looking for better health in all the wrong places: the road to “equality” hits a dead end

the graying of america: challenges and controversies • spring 2012 33

Looking for Better Health in All the Wrong Places: The Road to “Equality” Hits a Dead EndTom Miller

I was initially assigned the working title, “Pursuing Equality in Health Care for the Elderly Is Futile.” I prefer to think of that particular dead end of

health policy as one of listening to the wrong music for too long. Hence, this article reprises and revises the title song of the early 1980s movie, Urban Cow-boy, but with Johnny Lee’s original lyrics adapted as “Looking for better health [rather than either ‘love’ or ‘love of equality’] in all the wrong places.”1 The better goal is to achieve more progress in improving health for more people, including (but not limited to) the elderly. It need not be as futile as the pursuit of the elusive abstraction of “equality” for all — but only if we first move away from a path-dependent approach of recent times that remains too narrowly focused on statistical disparities in health care services received by particular groups.

A Thin Evidence BaseIn examining this topic, one finds that the actual evi-dence base for measures of health inequality among the elderly in the United States remains rather thin. We need to reconsider what, and how, we measure in searching for apparent differences in health across and among different parts of the population. Are we even asking the right questions? Once we begin to do so, we may discover a much more complex set of causes, correlations, and limiting factors facing both researchers and policymakers. They suggest that we proceed with greater caution and humility in setting more feasible priorities and targets of intervention. We should refrain from continuing to search for the lost keys to better health only where the political light appears to be brightest — under a well-funded lamp post called “health disparities” — rather than where those keys actually might be located.

The most common starting point for a purportedly comprehensive and evidence-based review of the health inequality issue in the United States is supposed to be the annual National Healthcare Disparities Report (NHDR), produced by the Agency for Healthcare Research and Quality.2 The most recent3 6th annual report, for 2008, defines health care disparities as “dif-ferences or gaps in care experienced by one population compared with another population.”4 Its broad mission is to measure differences, and changes over time, in

Tom Miller, J.D., is a resident fellow at the American En-terprise Institute. He is a former member of the National Ad-visory Council for the Agency for Healthcare Research and Quality (2007-2009) and a former senior health economist at the Joint Economic Committee, U.S. Congress. He received his J.D. from Duke University Law School (Durham, NC) and his B.A. from New York University’s University College (Bronx, NY).

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health care quality and access to care for various health conditions in different populations. Note that the resulting findings not-too-subtly appear to sound more intentional and unfair when they are termed “dispari-ties” than when they are called “differences.” The main emphasis of the annual report is on health disparities related to racial and ethnic minorities, and low-income groups. However, one of the seven “priority” popula-tions it addresses involves older adults — age 65 and over. (Other priority populations include women, chil-dren, residents of rural areas, and individuals with dis-abilities or special health care needs).5

The highlights of the latest NHDR include the find-ing that health disparities persist for all populations. Of course, that appears to be somewhat inevitable, because the report starts from the contestable premise that all population groups should receive an “equally high” quality of care. Other published research, along with a similar annual National Healthcare Quality Report, has reported that Americans too often fail to

receive the recommended care they need or receive care that causes harm.6 However, the NHDR tries to focus on those groups that receive even worse care than others (either between different population cohorts, or within them), or perhaps just do not get enough of it, despite its poor quality.

Although the NHDR uses 220 measures to assess health care quality, the 2008 report focuses on 45 core measures. Those core measures generally remain limited to assessing health care inputs and processes, rather than broader health outcomes. Overall, the report’s list of core measures for 2008 includes 27 for processes and only 18 for health outcomes.7 The receipts of medical care are easier to measure than its results, even though patients (and hopefully most pro-viders) should care much more about the latter.

In general, the report’s methodology — while acknowledging that many factors contribute to dispari-ties in quality and access, including cultural attitudes and health literacy — also places its greatest emphasis on increasing access to care through increasing the por-

tion of the population with health insurance. It mea-sures health care disparities primarily by race, ethnicity, and socioeconomic variables (emphasizing income vari-ables much more than education factors, for the latter). The NHDR approach appears either to lack the data sources, or the interest, to pursue very much bivariate or multivariate analyses to control for multiple factors, which would pinpoint better the extent to which each one in particular affects a health outcome.

