World Bank maternal mortality panel (MMR, 1985-2023)

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): the maternal mortality ratio (modeled estimate of pregnancy-related deaths per 100,000 live births) for ~240 economies, 1985-2023, with 5-year and 10-year changes, within-year MMR ranks, an SDG 3.1 target-met flag (MMR < 70) and a high-burden flag (MMR >= 300). The SDG 3.1 tracking layer for health systems, development agencies and insurers; joins on economy_code with the catalog's other World Bank panels.

Source: World Bank maternal mortality intelligence
Rows: 7,566
Columns: 12
Source cadence: Yearly
Last refreshed: Oct 11, 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 (1985-2023; 2023 is the latest broadly covered year — later years are null upstream, a genuine vintage lag).
mmr_per_100k	float	Maternal mortality ratio: modeled estimate of the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births (WDI indicator SH.STA.MMRT; WHO / UNICEF / UN Population Fund / World Bank Group, Trends in Maternal Mortality). Strictly positive; historical extremes (SSD 1987: 8,045; RWA 1994: 5,721) are genuine modeled estimates kept verbatim. (unit: deaths per 100,000 live births)
mmr_change_5y	float	Trailing 5-year change of the MMR; null when the base year is absent. (unit: deaths per 100,000 live births)
mmr_change_10y	float	Trailing 10-year change of the MMR; null when the base year is absent. (unit: deaths per 100,000 live births)
mmr_rank	integer	Rank of mmr_per_100k within the year (1 = lowest maternal mortality ratio). (unit: rank)
sdg31_met_flag	integer	1 when the MMR is below the SDG target 3.1 threshold of 70 deaths per 100,000 live births; 0 otherwise.
high_mmr_flag	integer	1 when the MMR is at or above 300 deaths per 100,000 live births (WHO high-burden band); 0 otherwise.
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	mmr_per_100k	mmr_change_5y	mmr_change_10y	mmr_rank	high_mmr_flag	row_hash
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,985	2,232	—	—	193	1	3e1b8495f1d2
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,986	1,857	—	—	192	1	cf89bf173fab
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,987	1,821	—	—	193	1	31a6d0371641
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,988	1,572	—	—	190	1	96a4fef1580f
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,989	1,520	—	—	190	1	6c7f53e0b5f0
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,990	1,550	-682	—	190	1	ced4e7b75201
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,991	1,534	-323	—	190	1	8a5cc540a6c6
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,992	1,443	-378	—	188	1	8b72a48eb771
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,993	1,347	-225	—	188	1	aac610f1a870
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,994	1,396	-124	—	189	1	33c8bbfff234

Profiled Oct 11, 2026 from snapshot 20261011T002751Z-0ceea9c44f61

Measured

Completeness: 96.8%
Rows: 7,566
Columns: 12
Columns with gaps: 2

Rows per yearyear · 1985–2023 · 39 years

19852023

Column	Missing	Distinct	Range	Distribution
economy_codevarchar	0%	248	—	AFG39 AGO39 ALB39
economy_namevarchar	0%	200	—	Afghanistan39 Albania39 Algeria39
regionvarchar	0%	6	—	Europe & Central Asia1,989 Sub-Saharan Africa 1,872 Latin America & Caribbean 1,326
income_groupvarchar	0%	4	—	High income2,496 Upper middle income2,262 Lower middle income1,833
yearbigint	0%	37	1,985 → 2,023median 2,004
mmr_per_100kdouble	0%	1,202	1 → 8,045median 71	115 outside 1st–99th percentile
mmr_change_5ydouble	12.8%	550	-6,315 → 4,483median -6	132 outside 1st–99th percentile
mmr_change_10ydouble	25.6%	806	-6,656 → 1,979median -12	114 outside 1st–99th percentile
mmr_rankbigint	0%	220	1 → 194median 97	100 outside 1st–99th percentile
sdg31_met_flagbigint	0%	2	0 → 1median 0
high_mmr_flagbigint	0%	2	0 → 1median 0
row_hashvarchar	0%	8,871	—	000df08048851 00190f572f921 001963f928311

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_maternal_mortality_intel/maternal_mortality_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/wb_maternal_mortality_intel/maternal_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_maternal_mortality_intel/maternal_mortality_panel

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

Cite this snapshot

Pinned to snapshot 20261011T002751Z-0ceea9c44f61 and its content hash, so readers get exactly the data you used.

APA

World Bank maternal mortality intelligence. (2026). World Bank maternal mortality panel (MMR, 1985-2023) [Data set, snapshot 20261011T002751Z-0ceea9c44f61, sha256 0ceea9c44f61]. Datazimuts. Retrieved 2026-10-11, from https://datazimuts.com/en/datasets/wb_maternal_mortality_intel/maternal_mortality_panel?snapshot=20261011T002751Z-0ceea9c44f61

BibTeX

@misc{dz_wb_maternal_mortality_intel_maternal_mor_0ceea9c4,
  title        = {{World Bank maternal mortality panel (MMR, 1985-2023)}},
  author       = {{World Bank maternal mortality intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/wb_maternal_mortality_intel/maternal_mortality_panel?snapshot=20261011T002751Z-0ceea9c44f61}},
  note         = {Snapshot 20261011T002751Z-0ceea9c44f61, sha256 0ceea9c44f61f7b940897b42f1d2c749a96a44f47b6624fc08f53980e6831614; accessed 2026-10-11}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=wb_maternal_mortality_intel%2Fmaternal_mortality_panel&lang=en&theme=auto&snapshot=20261011T002751Z-0ceea9c44f61&x=year&y=year&agg=avg" title="World Bank maternal mortality panel (MMR, 1985-2023)" width="100%" height="380" style="border:0" loading="lazy"></iframe>