Assessing differences in country-level estimates of maternal mortality: a comparison of GMatH, UN, and GBD model results for 2020

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

doi:10.1016/j.eclinm.2025.103505

Assessing differences in country-level estimates of maternal mortality: a comparison of GMatH, UN, and GBD model results for 2020

EClinicalMedicine. 2025 Sep 17:88:103505. doi: 10.1016/j.eclinm.2025.103505. eCollection 2025 Oct.

Authors

Zachary J Ward^{1

2}, Rifat Atun^{2

3

4}, Gary King⁵, Brenda Sequeira Dmello⁶, Sue J Goldie^{1

2

4

7}

Affiliations

¹ Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
² Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
³ Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
⁴ Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, USA.
⁵ Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA.
⁶ Maternal and Newborn Healthcare, Comprehensive Community Based Rehabilitation in Tanzania (CCBRT), Dar Es Salaam, Tanzania.
⁷ Global Health Education and Learning Incubator, Harvard University, Cambridge, MA, USA.

Abstract

Background: Estimates of maternal mortality are important for informing policy and resource allocation, both globally and for individual countries, and to track progress towards Sustainable Development Goals. The Global Maternal Health (GMatH) model was developed for policy analysis and produces global and country-level estimates of maternal mortality. Estimates are also produced by models from the United Nations (UN) and Global Burden of Disease (GBD).

Methods: We compared country-level estimates for 2020 of maternal deaths and the maternal mortality ratio (MMR) across the UN (v2023), GBD (v2021), and GMatH (v2023) models. We summarized the differences, assessed model convergence, and characterized the available empirical mortality data for countries with large differences to shed light on potential reasons for these differences.

Findings: On average, the GMatH estimates of country-level maternal deaths in 2020 were 272 larger (43% higher) than the UN estimates, and 728 larger (49% higher) than the GBD estimates. Country-level MMRs were on average 22.3 higher (19% higher) than the UN estimates and 48.1 higher (22% higher) than the GBD estimates. Overall, 87.9% of the UN country-level MMR estimates were convergent with the GMatH model, and 82.8% of the GBD MMR estimates were convergent, but large differences were found for some countries. Among countries with the largest differences across models, survey-based estimates of the pregnancy mortality ratio were usually the only empirical mortality data available.

Interpretation: Although estimates of maternal mortality are similar across the GMatH, UN, and GBD models for most countries, there are also large differences. Our structural modelling approach leverages multiple types of data across the reproductive life course, including pregnancy mortality ratios, allowing for more robust estimation of maternal health indicators. Comparing results across models helps to build confidence in estimates where they are similar and sheds light on potential reasons for differences where they diverge to help refine estimates and guide policies to reduce maternal mortality.

Funding: John D. and Catherine T. MacArthur Foundation, 10-97002-000-INP.

Keywords: Global health; Maternal health.