Large-scale study identifies factors related to uptake of first dose of COVID-19 vaccination

A large-scale registry study in Finland identified several factors associated with taking the first dose of COVID-19 vaccination. In particular, people with little or no work income and people with mental health or addiction problems were less likely to get vaccinated.

The study, carried out in collaboration between the University of Helsinki and the Finnish Institute of Health and Welfare, tested the association of nearly 3,000 health, demographic and socio-economic variables with the adoption of the first dose of vaccination against COVID-19 in the whole of Finland. population.

This book, which has just been published in the Nature Human behavioris the largest study to date on this topic.

The most important factors associated with a reduced likelihood of being vaccinated were lack of income from work in the year before the pandemic, mother tongue other than Finnish or Swedish, and having close relatives unvaccinated, especially the mother. Among the health-related variables, factors related to mental health and substance abuse problems associated with reduced vaccination.

The lack of income from work can be due to unemployment, illness or retirement. Additionally, among people with working income, we saw that low-income people were the least likely to vaccinate. »

Tuomo Hartonen, Postdoctoral Researcher, Institute of Molecular Medicine Finland FIMM, University of Helsinki

The study was based on data from FinRegistry. The researchers analyzed national health and population-wide registry data from the pre-pandemic period and compared it to data on vaccination status. Analyzes were limited to people aged 30 to 80.

“A particular strength of our study is that it is based on registers covering the entire Finnish population. This way we can avoid any selection bias, which is a major challenge of survey studies,” says the FIMM postdoctoral researcher Bradley Jermy.

The researchers point out that their results describe the association between the variables studied and the uptake of vaccination at the population level, but do not allow conclusions to be drawn about causal relationships. Also, the generalizability of the results outside of Finland requires further studies. However, it is clear from the results that in Finland the vaccination rate was lowest among those who were already in a vulnerable position.

Researchers have created a machine learning-based model to predict vaccination uptake

In addition to studying single predictors, the research team built a model based on machine learning to predict vaccination uptake. This prediction model allowed researchers to group individuals based on their likelihood of receiving the COVID-19 vaccine.

Approximately 90% of the total study population received at least one dose of COVID-19 vaccine. In contrast, the group with the lowest probability of being vaccinated according to the model had a vaccination rate of less than 19%.

“Our research has created a framework for using machine learning and statistical approaches to identify groups that are at higher risk of not vaccinating,” says the study’s corresponding author, Associate Professor Andrea Ganna of the FIMM.

“These results and the predictive model could be used in the future, for example in the design of vaccination campaigns,” says the principal investigator of the FinRegistry study, THL Research Professor Markus Perola.

“This study is an excellent example of the opportunities created by the FinRegistry study to investigate topical issues in a short period of time. The collaboration between THL genetics and registry researchers and FIMM scientists will help to understand the many pathways that lead to susceptibility to different diseases,” Perola continues.

The study is part of the FinRegistry project, a joint research project between the Finnish Institute for Health and Welfare (THL) and the Finnish Institute of Molecular Medicine (FIMM) at the University of Helsinki.