placestats

Anaemia in women vs. Physicians

Fades after adjusting for income

Across 185 countries, Anaemia in women and Physicians rise and fall together at r = -0.62 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.23, which could easily be chance (q = 0.26, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things.

Comparing countries across the middle half of physicians, 0.424 to 3.4 per 1,000 people, anaemia in women is better by 11.8% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 5.38% of women aged 15-49.

Link before adjusting (r)-0.622p = 3.6e-21 · 185 countries
After adjusting for national income-0.234p = 0.001 · likely range [-0.37, -0.09]
p, allowing for regions0.020standard error 0.075 · 7 regions
Check for luck (q)0.265across 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
Physicians · per 1,000 peoplelink with income +0.85 · proxy

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

Anaemia in women · % of women aged 15-49 →Physicians · per 1,000 people →10.5%59.9%0.0386.6Afghanistan: 45.4% of women aged 15-49, 0.317 per 1,000 peopleAngola: 43.8% of women aged 15-49, 0.244 per 1,000 peopleAlbania: 24.7% of women aged 15-49, 1.88 per 1,000 peopleAndorra: 16.4% of women aged 15-49, 5.07 per 1,000 peopleUnited Arab Emirates: 28.2% of women aged 15-49, 2.99 per 1,000 peopleArgentina: 23.5% of women aged 15-49, 5.11 per 1,000 peopleArmenia: 18.7% of women aged 15-49, 3.36 per 1,000 peopleAntigua and Barbuda: 20.3% of women aged 15-49, 2.92 per 1,000 peopleAustralia: 11.1% of women aged 15-49, 4.09 per 1,000 peopleAustria: 16.7% of women aged 15-49, 5.51 per 1,000 peopleAzerbaijan: 35.6% of women aged 15-49, 3.19 per 1,000 peopleBurundi: 39% of women aged 15-49, 0.078 per 1,000 peopleBelgium: 15% of women aged 15-49, 3.57 per 1,000 peopleBenin: 50% of women aged 15-49, 0.216 per 1,000 peopleBurkina Faso: 44.3% of women aged 15-49, 0.148 per 1,000 peopleBangladesh: 37.6% of women aged 15-49, 0.722 per 1,000 peopleBulgaria: 25.8% of women aged 15-49, 4.33 per 1,000 peopleBahrain: 38.4% of women aged 15-49, 0.741 per 1,000 peopleBahamas, The: 18.8% of women aged 15-49, 1.9 per 1,000 peopleBosnia and Herzegovina: 26% of women aged 15-49, 2.58 per 1,000 peopleBelarus: 22.1% of women aged 15-49, 4.72 per 1,000 peopleBelize: 21.3% of women aged 15-49, 1.09 per 1,000 peopleBolivia: 24.9% of women aged 15-49, 1.28 per 1,000 peopleBrazil: 21.3% of women aged 15-49, 2.36 per 1,000 peopleBarbados: 20.1% of women aged 15-49, 2.96 per 1,000 peopleBrunei Darussalam: 18.3% of women aged 15-49, 1.89 per 1,000 peopleBhutan: 33.8% of women aged 15-49, 0.553 per 1,000 peopleBotswana: 30.9% of women aged 15-49, 0.379 per 1,000 peopleCentral African Republic: 43.4% of women aged 15-49, 0.074 per 1,000 peopleCanada: 14% of women aged 15-49, 2.82 per 1,000 peopleSwitzerland: 16.6% of women aged 15-49, 4.48 per 1,000 peopleChile: 15.7% of women aged 15-49, 3.33 per 1,000 peopleChina: 15.6% of women aged 15-49, 3.11 per 1,000 peopleCote