Evidence map›Paper›PMID 37081226›Full record

ArticleNature medicine2023

Simulation-based estimates and projections of global, regional and country-level maternal mortality by cause, 1990-2050.

Zachary J Ward, Rifat Atun, Gary King, Brenda Sequeira Dmello, Sue J Goldie

Open access · hybridAbstract read
In one paragraph

Article in Nature medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
20.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Zachary J WardCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. zward@hsph.harvard.edu.ORCID http://orcid.org/0000-0002-4007-2207
Rifat AtunDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.ORCID http://orcid.org/0000-0002-1531-5983
Gary KingInstitute for Quantitative Social Science, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-5327-7631
Brenda Sequeira DmelloMaternal and Newborn Healthcare, Comprehensive Community Based Rehabilitation in Tanzania (CCBRT), Dar Es Salaam, Tanzania.ORCID http://orcid.org/0000-0002-1550-248X
Sue J GoldieCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Harvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Maternal mortality is a major global health challenge. Although progress has been made globally in reducing maternal deaths, measurement remains challenging given the many causes and frequent underreporting of maternal deaths. We developed the Global Maternal Health microsimulation model for women in 200 countries and territories, accounting for individual fertility preferences and clinical histories. Demographic, epidemiologic, clinical and health system data were synthesized from multiple sources, including the medical literature, Civil Registration Vital Statistics systems and Demographic and Health Survey data. We calibrated the model to empirical data from 1990 to 2015 and assessed the predictive accuracy of our model using indicators from 2016 to 2020. We projected maternal health indicators from 1990 to 2050 for each country and estimate that between 1990 and 2020 annual global maternal deaths declined by over 40% from 587,500 (95% uncertainty intervals (UI) 520,600-714,000) to 337,600 (95% UI 307,900-364,100), and are projected to decrease to 327,400 (95% UI 287,800-360,700) in 2030 and 320,200 (95% UI 267,100-374,600) in 2050. The global maternal mortality ratio is projected to decline to 167 (95% UI 142-188) in 2030, with 58 countries above 140, suggesting that on current trends, maternal mortality Sustainable Development Goal targets are unlikely to be met. Building on the development of our structural model, future research can identify context-specific policy interventions that could allow countries to accelerate reductions in maternal deaths.

Indexed as

Maternal DeathMaternal MortalityFemaleForecastingGlobal HealthHumansMortalityUncertainty

Identifiers

PMID37081226
PMCPMC10202807
OpenAlexW4366597800

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.