surely, is that the revision be made. But for both consistency and expertise reasons, it seems better to make the revision in the health account—that is, national output for 1799 29 Interarea differences in medical practices may be errors or may be differences of opinion about best practice. But even if the latter is true, presumably more knowledge will eventually show that some treatments that were thought to be best practice in some areas were in fact errors.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement remains unrevised by the new scientific knowledge, but the estimate of national welfare is revised downward. Consider also the role of medical care output in the account for health, for which it is an input; the output of the health account is health. One never wants to measure an input by its output (nor an output by an input). It must in principle be possible that the output effect of a change in input quantity differs from the input change. If the output of the medical care sector were measured as a health outcome, and that measure then used as an input in the health account, the possibility of productivity change in the health account is largely eliminated by convention. One of the things that a health account should be designed to reveal is the productivity of the medical care sector in the production of health. To estimate that, the measure of medical sector output must not be identical to the output of the health sector.30 A third reason is also compelling: a health care output measure that is based on disease treatments (with medical outcome measures as quality adjustments) is grounded on a more precise statistic than an output measure that is based entirely on medical outcome measures. Our ability to measure health care output by treatments, that is, by a disease classification system, may not be that far along, as we emphasize in this report. Nevertheless, information on expenditure by disease, on numbers of treatments by disease, and even on health care prices by disease is further developed than are medical outcome measures. Treatment information is inherently more concrete and therefore more precisely measured information. For a medical care account to attain public confidence, it needs to be seen as transparent, at least in relation to other comparable economic measurements. Measuring medical care output by treatments is not that different from the way car repair is measured in national accounts (Triplett, 2001) and can readily be understood within the usual framework of economic statistics. In contrast, even health economics professionals raise difficulties, both conceptual and practical, with existing medical outcomes measures (Meltzer, 2001). A sound measurement principle is to minimize the use of undeveloped and potentially controversial measures, using them only when they are necessary and not when more straightforward alternatives exist. We are not minimizing the potential contributions of such medical outcome measures as QALY and QALE. Indeed, we believe that they should be developed as rapidly as possible (see Chapters 5 and 6). We advocate using them in the health account and also in the medical care account. Nonetheless, at this stage in their development, the time is not propitious to rely on medical outcome measures exclusively as the output measure in a medical care account. 30 To avoid confusion, it is not inconsistent to make the quality adjustment for a changed treatment depend on the ratio of medical outcomes for the new and old treatments. This thorny question is addressed in Chapter 4.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement ANNEX: PRODUCT DETAIL FOR ELECTROMEDICAL EQUIPMENT DATA Part I: Problems with Available Data on Medical Equipment Problems occur in estimates of capital stocks for medical equipment. Capital goods (including computer software, as well as medical equipment) are allocated among using industries by the Bureau of Economic Analysis (BEA) capital flow table, an adjunct to the input-output table that tracks flows from capital goods–producing industries, or imports, to using industries. The 1997 capital flow table shows, for example, that two-thirds of hospitals’ capital investment goes to equipment, which is about the same as for the sector as a whole. Of hospitals’ equipment expenditures, 38 percent are for medical instruments and related equipment and 29 percent are for electromedical equipment (the category that includes scanners). Other hospital investment expenditures are for structures and a range of capital equipment that is also used by other industries—computers, software, and other (nonmedical) electronic products account for a quarter of hospital equipment. Not surprisingly, nursing homes spend less, relatively, for equipment, and they spend their equipment money in different ways. Data on medical equipment in the capital flow table are quite coarse: