Large study identifies factors associated with uptake of first COVID-19 vaccine

A large registry study in Finland identified several factors associated with uptake of the first dose of the COVID-19 vaccine. In particular, those with low or no work income and those with mental health or substance abuse problems were less likely to be vaccinated.

The study, conducted jointly by the University of Helsinki and the Finnish Institute of Health and Welfare, tested the uptake of the first COVID-19 vaccine across Finland and the association of approximately 3000 health, demographic and socioeconomic variables. bottom. population.

This work, nature human behaviorthe largest study to date on this topic.

The most important factors associated with reduced likelihood of vaccination were lack of work income in the year before the pandemic, mother tongues other than Finnish or Swedish, non-vaccination of close relatives, especially mothers. . Among health-related variables, factors related to mental health and substance abuse problems associated with reduced immunization.

A lack of working income may be due to unemployment, illness, or retirement. Furthermore, among individuals with working income, low-income individuals were found to be the least likely to be vaccinated. ”

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

This research is based on FinRegistry data. Researchers analyzed population-wide national health and population registry data from the pre-pandemic period and compared these with vaccination status data. The analysis was limited to those aged 30 to her 80.

“A particular strength of our study is that it is based on a registry that covers the entire Finnish population. In this way, we avoid all selection biases, which is a major challenge for research studies,” he said. said Bradley Jermy, FIMM Postdoctoral Fellow.

The researchers emphasize that their results explain the association between the variables studied and vaccination coverage at the population level, but they cannot draw any conclusions about causality. Further research is needed to generalize the findings outside of Finland. However, the results show that Finland had the lowest vaccination coverage among already vulnerable populations.

Researchers created a machine learning-based model to predict vaccine uptake

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

Approximately 90% of all study participants received at least one COVID-19 vaccination. In contrast, vaccination coverage was less than 19% for the group least likely to be vaccinated based on the model.

“Our study used machine learning and statistical approaches to create a framework for identifying groups at high risk of not being vaccinated,” said Andrea of ​​the FIMM, corresponding author of the study. Associate Professor Ganna said.

“These results and predictive models may have future uses, for example in designing vaccination campaigns,” said Markus Perola, principal investigator of the FinRegistry study and research professor at THL.

“This study is a great example of what the FinRegistry study has created to investigate a very topical issue in a short period of time. It helps us understand the many pathways that lead to susceptibility to ,” Perola continues.

This research is part of the FinRegistry project, a collaborative research project between the Finnish Institute for Health and Human Services (THL) and the Finnish Institute for Molecular Medicine (FIMM) at the University of Helsinki.