Global health-workforce and hospital-capacity panel

Commercial use OKFreshLicense: CC-BY-4.0

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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.

Source: World Bank health-workforce intelligence
Rows: 6,684
Columns: 18
Source cadence: Yearly
Last refreshed: Oct 8, 2026
Theme: health

Column	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).

First 10 sample rows — a preview, not the complete dataset.

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

Profiled Oct 9, 2026 from snapshot 20261008T143728Z-4987093e28eb

Measured

Completeness: 69%
Rows: 6,684
Columns: 18
Columns with gaps: 12

Rows per yearyear · 1960–2023 · 64 years

19602023

Column	Missing	Distinct	Range	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 → 2,023median 2,002
beds_per_1kdouble	29.4%	2,292	0.1158 → 40.32median 3.32	96 outside 1st–99th percentile
physicians_per_1kdouble	19.8%	2,840	0.004 → 9.54median 1.43	105 outside 1st–99th percentile
nurses_midwives_per_1kdouble	49%	3,196	0.047 → 20.83median 4.23	70 outside 1st–99th percentile
clinical_workforce_per_1kdouble	53.9%	2,775	0.072 → 28.59median 7.02	62 outside 1st–99th percentile
nurse_physician_ratiodouble	53.9%	3,282	0.2005 → 49.69median 2.46	62 outside 1st–99th percentile
beds_per_clinical_workerdouble	64%	2,250	0.0664 → 8.24median 0.5696	50 outside 1st–99th percentile
beds_change_10y_pctdouble	59%	2,714	-83 → 712.4median -9.26	56 outside 1st–99th percentile
physicians_change_10y_pctdouble	55.8%	2,433	-66.11 → 2,091median 20	60 outside 1st–99th percentile
nurses_change_10y_pctdouble	74.9%	1,609	-79.41 → 729.72median 12.53	34 outside 1st–99th percentile
beds_rankbigint	29.4%	153	1 → 149median 50	46 outside 1st–99th percentile
physicians_rankbigint	19.8%	156	1 → 157median 49	48 outside 1st–99th percentile
nurses_rankbigint	49%	194	1 → 179median 51.5	68 outside 1st–99th percentile
row_hashvarchar	0%	6,893	—	000df08048851 00190f572f921 001d63a2841d1

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)

API endpoint: https://datazimuts.com/v1/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261008T143728Z-4987093e28eb and its content hash, so readers get exactly the data you used.

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/en/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/en/datasets/wb_healthworkforce_intel/healthworkforce_capacity_panel?snapshot=20261008T143728Z-4987093e28eb}},
  note         = {Snapshot 20261008T143728Z-4987093e28eb, sha256 4987093e28eb0e352a35d19725882b022416487f2763bef36140424fea07b090; accessed 2026-10-09}
}

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