Value-added panel of the World Bank's World Development Indicators (keyless API v2, CC BY 4.0): adult mortality rates (probability per 1,000 of dying between ages 15 and 60) for males and females in ~230 economies, 1960-2024, with the male-female mortality gap, 10-year changes, within-year mortality ranks and a joint-improvement flag. The prime-working-age mortality layer for health systems, insurers and pension models; 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 adult mortality intelligence
Lignes: 13 970
Colonnes: 15
Cadence de la source: Annuelle
Dernière actualisation: 7 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 (1960-2024; 2024 is the latest broadly covered year — 2025 is null upstream, a genuine vintage lag).
male_adult_mortality_per_1000	float	Adult mortality rate, male: the probability per 1,000 that a 15-year-old male will die before reaching age 60, if subject to age-specific mortality rates of the specified year (WDI indicator SP.DYN.AMRT.MA; UN World Population Prospects and the Human Mortality Database). Capped upstream at 1,000. (unit: per 1,000 male adults)
female_adult_mortality_per_1000	float	Adult mortality rate, female: the probability per 1,000 that a 15-year-old female will die before reaching age 60, if subject to age-specific mortality rates of the specified year (WDI indicator SP.DYN.AMRT.FE; UN World Population Prospects and the Human Mortality Database). Capped upstream at 1,000. (unit: per 1,000 female adults)
gender_gap_male_minus_female	float	Male adult mortality minus female adult mortality. Positive values mean higher male mortality; the direction is never gated (it can run either way). (unit: per 1,000 adults)
male_change_10y	float	Trailing 10-year change of male adult mortality; null when the base year is absent. (unit: per 1,000 male adults)
female_change_10y	float	Trailing 10-year change of female adult mortality; null when the base year is absent. (unit: per 1,000 female adults)
gap_change_10y	float	Trailing 10-year change of the gender gap; null when the base year is absent. (unit: per 1,000 adults)
male_rank	integer	Rank of male_adult_mortality_per_1000 within the year (1 = highest male adult mortality); null where the male rate is null. (unit: rank)
female_rank	integer	Rank of female_adult_mortality_per_1000 within the year (1 = highest female adult mortality); null where the female rate is null. (unit: rank)
improvement_10y_flag	integer	1 when both male and female adult mortality fell over the trailing 10 years; null when either 10-year change is null.
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	male_adult_mortality_per_1000	female_adult_mortality_per_1000	gender_gap_male_minus_female	male_change_10y	female_change_10y	gap_change_10y	male_rank	female_rank	improvement_10y_flag	row_hash
ABW	Aruba	Latin America & Caribbean	High income	1 960	292,373	165,942	126,431	—	—	—	133	157	—	82d7dbb3004e
ABW	Aruba	Latin America & Caribbean	High income	1 961	293,03	163,605	129,425	—	—	—	132	155	—	2861451dba3e
ABW	Aruba	Latin America & Caribbean	High income	1 962	290,848	158,801	132,047	—	—	—	130	156	—	0ff75b5d0c5d
ABW	Aruba	Latin America & Caribbean	High income	1 963	289,051	154,622	134,429	—	—	—	128	158	—	f6dcac872121
ABW	Aruba	Latin America & Caribbean	High income	1 964	287,185	149,818	137,367	—	—	—	128	157	—	c3dd8337dd1a
ABW	Aruba	Latin America & Caribbean	High income	1 965	285,518	146,088	139,43	—	—	—	126	158	—	9511b277e1aa
ABW	Aruba	Latin America & Caribbean	High income	1 966	277,235	141,795	135,44	—	—	—	126	159	—	a0951a4ed836
ABW	Aruba	Latin America & Caribbean	High income	1 967	276,179	139,151	137,028	—	—	—	124	159	—	589f4bb72296
ABW	Aruba	Latin America & Caribbean	High income	1 968	272,024	135,949	136,075	—	—	—	124	159	—	6e776bd38f5e
ABW	Aruba	Latin America & Caribbean	High income	1 969	267,693	133,097	134,596	—	—	—	128	158	—	ca8d34b45651

7 oct. 2026Actuelle
20261007T101739Z-b65edddf1488 · sha256 b65edddf1488…
13 970 lignes · premier instantané

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_adult_mortality_intel/adult_mortality_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/wb_adult_mortality_intel/adult_mortality_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_adult_mortality_intel/adult_mortality_panel

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

D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.

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Épinglé à l’instantané 20261007T101739Z-b65edddf1488 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

APA

World Bank adult mortality intelligence. (2026). World Bank adult mortality panel (male/female, 15-60) [Data set, snapshot 20261007T101739Z-b65edddf1488, sha256 b65edddf1488]. Datazimuts. Retrieved 2026-10-07, from https://datazimuts.com/fr/datasets/wb_adult_mortality_intel/adult_mortality_panel?snapshot=20261007T101739Z-b65edddf1488

BibTeX

@misc{dz_wb_adult_mortality_intel_adult_mortality_b65edddf,
  title        = {{World Bank adult mortality panel (male/female, 15-60)}},
  author       = {{World Bank adult mortality intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/wb_adult_mortality_intel/adult_mortality_panel?snapshot=20261007T101739Z-b65edddf1488}},
  note         = {Snapshot 20261007T101739Z-b65edddf1488, sha256 b65edddf1488e289945cdeb32aca1ae36f2298a86061d05348ccdf44b15d34da; accessed 2026-10-07}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=wb_adult_mortality_intel%2Fadult_mortality_panel&lang=fr&theme=auto&snapshot=20261007T101739Z-b65edddf1488&x=year&y=year&agg=avg" title="World Bank adult mortality panel (male/female, 15-60)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

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