placestats

HIV prevalence vs. Wellbeing score

Holds up after adjusting for income

Across 81 countries, HIV prevalence and Wellbeing score rise and fall together at r = -0.36 (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 = -0.23, q = 0.047, allowing for the many pairs tested). Caution: leave-one-out range spans 0.15 in r.

Comparing countries across the middle half of hiv prevalence, 0.1% to 0.5% of adults aged 15-49, wellbeing score is worse by 0.0485 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0209 typical gaps.

Link before adjusting (r)-0.363p = 0.001 · 81 countries
After adjusting for national income-0.230p = 0.040 · likely range [-0.43, -0.01]
p, allowing for regions0.004standard error 0.052 · 7 regions
Check for luck (q)0.047across 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
Wellbeing score · typical gaps (standard deviations)link with income +0.80 · 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 →Wellbeing score · typical gaps (standard deviations) →0.1%17.2%-1.981.89Albania: 0.1% of adults aged 15-49, -0.617 typical gaps (standard deviations)Argentina: 0.4% of adults aged 15-49, 0.216 typical gaps (standard deviations)Armenia: 0.4% of adults aged 15-49, -0.118 typical gaps (standard deviations)Australia: 0.1% of adults aged 15-49, 1.25 typical gaps (standard deviations)Azerbaijan: 0.1% of adults aged 15-49, -0.0603 typical gaps (standard deviations)Belgium: 0.2% of adults aged 15-49, 0.818 typical gaps (standard deviations)Burkina Faso: 0.5% of adults aged 15-49, -0.476 typical gaps (standard deviations)Bangladesh: 0.1% of adults aged 15-49, -0.561 typical gaps (standard deviations)Bulgaria: 0.1% of adults aged 15-49, -0.572 typical gaps (standard deviations)Bolivia: 0.4% of adults aged 15-49, -0.361 typical gaps (standard deviations)Brazil: 0.7% of adults aged 15-49, 0.123 typical gaps (standard deviations)Canada: 0.1% of adults aged 15-49, 1.06 typical gaps (standard deviations)Switzerland: 0.2% of adults aged 15-49, 1.55 typical gaps (standard deviations)Chile: 0.7% of adults aged 15-49, 0.19 typical gaps (standard deviations)Colombia: 0.6% of adults aged 15-49, -0.125 typical gaps (standard deviations)Germany: 0.2% of adults aged 15-49, 0.981 typical gaps (standard deviations)Denmark: 0.1% of adults aged 15-49, 1.89 typical gaps (standard deviations)Algeria: 0.1% of adults aged 15-49, -0.205 typical gaps (standard deviations)Ecuador: 0.4% of adults aged 15-49, -0.164 typical gaps (standard deviations)Egypt, Arab Rep.: 0.1% of adults aged 15-49, -1.08 typical gaps (standard deviations)Spain: 0.3% of adults aged 15-49, 0.816 typical gaps (standard deviations)Estonia: 0.7% of adults aged 15-49, 0.622 typical gaps (standard deviations)Ethiopia: 0.7% of adults aged 15-49, -0.726 typical gaps (standard deviations)France: 0.3% of adults aged 15-49, 0.717 typical gaps (standard deviations)United Kingdom: 0.2% of adults aged 15-49, 1.07 typical gaps (standard deviations)Georgia: 0.4% of adults aged 15-49, -0.493 typical gaps (standard deviations)Ghana: 1.5% of adults aged 15-49, -0.73 typical gaps (standard deviations)Greece: 0.3% of adults aged 15-49, -0.284 typical gaps (standard deviations)Guatemala: 0.2% of adults aged 15-49, 0.352 typical gaps (standard deviations)Croatia: 0.1% of adults aged 15-49, 0.0883 typical gaps (standard deviations)Haiti: 1.6% of adults aged 15-49, -0.461 typical gaps (standard deviations)Indonesia: 0.4% of adults aged 15-49, -0.192 typical gaps (standard deviations)India: 0.2% of adults aged 15-49, -0.736 typical gaps (standard deviations)Ireland: 0.2% of adults aged 15-49, 0.894 typical gaps (standard deviations)Iraq: 0.1% of adults aged 15-49, -0.726 typical gaps (standard deviations)Iceland: 0.1% of adults aged 15-49, 1.78 typical gaps (standard deviations)Italy: 0.3% of adults aged 15-49, 0.438 typical gaps (standard deviations)Jordan: 0.1% of adults aged 15-49, -0.666 typical gaps (standard deviations)Kazakhstan: 0.3% of adults aged 15-49, 0.304 typical gaps (standard deviations)Kenya: 3% of adults aged 15-49, -0.678 typical gaps (standard deviations)Kyrgyz Republic: 0.3% of adults aged 15-49, 0.225 typical gaps (standard deviations)Kuwait: 0.1% of adults aged 15-49, 0.607 typical gaps (standard deviations)Lebanon: 0.1% of adults aged 15-49, -1.54 typical gaps (standard deviations)Libya: 0.1% of adults aged 15-49, -0.158 typical gaps (standard deviations)Lithuania: 0.2% of adults aged 15-49, 0.51 typical gaps (standard deviations)Luxembourg: 0.3% of adults aged 15-49, 0.652 typical gaps (standard deviations)Latvia: 0.5% of adults aged 15-49, 0.212 typical gaps (standard deviations)Morocco: 0.1% of adults aged 15-49, -0.382 typical gaps (standard deviations)Moldova: 0.9% of adults aged 15-49, -1.29 typical gaps (standard deviations)Maldives: 0.1% of adults aged 15-49, 0.0845 typical gaps (standard deviations)Mexico: 0.5% of adults aged 15-49, 0.335 typical gaps (standard deviations)North Macedonia: 0.1% of adults aged 15-49, -0.171 typical gaps (standard deviations)Mali: 0.8% of adults aged 15-49, -0.328 typical gaps (standard deviations)Malta: 0.3% of adults aged 15-49, 0.15 typical gaps (standard deviations)Myanmar: 0.9% of adults aged 15-49, -0.547 typical gaps (standard deviations)Montenegro: 0.1% of adults aged 15-49, 0.239 typical gaps (standard deviations)Mongolia: 0.1% of adults aged 15-49, 0.000193 typical gaps (standard deviations)Malaysia: 0.3% of adults aged 15-49, 0.215 typical gaps (standard deviations)Nigeria: 1.2% of adults aged 15-49, -0.611 typical gaps (standard deviations)Netherlands: 0.1% of adults aged 15-49, 1.54 typical gaps (standard deviations)Norway: 0.1% of adults aged 15-49, 1.82 typical gaps (standard deviations)New Zealand: 0.1% of adults aged 15-49, 1.46 typical gaps (standard deviations)Pakistan: 0.2% of adults aged 15-49, -0.299 typical gaps (standard deviations)Peru: 0.4% of adults aged 15-49, -0.348 typical gaps (standard deviations)Philippines: 0.3% of adults aged 15-49, -0.119 typical gaps (standard deviations)Portugal: 0.5% of adults aged 15-49, 0.195 typical gaps (standard deviations)Qatar: 0.1% of adults aged 15-49, 0.756 typical gaps (standard deviations)Romania: 0.2% of adults aged 15-49, 0.197 typical gaps (standard deviations)Rwanda: 2% of adults aged 15-49, -0.867 typical gaps (standard deviations)Singapore: 0.1% of adults aged 15-49, 0.779 typical gaps (standard deviations)Serbia: 0.1% of adults aged 15-49, 0.105 typical gaps (standard deviations)Slovak Republic: 0.1% of adults aged 15-49, 0.318 typical gaps (standard deviations)Slovenia: 0.1% of adults aged 15-49, 0.55 typical gaps (standard deviations)Sweden: 0.1% of adults aged 15-49, 1.7 typical gaps (standard deviations)Thailand: 1% of adults aged 15-49, 0.256 typical gaps (standard deviations)Tajikistan: 0.2% of adults aged 15-49, 0.104 typical gaps (standard deviations)Tunisia: 0.1% of adults aged 15-49, -0.73 typical gaps (standard deviations)Viet Nam: 0.4% of adults aged 15-49, 0.469 typical gaps (standard deviations)South Africa: 17.2% of adults aged 15-49, -0.515 typical gaps (standard deviations)Zambia: 9.4% of adults aged 15-49, -1.67 typical gaps (standard deviations)Zimbabwe: 9.8% of adults aged 15-49, -1.98 typical gaps (standard deviations)DNKEGYISLLBNMDANORZAFZMBZWE