the table distinguishes only the two gross aggregates “medical instruments and related equipment” and “electromedical equipment.” One reason is that the table presents flows for all sectors of the economy. Most electromedical equipment, not surprisingly, flows to the medical care industries (according to the capital flow table, education is the second largest using industry). Providing more detail on medical equipment would not suit other industries in the capital flow table, even though more detail would be useful for the analysis of medical care. The available survey data on medical care investments do not contain that much more detail. Several Census Bureau data sources present different and sometimes conflicting information, but for most of them, the useful detail is not appreciably greater than in BEA’s capital flow table. Worse, the detail present in different surveys does not match up, which greatly diminishes the usefulness of the data. These data sources—which include the Economic Census, the Annual Surveys of Manufactures (ASM), Current Industrial Reports (CIR), and the Annual Capital Expenditures Survey (ACES)—are not well integrated and can be confusing, so we present a brief summary of their data on medical equipment. Data for Medical Care Capital Equipment As explained in the text, the Census Bureau, in the Economic Census, still does not collect the range of input data for services industries that it has long collected for manufacturing and other goods-producing industries. The resulting data incongruity handicaps analysis of services industries, including medical care
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement industries, which require the same kinds of information that has long been provided for goods-producing sectors. The same flaw carries through to the Annual Services Surveys, which also are deficient in input data.31 The Census Bureau has instituted ACES to fill the gap, but this survey has been directed toward obtaining investment data for the economy as a whole, and its usefulness is greatly limited by inadequate industry and commodity detail. Promising for our purposes is an information and communications technology (ICT) supplement to ACES that distinguishes the category “electromedical and electrotherapeutic equipment.” This ICT category in ACES includes major types of equipment used in the medical care industries and matches the electro-medical equipment category in the BEA capital flow table. The ACES survey form collects 4-digit North American Industry Classification System (NAICS) industry codes. However, the promise of ACES has not been fulfilled. ACES published for 2005 and 2006 only at the 2-digit NAICS level (NAICS 62). No detail is published, only the total for investment in electromedical and electrotherapeutic equipment, plus information by type of acquisition (capitalized, leased, and so forth). ACES thus provides only a very limited benchmark—for one aggregated type of medical equipment, at the level of the medical sector as a whole. Other relevant Census Bureau surveys collect data on U.S. production of medical equipment, not investment. The most informative, CIR, collects data on U.S. production of the products of NAICS 33451, electromedical apparatus manufacturing, and of some other medical equipment, at considerably more detail than ACES. ASM distinguish as the main product of NAICS 33451 “diagnostic and therapeutic” equipment, presumably the same “electromedical and electro-therapeutic equipment” products that are collected in ACES. No detail beyond this aggregate is published in ASM.32 The 2007 Economic Census form for the industry “Electromedical and Electro-therapeutic Apparatus” gathered information on receipts from “electromedical equipment including diagnostic, therapeutic and patient monitoring equipment.” This is the same level of fairly gross aggregation as in ASM, and the Census Bureau form specifies that it is the same aggregate as on CIR. Thus, the Economic Census, which collects in many industries more detail than in annual collections, in this case does not approach the detail in the CIR.33 The Economic Census also collected data for NAICS 33911; this industry makes nonelectronic medical equipment. 31 This old data lacuna in services-producing industries is discussed more fully in Triplett and Bosworth (2004, Chapters 10 and 11). 32 Both CIR and ASM record that production of these products was considerably greater than total U.S. investment in them in 2005-2006, suggesting that net exports were high, but it is known that, for some of these products, foreign producers are important suppliers. 33 The Economic Census also collected information on other products, including irradiation equipment, scientific instruments, nonelectromedical surgical and medical apparatus, catheters, and so forth, that are also made in this industry (in which they are secondary products).
