Is it possible to predict COVID-19 vaccination status prior to the existence and availability of COVID-19 vaccines? Here, we present a logistic model by regressing decisions to vaccinate in late 2021 on lagged sociodemographic, health, social, and political indicators from 2019 in a sample of New Zealand adults aged between 18 and 94 (M-age = 52.92, SD = 14.10; 62.21% women; N = 5324). We explain 31% of the variance in decision making across New Zealand. Significant predictors of being unvaccinated were being younger, more deprived, reporting less satisfaction with general practitioners, lower levels of neuroticism, greater levels of subjective health and meaning in life, higher distrust in science and in the police, lower satisfaction in the government, as well as political conservatism. Additional cross-sectional models specified using the same, and additional COVID-19-specific factors are also presented. These findings reveal that vaccination decisions are neither artefacts of context nor chance, but rather can be predicted in advance of the availability of vaccines.
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Ho Chi Minh City Open Univ, Fac Econ & Publ Management, Ho Chi Minh City, VietnamHo Chi Minh City Open Univ, Fac Econ & Publ Management, Ho Chi Minh City, Vietnam
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Population Modelling group, National Institute of Water and Atmospheric Research, Wellington, New ZealandPopulation Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
Datta, Samik
Vattiato, Giorgia
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School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
Manaaki Whenua, Lincoln, New ZealandPopulation Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
Vattiato, Giorgia
Maclaren, Oliver J.
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Department of Engineering Science, University of Auckland, Auckland, New ZealandPopulation Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
Maclaren, Oliver J.
Hua, Ning
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Precision Driven Health, Auckland, New ZealandPopulation Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand
Hua, Ning
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Sporle, Andrew
Plank, Michael J.
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School of Mathematics and Statistics, University of Canterbury, Christchurch, New ZealandPopulation Modelling group, National Institute of Water and Atmospheric Research, Wellington, New Zealand