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1000 Titel
  • Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011
1000 Autor/in
  1. Tahden, Maike |
  2. Manitz, Juliane |
  3. Baumgardt, Klaus |
  4. Fell, Gerhard |
  5. Kneib, Thomas |
  6. Hegasy, Guido |
1000 Erscheinungsjahr 2016
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2016-10-10
1000 Erschienen in
1000 Quellenangabe
  • 11(10): e0164508
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2016
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0164508 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5056673/ |
1000 Ergänzendes Material
  • http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164508#sec014 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence.
1000 Sacherschließung
lokal Epidemiology
lokal Principal component analysis
lokal Disease ecology
lokal Theoretical ecology
lokal Germany
lokal Elections
lokal Enterohaemorrhagic Escherichia coli
lokal Hemolytic-uremic syndrome
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/VGFoZGVuLCBNYWlrZQ==|https://frl.publisso.de/adhoc/creator/TWFuaXR6LCBKdWxpYW5l|https://frl.publisso.de/adhoc/creator/QmF1bWdhcmR0LCBLbGF1cw==|https://frl.publisso.de/adhoc/creator/RmVsbCwgR2VyaGFyZA==|https://frl.publisso.de/adhoc/creator/S25laWIsIFRob21hcw==|https://frl.publisso.de/adhoc/creator/SGVnYXN5LCBHdWlkbw==
1000 Label
1000 Förderer
  1. German Research Association (DFG) |
  2. Research Training Group ”Scaling Problems in Statistics” |
1000 Fördernummer
  1. -
  2. RTG 1644
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Research Association (DFG) |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Research Training Group ”Scaling Problems in Statistics” |
    1000 Förderprogramm -
    1000 Fördernummer RTG 1644
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6406217.rdf
1000 Erstellt am 2018-01-08T15:45:50.328+0100
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1000 Bearbeitet von 288
1000 Zuletzt bearbeitet Thu Apr 01 13:19:48 CEST 2021
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1000 Oai Id
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