placestats

HIV prevalence vs. Tuberculosis incidence

Holds up after adjusting for income

Across 148 countries, HIV prevalence and Tuberculosis incidence rise and fall together at r = +0.41 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = +0.35, q = 0.019, allowing for the many pairs tested). Caution: correlation moves sharply once outliers are trimmed.

Comparing countries across the middle half of hiv prevalence, 0.1% to 1% of adults aged 15-49, tuberculosis incidence is worse by 15.7 per 100,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 11.9 per 100,000 people.

Link before adjusting (r)+0.412p = 1.9e-7 · 148 countries
After adjusting for national income+0.355p = 1.0e-5 · likely range [+0.20, +0.49]
p, allowing for regions0.001standard error 0.058 · 7 regions
Check for luck (q)0.019across 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.

HIV prevalence · % of adults aged 15-49link with income -0.40 · moderate
Tuberculosis incidence · per 100,000 peoplelink with income -0.67 · high

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

Every country as a dot

HIV prevalence · % of adults aged 15-49 →Tuberculosis incidence · per 100,000 people →0.1%23.4%0.56664Afghanistan: 0.1% of adults aged 15-49, 203 per 100,000 peopleAngola: 1.6% of adults aged 15-49, 373 per 100,000 peopleAlbania: 0.1% of adults aged 15-49, 15 per 100,000 peopleUnited Arab Emirates: 0.1% of adults aged 15-49, 0.63 per 100,000 peopleArgentina: 0.4% of adults aged 15-49, 44 per 100,000 peopleArmenia: 0.4% of adults aged 15-49, 19 per 100,000 peopleAntigua and Barbuda: 1.1% of adults aged 15-49, 1.1 per 100,000 peopleAustralia: 0.1% of adults aged 15-49, 6.9 per 100,000 peopleAzerbaijan: 0.1% of adults aged 15-49, 64 per 100,000 peopleBurundi: 0.8% of adults aged 15-49, 83 per 100,000 peopleBelgium: 0.2% of adults aged 15-49, 2.7 per 100,000 peopleBenin: 0.7% of adults aged 15-49, 40 per 100,000 peopleBurkina Faso: 0.5% of adults aged 15-49, 57 per 100,000 peopleBangladesh: 0.1% of adults aged 15-49, 221 per 100,000 peopleBulgaria: 0.1% of adults aged 15-49, 12 per 100,000 peopleBahrain: 0.1% of adults aged 15-49, 12 per 100,000 peopleBahamas, The: 1.1% of adults aged 15-49, 18 per 100,000 peopleBelize: 1.2% of adults aged 15-49, 34 per 100,000 peopleBolivia: 0.4% of adults aged 15-49, 114 per 100,000 peopleBrazil: 0.7% of adults aged 15-49, 49 per 100,000 peopleBarbados: 1% of adults aged 15-49, 1 per 100,000 peopleBhutan: 0.2% of adults aged 15-49, 191 per 100,000 peopleBotswana: 15.7% of adults aged 15-49, 143 per 100,000 peopleCentral African Republic: 2.4% of adults aged 15-49, 419 per 100,000 peopleCanada: 0.1% of adults aged 15-49, 6.2 per 100,000 peopleSwitzerland: 0.2% of adults aged 15-49, 5.4 per 100,000 peopleChile: 0.7% of adults aged 15-49, 19 per 100,000 peopleCote d'Ivoire: 1.7% of adults aged 15-49, 99 per 100,000 peopleCameroon: 2.5% of adults aged 15-49, 132 per 100,000 peopleCongo, Dem. Rep.: 0.7% of adults aged 15-49, 377 per 100,000 peopleCongo, Rep.: 3.2% of adults aged 15-49, 302 per 100,000 peopleColombia: 0.6% of adults aged 15-49, 49 per 100,000 peopleComoros: 0.1% of adults aged 15-49, 22 per 100,000 peopleCabo Verde: 0.9% of adults aged 15-49, 47 per 100,000 peopleCosta Rica: 0.6% of adults aged 15-49, 9.3 per 100,000 peopleGermany: 0.2% of adults aged 15-49, 5.4 per 100,000 peopleDjibouti: 0.9% of adults aged 15-49, 495 per 