Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.
Identifieur interne : 000421 ( Main/Exploration ); précédent : 000420; suivant : 000422Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.
Auteurs : Geneviève Cadieux [Canada] ; David L. Buckeridge ; André Jacques ; Michael Libman ; Nandini Dendukuri ; Robyn TamblynSource :
- BMC public health [ 1471-2458 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- geographic : Quebec.
- methods : Population Surveillance.
- Female, Humans, Logistic Models, Male, Medical Audit, Office Visits, Patient Credit and Collection, Syndrome.
Abstract
BACKGROUND
Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).
METHODS
The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.
RESULTS
The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).
CONCLUSIONS
Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.
DOI: 10.1186/1471-2458-12-166
PubMed: 22397597
Affiliations:
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Buckeridge</name></author><author><name sortKey="Jacques, Andre" sort="Jacques, Andre" uniqKey="Jacques A" first="André" last="Jacques">André Jacques</name></author><author><name sortKey="Libman, Michael" sort="Libman, Michael" uniqKey="Libman M" first="Michael" last="Libman">Michael Libman</name></author><author><name sortKey="Dendukuri, Nandini" sort="Dendukuri, Nandini" uniqKey="Dendukuri N" first="Nandini" last="Dendukuri">Nandini Dendukuri</name></author><author><name sortKey="Tamblyn, Robyn" sort="Tamblyn, Robyn" uniqKey="Tamblyn R" first="Robyn" last="Tamblyn">Robyn Tamblyn</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22397597</idno><idno type="pmid">22397597</idno><idno type="doi">10.1186/1471-2458-12-166</idno><idno type="wicri:Area/Main/Corpus">000452</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000452</idno><idno type="wicri:Area/Main/Curation">000452</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000452</idno><idno type="wicri:Area/Main/Exploration">000452</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.</title><author><name sortKey="Cadieux, Genevieve" sort="Cadieux, Genevieve" uniqKey="Cadieux G" first="Geneviève" last="Cadieux">Geneviève Cadieux</name><affiliation wicri:level="4"><nlm:affiliation>Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2, Canada. genevieve.cadieux@mail.mcgill.ca</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Epidemiology and Biostatistics, McGill University, 1020 Pine Avenue West, Montreal, QC, H3A 1A2</wicri:regionArea><orgName type="university">Université McGill</orgName><placeName><settlement type="city">Montréal</settlement><region type="state">Québec</region></placeName></affiliation></author><author><name sortKey="Buckeridge, David L" sort="Buckeridge, David L" uniqKey="Buckeridge D" first="David L" last="Buckeridge">David L. Buckeridge</name></author><author><name sortKey="Jacques, Andre" sort="Jacques, Andre" uniqKey="Jacques A" first="André" last="Jacques">André Jacques</name></author><author><name sortKey="Libman, Michael" sort="Libman, Michael" uniqKey="Libman M" first="Michael" last="Libman">Michael Libman</name></author><author><name sortKey="Dendukuri, Nandini" sort="Dendukuri, Nandini" uniqKey="Dendukuri N" first="Nandini" last="Dendukuri">Nandini Dendukuri</name></author><author><name sortKey="Tamblyn, Robyn" sort="Tamblyn, Robyn" uniqKey="Tamblyn R" first="Robyn" last="Tamblyn">Robyn Tamblyn</name></author></analytic><series><title level="j">BMC public health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Female</term><term>Humans</term><term>Logistic Models</term><term>Male</term><term>Medical Audit</term><term>Office Visits</term><term>Patient Credit and Collection</term><term>Population Surveillance (methods)</term><term>Quebec</term><term>Syndrome</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Audit médical</term><term>Consultation médicale</term><term>Crédit et recouvrement patient</term><term>Femelle</term><term>Humains</term><term>Modèles logistiques</term><term>Mâle</term><term>Québec</term><term>Surveillance de la population ()</term><term>Syndrome</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Quebec</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Female</term><term>Humans</term><term>Logistic Models</term><term>Male</term><term>Medical Audit</term><term>Office Visits</term><term>Patient Credit and Collection</term><term>Syndrome</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Audit médical</term><term>Consultation médicale</term><term>Crédit et recouvrement patient</term><term>Femelle</term><term>Humains</term><term>Modèles logistiques</term><term>Mâle</term><term>Québec</term><term>Surveillance de la population</term><term>Syndrome</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce false-positive alerts by surveillance systems, either by focusing on the data most likely to be accurate, or by adjusting the observed data for known biases in diagnosis reporting and performing surveillance using the adjusted values.