placestats

Premature NCD mortality vs. Tuberculosis incidence

Holds up after adjusting for income

Across 179 countries, Premature NCD mortality and Tuberculosis incidence 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 +0.35 (q = 0.0081, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains. Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of tuberculosis incidence, 9.9 to 139 per 100,000 people, premature ncd mortality is worse by 3.57% probability, ages 30-70 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.93% probability, ages 30-70.

Link before adjusting (r)+0.568p = 1.1e-16 · 179 countries
After adjusting for national income+0.354p = 1.3e-6 · likely range [+0.22, +0.48]
p, allowing for regions0.037standard error 0.132 · 7 regions
Check for luck (q)0.008across 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
Tuberculosis incidence · per 100,000 peoplelink with income -0.67 · 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 →Tuberculosis incidence · per 100,000 people →6.9%44.1%0.29945Afghanistan: 32.7% probability, ages 30-70, 203 per 100,000 peopleAngola: 24.7% probability, ages 30-70, 373 per 100,000 peopleAlbania: 10% probability, ages 30-70, 15 per 100,000 peopleUnited Arab Emirates: 11.6% probability, ages 30-70, 0.63 per 100,000 peopleArgentina: 13.4% probability, ages 30-70, 44 per 100,000 peopleArmenia: 20.6% probability, ages 30-70, 19 per 100,000 peopleAntigua and Barbuda: 12.1% probability, ages 30-70, 1.1 per 100,000 peopleAustralia: 8.3% probability, ages 30-70, 6.9 per 100,000 peopleAustria: 9.9% probability, ages 30-70, 4.9 per 100,000 peopleAzerbaijan: 17.4% probability, ages 30-70, 64 per 100,000 peopleBurundi: 24.8% probability, ages 30-70, 83 per 100,000 peopleBelgium: 9.2% probability, ages 30-70, 2.7 per 100,000 peopleBenin: 20.8% probability, ages 30-70, 40 per 100,000 peopleBurkina Faso: 23.4% probability, ages 30-70, 57 per 100,000 peopleBangladesh: 18.1% probability, ages 30-70, 221 per 100,000 peopleBulgaria: 25.6% probability, ages 30-70, 12 per 100,000 peopleBahrain: 14.8% probability, ages 30-70, 12 per 100,000 peopleBahamas, The: 20.4% probability, ages 30-70, 18 per 100,000 peopleBosnia and Herzegovina: 17.2% probability, ages 30-70, 21 per 100,000 peopleBelarus: 23.8% probability, ages 30-70, 17 per 100,000 peopleBelize: 15.4% probability, ages 30-70, 34 per 100,000 peopleBolivia: 19.9% probability, ages 30-70, 114 per 100,000 peopleBrazil: 14.5% probability, ages 30-70, 49 per 100,000 peopleBarbados: 14% probability, ages 30-70, 1 per 100,000 peopleBrunei Darussalam: 14.9% probability, ages 30-70, 65 per 100,000 peopleBhutan: 15.9% probability, ages 30-70, 191 per 100,000 peopleBotswana: 19% probability, ages 30-70, 143 per 100,000 peopleCentral African Republic: 30.8% probability, ages 30-70, 419 per 100,000 peopleCanada: 9.7% probability, ages 30-70, 6.2 per 100,000 peopleSwitzerland: 7.5% probability, ages 30-70, 5.4 per 100,000 peopleChile: 9.4% probability, ages 30-70, 19 per 100,000 peopleChina: 15.9% probability, ages 30-70, 49 per 100,000 peopleCote d'Ivoire: 22.7% probability, ages 30-70, 99 per 100,000 peopleCameroon: 