Doug Dix, Ph.D.
Professor of Biology and Medical Technology
Department of Health Science
University of Hartford
West Hartford, CT 06117
Of the 144 nations reporting complete health and economic data in the World Health Organization 2011 Report, 87 are objectively normal by 5 different health parameters and are considered healthy. The 57 remaining nations are considered sick because they are all abnormal by 1 health parameter and 51 are abnormal by at least 3 parameters. Of the 87 healthy nations, only 5 have health expenditure per capita (Health $/c) < 186. Of the 57 sick nations, only 10 have Health $/c > 186. By considering Health $/c = 186 as the minimum required for a nation to be healthy, I ranked all sick nations according to need and all healthy nations according to excess. Triage would encourage healthy nations to donate according to their excess to sick nations according to their need.
Better understanding of the relationship between the health and wealth of nations could improve the cost-effectiveness of humanitarian assistance and foster fulfillment of the Millennium Development Goals. Initially, health was considered an effect of wealth (1). Currently, however, the relationship is viewed as a positive feedback loop: Wealth enhances health, which then enhances wealth (2). While the merit in the current perspective is obvious, the definitions of national health and wealth leave something to be desired.
In an effort to measure each nation’s overall welfare, the United Nations Development Programme invented the Human Development Index (HDI), an aggregate of per capita health, wealth, and education statistics (3). While the HDI is a reasonable measure of a nation’s per capita welfare, per capita perspectives can mislead, for large nations with better per capita statistics than small ones can harbor more poor or sick people. When we want to compare nations, we cannot ignore the size of their populations. Needy people in rich nations are no less important than equally needy people in poor ones.
Aggregating health, wealth, and education data can also mislead, for health and wealth can vary oppositely. Wealth that is pursued by socially or environmentally destructive means is inherently unhealthy, while health that is pursued without fiscal responsibility, is economically destructive. And education that fosters destructive pursuits of health or wealth stifles both. A single aggregate of per capita health, wealth, and education data, therefore, is not the ideal measure by which to rank nations according to need or abundance.
The focus on per capita statistics might be one reason for sluggishness in pursuit of the Millennium Development Goals. There’s no consensus on the nations in greatest need. Is Brazil needy? Is Mexico or India? Which nation is most needy? Which is second, third, etc.? International triage requires that nations be ranked according to need or abundance by some objective criteria. But the only accepted criteria are per capita statistics.
It has been common to interpret a nation’s life expectancy at birth (LE) as a measure of national health, and a nation’s gross national income per capita (GNI/c) as a measure of national wealth. But no specific values for either parameter have been identified as distinguishing healthy from sick, or rich from poor nations, respectively. In this paper, I expand the definitions of national health and wealth, and objectively identify the specific values that distinguish healthy from sick nations. I identify the minimum values of wealth associated with national health and offer these values as international poverty lines. Nations are poor in proportion as they fall below these lines, and rich in proportion as they exceed them. Fair humanitarian assistance, perhaps, ideally, as World Tax, would flow from rich nations in proportion to their excess to poor nations in proportion to their need.
The World Health Organization lists various health and wealth statistics for 193 nations (4). For 49 of these nations, statistics are sparse, and, for this reason, these nations are ignored: Afghanistan, Andorra, Antigua & Barbuda, Bahamas, Bahrain, Barbados, Belize, Brunei Darussalam, Chad, Congo, Cook Islands, Cuba, Cyprus, Democratic People’s Republic of Korea, Dominica, Eritrea, Grenada, Guyana, Haiti, Kiribati, Kuwait, Madagascar, Malta, Marshall Islands, Micronesia, Monaco, Myanmar, Nauru, Niue, Oman, Palau, Qatar, Romania, St. Kitts & Nevis, St. Lucia, St. Vincent & Grenadines, Samoa, San Marino, Sao Tome & Principe, Saudi Arabia, Seychelles, Somalia, Suriname, Timor-Leste, Tonga, Tuvalu, United Arab Emirates, Vanuatu, and Zimbabwe. The remaining 144 nations are the subject of study.
Distinguishing Healthy from Sick Nations
For some clinical tests, it is possible to distinguish patients from normal subjects by plotting the clinical test results vs their percentile rank in the population (5). With colleagues, I quantified and simplified this method and demonstrated its utility in distinguishing normal from abnormal values without subjective assumptions (6-7). It is an ideal method for distinguishing normal from abnormal nations, and I have applied it to the nations under study.
