01 Jul 2006 Don’t Fall Prey to Propaganda: Life Expectancy and Infant Mortality are Unreliable Measures for Comparing the U.S. Health Care System to Others
How does the United States health care system fare when compared to the rest of the industrialized world? This is an important question. Accurately measuring our health care system relative to those of other nations can yield insight into the types of health care policies America should pursue.
New York Times columnist Paul Krugman has expressed the view that the U.S. health care system is inferior:
The United States spends far more on health care than other advanced countries. Yet we don’t appear to receive more medical services. And we have lower life expectancy and higher infant mortality rates than countries that spend less than half as much per person. How do we do it?1
Life expectancy and infant mortality are two measures that are widely cited, yet seldom questioned. This is unfortunate, because life expectancy and infant mortality tell us little about the efficacy of a health care system.
This paper examines the deficiencies of using life expectancy and infant mortality to measure a health care system. It also examines the question: How should we measure a health care system?
Why Life Expectancy and Infant Mortality are Popular Measures
Type in the terms “life expectancy,” “infant mortality” and “health care” into the popular search engine Google, and it will yield about 449,000 results. Clearly, linking these two measures to health care is very popular. It is easy to understand why.
Life expectancy and infant mortality are powerful tools for those who support some form of socialized medicine. On those measures the United States fares worse than all other industrialized nations. Most other industrialized nations have some form of government-run, universal health insurance. Thus, the reasoning goes, America’s inferior performance on life expectancy and infant
mortality is due to its heavy reliance on a system of private sector care.
Paul Krugman is in good company. Liberal pundit Sebastian Mallaby recently lamented that the American health care system
…achieves shorter life expectancy than the British, French, German, Canadian or Japanese systems, but it eats up 16 percent of the resources in the economy, compared with between eight and 11 percent in those other countries. The difference – six percent or so of economic output – suggests that the waste in the American system comes to $700 billion a year.
He concludes that the “most plausible subsidizer of universal insurance is government, and the only entity with a stake in lifelong wellness is the government.”2
A recent study that compared the U.S. to Canada and garnered some media attention used life expectancy as a measure of the efficacy of each nation’s health care system. Noting that Canada spends about half as much on health care as does the U.S., the scholars stated, “Canadians live two to three years longer.”3 The scholars concluded, “Universal health care attenuates inequities in health care and should be implemented in the United States.”4
Physicians for a National Health Program, a vocal advocacy group, recently examined the health care systems in 16 industrialized countries. The only measures that the study used to compare the different nations were, not surprisingly, life expectancy and infant mortality.5 The Center for Economic and Policy Research, a Washington D.C. think tank that supports government-run health care, produced the following table. Using expenditure per capita on health care as a proxy for health care system, it shows that America spent more on health care but got less return than countries that had some form of universal health insurance. “The high costs and poor outcomes seem to stem from inefficiencies that are unique to the U.S. health care system,” the Center for Economic and Policy Research claimed.6
Life expectancy and infant mortality are widely used as measures of a health care system because doing so serves an ideological agenda of greater government involvement in health care. However, these measures are useless for trying to determine the effectiveness of a health care system. Even some advocates of government-run health care acknowledge this. For example, Jonathan Cohn of The New Republic states “those statistics are pretty crude measures.”7
The next three sections offer an explanation why.
Measuring Health Care Systems
Any statistic that accurately measures health-care systems across nations must satisfy three criteria. First, the statistic must assume actual interaction with the health care system. Second, it must measure a phenomenon that the health care system can actually affect. Finally, the statistic must be collected consistently across nations.
Under the first criterion, the phenomenon being measured must be one in which the individual actually has contact with the health care system. More specifically, he must have contact with a health care professional, be it a doctor, nurse, lab technician, etc. A statistic measuring the rate of cancer survival satisfies this criterion, since diagnosis and treatment of cancer requires health care professionals. By contrast, a statistic measuring the rate of car accidents would not satisfy such a criteria since health care professionals are not essential to identifying car accidents.
