The power of numbers, a data driven understanding of excess mortality during the COVID pandemic



The COVID pandemic impacted every aspect of our daily lives, our economy, and our relationships. Too many people lost loved ones and most of us either caught the virus or knew someone else who did. For two years, the pandemic dominated the news and our discussions with friends; everyone had an opinion about lockdowns and subjects like vaccinations were sensitive topics for many.  

The opinions we held were based on feelings or best-available data at the time. Three years on, we have access to data that can help us better understand the impact the pandemic had on society. Specifically, we can see how the causes of death changed as a result of the pandemic, how these impacted different groups in society and how effective interventions, like vaccinations, were adopted by people of different ages and socio-economic backgrounds.   

On the 18th of April, Dr. Frank van Berkum and Dr. Irene Simonetti from the Research Centre for Longevity Risk presented findings from research they are doing as part of a project sponsored by ZonMw and the Ministry of Health, Welfare and Sport to understand the causes of excess mortality during the COVID pandemic.

The robustness and strength of their research was made possible by the access they had microdata from Statistics Netherlands and the super computing power of ODISSEI (Open Data Infrastructure for Social Science and Economic Innovations). Using one billion pieces of anonymised microdata that combined personal, medical, socio economic and educational data along with information about vaccination update, COVID tests, and cause of death, Frank was able to: 

  • Establish a cause-specific, pre-pandemic baseline mortality level taking socio-economic factors into consideration. 
  • Quantify cause-specific excess mortality and 
  • Analyse the extent to which excess mortality can be attributed to socio-economic risk factors and information on COVID testing and vaccination.  

During his presentation, Frank explained how he analysed the impact of socio-economic factors on mortality in developing a baseline model and in looking at excess mortality during the COVID pandemic. In modelling pre-pandemic mortality, he found that information on wealth/income and medical expenses are key in predicting the level of individuals’ mortality. However, in explaining excess mortality during the pandemic, COVID information was more relevant than socio-economic factors. Further, besides the obvious new cause of death being COVID-19, he found that for many causes, observed mortality was lower than predicted.  

Dr. Irene Simonetti’s researched which groups in Dutch society were less likely to take up the opportunity to get vaccinated against COVID. Irene separately examined the probability of an individual completing the primary series of COVID vaccinations and subsequently receiving one booster dose. Her analysis grouped people by characteristics like age, migration status, country of birth, socio economic group and personal gross income to develop an Odds Ratio showing which segment within each cohort was more or less likely to get vaccinated compared with the baseline. 

Irene’s research showed that vaccination uptake varies considerably between demographic and socio-economic groups; almost 40 percent of individuals aged between 18 and 35 and the same percentage of people receiving social welfare payments did not complete the primary series of vaccinations. Other factors influencing the probability of an individual completing the primary series of vaccinations were migration background and socio-economic variables like the personal income.  

After the presentations, the speakers answered questions from the audience of over 40 academics and industry practitioners. Frank and Irene will continue refining their research over the coming months and we will update you with new findings and insights as they become available. Please follow our work on LinkedIn by clicking on the QR code below. 


RCLR LinkedIn Page QR Code Research Centre for Longevity Risk