Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic.
Identifieur interne : 000442 ( Main/Exploration ); précédent : 000441; suivant : 000443Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic.
Auteurs : Rachel Savage [Canada] ; Anna Chu ; Laura C. Rosella ; Natasha S. Crowcroft ; Monali Varia ; Michelle E. Policarpio ; Norman G. Vinson ; Anne-Luise Winter ; Karen Hay ; Richard F. Davies ; Ian Gemmill ; Don Willison ; Ian JohnsonSource :
- Journal of public health (Oxford, England) [ 1741-3850 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
- méthodes : Surveillance de la population.
- épidémiologie : Grippe humaine, Ontario.
- Collecte de données, Humains, Pandémies, Personnel de santé, Sous-type H1N1 du virus de la grippe A.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Ontario.
- epidemiology : Influenza, Human.
- methods : Population Surveillance.
- Data Collection, Health Personnel, Humans, Influenza A Virus, H1N1 Subtype, Pandemics.
Abstract
BACKGROUND
Despite the growing popularity of syndromic surveillance, little is known about if or how these systems are accepted, utilized and valued by end users. This study seeks to describe the use of syndromic surveillance systems in Ontario and users' perceptions of the value of these systems within the context of other surveillance systems.
METHODS
Ontario's 36 public health units, the provincial ministry of health and federal public health agency completed a web survey to identify traditional and syndromic surveillance systems used routinely and during the pandemic and to describe system attributes and utility in monitoring pandemic activity and informing decision-making.
RESULTS
Syndromic surveillance systems are used by 20/38 (53%) organizations. For routine surveillance, laboratory, integrated Public Health Information System and school absenteeism data are the most frequently used sources. Laboratory data received the highest ratings for reliability, timeliness and accuracy ('very acceptable' by 92, 51 and 89%). Hospital/clinic screening data were rated as the most reliable and timely syndromic data source (50 and 43%) and ED visit data the most accurate (48%). During the pandemic, laboratory data were considered the most useful for monitoring the epidemiology and informing decision-making while ED screening and visit data were considered the most useful syndromic sources.
CONCLUSIONS
End user perceptions are valuable for identifying opportunities for improvement and guiding further investments in public health surveillance.
DOI: 10.1093/pubmed/fdr088
PubMed: 22194318
PubMed Central: PMC7313939
Affiliations:
Links toward previous steps (curation, corpus...)
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Rosella</name></author><author><name sortKey="Crowcroft, Natasha S" sort="Crowcroft, Natasha S" uniqKey="Crowcroft N" first="Natasha S" last="Crowcroft">Natasha S. Crowcroft</name></author><author><name sortKey="Varia, Monali" sort="Varia, Monali" uniqKey="Varia M" first="Monali" last="Varia">Monali Varia</name></author><author><name sortKey="Policarpio, Michelle E" sort="Policarpio, Michelle E" uniqKey="Policarpio M" first="Michelle E" last="Policarpio">Michelle E. Policarpio</name></author><author><name sortKey="Vinson, Norman G" sort="Vinson, Norman G" uniqKey="Vinson N" first="Norman G" last="Vinson">Norman G. Vinson</name></author><author><name sortKey="Winter, Anne Luise" sort="Winter, Anne Luise" uniqKey="Winter A" first="Anne-Luise" last="Winter">Anne-Luise Winter</name></author><author><name sortKey="Hay, Karen" sort="Hay, Karen" uniqKey="Hay K" first="Karen" last="Hay">Karen Hay</name></author><author><name sortKey="Davies, Richard F" sort="Davies, Richard F" uniqKey="Davies R" first="Richard F" last="Davies">Richard F. 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Rosella</name></author><author><name sortKey="Crowcroft, Natasha S" sort="Crowcroft, Natasha S" uniqKey="Crowcroft N" first="Natasha S" last="Crowcroft">Natasha S. Crowcroft</name></author><author><name sortKey="Varia, Monali" sort="Varia, Monali" uniqKey="Varia M" first="Monali" last="Varia">Monali Varia</name></author><author><name sortKey="Policarpio, Michelle E" sort="Policarpio, Michelle E" uniqKey="Policarpio M" first="Michelle E" last="Policarpio">Michelle E. Policarpio</name></author><author><name sortKey="Vinson, Norman G" sort="Vinson, Norman G" uniqKey="Vinson N" first="Norman G" last="Vinson">Norman G. Vinson</name></author><author><name sortKey="Winter, Anne Luise" sort="Winter, Anne Luise" uniqKey="Winter