For older adults (above age 65), the sixth annual NHDR discusses just four measures — involving influ-enza vaccination, vision screening, delayed care due to cost, and health literacy, respectively. It notes progress in increasing the percentage of Medicare beneficiaries with an influenza vaccination from 1998 to 2004 (up from 68.7% to 71.7%) but points to a significant gap based on income (ranging from 61.3% for poor Medi-care beneficiaries to 77.6% for high-income ones). For vision screening, the gap between blacks and whites decreased from 1998 to 2004 with no change in the

gap between poor and high-income Medicare benefi-ciaries. There also were no significant changes based on gaps in income for delayed care due to cost, from 1998 to 2005, although Hispanic beneficiaries were more likely than non-Hispanic whites to delay care for this reason (7.8% versus 4.4%). Finally, health literacy was a broad problem for adults age 65 and over, and it was particularly poor for adults over age 75 (more than two-thirds of those Medicare beneficiaries had below basic or basic health literacy).8

The broader health picture for Americans over age 65 is that they represent a growing share of the popu-lation and their life expectancy has increased signifi-cantly. They naturally face greater health care concerns than do younger populations, but they also experience lower rates of poverty. Based on the limited evidence in the latest NHDR for health disparities among the elderly, one might be tempted to ask, “Is that all there is?”

The broader health picture for Americans over age 65 is that they represent a growing share of the population and their life expectancy has increased

significantly. They naturally face greater health care concerns than do younger populations, but they also experience lower rates of poverty. Based on

the limited evidence in the latest NHDR for health disparities among the elderly, one might be tempted to ask, “Is that all there is?”


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The Role of Medicare in Reducing Health Inequality One initial presumption is that the enactment of Medicare coverage in 1965 and its subsequent expan-sion must have improved the health of Americans over age 65, and reduced health disparities between the young and the old. However, health researchers Amy Finkelstein and Robin McKnight actually found that the establishment of universal health insurance for the elderly had “no discernible impact” on their mortality during the first 10 years of the Medicare program.9 Although Medicare played essentially no role in the dramatic decline in mortality rates for the elderly that began in the late 1960s, it neverthe-less did help reduce the elderly’s financial exposure to out-of-pocket medical spending risk. The Medi-care-induced increase in health care consumption was ineffective at least in regard to mortality, con-cluded Finkelstein and McKnight, because lack of legal access to care — not lack of insurance — was the primary problem that was corrected with regard to mortality post-1965. The broader effects of deseg-regation and new civil rights enforcement on the overall black population included improved access to hospital care for individuals with life-threatening, treatable conditions.10

Medicare’s expansion, however, had other major effects within the overall U.S. health care system. In related research, Finkelstein concluded that Medi-care’s growth, along with the overall spread of health insurance between 1950 and 1990, may explain as much as half of the six-fold increase in real per capita health spending during those four decades.11 Medicare stimulated entry of new hospitals and the adoption of new medical technologies. The market-wide changes in health insurance it helped create may have funda-mentally altered the character of medical care, both for individuals who experienced a change in coverage and for those who did not.12

Other potentially more positive evidence of Medi-care’s effects on the health of the elderly comes from the work of University of California-Berkeley econo-mist David Card and several colleagues. They suggest that Medicare eligibility at age 65 reduces the death rate of a relatively sick population (people admitted to hospitals through the emergency room for “non-deferrable” conditions) by 20 percent, and that this mortality improvement persists for at least two years following the initial hospital admission.13 However, those findings reflect the relatively greater generos-ity of services (or more timely delivery of them) for patients covered by Medicare and supplemental pri-vate insurance relative to insurance benefits held by people just under age 65. The study also did not exam-

ine whether the total costs associated with the overall Medicare program (which include large increases in the use of services by other “healthier” patients who experience small, or no, mortality effects) were justi-fied by the total gains in health for this particular sub-population of beneficiaries.14

Health Differences among the Elderly Based on Income A different area for investigating health inequality among the elderly involves measures of income-related disparities within that age cohort. In other words, how progressive is the Medicare program’s structure of benefits and financing for its beneficiaries. At first glance, as an age-based entitlement program, Medi-care appears to offer the same core health benefits to almost all Americans age 65 and above, regardless of income. On the other side of the beneficiary ledger, however, Medicare relies on a mixed combination of revenue sources — based on payroll taxes and income taxes. This overall financing scheme has produced less consistent net distributional effects (benefits received minus taxes paid) over time than might be first assumed.