d'Ivoire: 51.4% of women aged 15-49, 0.166 per 1,000 peopleCameroon: 38.9% of women aged 15-49, 0.136 per 1,000 peopleCongo, Dem. Rep.: 40% of women aged 15-49, 0.208 per 1,000 peopleCongo, Rep.: 46.7% of women aged 15-49, 0.173 per 1,000 peopleColombia: 22.4% of women aged 15-49, 2.54 per 1,000 peopleComoros: 29.9% of women aged 15-49, 0.424 per 1,000 peopleCabo Verde: 40.6% of women aged 15-49, 0.73 per 1,000 peopleCosta Rica: 13.7% of women aged 15-49, 2.69 per 1,000 peopleCyprus: 16.4% of women aged 15-49, 3.56 per 1,000 peopleCzechia: 24.6% of women aged 15-49, 4.35 per 1,000 peopleGermany: 14% of women aged 15-49, 4.53 per 1,000 peopleDjibouti: 31.8% of women aged 15-49, 0.211 per 1,000 peopleDominica: 22% of women aged 15-49, 1.16 per 1,000 peopleDenmark: 17% of women aged 15-49, 4.5 per 1,000 peopleDominican Republic: 22.4% of women aged 15-49, 2.43 per 1,000 peopleAlgeria: 31.6% of women aged 15-49, 1.66 per 1,000 peopleEcuador: 20.2% of women aged 15-49, 2.31 per 1,000 peopleEgypt, Arab Rep.: 32.7% of women aged 15-49, 0.671 per 1,000 peopleSpain: 15.6% of women aged 15-49, 4.29 per 1,000 peopleEstonia: 24.7% of women aged 15-49, 3.47 per 1,000 peopleEthiopia: 23.1% of women aged 15-49, 0.143 per 1,000 peopleFinland: 13.6% of women aged 15-49, 3.61 per 1,000 peopleFiji: 29.9% of women aged 15-49, 0.813 per 1,000 peopleFrance: 13.1% of women aged 15-49, 3.28 per 1,000 peopleMicronesia, Fed. Sts.: 22.1% of women aged 15-49, 0.974 per 1,000 peopleGabon: 59.9% of women aged 15-49, 0.521 per 1,000 peopleUnited Kingdom: 13.6% of women aged 15-49, 3.3 per 1,000 peopleGeorgia: 30.2% of women aged 15-49, 5.64 per 1,000 peopleGhana: 36% of women aged 15-49, 0.266 per 1,000 peopleGuinea: 45.7% of women aged 15-49, 0.21 per 1,000 peopleGambia, The: 46.1% of women aged 15-49, 0.09 per 1,000 peopleGuinea-Bissau: 45.7% of women aged 15-49, 0.252 per 1,000 peopleEquatorial Guinea: 41.3% of women aged 15-49, 0.153 per 1,000 peopleGreece: 15.6% of women aged 15-49, 6.58 per 1,000 peopleGrenada: 20.9% of women aged 15-49, 1.38 per 1,000 peopleGuatemala: 10.5% of women aged 15-49, 1.28 per 1,000 peopleGuyana: 37.5% of women aged 15-49, 1.39 per 1,000 peopleHonduras: 18.8% of women aged 15-49, 0.489 per 1,000 peopleCroatia: 23.3% of women aged 15-49, 3.91 per 1,000 peopleHaiti: 45.9% of women aged 15-49, 0.292 per 1,000 peopleHungary: 24.1% of women aged 15-49, 3.46 per 1,000 peopleIndonesia: 26.7% of women aged 15-49, 0.524 per 1,000 peopleIndia: 53.7% of women aged 15-49, 0.723 per 1,000 peopleIreland: 14% of women aged 15-49, 3.88 per 1,000 peopleIran, Islamic Rep.: 25.5% of women aged 15-49, 1.81 per 1,000 peopleIraq: 27.1% of women aged 15-49, 1.02 per 1,000 peopleIceland: 14.7% of women aged 15-49, 4.37 per 1,000 peopleIsrael: 14.6% of women aged 15-49, 3.8 per 1,000 peopleItaly: 16.4% of women aged 15-49, 4.19 per 1,000 peopleJamaica: 21.5% of women aged 15-49, 0.46 per 1,000 peopleJordan: 33% of women aged 15-49, 2.85 per 1,000 peopleJapan: 18.7% of