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement The problem of inadequate capital data is not unique to medical care industries. However, medical care is the largest sector of the economy for which detailed data are not available on purchased inputs, including capital inputs. Moreover, medical care industries purchase a range of very highly technological equipment, which is importantly linked with technical change. Thus, unlike some other services industries in which absence of capital expenditure detail is merely an annoyance (for example, NAICS 812, Personal and Laundry Services), in medical care the data gap threatens understanding of essential aspects of recent developments in the sector. Critics of the U.S. medical care system have frequently asserted that it over-uses imaging devices. It is accordingly bizarre from a research and policy analysis standpoint that data collections in the Economic Census do not reveal how much imaging equipment is going into the medical care sector, let alone the total stock of it that is in place. Greater data detail on technological capital goods used by the medical care sector is essential. The model for improving medical equipment data is the data published for computers and office equipment—the second largest category of medical industry equipment investment and another notable category of technological investment products. Some years ago, government data on computers and related equipment were as seriously undeveloped as medical equipment data are today. A multipronged effort by all three major statistical agencies (BEA, Census Bureau, Bureau of Labor Statistics [BLS]) involved a new and more relevantly descriptive system of product codes; improved and more detailed data on shipments and sales receipts by detailed product; and improved deflators that, using hedonic price index methods, allowed for the rapid rate of technological changes characteristic of nearly all electronic goods. The value of this extensive data development exercise was shown in the analysis of the substantial influence of information technology investment in the post-1995 U.S. productivity expansion—see, for example, Jorgenson (2001) and Jorgenson and Stiroh (2000). Without the development of a comprehensive data set on the production of—and investment in—computer and related equipment, analysis of the role of information technology in the U.S. economy would have been, if not impossible, certainly greatly handicapped.34 Medical equipment performs a similar role in sparking, facilitating, and implementing technological innovation, except it does it exclusively in the medical care industries and not economy wide. Much anecdotal information exists 34 The Federal Reserve Board has also contributed more recently to improving the deflators.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement about medical equipment investment, but it is not quantified in the way data on other ICT equipment are for purposes of economic analysis. The lack of good information on medical equipment is one more way in which data for the analysis of medical care suffer from long-term neglect. The first step in a data improvement project such as the one needed for medical equipment is getting agreement among the agencies on a common product classification scheme. This seemingly mundane task is necessary because otherwise data, especially from BLS and the Census Bureau but also from different Census Bureau surveys, do not fit together, and data expansions by individual programs and agencies proceed in inconsistent directions. Moreover, there is no center in the U.S. statistical system for coordinating such matters; it usually takes a special task force composed of agency representatives.35 We explore ways for improving a specific type of capital equipment in greater detail below. Part II: Product Detail for Electromedical Equipment Data In this part, we discuss product detail for electromedical equipment and its inadequacy for producing consistent and meaningful data. Similar reviews could be carried out for the other categories of medical equipment and indeed for investment in medical structures, which have their own unique problems. The example we provide illustrates principles that we think should be followed. In most goods-producing industries, the Census Bureau 10-digit commodity codes provide the standard for product nomenclature. In the case of electro-medical equipment (primarily, NAICS 33451), the list is (the last four digits only are shown): 1100 electromedical equipment, including diagnostic, therapeutic, and patient monitoring equipment; 1103 magnetic resonance imaging equipment (MRI); 1106 ultrasound scanning devices; 1109 electrocardiograp; 1112 electroencephalograph and electromyograph; 1115 audiological equipment; 1118 endoscopic equipment; 1121 respiratory analysis equipment; 1124 all other medical diagnostic equipment; and 3100 electronic hearing aids. The categories 1103-1124 are subdivisions of the first one. Hence, no distinction is made between uses. For example, ultrasound diagnostic equipment and ultrasound therapy equipment are in 1106. 35 The industry classification system, NAICS, is an Office of Management and Budget standard, and there is an emerging NAPCS for products in the services sector. But no similar, formal standard exists for goods-sector products.