Drivers of herpes zoster vaccine hesitancy in adults aged 50 and above: A machine learning approach

Herpes zoster (HZ) poses a growing public health challenge among adults aged 50 and above, with vaccine hesitancy being a major barrier to improving immunization rates. Understanding the factors driving HZ vaccine hesitancy is essential for developing strategies to enhance vaccination uptake in this population. This study aims to identify and analyze the key determinants of HZ vaccine hesitancy using machine learning models, providing insights to guide targeted educational strategies and interventions.

A cross-sectional study was conducted from April to August 2024, collecting data from individuals aged 50 and older at four community health service centers in Shanghai. Data collection included demographic information, HZ disease and vaccine knowledge, the 5C scale, and the vaccine health literacy. We employed LASSO regression for variable selection, followed by analysis of key variables using four machine learning algorithms: logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost). Model performance was evaluated using AUC and calibration plots. Shapley Additive Explanations (SHAP) were used to interpret and identify key predictors.

A total of 1152 participants (mean age 66.1 ± 8.8 years; 49.7 % male) were included, with 73.95 % reporting hesitancy toward the HZ vaccine. Ten out of 21 features were selected for modeling. XGBoost model showed the best performance with an AUC of 0.960 (95 % CI: 0.937-0.983). SHAP analysis identified confidence, vaccine literacy, complacency, disease knowledge, and calculation as the primary predictors.

The SHAP-XGBoost model showed strong predictive performance for herpes zoster vaccine hesitancy, with vaccine literacy and the 5C psychological antecedents identified as key predictors. This tool can inform targeted health interventions, and future work may integrate community databases for large-scale identification and tailored group strategies.