Values for maternal mortality ratio (MMR = maternal pregnancy-related deaths per 100,000 live births) were calculated at each whole percentile over the 144 nations studied and plotted vs their percentile rank. The graph is linear with a single break in slope at the 65th percentile (MMR = 119.5) (Fig. 1). Abnormal values (the minority) exhibit a greater slope that normal values (the majority). By this method, nations are abnormal because their values for MMR differ more from each other than do the values for normal nations. Eighty-six nations are normal by this method: Albania, Argentina, Armenia, Australia, Austria, Azerbaijan, Belarus, Belgium, Bosnia & Herzegovina, Brazil, Bulgaria, Canada, Cape Verde, Chile, China, Columbia, Costa Rica, Croatia, Czech Republic, Denmark, Dominican Republic, Egypt, El Salvador, Estonia, Fiji, Finland, France, Georgia, Germany, Greece, Guatemala, Honduras, Hungary, Iceland, Iran, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Latvia, Lebanon, Libya, Lithuania, Luxembourg, Malaysia, Maldives, Mauritius, Mexico, Mongolia, Montenegro, Morocco, Netherlands, New Zealand, Nicaragua, Norway, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Republic of Korea, Republic of Moldova, Russia, Serbia, Singapore, Slovakia, Slovenia, Spain, Sri Lanka, Sweden, Switzerland, Syria, Thailand, Macedonia, Trinidad & Tobago, Tunisia, Turkey, Ukraine, United Kingdom, United States, Uruguay, Venezuela, and Viet Nam.
All of these nations also report values for infant mortality rate (IMR = deaths between birth and age 1 per 1000 live births), and under-five mortality rate (U5MR = deaths between birth and age 5 per1000 live births), less that the 65th percentile rank (IMR = 33.9 and U5MR = 43.8). The above mortality rates are average statistics and say nothing about the distribution of health within their respective nations. To incorporate within-nation health disparity into health assessment, I utilized each nation’s values for the United Nations Development Programme’s new inequality-adjusted life-expectancy index expressed as percentage life-expectancy loss (LE Loss) (3). All the above nations report values for LE loss less than 21.1, which is the value at the 65th percentile. In addition, all the above nations report values for life-expectancy at birth (LE) greater than 67 years, which is the value at the 39th percentile.
Data for MMR is for 2008 as listed in Table 2 of reference 4, and data for LE, IMR, and U5MR is for 2009 as listed in Table 1 of reference 4. Data for LE loss is for 2011 from Table 3 of reference 3. By this method, nations are abnormal if their values are as follows: MMR > 119.5, and/or IMR > 33.9 and/or U5MR > 43.8 and/or LE < 68 and/or LE Loss >21.0. I consider the above 86 nations that are normal by all 5 criteria to be healthy. In addition, I consider Algeria with MMR = 120 and all other statistics normal, to be healthy.
This definition of health is reasonable, but minimal. Egalitarian nations with small mortality rates and long LE can be sick by criteria not presently under consideration, e.g. stress, crime, psychosis. But non-egalitarian nations with large mortality rates or short LE cannot be considered healthy by any criterion.
Of the remaining 57 nations, 47 are abnormal by all five criteria: Angola, Bangladesh, Benin, Bhutan, Botswana, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Comoros, Cote d’Ivoire, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, India, Kenya, Laos, Lesotho, Liberia, Malawi, Mali, Mauritania, Mozambique, Namibia, Nepal, Niger, Nigeria, Pakistan, Papua, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Togo, Uganda, Tanzania, Yemen, Zambia. Of the remaining 10 nations, Bolivia is abnormal for MMR, IMR, U5MR, and LE Loss, and Turkmenistan for IMR, U5MR, LE, and LE Loss. Iraq is abnormal for IMR, U5MR and LE, and Tajikistan for IMR, U5MR, and LE Loss. Kazakhstan and Kyrgyzstan are abnormal for LE, Solomon Islands and Uzbekistan for LE Loss, and Indonesia and Ecuador for MMR.
In the final analysis of the 144 nations studied, 87 (including Algeria) are normal by all five criteria and considered healthy, 47 are abnormal by all five criteria, 2 by four criteria, 2 by three criteria, and 6 by one criterion. Except for Algeria, nations with abnormal values are considered sick. Some or all of the 6 nations sick by only one criterion might be false positive errors, i.e., they might, in fact, be healthy.
