Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Nat Hum Behav. 2023 Jul;7(7):1069-1083. doi: 10.1038/s41562-023-01591-z. Epub 2023 Apr 20.
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.
了解与 COVID-19 疫苗接种相关的因素可以突出公共卫生系统中的问题。我们使用机器学习考虑了芬兰所有 30-80 岁人群中 2890 个健康、社会经济和人口因素以及 273765 个人的全基因组信息的影响。疫苗接种状况的最强预测因素是劳动收入和用药购买史。心理健康状况和未接种疫苗的一级亲属与疫苗接种减少有关。结合所有预测因素的预测模型实现了良好的区分度(接受者操作特征曲线下面积,0.801;95%置信区间,0.799-0.803)。预测不接种疫苗风险最高的 1%个体的实际接种率为 18.8%,而研究人群中的接种率为 90.3%。我们确定了与疫苗接种率相关的 8 个遗传位点,并得出了一个多基因评分,该评分在一个独立的子集中是一个较弱的预测指标。我们的研究结果表明,感染 COVID-19 后后果最严重风险较高的个体也不太可能接种疫苗。