Some statistics may assume interaction with the health care system, but the phenomena they measure are not ones on which the health care system can have any meaningful impact. Take, for example, the rate of cancer incidence. While this statistic assumes interaction with the health care system (an incidence of cancer cannot be known without the diagnosis of a health care professional), there is little a health care system can do about the rate of cancer. Rather, cancer incidence is affected by factors such as genetics, diet, lifestyle, etc., over which the health care system has no control. Thus, to be an adequate measure of the effectiveness of a health care system, a statistic must measure a phenomenon that health care professionals can actually affect.
Finally, a statistic must be collected consistently across nations. While this seems simple in theory, in practice it is quite complicated. Nations use diverse definitions of health phenomena. This leads to some nations excluding a segment of their populations from the collection of a statistic while other nations include those segments. In such circumstances, cross-national comparisons are largely meaningless. Thus, for health care systems across countries to be meaningful, there should be little to no variation in how statistics are collected.
As shown below, both life expectancy and infant mortality are poor measures of a health care system because each fails to satisfy at least one of the above criteria.
Life Expectancy
Life expectancy is a poor statistic for determining the efficacy of a health care system because it fails the first criterion of assuming interaction with the health care system. For example, open any newspaper and, chances are, there are stories about people who die “in their sleep,” in a car accident or of some medical ailment before an ambulance ever arrives. If an individual dies with no interaction with the health care system, then his death tells us little about the quality of a health care system. Yet all such deaths are computed into the life expectancy statistic.
Life expectancy also largely violates the second criterion – a health care system has, at most, minimal impact on longevity. One way to see this is to reexamine the table constructed by the Center for Economic and Policy Research. The interpretation that the Center for Economic and Policy Research wants readers to derive from Table 1 is that the United States would be better off with a system of universal health care. However, a careful examination of that table yields a more accurate interpretation: There is no relationship between life expectancy and spending on health care. Greece, the country that spends the least per capita on health care, has higher life expectancy than seven other countries, including Belgium, Denmark, Finland, Germany, Netherlands, the United Kingdom and the United States. Spain, which spends the second least per capita on health care, has higher life expectancy than ten other countries that spend more.
More robust statistical analysis confirms that health care spending is not related to life expectancy. Studies of multiple countries using regression analysis found no significant relationship between life expectancy and the number of physicians and hospital beds per 100,000 population or health care expenditures as a percentage of GDP. Rather, life expectancy was associated with factors such as sanitation, clean water, income, and literacy rate.8 A recent study examined cross-national data from 1980 to 1998. Although the regression model used initially found an association between health care expenditure and life expectancy, that association was no longer significant when gross domestic product (GDP) per capita was added to the model.9 Indeed, GDP per capita is one of the more consistent predictors of life expectancy.
Yet the United States has the highest GDP per capita in the world, so why does it have a life expectancy lower than most of the industrialized world? The primary reason is that the U.S. is ethnically a far more diverse nation than most other industrialized nations. Factors associated with different ethnic backgrounds – culture, diet, etc. – can have a substantial impact on life expectancy. Comparisons of distinct ethnic populations in the U.S. with their country of origin find similar rates of life expectancy. For example, Japanese-Americans have an average life expectancy similar to that of Japanese.10
A good deal of the lower life expectancy rate in the U.S. is accounted for by the difference in life expectancy of African-Americans versus other populations in the United States. Life expectancy for African-Americans is about 72.3 years, while for whites it is about 77.7 years.11 What accounts for the difference? Numerous scholars have investigated this question.12 The most prevalent explanations are differences in income and personal risk factors. One study found that about one-third of the difference between white and African-American life expectancies in the United States was accounted for by income; another third was accounted for by personal risk factors such as obesity, blood pressure, alcohol intake, diabetes, cholesterol concentration, and smoking and the final third was due to unexplained factors.13 Another study found that much of the disparity was due to higher rates of HIV, diabetes and hypertension among African Americans.14 Even studies that suggest the health care system may have some effect on the disparity still emphasize the importance of factors such as income, education, and social environment.15
A plethora of factors influence life expectancy, including genetics, lifestyle, diet, income and educational levels. A health care system has, at best, minimal impact. Thus, life expectancy is not a statistic that should be used to inform the public policy debate on health care.
