Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan
The goals of this study were to (i) identify issues that affect the ability of discriminant function analysis (DA) of antimicrobial resistance profiles to differentiate sources of fecal contamination, (ii) test the accuracy of DA from a known-source library of fecal Escherichia coli isolates with is...
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creator | Kaneene, John B Miller, RoseAnn Sayah, Raida Johnson, Yvette J Gilliland, Dennis Gardiner, Joseph C |
description | The goals of this study were to (i) identify issues that affect the ability of discriminant function analysis (DA) of antimicrobial resistance profiles to differentiate sources of fecal contamination, (ii) test the accuracy of DA from a known-source library of fecal Escherichia coli isolates with isolates from environmental samples, and (iii) apply this DA to classify E. coli from surface water. A repeated cross-sectional study was used to collect fecal and environmental samples from Michigan livestock, wild geese, and surface water for bacterial isolation, identification, and antimicrobial susceptibility testing using disk diffusion for 12 agents chosen for their importance in treating E. coli infections or for their use as animal feed additives. Nonparametric DA was used to classify E. coli by source species individually and by groups according to antimicrobial exposure. A modified backwards model-building approach was applied to create the best decision rules for isolate differentiation with the smallest number of antimicrobial agents. Decision rules were generated from fecal isolates and applied to environmental isolates to determine the effectiveness of DA for identifying sources of contamination. Principal component analysis was applied to describe differences in resistance patterns between species groups. The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. An evaluation of the performance of DA must take into consideration relative contributions of random chance and the true discriminatory power of the decision rules. |
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A repeated cross-sectional study was used to collect fecal and environmental samples from Michigan livestock, wild geese, and surface water for bacterial isolation, identification, and antimicrobial susceptibility testing using disk diffusion for 12 agents chosen for their importance in treating E. coli infections or for their use as animal feed additives. Nonparametric DA was used to classify E. coli by source species individually and by groups according to antimicrobial exposure. A modified backwards model-building approach was applied to create the best decision rules for isolate differentiation with the smallest number of antimicrobial agents. Decision rules were generated from fecal isolates and applied to environmental isolates to determine the effectiveness of DA for identifying sources of contamination. Principal component analysis was applied to describe differences in resistance patterns between species groups. The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. An evaluation of the performance of DA must take into consideration relative contributions of random chance and the true discriminatory power of the decision rules.</description><identifier>ISSN: 0099-2240</identifier><identifier>EISSN: 1098-5336</identifier><identifier>DOI: 10.1128/aem.02376-06</identifier><identifier>PMID: 17337537</identifier><identifier>CODEN: AEMIDF</identifier><language>eng</language><publisher>Washington, DC: American Society for Microbiology</publisher><subject>Animals ; Animals, Domestic - microbiology ; Anti-Infective Agents - toxicity ; Biological and medical sciences ; Contamination ; Discriminant Analysis ; Drug resistance ; Drug Resistance, Bacterial - genetics ; E coli ; Escherichia coli - drug effects ; Escherichia coli - isolation & purification ; Feces - microbiology ; Fundamental and applied biological sciences. Psychology ; Geese - microbiology ; Michigan ; Microbial Sensitivity Tests ; Microbiology ; Principal Component Analysis ; Public Health Microbiology ; Species Specificity ; Surface water ; Water Microbiology</subject><ispartof>Applied and Environmental Microbiology, 2007-05, Vol.73 (9), p.2878-2890</ispartof><rights>2007 INIST-CNRS</rights><rights>Copyright American Society for Microbiology May 2007</rights><rights>Copyright © 2007, American Society for Microbiology 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c586t-8019923e2093121457f64853cf26aed71223aae52c68be46974c4c7061eb1cb53</citedby><cites>FETCH-LOGICAL-c586t-8019923e2093121457f64853cf26aed71223aae52c68be46974c4c7061eb1cb53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1892867/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC1892867/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,3188,3189,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18715147$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17337537$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kaneene, John B</creatorcontrib><creatorcontrib>Miller, RoseAnn</creatorcontrib><creatorcontrib>Sayah, Raida</creatorcontrib><creatorcontrib>Johnson, Yvette J</creatorcontrib><creatorcontrib>Gilliland, Dennis</creatorcontrib><creatorcontrib>Gardiner, Joseph C</creatorcontrib><title>Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan</title><title>Applied and Environmental Microbiology</title><addtitle>Appl Environ Microbiol</addtitle><description>The goals of this study were to (i) identify issues that affect the ability of discriminant function analysis (DA) of antimicrobial resistance profiles to differentiate sources of fecal contamination, (ii) test the accuracy of DA from a known-source library of fecal Escherichia coli isolates with isolates from environmental samples, and (iii) apply this DA to classify E. coli from surface water. A repeated cross-sectional study was used to collect fecal and environmental samples from Michigan livestock, wild geese, and surface water for bacterial isolation, identification, and antimicrobial susceptibility testing using disk diffusion for 12 agents chosen for their importance in treating E. coli infections or for their use as animal feed additives. Nonparametric DA was used to classify E. coli by source species individually and by groups according to antimicrobial exposure. A modified backwards model-building approach was applied to create the best decision rules for isolate differentiation with the smallest number of antimicrobial agents. Decision rules were generated from fecal isolates and applied to environmental isolates to determine the effectiveness of DA for identifying sources of contamination. Principal component analysis was applied to describe differences in resistance patterns between species groups. The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. An evaluation of the performance of DA must take into consideration relative contributions of random chance and the true discriminatory power of the decision rules.