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We used a \"One Health\" perspective to evaluate the association of human and veterinary antibiotic usage, environmental factors, socio-economic factors, and health care factors with clinical AMR in China.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We analyzed data from 31 provincial-level administrative divisions in China, encompassing 20,762,383 bacterial isolates sourced from the China Antimicrobial Resistance Surveillance System dataset between 2014 and 2022. A <jats:inline-formula>\n                <jats:alternatives>\n                  <jats:tex-math>$$\\beta$$<\/jats:tex-math>\n                  <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>\u03b2<\/mml:mi>\n                  <\/mml:math>\n                <\/jats:alternatives>\n              <\/jats:inline-formula> regression model was used to explore the relationship of AMR with multiple variables. We also estimated the contribution of factors associated with AMR, and evaluated the avoidable risk of AMR under six different measures during 2019 according to available guidelines.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>AMR had positive associations with human antibiotic usage, veterinary antibiotic usage, particulate matter smaller than 2.5\u00a0\u00b5m (PM<jats:sub>2.5<\/jats:sub>) level, population density, gross domestic product per capita, and length of hospital stay, and a 1 unit increase in the level of above independent variables were associated with a percentage change in the aggregate AMR of 1.8% (95% <jats:italic>CI<\/jats:italic>: 1.1, 2.5), 2.0% (95% <jats:italic>CI<\/jats:italic>: 0.6, 3.4), 0.9% (95% <jats:italic>CI<\/jats:italic>: 0.4, 1.4), 0.02% (95% <jats:italic>CI<\/jats:italic>: 0.01, 0.03), 0.5% (95% <jats:italic>CI<\/jats:italic>: 0.1, 0.8), and 8.0% (95% <jats:italic>CI<\/jats:italic>: 1.2, 15.3), respectively. AMR had negative associations with city water popularity, city greenery area per capita, and health expenditure per capita, and a 1 unit increase in the level of above independent variables were associated with a percentage change in the aggregate AMR of \u22124.2% (95% <jats:italic>CI<\/jats:italic>: \u22126.4, \u22121.9), \u22120.4% (95% <jats:italic>CI<\/jats:italic>: \u22120.8, \u22120.07), and \u22120.02% (95% <jats:italic>CI<\/jats:italic>: \u22120.04, \u22120.01), respectively. PM<jats:sub>2.5<\/jats:sub> might be a major influencing factor of AMR, accounting for 13.7% of variation in aggregate AMR. During 2019, there was estimated 5.1% aggregate AMR could be attributed to PM<jats:sub>2.5<\/jats:sub>, corresponding to 25.7 thousand premature deaths, 691.8 thousand years of life lost, and 63.9 billion Chinese yuan in the whole country. Human antibiotic usage halved, veterinary antibiotic usage halved, city water popularity improved, city greenery area improved, and comprehensive measures could decrease nationwide aggregate AMR by 8.5, 0.5, 1.3, 4.4, and 17.2%, respectively.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>The study highlights the complex and multi-dimensional nature of AMR in China and finds PM<jats:sub>2.5<\/jats:sub> as a possible major influencing factor. Despite improvements in decreasing AMR, future initiatives should consider integrated strategies to control PM<jats:sub>2.5<\/jats:sub> and other factors simultaneously to decrease AMR.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1186\/s40249-025-01289-6","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T06:26:34Z","timestamp":1743488794000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Factors associated with clinical antimicrobial resistance in China: a nationwide analysis"],"prefix":"10.1186","volume":"14","author":[{"given":"Wenyong","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zexuan","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4497-5251","authenticated-orcid":false,"given":"Weibing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"1289_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drup.2023.101012","volume":"71","author":"C de la Fuente-Nunez","year":"2023","unstructured":"de la Fuente-Nunez C, Cesaro A, Hancock REW. 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