Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces
The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System,...
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| Veröffentlicht in: | Environmental research 2024-03, Vol.245, p.117995-117995, Article 117995 |
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| container_title | Environmental research |
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| creator | Zhao, Yi-Chang Sun, Zhi-Hua Xiao, Ming-Xuan Li, Jia-Kai Liu, Huai-yuan Cai, Hua-Lin Cao, Wei Feng, Yu Zhang, Bi-Kui Yan, Miao |
| description | The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.
We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.
Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P |
| doi_str_mv | 10.1016/j.envres.2023.117995 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2906177991</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0013935123027998</els_id><sourcerecordid>2906177991</sourcerecordid><originalsourceid>FETCH-LOGICAL-c441t-5e4066370d4bd2a51b45f73d07463a91c49060bdea30b79e1759b3ccd578dd553</originalsourceid><addsrcrecordid>eNqFkU9vEzEQxS0EoqHwDRDykcsGT2yvsxyQoqj8kSpxgbPltSeso403tZ1U5dvwTZmyLUc4WSP_3jzNe4y9BrEEAe27_RLTOWNZrsRKLgFM1-knbAGiaxvRafmULYQA2XRSwwV7UcqeRtBSPGcXcg1KGwkL9muT3Hj3M6YfvA7I_ZQzjq7GKfEe6y1i4jenmKZxStiQWyzVpcqvih8wRz9ER5ox8scvjzy7ioW7FLgf44EGvnO-Trm85xuCD8eMA6YSz0gQmZOQO5-nUrgEvh1iwoL8mKdzpHXlJXu2c2PBVw_vJfv-8erb9nNz_fXTl-3muvFKQW00KtG20oig-rByGnqld0YGYVQrXQdedaIVfUAnRW86BKO7XnoftFmHoLW8ZG_nveR8c8JS7SEWj-PoEk6nYiVl19I2tf4vuiIrMFQIEKpm9M-BGXf2mCmUfGdB2Pse7d7OPdr7Hu3cI8nePDic-gOGv6LH4gj4MANIkZwjZlt8RMorxIy-2jDFfzv8BoeGs7o</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2906177991</pqid></control><display><type>article</type><title>Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Zhao, Yi-Chang ; Sun, Zhi-Hua ; Xiao, Ming-Xuan ; Li, Jia-Kai ; Liu, Huai-yuan ; Cai, Hua-Lin ; Cao, Wei ; Feng, Yu ; Zhang, Bi-Kui ; Yan, Miao</creator><creatorcontrib>Zhao, Yi-Chang ; Sun, Zhi-Hua ; Xiao, Ming-Xuan ; Li, Jia-Kai ; Liu, Huai-yuan ; Cai, Hua-Lin ; Cao, Wei ; Feng, Yu ; Zhang, Bi-Kui ; Yan, Miao</creatorcontrib><description>The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.
We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.
Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences.
The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.
[Display omitted]
•E. coli's resistance rates change with climate and Hu line.•Region-specific health strategies against antibiotic resistance is needed.•The model has a higher explanatory power.</description><identifier>ISSN: 0013-9351</identifier><identifier>EISSN: 1096-0953</identifier><identifier>DOI: 10.1016/j.envres.2023.117995</identifier><identifier>PMID: 38145731</identifier><language>eng</language><publisher>Netherlands: Elsevier Inc</publisher><subject>Anti-Bacterial Agents - pharmacology ; antibiotic resistance ; China ; China - epidemiology ; China mainland ; Climate ; Drug Resistance, Bacterial ; Escherichia coli ; Escherichia coli Proteins ; geographical variation ; health services ; meteorological data ; Meteorology ; monitoring ; monsoon season ; mountains ; public health ; Quinolone-resistant ; Quinolones ; rain ; Region ; variance</subject><ispartof>Environmental research, 2024-03, Vol.245, p.117995-117995, Article 117995</ispartof><rights>2023 The Authors</rights><rights>Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-5e4066370d4bd2a51b45f73d07463a91c49060bdea30b79e1759b3ccd578dd553</citedby><cites>FETCH-LOGICAL-c441t-5e4066370d4bd2a51b45f73d07463a91c49060bdea30b79e1759b3ccd578dd553</cites><orcidid>0000-0002-3582-8305</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0013935123027998$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38145731$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Yi-Chang</creatorcontrib><creatorcontrib>Sun, Zhi-Hua</creatorcontrib><creatorcontrib>Xiao, Ming-Xuan</creatorcontrib><creatorcontrib>Li, Jia-Kai</creatorcontrib><creatorcontrib>Liu, Huai-yuan</creatorcontrib><creatorcontrib>Cai, Hua-Lin</creatorcontrib><creatorcontrib>Cao, Wei</creatorcontrib><creatorcontrib>Feng, Yu</creatorcontrib><creatorcontrib>Zhang, Bi-Kui</creatorcontrib><creatorcontrib>Yan, Miao</creatorcontrib><title>Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces</title><title>Environmental research</title><addtitle>Environ Res</addtitle><description>The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.
We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.
Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences.
The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.