24.3% probability, ages 30-70, 132 per 100,000 peopleCongo, Dem. Rep.: 25.1% probability, ages 30-70, 377 per 100,000 peopleCongo, Rep.: 21.2% probability, ages 30-70, 302 per 100,000 peopleColombia: 10.3% probability, ages 30-70, 49 per 100,000 peopleComoros: 20.2% probability, ages 30-70, 22 per 100,000 peopleCabo Verde: 17.2% probability, ages 30-70, 47 per 100,000 peopleCosta Rica: 9.9% probability, ages 30-70, 9.3 per 100,000 peopleCyprus: 8.5% probability, ages 30-70, 5.5 per 100,000 peopleCzechia: 14.2% probability, ages 30-70, 4.9 per 100,000 peopleGermany: 11.6% probability, ages 30-70, 5.4 per 100,000 peopleDjibouti: 20.6% probability, ages 30-70, 495 per 100,000 peopleDenmark: 10.7% probability, ages 30-70, 3.2 per 100,000 peopleDominican Republic: 17.4% probability, ages 30-70, 51 per 100,000 peopleAlgeria: 13.3% probability, ages 30-70, 54 per 100,000 peopleEcuador: 13.2% probability, ages 30-70, 66 per 100,000 peopleEgypt, Arab Rep.: 26% probability, ages 30-70, 10 per 100,000 peopleSpain: 9.3% probability, ages 30-70, 7.9 per 100,000 peopleEstonia: 15.5% probability, ages 30-70, 8.2 per 100,000 peopleEthiopia: 16.8% probability, ages 30-70, 141 per 100,000 peopleFinland: 9.6% probability, ages 30-70, 3.7 per 100,000 peopleFiji: 37.9% probability, ages 30-70, 104 per 100,000 peopleFrance: 10.2% probability, ages 30-70, 7.8 per 100,000 peopleMicronesia, Fed. Sts.: 40.5% probability, ages 30-70, 264 per 100,000 peopleGabon: 18.1% probability, ages 30-70, 371 per 100,000 peopleUnited Kingdom: 11% probability, ages 30-70, 9.7 per 100,000 peopleGeorgia: 22.1% probability, ages 30-70, 45 per 100,000 peopleGhana: 22.2% probability, ages 30-70, 126 per 100,000 peopleGuinea: 23.6% probability, ages 30-70, 178 per 100,000 peopleGambia, The: 21.8% probability, ages 30-70, 138 per 100,000 peopleGuinea-Bissau: 25.3% probability, ages 30-70, 176 per 100,000 peopleEquatorial Guinea: 20.3% probability, ages 30-70, 199 per 100,000 peopleGreece: 12% probability, ages 30-70, 3.6 per 100,000 peopleGrenada: 17.1% probability, ages 30-70, 1.7 per 100,000 peopleGuatemala: 14.6% probability, ages 30-70, 28 per 100,000 peopleGuyana: 25.4% probability, ages 30-70, 67 per 100,000 peopleHonduras: 17.8% probability, ages 30-70, 38 per 100,000 peopleCroatia: 15.9% probability, ages 30-70, 6.6 per 100,000 peopleHaiti: 31.6% probability, ages 30-70, 175 per 100,000 peopleHungary: 21.7% probability, ages 30-70, 5.6 per 100,000 peopleIndonesia: 21.9% probability, ages 30-70, 382 per 100,000 peopleIndia: 23.6% probability, ages 30-70, 187 per 100,000 peopleIreland: 9.6% probability, ages 30-70, 6.2 per 100,000 peopleIran, Islamic Rep.: 13.9% probability, ages 30-70, 10 per 100,000 peopleIraq: 22.7% probability, ages 30-70, 21 per 100,000 peopleIceland: 8.5% probability, ages 30-70, 3 per 100,000 peopleIsrael: 7.6% probability, ages 30-70, 2.4 per 100,000 peopleItaly: 9.1% probability, ages 30-70, 5 per 100,000 peopleJamaica: 20.2% probability, ages 30-70, 2.3 per 100,000 peopleJordan: 11.6% probability, ages 30-70, 3.5 per 100,000 peopleJapan: 8% probability, ages 30-70, 9.8 per 100,000 peopleKazakhstan: 21.2% probability, ages 30-70, 