Back

Excess maternal deaths and maternal mortality ratios during the COVID-19 pandemic period: a global country-level counterfactual modelling study

Gajjar, J. H.; Karami, H.; Hayes, H. A.; Dixon, M. A.; Massetti, G. M.; Chowell, G.

2026-07-02 epidemiology
10.64898/2026.06.30.26356947 medRxiv
Show abstract

Background: Maternal mortality remains uneven globally, and the COVID-19 pandemic disrupted maternal health services through direct infection-related risks and indirect health-system pathways. We estimated country-level deviations in maternal deaths and maternal mortality ratios (MMR) during 2020-2023 relative to pre-pandemic trends. Methods: We used WHO/UN MMEIG model-based country-level estimates of maternal deaths and MMR from 2000-2019 to fit an ensemble n-sub-epidemic forecasting model. We generated no-pandemic counterfactual projections for 2020-2023 and compared them with WHO/UN MMEIG estimates for the same years. Excess was defined as the positive difference between the WHO/UN MMEIG estimate and the counterfactual prediction; uncertainty was quantified using bootstrap-based prediction intervals. Results: Globally, estimated cumulative excess maternal deaths were 68,489 (95% UI 34,706-147,118) during 2020-2023, and the aggregate excess MMR was 10,154 (95% UI 4,568-23,744). The largest regional excess death burdens were observed in the South-East Asia Region, Eastern Mediterranean Region, and African Region. Among the eight illustrative high-burden countries, Afghanistan and Somalia had statistically detectable excess maternal deaths, with totals of 2,335 (95% UI 1,148-4,350) and 1,815 (639-3,401), respectively. Liberia had a positive median estimate of 265 excess maternal deaths, but its interval included zero (0-990). Nigeria, Chad, and South Sudan had median totals of zero, although uncertainty intervals indicated that nonzero excess could not be excluded in Chad and South Sudan. Conclusion: Pandemic-period WHO/UN MMEIG estimates deviated heterogeneously from pre-pandemic counterfactual trends. These findings should be interpreted as modeled excess relative to a no-pandemic baseline and may reflect pandemic-related disruptions together with other contemporaneous health-system, political, and social shocks, rather than directly observed deaths or causal effects attributable solely to COVID-19.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.