{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T06:04:18Z","timestamp":1775714658898,"version":"3.50.1"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      This study aims to analyze the characteristics of multidrug-resistant\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      isolates and to identify the factors influencing multidrug resistance in the Nanning area.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study retrospectively analyzed all sputum specimens from pulmonary tuberculosis patients collected at the Fourth People\u2019s Hospital of Nanning from January 2021 to June 2022, including a total of 337 strains of\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      . Univariate analysis and binary logistics regression analysis were used to identify factors influencing multidrug resistance. A predictive model was constructed with SPSS software, and the predictive value of the model was evaluated with the Receiver Operating Characteristic (ROC) curve.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The results of binary logistics regression analysis indicated that treatment status and high-risk population were independent factors influencing multidrug resistance (\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt;\u202f0.05). According to the logistics regression analysis results, the model was constructed as follows: Logit(P)\u202f=\u202f\u22121.874\u202f+\u202f(1.187X\n                      <jats:sub>1<\/jats:sub>\n                      )\u202f+\u202f(0.837X\n                      <jats:sub>2<\/jats:sub>\n                      ). ROC analysis showed that the area under the curve (AUC) of the model was 0.936. In the validation group, the AUC was 0.853.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study results provide a basis for precise prevention and control of multidrug-resistant tuberculosis bacteria in Nanning, help reduce the risk of transmission, and ensure public health safety of local and surrounding populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1599578","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T06:40:08Z","timestamp":1764744008000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of influencing factors and construction of prediction model for multidrug-resistant tuberculosis in Nanning 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