placestats

Premature NCD mortality vs. Wellbeing score

Fades after adjusting for income

Across 103 countries, Premature NCD mortality and Wellbeing score rise and fall together at r = -0.58 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.23, which could easily be chance (q = 0.33, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things. Caution: direction is not consistent across regions.

Comparing countries across the middle half of premature ncd mortality, 10.3% to 21.4% probability, ages 30-70, wellbeing score is worse by 0.802 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.281 typical gaps.

Link before adjusting (r)-0.584p = 9.8e-11 · 103 countries
After adjusting for national income-0.235p = 0.018 · likely range [-0.41, -0.04]
p, allowing for regions0.140standard error 0.138 · 7 regions
Check for luck (q)0.330across 12,497 pairs

How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.

Premature NCD mortality · % probability, ages 30-70link with income -0.70 · high
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Premature NCD mortality · % probability, ages 30-70 →Wellbeing score · typical gaps (standard deviations) →6.9%31.9%-1.981.89Albania: 10% probability, ages 30-70, -0.617 typical gaps (standard deviations)Argentina: 13.4% probability, ages 30-70, 0.216 typical gaps (standard deviations)Armenia: 20.6% probability, ages 30-70, -0.118 typical gaps (standard deviations)Australia: 8.3% probability, ages 30-70, 1.25 typical gaps (standard deviations)Austria: 9.9% probability, ages 30-70, 1.26 typical gaps (standard deviations)Azerbaijan: 17.4% probability, ages 30-70, -0.0603 typical gaps (standard deviations)Belgium: 9.2% probability, ages 30-70, 0.818 typical gaps (standard deviations)Burkina Faso: 23.4% probability, ages 30-70, -0.476 typical gaps (standard deviations)Bangladesh: 18.1% probability, ages 30-70, -0.561 typical gaps (standard deviations)Bulgaria: 25.6% probability, ages 30-70, -0.572 typical gaps (standard deviations)Bosnia and Herzegovina: 17.2% probability, ages 30-70, -0.087 typical gaps (standard deviations)Belarus: 23.8% probability, ages 30-70, 0.732 typical gaps (standard deviations)Bolivia: 19.9% probability, ages 30-70, -0.361 typical gaps (standard deviations)Brazil: 14.5% probability, ages 30-70, 0.123 typical gaps (standard deviations)Canada: 9.7% probability, ages 30-70, 1.06 typical gaps (standard deviations)Switzerland: 7.5% probability, ages 30-70, 1.55 typical gaps (standard deviations)Chile: 9.4% probability, ages 30-70, 0.19 typical gaps (standard deviations)China: 15.9% probability, ages 30-70, 0.913 typical gaps (standard deviations)Colombia: 10.3% probability, ages 30-70, -0.125 typical gaps (standard deviations)Cyprus: 8.5% probability, ages 30-70, -0.0176 typical gaps (standard deviations)Czechia: 14.2% probability, ages 30-70, 0.726 typical gaps (standard deviations)Germany: 11.6% probability, ages 30-70, 0.981 typical gaps (standard deviations)Denmark: 10.7% probability, ages 30-70, 1.89 typical gaps (standard deviations)Algeria: 13.3% probability, ages 30-70, -0.205 typical gaps (standard deviations)Ecuador: 13.2% probability, ages 30-70, -0.164 typical gaps (standard deviations)Egypt, Arab Rep.: 26% probability, ages 30-70, -1.08 typical gaps (standard deviations)Spain: 9.3% probability, ages 30-70, 0.816 typical gaps (standard deviations)Estonia: 15.5% probability, ages 30-70, 0.622 typical gaps (standard deviations)Ethiopia: 16.8% probability, ages 30-70, -0.726 typical gaps (standard deviations)Finland: 9.6% probability, ages 30-70, 1.87 typical gaps (standard deviations)France: 10.2% probability, ages 30-70, 0.717 typical gaps (standard deviations)United Kingdom: 11% probability, ages 30-70, 1.07 typical gaps (standard deviations)Georgia: 22.1% probability, ages 30-70, -0.493 typical gaps (standard deviations)Ghana: 22.2% probability, ages 30-70, -0.73 typical gaps (standard deviations)Greece: 12% probability, ages 30-70, -0.284 typical gaps (standard deviations)Guatemala: 14.6% probability, ages 30-70, 0.352 typical gaps (standard deviations)Croatia: 15.9% probability, ages 30-70, 0.0883 typical gaps (standard deviations)Haiti: 31.6% probability, ages 30-70, -0.461 typical gaps (standard deviations)Hungary: 21.7% probability, ages 30-70, 0.266 typical gaps (standard deviations)Indonesia: 21.9% probability, ages 30-70, -0.192 typical gaps (standard deviations)India: 23.6% probability, ages 30-70, -0.736 typical gaps (standard deviations)Ireland: 9.6% probability, ages 30-70, 0.894 typical gaps (standard deviations)Iran, Islamic Rep.: 13.9% probability, ages 30-70, -0.684 typical gaps (standard deviations)Iraq: 22.7% probability, ages 30-70, -0.726 typical gaps (standard deviations)Iceland: 8.5% probability, ages 30-70, 1.78 typical gaps (standard deviations)Italy: 9.1% probability, ages 30-70, 0.438 typical gaps (standard deviations)Jordan: 