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Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>China has made progress in reducing maternal mortality ratio (MMR), yet county-level spatiotemporal heterogeneity persists. This study aims to identify spatiotemporal disparities in MMR and quantify the impacts of various administrative levels on these disparities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed county-level MMR panel data from 1996 to 2015, employing the spatial Gini coefficient, Anselin Local Moran's I, and Getis-Ord Gi<jats:sup>*<\/jats:sup> to assess spatiotemporal disparities related to spatial inequity and geographic clustering. Additionally, we applied a Bayesian multiscale spatiotemporally varying intercepts (BMSTVI) model to unveil the national temporal trend and multiple sub-national spatial patterns in maternal mortality risk. We further quantified the relative contributions of five sub-national administrative levels using the spatiotemporal variance partitioning index (STVPI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results suggested that from 1996 to 2015, the proportion of MMR in counties achieving Sustainable Development Goals (SDGs) increased from 27.05% to 93.40%, yet spatiotemporal disparities remained. The spatial Gini coefficient and geographic clustering analyses indicated temporally varying but spatially stable inequities patterns, highlighting the Spatial Inequity Lock-in (SILI) effect. Hotspot analysis identified sensitive and exemplary counties, underscoring the need for targeted regional interventions. The BMSTVI model indicated a declining trend in MMR risk over 20 years, with the most substantial reduction from 2003 to 2012. While the geographic distribution of high-risk areas remained relatively stable, analyses at finer administrative levels enabled more precise identification of affected locations and improved intervention effectiveness. Finally, the STVPI revealed that spatial effects contributed 83.91% (95% CIs: 78.66%\u221289.47%) to MMR variations, far exceeding the 11.60% (95% CIs: 7.27%\u221216.55%) from temporal effects. The contribution from the administrative county-level was the highest (29.15%, 95% CIs: 19.69%\u221235.06%), followed by contributions from the seven geographical regions (14.10%, 95% CIs: 6.61%\u221234.06%), rural\u2013urban differences (13.77%, 95% CIs: 4.93%\u221239.2%), provincial level (12.41%, 95% CIs: 8.06%\u221216.85%), and city level (11.21%, 95% CIs: 7.53%\u221213.84%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>These findings underscore the crucial need for region-specific, time-sensitive policies to achieve maternal health equity across Chinese counties. This study provides a robust empirical foundation for a multi-tiered adaptive policy framework grounded in systematic spatiotemporal assessment across macro, meso, and micro scales to guide targeted maternal health interventions globally.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1572382","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T13:09:47Z","timestamp":1746796187000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Spatiotemporal disparities in maternal mortality and the role of multiscale administrative levels: a 20-year study across Chinese 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