Distinguishing Rich from Poor Nations
To assess wealth, I plotted MMR vs total health expenditure per capita (Health $/c) (Fig. 2). Health expenditure data is in purchasing power parity in international dollars for 2008 as listed in Table 7 of reference 4. To clarify the difference between healthy and sick nations in Fig. 2, I focused only those 54 nations that were very poor, i.e., reporting Health $/c < 200 and those 32 nations that were very rich, i.e., reporting Health $/c greater than 1500 (Fig. 2). This focus makes the difference between rich (horizontal line) and poor (vertical line) conspicuous. Some of the 58 nations with Health $/c between 200 and 1500 fall on neither line (Fig. 2). This proves that the relationship between MMR and health expenditure is not a simple dichotomy. But the discrepant points are uncommon and not far from one or the other line. The simple dichotomy, therefore, is a reasonable first-approximation of the relationship between health and wealth. For the vertical line, the slope is -3.47 and the intercept 764. For the horizontal line, the slope is .0005 and the intercept 8. The lines intersect at Health $/c = 218. This is one measure of the minimum health expenditure for a nation to be rich. It approximates a more relevant parameter, i.e., the minimum health expenditure for a nation to be healthy. This is estimated by solving the equation of the vertical line for Health $/c at the critical MMR = 119.5. This value is 186.
Of the 87 healthy nations, only 5 (Cape Verde, Fiji, Mongolia, Philippines, and Syria) have Health $/c < 186. Of the 57 sick nations, only 10 (Bhutan, Bolivia, Botswana, Ecuador, Equatorial Guinea, Gabon, Kazakhstan, Namibia, South Africa, and Swaziland) have Health $/c > 186. Except for these 15 outliers, the 144 nations break into a natural dichotomy of 82 rich and healthy nations and 47 poor and sick nations. Of the sick outliers, Ecuador and Kazakhstan are each sick by only one criterion and might, in fact, be healthy. As a predictor of national sickness, Health $/c < 186 is at least 82% sensitive (positive in sick nations) and 94% specific (negative in healthy nations). I suggest that nations with Health $/c > 186 are rich enough to be healthy.
Cape Verde, Fiji, Mongolia, Philippines, and Syria manage to be healthy at lower health expenditure. They are the champions of cost-effectiveness, and their health budgets might be models to emulate. How do they achieve health with sub-par health expenditures? The answer is unknown. But all 5 of these nations report values for LE loss < 19, i.e., they are egalitarian. There is strong evidence that egalitarianism fosters health (8-9).
At the other extreme, Bhutan, Bolivia, Botswana, Ecuador, Equatorial Guinea, Gabon, Kazakhstan, Namibia, South Africa, and Swaziland fail to achieve health at high health expenditure. They are models of inefficiency. Why do they fail to achieve health in spite of substantial health expenditures? The answer is unknown, but all 10 of these nations report values for LE loss > 20. They are non-egalitarian.
The Need of Nations
On the suggestion that health expenditure per capita of $186 is the minimum necessary for health, a poor nation’s per capita need for health spending can be estimated as the difference between $186 and that nation’s Health $/c. A nation’s total need is the product of its per capita need and its population. Each nation’s total population for 2009 (in thousands) is listed in Table 9 of reference 4. The 47 poor, sick nations are listed according to need in Table 1. The total need for Health $ of all 47 needy nations is $231 billion.
To provide health expenditure, a nation needs income. The graph of Health $/c, on the vertical axis, vs gross national income per capita (GNI/c), on the horizontal, is linear over all nations studied, with slope = .098, intercept = -205, and Pearson correlation coefficient, r, = .948. Values for GNI/c in purchasing power parity in international dollars for 2009 are listed in Table 9 of reference 4. While nations can convert income into health expenditure without limit, their ability to convert health expenditure into health reaches saturation at Health $/c near 500 (Fig. 2).
I suggest that Health $/c is the effect of GNI/c. We can find the minimum GNI/c necessary for health by solving the above equation for Health $/c = 186. This value is $3990. A poor nation’s per capita need for income can then be estimated as the difference between $3990 and its GNI/c. A nation’s total need is the product of its per capita need and its population. The 47 poor, sick nations are listed according to need for income in Table 1. The total need for GNI of all needy nations is $3495 billion.
Of the 89 nations with GNI/c > 3990, only two, Angola and Turkmenistan, fail to achieve Health $/c = 186, and Angola is only $3/c short of this goal. These nations are models of inefficiency at converting income into health expenditure. Of the 55 nations with GNI/c < 3990, only 4, Honduras, Moldova, Nicaragua, and Viet Nam managed to achieve Health $/c = 186. These nations are models of efficient conversion of income into health expenditure. We might wonder about the factors that influence a nation’s ability to translate income into health expenditure, and vice versa, and look to these exceptional nations for answers.
