Risk aversion and COVID‐19 vaccine hesitancy

Lepinteur, Anthony; Borga, Liyousew; Clark, Andrew E.; Vögele, Claus; D’Ambrosio, Conchita

doi:10.1002/hec.4693

Cited by 2 publications

(4 citation statements)

References 35 publications

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“…Higher risk aversion in vaccinated participants suggests that these participants viewed contracting COVID-19 as a larger risk and, therefore, were more likely to receive the full dose. These findings are consistent with those of a study by Lepinteur et al [ 58 ], who found that risk-averse individuals were more likely to accept the COVID-19 vaccination, indicating that the perceived risk of contracting COVID-19 was greater than any risk from the vaccine. Hudson and Montelpare [ 54 ] also found that risk aversion may promote vaccine adherence when people perceive contracting a disease as more dangerous or likely.…”

Section: Discussionsupporting

confidence: 92%

“…Relatively few studies have sought to predict COVID-19 vaccine uptake using machine learning approaches [ 8 , 59 ]. Given that a small set of studies has assessed the psychological basis that may underlie vaccine uptake and choices [ 6 , 52 , 53 , 56 , 58 , 59 , 83 ], but none have used computational cognition variables based on reward and aversion judgment to predict vaccine uptake , we sought to assess whether variables quantifying human judgment predicted vaccine uptake . This study found that 7 demographic and 15 judgment variables predicted vaccine uptake with balanced and moderate recall and specificity, moderate accuracy, high AUROC, and high precision using a BRF framework.…”

Section: Discussionmentioning

confidence: 99%

“…A number of studies have described how risk aversion and other judgment variables are important for individual vaccine choices and hesitancies [ 52 - 58 ]. Hudson and Montelpare [ 54 ] found that risk aversion may promote vaccine adherence when people perceive contracting a disease as more dangerous or likely.…”

Section: Introductionmentioning

confidence: 99%

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Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Vike,

Bari,

Stefanopoulos

et al. 2024

JMIR Public Health Surveill

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Background Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake. Objective This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction. Methods We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R2>0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake. Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction. Results Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P<.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P<.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake. Conclusions The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Vike,

Bari,

Stefanopoulos

et al. 2024

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…The third population group consists of risk-averse individuals who perceive vaccination-related adverse events to be common or serious [ 6 ]. These individuals include those with prior severe drug-, food-, or insect sting-related allergic reactions like anaphylaxis, who were found by Asperti and colleagues to be more anxious when receiving COVID-19 vaccination compared with those with a mild allergy.…”

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confidence: 99%

The Impact of COVID-19 and Non-COVID-19 Vaccinations in Special Populations

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2024

Vaccines

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Risk aversion and COVID‐19 vaccine hesitancy

Cited by 2 publications

References 35 publications

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

The Impact of COVID-19 and Non-COVID-19 Vaccinations in Special Populations

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