One earlier line of research, begun by Mark McClel-lan and Jonathan Skinner, suggested that, as of 1990, Medicare produced net transfers of funds from low-income beneficiaries to higher-income ones.15 Up to that point, Medicare relied on relatively regressive payroll-tax financing — with a capped amount of wages subject to its flat rate. Higher-income benefi-ciaries received greater lifetime benefits due to their longer survival times. McClellan and Skinner con-cluded that Medicare was regressive if measured as a cash-transfer mechanism, but acknowledged that if the “insurance value” of Medicare benefits was taken into account, then the program might instead produce “faint redistribution” from the highest-income benefi-ciaries to the lowest-income ones.16

A subsequent paper by McClellan, Skinner, and Julia Lee identified a dramatic shift in the pattern of Medicare spending between 1990 and 1995, with increased redistribution toward the lowest-income neighborhoods, where Medicare per-capita spending grew much more rapidly.17 Although the cap on wage-based earnings subject to the Medicare share of the payroll tax was removed in the early 1990s, this shift toward more progressive net transfers was primarily due to some unusual short-term distortions in more intensive Medicare services (such as home health care spending) delivered in lower-income neighborhoods. McClellan, Skinner, and Lee described those services as of more questionable value to the beneficiaries receiving them.


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In a final, updated version of this work published in 2006, McClellan and Skinner moved back toward their original findings, concluding that net Medicare bene-fits (lifetime expenditures versus taxes paid) remained slightly higher for those in the highest-income house-holds than those in lower-income groups.18 How-ever, this line of analysis remained limited in several respects. It relied on income data by zip code, rather than per Medicare beneficiary. It was also hampered by the researchers’ inability to measure easily the over-all value of the health consequences produced by addi-tional health care services received due to more Medi-care spending, even while referencing evidence that suggested the low marginal value of many intensive Medicare services. In fact, the 1999 study by McClel-lan, Skinner, and Lee had noted that the large shift in Medicare resources toward people in lower-income neighborhoods failed to improve their survival rates and might have even slightly increased disparities in mortality rates.

Nevertheless, the McClellan and Skinner line of studies ultimately decided to rely on the “presumed value” of income transfers to low-income recipients in the form of generous, community-rated insurance benefits, despite evidence that those transfers also produced inefficient overconsumption of medical care by those elderly beneficiaries and more insurance than they wanted.19

Jay Bhattacharya and Darius Lakdawalla have offered a different approach to measuring the relative progressivity of Medicare. They found that the finan-cial returns to Medicare are actually much higher for poorer groups in the population and concluded that Medicare is a highly progressive public program.20 Their study used educational attainment as a better measure of permanent income and socioeconomic status (SES), arguing that this would correct for the aggregation bias in the McClellan/Skinner measures of SES based only on geography.21 Bhattacharya and Lakdawalla account for the role of geographic mobil-ity by the elderly, noting that those Medicare benefi-ciaries who move to richer ZIP codes (with presum-ably higher quality medical facilities) tend to increase their total medical spending, while those moving to poorer areas lower their spending level.22 They con-cluded that, at any given age, Medicare spends far more on the poor (less educated) than it does on the rich (more educated) and found that the advantage of the poor in receipt of Medicare benefits even overcame their higher death rates. This is partly related to differ-ences in health status, because less-educated people are sicker and therefore cost Medicare more.23

However, this particular finding is limited, because other adjustments for education-related longevity and

Medicare-benefit growth on a lifetime basis (rather than measured at a single point in time) erode some of Medicare’s progressivity, particularly for beneficiaries living beyond age 75. Bhattacharya and Lakdawalla also note in passing that they found a positive gradi-ent in privately financed medical expenditures (more education leads to greater spending) once one controls for health status.24 More significantly, their analysis remains wedded to measuring only differences in the dollar amounts of Medicare benefits received, instead of more significant disparities in the health outcomes that such spending on health care services may only partly help produce.

Asking a Better Question Delivers a Better Answer A better way to measure disparities within the Medi-care beneficiary population is to ask the right ques-tion. Jonathan Skinner and Weiping Zhou drew dis-tinctions between inequality in health spending and inequality in health outcomes.25 They found that when inequality is measured by differences in Medicare spending, health care for the elderly became more equitable during the past several decades. However, this relative growth in health spending directed at low-income elderly people did not translate into rela-tive improvement either in survival or rates of effective care.26 When Skinner and Zhou considered how sev-eral different cohorts of Medicare beneficiaries fared in survival rates between 1982 and 1992, they found that the highest income groups gained the most, both in percentage and absolute terms. Whereas the relative levels of health spending by different income groups depend on preferences, health status, and prices, their health outcomes are more strongly influenced by health behavior, diet, and past life-course events (such as past illness) that extend beyond the health care system alone. Given the association between those behavioral factors and income and SES, Skinner and Zhou emphasized that inequalities in health can reflect wider inequalities in society.27 Noting the prob-lem in trying to infer that spending more Medicare money on lower-income groups necessarily improves their health, Skinner and Zhou suggest that policy-makers might focus more effectively on what matters most — improving the delivery of “effective” care to lower-income patients.28

