Global health-workforce and hospital-capacity panel

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Value-added panel of the World Bank's World Development Indicators (keyless API v2, CC BY 4.0; WHO data): hospital beds, physicians and nurses/midwives per 1,000 people for 200+ economies, 1960-2023, with clinical-workforce density (physicians + nurses/midwives), the WHO staffing-mix gauge (nurses per physician), beds per clinical worker, trailing 10-year changes and within-year density ranks. Joins on economy_code with the catalog's other World Bank panels.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: World Bank health-workforce intelligence
Lignes: 6 684
Colonnes: 18
Cadence de la source: Annuelle
Dernière actualisation: 8 oct. 2026
Thème: health

Colonne	Type	Description
economy_code	string	ISO 3166-1 alpha-3 economy code (World Bank API field countryiso3code).
economy_name	string	Economy name as published by the World Bank API.
region	string	World Bank region (API field region.value).
income_group	string	World Bank income group (API field incomeLevel.value).
year	integer	Reference year (1960-2023).
beds_per_1k	float	Hospital beds per 1,000 people (WDI indicator SH.MED.BEDS.ZS, WHO data). Inpatient beds available in public, private, general and specialized hospitals and rehabilitation centers; in most cases both acute and chronic care beds. (unit: beds per 1,000 people)
physicians_per_1k	float	Physicians per 1,000 people (WDI indicator SH.MED.PHYS.ZS, WHO data). Generalist and specialist medical practitioners. (unit: physicians per 1,000 people)
nurses_midwives_per_1k	float	Nurses and midwives per 1,000 people (WDI indicator SH.MED.NUMW.P3, WHO data). Professional, auxiliary and enrolled nurses and midwives plus associated personnel. (unit: nurses/midwives per 1,000 people)
clinical_workforce_per_1k	float	Clinical-workforce density = physicians + nurses/midwives per 1,000 (connector-derived, informational). Null unless both inputs are present — never summed from a partial pair. (unit: workers per 1,000 people)
nurse_physician_ratio	float	Nurses and midwives per physician (connector-derived WHO staffing-mix gauge, informational). Null when either input is null or physicians is zero. (unit: nurses per physician)
beds_per_clinical_worker	float	Hospital beds per clinical worker = beds / clinical_workforce_per_1k (connector-derived capacity-vs-staffing readiness proxy, informational). Null unless both inputs are present. (unit: beds per clinical worker)
beds_change_10y_pct	float	Trailing 10-year percent change of hospital beds per 1,000 (the structural capacity lens); null when the base year is absent or zero. (unit: %)
physicians_change_10y_pct	float	Trailing 10-year percent change of physicians per 1,000; null when the base year is absent or zero. (unit: %)
nurses_change_10y_pct	float	Trailing 10-year percent change of nurses/midwives per 1,000; null when the base year is absent or zero. (unit: %)
beds_rank	integer	Rank of beds_per_1k within the year (1 = most beds); null where beds_per_1k is null. (unit: rank)
physicians_rank	integer	Rank of physicians_per_1k within the year (1 = most physicians); null where physicians_per_1k is null. (unit: rank)
nurses_rank	integer	Rank of nurses_midwives_per_1k within the year (1 = most nurses/midwives); null where the value is null. (unit: rank)
row_hash	string	Deterministic 12-hex row identity hash (economy_code\|year).

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

economy_code	economy_name	region	income_group	year	beds_per_1k	physicians_per_1k	nurses_midwives_per_1k	clinical_workforce_per_1k	nurse_physician_ratio	beds_per_clinical_worker	beds_change_10y_pct	physicians_change_10y_pct	nurses_change_10y_pct	beds_rank	physicians_rank	nurses_rank	row_hash
ABW	Aruba	Latin America & Caribbean	High income	1 995	—	1,12	—	—	—	—	—	—	—	—	68	—	65a5a89d7667
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 960	0,171	0,035	—	—	—	—	—	—	—	132	114	—	de4f672a4c0b
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 965	—	0,063	—	—	—	—	—	—	—	—	77	—	b87f09371b7f
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 970	0,199	0,065	—	—	—	—	16,629	85,714	—	133	115	—	e46415eeb918
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 981	0,276	0,077	—	—	—	—	—	—	—	58	83	—	2d278cca6f49
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 986	—	0,183	—	—	—	—	—	—	—	—	58	—	cf89bf173fab
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 987	0,309	0,179	—	—	—	—	—	—	—	50	45	—	31a6d0371641
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 989	—	0,129	—	—	—	—	—	—	—	—	42	—	6c7f53e0b5f0
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 990	0,25	0,109	—	—	—	—	—	—	—	147	116	—	ced4e7b75201
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1 993	—	0,143	—	—	—	—	—	—	—	—	92	—	aac610f1a870