</description><subject>Animals</subject><subject>Animals, Domestic - microbiology</subject><subject>Anti-Infective Agents - toxicity</subject><subject>Biological and medical sciences</subject><subject>Contamination</subject><subject>Discriminant Analysis</subject><subject>Drug resistance</subject><subject>Drug Resistance, Bacterial - genetics</subject><subject>E coli</subject><subject>Escherichia coli - drug effects</subject><subject>Escherichia coli - isolation & purification</subject><subject>Feces - microbiology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Geese - microbiology</subject><subject>Michigan</subject><subject>Microbial Sensitivity Tests</subject><subject>Microbiology</subject><subject>Principal Component Analysis</subject><subject>Public Health Microbiology</subject><subject>Species Specificity</subject><subject>Surface water</subject><subject>Water Microbiology</subject><issn>0099-2240</issn><issn>1098-5336</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkktvEzEUhS0EoiWwYw0GCVZM8WPGjw1SFBqo1ApEGnVpOY4ncTWxiz0Dyg_if3LzEAVWvvL97vHVOUboOSVnlDL13vrNGWFcioqIB-iUEq2qhnPxEJ0SonXFWE1O0JNSbgkhNRHqMTqhknPZcHmKfk1SLGHps-0DVPhm7SOelxBX-GMoLodNiDb2eDpEtyPwONpuW0LBqYW6h77LaRFsh795uO5tdB5_zakNnS_4OuGLpQes3eJZGrLz-8GpdzAAT_d2p78XhuvZkFsL4ze29xmHiK-CW4eVjU_Ro9Z2xT87niM0n55fTz5Xl18-XUzGl5VrlOgrRajWjHtGNKeM1o1sRa0a7lomrF9Kyhi31jfMCbXwtdCydrWTRFC_oG7R8BH6cNC9GxYbv3SwebaduQMbbN6aZIP5txPD2qzSD0OVZkpIEHh7FMjp--BLbzbgou86G30aiqFaKtLAjiP0-j_wFuwBb4thAEjNmx307gCBxaVk3_7ZhBKzC9-Mz6_MPnxDBOAv_t7-Hj6mDcCbI2ALBNBmCCuUe05J2tB6x706cGD--mfI3tiyMfDRjORGG6akAublgWltMnaVQWc-Y4RyQqRklGv-G2WMzk0</recordid><startdate>20070501</startdate><enddate>20070501</enddate><creator>Kaneene, John B</creator><creator>Miller, RoseAnn</creator><creator>Sayah, Raida</creator><creator>Johnson, Yvette J</creator><creator>Gilliland, Dennis</creator><creator>Gardiner, Joseph C</creator><general>American Society for Microbiology</general><scope>FBQ</scope><scope>IQODW</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>7QL</scope><scope>7QO</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>SOI</scope><scope>7QH</scope><scope>7TV</scope><scope>7UA</scope><scope>F1W</scope><scope>H97</scope><scope>L.G</scope><scope>5PM</scope></search><sort><creationdate>20070501</creationdate><title>Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan</title><author>Kaneene, John B ; Miller, RoseAnn ; Sayah, Raida ; Johnson, Yvette J ; Gilliland, Dennis ; Gardiner, Joseph C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c586t-8019923e2093121457f64853cf26aed71223aae52c68be46974c4c7061eb1cb53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Animals</topic><topic>Animals, Domestic - microbiology</topic><topic>Anti-Infective Agents - toxicity</topic><topic>Biological and medical sciences</topic><topic>Contamination</topic><topic>Discriminant Analysis</topic><topic>Drug resistance</topic><topic>Drug Resistance, Bacterial - genetics</topic><topic>E coli</topic><topic>Escherichia coli - drug effects</topic><topic>Escherichia coli - isolation & purification</topic><topic>Feces - microbiology</topic><topic>Fundamental and applied biological sciences. 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A repeated cross-sectional study was used to collect fecal and environmental samples from Michigan livestock, wild geese, and surface water for bacterial isolation, identification, and antimicrobial susceptibility testing using disk diffusion for 12 agents chosen for their importance in treating E. coli infections or for their use as animal feed additives. Nonparametric DA was used to classify E. coli by source species individually and by groups according to antimicrobial exposure. A modified backwards model-building approach was applied to create the best decision rules for isolate differentiation with the smallest number of antimicrobial agents. Decision rules were generated from fecal isolates and applied to environmental isolates to determine the effectiveness of DA for identifying sources of contamination. Principal component analysis was applied to describe differences in resistance patterns between species groups. The average rate of correct classification by DA was improved by reducing the numbers of species classifications and antimicrobial agents. DA was able to correctly classify environmental isolates when fewer than four classifications were used. Water sample isolates were classified by livestock type. An evaluation of the performance of DA must take into consideration relative contributions of random chance and the true discriminatory power of the decision rules.</abstract><cop>Washington, DC</cop><pub>American Society for Microbiology</pub><pmid>17337537</pmid><doi>10.1128/aem.02376-06</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Animals Animals, Domestic - microbiology Anti-Infective Agents - toxicity Biological and medical sciences Contamination Discriminant Analysis Drug resistance Drug Resistance, Bacterial - genetics E coli Escherichia coli - drug effects Escherichia coli - isolation & purification Feces - microbiology Fundamental and applied biological sciences. Psychology Geese - microbiology Michigan Microbial Sensitivity Tests Microbiology Principal Component Analysis Public Health Microbiology Species Specificity Surface water Water Microbiology |
title | Considerations When Using Discriminant Function Analysis of Antimicrobial Resistance Profiles To Identify Sources of Fecal Contamination of Surface Water in Michigan |
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