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Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern
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 i...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358915/ https://www.ncbi.nlm.nih.gov/pubmed/34383083 http://dx.doi.org/10.1007/s00103-021-03397-8 |
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author | Manz, Kirsi Mansmann, Ulrich |
author_facet | Manz, Kirsi Mansmann, Ulrich |
author_sort | Manz, Kirsi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8358915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83589152021-08-12 Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern Manz, Kirsi Mansmann, Ulrich Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz Leitthema 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. Springer Berlin Heidelberg 2021-08-12 2021 /pmc/articles/PMC8358915/ /pubmed/34383083 http://dx.doi.org/10.1007/s00103-021-03397-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access Dieser Artikel wird unter der Creative Commons Namensnennung 4.0 International Lizenz veröffentlicht, welche die Nutzung, Vervielfältigung, Bearbeitung, Verbreitung und Wiedergabe in jeglichem Medium und Format erlaubt, sofern Sie den/die ursprünglichen Autor(en) und die Quelle ordnungsgemäß nennen, einen Link zur Creative Commons Lizenz beifügen und angeben, ob Änderungen vorgenommen wurden. Die in diesem Artikel enthaltenen Bilder und sonstiges Drittmaterial unterliegen ebenfalls der genannten Creative Commons Lizenz, sofern sich aus der Abbildungslegende nichts anderes ergibt. Sofern das betreffende Material nicht unter der genannten Creative Commons Lizenz steht und die betreffende Handlung nicht nach gesetzlichen Vorschriften erlaubt ist, ist für die oben aufgeführten Weiterverwendungen des Materials die Einwilligung des jeweiligen Rechteinhabers einzuholen. Weitere Details zur Lizenz entnehmen Sie bitte der Lizenzinformation auf http://creativecommons.org/licenses/by/4.0/deed.de (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Leitthema Manz, Kirsi Mansmann, Ulrich Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title | Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title_full | Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title_fullStr | Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title_full_unstemmed | Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title_short | Regionales Monitoring von Infektionen mittels standardisierter Fallfatalitätsraten am Beispiel von SARS-CoV-2 in Bayern |
title_sort | regionales monitoring von infektionen mittels standardisierter fallfatalitätsraten am beispiel von sars-cov-2 in bayern |
topic | Leitthema |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358915/ https://www.ncbi.nlm.nih.gov/pubmed/34383083 http://dx.doi.org/10.1007/s00103-021-03397-8 |
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