Profilé le 9 oct. 2026 à partir de l’instantané 20261008T143728Z-4987093e28eb

Mesuré

Complétude: 69 %
Lignes: 6 684
Colonnes: 18
Colonnes incomplètes: 12

Lignes par annéeyear · 1960–2023 · 64 ans

19602023

Colonne	Manquant	Distinctes	Plage	Distribution
economy_codevarchar	0 %	257	—	BEL64 CAN64 CHE64
economy_namevarchar	0 %	220	—	Belgium64 Canada64 Iceland64
regionvarchar	0 %	6	—	Europe & Central Asia2 400 Sub-Saharan Africa 1 176 Latin America & Caribbean 1 070
income_groupvarchar	0 %	4	—	High income2 819 Upper middle income1 946 Lower middle income1 294
yearbigint	0 %	60	1 960 → 2 023médiane 2 002
beds_per_1kdouble	29,4 %	2 292	0,1158 → 40,32médiane 3,32	96 hors du 1er–99e centile
physicians_per_1kdouble	19,8 %	2 840	0,004 → 9,54médiane 1,43	105 hors du 1er–99e centile
nurses_midwives_per_1kdouble	49 %	3 196	0,047 → 20,83médiane 4,23	70 hors du 1er–99e centile
clinical_workforce_per_1kdouble	53,9 %	2 775	0,072 → 28,59médiane 7,02	62 hors du 1er–99e centile
nurse_physician_ratiodouble	53,9 %	3 282	0,2005 → 49,69médiane 2,46	62 hors du 1er–99e centile
beds_per_clinical_workerdouble	64 %	2 250	0,0664 → 8,24médiane 0,5696	50 hors du 1er–99e centile
beds_change_10y_pctdouble	59 %	2 714	-83 → 712,4médiane -9,26	56 hors du 1er–99e centile
physicians_change_10y_pctdouble	55,8 %	2 433	-66,11 → 2 091médiane 20	60 hors du 1er–99e centile
nurses_change_10y_pctdouble	74,9 %	1 609	-79,41 → 729,72médiane 12,53	34 hors du 1er–99e centile
beds_rankbigint	29,4 %	153	1 → 149médiane 50	46 hors du 1er–99e centile
physicians_rankbigint	19,8 %	156	1 → 157médiane 49	48 hors du 1er–99e centile
nurses_rankbigint	49 %	194	1 → 179médiane 51,5	68 hors du 1er–99e centile
row_hashvarchar	0 %	6 893	—	000df08048851 00190f572f921 001d63a2841d1

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API : https://datazimuts.com/v1/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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Épinglé à l’instantané 20261008T143728Z-4987093e28eb et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

APA

World Bank health-workforce intelligence. (2026). Global health-workforce and hospital-capacity panel [Data set, snapshot 20261008T143728Z-4987093e28eb, sha256 4987093e28eb]. Datazimuts. Retrieved 2026-10-09, from https://datazimuts.com/fr/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel?snapshot=20261008T143728Z-4987093e28eb

BibTeX

@misc{dz_wb_healthworkforce_intel_healthworkforce_4987093e,
  title        = {{Global health-workforce and hospital-capacity panel}},
  author       = {{World Bank health-workforce intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel?snapshot=20261008T143728Z-4987093e28eb}},
  note         = {Snapshot 20261008T143728Z-4987093e28eb, sha256 4987093e28eb0e352a35d19725882b022416487f2763bef36140424fea07b090; accessed 2026-10-09}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=wb_healthworkforce_intel%2Fhealthworkforce_capacity_panel&lang=fr&theme=auto&snapshot=20261008T143728Z-4987093e28eb&x=year&y=year&agg=avg" title="Global health-workforce and hospital-capacity panel" width="100%" height="380" style="border:0" loading="lazy"></iframe>