< COVID-19

This learning resource is about all-cause deaths under the learning resource COVID-19.

What follows are all-cause death charts for multiple countries, most of which were created from data from Human Mortality Database (HMD), mortality.org, a collaboration between organizations from Germany, the U.S., and France: Max Planck Institute for Demographic Research; University of California, Berkeley and INED, Paris. Some charts were created from other sources.

Disclaimer

Please, keep in mind, that all Medical and Public Health-related decision making is complex task. Public health activities include preventive activities and for those activities you should rely on official public health agencies, research institutes and their epidemiological risk assessment for up-to-date information and recommendation and not on this learning resource. According to scientific evidence look into peer-reviewed journals, where quality assurance was implemented before publication. This is very important for health related issues.

  • Death is a very late indicator for infectious diseases. Epidemiological responses and preventive measures try to act as early as possible to prevent high mortality and keep the health system operational.
  • Epidemiological responses include preventive measures, that depend on a risk literate society,
  • Learning resources look on charts and pattern in charts, e.g. patterns in all-cause death a seasonal patterns may add additional workload to health system. A learning resource can only be a basic introduction in chart and data analysis. For a scientific view please use peer-reviewed journals for your focus of interest.

Learning Objectives

All-cause death charts are presented region by region.

  • (Preventive Measures) What elevation above the normal mortality could be observed during the covid pandemic? (Not all elevation in necessarily caused directly by the covid infection.) How rare are the peaks in mortality in 2020 compared to other years? (Peaks in mortality in COVID-19 specific death seem at least somewhat indicative of peak hospitalizations and the associated healthcare overload.) Please keep in mind, that preventive measures were implemented in many countries and you see the results of all-cause death with the implemented preventive measures. Preventive measures in epidemiology are implemented to prevent very high numbers of death.
  • (Aggregated Data vs. Death cause specific data) Keep in mind that aggregating data for different causes makes it difficult access what the development for a specific cause of death is. E.g. if
    • (A) the death of bicycle riders in traffic accidents increase and
    • (B) at the same time the mortality (B) of car drivers in accidents decreases.
Assume the decrease in deadly car accidents neutralizes the increase deadly accidents with bicycle. Then you cannot argue that nothing happend beside the result that all-cause death did not increase. Looking into the details of causes provide insights, e.g. that a specific measure reduced deadly car accidents and protective measures for bicycle riders must be improved. This is just a general mathematical comment to be careful with interpretation of aggregated data in comparision to death cause specific data.
Explain how an analysis can be performed, that identifies the impact of COVID-19 death (e.g. improvement of health service delivery for COVID-19, encoding of death causes, ...) Keep in mind that mortality represent the fraction of infected patients that die, so preventive measure to keep the total number of infections low keeps the total number of death low independent of the mortality rate for COVID-19.
  • (Encoding of COVID-19 Death) Look at the difference between covid-coded deaths and covid-positive deaths in the section below. Keep in mind that any infection is a burdon to the immune system, even if the patient does not show any symptons (asymptomatic). Patients that have other risk factors are more vulnerable than patients without any previous diseases. Counting COVID-19 requires international standardized reporting mechanisms to make them comparable between countries. For a new disease the standardization is dependent on the scientific knowledge about the disease, that is increasing over time.
Beside the administrative challenge of international comparable statistics, keep in mind that the primary objective is to prevent people from getting the disease, getting to hospital, die and appear in the all-cause death statistics.
  • (Death and Infectious Diseases) If a virus will immediatly kill a patients or makes the majority patients very sick, seem to have a stronger impact on the virus caused death than a virus that leaves a majority of people unaffected and kills only a small minority of infected. But infectious diseases must be treated differently. If people are hospitalized or have to stay at home due to the health impact of virus disease, then they could not infect other people, while virus that leave many people asymptomatic or with mild symptoms, then they might interact with other citizens and spread the disease further and total number of infected might be much higher than for the infectious disease that kill the patient immediately or hospitalizes the patients before he/she could spread the disease. A basic example shows the difference:
  • Virus A kills 90% of infected people, but the virus infects 10.000 people in a specific time span,
  • Virus B kills 1% of infected people and the virus is able to infected 1 million citizens (1,000,000)
From the risk perception the virus A seems to be more dangerous but basic calculation show, that virus A kills 9000 patients and virus B kills 10,000 patients. So the preventive measures for the infections are the key to keep the number of infections low and the health system operational for the treatment of other diseases. This is just a basic example to understand the link between preventive measures and mortality.

Source of Data

There are repetitive disclaimers under the charts about the data being preliminary.

Registration delay: The data from 2020 suffer from registration delay. The last two weeks suffer especially badly, but other weeks are also not free from the registration delay effect.