A" first="Anne-Luise" last="Winter">Anne-Luise Winter</name></author><author><name sortKey="Hay, Karen" sort="Hay, Karen" uniqKey="Hay K" first="Karen" last="Hay">Karen Hay</name></author><author><name sortKey="Davies, Richard F" sort="Davies, Richard F" uniqKey="Davies R" first="Richard F" last="Davies">Richard F. Davies</name></author><author><name sortKey="Gemmill, Ian" sort="Gemmill, Ian" uniqKey="Gemmill I" first="Ian" last="Gemmill">Ian Gemmill</name></author><author><name sortKey="Willison, Don" sort="Willison, Don" uniqKey="Willison D" first="Don" last="Willison">Don Willison</name></author><author><name sortKey="Johnson, Ian" sort="Johnson, Ian" uniqKey="Johnson I" first="Ian" last="Johnson">Ian Johnson</name></author></analytic><series><title level="j">Journal of public health (Oxford, England)</title><idno type="eISSN">1741-3850</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Data Collection (MeSH)</term><term>Health Personnel (MeSH)</term><term>Humans (MeSH)</term><term>Influenza A Virus, H1N1 Subtype (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Ontario (epidemiology)</term><term>Pandemics (MeSH)</term><term>Population Surveillance (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Collecte de données (MeSH)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Ontario (épidémiologie)</term><term>Pandémies (MeSH)</term><term>Personnel de santé (MeSH)</term><term>Sous-type H1N1 du virus de la grippe A (MeSH)</term><term>Surveillance de la population (méthodes)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Ontario</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de la population</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Ontario</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Data Collection</term><term>Health Personnel</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Pandemics</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Collecte de données</term><term>Humains</term><term>Pandémies</term><term>Personnel de santé</term><term>Sous-type H1N1 du virus de la grippe A</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Despite the growing popularity of syndromic surveillance, little is known about if or how these systems are accepted, utilized and valued by end users. This study seeks to describe the use of syndromic surveillance systems in Ontario and users' perceptions of the value of these systems within the context of other surveillance systems.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Ontario's 36 public health units, the provincial ministry of health and federal public health agency completed a web survey to identify traditional and syndromic surveillance systems used routinely and during the pandemic and to describe system attributes and utility in monitoring pandemic activity and informing decision-making.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Syndromic surveillance systems are used by 20/38 (53%) organizations. For routine surveillance, laboratory, integrated Public Health Information System and school absenteeism data are the most frequently used sources. Laboratory data received the highest ratings for reliability, timeliness and accuracy ('very acceptable' by 92, 51 and 89%). Hospital/clinic screening data were rated as the most reliable and timely syndromic data source (50 and 43%) and ED visit data the most accurate (48%). During the pandemic, laboratory data were considered the most useful for monitoring the epidemiology and informing decision-making while ED screening and visit data were considered the most useful syndromic sources.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>End user perceptions are valuable for identifying opportunities for improvement and guiding further investments in public health surveillance.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22194318</PMID><DateCompleted><Year>2012</Year><Month>09</Month><Day>06</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>26</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1741-3850</ISSN><JournalIssue CitedMedium="Internet"><Volume>34</Volume><Issue>2</Issue><PubDate><Year>2012</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Journal of public health (Oxford, England)</Title><ISOAbbreviation>J Public Health (Oxf)</ISOAbbreviation></Journal><ArticleTitle>Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic.</ArticleTitle><Pagination><MedlinePgn>195-202</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/pubmed/fdr088</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Despite the growing popularity of syndromic surveillance, little is known about if or how these systems are accepted, utilized and valued by end users. This study seeks to describe the use of syndromic surveillance systems in Ontario and users' perceptions of the value of these systems within the context of other surveillance systems.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Ontario's 36 public health units, the provincial ministry of health and federal public health agency completed a web survey to identify traditional and syndromic surveillance systems used routinely and during the pandemic and to describe system attributes and utility in monitoring pandemic activity and informing decision-making.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Syndromic surveillance systems are used by 20/38 (53%) organizations. For routine surveillance, laboratory, integrated Public Health Information System and school absenteeism data are the most frequently used sources. Laboratory data received the highest ratings for reliability, timeliness and accuracy ('very acceptable' by 92, 51 and 89%). Hospital/clinic screening data were rated as the most reliable and timely syndromic data source (50 and 43%) and ED visit data the most accurate (48%). During the pandemic, laboratory data were considered the most useful for monitoring the epidemiology and informing decision-making while ED screening and visit data were considered the most useful syndromic sources.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">End user perceptions are valuable for identifying opportunities for improvement and guiding further investments in public health surveillance.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Savage</LastName><ForeName>Rachel</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Surveillance and Epidemiology, Public Health Ontario, Toronto, ON, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chu</LastName><ForeName>Anna</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Rosella</LastName><ForeName>Laura C</ForeName><Initials>LC</Initials></Author><Author ValidYN="Y"><LastName>Crowcroft</LastName><ForeName>Natasha S</ForeName><Initials>NS</Initials></Author><Author ValidYN="Y"><LastName>Varia</LastName><ForeName>Monali</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Policarpio</LastName><ForeName>Michelle E</ForeName><Initials>ME</Initials></Author><Author ValidYN="Y"><LastName>Vinson</LastName><ForeName>Norman G</ForeName><Initials>NG</Initials></Author><Author ValidYN="Y"><LastName>Winter</LastName><ForeName>Anne-Luise</ForeName><Initials>AL</Initials></Author><Author ValidYN="Y"><LastName>Hay</LastName><ForeName>Karen</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Davies</LastName><ForeName>Richard F</ForeName><Initials>RF</Initials></Author><Author ValidYN="Y"><LastName>Gemmill</LastName><ForeName>Ian</ForeName><Initials>I</Initials></Author><Author ValidYN="Y"><LastName>Willison</LastName><ForeName>Don</ForeName><Initials>D</Initials></Author><Author 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Crowcroft</name><name sortKey="Davies, Richard F" sort="Davies, Richard F" uniqKey="Davies R" first="Richard F" last="Davies">Richard F. Davies</name><name sortKey="Gemmill, Ian" sort="Gemmill, Ian" uniqKey="Gemmill I" first="Ian" last="Gemmill">Ian Gemmill</name><name sortKey="Hay, Karen" sort="Hay, Karen" uniqKey="Hay K" first="Karen" last="Hay">Karen Hay</name><name sortKey="Johnson, Ian" sort="Johnson, Ian" uniqKey="Johnson I" first="Ian" last="Johnson">Ian Johnson</name><name sortKey="Policarpio, Michelle E" sort="Policarpio, Michelle E" uniqKey="Policarpio M" first="Michelle E" last="Policarpio">Michelle E. Policarpio</name><name sortKey="Rosella, Laura C" sort="Rosella, Laura C" uniqKey="Rosella L" first="Laura C" last="Rosella">Laura C. Rosella</name><name sortKey="Varia, Monali" sort="Varia, Monali" uniqKey="Varia M" first="Monali" last="Varia">Monali Varia</name><name sortKey="Vinson, Norman G" sort="Vinson, Norman G" uniqKey="Vinson N" first="Norman G" last="Vinson">Norman G. Vinson</name><name sortKey="Willison, Don" sort="Willison, Don" uniqKey="Willison D" first="Don" last="Willison">Don Willison</name><name sortKey="Winter, Anne Luise" sort="Winter, Anne Luise" uniqKey="Winter A" first="Anne-Luise" last="Winter">Anne-Luise Winter</name></noCountry><country name="Canada"><noRegion><name sortKey="Savage, Rachel" sort="Savage, Rachel" uniqKey="Savage R" first="Rachel" last="Savage">Rachel Savage</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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