Even Universal Insurance Coverage under a National Health Program Does Not Ensure More Equal Health OutcomesMore cursory examinations of how fairly Medicare’s health care services are distributed among the elderly based on income may assume that essentially the same


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set of benefits is delivered in the same way through-out the United States. After all, a national program providing near-universal coverage to seniors age 65 and over is supposed to reduce the role of money in accessing care. However, several decades of analysis of geographic variation in health spending and patterns of medical practice suggest how any such pursuit of equality remains elusive. For example, researchers involved in producing the Dartmouth Atlas of Health Care have estimated that among groups of Medicare beneficiaries who are otherwise similar, those living in high-spending areas receive approximately 60 percent more in services than do those living in low-spending areas. Although the prices of health care services and severity of illness are important fac-tors in explaining geographic varia-tion in health care spending, they combine to account for, at most, less than half (and possibly much less than half ) of the geographic variation in spending.29 Income and the prefer-ences of individuals for specific types of care also appear to explain little of the variation in Medicare spend-ing for the elderly. Some of the varia-tion in medical practice probably is attributable to regional differences in medical resources for so-called sup-ply-sensitive services (e.g., physician visits, specialist consultations, diagnostic tests, and hospitalizations) and the propensity to take advantage of the financial incentives provided by Medicare or other payers in developing and using those resources, according to a 2008 Congressional Budget Office review of the existing literature and evidence.30

With some regions more prone to adopt low-cost, highly effective patterns of care and others more likely to adopt high-cost patterns of care and to deliver treat-ments that provide little benefit or are even harmful, any possible residual evidence of health inequal-ity based on the income characteristics of beneficia-ries remains hard to find. By one older estimate, the Dartmouth Atlas researchers calculated that Medi-care spending would fall by 29 percent if spending in medium- and high-spending regions were the same as in their benchmark regions, defined as those with spending in the lowest decile.31

Moreover, efforts to find “unfair” differences among beneficiaries in regard to the health services they receive and/or health outcomes they experience within a purportedly “uniform” national program are further complicated by the fact that various types of supplemental insurance for the elderly produce addi-tional differences. Medicaid coverage for dual-eligible

elderly Americans, additional supplemental benefits for certain lower-income groups, and private Medigap coverage for many other beneficiaries means that even enrollees within the traditional Medicare, let alone those in more generous private Medicare Advantage plans (which experience surprisingly higher enroll-ment by lower-income African-American beneficiaries in many urban areas), create a much more complex, layered pattern of benefits relative to income than Medicare’s core benefit structure might suggest.

Even when one examines national health systems in other countries, where promised health benefits appear to be more uniform, substantial differences in the health outcomes they seemingly produce remain.

The Whitehall studies are the most notable set of long-term examinations of socioeconomic differences in physical and mental illness and mortality. Led by chief investigator Sir Michael Marmot, the initial White-hall I Study was conducted over 10 years, beginning in 1967. It examined social determinants of health in terms of cardio respiratory disease prevalence and mortality rates among British male civil servants between ages 20 and 64. The Whitehall I study dem-onstrated that there was a social gradient in mortality rates from a range of causes, running from the bottom to the top of the grade levels of employment within the British civil service. Although the lower the grade, the higher the risk of death, the gradient of health involved more than just a clear dividing line between poor health for the disadvantaged and good health for everyone else.32

The effects of social and occupational influences on health and illness (morbidity) were investigated fur-ther in the next round of Whitehall II studies, involv-ing a variety of diseases affecting both men and women in civil service jobs. Whitehall researchers found that work, stress, and health were inter-related. The social gradient in health was shaped by such factors as one’s degree of social participation and sense of control over

The Whitehall studies demonstrated that other inequalities in the broader society of the United Kingdom shaped the social determinants of health, and they were stronger than any potentially “equalizing” factors stemming from a single-payer National Health Service. Universal provision of health care was no guarantee of universal equality in the outcomes it produced.


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their life, as well as their work climate and early life experiences.33

The important takeaway point here regarding sources of health inequality is that the Whitehall stud-ies demonstrated that other inequalities in the broader society of the United Kingdom shaped the social determinants of health, and they were stronger than any potentially “equalizing” factors stemming from a single-payer National Health Service. Universal pro-vision of health care was no guarantee of universal equality in the outcomes it produced.