45 per 100,000 peopleKenya: 18% probability, ages 30-70, 207 per 100,000 peopleKyrgyz Republic: 18.8% probability, ages 30-70, 128 per 100,000 peopleCambodia: 22.6% probability, ages 30-70, 272 per 100,000 peopleKiribati: 44.1% probability, ages 30-70, 945 per 100,000 peopleKorea, Rep.: 6.9% probability, ages 30-70, 35 per 100,000 peopleKuwait: 9.3% probability, ages 30-70, 10 per 100,000 peopleLao PDR: 26.5% probability, ages 30-70, 127 per 100,000 peopleLebanon: 11.9% probability, ages 30-70, 13 per 100,000 peopleLiberia: 22% probability, ages 30-70, 207 per 100,000 peopleLibya: 19.8% probability, ages 30-70, 63 per 100,000 peopleSt. Lucia: 15.5% probability, ages 30-70, 0.56 per 100,000 peopleSri Lanka: 13.9% probability, ages 30-70, 59 per 100,000 peopleLesotho: 36.3% probability, ages 30-70, 548 per 100,000 peopleLithuania: 19.8% probability, ages 30-70, 31 per 100,000 peopleLuxembourg: 7.8% probability, ages 30-70, 7.5 per 100,000 peopleLatvia: 22.7% probability, ages 30-70, 21 per 100,000 peopleMorocco: 22% probability, ages 30-70, 98 per 100,000 peopleMoldova: 24.7% probability, ages 30-70, 64 per 100,000 peopleMadagascar: 25.9% probability, ages 30-70, 224 per 100,000 peopleMaldives: 9% probability, ages 30-70, 40 per 100,000 peopleMexico: 16.3% probability, ages 30-70, 28 per 100,000 peopleNorth Macedonia: 19.6% probability, ages 30-70, 11 per 100,000 peopleMali: 23% probability, ages 30-70, 51 per 100,000 peopleMalta: 9.8% probability, ages 30-70, 15 per 100,000 peopleMyanmar: 24% probability, ages 30-70, 482 per 100,000 peopleMontenegro: 18% probability, ages 30-70, 16 per 100,000 peopleMongolia: 26.3% probability, ages 30-70, 446 per 100,000 peopleMozambique: 26.6% probability, ages 30-70, 361 per 100,000 peopleMauritania: 18.7% probability, ages 30-70, 65 per 100,000 peopleMauritius: 21.7% probability, ages 30-70, 13 per 100,000 peopleMalawi: 24.9% probability, ages 30-70, 113 per 100,000 peopleMalaysia: 19.9% probability, ages 30-70, 96 per 100,000 peopleNamibia: 23.1% probability, ages 30-70, 422 per 100,000 peopleNiger: 20.2% probability, ages 30-70, 86 per 100,000 peopleNigeria: 17.7% probability, ages 30-70, 219 per 100,000 peopleNicaragua: 12.6% probability, ages 30-70, 43 per 100,000 peopleNetherlands: 9.9% probability, ages 30-70, 4.7 per 100,000 peopleNorway: 7.8% probability, ages 30-70, 3.3 per 100,000 peopleNepal: 19.4% probability, ages 30-70, 227 per 100,000 peopleNew Zealand: 10.3% probability, ages 30-70, 7.9 per 100,000 peopleOman: 14.6% probability, ages 30-70, 11 per 100,000 peoplePakistan: 25.5% probability, ages 30-70, 266 per 100,000 peoplePanama: 10.9% probability, ages 30-70, 63 per 100,000 peoplePeru: 12.3% probability, ages 30-70, 139 per 100,000 peoplePhilippines: 31.9% probability, ages 30-70, 625 per 100,000 peoplePapua New Guinea: 28.5% probability, ages 30-70, 664 per 100,000 peoplePoland: 17% probability, ages 30-70, 11 per 100,000 peoplePuerto Rico (US): 10.1% probability, ages 30-70, 0.55 per 100,000 peoplePortugal: 10.5% probability, ages 30-70, 15 per 100,000 peopleParaguay: 16.1% probability, ages 30-70, 81 per 100,000 peopleWest Bank and Gaza: 