Serveur d'exploration sur la grippe en Allemagne

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[Hospital surveillance during major outbreaks of community-acquired diseases. Pandemic Influenza Hospital Surveillance (PIKS) 2009/2010 and Surveillance of Bloody Diarrhea (SBD) 2011].

Identifieur interne : 000197 ( Main/Exploration ); précédent : 000196; suivant : 000198

[Hospital surveillance during major outbreaks of community-acquired diseases. Pandemic Influenza Hospital Surveillance (PIKS) 2009/2010 and Surveillance of Bloody Diarrhea (SBD) 2011].

Auteurs : B. Greutélaers [Allemagne] ; M. Wadl ; M. Nachtnebel ; T. Rieck ; B. Hogan ; C. Adlhoch ; T. Eckmanns ; J. Benzler

Source :

RBID : pubmed:23512362

Descripteurs français

English descriptors

Abstract

BACKGROUND AND OBJECTIVE

During the influenza pandemic 2009/2010 and the outbreak of entero-haemorrhagic Escherichia coli (EHEC)/hemolytic-uremic syndrome (HUS) 2011, the statutory reporting system in Germany was complemented by additional event-related surveillance systems in hospitals. The Pandemic Influenza Hospital Surveillance (PIKS) and the Surveillance of Bloody Diarrhea (SBD) were evaluated, to make experiences available for similar future situations.

METHODS

The description and evaluation of our surveillance systems is based on the "Updated Guidelines for Evaluating Public Health Surveillance Systems" published by the U.S. Centers for Disease Control and Prevention in 2001.

RESULTS

PIKS and SBD could be implemented quickly and were able to capture resilient data in a timely manner both on the severity and course of the influenza pandemic 2009/2010 and the outbreak of EHEC and HUS 2011. Although lacking in representativeness, sensitive and useful data were generated.

CONCLUSION

In large outbreaks of severe diseases, the establishment of specific hospital surveillance should be considered as early as possible. In Germany, the participating hospitals were able to rapidly implement the required measures.