One overly ambitious, if not utopian, policy response that might flow from examination of the many factors shaping differences in health is the one recently recommended by the Commission on Social Determinants of Health, set up by the World Health Organization in 2005 and chaired by Sir Marmot. It urged that “[a]ction on the social determinants of health must involve the whole of government, civil society and local communities, business, global fora, and international agencies. Policies and programmes must embrace all the key sectors of society not just the health sector.”34 Although the Commission may have suffered from an overdose of mission creep in insisting on addressing inequities in “the way society is orga-nized,” its more down-to-earth recommendations for starting to improve inequitable conditions of daily living centered on early childhood development and education.35

Lesson: Measure What MattersThe first broad lesson from the evidence above is that diagnosing the real causes of health inequalities and finding effective ways to reduce them (whether just limited to the elderly or to other categories of indi-viduals within the broader population) begins with better measurement that focuses on outputs instead of inputs, and health outcomes rather than health care processes. It needs to go well beyond surface indica-tors of relative access to different quantities and quali-ties of health services. More effective measurement should capture the influence on health outcomes of a more complex mix of possible factors, such as health behavior, diet, and life-course events. The public pol-icy objective should be to discover the reasons for dif-ferences in health, not just stop at detecting and high-lighting apparent statistical differences at the group level. This alternative path would focus more on devel-oping effective strategies to reduce health inequalities and improve health at the individual level.

Lesson: Examine the Real Causes of Health DifferencesOnce one begins to ask better questions regarding the true determinants of different health outcomes, different policy answers appear. At a basic level, the work of Michael McGinnis regarding the causes of early deaths (avoidable mortality) in the United States indicates that some 40 percent of them are caused by behavioral patterns that could be modified by preven-tive interventions, but only a much smaller proportion — perhaps 10-15 percent — could be avoided by better availability or quality of health care.36

The impacts of other domains on early deaths in the U.S. include 30 percent due to genetic predis-positions, 15 percent linked to social circumstances, and 5 percent related to environmental exposures. At a minimum, this evidence suggests that if we retar-geted our public resource commitments toward deal-ing more effectively and earlier in life with the non-medical determinants of population health, then we not only might rely somewhat less heavily on conven-tional medical interventions yet deliver better health outcomes; we also might even make more progress in improving the health of vulnerable groups such as the elderly.

Consumption of health care services provides only one input into the health-production function, in the “health capital” model of Michael Grossman.37 His extensive body of research reveals that other non-medical factors — such as exercise, nutrition, health-related behaviors, and social norms — account for much more of the differences in likely health out-comes among individuals and groups. The “upstream” determinants of future health — which begin to shape health outcomes even before one engages the health services sector more extensively and continue to oper-ate as powerful outside forces afterward — include one’s education level, culture, family, geography, neigh-borhood, health literacy, and self-care capability.

Let’s focus here primarily on education, because Grossman concludes that one’s education level is the most important correlate of good health, even more powerful than other socioeconomic variables like income or occupation. Related research by James Smith finds that additional schooling more strongly predicts disease onset than does either the income component of socioeconomic status (SES) or one’s health insurance status.38 Adreana Lleras-Muney esti-mates that one additional year of education increases life expectancy at age 35 by 1.7 years.39 In their exami-nation of the determinants of mortality, David Cut-ler, Angus Deaton, and Lleras-Muney conclude that, although the relationship between SES and health works to various degrees in both directions, the effects


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of the “education” component of SES on health are more consistent than those of the “income” compo-nent.40 Much of the link between income and health is the result of the latter causing the former, rather than the reverse.

Cutler, Deaton, and Lleras-Muney observe that greater levels of education are likely to provide gen-eral human capital that can be used to maintain and improve health in a wide range of circumstances.41 Education makes it easier (or cheaper) for people to obtain and process information about the causes and

consequences of one’s health. Education may change a person’s time preferences in a manner that encour-ages deferred gratification and future-oriented behav-ior, which favors investments in health relative to con-sumption of other commodities.42 Grossman suggests that more-educated people not only use the health care system more effectively, they also demand more from it.43 More broadly, those individuals are likely to feel more in control of their lives and more engaged in patient self-management, which is related to bet-ter problem-solving skills, richer social networks, and healthier life style norms.

Education differentials may best explain growing gaps in life expectancy among the elderly, accord-ing to Ellen Meara, Seth Richards, and David Cutler. They observe that all recent gains in life expectancy at age 25 have occurred among better-educated groups, with education-related gaps increasing by about 30 percent between the 1980s and 2000.44 Moreover, increased education differences among the elderly account for much of this growing gap in mortality and life expectancy. Meara and her colleagues note that these education-related changes “occurred during a period of increasing attention to health disparities and increased public spending designed to improve the health of less-advantaged populations.”45 One sig-nificant factor contributing to the growing differences in life expectancy appears to be the success of tobacco control policies that substantially reduced consump-tion of cigarettes. Meara, Richard, and Cutler point out that declines were greatest among the most-edu-

cated groups and cite this as another illustration of how prevention can widen disparities in health across education and income groups. Looking ahead to our current public health challenge regarding increased obesity, the authors conclude that better-targeted efforts to push successful health interventions into less-educated groups may be necessary to reduce socioeconomic disparities in health.46 Interestingly, they fail to suggest a more direct and potentially more effective policy approach — that is, by first improving the education level of those groups.

