Geographical distribution and space–time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017–2018
Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 wh...
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Veröffentlicht in: | Epidemiology and infection 2022-10, Vol.150, p.e175, Article e175 |
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description | Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017–2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space–time clustering of isolates at the zip code level using scan statistic models. Space–time clusters were detected for each major serotype during 2017–2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida. |
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Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017–2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space–time clustering of isolates at the zip code level using scan statistic models. Space–time clusters were detected for each major serotype during 2017–2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.</description><identifier>ISSN: 0950-2688</identifier><identifier>EISSN: 1469-4409</identifier><identifier>DOI: 10.1017/S0950268822001558</identifier><identifier>PMID: 36315003</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Cluster analysis ; Clustering ; Florida - epidemiology ; Foodborne diseases ; Genomes ; Geographical distribution ; Health surveillance ; Humans ; Hypotheses ; Illnesses ; Laboratories ; Original Paper ; Population ; Postal codes ; Public health ; Salmonella ; Salmonella Food Poisoning - epidemiology ; Salmonellosis ; Serogroup ; Serotypes ; Serotyping ; Space-Time Clustering ; Statistical analysis ; Statistics</subject><ispartof>Epidemiology and infection, 2022-10, Vol.150, p.e175, Article e175</ispartof><rights>Copyright © The Author(s), 2022. Published by Cambridge University Press</rights><rights>Copyright © The Author(s), 2022. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022 2022 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c498t-966429ddd95fe4ca57d51b4b5255a76586cebe11ee0bc1f2448dde021bfe2e703</citedby><cites>FETCH-LOGICAL-c498t-966429ddd95fe4ca57d51b4b5255a76586cebe11ee0bc1f2448dde021bfe2e703</cites><orcidid>0000-0002-8396-2912 ; 0000-0002-6456-5460</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980922/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0950268822001558/type/journal_article$$EHTML$$P50$$Gcambridge$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,23297,27901,27902,53766,53768,55779</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36315003$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xiaolong</creatorcontrib><creatorcontrib>Singh, Nitya</creatorcontrib><creatorcontrib>Havelaar, Arie H.</creatorcontrib><creatorcontrib>Blackburn, Jason K.</creatorcontrib><title>Geographical distribution and space–time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017–2018</title><title>Epidemiology and infection</title><addtitle>Epidemiol. Infect</addtitle><description>Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017–2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space–time clustering of isolates at the zip code level using scan statistic models. Space–time clusters were detected for each major serotype during 2017–2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.</description><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Florida - epidemiology</subject><subject>Foodborne diseases</subject><subject>Genomes</subject><subject>Geographical distribution</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Illnesses</subject><subject>Laboratories</subject><subject>Original Paper</subject><subject>Population</subject><subject>Postal codes</subject><subject>Public health</subject><subject>Salmonella</subject><subject>Salmonella Food Poisoning - epidemiology</subject><subject>Salmonellosis</subject><subject>Serogroup</subject><subject>Serotypes</subject><subject>Serotyping</subject><subject>Space-Time Clustering</subject><subject>Statistical analysis</subject><subject>Statistics</subject><issn>0950-2688</issn><issn>1469-4409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>IKXGN</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kc1u1DAUhS0EokPhAdggS2wbsB07sTdIVUULUiUW064jx76Z8cixg52AumPDE_CGPAkedVqKUFd3cc797s9B6DUl7yih7fs1UYKwRkrGCKFCyCdoRXmjKs6JeopWe7na60foRc47Qohisn2OjuqmpoKQeoV-XkDcJD1tndEeW5fn5PpldjFgHSzOkzbw-8ev2Y2AjV_yDMmFDY4D3i6jDth5HyBnyPi7m7d41LuY8Fr7MQbwXuMMKc43U9FdwOc-Jmf1Cb5en55gVk4o6FLkS_Rs0D7Dq0M9RtfnH6_OPlWXXy4-n51eVoYrOVeqaThT1lolBuBGi9YK2vNeMCF02wjZGOiBUgDSGzowzqW1QBjtB2DQkvoYfbjlTks_gjUQ5qR9NyU36nTTRe26f5Xgtt0mfuuUkuV3rADeHgApfl0gz90uLimUnTvW1oQp3tKmuOity6SYc4LhfgIl3T657r_kSs-bh6vdd9xFVQz1AarHvrxxA39nP479A_sepsw</recordid><startdate>20221031</startdate><enddate>20221031</enddate><creator>Li, Xiaolong</creator><creator>Singh, Nitya</creator><creator>Havelaar, Arie H.</creator><creator>Blackburn, Jason K.</creator><general>Cambridge University Press</general><scope>IKXGN</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>3V.</scope><scope>7QL</scope><scope>7RV</scope><scope>7T2</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8396-2912</orcidid><orcidid>https://orcid.org/0000-0002-6456-5460</orcidid></search><sort><creationdate>20221031</creationdate><title>Geographical distribution and space–time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017–2018</title><author>Li, Xiaolong ; Singh, Nitya ; Havelaar, Arie H. ; Blackburn, Jason K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c498t-966429ddd95fe4ca57d51b4b5255a76586cebe11ee0bc1f2448dde021bfe2e703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Florida - 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Infect</addtitle><date>2022-10-31</date><risdate>2022</risdate><volume>150</volume><spage>e175</spage><pages>e175-</pages><artnum>e175</artnum><issn>0950-2688</issn><eissn>1469-4409</eissn><abstract>Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017–2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space–time clustering of isolates at the zip code level using scan statistic models. Space–time clusters were detected for each major serotype during 2017–2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><pmid>36315003</pmid><doi>10.1017/S0950268822001558</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-8396-2912</orcidid><orcidid>https://orcid.org/0000-0002-6456-5460</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Cluster analysis Clustering Florida - epidemiology Foodborne diseases Genomes Geographical distribution Health surveillance Humans Hypotheses Illnesses Laboratories Original Paper Population Postal codes Public health Salmonella Salmonella Food Poisoning - epidemiology Salmonellosis Serogroup Serotypes Serotyping Space-Time Clustering Statistical analysis Statistics |
title | Geographical distribution and space–time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017–2018 |
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