by Ramon Bauer, Peter Frühwirt, Daniel Jost, Roman Seidl, Markus Speringer and Franz Trautinger
Last update: 03 December 2020 – auf Deutsch lesen
In Austria, the first corona death occurred in mid-March 2020. Since then, there is a growing interest in the question as to whether the COVID-19 pandemic has led to increased overall mortality. For this reason, Statistics Vienna (MA 23) evaluates mortality trends in Austria’s nine provinces with regard to unusual events on a weekly basis applying the methodology developed for the Vienna Mortality Monitoring and on data provided by Statistics Austria.
In order to assess whether there was any excess mortality we defined the range of the expected number of weekly deaths. The prediction intervals (“bands”) comprise 99% of the expected values assuming a random and independent distribution of the weekly number of deaths by age group (0 to 64 years or 65 years and older). The “bands” take into account seasonal fluctuations and changes in the population size and age structure – see our method report (in German).
Our analyses include all deaths of people with a residence in the Austrian provinces who died in Austria. The data originates from the Register of Vital Statistics (ZPR) and is provided by Statistics Austria. It must be noted that the data is still preliminary. The number of deaths of the two latest available weeks have not yet been fully registered and are therefore partially estimated by Statistics Austria. Data on weekly deaths since 2007 in Austria at the scale of provinces (NUTS-2) as well as the prediction intervals are published at the Austrian Open Data portal.
Weekly deaths in Austria’s nine provinces in 2020
The following charts show the number of weekly deaths in Austria (excluding deaths of Austrian residents that happened abroad) and the range of the expected values (i.e. the prediction interval). When interpreting the charts it should be taken into account that the prediction intervals are not intended to show events of a comparable strength. The prediction intervals rather show how clearly excess mortality can be assessed for the respective week in the respective region (statistical significance).
Population size determines the width of the prediction interval and thus how clearly excess mortality can be assessed. The greater the population (and, hence, number of corresponding deaths), the narrower is the expected variance in weekly deaths, which in turn allows a more precise identification of excess mortality. Therefore the magnitude of the deviation of actual deaths from the prediction interval is only comparable between provinces with a similar population size (e.g. between Vienna and Lower Austria).
Mortality in European cities since 2015
Additionally, we prepared descriptive statistics of weekly deaths in selected European cities or urban regions (NUTS-3/2) since 2015 on a separate website (in German). These descriptive analyses show clear deviations in 2020 from previous years in some cities, while mortality trends in other cities do not seem to be affected by the COVID-19 pandemic. However, these descriptive analyses do not take into account the seasonality of mortality, nor the changing population and age structures in the selected cities and urban regions.
About the Authors
- Ramon Bauer is Deputy Head of Statistics Vienna at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).
- Peter Frühwirt works in the Basic Research Section at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).
- Daniel Jost works in the Communication at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).
- Roman Seidl works in the Basic Research Section at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).
- Markus Speringer works in the Statistics Vienna at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).
- Franz Trautinger is Head of Communication at the City of Vienna’s Department for Economic Affairs, Labour and Statistics (MA 23).