Although I have emphasized the crucial role of edu-cation as perhaps the most significant upstream deter-minant of differences in health, another “downstream” determinant involves the effectiveness and efficiency of the particular health care delivery system that a patient encounters. Despite the temptation to chase after narrowing age- and race-based health disparities at a crude aggregate level with premature inferences about provider bias, the important role of geography needs closer consideration. Health care is local. Its potential effect on the health of a particular patient depends greatly on where the latter actually lives. For example, geography shapes such factors as the volume of procedures conducted at particular hospitals and the availability of primary and specialist care.47 Hence, learning how to deliver more effective care, measur-ing and reporting how well various providers perform in meeting that standard, and designing more robust methods to ensure better accountability and incentiv-ize improved care delivery remain essential elements in reducing broader geographic variations in care, and resulting health — instead of mislabeling those varia-tions as health inequalities based primarily on age, race, or other demographic characteristics.

Lesson: Account for Time Lags in the Causes of Future Health OutcomesThe bias of our health care system toward interven-ing with relatively expensive health care services dur-ing the stages of life when symptoms of poor health become more acute, then chronic, and ultimately fatal

Learning how to deliver more effective care, measuring and reporting how well various providers perform in meeting that standard, and designing more robust methods to ensure better accountability and incentivize improved care delivery remain essential elements in reducing broader geographic variations

in care, and resulting health — instead of mislabeling those variations as health inequalities based primarily on age, race, or other demographic characteristics.


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reaches its peak in care for the elderly. This “just in time” mode of medical intervention, even allowing for its occasionally more efficient instances of preventive screening or even rarer instances of customized but comprehensive treatment of the “whole person” that incorporates social welfare needs along with medi-cal ones, still tends to overlook and obscure the long latency of early developmental factors behind many chronic illnesses that manifest much later in life.

The roots of those seemingly age-related chronic conditions actually may extend back to early childhood environments (if not initial fetal programming). David Barker’s ideas on the fetal origins of adult chronic dis-eases (the so-called womb with a view hypothesis) highlight the key role of the prenatal environment in causing gene expression that gives rise to susceptibil-ity to different diseases, abilities, and personality char-acteristics.48 As Robert Fogel observes, “[T]he severity and extent of chronic diseases at middle and late ages are, to a large extent, due to environmental insults at early ages, including in utero.”49 For example, nutri-tional deficiency for a developing fetus (e.g., low birth weight) will differentially compromise important functions that are operative only much later in the entire life cycle.

Because the visible onset and the consequences of many chronic conditions do not become apparent until those later ages (even until after age 65), some may tend both to understate the role that improving environmental factors increases what Fogel terms the initial stock of “physiological capital” with which a given generation, population, or individual begins life, and to overstate the degree to which the decline in mortality and morbidity during recent decades was due to improved medical technology.50 Hence, what we measure today as apparent “health inequalities” among the elderly may be largely due to environmental factors (such as nutrition) of an earlier period that are having their effects only much later. However, there is a broader trend over the past two centuries toward more equal distribution of physiological capital.

Fogel’s research reaches several important con-clusions regarding the sources of, and solutions to, remaining health status inequities. Prenatal and early childhood care and environmental issues are the most important areas of intervention to enhance the robust-ness and capacity of vital organ systems (the initial stock of “physiological capital”) as well as to affect its rate of depreciation. The main contribution of more advanced medical treatment over the last third of the 20th century has been to slow down the rate of depre-ciation in the stock of physiological capital that mem-bers of particular cohorts accumulated during much earlier developmental ages. Hence, greater emphasis

on lifestyle change is the key to improving health equity in rich countries like the United States.51 Fogel notes that the risky behaviors that undermine the accumula-tion of enhanced physiological capital are most preva-lent among the poor and poorly educated. Although he also concludes that greater access to clinical care is a high priority in promoting greater health equity, he finds that we cannot simply rely upon the extension of health insurance alone to achieve it. More aggres-sive outreach programs and more convenient access to health care services are needed most of all.