women aged 15-49, 2.65 per 1,000 peopleKazakhstan: 30.5% of women aged 15-49, 3.75 per 1,000 peopleKenya: 32.2% of women aged 15-49, 0.289 per 1,000 peopleKyrgyz Republic: 32.9% of women aged 15-49, 1.57 per 1,000 peopleCambodia: 38% of women aged 15-49, 0.211 per 1,000 peopleKiribati: 26.9% of women aged 15-49, 0.193 per 1,000 peopleSt. Kitts and Nevis: 19.4% of women aged 15-49, 3.08 per 1,000 peopleKorea, Rep.: 15.7% of women aged 15-49, 2.61 per 1,000 peopleKuwait: 27.7% of women aged 15-49, 2.27 per 1,000 peopleLao PDR: 28.6% of women aged 15-49, 0.331 per 1,000 peopleLebanon: 34% of women aged 15-49, 2.68 per 1,000 peopleLiberia: 42.5% of women aged 15-49, 0.178 per 1,000 peopleLibya: 29.3% of women aged 15-49, 2.04 per 1,000 peopleSt. Lucia: 19.9% of women aged 15-49, 4.23 per 1,000 peopleSri Lanka: 20.9% of women aged 15-49, 1.14 per 1,000 peopleLesotho: 34.2% of women aged 15-49, 0.235 per 1,000 peopleLithuania: 23.7% of women aged 15-49, 4.47 per 1,000 peopleLuxembourg: 15.4% of women aged 15-49, 2.98 per 1,000 peopleLatvia: 21.4% of women aged 15-49, 3.4 per 1,000 peopleMorocco: 29.5% of women aged 15-49, 0.74 per 1,000 peopleMoldova: 28.5% of women aged 15-49, 4.02 per 1,000 peopleMadagascar: 37.2% of women aged 15-49, 0.172 per 1,000 peopleMaldives: 43.4% of women aged 15-49, 2.23 per 1,000 peopleMexico: 13.2% of women aged 15-49, 2.59 per 1,000 peopleMarshall Islands: 23.5% of women aged 15-49, 0.467 per 1,000 peopleNorth Macedonia: 21.5% of women aged 15-49, 2.94 per 1,000 peopleMali: 56.5% of women aged 15-49, 0.188 per 1,000 peopleMalta: 15.7% of women aged 15-49, 4.51 per 1,000 peopleMyanmar: 39.7% of women aged 15-49, 0.757 per 1,000 peopleMontenegro: 25.8% of women aged 15-49, 2.78 per 1,000 peopleMongolia: 19.1% of women aged 15-49, 4.13 per 1,000 peopleMozambique: 47% of women aged 15-49, 0.175 per 1,000 peopleMauritania: 54.5% of women aged 15-49, 0.261 per 1,000 peopleMauritius: 32.8% of women aged 15-49, 1.44 per 1,000 peopleMalawi: 32.4% of women aged 15-49, 0.054 per 1,000 peopleMalaysia: 31.3% of women aged 15-49, 2.34 per 1,000 peopleNamibia: 24.9% of women aged 15-49, 0.553 per 1,000 peopleNiger: 47.1% of women aged 15-49, 0.038 per 1,000 peopleNigeria: 37.9% of women aged 15-49, 0.38 per 1,000 peopleNetherlands: 16.5% of women aged 15-49, 3.88 per 1,000 peopleNorway: 16.3% of women aged 15-49, 4.97 per 1,000 peopleNepal: 33.2% of women aged 15-49, 1.01 per 1,000 peopleNaoero: 22.5% of women aged 15-49, 1.27 per 1,000 peopleNew Zealand: 12.4% of women aged 15-49, 3.61 per 1,000 peopleOman: 34.4% of women aged 15-49, 1.99 per 1,000 peoplePakistan: 46.5% of women aged 15-49, 1.16 per 1,000 peoplePanama: 25.5% of women aged 15-49, 1.63 per 1,000 peoplePeru: 21% of women aged 15-49, 1.69 per 1,000 peoplePhilippines: 12% of women aged 15-49, 0.792 per 1,000 peoplePalau: 25.9% of women aged 15-49, 1.81 per 1,000 peoplePapua New Guinea: 28.8% of women aged 15-49, 0.061 per 1,000 peoplePoland: 23.4% of women aged 15-49, 4.03 