placestats

Internet users vs. Premature NCD mortality

Fades after adjusting for income

Across 179 countries, Internet users and Premature NCD mortality rise and fall together at r = -0.57 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.02, which could easily be chance (q = 0.92, 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: sign flips when a single country is removed.

Comparing countries across the middle half of internet users, 50.6% to 91.1% of population, premature ncd mortality is better by 6.39% probability, ages 30-70 at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.332% probability, ages 30-70 the other way.

Link before adjusting (r)-0.575p = 4.0e-17 · 179 countries
After adjusting for national income+0.020p = 0.795 · likely range [-0.13, +0.17]
p, allowing for regions0.863standard error 0.109 · 7 regions
Check for luck (q)0.923across 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.

Internet users · % of populationlink with income +0.85 · 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

Internet users · % of population →Premature NCD mortality · % probability, ages 30-70 →8.6%100%6.9%44.1%Afghanistan: 16.1% of population, 32.7% probability, ages 30-70Angola: 40.7% of population, 24.7% probability, ages 30-70Albania: 85.9% of population, 10% probability, ages 30-70United Arab Emirates: 100% of population, 11.6% probability, ages 30-70Argentina: 89.7% of population, 13.4% probability, ages 30-70Armenia: 81.3% of population, 20.6% probability, ages 30-70Antigua and Barbuda: 72.7% of population, 12.1% probability, ages 30-70Australia: 96.1% of population, 8.3% probability, ages 30-70Austria: 91.9% of population, 9.9% probability, ages 30-70Azerbaijan: 90.8% of population, 17.4% probability, ages 30-70Burundi: 8.6% of population, 24.8% probability, ages 30-70Belgium: 95.8% of population, 9.2% probability, ages 30-70Benin: 34% of population, 20.8% probability, ages 30-70Burkina Faso: 28.3% of population, 23.4% probability, ages 30-70Bangladesh: 53.4% of population, 18.1% probability, ages 30-70Bulgaria: 82.4% of population, 25.6% probability, ages 30-70Bahrain: 100% of population, 14.8% probability, ages 30-70Bahamas, The: 92.5% of population, 20.4% probability, ages 30-70Bosnia and Herzegovina: 86.1% of population, 17.2% probability, ages 30-70Belarus: 94.3% of population, 23.8% probability, ages 30-70Belize: 80% of population, 15.4% probability, ages 30-70Bolivia: 79.7% of population, 19.9% probability, ages 30-70Brazil: 84.5% of population, 14.5% probability, ages 30-70Barbados: 70.4% of population, 14% probability, ages 30-70Brunei Darussalam: 96.3% of population, 14.9% probability, ages 30-70Bhutan: 91.3% of population, 15.9% probability, ages 30-70Botswana: 57.5% of population, 19% probability, ages 30-70Central African Republic: 13.8% of population, 30.8% probability, ages 30-70Canada: 94.4% of population, 9.7% probability, ages 30-70Switzerland: 97.3% of population, 7.5% probability, ages 30-70Chile: 95.6% of population, 9.4% probability, ages 30-70China: 91.6% of population, 15.9% probability, ages 30-70Cote d'Ivoire: 41.4% of population, 22.7% probability, ages 30-70Cameroon: 46.3% of population, 24.3% probability, ages 30-70Congo, Dem. Rep.: 19.7% of population, 25.1% probability, ages 30-70Congo, Rep.: 47.3% of population, 21.2% probability, ages 30-70Colombia: 79.3% of population, 10.3% probability, ages 30-70Comoros: 32.5% of population, 20.2% probability, ages 30-70Cabo Verde: 74.7% of population, 17.2% probability, ages 30-70Costa Rica: 87.2% of population, 9.9% probability, ages 30-70Cyprus: 89.6% of population, 8.5% probability, ages 30-70Czechia: 87.7% of population, 14.2% probability, ages 30-70Germany: 93.5% of population, 11.6% probability, ages 30-70Djibouti: 65.3% of population, 20.6% probability, ages 30-70Denmark: 99.8% of population, 10.7% probability, ages 30-70Dominican Republic: 91% of population, 17.4% probability, ages 30-70Algeria: 77.4% of population, 13.3% probability, ages 30-70Ecuador: 77.2% of population, 13.2% probability, ages 