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1000 Titel
  • Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern
1000 Titelzusatz
  • Regional monitoring of infections by means of standardized case fatality rates using the example of SARS-CoV-2 in Bavaria
1000 Autor/in
  1. Manz, Kirsi |
  2. Mansmann, Ulrich |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-08-12
1000 Erschienen in
1000 Quellenangabe
  • 64(9):1146-1156
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s00103-021-03397-8 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358915/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Background!#!Maps of the temporal evolution of the regional distribution of a health-related measure enable public health-relevant assessments of health outcomes.!##!Objectives!#!The paper introduces the concept of standardized case fatality rate (sCFR). It describes the ratio of the regional variation in mortality to the regional variation in the documented infection process. The regional sCFR values are presented in maps and the time-varying regional heterogeneity observed in them is interpreted.!##!Materials and methods!#!The regional sCFR is the quotient of the regional standardized mortality and case rate. It is estimated using a bivariate model. The sCFR values presented in maps are based on SARS-CoV‑2 reporting data from Bavaria since the beginning of April 2020 until the end of March 2021. Four quarters (Q2/20, Q3/20, Q4/20, and Q1/21) are considered.!##!Results!#!In the quarters considered, the naïve CFR values in Bavaria are 5.0%, 0.5%, 2.5%, and 2.8%. In Q2/20, regional sCFR values are irregularly distributed across the state. This heterogeneity weakens in the second wave of the epidemic. In Q1/21, only isolated regions with elevated sCFR (> 1.25) appear in southern Bavaria. Clusters of regions with sCFR > 1.25 form in northern Bavaria, with Oberallgäu being the region with the lowest sCFR (0.39, 95% credibility interval: 0.25-0.55).!##!Conclusions!#!In Bavaria, heterogeneous regional SARS-CoV-2-specific sCFR values are shown to change over time. They estimate the relative risk of dying from or with COVID-19 as a documented case. Strong small-scale variability in sCFR suggests a preference for regional over higher-level measures to manage the incidence of infection.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Standardized mortality rate
lokal Leitthema
lokal Humans [MeSH]
lokal Incidence [MeSH]
lokal Standardisierte Inzidenzrate
lokal Geographical epidemiology
lokal Geographische Epidemiologie
lokal Risk [MeSH]
lokal Germany/epidemiology [MeSH]
lokal Indirect standardization
lokal Standardized incidence rate
lokal COVID-19/mortality [MeSH]
lokal Indirekte Standardisierung
lokal Bayesian hierarchical models
lokal Bayesianische hierarchische Modelle
lokal Standardisierte Mortalitätsrate
lokal SARS-CoV-2 [MeSH]
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/TWFueiwgS2lyc2k=|https://frl.publisso.de/adhoc/uri/TWFuc21hbm4sIFVscmljaA==
1000 Hinweis
  • DeepGreen-ID: 4f9baaadd46d492ea0a45ad650497829 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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1000 Erstellt am 2023-05-12T11:06:55.630+0200
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1000 Zuletzt bearbeitet 2023-10-24T08:13:02.352+0200
1000 Objekt bearb. Tue Oct 24 08:13:02 CEST 2023
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1000 Oai Id
  1. oai:frl.publisso.de:frl:6452004 |
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