Infant Mortality
At first glance, infant mortality appears to be a good measure of a health care system. First, it assumes interaction with a health care system since most babies born in the industrialized world are born in a hospital or other health care facility. It also satisfies the second criterion of assuming that health care professionals can affect the outcome, since doctors and nurses have a direct impact on the survival chances of a newborn. If infant mortality were accepted as an adequate measure based on those two criteria alone, then the U.S. health care system is one of the least effective in the industrialized world. This can be seen by constructing a table using the data on infant mortality utilized in the report from the Physicians for a National Health Program. Table 2 shows that on infant mortality, the U.S. ranks below all nations save New Zealand.
But infant mortality tells us a lot less about a health care system than one might think. The main problem is inconsistent measurement across nations. The United Nations Statistics Division, which collects data on infant mortality, stipulates that an infant, once it is removed from its mother and then “breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles… is considered live-born regardless of gestational age.”16 While the U.S. follows that definition, many other nations do not. Demographer Nicholas Eberstadt notes that in Switzerland “an infant must be at least 30 centimeters long at birth to be counted as living.”17 This excludes many of the most vulnerable infants from Switzerland’s infant mortality measure.
Switzerland is far from the only nation to have peculiarities in its measure. Italy has at least three different definitions for infant deaths in different regions of the nation.18 The United Nations Statistics Division notes many other differences.19 Japan counts only births to Japanese nationals living in Japan, not abroad. Finland, France and Norway, by contrast, do count births to nationals living outside of the country. Belgium includes births to its armed forces living outside Belgium but not births to foreign armed forces living in Belgium. Finally, Canada counts births to Canadians living in the U.S., but not Americans living in Canada. In short, many nations count births that are in no way an indication of the efficacy of their own health care systems.
The United Nations Statistics Division explains another factor hampering consistent measurement across nations:
…some infant deaths are tabulated by date of registration and not by date of occurrence… Whenever the lag between the date of occurrence and date of registration is prolonged and therefore, a large proportion of the infant-death registrations are delayed, infant-death statistics for any given year may be seriously affected.20
The nations of Australia, Ireland and New Zealand fall into this category.
Registration problems hamper accurate collection of data on infant mortality in another way. Looking at data from 1984-1985, Eberstadt argued that, “Underregistration of infant deaths may also be indicated by the proportion of infant deaths reported for the first twenty-four hours after birth.”21 Eberstadt found that in the U.S. and Canada more than a third of all infant death occurred during the first day, but in Sweden and France they accounted for less than one-fifth. Table 3 shows that the pattern still holds today.
Inconsistent measurement explains only part of the difference between the U.S. and the rest of the world. Were measurements to be standardized, according to Eberstadt, “America might move from the bottom third toward the middle, but it would be unlikely to advance into the top half.”22 Another factor affecting infant mortality Eberstadt identifies is parental behavior.23 Pregnant women in other countries are more likely to either be married or living with a partner. Pregnant women in such households are more likely to receive prenatal care than pregnant women living on their own. In the U.S., pregnant women are far more likely to be living alone. Although the nature of the relationship is still unclear (it is possible that mothers living on their own are less likely to want to be pregnant), it likely leads to a higher rate of infant mortality in the U.S.
In summary, infant mortality is measured far too inconsistently to make cross-national comparisons useful. Thus, just like life expectancy, infant mortality is not a reliable measure of the relative merits of health care systems.
