Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland

The authors use data on the entire Finnish population to develop a machine learning model for predicting COVID-19 vaccination uptake. Important predictors are proxies of socio-economic status, and those at high risk for COVID-19 consequences are less likely to get vaccinated. Understanding factors associated with COVID-19 vaccination can highlight issues in public health systems. Using machine learning, we considered the effects of 2,890 health, socio-economic and demographic factors in the entire Finnish population aged 30-80 and genome-wide information from 273,765 individuals. The strongest predictors of vaccination status were labour income and medication purchase history. Mental health conditions and having unvaccinated first-degree relatives were associated with reduced vaccination. A prediction model combining all predictors achieved good discrimination (area under the receiver operating characteristic curve, 0.801; 95% confidence interval, 0.799-0.803). The 1% of individuals with the highest predicted risk of not vaccinating had an observed vaccination rate of 18.8%, compared with 90.3% in the study population. We identified eight genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor in an independent subset. Our results suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also less likely to vaccinate.

Automated summary

The authors utilized data on the entire Finnish population to develop a machine learning model that predicts COVID-19 vaccination uptake. Socio-economic proxies are important predictors, and those at a higher risk of COVID-19 consequences are less likely to get vaccinated. Understanding factors associated with COVID-19 vaccination can shed light on issues in public health systems.