Scripting to create these charts are at /Scripts.

The number of years covered varies, depending on how much data HMD was able to obtain from the countries. Some data starts in 2000. The more years included, the stronger conclusion one can make about how rare the covid death elevation in 2020 is compared to other years.

Aggregation of all Causes and Decision Making

Death is a very late and rough indicator for epidemiological response activities. In epidemiology for infectious diseases it is key to act as early as possible (containment of the disease, avoid lockdowns) and not wait until even the aggregated mortality of all-cause deaths shows a significant increase.

The disease control tries to flatten the curve that the health system is able to deal with the number of cases. The health service delivery is responsible for more than one disease so an extreme increase of COVID-19 treatments in hospital assigns medical staff and resources to COVID-19 treatment that might be necessary in other areas. So early detection and early interventions for COVID-19 are required.

Nevertheless, it makes sense to keep an eye at other causes of deaths that COVID-19 might have an impact on.

  • reduced influenza caused death due to risk mitigations strategies for COVID-19,
  • impact on traffic accidents,
  • impact on suicides,
  • impact on drug abuse,
  • long term impact on death cases due to postponed treatments,
  • etc.

In that case all-death cases can be rough indicator.

Learning Tasks

The learning tasks is a statistical learning task of aggregated data and disease specific data. If you want to follow current development of COVID-19 follow the actual number of cases and the recommended risk mitigation strategies of public health agencies, scientific results of centers for epidemiology and disease control with the epidemiological expertise.

Now we analyze all causes of death in learning tasks with the following charts that show aggregated mortality of all causes (e.g. death without any disease or incident, accidents, drug abuse, suicid, communicable and non-communicable diseases, ...), region by region:

  • (Pattern in Curves) Look at the pattern in these charts, what are the causes of these pattern? Try to find scientific evidence for these patterns?
  • (Environmental Conditions) Now let us analyze the communicable disease (because this learning resource was created under COVID-19 - there is more to examine - see first question).
    • How do environmental seasonal conditions have an overall impact on all causes of death (accidents, suicides, diseases, ...)?
    • What are environmental conditions that could increase spreading of communicable diseases in general and what are environmental conditions that help us to reduce the number of cases?
    • Explore the current scientific knowledge about COVID-19 and the impact of environmental conditions on aerosols, droplets directly and on the behaviour of people (staying more inside, closed windows in colder season, ...).
  • (Aggregated mortality and specific disease data) Now we compare aggregated mortality and disease specific data.
    • Aggregated data (as the title of this learning resource suggests) add up all causes of death and not only the data of a single disease. Take a non-communicable disease (e.g. breast cancer and remove the mortality of breast cancer from all years in mortality chart of all cases and add the breast cancer data of mortality just in 2020. Do you see an elevation above the normal mortality? If you do not see any elevation, would that justify to stop medical treatment because you analyzed aggregated data? What is the scientific reason to look at specific disease data, that is not aggregated to follow the development of the disease and to access the impact of specific risk mitigation strategies.
    • The charts below aggregate all cause of death. Assume we aggregate not all causes of death but only causes of death of the class of influenza viruses to be more closer to virus disease. What are the seasonal pattern of influenca caused death and how do you explain that (look for epidemiological scientific evidence). Compare Influenza and COVID-19 in terms risk mitigation strategies for the vulnerable patients. What are similarities and what are the difference between Influenza and COVID-19? If you apply the precautionary principle what are consequences for the response in public health and risk mitigation strategies? Keep in mind that the seasonal data for class of aggregated Influenza have a longer scientific history.
    • Look at the peaks in mortality in 2020 compared to other years? Explain why is it necessary to collect the data from hospitals with the available medical equipments to treat patients for specific medical support for COVID-19 and not look at aggregated data of mortality to adjust prepardness for communicable and non-communicable disease, treatment if injuries, ... (e.g. analyze mortality that is caused of peak hospitalizations and the associated healthcare overload by a higher demand for treatment of respiratory disease that cannot be covered by health system.)
    • An infection causes "work" for the immune system even the patient is asymptomatic i.e. the COVID-positive patient is showing no COVID-19 symptoms. In aggregated mortality you get 0 or 1 for an covid-coded deaths. To what extend the disease contributed is difficult to see in aggregated mortality. A comparision of aggregated covid-positive death
      • in less vulnerable cohort without any other risk factors with
      • a more vulnerable population with additional risk factors
    • helps to understand how a COVID-19 infection has an impact on the number of deaths in the group? This is not entirely easy to get right in aggregated mortality data: deaths can in principle be contributed to by
      • improper treatment, partially disconntinued treatment without any medical advice, ...
      • unavailable medical resources for treatment (e.g. Intensive Care Units, ...)
      • unavailable scientific knowledge about the best treatment, which can be identified by medical studies, ...)
      • the missing communication about risks, Risk Literacy of patients (e.g. avoidance of necessary adviced treatments, that are recommended for the increasing the life expectancy of the patient)
      • etc.
  • (Technical Learning Task) Learn about the technical approach in providing the charts. What lessons can be learned to update other COVID-19 specific charts in this learning resource and keep them up to date with automated scripts.