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22397597</PMID><DateCompleted><Year>2012</Year><Month>10</Month><Day>11</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2458</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><PubDate><Year>2012</Year><Month>Mar</Month><Day>08</Day></PubDate></JournalIssue><Title>BMC public health</Title><ISOAbbreviation>BMC Public Health</ISOAbbreviation></Journal><ArticleTitle>Patient, physician, encounter, and billing characteristics predict the accuracy of syndromic surveillance case definitions.</ArticleTitle><Pagination><MedlinePgn>166</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2458-12-166</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Syndromic surveillance systems are plagued by high false-positive rates. In chronic disease monitoring, investigators have identified several factors that predict the accuracy of case definitions based on diagnoses in administrative data, and some have even incorporated these predictors into novel case detection methods, resulting in a significant improvement in case definition accuracy. Based on findings from these studies, we sought to identify physician, patient, encounter, and billing characteristics associated with the positive predictive value (PPV) of case definitions for 5 syndromes (fever, gastrointestinal, neurological, rash, and respiratory (including influenza-like illness)).</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">The study sample comprised 4,330 syndrome-positive visits from the claims of 1,098 randomly-selected physicians working in Quebec, Canada in 2005-2007. For each visit, physician-facilitated chart review was used to assess whether the same syndrome was present in the medical chart (gold standard). We used multivariate logistic regression analyses to estimate the association between claim-chart agreement about the presence of a syndrome and physician, patient, encounter, and billing characteristics.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The likelihood of the medical chart agreeing with the physician claim about the presence of a syndrome was higher when the treating physician had billed many visits for the same syndrome recently (ORper 10 visit, 1.05; 95% CI, 1.01-1.08), had a lower workload (ORper 10 claims, 0.93; 95% CI, 0.90-0.97), and when the patient was younger (ORper 5 years of age, 0.96; 95% CI, 0.94-0.97), and less socially deprived (ORmost versus least deprived, 0.76; 95% CI, 0.60-0.95).</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Many physician, patient, encounter, and billing characteristics associated with the PPV of surveillance case definition are accessible to public health, and could be used to reduce 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ValidYN="Y"><LastName>Dendukuri</LastName><ForeName>Nandini</ForeName><Initials>N</Initials></Author><Author ValidYN="Y"><LastName>Tamblyn</LastName><ForeName>Robyn</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><Agency>Canadian Institutes of Health Research</Agency><Country>Canada</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>03</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Public Health</MedlineTA><NlmUniqueID>100968562</NlmUniqueID><ISSNLinking>1471-2458</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D005260" 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Buckeridge</name><name sortKey="Dendukuri, Nandini" sort="Dendukuri, Nandini" uniqKey="Dendukuri N" first="Nandini" last="Dendukuri">Nandini Dendukuri</name><name sortKey="Jacques, Andre" sort="Jacques, Andre" uniqKey="Jacques A" first="André" last="Jacques">André Jacques</name><name sortKey="Libman, Michael" sort="Libman, Michael" uniqKey="Libman M" first="Michael" last="Libman">Michael Libman</name><name sortKey="Tamblyn, Robyn" sort="Tamblyn, Robyn" uniqKey="Tamblyn R" first="Robyn" last="Tamblyn">Robyn Tamblyn</name></noCountry><country name="Canada"><region name="Québec"><name sortKey="Cadieux, Genevieve" sort="Cadieux, Genevieve" uniqKey="Cadieux G" first="Geneviève" last="Cadieux">Geneviève Cadieux</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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