DOI: 10.1055/s-0032-1332962
PubMed: 23512362


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<term>Community-Acquired Infections (transmission)</term>
<term>Cross Infection (epidemiology)</term>
<term>Cross Infection (prevention & control)</term>
<term>Cross Infection (transmission)</term>
<term>Cross-Sectional Studies (MeSH)</term>
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<term>Dysentery (etiology)</term>
<term>Dysentery (prevention & control)</term>
<term>Emergency Service, Hospital (statistics & numerical data)</term>
<term>Enterohemorrhagic Escherichia coli (MeSH)</term>
<term>Germany (MeSH)</term>
<term>Hemolytic-Uremic Syndrome (epidemiology)</term>
<term>Hemolytic-Uremic Syndrome (etiology)</term>
<term>Hemolytic-Uremic Syndrome (prevention & control)</term>
<term>Hospital Information Systems (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Influenza, Human (epidemiology)</term>
<term>Influenza, Human (prevention & control)</term>
<term>Influenza, Human (transmission)</term>
<term>Intensive Care Units (statistics & numerical data)</term>
<term>Internet (MeSH)</term>
<term>Patient Admission (statistics & numerical data)</term>
<term>Population Surveillance (methods)</term>
<term>Software Design (MeSH)</term>
<term>United States (MeSH)</term>
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<term>Allemagne (MeSH)</term>
<term>Collecte de données (MeSH)</term>
<term>Conception de logiciel (MeSH)</term>
<term>Dysenterie (prévention et contrôle)</term>
<term>Dysenterie (épidémiologie)</term>
<term>Dysenterie (étiologie)</term>
<term>Escherichia coli entérohémorrhagique (MeSH)</term>
<term>Grippe humaine (prévention et contrôle)</term>
<term>Grippe humaine (transmission)</term>
<term>Grippe humaine (épidémiologie)</term>
<term>Humains (MeSH)</term>
<term>Infection croisée (prévention et contrôle)</term>
<term>Infection croisée (transmission)</term>
<term>Infection croisée (épidémiologie)</term>
<term>Infections communautaires (transmission)</term>
<term>Infections communautaires (épidémiologie)</term>
<term>Internet (MeSH)</term>
<term>Notification des maladies (statistiques et données numériques)</term>
<term>Service hospitalier d'urgences (statistiques et données numériques)</term>
<term>Surveillance de la population (méthodes)</term>
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<term>Syndrome hémolytique et urémique (épidémiologie)</term>
<term>Syndrome hémolytique et urémique (étiologie)</term>
<term>Systèmes d'information hospitaliers (MeSH)</term>
<term>Unités de soins intensifs (statistiques et données numériques)</term>
<term>Épidémies de maladies (prévention et contrôle)</term>
<term>Épidémies de maladies (statistiques et données numériques)</term>
<term>États-Unis (MeSH)</term>
<term>Études transversales (MeSH)</term>
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<term>United States</term>
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<term>Cross Infection</term>
<term>Dysentery</term>
<term>Hemolytic-Uremic Syndrome</term>
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<term>Disease Outbreaks</term>
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<term>Hemolytic-Uremic Syndrome</term>
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<term>Grippe humaine</term>
<term>Infection croisée</term>
<term>Syndrome hémolytique et urémique</term>
<term>Épidémies de maladies</term>
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<term>Disease Outbreaks</term>
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<term>Notification des maladies</term>
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<term>Unités de soins intensifs</term>
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<term>Grippe humaine</term>
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<term>Cross-Sectional Studies</term>
<term>Data Collection</term>
<term>Enterohemorrhagic Escherichia coli</term>
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<term>Internet</term>
<term>Software Design</term>
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<term>Collecte de données</term>
<term>Conception de logiciel</term>
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<div type="abstract" xml:lang="en">
<p>
<b>BACKGROUND AND OBJECTIVE</b>
</p>
<p>During the influenza pandemic 2009/2010 and the outbreak of entero-haemorrhagic Escherichia coli (EHEC)/hemolytic-uremic syndrome (HUS) 2011, the statutory reporting system in Germany was complemented by additional event-related surveillance systems in hospitals. The Pandemic Influenza Hospital Surveillance (PIKS) and the Surveillance of Bloody Diarrhea (SBD) were evaluated, to make experiences available for similar future situations.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>METHODS</b>
</p>
<p>The description and evaluation of our surveillance systems is based on the "Updated Guidelines for Evaluating Public Health Surveillance Systems" published by the U.S. Centers for Disease Control and Prevention in 2001.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>RESULTS</b>
</p>
<p>PIKS and SBD could be implemented quickly and were able to capture resilient data in a timely manner both on the severity and course of the influenza pandemic 2009/2010 and the outbreak of EHEC and HUS 2011. Although lacking in representativeness, sensitive and useful data were generated.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>CONCLUSION</b>
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<p>In large outbreaks of severe diseases, the establishment of specific hospital surveillance should be considered as early as possible. In Germany, the participating hospitals were able to rapidly implement the required measures.</p>
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<Month>3</Month>
<Day>21</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline">
<Year>2013</Year>
<Month>5</Month>
<Day>15</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList>
<ArticleId IdType="pubmed">23512362</ArticleId>
<ArticleId IdType="doi">10.1055/s-0032-1332962</ArticleId>
</ArticleIdList>
</PubmedData>
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<affiliations>
<list>
<country>
<li>Allemagne</li>
</country>
<region>
<li>Berlin</li>
</region>
<settlement>
<li>Berlin</li>
</settlement>
</list>
<tree>
<noCountry>
<name sortKey="Adlhoch, C" sort="Adlhoch, C" uniqKey="Adlhoch C" first="C" last="Adlhoch">C. Adlhoch</name>
<name sortKey="Benzler, J" sort="Benzler, J" uniqKey="Benzler J" first="J" last="Benzler">J. Benzler</name>
<name sortKey="Eckmanns, T" sort="Eckmanns, T" uniqKey="Eckmanns T" first="T" last="Eckmanns">T. Eckmanns</name>
<name sortKey="Hogan, B" sort="Hogan, B" uniqKey="Hogan B" first="B" last="Hogan">B. Hogan</name>
<name sortKey="Nachtnebel, M" sort="Nachtnebel, M" uniqKey="Nachtnebel M" first="M" last="Nachtnebel">M. Nachtnebel</name>
<name sortKey="Rieck, T" sort="Rieck, T" uniqKey="Rieck T" first="T" last="Rieck">T. Rieck</name>
<name sortKey="Wadl, M" sort="Wadl, M" uniqKey="Wadl M" first="M" last="Wadl">M. Wadl</name>
</noCountry>
<country name="Allemagne">
<region name="Berlin">
<name sortKey="Greutelaers, B" sort="Greutelaers, B" uniqKey="Greutelaers B" first="B" last="Greutélaers">B. Greutélaers</name>
</region>
</country>
</tree>
</affiliations>
</record>

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