11.6% probability, ages 30-70, -0.666 typical gaps (standard deviations)Japan: 8% probability, ages 30-70, 0.552 typical gaps (standard deviations)Kazakhstan: 21.2% probability, ages 30-70, 0.304 typical gaps (standard deviations)Kenya: 18% probability, ages 30-70, -0.678 typical gaps (standard deviations)Kyrgyz Republic: 18.8% probability, ages 30-70, 0.225 typical gaps (standard deviations)Korea, Rep.: 6.9% probability, ages 30-70, 0.475 typical gaps (standard deviations)Kuwait: 9.3% probability, ages 30-70, 0.607 typical gaps (standard deviations)Lebanon: 11.9% probability, ages 30-70, -1.54 typical gaps (standard deviations)Libya: 19.8% probability, ages 30-70, -0.158 typical gaps (standard deviations)Lithuania: 19.8% probability, ages 30-70, 0.51 typical gaps (standard deviations)Luxembourg: 7.8% probability, ages 30-70, 0.652 typical gaps (standard deviations)Latvia: 22.7% probability, ages 30-70, 0.212 typical gaps (standard deviations)Morocco: 22% probability, ages 30-70, -0.382 typical gaps (standard deviations)Moldova: 24.7% probability, ages 30-70, -1.29 typical gaps (standard deviations)Maldives: 9% probability, ages 30-70, 0.0845 typical gaps (standard deviations)Mexico: 16.3% probability, ages 30-70, 0.335 typical gaps (standard deviations)North Macedonia: 19.6% probability, ages 30-70, -0.171 typical gaps (standard deviations)Mali: 23% probability, ages 30-70, -0.328 typical gaps (standard deviations)Malta: 9.8% probability, ages 30-70, 0.15 typical gaps (standard deviations)Myanmar: 24% probability, ages 30-70, -0.547 typical gaps (standard deviations)Montenegro: 18% probability, ages 30-70, 0.239 typical gaps (standard deviations)Mongolia: 26.3% probability, ages 30-70, 0.000193 typical gaps (standard deviations)Malaysia: 19.9% probability, ages 30-70, 0.215 typical gaps (standard deviations)Nigeria: 17.7% probability, ages 30-70, -0.611 typical gaps (standard deviations)Nicaragua: 12.6% probability, ages 30-70, 0.0736 typical gaps (standard deviations)Netherlands: 9.9% probability, ages 30-70, 1.54 typical gaps (standard deviations)Norway: 7.8% probability, ages 30-70, 1.82 typical gaps (standard deviations)New Zealand: 10.3% probability, ages 30-70, 1.46 typical gaps (standard deviations)Pakistan: 25.5% probability, ages 30-70, -0.299 typical gaps (standard deviations)Peru: 12.3% probability, ages 30-70, -0.348 typical gaps (standard deviations)Philippines: 31.9% probability, ages 30-70, -0.119 typical gaps (standard deviations)Poland: 17% probability, ages 30-70, 0.505 typical gaps (standard deviations)Puerto Rico (US): 10.1% probability, ages 30-70, 0.077 typical gaps (standard deviations)Portugal: 10.5% probability, ages 30-70, 0.195 typical gaps (standard deviations)West Bank and Gaza: 18.3% probability, ages 30-70, -0.644 typical gaps (standard deviations)Qatar: 12.1% probability, ages 30-70, 0.756 typical gaps (standard deviations)Romania: 21.5% probability, ages 30-70, 0.197 typical gaps (standard deviations)Russian Federation: 22.4% probability, ages 30-70, -0.00878 typical gaps (standard deviations)Rwanda: 20% probability, ages 30-70, -0.867 typical gaps (standard deviations)Singapore: 10.3% probability, ages 30-70, 0.779 typical gaps (standard deviations)Serbia: 20.7% probability, ages 30-70, 0.105 typical gaps (standard deviations)Slovak Republic: 16.6% probability, ages 30-70, 0.318 typical gaps (standard deviations)Slovenia: 11.6% probability, ages 30-70, 0.55 typical gaps (standard deviations)Sweden: 7.9% probability, ages 30-70, 1.7 typical gaps (standard deviations)Thailand: 14.5% probability, ages 30-70, 0.256 typical gaps (standard deviations)Tajikistan: 18.1% probability, ages 30-70, 0.104 typical gaps (standard deviations)Trinidad and Tobago: 20.3% probability, ages 30-70, 0.0637 typical gaps (standard deviations)Tunisia: 13% probability, ages 30-70, -0.73 typical gaps (standard deviations)Turkiye: 15.4% probability, ages 30-70, -0.508 typical gaps (standard deviations)Ukraine: 24.8% probability, ages 30-70, -0.244 typical gaps (standard deviations)Uruguay: 17.4% probability, ages 30-70, 0.449 typical gaps (standard deviations)United States: 13.7% probability, ages 30-70, 0.943 typical gaps (standard deviations)Uzbekistan: 24.6% probability, ages 30-70, 0.617 typical gaps (standard deviations)Viet Nam: 20.1% probability, ages 30-70, 0.469 typical gaps (standard deviations)South Africa: 22.7% probability, ages 30-70, -0.515 typical gaps (standard deviations)Zambia: 23.6% probability, ages 30-70, -1.67 typical gaps (standard deviations)Zimbabwe: 31.2% probability, ages 30-70, -1.98 typical gaps (standard deviations)ALBBLRHTIISLJORLBNNORPHLSWEUZBZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5849.8e-11103
By rank (Spearman), not adjusted-0.5791.5e-10103
Standard (Pearson), adjusted for national income-0.2350.018103
By rank (Spearman), adjusted for national income-0.0930.353103