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement No one seems to use the Census Bureau 10-digit list for this industry. For example, the Census Bureau’s CIR imposes use-categories as its first disaggregation: medical diagnostic equipment; patient monitoring equipment; medical therapy equipment; surgical systems; and other electromedical and electrotherapeutic apparatus. CIR’s second disaggregation is by product. But even though CIR presents much more product detail than the ASM or the Economic Census, the CIR published product detail does not map exactly into the Census Bureau 10-digit product list. For example, where do defibrillators go in the 10-digit list? They are not diagnostic, so there is not even a place for them in the “all other” grouping. The classification used by BEA (in its investment series) more or less follows CIR’s first-level disaggregation, even though BEA does not use CIR data for medical equipment investment. BEA’s preferred classification scheme is also inconsistent with the Census Bureau 10-digit product codes. Product codes in the PPI industry classification are also broadly consistent with CIR’s first-level disaggregation, although a separate PPI commodity code scheme disaggregates differently. In addition to measures for the industry aggregate (electromedical apparatus), both the PPI and CIR contain data at the first-level disaggregation (that is, “diagnostic equipment” and so forth). But “medical diagnostic equipment” is still far too broad: CIR is right that meaningful disaggregation would produce series such as “ultrasound scanning devices” and “EKG.” The BEA and PPI codes suggest an incipient interagency agreement on the CIR first-level disaggregation, except for possibly the Economic Census and ASM. However, even incipient agreement between PPI and CIR does not yield detailed data on medical investment because BEA does not use the PPI for deflation at this level, and indeed it does not use CIR for any of its investment estimates. CIR contains much product detail, but the PPI is insufficiently fleshed out to match it.36 The CIR contains more detail, and more meaningful categories, than do any of the other surveys, including the PPI. At the product level, however, the CIR is problematic. For one thing, the size of CIR product categories labeled “all other” equipment makes the CIR categories less informative than they ought to be (see Table 2A-1). To take the worst case, the largest—by far—category of patient monitoring equipment is “all other patient monitoring” equipment; it accounts for 86 percent of the total. “All other” miscellaneous categories are not useful 36 We leave aside any judgments about whether CIR data are equal in quality to ASM or Economic Census data.
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement TABLE 2A-1 “All Other” as a Proportion of Shipments in Electromedical Equipment Categories, Current Industrial Reports Proportion of Category (%) Proportion of All Electromedical Equipment (%) Medical diagnostic equipment 44 7.6 Patient monitoring equipment 86 12.3 Medical therapy equipment 43 17.6 Surgical systems 42 5.7 Other 100 6.6 Total (other and all other) 49.8 SOURCE: U.S. Census Bureau, Current Industrial Reports, MA334A; available at http://www.census.gov/manufacturing/cir/index.html. ones, and when they are large and growing they tend to hide the most vigorous technologies where they cannot be observed. In some cases, the “all other” category may be large because of disclosure problems. For example, the number of producers of MRI machines is small, so if there were a line for MRI equipment in the CIR medical diagnostic equipment category, it could not be published, to avoid disclosure of individual producer information. However, it is hard to believe that the disclosure problem applies to each of the “all other” equipment categories. Moreover, in addition to the “all other” categories within the major types of electromedical equipment (e.g., “all other patient-monitoring equipment” as part of “patient-monitoring equipment”), CIR electromedical equipment contains a whole first-level category labeled “other electromedical and electrotherapeutic apparatus.” Accordingly, almost half of the total shipments of electromedical equipment falls into “all other” classifications. Substantial government funding and a substantial amount of respondent burden are costs of the CIR medical equipment survey. Because the administration of this program has not optimized the value of the survey, it is not producing sufficient information to justify those costs. These are old problems that urgently need attention.37 We believe that careful study by an interagency team would produce a more detailed, workable product classification scheme that could be implemented by CIR, the Economic Census, and the PPI. Disclosure difficulties are likely to arise in implementing a detailed classification scheme in production data. However, obtaining information on investment purchases of MRI and other technological 37 A previous problem with CIR that prevented publication of consistent aggregates has been corrected, at least at the level of electromedical equipment as a whole (formerly, there were gaps in the published aggregations that were caused by the methods adopted to prevent potential disclosure problems).
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Accounting for Health and Health Care: Approaches to Measuring the Sources and Costs of their Improvement equipment by hospitals and other medical sector units presents no disclosure possibilities and so obviates the difficulty in collecting information from domestic producers. In medical care analysis, the investment data—that is to say, information from the buyers—are more crucial than domestic production data (information from the sellers), although both are valuable.
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