30-70Egypt, Arab Rep.: 74.6% of population, 26% probability, ages 30-70Spain: 95.8% of population, 9.3% probability, ages 30-70Estonia: 92.2% of population, 15.5% probability, ages 30-70Ethiopia: 21.9% of population, 16.8% probability, ages 30-70Finland: 93.7% of population, 9.6% probability, ages 30-70Fiji: 74.7% of population, 37.9% probability, ages 30-70France: 88.7% of population, 10.2% probability, ages 30-70Micronesia, Fed. Sts.: 39.5% of population, 40.5% probability, ages 30-70Gabon: 68.7% of population, 18.1% probability, ages 30-70United Kingdom: 95.5% of population, 11% probability, ages 30-70Georgia: 83.8% of population, 22.1% probability, ages 30-70Ghana: 72.2% of population, 22.2% probability, ages 30-70Guinea: 33.3% of population, 23.6% probability, ages 30-70Gambia, The: 49.5% of population, 21.8% probability, ages 30-70Guinea-Bissau: 29.8% of population, 25.3% probability, ages 30-70Equatorial Guinea: 63.3% of population, 20.3% probability, ages 30-70Greece: 86.3% of population, 12% probability, ages 30-70Grenada: 70.2% of population, 17.1% probability, ages 30-70Guatemala: 60.2% of population, 14.6% probability, ages 30-70Guyana: 83% of population, 25.4% probability, ages 30-70Honduras: 58.6% of population, 17.8% probability, ages 30-70Croatia: 83.6% of population, 15.9% probability, ages 30-70Haiti: 47.9% of population, 31.6% probability, ages 30-70Hungary: 93.8% of population, 21.7% probability, ages 30-70Indonesia: 72.8% of population, 21.9% probability, ages 30-70India: 70% of population, 23.6% probability, ages 30-70Ireland: 97.2% of population, 9.6% probability, ages 30-70Iran, Islamic Rep.: 85.3% of population, 13.9% probability, ages 30-70Iraq: 81.5% of population, 22.7% probability, ages 30-70Iceland: 98.2% of population, 8.5% probability, ages 30-70Israel: 88.2% of population, 7.6% probability, ages 30-70Italy: 89.2% of population, 9.1% probability, ages 30-70Jamaica: 90.1% of population, 20.2% probability, ages 30-70Jordan: 95.6% of population, 11.6% probability, ages 30-70Japan: 85.5% of population, 8% probability, ages 30-70Kazakhstan: 93.4% of population, 21.2% probability, ages 30-70Kenya: 35% of population, 18% probability, ages 30-70Kyrgyz Republic: 92% of population, 18.8% probability, ages 30-70Cambodia: 68.5% of population, 22.6% probability, ages 30-70Kiribati: 89.4% of population, 44.1% probability, ages 30-70Korea, Rep.: 97.9% of population, 6.9% probability, ages 30-70Kuwait: 100% of population, 9.3% probability, ages 30-70Lao PDR: 65.6% of population, 26.5% probability, ages 30-70Lebanon: 80.6% of population, 11.9% probability, ages 30-70Liberia: 32.2% of population, 22% probability, ages 30-70Libya: 82% of population, 19.8% probability, ages 30-70St. Lucia: 68.2% of population, 15.5% probability, ages 30-70Sri Lanka: 54.6% of population, 13.9% probability, ages 30-70Lesotho: 51.8% of population, 36.3% probability, ages 30-70Lithuania: 89.2% of population, 19.8% probability, ages 30-70Luxembourg: 99.1% of population, 7.8% probability, ages 30-70Latvia: 92.7% of population, 22.7% probability, ages 30-70Morocco: 91.2% of population, 22% probability, ages 30-70Moldova: 77.4% of population, 24.7% probability, ages 30-70Madagascar: 18.7% of population, 25.9% probability, ages 30-70Maldives: 85.2% of population, 9% probability, ages 30-70Mexico: 83.1% of population, 16.3% probability, ages 30-70North Macedonia: 93.1% of population, 19.6% probability, ages 30-70Mali: 36.8% of population, 23% probability, ages 30-70Malta: 93.9% of population, 9.8% probability, ages 30-70Myanmar: 45.4% of population, 24% probability, ages 30-70Montenegro: 88.9% of population, 18% probability, ages 30-70Mongolia: 85.1% of population, 26.3% probability, ages 30-70Mozambique: 20.5% of population, 26.6% probability, ages 30-70Mauritania: 45.8% of population, 18.7% probability, ages 30-70Mauritius: 73.3% of population, 21.7% probability, ages 30-70Malawi: 19% of population, 24.9% probability, ages 30-70Malaysia: 98% of population, 19.9% probability, ages 30-70Namibia: 64.9% of population, 23.1% probability, ages 