Conclusion
Life expectancy and infant mortality are wholly inadequate comparative measures for health care systems. Life expectancy is influenced by a host of factors other than a health care system, while infant mortality is measured inconsistently across nations. Neither of these measures provides the United States with conclusive guidance on health care policy, let alone serve as reliable evidence that a system of universal health care “should be implemented in the United States.”24
Do measures that would permit accurate cross-national comparisons of health care systems exist? The most exhaustive source of cross-national data is the Organization for Economic Co-operation and Development (OECD). Yet the OECD notes that in most cases its data is not “internationally comparable” because “there is a lack of international agreement on the most promising indicators and many definitions of each indicator that could be adopted.”25
To rectify this problem, the OECD and the Commonwealth Fund have embarked on a collaborative effort to develop comparable measures across nations. Called the “OECD Health Care Quality Indicators Project,” it is taking the “first steps towards a comprehensive reporting system for quality of care in OECD member countries.”26 A recent report updating the progress of this project looks promising. For example, one standard that an indicator must meet is its “susceptibility to being influenced by the health care system.”27 The researchers pose important questions on this regard, including, “Can the health care system meaningfully address this aspect or problem?” and “Does the health care system impact on the indicator independent of confounders like patient risk?”28 In other words, these statistics will assume interaction with a health care system and measure phenomena that a health care system actually affects. Furthermore, the aim of this project is to assure that data is collected consistently across nations, so that national policymakers have “the opportunity to compare the performance of their health care delivery systems against a peer group”29
While the project researchers have chosen many indicators that measure phenomena that are actually affected by a health care system, comparability issues across nations remain. For example, one indicator measures the fatality rate within 30 days of those diagnosed with acute myocardial infarction (heart attack). However, the report notes that some “countries are able to track patients after hospital discharge, [while] some are not.”30
Hopefully such difficulties can be resolved as the project progresses. In the meantime, policymakers, pundits and reporters should stop referring to life expectancy and infant mortality as meaningful comparative measures of health care systems.
David Hogberg, Ph.D. is a senior policy analyst for The National Center for Public Policy Research.
Footnotes:
1 Paul Krugman, “Passing the Buck,” New York Times, April 22, 2005, p. A23.
2 Sebastian Mallaby, “Bush’s Turn To Health Care. President Ready to Expand the Public Role?” Washington Post, January 16, 2006, available at http://www.washingtonpost.com/wp-dyn/content/article/2006/01/15/AR2006011500929.html as of June 27, 2006.
3 Karen E. Lasser, David U. Himmelstein and Steffie Wollhandleer, “Access to Care, Health Status, and Health Disparities in the United States and Canada: Results of a Cross-National Population-Based Survey,” American
Journal of Public Health, July 2006, Vol. 96, No. 7, p. 1300.
4 Ibid, p. 1306
5 Physicians For A National Health Program, “International Health Systems,” December 12, 2005, available at http://www.pnhp.org/facts/international_health_systems.php as of June 27, 2006.
6 Dean Baker and David Rosnick, “The Burden of Social Security Taxes and the Burden of Excessive Health Care Costs,” Issue Brief, Center for Economic and Policy Research, p.4, available at http://cepr.net/publications/social_security_2005_03_24.pdf as of June 27, 2006.
7 “Healthy Competition: What’s Holding Back Health Care and How to Free It,” Book Forum, Cato Institute, Washington, D.C., November 29, 2005, viewable at http://www.cato.org/event.php?eventid=2420 as of June 27, 2006.
8 M. Furukawa, “Factor Analysis of Attributive Determinant for Life Expectancy and Infant Mortality Rate With Recipient Country Data in Consideration of Socioeconomic Environment,” Nippon Eiseigaku Zasshi, 2005, Vol. 60, pp. 335-344; Gabriel Gulis, “Life Expectancy as an Indicator of Environmental Health,” European Journal of Epidemiology, 2000, Vol. 16, pp. 161-165 and
Erica Hertz, James R. Herbert and Joan Landon, “Social and Environmental Factors and Life Expectancy, Infant Mortality, and Maternal Mortality Rates: Results of a Cross-National Comparison,” Social Science and Medicine, 1994, Vol. 39, pp. 105-114.
9 Cynthia Ramsay, “Beyond the Public-Private Debate: An Examination of Quality, Access and Cost in the Health Care Systems of Eight Countries,” Marigold Foundation, Alberta, Canada, July 2001, available at http://www.davidgratzer.com/report1/MarigoldStudyPDF.pdf as of June 27, 2006. Especially see page 33.