The Excess of Nations
By the above reasoning, a rich nation’s health expenditure per capita in excess of what it needs for health is the difference between that nation’s Health $/c and $186. A nation’s total excess is the product of its per capita excess and its population. The 87 rich, healthy nations are ranked according to excess in Table 2. The total excess health expenditure of all rich, healthy nations is $5009 billion.
By the above reasoning, a rich nation’s income per capita in excess of what it needs for health is the difference between that nation’s GNI/c and $3990. A nation’s total excess is the product of its per capita excess and its population. The 87 rich, healthy nations are ranked according to excess in Table 2. The total excess GNI of all rich, healthy nations is $46357 billion.
Converting Humanitarian Assistance to International Triage
How should we judge the overall welfare of nations? The HDI is a convenient per capita statistic and correlates well with per capita health and wealth statistics, but does not correlate well with national population or with the above measures of national need or excess (Table 3). The Pearson correlation coefficient, r, between the two measures of need, Health $ and GNI, is .94 over all 45 nations that are needy for both. Both measures of national need also correlate very strongly with national population: Need for Health $ vs population, r = .97 over the 47 nations in need of Health $; Need for GNI vs population, r = .92 over the 45 nations in need of GNI. Turkmenistan and Angola need Health $ but not GNI.
The r between the two measures of excess, Health $ and GNI, is .96 over the 78 nations that are excessive for both. Neither measure of national excess correlates well with national population: Excess for Health $ vs population, r = .26 over the 82 nations that are excessive for Health $; Excess for GNI vs population, r = .47 over the 78 nations that are excessive for GNI. Nicaragua, Viet Nam, Moldova, and Honduras are excessive for Health $, but not GNI. For the purpose of converting international humanitarian assistance to international triage, the above measures of national need and excess are more relevant than HDI and the other per capita statistics.
The Cost of Health for All
The cost of making all poor nations rich enough to be healthy by the above reasoning is 4.6% of all rich, healthy nations’ excess health expenditure. Among the healthy nations, 3 (USA, Japan, and Germany) each have excess Health $ greater than the total need for Health $ of all poor nations. The cost of raising GNI in all needy nations to the minimum to support adequate health expenditure is 7.5% of all rich, healthy nations’ excess GNI. Among the healthy nations, 3 (USA, China, and Japan) each have excess GNI greater than the total need for GNI of all poor nations. Fair humanitarian assistance, perhaps, ideally in the form of a World Tax would solicit 4.6% from each rich, healthy nation’s excess Health $ as listed in Table 2 for donation to each poor, sick nation according to need as listed in Table 1.
The final Millennium Development Goal calls for a global partnership. A fair basis for such a partnership would include an international business plan to move 7.5% from each rich, healthy nation’s excess GNI as listed in Table 2 into each poor, sick nation according to need as listed in Table 1. This might be in the form of contracts to buy health and ecology. Rich nations might pay poor nations to hire their people to build health and preserve their environments. Rich nations might look to the champions of cost-effectiveness, Cape Verde, Fiji, Mongolia, Philippines, and Syria, and also to the expert translators of income into health expenditure, Honduras, Moldova, Nicaragua, and Viet Nam for strategic guidance.
Brazil and Mexico are commonly considered poor, but both rank among the nations with greatest abundance (Table 2). It is more accurate to consider Brazil and Mexico to be rich nations that harbor large populations of poor, sick people because rich Brazilians and rich Mexicans hoard wealth. The income GINI coefficient, a measure of a nation’s economic disparity, is 52.9 for Brazil and 51.7 for Mexico, as listed in Table 3 of reference 3. By comparison, the USA, with the world’s greatest excess and a reputation for economic disparity, has a GINI of “only” 40.8.
India is newly considered rich, but ranks as the most needy nation (Table 1). The GINI for India is 36.8, but with prosperity will likely rise. Seychelles is most egalitarian with a GINI at 19.0, followed by Sweden at 25.0, Norway at 25.8, and Belarus at 27.2. Comoros is least egalitarian with a GINI at 64.3, followed by Haiti at 59.5 and Angola at 58.6, according to Table 3 of reference 3.
Dissecting the Relationship Between Wealth and Health
Money, at least in the form of health expenditure, can be viewed as a nutrient, and nations can suffer from excess (financial obesity) as well as deficiency (Kwasiorkor). Most healthy nations are suffering from financial, and, in many cases, also nutritional, obesity. If wealth is a necessary condition for health, 44 nations have less of it than they need. Rich nations would get more health for their dollars, and less obesity, if they donated them to such needy nations.