30-70Niger: 15.6% of population, 20.2% probability, ages 30-70Nigeria: 41.2% of population, 17.7% probability, ages 30-70Nicaragua: 61.4% of population, 12.6% probability, ages 30-70Netherlands: 97% of population, 9.9% probability, ages 30-70Norway: 99% of population, 7.8% probability, ages 30-70Nepal: 46.3% of population, 19.4% probability, ages 30-70New Zealand: 93.5% of population, 10.3% probability, ages 30-70Oman: 95.3% of population, 14.6% probability, ages 30-70Pakistan: 57.3% of population, 25.5% probability, ages 30-70Panama: 72.8% of population, 10.9% probability, ages 30-70Peru: 82% of population, 12.3% probability, ages 30-70Philippines: 67.3% of population, 31.9% probability, ages 30-70Papua New Guinea: 18.8% of population, 28.5% probability, ages 30-70Poland: 88.6% of population, 17% probability, ages 30-70Puerto Rico (US): 87.3% of population, 10.1% probability, ages 30-70Portugal: 88.5% of population, 10.5% probability, ages 30-70Paraguay: 81.6% of population, 16.1% probability, ages 30-70West Bank and Gaza: 86.6% of population, 18.3% probability, ages 30-70Qatar: 98.1% of population, 12.1% probability, ages 30-70Romania: 91.3% of population, 21.5% probability, ages 30-70Russian Federation: 94.4% of population, 22.4% probability, ages 30-70Rwanda: 31.7% of population, 20% probability, ages 30-70Saudi Arabia: 100% of population, 13.6% probability, ages 30-70Sudan: 18.6% of population, 21.2% probability, ages 30-70Senegal: 60.1% of population, 21.4% probability, ages 30-70Singapore: 94.4% of population, 10.3% probability, ages 30-70Solomon Islands: 29% of population, 40.6% probability, ages 30-70Sierra Leone: 25.1% of population, 23.9% probability, ages 30-70El Salvador: 66.5% of population, 12.6% probability, ages 30-70Somalia, Fed. Rep.: 27.9% of population, 27.6% probability, ages 30-70Serbia: 87.7% of population, 20.7% probability, ages 30-70Sao Tome and Principe: 59.1% of population, 19.5% probability, ages 30-70Suriname: 87.4% of population, 18.7% probability, ages 30-70Slovak Republic: 89.8% of population, 16.6% probability, ages 30-70Slovenia: 90.8% of population, 11.6% probability, ages 30-70Sweden: 95.8% of population, 7.9% probability, ages 30-70Eswatini: 63.4% of population, 32.3% probability, ages 30-70Seychelles: 87.8% of population, 15.6% probability, ages 30-70Syrian Arab Republic: 33.8% of population, 21.3% probability, ages 30-70Chad: 12.6% of population, 23.4% probability, ages 30-70Togo: 39.5% of population, 25.4% probability, ages 30-70Thailand: 90.9% of population, 14.5% probability, ages 30-70Tajikistan: 55.8% of population, 18.1% probability, ages 30-70Turkmenistan: 18% of population, 25.7% probability, ages 30-70Timor-Leste: 35% of population, 20.1% probability, ages 30-70Tonga: 67.4% of population, 26.9% probability, ages 30-70Trinidad and Tobago: 82.2% of population, 20.3% probability, ages 30-70Tunisia: 76.5% of population, 13% probability, ages 30-70Turkiye: 89.8% of population, 15.4% probability, ages 30-70Tanzania: 31.2% of population, 18.8% probability, ages 30-70Uganda: 8.95% of population, 21.5% probability, ages 30-70Ukraine: 82.5% of population, 24.8% probability, ages 30-70Uruguay: 92% of population, 17.4% probability, ages 30-70United States: 94.7% of population, 13.7% probability, ages 30-70Uzbekistan: 89.5% of population, 24.6% probability, ages 30-70St. Vincent and the Grenadines: 76.2% of population, 23.6% probability, ages 30-70Viet Nam: 84.2% of population, 20.1% probability, ages 30-70Vanuatu: 22.4% of population, 36.8% probability, ages 30-70Samoa: 21.2% of population, 32.3% probability, ages 30-70South Africa: 78.4% of population, 22.7% probability, ages 30-70Zambia: 17.1% of population, 23.6% probability, ages 30-70Zimbabwe: 41.6% of population, 31.2% probability, ages 30-70ETHFJIFSMKIRKORLUXNERNORSLBUGAVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5754.0e-17179
By rank (Spearman), not adjusted-0.6433.1e-22179
Standard (Pearson), adjusted for national income+0.0200.795179
By rank (Spearman), adjusted for national income-0.0920.219179

