Do Infant mortality and Skilled birth attendance go together across countries?

Across 176 countries the link before adjusting for anything is r = -0.76, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.70.

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

Comparing each country only with itself across 1990-2022, and setting aside what happened worldwide in each year, the link is -0.518 (168 countries, 2,825 country-years). Using year-to-year changes, it is +0.005.

Does Infant mortality cause Skilled birth attendance?

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.

Infant mortality vs. Skilled birth attendance

Holds up after adjusting for income

Across 176 countries, Infant mortality and Skilled birth attendance rise and fall together at r = -0.76 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.48 (q = 0.0034, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains.

Comparing countries across the middle half of infant mortality, 5.5 to 30.3 per 1,000 live births, skilled birth attendance is worse by 17.3% of births at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 13.3% of births.

Link before adjusting (r)-0.758p = 4.7e-34 · 176 countries

After adjusting for national income-0.476p = 2.8e-11 · likely range [-0.58, -0.35]

p, allowing for regions5.6e-5standard error 0.047 · 7 regions

Check for luck (q)0.003across 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.

Infant mortality · per 1,000 live birthslink with income -0.86 · proxy

Skilled birth attendance · % of birthslink with income +0.69 · 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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.758	4.7e-34	176
By rank (Spearman), not adjusted	-0.698	4.8e-27	176
Standard (Pearson), adjusted for national income	-0.476	2.8e-11	176
By rank (Spearman), adjusted for national income	-0.288	0.000	176

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 176 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: +0.282 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.499 (without ZWE) to -0.436 (without NGA). 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: NGA (+0.039).

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	27	-0.57
Europe & Central Asia	44	-0.14
Latin America & Caribbean	32	-0.47
Middle East, North Africa, Afghanistan & Pakistan	19	-0.76
Sub-Saharan Africa	46	-0.41

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	59	-0.48
Middle third	58	-0.39
Richest third	59	-0.08

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	102	-0.30
Muslims	48	-0.70

By last european coloniser

	n	r
France	23	-0.37
Never a European colony	21	-0.08
Spain	19	-0.67
United Kingdom	61	-0.44

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 = -0.429 after dropping 18 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 33years. 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 = -0.518) but mostly disappears in year-to-year changes (r = +0.005). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.476for comparison

Within countries-0.621each country vs itself

…and vs the same year elsewhere-0.518q = 0.000 · 168 countries

Year-on-year changes+0.005q = 0.893

Read with care.

The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

2,825 country-years (one country in one year), 168 countries, 1990–2022, about 17 years each on average. Share of the variation that happens within countries over time: 17% for Infant mortality, 13% for Skilled birth attendance. 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): -0.518. 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 (-0.61 at lag +5) is higher than the same-year one by 0.09. 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

	per 1,000 live births	% of births	Years
Nigeria NGA	69.8	50.7%	2024, 2022
Niger NER	65.6	43.7%	2024, 2021
Somalia, Fed. Rep. SOM	65.4	31.9%	2024, 2019
Liberia LBR	63.2	84.4%	2024, 2020
Zimbabwe ZWE	62.4	86%	2024, 2019
Guinea GIN	59.8	55.3%	2024, 2018
Central African Republic CAF	58.8	40.3%	2024, 2019
Congo, Dem. Rep. COD	56.4	85.2%	2024, 2018
Chad TCD	56.3	47.2%	2024, 2019
Lesotho LSO	56.3	86.6%	2024, 2018
Sierra Leone SLE	54.6	86.9%	2024, 2019
Afghanistan AFG	48.3	61.8%	2024, 2020
Pakistan PAK	48.2	68%	2024, 2020
Equatorial Guinea GNQ	47.6	68.3%	2024, 2011
Mali MLI	47.5	67.3%	2024, 2018

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

Sources

Infant mortality

Mortality rate, infant (per 1,000 live births)

World Bank · CC BY 4.0 · updated quarterly

Skilled birth attendance

Births attended by skilled health staff (% of total)

UNICEF and WHO · CC BY 4.0 · updated quarterly

Based on surveys, so a country reports it every few years rather than yearly.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2024. 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 Infant mortality and Skilled birth attendance

This page shows that two measures go together. It does not show that Infant mortality changes Skilled birth attendance, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.