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Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2021 Sep;64(9):1146-1156. doi: 10.1007/s00103-021-03397-8. Epub 2021 Aug 12.

[Regional monitoring of infections by means of standardized case fatality rates using the example of SARS-CoV-2 in Bavaria].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz

[Article in German]
Kirsi Manz, Ulrich Mansmann

Affiliations

  1. Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland. [email protected].
  2. Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie (IBE), Ludwig-Maximilians-Universität München, Marchioninistr. 15, 81377, München, Deutschland.
  3. Pettenkofer School of Public Health (PSPH), Ludwig-Maximilians-Universität München, München, Deutschland.

PMID: 34383083 PMCID: PMC8358915 DOI: 10.1007/s00103-021-03397-8

Abstract

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.

© 2021. The Author(s).

Keywords: Bayesian hierarchical models; Geographical epidemiology; Indirect standardization; Standardized incidence rate; Standardized mortality rate

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