Geographic Model and Biomarker‐Derived Measures of Pesticide Exposure and Parkinson's Disease
Identifieur interne : 000517 ( Istex/Corpus ); précédent : 000516; suivant : 000518Geographic Model and Biomarker‐Derived Measures of Pesticide Exposure and Parkinson's Disease
Auteurs : Beate Ritz ; Sadie CostelloSource :
- Annals of the New York Academy of Sciences [ 0077-8923 ] ; 2006-09.
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Abstract
Abstract: For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson's disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)‐based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid‐adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population‐based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR‐based organochlorine (OC) estimates were derived for each subject and compared to lipid‐adjusted DDE serum levels. Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). Our environmental GIS–PUR‐based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides.
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DOI: 10.1196/annals.1371.074
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To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)‐based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid‐adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population‐based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR‐based organochlorine (OC) estimates were derived for each subject and compared to lipid‐adjusted DDE serum levels. Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). 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Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). 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To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)‐based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid‐adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population‐based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR‐based organochlorine (OC) estimates were derived for each subject and compared to lipid‐adjusted DDE serum levels. Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). Our environmental GIS–PUR‐based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>keywords</head><item><term>pesticides</term></item><item><term>Geographic Information Systems (GIS)</term></item><item><term>validation</term></item><item><term>exposure assessment</term></item><item><term>biomarker</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2006-09">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/E23353CCBBDA4EAFB04DCEE441704D2CDEAAE10A/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Inc</publisherName><publisherLoc>Malden, USA</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1749-6632</doi><issn type="print">0077-8923</issn><issn type="electronic">1749-6632</issn><idGroup><id type="product" value="NYAS"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="ANNALS OF NEW YORK ACADEMY SCIENCES">Annals of the New York Academy of Sciences</title></titleGroup></publicationMeta><publicationMeta level="part" position="09001"><doi origin="wiley">10.1111/nyas.2006.1076.issue-1</doi><titleGroup><title type="main">Living in a Chemical World: Framing the Future in Light of the Past</title></titleGroup><numberingGroup><numbering type="journalVolume" number="1076">1076</numbering><numbering type="journalIssue">1</numbering></numberingGroup><coverDate startDate="2006-09">September 2006</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="30" status="forIssue"><doi origin="wiley">10.1196/annals.1371.074</doi><idGroup><id type="unit" value="NYAS74"></id></idGroup><countGroup><count type="pageTotal" number="10"></count></countGroup><titleGroup><title type="tocHeading1">Part III. Reducing and Managing Risk in High‐Hazard Sectors</title><title type="tocHeading2"><i>Agriculture</i></title></titleGroup><eventGroup><event type="firstOnline" date="2006-10-05"></event><event type="publishedOnlineFinalForm" date="2006-10-05"></event><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:2.3.5 mode:FullText source:FullText result:FullText" date="2010-04-06"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:4.0.1" date="2014-03-19"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-14"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="378">378</numbering><numbering type="pageLast" number="387">387</numbering></numberingGroup><correspondenceTo>Address for correspondence: Beate Ritz, M.D., Ph.D., Associate Professor of Epidemiology and Environmental Health Sciences, UCLA School of Public Health, Box 951772, 650 Charles E. Young Drive, Los Angeles, CA 90095‐1772. Voice: 310‐206‐7458; fax: 310‐206‐6039.
e‐mail: <email>britz@ucla.edu</email></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:NYAS.NYAS74.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="0"></count><count type="tableTotal" number="3"></count><count type="formulaTotal" number="0"></count><count type="referenceTotal" number="35"></count><count type="linksCrossRef" number="21"></count></countGroup><titleGroup><title type="main">Geographic Model and Biomarker‐Derived Measures of Pesticide Exposure and Parkinson's Disease</title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1"><personName><givenNames>BEATE</givenNames><familyName>RITZ</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a1"><personName><givenNames>SADIE</givenNames><familyName>COSTELLO</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1" countryCode="US"><unparsedAffiliation>UCLA School of Public Health, Los Angeles, California 90095‐1772, USA</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en"><keyword xml:id="k1">pesticides</keyword><keyword xml:id="k2">Geographic Information Systems (GIS)</keyword><keyword xml:id="k3">validation</keyword><keyword xml:id="k4">exposure assessment</keyword><keyword xml:id="k5">biomarker</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><p><b><sc>Abstract: </sc></b> <b>For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson's disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)‐based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid‐adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population‐based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR‐based organochlorine (OC) estimates were derived for each subject and compared to lipid‐adjusted DDE serum levels. Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted <i>r</i><sup>2</sup>= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). Our environmental GIS–PUR‐based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides.</b> </p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Geographic Model and Biomarker‐Derived Measures of Pesticide Exposure and Parkinson's Disease</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Geographic Model and Biomarker‐Derived Measures of Pesticide Exposure and Parkinson's Disease</title></titleInfo><name type="personal"><namePart type="given">BEATE</namePart><namePart type="family">RITZ</namePart><affiliation>UCLA School of Public Health, Los Angeles, California 90095‐1772, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">SADIE</namePart><namePart type="family">COSTELLO</namePart><affiliation>UCLA School of Public Health, Los Angeles, California 90095‐1772, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Inc</publisher><place><placeTerm type="text">Malden, USA</placeTerm></place><dateIssued encoding="w3cdtf">2006-09</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="tables">3</extent><extent unit="references">35</extent></physicalDescription><abstract>Abstract: For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson's disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)‐based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid‐adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population‐based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR‐based organochlorine (OC) estimates were derived for each subject and compared to lipid‐adjusted DDE serum levels. Relying on a linear regression model, we predicted log‐transformed lipid‐adjusted DDE serum levels. GIS–PUR‐derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2= 0.47). The specificity of using our environmental GIS–PUR‐derived OC measures to identify those with high‐serum DDE levels was reasonably good (87%). Our environmental GIS–PUR‐based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides.</abstract><subject lang="en"><genre>keywords</genre><topic>pesticides</topic><topic>Geographic Information Systems (GIS)</topic><topic>validation</topic><topic>exposure assessment</topic><topic>biomarker</topic></subject><relatedItem type="host"><titleInfo><title>Annals of the New York Academy of Sciences</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">0077-8923</identifier><identifier type="eISSN">1749-6632</identifier><identifier type="DOI">10.1111/(ISSN)1749-6632</identifier><identifier type="PublisherID">NYAS</identifier><part><date>2006</date><detail type="title"><title>Living in a Chemical World: Framing the Future in Light of the Past</title></detail><detail type="volume"><caption>vol.</caption><number>1076</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>378</start><end>387</end><total>10</total></extent></part></relatedItem><identifier type="istex">E23353CCBBDA4EAFB04DCEE441704D2CDEAAE10A</identifier><identifier type="DOI">10.1196/annals.1371.074</identifier><identifier type="ArticleID">NYAS74</identifier><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Inc</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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