18.3% probability, ages 30-70, 0.29 per 100,000 peopleQatar: 12.1% probability, ages 30-70, 37 per 100,000 peopleRomania: 21.5% probability, ages 30-70, 60 per 100,000 peopleRussian Federation: 22.4% probability, ages 30-70, 34 per 100,000 peopleRwanda: 20% probability, ages 30-70, 62 per 100,000 peopleSaudi Arabia: 13.6% probability, ages 30-70, 8.4 per 100,000 peopleSudan: 21.2% probability, ages 30-70, 47 per 100,000 peopleSenegal: 21.4% probability, ages 30-70, 132 per 100,000 peopleSingapore: 10.3% probability, ages 30-70, 43 per 100,000 peopleSolomon Islands: 40.6% probability, ages 30-70, 123 per 100,000 peopleSierra Leone: 23.9% probability, ages 30-70, 372 per 100,000 peopleEl Salvador: 12.6% probability, ages 30-70, 116 per 100,000 peopleSomalia, Fed. Rep.: 27.6% probability, ages 30-70, 151 per 100,000 peopleSerbia: 20.7% probability, ages 30-70, 8.7 per 100,000 peopleSao Tome and Principe: 19.5% probability, ages 30-70, 87 per 100,000 peopleSuriname: 18.7% probability, ages 30-70, 34 per 100,000 peopleSlovak Republic: 16.6% probability, ages 30-70, 3.3 per 100,000 peopleSlovenia: 11.6% probability, ages 30-70, 2.6 per 100,000 peopleSweden: 7.9% probability, ages 30-70, 3.3 per 100,000 peopleEswatini: 32.3% probability, ages 30-70, 319 per 100,000 peopleSeychelles: 15.6% probability, ages 30-70, 17 per 100,000 peopleSyrian Arab Republic: 21.3% probability, ages 30-70, 23 per 100,000 peopleChad: 23.4% probability, ages 30-70, 118 per 100,000 peopleTogo: 25.4% probability, ages 30-70, 48 per 100,000 peopleThailand: 14.5% probability, ages 30-70, 146 per 100,000 peopleTajikistan: 18.1% probability, ages 30-70, 75 per 100,000 peopleTurkmenistan: 25.7% probability, ages 30-70, 40 per 100,000 peopleTimor-Leste: 20.1% probability, ages 30-70, 496 per 100,000 peopleTonga: 26.9% probability, ages 30-70, 1.9 per 100,000 peopleTrinidad and Tobago: 20.3% probability, ages 30-70, 27 per 100,000 peopleTunisia: 13% probability, ages 30-70, 39 per 100,000 peopleTurkiye: 15.4% probability, ages 30-70, 13 per 100,000 peopleTanzania: 18.8% probability, ages 30-70, 172 per 100,000 peopleUganda: 21.5% probability, ages 30-70, 197 per 100,000 peopleUkraine: 24.8% probability, ages 30-70, 86 per 100,000 peopleUruguay: 17.4% probability, ages 30-70, 47 per 100,000 peopleUnited States: 13.7% probability, ages 30-70, 3.2 per 100,000 peopleUzbekistan: 24.6% probability, ages 30-70, 51 per 100,000 peopleSt. Vincent and the Grenadines: 23.6% probability, ages 30-70, 4.6 per 100,000 peopleViet Nam: 20.1% probability, ages 30-70, 182 per 100,000 peopleVanuatu: 36.8% probability, ages 30-70, 44 per 100,000 peopleSamoa: 32.3% probability, ages 30-70, 5.5 per 100,000 peopleSouth Africa: 22.7% probability, ages 30-70, 389 per 100,000 peopleZambia: 23.6% probability, ages 30-70, 272 per 100,000 peopleZimbabwe: 31.2% probability, ages 30-70, 203 per 100,000 peopleCHEDJIFJIISRKIRLSONORPHLPNGSLBTLSVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5681.1e-16179
By rank (Spearman), not adjusted+0.6192.7e-20179
Standard (Pearson), adjusted for national income+0.3541.3e-6179
By rank (Spearman), adjusted for national income+0.2710.000179