10 “How Not To Judge Our Health Care System,” Brief Analysis, National Center for Policy Analysis, Dallas, Texas, November 15, 1994, available at http://www.ncpa.org/ba/ba141.html as of June 27, 2006. Also see Chapter 4 of John C. Goodman, Gerald L. Musgrave and Devon M. Herrick, Lives At Risk: Single-Payer National Health Insurance around the World, (Lanham, Maryland: Rowman and Littlefield Publishers, 2004).
11 “United States Life Tables, 2002,” National Vital Statistics Reports, November 10, 2004, Vol. 53, No. 6, Centers for Disease Control, Atlanta, Georgia, available at http://www.cdc.gov/nchs/data/nvsr/nvsr53/nvsr53_6.pdf as of June 27, 2006.
12 For a summary of the research on race and life expectancy, see Robert H. Hummer, “Black-White Differences In Health And Mortality: A Review And Conceptual Model,” The Sociological Quarterly, 1996, Vol. 37, No. 1, pp. 105-125.
13 M. W. Otten Jr, S. M. Teutsch, D. F. Williamson and J. S. Marks, “The Effect of Known Risk Factors on the Excess Mortality of Black Adults in the United States,” Journal of the American Medical Association, 1990, Vol. 263, No. 6, pp. 845-50.
14 Mitchell D. Wong, Martin F. Shapiro, W. John Boscardin and Susan L. Ettner, “Contribution of Major Diseases to Disparities in Mortality,” The New England Journal of Medicine, 2002, Vol. 347, pp. 1585-1592.
15 Paul Sorlie, Eugene Rogot, Roger Anderson, Norman J. Johnson, and Erick Backlund, “Black-White Mortality Difference by Family Income,” The Lancet, 1992, Vol. 340, No. 8815, p. 350.
16 “Table 4 – Notes,” Demographic Yearbook 2002, United Nations Statistics Division, p. 1, available at http://unstats.un.org/unsd/demographic/products/dyb/dyb2002/NotesTab04.pdf as of June 27, 2006.
17 Nicholas Eberstadt, The Tyranny of Numbers: Measurement and Misrule, (Washington: The AEI Press, 1995), p. 50.
18 Ibid, p. 50. Also see Miranda Mugford, “A Comparison of Reported Differences in Definitions of Vital Events and Statistics,” World Health Statistics, vol. 36, 1987.
19 “Infant Deaths and Infant Mortality Rates by Age and Sex: Latest Available Year, 1993-2002,” Demographic Yearbook 2002, United Nations Statistics Division, p. 17, available at http://unstats.un.org/unsd/demographic/products/dyb/dyb2002/Table16.pdf as of June 27, 2006.
20 “Table 16,” Demographic Yearbook 2002, United Nations Statistics Division, p. 1, available at http://unstats.un.org/unsd/demographic/products/dyb/dyb2002/NotesTab16.pdf as of June 27, 2006.
21 Eberstadt, p.50.
22 Ibid, p. 51.
23 Ibid, pp. 57-60.
24 Karen E. Lasser, David U. Himmelstein and Steffie Wollhandleer, “Access to Care, Health Status, and Health Disparities in the United States and Canada: Results of a Cross-National Population-Based Survey,” Health Affairs, July 2006, Vol. 96, No. 7, p. 1306.
25 “OECD Health Care Quality Indicators Project,” Organization for Economic Co-operation and Development, Paris, France, available at http://www.oecd.org/document/31/0,2340,en_2649_34629_2484127_1_1_1_1,00.html as of June 27, 2006.
26 Ibid.
27 Edward Kelley and Jeremy Hurst, “Health Care Quality Indicators Project Initial Indicators Report,” OECD Health Working Papers, March 9, 2006, p. 13, available at http://www.oecd.org/dataoecd/1/34/36262514.pdf as of June 27, 2006.
28 Ibid.
29 “OECD Health Care Quality Indicators Project.”
30 Kelley and Hurst.