ArticleFrontiers in public health2026
Developing and validating machine learning models to predict vaccine hesitancy and literacy among adults in the United States.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Vaccine hesitancy and literacy are multifaceted and context-specific phenomena that affect vaccination uptake. Comprehensively examining the simultaneous effects of various factors influencing vaccine hesitancy and literacy remains a challenge. This study aimed to better understand key determinants of adults' vaccination decision-making, regarding both their own vaccinations and those of their children, using different machine learning algorithms to analyze survey data. Methods: A cross-sectional survey of US adults was conducted in 2022. Participants were categorized based on whether they had children under age 18 ("parents," Results: Among parents making vaccination decisions for their children, the random forest model achieved the highest predictive performance for vaccine hesitancy (F1 = 0.86, AUROC = 93.0%), and the XGBoost model performed best when predicting vaccine literacy (F1 = 0.64, AUPRC = 81.3%). Based on these models, the belief that "there is no need for my child to get vaccinated because everybody else does" emerged as the strongest predictor of hesitancy among parents, whereas low familiarity with the pediatric vaccination schedule was the main predictor of low literacy. Among adults making vaccination decisions for themselves, the XGBoost outperformed other models for both vaccine hesitancy (F1 = 0.77, AUROC = 90.3%) and vaccine literacy (F1 = 0.80, AUPRC = 86.0%). According to this model, having received an influenza vaccine was the strongest predictor of non-hesitancy among adults, and low familiarity with the adult vaccination schedule was the strongest predictor of low literacy. Conclusion: This study demonstrated the effectiveness of machine learning approaches in analyzing robust survey data. These models identified key determinants of vaccine hesitancy and literacy, offering valuable insights into the behavioral and informational factors influencing vaccination decisions among US adults.
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