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: +0.214 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.273 (without KIR) to +0.376 (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: KIR (-0.081).

Fragile: this link may not hold up.
  • correlation moves sharply once outliers are trimmed

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+0.05
Europe & Central Asia48+0.13
Latin America & Caribbean30+0.20
Middle East, North Africa, Afghanistan & Pakistan22+0.27
Sub-Saharan Africa46+0.54

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+0.40
Middle third59+0.40
Richest third60+0.48

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+0.33
Muslims49+0.28

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+0.43
Never a European colony21+0.30
Spain20+0.66
United Kingdom59+0.39

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 = +0.172 after dropping 19 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.

Within countries over time, there is no link that is clearly more than chance (r = -0.092, 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 premature ncd mortality moved did not tend to see their tuberculosis incidence move with it.

Across countries (adjusted)+0.354for comparison
Within countries+0.179each country vs itself
…and vs the same year elsewhere-0.092q = 0.104 · 184 countries
Year-on-year changes+0.007q = 0.927
The direction flips. Comparing each country only with itself gives +0.18. Also taking out what happened to the whole world each year gives -0.09. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.
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.

4,025 country-years (one country in one year), 184 countries, 2000–2021, about 22 years each on average. Share of the variation that happens within countries over time: 14% for Premature NCD mortality, 9% for Tuberculosis incidence. 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

0.30−0.3-100+10years Tuberculosis incidence trails Premature NCD mortality →lag -10: r = -0.058 (n = 2,184)lag -9: r = -0.046 (n = 2,367)lag -8: r = -0.044 (n = 2,550)lag -7: r = -0.046 (n = 2,733)lag -6: r = -0.056 (n = 2,921)lag -5: r = -0.062 (n = 3,105)lag -4: r = -0.068 (n = 3,289)lag -3: r = -0.077 (n = 3,473)lag -2: r = -0.077 (n = 3,657)lag -1: r = -0.082 (n = 3,841)lag +0: r = -0.092 (n = 4,025)lag +1: r = -0.117 (n = 4,029)lag +2: r = -0.134 (n = 4,033)lag +3: r = -0.146 (n = 4,036)lag +4: r = -0.167 (n = 3,855)lag +5: r = -0.178 (n = 3,674)lag +6: r = -0.194 (n = 3,491)lag +7: r = -0.206 (n = 3,308)lag +8: r = -0.217 (n = 3,125)lag +9: r = -0.234 (n = 2,942)lag +10: r = -0.246 (n = 2,759)

Same year (lag 0): -0.092. 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.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


% probability, ages 30-70
per 100,000 people		Years
Kiribati KIR44.1%9452021, 2024
Solomon Islands SLB40.6%1232021, 2024
Micronesia, Fed. Sts. FSM40.5%2642021, 2024
Fiji FJI37.9%1042021, 2024
Vanuatu VUT36.8%442021, 2024
Lesotho LSO36.3%5482021, 2024
Afghanistan AFG32.7%2032021, 2024
Samoa WSM32.3%5.52021, 2024
Eswatini SWZ32.3%3192021, 2024
Philippines PHL31.9%6252021, 2024
Haiti HTI31.6%1752021, 2024
Zimbabwe ZWE31.2%2032021, 2024
Central African Republic CAF30.8%4192021, 2024
Papua New Guinea PNG28.5%6642021, 2024
Somalia, Fed. Rep. SOM27.6%1512021, 2024

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

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.

Tuberculosis incidence

Incidence of tuberculosis (per 100,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2024. 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 Tuberculosis incidence

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

More links with Premature NCD mortality or Tuberculosis incidence