A Classical Hypothesis
What causes a nation to be sick? Inadequate health expenditure. What causes a nation’s health expenditure to be inadequate? Deficient income or excessive expense. What evidence, in addition to the above, supports this reasoning? Indonesia (GNI/c = $3720) is the poor, sick nation with the highest GNI/c that is less than $3990. Every nation with GNI/c > $3720 can support Health $/c > 186. As a test of inadequate health expenditure (Health $/c < 186), GNI/c < 3721 is 90% sensitive (positive in poor nations) and 95% specific (negative in healthy nations). Total fertility rate (TFR) is one measure of a nation’s expenses. Values for TFR are listed as children/woman in Table 9 of reference 4. In all poor nations, TFR is greater than 1.9. Among all rich nations, 57% report TFR less than 1.9. As a test of inadequate health expenditure, the combination of GNI/c < 3721 and TFR > 1.9 is 90% sensitive and 97% specific. In other words, the combination of inadequate income and excessive expense is an excellent predictor of inadequate health expenditure, which is an excellent predictor of a nation being sick
How will we know when we’ve achieved health for all? We will have delivered “the most appropriate interventions for the most common health problems in the communities in greatest need” (10). As a result, the value vs percentile plots of health statistics will loose the break they now possess (Fig. 1). All nations will be on the same slope. How can we get that result? By practicing international triage, ie., by calculating each nation’s need for income if needy, or excess income if wealthy. With that information, we can solicit from each rich nation according to its excess in order to give to each poor nation according to its need, and, in this way, achieve health and wealth for all.
The author is grateful for advice and criticism from Patricia Cohen, Ph.D.
|Table 1: The Need of Nations ($ million)|
|Nation||Health $ Need||Nation||GNI Need|
|D. R. Congo||10758||Kenya||96316|
|Iraq||2425||D. R. Congo||62700|
|Cote d’Ivoire||2068||Cote d’Ivoire||49585|
|Burkina Faso||1643||Burkina Faso||44556|
|C. African Rep.||678||Liberia||14430|
|Laos||643||C. African Rep.||14256|
|Table 2: The Excess of Nations ($ millions)|
|Nation||Health $ Excess||Nation||GNI Excess|
|Bosnia & Herz||2854||Costa Rica||31924|
|Libya||2022||Trinidad & T||27274|
|Azerbaijan||1839||Bosnia & Herz||17822|
|Trinidad & T||1366||Mauritius||12064|
|Table 3: Pearson Correlation Coefficients with HDI|
|MMR||-0.85||all nations, n = 144|
|IMR||-0.91||all nations, n = 144|
|U5MR||-0.89||all nations, n = 144|
|LE||0.91||all nations, n = 144|
|LE Loss||-0.93||all nations, n = 144|
|TFR||-0.87||all nations, n = 144|
|Health $/c||0.74||all nations, n = 144|
|GNI/c||0.81||all nations, n = 144|
|Population||-0.01||all nations, n = 144|
|Health $ Need||0.19||all nations in need of Health $, n = 47|
|GNI Need||0.17||all nations in need of GNI, n = 45|
|Health $ Excess||0.25||all nations with excess Health $, n = 82|
|GNI Excess||0.25||all nations with excess GNI, n = 78|
1) Bloom D., Canning D. 2007, Commentary: The Preston curve 30 years on: Still sparking fires. Journal of Epidemiology, 36: 498-99.
2) Bloom D., Canning D. 2000, The health and wealth of nations. Science 287: 1207-09.
3) United Nations Development Programme. 2011, Human Development Report 2011, Sustainability and Equity, a Better Future for All, Human Development Statistical Tables, ISBN 9780230363311
4) World Health Organization, World Health Statistics 2011, Part 11, Global Health Indicators, ISBN 978 92 4 156419 9
5) Solberg, Helge E. 1994. Using a hospitalized population to establish reference intervals: Pros and cons. Clinical Chemistry 40: 2205-06.
6) Merkouriou, S., Dix D. 1988. Estimating reference ranges in clinical pathology: An objective approach. Statistics in Medicine 7: 377-85.
7) Morse M., Dix, D. 1995. Determining reference ranges. Laboratory Medicine 26: 282-85.
8) Vaupel, J., Zhang, Z., van Raalte A. 2012. Life expectancy and disparity: An international comparison of life table data. BMJ Open, doi:10.1136/bmjopen-2011-000128.
9) Grimm M. 2011. Does inequality in health impede economic growth?” Oxford Economic Papers 63:448-74.
10) Chen, L., Cash, R.1988. A decade after Alma Ata: Can primary health care lead to health for all? New England Journal of Medicine 319: 946-47.
Reply to Dix@hartford.edu