On the Role of Wealth in the Health of Nations: An Objective Approach to International Triage

Doug Dix, Ph.D.

Professor of Biology and Medical Technology

Department of Health Science

University of Hartford

West Hartford, CT 06117

Summary

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.

Introduction

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.

World Statistics

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.

Implications

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

Conclusion

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.

Acknowledgement

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
India 76672 India 886520
Bangladesh 23032 Bangladesh 395768
Pakistan 22419 Nigeria 297024
Indonesia 21841 Ethiopia 253368
Ethiopia 12337 Pakistan 236848
Nigeria 11293 Tanzania 115368
D. R. Congo 10758 Kenya 96316
Tanzania 5637 Uganda 91560
Kenya 4816 Sudan 84600
Nepal 3516 Nepal 82333
Mozambique 3366 Mozambique 71219
Iraq 2425 D. R. Congo 62700
Niger 2219 Indonesia 62073
Malawi 2096 Ghana 58310
Uganda 2093 Niger 50616
Cote d’Ivoire 2068 Cote d’Ivoire 49585
Sudan 1650 Malawi 49419
Burkina Faso 1643 Burkina Faso 44556
Mali 1573 Yemen 39176
Uzbekistan 1430 Mali 36400
Cameroon 1424 Cameroon 35100
Zambia 1367 Zambia 34959
Guinea 1293 Cambodia 32116
Yemen 1251 Guinea 30805
Burundi 1129 Burundi 29880
Benin 1113 Uzbekistan 29700
Senegal 1050 Rwanda 29300
Cambodia 1006 Senegal 27250
Ghana 999 Benin 22072
Rwanda 840 Togo 20724
Papua 777 Iraq 20262
Togo 766 Sierra Leone 18240
C. African Rep. 678 Liberia 14430
Laos 643 C. African Rep. 14256
Tajikistan 628 Tajikistan 14076
Liberia 546 Papua 11591
Sierra Leone 467 Laos 11277
Mauritania 436 Kyrgyzstan 9845
Kyrgyzstan 347 Mauritania 6699
Turkmenistan 316 Guinea Bissau 4688
Guinea Bissau 221 Lesotho 4599
Gambia 189 Gambia 4522
Lesotho 141 Comoros 1883
Angola 56 Djibouti 1359
Solomon 43 Solomon 1065
Comoros 40 Angola 0
Djibouti 30 Turkmenistan 0
Total 231000 Total 3495000
Table 2: The Excess of Nations ($ millions)
Nation Health $ Excess Nation GNI Excess
USA 2195977 USA 13107255
Japan 334663 China 3924570
Germany 255888 Japan 3749856
France 228329 Germany 2695338
UK 187018 UK 2047584
Italy 158735 Russia 2023324
Brazil 133459 France 1865262
Spain 123699 Italy 1639463
Canada 123682 Spain 1252261
Russia 112579 Brazil 1202877
China 106911 R. Korea 1126356
Finland 87529 Canada 1122912
R. Korea 78246 Mexico 1108056
Mexico 71350 Australia 729312
Australia 67713 Turkey 727056
Netherlands 67180 Netherlands 594114
Turkey 49293 Iran 555016
Belgium 41837 Poland 550545
Poland 41339 Argentina 407030
Argentina 40260 Belgium 348392
Switzerland 35180 Switzerland 326800
Austria 33298 Sweden 321780
Sweden 31955 Austria 285348
Iran 31683 Greece 278320
Greece 31405 Malaysia 267300
Portugal 25594 Thailand 247470
Norway 24101 Norway 244272
Denmark 19954 Venezuela 235378
Czech R 17098 Singapore 215213
Ireland 16245 Portugal 211432
Chile 15334 Columbia 210677
Columbia 15127 Czech R 204048
Ukraine 14441 Denmark 185955
Venezuela 14214 Israel 166464
Israel 13730 Finland 162922
Hungary 13200 Chile 160650
Malaysia 11963 Hungary 145800
New Zealand 10617 Algeria 143788
Thailand 9628 Egypt 140270
Slovakia 8980 Ireland 132840
Algeria 8759 Peru 120596
Singapore 7741 Ukraine 100083
Serbia 6742 Slovakia 95094
Egypt 6225 Belarus 84000
Croatia 6015 Libya 79424
Bulgaria 5910 Serbia 74844
Peru 5694 Croatia 66220
Belarus 4819 Bulgaria 65700
Slovenia 4468 Slovenia 44700
Costa Rica 4016 Azerbaijan 44264
Lithuania 3736 Lithuania 42108
Lebanon 3457 Dominican R 41612
Tunisia 3234 Lebanon 39522
Lux 2905 Tunisia 39345
Bosnia & Herz 2854 Costa Rica 31924
Dominican R 2818 Uruguay 30294
Uruguay 2706 Panama 28665
Panama 2583 Lux 27780
Latvia 2244 Latvia 27544
Libya 2022 Trinidad & T 27274
Jordan 1953 Estonia 19370
Azerbaijan 1839 Bosnia & Herz 17822
Guatemala 1708 El Salvador 15066
Estonia 1481 Sri Lanka 14746
Morocco 1440 Albania 13792
Viet Nam 1409 Macedonia 13120
El Salvador 1389 Morocco 13120
Trinidad & T 1366 Mauritius 12064
Albania 1226 Jordan 10962
Macedonia 1104 New Zealand 10234
Georgia 1062 Iceland 8868
Iceland 1019 Jamaica 8748
Mauritius 644 Guatemala 8120
Paraguay 598 Montenegro 5598
Montenegro 586 Armenia 4402
Moldova 482 Georgia 3053
Honduras 465 Paraguay 2772
Jamaica 481 Maldives 378
Nicaragua 371 Nicaragua 0
Maldives 175 Viet Nam 0
Armenia 118 Moldova 0
Sri Lanka 20 Honduras 0
Total 5009000 Total 46357000
Table 3: Pearson Correlation Coefficients with HDI
Parameter r population
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

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