placestats

Food insecurity vs. Premature NCD mortality

Fades after adjusting for income

Across 151 countries, Food insecurity and Premature NCD mortality rise and fall together at r = +0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.06, which could easily be chance (q = 0.82, 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, 12.4% to 23.3% probability, ages 30-70, food insecurity is worse by 19% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 1.59% of population the other way.

Link before adjusting (r)+0.531p = 2.4e-12 · 151 countries
After adjusting for national income-0.060p = 0.469 · likely range [-0.22, +0.10]
p, allowing for regions0.718standard error 0.157 · 7 regions
Check for luck (q)0.818across 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.

Food insecurity · % of populationlink with income -0.83 · proxy
Premature NCD mortality · % probability, ages 30-70link with income -0.70 · 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

Food insecurity · % of population →Premature NCD mortality · % probability, ages 30-70 →1.1%89.8%6.9%44.1%Afghanistan: 80.8% of population, 32.7% probability, ages 30-70Angola: 78.5% of population, 24.7% probability, ages 30-70Albania: 33% of population, 10% probability, ages 30-70United Arab Emirates: 3.6% of population, 11.6% probability, ages 30-70Argentina: 33.8% of population, 13.4% probability, ages 30-70Armenia: 7.8% of population, 20.6% probability, ages 30-70Antigua and Barbuda: 13.5% of population, 12.1% probability, ages 30-70Australia: 13.8% of population, 8.3% probability, ages 30-70Austria: 4.7% of population, 9.9% probability, ages 30-70Azerbaijan: 14% of population, 17.4% probability, ages 30-70Burundi: 70.8% of population, 24.8% probability, ages 30-70Belgium: 8.1% of population, 9.2% probability, ages 30-70Benin: 65.6% of population, 20.8% probability, ages 30-70Burkina Faso: 41.4% of population, 23.4% probability, ages 30-70Bulgaria: 11.6% of population, 25.6% probability, ages 30-70Bahamas, The: 17.2% of population, 20.4% probability, ages 30-70Bosnia and Herzegovina: 9.9% of population, 17.2% probability, ages 30-70Belarus: 1.1% of population, 23.8% probability, ages 30-70Belize: 45.5% of population, 15.4% probability, ages 30-70Brazil: 13.5% of population, 14.5% probability, ages 30-70Barbados: 31.1% of population, 14% probability, ages 30-70Botswana: 53.9% of population, 19% probability, ages 30-70Central African Republic: 88.1% of population, 30.8% probability, ages 30-70Canada: 10.2% of population, 9.7% probability, ages 30-70Switzerland: 2% of population, 7.5% probability, ages 30-70Chile: 19.2% of population, 9.4% probability, ages 30-70Cote d'Ivoire: 38.4% of population, 22.7% probability, ages 30-70Cameroon: 61.2% of population, 24.3% probability, ages 30-70Congo, Dem. Rep.: 81.5% of population, 25.1% probability, ages 30-70Colombia: 27.8% of population, 10.3% probability, ages 30-70Comoros: 79.7% of population, 20.2% probability, ages 30-70Cabo Verde: 32.1% of population, 17.2% probability, ages 30-70Costa Rica: 15.4% of population, 9.9% probability, ages 30-70Cyprus: 1.4% of population, 8.5% probability, ages 30-70Czechia: 10% of population, 14.2% probability, ages 30-70Germany: 4.1% of population, 11.6% probability, ages 30-70Djibouti: 49.2% of population, 20.6% probability, ages 30-70Denmark: 6.9% of population, 10.7% probability, ages 30-70Dominican Republic: 43.8% of population, 17.4% probability, ages 30-70Algeria: 17.6% of population, 13.3% probability, ages 30-70Ecuador: 33.3% of population, 13.2% probability, ages 30-70Egypt, Arab Rep.: 30.8% of population, 26% probability, ages 30-70Spain: 6.5% of population, 9.3% probability, ages 30-70Estonia: 10.8% of population, 15.5% probability, ages 30-70Ethiopia: 61.4% of population, 16.8% probability, ages 30-70Finland: 13.4% of population, 9.6% probability, ages 30-70Fiji: 29.2% of population, 37.9% probability, ages 30-70France: 8.4% of population, 10.2% probability, ages 30-70United Kingdom: 6.9% of population, 11% probability, ages 30-70Georgia: 26.6% of population, 22.1% probability, ages 30-70Ghana: 44.5% of population, 22.2% probability, ages 30-70Gambia, The: 54.8% of population, 21.8% probability, ages 30-70Guinea-Bissau: 62.5% of population, 25.3% probability, ages 30-70Greece: 6.6% of population, 12% probability, ages 30-70Grenada: 17.3% of population, 17.1% probability, ages 30-70Guatemala: 48.8% of population, 14.6% probability, ages 30-70Guyana: 25.5% of population, 25.4% probability, ages 30-70Honduras: 41.3% of population, 17.8% probability, ages 30-70Croatia: 6% of population, 15.9% probability, ages 30-70Haiti: 83.2% of population, 31.6% probability, ages 30-70Hungary: 15.6% of population, 21.7% probability, ages 30-70Indonesia: 4.5% of population, 21.9% probability, ages 30-70Ireland: 4.8% of population, 9.6% probability, ages 30-70Iran, Islamic Rep.: 38.8% of population, 13.9% probability, ages 30-70Iceland: 7.8% of population, 8.5% probability, ages 30-70Israel: 9.2% of population, 7.6% probability, ages 30-70Italy: 1.7% of population, 9.1% probability, ages 30-70Jamaica: 56.4% of population, 20.2% probability, ages 30-70Japan: 5.8% of population, 8% probability, ages 