November 2023
Navigation:
Go to Yearly Analysis.
Go to Weekly Analysis.
The data is based upon weekly deaths data from eurostat spanning from 2010 to 2022.
Countries: DE (Germany)
Source for Weekly Deaths (Eurostat): Eurostat table: Deaths by week, sex and 5-year age group.
Source for Vaccination data: European Centre for Disease Prevention and Control.
Source for Population Estimates (Eurostat and UN): Eurostat table: Population on 1st January by age, sex and type of projection.
Comment on the available data and its limitations.
The data on excess deaths and vaccination has several limitations that one should be aware of when analysing the charts below. Unlike the data from Eurostat for the majority of European countries), the data for weekly deaths in the Germany is only available in older age groups, namely (40-49, 50-59, 60-69, 70-79, 80+ and Total). Any analysis of the data does not allow us to observe the excess mortality in younger age groups with sufficient detail.
Additionally, the European CDPC data on vaccination for Germany is limited to the whole population. This poses a problem as we are unable to show excess deaths versus vaccination rates for different age groups.
In the charts below, we can also show the excess deaths age groups compared to total vaccinations administered, in the following way:
| Excess Deaths Age Group | Vaccination Age Group |
|---|---|
| 40-49 | Total |
| 50-59 | Total |
| 60-69 | Total |
| 70-79 | Total |
| 80+ | Total |
| Total | Total |
We obtain quarterly excess deaths estimates by aggregating in quarters, data that is based on our weekly analysis of excess deaths.
In order to estimate weekly excess mortality we perform a 2-step approach to estimate the baseline deaths. The first step is by estimating the trend in death rates using annual data as described in our methodology papers, while using method 2C.
The second step is to estimate weekly excess deaths by comparing deaths or death rates in a given week with the average death rate, which is computed using the average weekly frequency of deaths over a period of N-years (typically 5 to 10 years depending on the data availability). By using both methods in conjunction we obtain a trend adjusted and week of year adjusted estimate for excess mortality.
A quarterly analysis of excess deaths allows us to then use different metrics to estimate seasonally adjusted pattern in excess mortality. For example, we can compare excess mortality in Q1 of 2022 versus Q1 of 2021 to observe the impact of the vaccination policy on excess mortality.
The following chart shows the quarterly (annualised) excess mortality from 2020 to 2023, for different age groups. The Covid-19 vaccinations data (right hand scale) refers to the total accumulated doses at the end of each quarter, as a percentage of the respective age group population.
Please be aware that we matched the age groups as described in the data section above.
The user can specify the age group.
The following chart allows the user to perform quarter to quarter comparisons in excess mortality during 2020, 2021, 2022 and 2023, for the different age groups.
This chart is particularly interesting to investigate excess mortality in seasonally equivalent periods in different years. The vaccine penetration rate for the end 0f 2021 is also shown. Please note that we matched the age groups as described in the data section above.
The interactive chart allows the user to specify the desired comparison quarter.
PHINANCE TECHNOLOGIES