World Bank adult mortality panel (male/female, 15-60)
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.
- Rows
- 13,970
- Columns
- 15
- Source cadence
- Yearly
- Last refreshed
- Oct 7, 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 (1960-2024; 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). |
First 10 sample rows — a preview, not the complete dataset.
| 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 |
- Current
20261007T101739Z-b65edddf1488 · sha256 b65edddf1488…
13,970 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/wb_adult_mortality_intel/adult_mortality_panel" | jq '{title, rows, columns_count, license}'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)API endpoint: https://datazimuts.com/v1/datasets/wb_adult_mortality_intel/adult_mortality_panel
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20261007T101739Z-b65edddf1488 and its content hash, so readers get exactly the data you used.
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/en/datasets/wb_adult_mortality_intel/adult_mortality_panel?snapshot=20261007T101739Z-b65edddf1488
@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/en/datasets/wb_adult_mortality_intel/adult_mortality_panel?snapshot=20261007T101739Z-b65edddf1488}},
note = {Snapshot 20261007T101739Z-b65edddf1488, sha256 b65edddf1488e289945cdeb32aca1ae36f2298a86061d05348ccdf44b15d34da; accessed 2026-10-07}
}Embed a table or a chart
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