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

IF 10 1区医学 Q1 MEDICINE, GENERAL & INTERNAL

EClinicalMedicine Pub Date : 2025-09-17 eCollection Date: 2025-10-01 DOI:10.1016/j.eclinm.2025.103505

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

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引用次数: 0

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.

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评估国家一级孕产妇死亡率估计的差异：2020年GMatH、UN和GBD模型结果的比较

背景：孕产妇死亡率估计对于为全球和个别国家的政策和资源分配提供信息以及跟踪实现可持续发展目标的进展情况非常重要。全球产妇保健（GMatH）模型是为政策分析而开发的，它产生了全球和国家一级的产妇死亡率估计数。联合国（UN）和全球疾病负担（GBD）的模型也做出了估计。方法：我们比较了UN （v2023）、GBD （v2021）和GMatH （v2023）模型对2020年孕产妇死亡和孕产妇死亡率（MMR）的国家级估计值。我们总结了差异，评估了模型收敛性，并对差异较大的国家的现有经验死亡率数据进行了特征化，以揭示这些差异的潜在原因。研究结果：平均而言，GMatH对2020年国家级孕产妇死亡的估计比联合国的估计高出272例（高出43%），比GBD的估计高出728例（高出49%）。国家一级的产妇死亡率比联合国的估计平均高出22.3（高出19%），比GBD的估计平均高出48.1（高出22%）。总体而言，87.9%的联合国国家级MMR估计值与GMatH模型是收敛的，82.8%的GBD MMR估计值是收敛的，但在一些国家发现了很大的差异。在各模式差异最大的国家中，基于调查的怀孕死亡率估计数通常是唯一可用的经验死亡率数据。解释：尽管大多数国家的GMatH、UN和GBD模型对孕产妇死亡率的估计是相似的，但也存在很大差异。我们的结构建模方法利用了整个生殖生命过程中的多种类型的数据，包括怀孕死亡率，从而可以对孕产妇健康指标进行更可靠的估计。比较各模型之间的结果有助于建立对相似估计的信心，并揭示差异的潜在原因，以帮助改进估计并指导降低孕产妇死亡率的政策。资助：John D. and Catherine T. MacArthur Foundation, 10-97002- 2000 - inp。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EClinicalMedicine Medicine-Medicine (all)

CiteScore

18.90

自引率

1.30%

发文量

506

审稿时长

22 days

期刊介绍： eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.