Do Anaemia in women and Skilled birth attendance go together across countries?

Across 173 countries the link before adjusting for anything is r = -0.58, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.51.

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

Comparing each country only with itself across 2000-2022, and setting aside what happened worldwide in each year, the link is -0.144 (155 countries, 2,113 country-years). Using year-to-year changes, it is -0.023.

Does Anaemia in women 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.

Anaemia in women vs. Skilled birth attendance

Holds up after adjusting for income

Across 173 countries, Anaemia in women and Skilled birth attendance rise and fall together at r = -0.58 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.25 (q = 0.049, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak negative link remains.

Comparing countries across the middle half of skilled birth attendance, 86% to 99.7% of births, anaemia in women is better by 5.41% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 2.48% of women aged 15-49.

Link before adjusting (r)-0.579p = 7.3e-17 · 173 countries

After adjusting for national income-0.251p = 0.001 · likely range [-0.39, -0.10]

p, allowing for regions0.005standard error 0.059 · 7 regions

Check for luck (q)0.049across 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.

Anaemia in women · % of women aged 15-49link with income -0.67 · high

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	-0.579	7.3e-17	173
By rank (Spearman), not adjusted	-0.505	1.3e-12	173
Standard (Pearson), adjusted for national income	-0.251	0.001	173
By rank (Spearman), adjusted for national income	-0.107	0.162	173

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 173 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: +0.328 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.280 (without ETH) to -0.231 (without AGO). 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: ETH (-0.029).

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	-0.16
Europe & Central Asia	43	-0.04
Latin America & Caribbean	30	-0.54
Middle East, North Africa, Afghanistan & Pakistan	19	-0.51
Sub-Saharan Africa	46	-0.27

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	58	-0.39
Middle third	57	-0.03
Richest third	58	+0.09

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	101	-0.21
Muslims	47	-0.38

By last european coloniser

	n	r
France	23	-0.14
Never a European colony	21	+0.30
Spain	18	-0.33
United Kingdom	61	-0.30

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 = -0.223 after dropping 20 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 23years. 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 = -0.144, 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 anaemia in women moved did not tend to see their skilled birth attendance move with it.

Across countries (adjusted)-0.251for comparison

Within countries-0.278each country vs itself

…and vs the same year elsewhere-0.144q = 0.231 · 155 countries

Year-on-year changes-0.023q = 0.710

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.

2,113 country-years (one country in one year), 155 countries, 2000–2022, about 14 years each on average. Share of the variation that happens within countries over time: 12% for Anaemia in women, 4% 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): -0.144. 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

	% of women aged 15-49	% of births	Years
Gabon GAB	59.9%	89.3%	2023, 2012
Mali MLI	56.5%	67.3%	2023, 2018
Mauritania MRT	54.5%	70.4%	2023, 2021
India IND	53.7%	89.4%	2023, 2021
Cote d'Ivoire CIV	51.4%	84%	2023, 2021
Benin BEN	50%	78.1%	2023, 2018
Niger NER	47.1%	43.7%	2023, 2021
Mozambique MOZ	47%	73%	2023, 2015
Congo, Rep. COG	46.7%	91.2%	2023, 2015
Chad TCD	46.5%	47.2%	2023, 2019
Pakistan PAK	46.5%	68%	2023, 2020
Somalia, Fed. Rep. SOM	46.2%	31.9%	2023, 2019
Gambia, The GMB	46.1%	83.8%	2023, 2020
Haiti HTI	45.9%	41.6%	2023, 2017
Guinea-Bissau GNB	45.7%	53.8%	2023, 2019

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

Sources

Anaemia in women

Prevalence of anaemia among women of reproductive age (% of women ages 15-49)

World Health Organization · 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 2011–2023. 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 Anaemia in women and Skilled birth attendance

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