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.3630.00181
By rank (Spearman), not adjusted-0.3310.00381
Standard (Pearson), adjusted for national income-0.2300.04081
By rank (Spearman), adjusted for national income+0.0490.66681

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 81 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: +0.133 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.292 (without ZAF) to -0.139 (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: ZWE (+0.091).

Fragile: this link may not hold up.
  • leave-one-out range spans 0.15 in r
  • correlation moves sharply 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
Europe & Central Asia35-0.36
Middle East, North Africa, Afghanistan & Pakistan12+0.11

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 third27-0.44
Middle third27+0.08
Richest third27-0.38

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
Christians50-0.26
Muslims23+0.21

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
United Kingdom22-0.34

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 = -0.010 after dropping 8 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


% of adults aged 15-49
typical gaps (standard deviations)		Years
South Africa ZAF17.2%-0.5152024, 2014
Zimbabwe ZWE9.8%-1.982024, 2022
Zambia ZMB9.4%-1.672024, 2010
Kenya KEN3%-0.6782024, 2022
Rwanda RWA2%-0.8672024, 2014
Haiti HTI1.6%-0.4612024, 2014
Ghana GHA1.5%-0.732024, 2014
Nigeria NGA1.2%-0.6112024, 2022
Thailand THA1%0.2562024, 2022
Myanmar MMR0.9%-0.5472024, 2022
Moldova MDA0.9%-1.292024, 2010
Mali MLI0.8%-0.3282024, 2010
Ethiopia ETH0.7%-0.7262024, 2022
Estonia EST0.7%0.6222024, 2022
Chile CHL0.7%0.192024, 2022

World Bank, World Development Indicators
ivs; whr

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.

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 2010–2024. 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 Wellbeing score

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

More links with HIV prevalence or Wellbeing score