Predictive modeling and explainable AI in addressing disparities in adolescent vaccination uptake: Insights from the...

Vaccination uptake refers to the proportion of individuals within a specific population who receive recommended vaccinations within a given timeframe. While previous studies have primarily focused on describing trends in adolescent vaccination coverage, this study introduces advanced predictive modeling and explainable AI to enhance immunization strategies.

The research team explored the NIS-Teen (National Immunization Survey-Teen) with 27,565 records. After much preprocessing, exploratory data analysis (EDA), and model development, Linear Regression, Balanced Random Forest, XGBoost, and Support Vector Regression were performed on the data. Outputs of models were explained employing SHAP analysis to discern key predictors, such as vaccine type, dose history, geography, and the survey year.

Our analysis showed that geographic location, including state and HHS region, is the biggest predictor of adolescent vaccination coverage. This finding points to significant differences between states and regions. After geography, the type of vaccine and the survey year were also important factors. This highlights the specific challenges related to certain vaccines, like HPV, and how coverage trends change over time. Other key factors included poverty level and race or ethnicity. This underscores how social and demographic inequalities affect vaccination rates.

Adolescent vaccination coverage in the U.S. varies based on different influences, mainly geographic location and vaccine type. These findings underscore the urgent need to move beyond one-size-fits-all strategies and toward geographically-targeted, vaccine-specific public health campaigns that address the socioeconomic and demographic barriers identified in this study. By focusing on these key drivers, public health officials can better allocate resources to close critical vaccination gaps and protect adolescent health.