Do Conflict deaths and Physicians go together across countries?

Across 186 countries the link before adjusting for anything is r = -0.01, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.35.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 1990-2023, and setting aside what happened worldwide in each year, the link is +0.007 (189 countries, 3,841 country-years). Using year-to-year changes, it is +0.001.

Does Conflict deaths cause Physicians?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Conflict deaths vs. Physicians

Holds up after adjusting for income

Across 186 countries, Conflict deaths and Physicians rise and fall together at r = -0.01 (on a scale from −1 to +1, where 0 means no link). There is almost no link to explain.

Comparing countries across the middle half of conflict deaths, 0 to 0.0126 per 100,000 people, physicians is better by 0 per 1,000 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.00008 per 1,000 people.

Link before adjusting (r)-0.006p = 0.937 · 186 countries

After adjusting for national income+0.170p = 0.021 · likely range [+0.03, +0.31]

p, allowing for regions1.1e-5standard error 0.013 · 7 regions

Check for luck (q)0.001across 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.

Conflict deaths · per 100,000 peoplelink with income -0.42 · moderate

Physicians · per 1,000 peoplelink with income +0.85 · proxy

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

Every country as a dot

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.006	0.937	186
By rank (Spearman), not adjusted	-0.354	6.9e-7	186
Standard (Pearson), adjusted for national income	+0.170	0.021	186
By rank (Spearman), adjusted for national income	-0.006	0.931	186

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 186 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: -0.164 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.128 (without PSE) to +0.178 (without SYC). 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: PSE (-0.042).

Fragile: this link may not hold up.

correlation moves sharply once outliers are trimmed
sign flips 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.

	n	r
East Asia & Pacific	29	-0.02
Europe & Central Asia	50	+0.08
Latin America & Caribbean	31	+0.09
Middle East, North Africa, Afghanistan & Pakistan	22	+0.31
Sub-Saharan Africa	46	+0.08

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.

	n	r
Poorest third	62	+0.47
Middle third	62	+0.21
Richest third	62	+0.15

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.

	n	r
Christians	108	+0.12
Muslims	49	+0.28

By last european coloniser

	n	r
France	25	+0.36
Never a European colony	21	-0.24
Spain	20	-0.00
United Kingdom	63	+0.08

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 = -0.034 after dropping 46 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 34years. 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 = +0.007, 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 conflict deaths moved did not tend to see their physicians move with it.

Across countries (adjusted)+0.170for comparison

Within countries-0.000each country vs itself

…and vs the same year elsewhere+0.007q = 0.629 · 189 countries

Year-on-year changes+0.001q = 0.868

3,841 country-years (one country in one year), 189 countries, 1990–2023, about 20 years each on average. Share of the variation that happens within countries over time: 95% for Conflict deaths, 11% for Physicians. 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

Same year (lag 0): +0.007. 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

	per 100,000 people	per 1,000 people	Years
West Bank and Gaza PSE	259	2.17	2025, 2018
Ukraine UKR	232	3.53	2025, 2023
Sudan SDN	145	0.25	2025, 2017
Haiti HTI	20.4	0.292	2025, 2022
Somalia, Fed. Rep. SOM	19.7	0.048	2025, 2014
Burkina Faso BFA	18.6	0.148	2025, 2022
Syrian Arab Republic SYR	13.4	1.52	2025, 2021
Mali MLI	8.54	0.188	2025, 2023
Congo, Dem. Rep. COD	7.85	0.208	2025, 2022
Central African Republic CAF	6.28	0.074	2025, 2023
Mexico MEX	5.94	2.59	2025, 2022
Lebanon LBN	5.93	2.68	2025, 2020
Congo, Rep. COG	4.64	0.173	2025, 2022
Myanmar MMR	4.21	0.757	2025, 2019
Ethiopia ETH	4	0.143	2025, 2023

Uppsala Conflict Data Program, Georeferenced Event Dataset; rate per population from Our World in Data
World Bank, World Development Indicators

Sources

Conflict deaths

Deaths in armed conflicts, per 100,000 people

Uppsala Conflict Data Program, Uppsala University · via Our World in Data · CC BY 4.0 · updated annual

Deaths from organised violence on a country's territory, per 100,000 of its people. That covers fighting involving a government, fighting between other armed groups, and attacks on civilians. Deaths are counted where the fighting happens, not by who was fighting. So a war fought on one country's soil by another country's army counts here. It is zero for most countries in most years, and in the hundreds per hundred thousand for a few at war. That is why it is put on a log scale (which squeezes very large values together) where it is used for adjusting. It is also why the "leave one out" check matters on every pair involving it: a handful of countries carry the whole link. The site's only other measure of violence is the homicide rate, which counts something different. A peaceful country can have a high homicide rate, and a country at war can have a low one. It is kept out of every topic score. In 2023, 143 of the 197 countries reporting it report exactly zero. A log scale, used here since a few values are huge, cannot handle zero, so every zero becomes a blank. A safety score built on it covered 79 countries instead of 172. It silently dropped the peaceful ones, the opposite of what such a score should say. A score needs parts that vary across nearly every country. This one is a rare event almost everywhere and the biggest fact in a dozen places. That makes it worth its own page, and useless inside an average.

Physicians

Physicians (per 1,000 people)

World Health Organization · CC BY 4.0 · updated quarterly

Reported irregularly, so the latest value in the time window used is from a different year for different countries. Hospital beds, already on the site, have no "better" direction, since more beds is not plainly better. More doctors is closer to plainly better, so a direction is set on that basis.

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 Conflict deaths and Physicians

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