per 1,000 peoplePortugal: 15% of women aged 15-49, 5.85 per 1,000 peopleParaguay: 23.2% of women aged 15-49, 3.89 per 1,000 peopleWest Bank and Gaza: 24.8% of women aged 15-49, 2.17 per 1,000 peopleQatar: 23.6% of women aged 15-49, 3.02 per 1,000 peopleRomania: 24.3% of women aged 15-49, 3.63 per 1,000 peopleRussian Federation: 24.1% of women aged 15-49, 5.11 per 1,000 peopleRwanda: 16.1% of women aged 15-49, 0.09 per 1,000 peopleSaudi Arabia: 17.6% of women aged 15-49, 3.41 per 1,000 peopleSudan: 34% of women aged 15-49, 0.25 per 1,000 peopleSenegal: 43.1% of women aged 15-49, 0.109 per 1,000 peopleSingapore: 16.6% of women aged 15-49, 2.83 per 1,000 peopleSolomon Islands: 32.6% of women aged 15-49, 0.238 per 1,000 peopleSierra Leone: 44.6% of women aged 15-49, 0.134 per 1,000 peopleEl Salvador: 14.4% of women aged 15-49, 1.62 per 1,000 peopleSan Marino: 15.7% of women aged 15-49, 4.63 per 1,000 peopleSomalia, Fed. Rep.: 46.2% of women aged 15-49, 0.048 per 1,000 peopleSerbia: 24.9% of women aged 15-49, 3.1 per 1,000 peopleSao Tome and Principe: 39.7% of women aged 15-49, 0.464 per 1,000 peopleSuriname: 22.2% of women aged 15-49, 1.35 per 1,000 peopleSlovak Republic: 25.3% of women aged 15-49, 3.7 per 1,000 peopleSlovenia: 25.7% of women aged 15-49, 3.37 per 1,000 peopleSweden: 16.8% of women aged 15-49, 4.41 per 1,000 peopleEswatini: 28.3% of women aged 15-49, 0.557 per 1,000 peopleSeychelles: 25.8% of women aged 15-49, 6.6 per 1,000 peopleSyrian Arab Republic: 31% of women aged 15-49, 1.52 per 1,000 peopleChad: 46.5% of women aged 15-49, 0.085 per 1,000 peopleTogo: 41.5% of women aged 15-49, 0.08 per 1,000 peopleThailand: 20.7% of women aged 15-49, 0.541 per 1,000 peopleTajikistan: 37.3% of women aged 15-49, 1.87 per 1,000 peopleTurkmenistan: 34.8% of women aged 15-49, 1.93 per 1,000 peopleTimor-Leste: 29.7% of women aged 15-49, 0.752 per 1,000 peopleTonga: 20.7% of women aged 15-49, 1.01 per 1,000 peopleTrinidad and Tobago: 22.1% of women aged 15-49, 4.16 per 1,000 peopleTunisia: 27.7% of women aged 15-49, 1.31 per 1,000 peopleTurkiye: 29.3% of women aged 15-49, 2.24 per 1,000 peopleTuvalu: 23.2% of women aged 15-49, 1.35 per 1,000 peopleTanzania: 38.8% of women aged 15-49, 0.134 per 1,000 peopleUganda: 27.2% of women aged 15-49, 0.191 per 1,000 peopleUkraine: 19.4% of women aged 15-49, 3.53 per 1,000 peopleUruguay: 24.5% of women aged 15-49, 4.67 per 1,000 peopleUnited States: 15% of women aged 15-49, 3.68 per 1,000 peopleUzbekistan: 30.4% of women aged 15-49, 2.81 per 1,000 peopleSt. Vincent and the Grenadines: 18.8% of women aged 15-49, 0.938 per 1,000 peopleViet Nam: 20.5% of women aged 15-49, 1.11 per 1,000 peopleVanuatu: 30.4% of women aged 15-49, 0.164 per 1,000 peopleSamoa: 21.9% of women aged 15-49, 0.561 per 1,000 peopleSouth Africa: 31.2% of women aged 15-49, 0.794 per 1,000 peopleZambia: 27.1% of women aged 15-49, 0.324 per 1,000 peopleZimbabwe: 27.5% of women aged 15-49, 0.136 per 1,000 peopleAUTGABGEOGRCGTMMLIMRTPHLPRTRWASYC