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 179 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: +0.555 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.062 (without KIR) to +0.060 (without TKM). 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: KIR (-0.081).

Fragile: this link may not hold up.
  • sign flips when a single country is removed
  • sign flips once outliers are trimmed
  • 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 & Pacific25+0.13
Europe & Central Asia48-0.13
Latin America & Caribbean30-0.05
Middle East, North Africa, Afghanistan & Pakistan22-0.41
Sub-Saharan Africa46+0.25

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 third60+0.11
Middle third59-0.21
Richest third60-0.03

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
Christians109+0.14
Muslims49-0.29

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
France25+0.17
Never a European colony21-0.19
Spain20-0.07
United Kingdom59+0.13

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 = -0.055 after dropping 3 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 22years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link shows up within countries over time (r = -0.263) but mostly disappears in year-to-year changes (r = -0.040). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)+0.020for comparison
Within countries-0.637each country vs itself
…and vs the same year elsewhere-0.263q = 0.000 · 185 countries
Year-on-year changes-0.040q = 0.229
Read with care.
  • Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.
  • Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.
  • The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

3,950 country-years (one country in one year), 185 countries, 2000–2021, about 21 years each on average. Share of the variation that happens within countries over time: 42% for Internet users, 9% 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

0.40−0.4-100+10years Premature NCD mortality trails Internet users →lag -10: r = 0.032 (n = 2,672)lag -9: r = 0.003 (n = 2,853)lag -8: r = -0.028 (n = 3,035)lag -7: r = -0.061 (n = 3,217)lag -6: r = -0.098 (n = 3,397)lag -5: r = -0.125 (n = 3,577)lag -4: r = -0.149 (n = 3,758)lag -3: r = -0.174 (n = 3,922)lag -2: r = -0.204 (n = 3,931)lag -1: r = -0.233 (n = 3,940)lag +0: r = -0.263 (n = 3,950)lag +1: r = -0.287 (n = 3,958)lag +2: r = -0.304 (n = 3,957)lag +3: r = -0.308 (n = 3,946)lag +4: r = -0.306 (n = 3,926)lag +5: r = -0.303 (n = 3,875)lag +6: r = -0.291 (n = 3,773)lag +7: r = -0.267 (n = 3,653)lag +8: r = -0.239 (n = 3,519)lag +9: r = -0.208 (n = 3,374)lag +10: r = -0.180 (n = 3,374)

Same year (lag 0): -0.263. 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
Saudi Arabia SAU100%13.6%2024, 2021
Kuwait KWT100%9.3%2025, 2021
Bahrain BHR100%14.8%2024, 2021
United Arab Emirates ARE100%11.6%2024, 2021
Denmark DNK99.8%10.7%2024, 2021
Luxembourg LUX99.1%7.8%2025, 2021
Norway NOR99%7.8%2024, 2021
Iceland ISL98.2%8.5%2024, 2021
Qatar QAT98.1%12.1%2024, 2021
Malaysia MYS98%19.9%2024, 2021
Korea, Rep. KOR97.9%6.9%2024, 2021
Switzerland CHE97.3%7.5%2025, 2021
Ireland IRL97.2%9.6%2024, 2021
Netherlands NLD97%9.9%2024, 2021
Brunei Darussalam BRN96.3%14.9%2024, 2021

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

Sources

Internet users

Individuals using the Internet (% of population)

International Telecommunication Union · CC BY 4.0 · updated quarterly

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 2014–2025. 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 Internet users and Premature NCD mortality

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

More links with Internet users or Premature NCD mortality