Evidence map›Paper›PMID 38800798›Full record

ArticleEClinicalMedicine2024

Global maternal mortality projections by urban/rural location and education level: a simulation-based analysis.

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

Abstract read
In one paragraph

Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
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

17 citing papers in PubMed.

  1. Article
  2. Machine Learning Analysis of Maternal Mortality Determinants in Nigeria: An Exploratory Study.The International journal of health planning and management · 2026
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  5. A retrospective review of screening for HIV, syphilis and anaemia on first antenatal care visits at a midwife-run obstetric unit in South Africa.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2026
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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.

Zachary J WardCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Rifat AtunDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
Gary KingInstitute for Quantitative Social Sciences, Harvard University, Cambridge, MA, USA.
Brenda Sequeira DmelloMaternal and Newborn Healthcare, Comprehensive Community Based Rehabilitation in Tanzania (CCBRT), Dar Es Salaam, Tanzania.
Sue J GoldieCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Maternal mortality remains a challenge in global health, with well-known disparities across countries. However, less is known about disparities in maternal health by subgroups within countries. The aim of this study is to estimate maternal health indicators for subgroups of women within each country. Methods: In this simulation-based analysis, we used the empirically calibrated Global Maternal Health (GMatH) microsimulation model to estimate a range of maternal health indicators by subgroup (urban/rural location and level of education) for 200 countries/territories from 1990 to 2050. Education levels were defined as low (less than primary), middle (less than secondary), and high (completed secondary or higher). The model simulates the reproductive lifecycle of each woman, accounting for individual-level factors such as family planning preferences, biological factors (e.g., anemia), and history of maternal complications, and how these factors vary by subgroup. We also estimated the impact of scaling up women's education on projected maternal health outcomes compared to clinical and health system-focused interventions. Findings: We find large subgroup differences in maternal health outcomes, with an estimated global maternal mortality ratio (MMR) in 2022 of 292 (95% UI 250-341) for rural women and 100 (95% UI 84-116) for urban women, and 536 (95% UI 450-594), 143 (95% UI 117-174), and 85 (95% UI 67-108) for low, middle, and high education levels, respectively. Ensuring all women complete secondary school is associated with a large impact on the projected global MMR in 2030 (97 [95% UI 76-120]) compared to current trends (167 [95% UI 142-188]), with especially large improvements in countries such as Afghanistan, Chad, Madagascar, Niger, and Yemen. Interpretation: Substantial subgroup disparities present a challenge for global maternal health and health equity. Outcomes are especially poor for rural women with low education, highlighting the need to ensure that policy interventions adequately address barriers to care in rural areas, and the importance of investing in social determinants of health, such as women's education, in addition to health system interventions to improve maternal health for all women. Funding: John D. and Catherine T. MacArthur Foundation, 10-97002-000-INP.

Indexed as

Global healthHealth disparitiesMaternal healthSimulation modellingSocial determinants of health

Identifiers

PMID38800798
PMCPMC11126824

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LicenceCC BY-NC-ND
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Registered trials

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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.