100,000 peopleDominica: 0.5% of adults aged 15-49, 4.5 per 100,000 peopleDenmark: 0.1% of adults aged 15-49, 3.2 per 100,000 peopleDominican Republic: 1% of adults aged 15-49, 51 per 100,000 peopleAlgeria: 0.1% of adults aged 15-49, 54 per 100,000 peopleEcuador: 0.4% of adults aged 15-49, 66 per 100,000 peopleEgypt, Arab Rep.: 0.1% of adults aged 15-49, 10 per 100,000 peopleSpain: 0.3% of adults aged 15-49, 7.9 per 100,000 peopleEstonia: 0.7% of adults aged 15-49, 8.2 per 100,000 peopleEthiopia: 0.7% of adults aged 15-49, 141 per 100,000 peopleFiji: 1% of adults aged 15-49, 104 per 100,000 peopleFrance: 0.3% of adults aged 15-49, 7.8 per 100,000 peopleGabon: 2.8% of adults aged 15-49, 371 per 100,000 peopleUnited Kingdom: 0.2% of adults aged 15-49, 9.7 per 100,000 peopleGeorgia: 0.4% of adults aged 15-49, 45 per 100,000 peopleGhana: 1.5% of adults aged 15-49, 126 per 100,000 peopleGuinea: 1.3% of adults aged 15-49, 178 per 100,000 peopleGambia, The: 1.3% of adults aged 15-49, 138 per 100,000 peopleGuinea-Bissau: 2.2% of adults aged 15-49, 176 per 100,000 peopleGreece: 0.3% of adults aged 15-49, 3.6 per 100,000 peopleGuatemala: 0.2% of adults aged 15-49, 28 per 100,000 peopleGuyana: 1.6% of adults aged 15-49, 67 per 100,000 peopleHonduras: 0.2% of adults aged 15-49, 38 per 100,000 peopleCroatia: 0.1% of adults aged 15-49, 6.6 per 100,000 peopleHaiti: 1.6% of adults aged 15-49, 175 per 100,000 peopleIndonesia: 0.4% of adults aged 15-49, 382 per 100,000 peopleIndia: 0.2% of adults aged 15-49, 187 per 100,000 peopleIreland: 0.2% of adults aged 15-49, 6.2 per 100,000 peopleIraq: 0.1% of adults aged 15-49, 21 per 100,000 peopleIceland: 0.1% of adults aged 15-49, 3 per 100,000 peopleItaly: 0.3% of adults aged 15-49, 5 per 100,000 peopleJamaica: 1.1% of adults aged 15-49, 2.3 per 100,000 peopleJordan: 0.1% of adults aged 15-49, 3.5 per 100,000 peopleKazakhstan: 0.3% of adults aged 15-49, 45 per 100,000 peopleKenya: 3% of adults aged 15-49, 207 per 100,000 peopleKyrgyz Republic: 0.3% of adults aged 15-49, 128 per 100,000 peopleCambodia: 0.5% of adults aged 15-49, 272 per 100,000 peopleSt. Kitts and Nevis: 0.8% of adults aged 15-49, 1.1 per 100,000 peopleKuwait: 0.1% of adults aged 15-49, 10 per 100,000 peopleLao PDR: 0.4% of adults aged 15-49, 127 per 100,000 peopleLebanon: 0.1% of adults aged 15-49, 13 per 100,000 peopleLiberia: 0.9% of adults aged 15-49, 207 per 100,000 peopleLibya: 0.1% of adults aged 15-49, 63 per 100,000 peopleSt. Lucia: 0.4% of adults aged 15-49, 0.56 per 100,000 peopleSri Lanka: 0.1% of adults aged 15-49, 59 per 100,000 peopleLesotho: 17.1% of adults aged 15-49, 548 per 100,000 peopleLithuania: 0.2% of adults aged 15-49, 31 per 100,000 peopleLuxembourg: 0.3% of adults aged 15-49, 7.5 per 100,000 peopleLatvia: 0.5% of adults aged 15-49, 21 per 100,000 peopleMorocco: 0.1% of adults aged 15-49, 98 per 100,000 peopleMoldova: 0.9% of adults aged 15-49, 64 per 100,000 peopleMadagascar: 0.5% of adults aged 15-49, 224 per 100,000 peopleMaldives: 0.1% of adults aged 15-49, 40 per 100,000 peopleMexico: 0.5% of adults aged 15-49, 28 per 100,000 peopleNorth Macedonia: 0.1% of adults aged 15-49, 11 per 100,000 peopleMali: 0.8% of adults aged 15-49, 51 per 100,000 peopleMalta: 0.3% of adults aged 15-49, 15 per 100,000 peopleMyanmar: 0.9% of adults aged 15-49, 482 per 100,000 peopleMontenegro: 0.1% of adults aged 15-49, 16 per 100,000 peopleMongolia: 