The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 103 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.349 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.283 (without LBN) to -0.198 (without ZWE). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: LBN (-0.048).

Fragile: this link may not hold up.
  • direction is not consistent across regions

Within each region

Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.

nr
East Asia & Pacific13-0.34
Europe & Central Asia47-0.39
Latin America & Caribbean14+0.02
Middle East, North Africa, Afghanistan & Pakistan14+0.08

By income third

If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.

nr
Poorest third35-0.01
Middle third34-0.23
Richest third34-0.23

By largest religious group

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
Christians62-0.39
Muslims28+0.22

By last european coloniser

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
Never a European colony15-0.02
Spain12+0.00
United Kingdom25-0.45

Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = -0.198 after dropping 1 countries.

Two series, one country

Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.

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Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


% probability, ages 30-70

typical gaps (standard deviations)
Years
Philippines PHL31.9%-0.1192021, 2022
Haiti HTI31.6%-0.4612021, 2014
Zimbabwe ZWE31.2%-1.982021, 2022
Mongolia MNG26.3%0.0001932021, 2022
Egypt, Arab Rep. EGY26%-1.082021, 2022
Bulgaria BGR25.6%-0.5722021, 2022
Pakistan PAK25.5%-0.2992021, 2022
Ukraine UKR24.8%-0.2442021, 2022
Moldova MDA24.7%-1.292021, 2010
Uzbekistan UZB24.6%0.6172021, 2022
Myanmar MMR24%-0.5472021, 2022
Belarus BLR23.8%0.7322021, 2022
Zambia ZMB23.6%-1.672021, 2010
India IND23.6%-0.7362021, 2022
Burkina Faso BFA23.4%-0.4762021, 2010

World Bank, World Development Indicators
ivs; whr

Sources

Premature NCD mortality

Mortality from cardiovascular disease, cancer, diabetes or chronic respiratory disease between ages 30 and 70 (%)

World Health Organization · CC BY 4.0 · updated quarterly

The series ends in 2021.

Wellbeing score

Wellbeing score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 3 measures from 2 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Self-reported happiness, Interpersonal trust, Life satisfaction.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2022. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 7 World Bank regions. The q-value corrects for testing all 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Premature NCD mortality and Wellbeing score

This page shows that two measures go together. It does not show that Premature NCD mortality changes Wellbeing score, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Premature NCD mortality or Wellbeing score