[Display omitted]
•E. coli's resistance rates change with climate and Hu line.•Region-specific health strategies against antibiotic resistance is needed.•The model has a higher explanatory power.</description><subject>Anti-Bacterial Agents - pharmacology</subject><subject>antibiotic resistance</subject><subject>China</subject><subject>China - epidemiology</subject><subject>China mainland</subject><subject>Climate</subject><subject>Drug Resistance, Bacterial</subject><subject>Escherichia coli</subject><subject>Escherichia coli Proteins</subject><subject>geographical variation</subject><subject>health services</subject><subject>meteorological data</subject><subject>Meteorology</subject><subject>monitoring</subject><subject>monsoon season</subject><subject>mountains</subject><subject>public health</subject><subject>Quinolone-resistant</subject><subject>Quinolones</subject><subject>rain</subject><subject>Region</subject><subject>variance</subject><issn>0013-9351</issn><issn>1096-0953</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU9vEzEQxS0EoqHwDRDykcsGT2yvsxyQoqj8kSpxgbPltSeso403tZ1U5dvwTZmyLUc4WSP_3jzNe4y9BrEEAe27_RLTOWNZrsRKLgFM1-knbAGiaxvRafmULYQA2XRSwwV7UcqeRtBSPGcXcg1KGwkL9muT3Hj3M6YfvA7I_ZQzjq7GKfEe6y1i4jenmKZxStiQWyzVpcqvih8wRz9ER5ox8scvjzy7ioW7FLgf44EGvnO-Trm85xuCD8eMA6YSz0gQmZOQO5-nUrgEvh1iwoL8mKdzpHXlJXu2c2PBVw_vJfv-8erb9nNz_fXTl-3muvFKQW00KtG20oig-rByGnqld0YGYVQrXQdedaIVfUAnRW86BKO7XnoftFmHoLW8ZG_nveR8c8JS7SEWj-PoEk6nYiVl19I2tf4vuiIrMFQIEKpm9M-BGXf2mCmUfGdB2Pse7d7OPdr7Hu3cI8nePDic-gOGv6LH4gj4MANIkZwjZlt8RMorxIy-2jDFfzv8BoeGs7o</recordid><startdate>20240315</startdate><enddate>20240315</enddate><creator>Zhao, Yi-Chang</creator><creator>Sun, Zhi-Hua</creator><creator>Xiao, Ming-Xuan</creator><creator>Li, Jia-Kai</creator><creator>Liu, Huai-yuan</creator><creator>Cai, Hua-Lin</creator><creator>Cao, Wei</creator><creator>Feng, Yu</creator><creator>Zhang, Bi-Kui</creator><creator>Yan, Miao</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-3582-8305</orcidid></search><sort><creationdate>20240315</creationdate><title>Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces</title><author>Zhao, Yi-Chang ; Sun, Zhi-Hua ; Xiao, Ming-Xuan ; Li, Jia-Kai ; Liu, Huai-yuan ; Cai, Hua-Lin ; Cao, Wei ; Feng, Yu ; Zhang, Bi-Kui ; Yan, Miao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-5e4066370d4bd2a51b45f73d07463a91c49060bdea30b79e1759b3ccd578dd553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Anti-Bacterial Agents - pharmacology</topic><topic>antibiotic resistance</topic><topic>China</topic><topic>China - epidemiology</topic><topic>China mainland</topic><topic>Climate</topic><topic>Drug Resistance, Bacterial</topic><topic>Escherichia coli</topic><topic>Escherichia coli Proteins</topic><topic>geographical variation</topic><topic>health services</topic><topic>meteorological data</topic><topic>Meteorology</topic><topic>monitoring</topic><topic>monsoon season</topic><topic>mountains</topic><topic>public health</topic><topic>Quinolone-resistant</topic><topic>Quinolones</topic><topic>rain</topic><topic>Region</topic><topic>variance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Yi-Chang</creatorcontrib><creatorcontrib>Sun, Zhi-Hua</creatorcontrib><creatorcontrib>Xiao, Ming-Xuan</creatorcontrib><creatorcontrib>Li, Jia-Kai</creatorcontrib><creatorcontrib>Liu, Huai-yuan</creatorcontrib><creatorcontrib>Cai, Hua-Lin</creatorcontrib><creatorcontrib>Cao, Wei</creatorcontrib><creatorcontrib>Feng, Yu</creatorcontrib><creatorcontrib>Zhang, Bi-Kui</creatorcontrib><creatorcontrib>Yan, Miao</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environmental research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Yi-Chang</au><au>Sun, Zhi-Hua</au><au>Xiao, Ming-Xuan</au><au>Li, Jia-Kai</au><au>Liu, Huai-yuan</au><au>Cai, Hua-Lin</au><au>Cao, Wei</au><au>Feng, Yu</au><au>Zhang, Bi-Kui</au><au>Yan, Miao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces</atitle><jtitle>Environmental research</jtitle><addtitle>Environ Res</addtitle><date>2024-03-15</date><risdate>2024</risdate><volume>245</volume><spage>117995</spage><epage>117995</epage><pages>117995-117995</pages><artnum>117995</artnum><issn>0013-9351</issn><eissn>1096-0953</eissn><abstract>The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue.
We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis.
Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences.
The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance.
[Display omitted]
•E. coli's resistance rates change with climate and Hu line.•Region-specific health strategies against antibiotic resistance is needed.•The model has a higher explanatory power.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>38145731</pmid><doi>10.1016/j.envres.2023.117995</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-3582-8305</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Anti-Bacterial Agents - pharmacology antibiotic resistance China China - epidemiology China mainland Climate Drug Resistance, Bacterial Escherichia coli Escherichia coli Proteins geographical variation health services meteorological data Meteorology monitoring monsoon season mountains public health Quinolone-resistant Quinolones rain Region variance |
| title | Analyzing the correlation between quinolone-resistant Escherichia coli resistance rates and climate factors: A comprehensive analysis across 31 Chinese provinces |
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