All regions

Some countries and regions are transcluded in sections below, while some are only in their separate pages, to prevent chart rendering problems and speed up page loading. Regions covered:

See also Category:Pages with graphs.

Belgium

Weekly all-cause deaths in Belgium, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in Belgium for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded were excluded to prevent the worst effect of registration delay.

Observation: The above 2020 drop in the values could result from very pronounced registration delay, not visible to this degree for other countries; could it be something else?

All-cause deaths in Belgium in weeks 1-35, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in Belgium in weeks 40+ the year before and weeks 1-35 of the year, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Script oneliners to update the charts:

  • plotHmd.py stmf.csv BEL DTotal
  • plotHmd.py stmf.csv BEL D0_14
  • plotHmdPerYear.py stmf.csv BEL DTotal
  • plotHmdPerSeason.py stmf.csv BEL DTotal

External links:

England and Wales

Weekly all-cause deaths in England and Wales, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Observation: Above, the last week of each year generally seems to have a dip, a discontinuity in the data. This is not observed for other countries. To be researched.

Weekly all-cause deaths in England and Wales, based on mortality.org data, stmf.csv, 3-week average:

Weekly all-cause deaths in England and Wales for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in England and Wales for 0-14 year olds, based on mortality.org data, stmf.csv, 3-week average:

All-cause deaths in England and Wales in weeks 1-29, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in England and Wales in weeks 40+ the year before and weeks 1-29 of the year, year by year, based on mortality.org data, stmf.csv::

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

France

Weekly all-cause deaths in France, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in France for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded were excluded to prevent the worst effect of registration delay.

All-cause deaths in France in weeks 1-31, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in France in weeks 40+ the year before and weeks 1-31 of the year, year by year, based on mortality.org data, stmf.csv::

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Script oneliners to update the charts:

  • plotHmd.py stmf.csv FRATNP DTotal
  • plotHmd.py stmf.csv FRATNP D0_14
  • plotHmdPerYear.py stmf.csv FRATNP DTotal
  • plotHmdPerSeason.py stmf.csv FRATNP DTotal

External links:

Germany

Weekly all-cause deaths in Germany based on mortality.org data, stmf.csv :

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded as suffering significantly from registration delay.

Weekly all-cause deaths in Germany for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

All-cause deaths in Germany in weeks 1-25, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Italy

Weekly all-cause deaths in Italy based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in Italy for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

All-cause deaths in Italy in weeks 1-24, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in Italy in weeks 40+ the year before and weeks 1-24 of the year, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Script oneliners to update the charts:

  • plotHmd.py stmf.csv ITA DTotal
  • plotHmd.py stmf.csv ITA D0_14
  • plotHmdPerYear.py stmf.csv ITA DTotal
  • plotHmdPerSeason.py stmf.csv ITA DTotal

External links:

Netherlands

Weekly all-cause deaths in Netherlands, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in Netherlands for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded were excluded to prevent the worst effect of registration delay.

All-cause deaths in Netherlands in weeks 1-28, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in Netherlands in weeks 40+ the year before and weeks 1-28 of the year, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Spain

Weekly all-cause deaths in Spain, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay.

Weekly all-cause deaths in Spain for 0-14 year olds, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded were excluded to prevent the worst effect of registration delay.

All-cause deaths in Spain in weeks 1-29, year by year, based on mortality.org data, stmf.csv:

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

All-cause deaths in Spain in weeks 40+ the year before and weeks 1-29 of the year, year by year, based on mortality.org data, stmf.csv::

mortality.org indicates the data for 2020 to be preliminary; above, the last two weeks available from mortality.org were excluded to prevent the worst effect of registration delay. The above is not adjusted by population size.

Age-standardized mortality

ONS article linked below has weekly age-standardised mortality rates in 2020, which includes Montenegro, Serbia, Wales and Northern Ireland, and shows comparison between the selected country and England in a chart. The mortality rates are per 100,000 and are age-standardized.

A staggering observation is that the overall normal-mortality differences between countries make much more of a difference than the covid does, as apparent e.g. from comparing low-rate Switzerland and high-rate Serbia; they do so year after year and are going to in near future. Switzerland's mid-term low values (as opposed to peak values) are at or below 15 weekly deaths per 100,000 while Serbia's are about 25 weekly deaths per 100,000.

Links:

See also

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