0.1% of adults aged 15-49, 446 per 100,000 peopleMozambique: 11.5% of adults aged 15-49, 361 per 100,000 peopleMauritania: 0.1% of adults aged 15-49, 65 per 100,000 peopleMalawi: 6.2% of adults aged 15-49, 113 per 100,000 peopleMalaysia: 0.3% of adults aged 15-49, 96 per 100,000 peopleNamibia: 9% of adults aged 15-49, 422 per 100,000 peopleNiger: 0.2% of adults aged 15-49, 86 per 100,000 peopleNigeria: 1.2% of adults aged 15-49, 219 per 100,000 peopleNetherlands: 0.1% of adults aged 15-49, 4.7 per 100,000 peopleNorway: 0.1% of adults aged 15-49, 3.3 per 100,000 peopleNepal: 0.1% of adults aged 15-49, 227 per 100,000 peopleNew Zealand: 0.1% of adults aged 15-49, 7.9 per 100,000 peopleOman: 0.1% of adults aged 15-49, 11 per 100,000 peoplePakistan: 0.2% of adults aged 15-49, 266 per 100,000 peoplePanama: 1.1% of adults aged 15-49, 63 per 100,000 peoplePeru: 0.4% of adults aged 15-49, 139 per 100,000 peoplePhilippines: 0.3% of adults aged 15-49, 625 per 100,000 peoplePapua New Guinea: 1.5% of adults aged 15-49, 664 per 100,000 peoplePortugal: 0.5% of adults aged 15-49, 15 per 100,000 peopleParaguay: 0.5% of adults aged 15-49, 81 per 100,000 peopleQatar: 0.1% of adults aged 15-49, 37 per 100,000 peopleRomania: 0.2% of adults aged 15-49, 60 per 100,000 peopleRwanda: 2% of adults aged 15-49, 62 per 100,000 peopleSaudi Arabia: 0.1% of adults aged 15-49, 8.4 per 100,000 peopleSudan: 0.2% of adults aged 15-49, 47 per 100,000 peopleSenegal: 0.3% of adults aged 15-49, 132 per 100,000 peopleSingapore: 0.1% of adults aged 15-49, 43 per 100,000 peopleSolomon Islands: 0.1% of adults aged 15-49, 123 per 100,000 peopleSierra Leone: 1.4% of adults aged 15-49, 372 per 100,000 peopleEl Salvador: 0.4% of adults aged 15-49, 116 per 100,000 peopleSomalia, Fed. Rep.: 0.1% of adults aged 15-49, 151 per 100,000 peopleSerbia: 0.1% of adults aged 15-49, 8.7 per 100,000 peopleSuriname: 1.6% of adults aged 15-49, 34 per 100,000 peopleSlovak Republic: 0.1% of adults aged 15-49, 3.3 per 100,000 peopleSlovenia: 0.1% of adults aged 15-49, 2.6 per 100,000 peopleSweden: 0.1% of adults aged 15-49, 3.3 per 100,000 peopleEswatini: 23.4% of adults aged 15-49, 319 per 100,000 peopleSyrian Arab Republic: 0.1% of adults aged 15-49, 23 per 100,000 peopleChad: 0.9% of adults aged 15-49, 118 per 100,000 peopleTogo: 1.6% of adults aged 15-49, 48 per 100,000 peopleThailand: 1% of adults aged 15-49, 146 per 100,000 peopleTajikistan: 0.2% of adults aged 15-49, 75 per 100,000 peopleTimor-Leste: 0.2% of adults aged 15-49, 496 per 100,000 peopleTunisia: 0.1% of adults aged 15-49, 39 per 100,000 peopleTuvalu: 0.4% of adults aged 15-49, 223 per 100,000 peopleTanzania: 3.5% of adults aged 15-49, 172 per 100,000 peopleUganda: 4.9% of adults aged 15-49, 197 per 100,000 peopleSt. Vincent and the Grenadines: 0.8% of adults aged 15-49, 4.6 per 100,000 peopleViet Nam: 0.4% of adults aged 15-49, 182 per 100,000 peopleVanuatu: 0.1% of adults aged 15-49, 44 per 100,000 peopleSouth Africa: 17.2% of adults aged 15-49, 389 per 100,000 peopleZambia: 9.4% of adults aged 15-49, 272 per 100,000 peopleZimbabwe: 9.8% of adults aged 15-49, 203 per 100,000 peopleAREBWAISLLSOPHLPNGSVNSWZTLSZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.4121.9e-7148
By rank (Spearman), not adjusted+0.4701.7e-9148
Standard (Pearson), adjusted for national income+0.3551.0e-5148
By rank (Spearman), adjusted for national income+0.2850.000148

