{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:33:13Z","timestamp":1785407593054,"version":"3.56.0"},"reference-count":46,"publisher":"American Society for Microbiology","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Antimicrob Agents Chemother"],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Antimicrobial resistance (AMR) poses a critical and growing global health threat, directly causing millions of deaths, with China bearing a significant burden. Understanding the provincial dynamics and multifactorial one health drivers of AMR, especially amidst the transformative 2019\u20132023 coronavirus disease 2019 (COVID-19) pandemic, remains crucial but underexplored. This comprehensive study investigated the spatiotemporal patterns and multisectoral drivers of methicillin-resistant\n                      <jats:italic toggle=\"yes\">Staphylococcus aureus<\/jats:italic>\n                      (MRSA), carbapenem-resistant\n                      <jats:italic toggle=\"yes\">Klebsiella pneumoniae<\/jats:italic>\n                      (CRKP), and carbapenem-resistant\n                      <jats:italic toggle=\"yes\">Acinetobacter baumannii<\/jats:italic>\n                      (CRAB) prevalence across Chinese provinces using a robust 2019\u20132023 panel data set. Utilizing spatial autocorrelation (Global Moran\u2019s I) and a multimodel approach, including panel fixed-effects regression, least absolute shrinkage and selection operator, and random forest, we identified robust drivers across healthcare, agricultural, environmental, and socioeconomic domains. Significant positive spatial autocorrelation was found for CRKP (Moran\u2019s\n                      <jats:italic toggle=\"yes\">I<\/jats:italic>\n                      = 0.225;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05) and CRAB (Moran\u2019s\n                      <jats:italic toggle=\"yes\">I<\/jats:italic>\n                      = 0.159;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05), indicating geographical clustering, whereas MRSA exhibited no significant pattern. Pathogen-specific drivers emerged. MRSA prevalence was linked to livestock inventory and PM2.5; CRKP to healthcare expenditure and pig inventory; and CRAB to healthcare expenditure and hospital beds, alongside counterintuitive negative associations with population aging and average length of hospital stay. The direct annual effect of COVID-19 was not statistically significant. We conclude that Chinese AMR is a spatially heterogeneous challenge driven by complex one health factors. A striking \u201cparadox of progress\u201d suggests higher healthcare capacity correlates with dangerously increased carbapenem-resistant pathogens, emphasizing the urgent need for robust infection prevention and control. The pandemic\u2019s influence was predominantly indirect. These findings demand multisectoral, regionally tailored AMR strategies integrating healthcare, agricultural, and environmental policies for effective control.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/aac.01600-25","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T13:00:27Z","timestamp":1770642027000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":1,"title":["Spatiotemporal dynamics and multiple driving factors of antimicrobial resistance in China during the COVID-19 pandemic (2019\u20132023): a provincial panel data analysis"],"prefix":"10.1128","volume":"70","author":[{"given":"Xu","family":"Zheng","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02jqapy19","id-type":"ROR","asserted-by":"publisher"}],"name":"Qingdao Municipal Hospital","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"You","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02jqapy19","id-type":"ROR","asserted-by":"publisher"}],"name":"Qingdao Municipal Hospital","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/000qzf213","id-type":"ROR","asserted-by":"publisher"}],"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6310-915X","authenticated-orcid":true,"given":"Binwei","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02jqapy19","id-type":"ROR","asserted-by":"publisher"}],"name":"Qingdao Municipal Hospital","place":["Qingdao, 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