The Fogel analysis, to be sure, does not fit neatly into more conventional approaches to health inequal-ities within either the overall population or its more elderly cohorts. Nor does it offer a simplified alterna-tive health policy prescription. What it does strongly suggest is a rebalancing and broadening of our portfo-lio of public and private investments in improving the absolute and relative health of more vulnerable popu-lation groups, along with a greater appreciation of the long time line for measuring its results.

Lesson: The Long-Term Effects of Early Advantages and Disadvantages in Skills Development A related line of research by James Heckman further examines how broader “skill development” factors strongly influence resulting differences in health status across various groups.52 He finds that gaps in health status, like gaps in abilities and skills, are shaped and show up at early ages and persist — well before formal education in school begins. Individuals’ early behav-ioral traits and development of their cognitive and non-cognitive skills are strongly influenced by their family, and this early foundation will go on to affect greatly the evolution of one’s health capital into adult-hood.53 Heckman emphasizes that the development at early ages of non-cognitive abilities like perseverance, motivation, time preference, risk aversion, and self-control will have direct effects on future health choices and health outcomes as an adult, as well as on one’s social and economic well-being. Because those early advantages and disadvantages accumulate quickly, Heckman finds that interventions to boost health and skills development are more effective in early child-hood than later in life. Although later interventions for disadvantages may be possible, they are likely to be much more costly and less effective than early remediation, he counsels. Hence, his broader policy recommendation is that building mutually reinforc-ing early advantages for targeted populations is much less expensive than trying to correct developmental deficits and their likely consequences several years (or decades) later.54


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The broader message from the work of both Fogel and Heckman is that to improve overall population health as well as reduce longer-standing health dis-parities, interventions that improve environments during early childhood — particularly for more disad-vantaged and vulnerable populations — would provide more bang for the buck (at least if public policymakers were less prone to the sort of hyperbolic discounting more commonly attributed by researchers to poten-tially empowered health care consumers). However, this change in emphasis (at least in public policy) to move away from playing medically-intensive catch up games, with diminishing returns, would appear to conflict with the current tendencies of our health care politics, which encourage over-investment in pub-lic spending on more complex, costly, and intensive health care services for the elderly as a whole, rather than address much earlier the roots of the various dif-ferences in their health status later in life.

Lesson: Account for Patient Variation and Technological InnovationAnother important factor behind differences in patients’ health outcomes is how effectively they use the health care system and how it responds to their demands. This suggests that a more rapid overall dif-fusion of innovative health technology, but lags in its pattern of adoption, could appear to “worsen” health inequality even as it improves population health as a whole. Culter, Deaton, and Lleras-Muney describe an education-related health gradient whenever a mech-anism or technology exists that more knowledge-able and educated people can use more effectively to improve their health.55

Sherry Glied and Lleras-Muney found that more educated individuals have a larger survival advantage in those diseases and health conditions where there has been more technological progress in medicine.56 That is primarily because more educated people appear to benefit from development of new health care tech-nologies more rapidly than do less-educated people. Glied and Lleras-Muney observe that the former are better informed about medical innovation. They have a more positive view of its risks and benefits, and they do a better job in searching among providers that dif-fer in quality and practice patterns.57

Moreover, groups with greater levels of education not only use the health care system more effectively; they also demand more from it. Because educa-tion encourages future-oriented behavior, additional investments in health care are more valuable to those better educated in terms of their time preferences and opportunity costs.58

Apart from its effect on the rate of medical technol-ogy adoption by patients, education factors can shape differences in their compliance with effective treatment regimes and ability to self-manage their care. Individu-als with greater self-control and conscientiousness fol-low medical instructions and take better care of them-selves. Michael Grossman describes more-educated people as exhibiting better productive efficiency in obtaining better health outcomes, by allocating a given amount of health services inputs more effectively.59

The research of Dana Goldman and Darius Lakdawalla suggests that economy-wide growth in levels of education may encourage a particular kind of technological change, one that involves patient-inten-sive, “own-time” investments as opposed to simpler “time-saving” technologies.60 They imply that the lat-ter type of medical technology change is less likely for diseases confined more to the educated or rich. Hence, Goldman and Lakdawalla expect people with chronic, but treatable, conditions to exhibit greater health dis-parities.61 (Any connection to health inequality among the elderly remains unstated by them, but may not be coincidental). They therefore conclude that pre-vention of treatable conditions is more effective than prevention of untreatable disease in reducing health inequality.62

Finally, David Cutler, Ellen Meara, and Seth Rich-ards explain how research effort in medical technol-ogy and treatment tends to be targeted to address the most common health conditions in the popula-tion as a whole. They conclude that a byproduct of this “induced innovation,” which necessarily responds more to the medical needs of majority groups (like whites in the U.S.), is growth in mortality disparities between minority and majority groups.63

Accordingly, it should be no surprise that a health care system driven most effectively by its dominant payers and most articulate and active voters is likely to direct its technological development and deliver bet-ter results to those with more education and income.