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.6223.6e-21185
By rank (Spearman), not adjusted-0.6638.2e-25185
Standard (Pearson), adjusted for national income-0.2340.001185
By rank (Spearman), adjusted for national income-0.2490.001185

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 185 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: +0.387 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.252 (without GEO) to -0.206 (without BHR). 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: BHR (+0.028).

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 & Pacific29-0.34
Europe & Central Asia50-0.21
Latin America & Caribbean30-0.11
Middle East, North Africa, Afghanistan & Pakistan22-0.56
Sub-Saharan Africa46-0.21

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 third62-0.25
Middle third61-0.18
Richest third62-0.46

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
Christians107-0.16
Muslims49-0.32

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
France25-0.42
Never a European colony21+0.07
Spain19-0.15
United Kingdom63-0.27

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 = -0.212 after dropping 2 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 24years. 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 = +0.189, 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 physicians move with it.

Across countries (adjusted)-0.234for comparison
Within countries+0.063each country vs itself
…and vs the same year elsewhere+0.189q = 0.083 · 179 countries
Year-on-year changes+0.054q = 0.331
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.

2,799 country-years (one country in one year), 179 countries, 2000–2023, about 16 years each on average. Share of the variation that happens within countries over time: 8% for Anaemia in women, 3% 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

0.20−0.2-100+10years Physicians trails Anaemia in women →lag -10: r = 0.116 (n = 2,549)lag -9: r = 0.139 (n = 2,546)lag -8: r = 0.164 (n = 2,566)lag -7: r = 0.172 (n = 2,600)lag -6: r = 0.183 (n = 2,629)lag -5: r = 0.185 (n = 2,719)lag -4: r = 0.189 (n = 2,734)lag -3: r = 0.186 (n = 2,764)lag -2: r = 0.188 (n = 2,784)lag -1: r = 0.186 (n = 2,826)lag +0: r = 0.189 (n = 2,799)lag +1: r = 0.176 (n = 2,679)lag +2: r = 0.176 (n = 2,583)lag +3: r = 0.188 (n = 2,480)lag +4: r = 0.186 (n = 2,379)lag +5: r = 0.157 (n = 2,234)lag +6: r = 0.152 (n = 2,120)lag +7: r = 0.143 (n = 2,017)lag +8: r = 0.132 (n = 1,905)lag +9: r = 0.113 (n = 1,754)lag +10: r = 0.093 (n = 1,625)

Same year (lag 0): +0.189. 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 data


% of women aged 15-49
per 1,000 people		Years
Gabon GAB59.9%0.5212023, 2022
Mali MLI56.5%0.1882023
Mauritania MRT54.5%0.2612023, 2022
India IND53.7%0.7232023, 2020
Cote d'Ivoire CIV51.4%0.1662023
Benin BEN50%0.2162023
Niger NER47.1%0.0382023
Mozambique MOZ47%0.1752023, 2022
Congo, Rep. COG46.7%0.1732023, 2022
Chad TCD46.5%0.0852023
Pakistan PAK46.5%1.162023, 2021
Somalia, Fed. Rep. SOM46.2%0.0482023, 2014
Gambia, The GMB46.1%0.092023
Haiti HTI45.9%0.2922023, 2022
Guinea-Bissau GNB45.7%0.2522023, 2022

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

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 2012–2023. 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 Physicians

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

More links with Anaemia in women or Physicians