Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Hartonen T

PROVIDER: S-EPMC10365990 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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

Hartonen Tuomo T Jermy Bradley B Sõnajalg Hanna H Vartiainen Pekka P Krebs Kristi K Vabalas Andrius A Leino Tuija T Nohynek Hanna H Sivelä Jonas J Mägi Reedik R Daly Mark M Ollila Hanna M HM Milani Lili L Perola Markus M Ripatti Samuli S Ganna Andrea A

Nature human behaviour 20230420 7

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 wit ...[more]

PMID: 37081098

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Project description:Socio-economic conditions and social attitudes are known to represent epidemiological determinants. Credible knowledge on socio-economic driving factors of the COVID-19 epidemic is still incomplete. Based on linear random effects regression, an ecological model is derived to estimate COVID-19 incidence in German rural/urban districts from local socio-economic factors and popularity of political parties in terms of their share of vote. Thereby, records provided by Germany's public health institute (Robert Koch Institute) of weekly notified 7-day incidences per 100,000 inhabitants per district from the outset of the epidemic in 2020 up to December 1, 2021, are used to construct the dependent variable. Local socio-economic conditions including share of votes, retrieved from the Federal Statistical Office of Germany, have been used as potential risk factors. Socio-economic parameters like per capita income, proportions of protection seekers and social benefit claimants, and educational level have negligible impact on incidence. To the contrary, incidence significantly increases with population density and we observe a strong association with vote shares. Popularity of the right-wing party Alternative for Germany (AfD) bears a considerable risk of increasing COVID-19 incidence both in terms of predicting the maximum incidences during three epidemic periods (alternatively, cumulative incidences over the periods are used to quantify the dependent variable) and in a time-continuous sense. Thus, districts with high AfD popularity rank on top in the time-average regarding COVID-19 incidence. The impact of the popularity of the Free Democrats (FDP) is markedly intermittent in the course of time showing two pronounced peaks in incidence but also occasional drops. A moderate risk emanates from popularities of the Green Party (GRÜNE) and the Christian Democratic Union (CDU/CSU) compared to the other parties with lowest risk level. In order to effectively combat the COVID-19 epidemic, public health policymakers are well-advised to account for social attitudes and behavioral patterns reflected in local popularities of political parties, which are conceived as proper surrogates for these attitudes. Whilst causal relations between social attitudes and the presence of parties remain obscure, the political landscape in terms of share of votes constitutes at least viable predictive "markers" relevant for public health policy making.

Dataset Information

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

Publications

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

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