30-70Kazakhstan: 2% of population, 21.2% probability, ages 30-70Kenya: 73.9% of population, 18% probability, ages 30-70Kyrgyz Republic: 7% of population, 18.8% probability, ages 30-70Cambodia: 40% of population, 22.6% probability, ages 30-70Kiribati: 42.9% of population, 44.1% probability, ages 30-70Korea, Rep.: 5.4% of population, 6.9% probability, ages 30-70Kuwait: 8.4% of population, 9.3% probability, ages 30-70Lao PDR: 35.6% of population, 26.5% probability, ages 30-70Lebanon: 42.4% of population, 11.9% probability, ages 30-70Liberia: 80.8% of population, 22% probability, ages 30-70Libya: 35.1% of population, 19.8% probability, ages 30-70St. Lucia: 22.2% of population, 15.5% probability, ages 30-70Sri Lanka: 11.2% of population, 13.9% probability, ages 30-70Lesotho: 59.9% of population, 36.3% probability, ages 30-70Lithuania: 6.5% of population, 19.8% probability, ages 30-70Luxembourg: 2.6% of population, 7.8% probability, ages 30-70Latvia: 11.2% of population, 22.7% probability, ages 30-70Moldova: 25.1% of population, 24.7% probability, ages 30-70Madagascar: 72.6% of population, 25.9% probability, ages 30-70Maldives: 13.4% of population, 9% probability, ages 30-70Mexico: 19.3% of population, 16.3% probability, ages 30-70North Macedonia: 15.2% of population, 19.6% probability, ages 30-70Mali: 20.6% of population, 23% probability, ages 30-70Malta: 9.2% of population, 9.8% probability, ages 30-70Myanmar: 32.7% of population, 24% probability, ages 30-70Montenegro: 10.4% of population, 18% probability, ages 30-70Mongolia: 4.8% of population, 26.3% probability, ages 30-70Mauritania: 66.3% of population, 18.7% probability, ages 30-70Mauritius: 31.1% of population, 21.7% probability, ages 30-70Malawi: 81.7% of population, 24.9% probability, ages 30-70Malaysia: 16.7% of population, 19.9% probability, ages 30-70Namibia: 55.9% of population, 23.1% probability, ages 30-70Niger: 50.9% of population, 20.2% probability, ages 30-70Nigeria: 74.8% of population, 17.7% probability, ages 30-70Netherlands: 7.5% of population, 9.9% probability, ages 30-70Norway: 7.8% of population, 7.8% probability, ages 30-70Nepal: 36.6% of population, 19.4% probability, ages 30-70New Zealand: 17.3% of population, 10.3% probability, ages 30-70Pakistan: 42.1% of population, 25.5% probability, ages 30-70Peru: 41% of population, 12.3% probability, ages 30-70Philippines: 32.9% of population, 31.9% probability, ages 30-70Papua New Guinea: 57.3% of population, 28.5% probability, ages 30-70Poland: 3.4% of population, 17% probability, ages 30-70Portugal: 11.9% of population, 10.5% probability, ages 30-70Paraguay: 26.2% of population, 16.1% probability, ages 30-70West Bank and Gaza: 27% of population, 18.3% probability, ages 30-70Romania: 18.6% of population, 21.5% probability, ages 30-70Russian Federation: 2.8% of population, 22.4% probability, ages 30-70Senegal: 29.1% of population, 21.4% probability, ages 30-70Singapore: 9.5% of population, 10.3% probability, ages 30-70Sierra Leone: 89.8% of population, 23.9% probability, ages 30-70El Salvador: 45.5% of population, 12.6% probability, ages 30-70Somalia, Fed. Rep.: 79.5% of population, 27.6% probability, ages 30-70Serbia: 9.5% of population, 20.7% probability, ages 30-70Sao Tome and Principe: 54.6% of population, 19.5% probability, ages 30-70Suriname: 35.8% of population, 18.7% probability, ages 30-70Slovak Republic: 7.5% of population, 16.6% probability, ages 30-70Slovenia: 8.2% of population, 11.6% probability, ages 30-70Sweden: 6.9% of population, 7.9% probability, ages 30-70Eswatini: 59% of population, 32.3% probability, ages 30-70Seychelles: 14.3% of population, 15.6% probability, ages 30-70Chad: 66.3% of population, 23.4% probability, ages 30-70Togo: 58.5% of population, 25.4% probability, ages 30-70Thailand: 5.4% of population, 14.5% probability, ages 30-70Tajikistan: 25.1% of population, 18.1% probability, ages 30-70Timor-Leste: 53.7% of population, 20.1% probability, ages 30-70Tonga: 14.8% of population, 26.9% probability, ages 30-70Trinidad and Tobago: 27.6% of population, 20.3% probability, ages 30-70Tunisia: 25.4% of population, 13% probability, ages 30-70Tanzania: 58% of population, 18.8% probability, ages 30-70Uganda: 54.6% of population, 21.5% probability, ages 30-70Ukraine: 32.5% of population, 24.8% probability, ages 30-70Uruguay: 16.2% of population, 17.4% probability, ages 30-70United States: 10.3% of population, 13.7% probability, ages 30-70Uzbekistan: 24.4% of population, 24.6% probability, ages 30-70St. Vincent and the Grenadines: 33.3% of population, 23.6% probability, ages 30-70Viet Nam: 10.7% of population, 20.1% probability, ages 30-70Vanuatu: 23.3% of population, 36.8% probability, ages 30-70Samoa: 18.6% of population, 32.3% probability, ages 30-70South Africa: 20.7% of population, 22.7% probability, ages 30-70Zambia: 45.3% of population, 23.6% probability, ages 30-70Zimbabwe: 68.8% of population, 31.2% probability, ages 30-70AFGCAFCHECOMFJIKENKIRKORLUXNGAVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5312.4e-12151
By rank (Spearman), not adjusted+0.5673.2e-14151
Standard (Pearson), adjusted for national income-0.0600.469151
By rank (Spearman), adjusted for national income-0.0300.715151