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 148 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: +0.057 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.299 (without LSO) to +0.388 (without PHL). 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: LSO (-0.056).

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 & Pacific18+0.34
Europe & Central Asia35+0.17
Latin America & Caribbean27+0.12
Middle East, North Africa, Afghanistan & Pakistan19+0.88
Sub-Saharan Africa42+0.52

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 third50+0.36
Middle third49+0.35
Richest third49+0.33

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
Christians89+0.41
Muslims42+0.38

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+0.56
Never a European colony14-0.10
Spain16+0.07
United Kingdom51+0.46

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 = +0.146 after dropping 22 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 25years. 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 also shows up within countries over time (r = +0.358). It holds even when looking only at changes from one year to the next (r = +0.141). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.

Across countries (adjusted)+0.355for comparison
Within countries+0.418each country vs itself
…and vs the same year elsewhere+0.358q = 0.001 · 152 countries
Year-on-year changes+0.141q = 0.002
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.
  • The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

3,744 country-years (one country in one year), 152 countries, 2000–2024, about 25 years each on average. Share of the variation that happens within countries over time: 2% for HIV prevalence, 18% 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

0.50−0.5-100+10years Tuberculosis incidence trails HIV prevalence →lag -10: r = 0.271 (n = 2,240)lag -9: r = 0.348 (n = 2,394)lag -8: r = 0.405 (n = 2,544)lag -7: r = 0.437 (n = 2,694)lag -6: r = 0.454 (n = 2,844)lag -5: r = 0.456 (n = 2,994)lag -4: r = 0.446 (n = 3,144)lag -3: r = 0.427 (n = 3,294)lag -2: r = 0.405 (n = 3,444)lag -1: r = 0.381 (n = 3,594)lag +0: r = 0.358 (n = 3,744)lag +1: r = 0.313 (n = 3,747)lag +2: r = 0.267 (n = 3,747)lag +3: r = 0.219 (n = 3,745)lag +4: r = 0.170 (n = 3,743)lag +5: r = 0.120 (n = 3,737)lag +6: r = 0.068 (n = 3,736)lag +7: r = 0.014 (n = 3,735)lag +8: r = -0.041 (n = 3,734)lag +9: r = -0.098 (n = 3,733)lag +10: r = -0.157 (n = 3,732)

Same year (lag 0): +0.358. 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 largest value on this curve (+0.46 at lag -5) is higher than the same-year one by 0.10. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of adults aged 15-49
per 100,000 people		Years
Eswatini SWZ23.4%3192024
South Africa ZAF17.2%3892024
Lesotho LSO17.1%5482024
Botswana BWA15.7%1432024
Mozambique MOZ11.5%3612024
Zimbabwe ZWE9.8%2032024
Zambia ZMB9.4%2722024
Namibia NAM9%4222024
Malawi MWI6.2%1132024
Uganda UGA4.9%1972024
Tanzania TZA3.5%1722024
Congo, Rep. COG3.2%3022024
Kenya KEN3%2072024
Gabon GAB2.8%3712024
Cameroon CMR2.5%1322024

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

Sources

HIV prevalence

Prevalence of HIV, total (% of population ages 15-49)

UNAIDS · CC BY 4.0 · updated quarterly

Estimates from a statistical model. Very lopsided: a few southern African countries sit about ten times higher than everyone else. So a link with this is largely a link with them, and the "leave one out" check on each pair page shows by how much.

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 2022–2024. 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 HIV prevalence and Tuberculosis incidence

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

More links with HIV prevalence or Tuberculosis incidence