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Health, socioeconomic and genetic predictors of COVID-19 vaccination uptake: a nationwide machine-learning study
Tuomo Hartonen; Bradley Jermy; Hanna Sonajalg; Pekka Vartiainen; Kristi Krebs; Andrius Vabalas; - FinnGen; - Estonian Biobank research team; Tuija Leino; Hanna Nohynek; Jonas Sivela; Reedik Magi; Mark J Daly; Hanna M Ollila; Lili Milani; Markus Perola; Samuli Ripatti; Andrea Ganna.
Afiliação
  • Tuomo Hartonen; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
  • Bradley Jermy; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
  • Hanna Sonajalg; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
  • Pekka Vartiainen; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
  • Kristi Krebs; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
  • Andrius Vabalas; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
  • - FinnGen;
  • - Estonian Biobank research team;
  • Tuija Leino; The Finnish Institute for Health and Welfare, Helsinki, Finland
  • Hanna Nohynek; The Finnish Institute for Health and Welfare, Helsinki, Finland
  • Jonas Sivela; The Finnish Institute for Health and Welfare, Helsinki, Finland
  • Reedik Magi; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
  • Mark J Daly; Institute for Molecular Medicine Finland (FIMM)
  • Hanna M Ollila; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
  • Lili Milani; University of Tartu
  • Markus Perola; The Finnish Institute for Health and Welfare, Helsinki, Finland
  • Samuli Ripatti; FIMM
  • Andrea Ganna; Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22282213
ABSTRACT
Reduced participation in COVID-19 vaccination programs is a key societal concern. Understanding factors associated with vaccination uptake can help in planning effective immunization programs. We considered 2,890 health, socioeconomic, familial, and demographic factors measured on the entire Finnish population aged 30 to 80 (N=3,192,505) and genome-wide information for a subset of 273,765 individuals. Risk factors were further classified into 12 thematic categories and a machine learning model was trained for each category. The main outcome was uptaking the first COVID-19 vaccination dose by 31.10.2021, which has occurred for 90.3% of the individuals. The strongest predictor category was labor income in 2019 (AUC evaluated in a separate test set = 0.710, 95% CI 0.708-0.712), while drug purchase history, including 376 drug classes, achieved a similar prediction performance (AUC = 0.706, 95% CI 0.704-0.708). Higher relative risks of being unvaccinated were observed for some mental health diagnoses (e.g. dissocial personality disorder, OR=1.26, 95% CI 1.24-1.27) and when considering vaccination status of first-degree relatives (OR=1.31, 95% CI1.31-1.32 for unvaccinated mothers) We derived a prediction model for vaccination uptake by combining all the predictors and achieved good discrimination (AUC = 0.801, 95% CI 0.799-0.803). The 1% of individuals with the highest risk of not vaccinating according to the model predictions had an average observed vaccination rate of only 18.8%. We identified 8 genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor of vaccination status in an independent subset (AUC=0.612, 95% CI 0.601-0.623). Genetic effects were replicated in an additional 145,615 individuals from Estonia (genetic correlation=0.80, 95% CI 0.66-0.95) and, similarly to data from Finland, correlated with mental health and propensity to participate in scientific studies. Individuals at higher genetic risk for severe COVID-19 were less likely to get vaccinated (OR=1.03, 95% CI 1.02-1.05). Our results, while highlighting the importance of harmonized nationwide information, not limited to health, suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also those less likely to uptake COVID-19 vaccination. The results can support evidence-informed actions for COVID-19 and other areas of national immunization programs.
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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