Setting Priorities and TargetsBefore we are swallowed up in a never-ending effort to reverse apparent statistical indicators of health inequality among various groups (such as the elderly), we need to appreciate both their complex chain of causes and the quite diversified portfolio of policy approaches more likely to help ameliorate the most significant health disparities. Perceived opportunities to find and address any measurable differences in the health of various population groups, or even to target the most vulnerable ones for intervention, may appear limitless. However, the availability of resources and effective tools to alleviate, if not eliminate, them are


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not. Doing something, but not everything, and doing it better presumes a more realistic understanding of the degree to which more heightened levels and fre-quencies of intervention are advisable.

This article notes the lack of deep and robust evi-dence of health inequality among the elderly in this

country. Many supposed indicators of health inequal-ity are based on health inputs and processes of care rather than outcome-based measures that should matter most. They fail to focus on the more powerful causes of health differences which reach well beyond the quantity (and even the quality) of medical ser-vices delivered and received. Moreover, a humbler, but more effective approach to improving the health of vulnerable populations and reducing unnecessary differences in their health outcomes would take into account the entire time frame in which the health of the elderly is shaped and the long latency of cru-cial developmental factors in early life. It would also acknowledge the complicating factors of variation in patients’ characteristics and capabilities, and partic-ularly how they demand and use rapidly improving medical technologies.

One important starting point is to re-focus on achieving absolute, rather than relative, improvement in health outcomes. We should obsess less in poli-tics and policy over relative inequalities for different large subgroups of the population in accessing more quantities of health services and target instead efforts to achieve absolute improvements in their respective health outcomes.

Rather than pour more resources into just-in-time medical interventions to reduce slightly the predict-able findings of some health differences among the elderly that took a half-dozen or more decades to pro-duce, we should consider investing earlier, if not more often, in children from disadvantaged environments. Interventions to boost both health and skills develop-

ment (the mutually reinforcing components of health and human “capital”) in early childhood are much more effective than are medical investments later to maintain the depreciated health capital of the elderly. Early investments ultimately cost less and deliver larger returns later.

The implications for intervention priorities are to aim for more far-reaching improvement in the health of future generations of elderly Americans — through more focus on prenatal and early childhood care, encouragement of appropriate behavior by pregnant women, and reformation of destructive lifestyle prac-tices that “are more frequently practiced among the poor and the poorly educated than among the rich and the well-educated.”64

The physiological-capital strategy of early interven-tion also implies that improving outreach, mentoring, and education and providing more convenient access to and better delivery of health services represent more important factors than expanding the level and scope of health insurance coverage per se. It still needs to be complemented and bolstered by a health-capital strategy that helps more vulnerable people effectively “produce” better health. Important components of the latter include improving their educational opportuni-ties, deregulating the delivery and financing of medi-cal services to provide those patients with more choice and control, and increasing competition in health care markets. This strategy also would expand counseling support to encourage more future-oriented behav-ior, offer more assistance for consumers navigating a complex health system, and improve access to more actionable consumer health information.

To be sure, there will remain millions of chronically or acutely ill older patients in the meantime who need access to more effective and efficient medical treat-ment — no matter how much progress might be made in providing better incentives and tools for improving

Rather than pour more resources into just-in-time medical interventions to reduce slightly the predictable findings of some health differences among

the elderly that took a half-dozen or more decades to produce, we should consider investing earlier, if not more often, in children from disadvantaged

environments. Interventions to boost both health and skills development (the mutually reinforcing components of health and human “capital”)

are much more effective in early childhood than are medical investments later to maintain the depreciated health capital of the elderly. Early

investments ultimately cost less and deliver larger returns later.


Tom Miller

the installed base of health capital for younger and future generations. In determining which heightened levels and frequencies of medical intervention are advisable for the most vulnerable groups among the elderly, we should consider the degree to which they address more persistent and treatable, but less avoid-able, conditions. Unfortunately, targeting resources on the most medically significant problems among the elderly may conflict with the greater political market-ability of higher visibility (and broader-based) inter-ventions. In weighing the political factors between addressing the greatest needs versus claiming the greatest credit (serving fewer people, but the most vul-nerable ones, better — or simply serving more at the medical benefits buffet line), the temptation for office-holders and public program administrators remains to slice the salami so thin that all the elderly get is more baloney.

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looking for better health in all the wrong places: the road to “equality” hits a dead end

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