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 151 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: +0.471 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.095 (without GUY) to -0.024 (without VUT). 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: VUT (+0.035).

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 & Pacific21-0.11
Europe & Central Asia46+0.03
Latin America & Caribbean26+0.47
Middle East, North Africa, Afghanistan & Pakistan14+0.41
Sub-Saharan Africa39+0.13

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 third51-0.08
Middle third50-0.10
Richest third50+0.32

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
Christians98-0.17
Muslims35+0.01

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
France21+0.14
Never a European colony16-0.08
Spain14-0.17
United Kingdom49-0.03

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 = -0.037 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 7years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = +0.075, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose food insecurity moved did not tend to see their premature ncd mortality move with it.

Across countries (adjusted)-0.060for comparison
Within countries-0.143each country vs itself
…and vs the same year elsewhere+0.075q = 0.533 · 111 countries
Year-on-year changes+0.037q = 0.606
Read with care.
  • Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

749 country-years (one country in one year), 111 countries, 2015–2021, about 7 years each on average. Share of the variation that happens within countries over time: 1% for Food insecurity, 2% for Premature NCD mortality. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.10−0.1-100+2years Premature NCD mortality trails Food insecurity →lag -10: r = 0.015 (n = 1,051)lag -9: r = 0.047 (n = 1,051)lag -8: r = 0.053 (n = 1,051)lag -7: r = 0.046 (n = 1,051)lag -6: r = 0.040 (n = 1,051)lag -5: r = 0.032 (n = 1,051)lag -4: r = 0.037 (n = 1,051)lag -3: r = 0.073 (n = 1,051)lag -2: r = 0.079 (n = 1,051)lag -1: r = 0.069 (n = 900)lag +0: r = 0.075 (n = 749)lag +1: r = 0.031 (n = 586)lag +2: r = 0.024 (n = 480)

Same year (lag 0): +0.075. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

The data


% of population
% probability, ages 30-70		Years
Sierra Leone SLE89.8%23.9%2023, 2021
Central African Republic CAF88.1%30.8%2022, 2021
Haiti HTI83.2%31.6%2023, 2021
Malawi MWI81.7%24.9%2023, 2021
Congo, Dem. Rep. COD81.5%25.1%2023, 2021
Liberia LBR80.8%22%2023, 2021
Afghanistan AFG80.8%32.7%2023, 2021
Comoros COM79.7%20.2%2023, 2021
Somalia, Fed. Rep. SOM79.5%27.6%2021
Angola AGO78.5%24.7%2021
Nigeria NGA74.8%17.7%2023, 2021
Kenya KEN73.9%18%2023, 2021
Madagascar MDG72.6%25.9%2023, 2021
Burundi BDI70.8%24.8%2023, 2021
Zimbabwe ZWE68.8%31.2%2023, 2021

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Food insecurity

Prevalence of moderate or severe food insecurity in the population (%)

Food and Agriculture Organization, Food Insecurity Experience Scale · CC BY 4.0 · updated quarterly

Asked of people, in a survey about running out of food, and reported as a three-year average from 2015. It is the one measure of hunger here that comes from what people say they went through, rather than from a model.

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.

How this was computed

Data used: most recent value in 2010-2025; values come from 2017–2023. 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 Food insecurity and Premature NCD mortality

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

More links with Food insecurity or Premature NCD mortality