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Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland.
Hartonen, Tuomo; Jermy, Bradley; Sõnajalg, Hanna; Vartiainen, Pekka; Krebs, Kristi; Vabalas, Andrius; Leino, Tuija; Nohynek, Hanna; Sivelä, Jonas; Mägi, Reedik; Daly, Mark; Ollila, Hanna M; Milani, Lili; Perola, Markus; Ripatti, Samuli; Ganna, Andrea.
Afiliação
  • Hartonen T; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Jermy B; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Sõnajalg H; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
  • Vartiainen P; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Krebs K; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
  • Vabalas A; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Leino T; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Nohynek H; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Sivelä J; Finnish Institute for Health and Welfare, Helsinki, Finland.
  • Mägi R; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
  • Daly M; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Ollila HM; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Milani L; Massachusetts General Hospital, Cambridge, MA, USA.
  • Perola M; Harvard Medical School, Cambridge, MA, USA.
  • Ripatti S; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
  • Ganna A; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nat Hum Behav ; 7(7): 1069-1083, 2023 07.
Article em En | MEDLINE | ID: mdl-37081098
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Nat Hum Behav Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Finlândia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Europa Idioma: En Revista: Nat Hum Behav Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Finlândia