Predictors of Childhood Vaccination Uptake in England: An Explainable Machine Learning Analysis of Longitudinal Regional Data (2021-2024)

2025-04-18

AI Summary

This study addresses regional disparities in childhood vaccination coverage across England. Moving beyond conventional cross-sectional analyses, it pioneers a national-scale longitudinal investigation integrating vaccination records from 150 local authorities (2021–2024) with multidimensional Geospatial, Demographic, Socioeconomic, and Cultural (GDSC) indicators. Methodologically, it innovatively combines hierarchical clustering, CatBoost classification, and SHAP-based interpretability analysis. Results identify four key predictors—rurality, English language proficiency, proportion of foreign-born residents, and ethnic composition—whose predictive importance surpasses traditional indicators such as poverty. Notably, the analysis reveals a counterintuitive pattern: higher vaccination coverage in rural areas. The model achieves high accuracy across successive annual cohorts: 92.1% (2021–2022), 90.6% (2022–2023), and 86.3% (2023–2024). These findings provide actionable, data-driven evidence to inform targeted public health interventions and policy design.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Childhood vaccination is a cornerstone of public health, yet disparities in vaccination coverage persist across England. These disparities are shaped by complex interactions among various factors, including geographic, demographic, socioeconomic, and cultural (GDSC) factors. Previous studies mostly rely on cross-sectional data and traditional statistical approaches that assess individual or limited sets of variables in isolation. Such methods may fall short in capturing the dynamic and multivariate nature of vaccine uptake. In this paper, we conducted a longitudinal machine learning analysis of childhood vaccination coverage across 150 districts in England from 2021 to 2024. Using vaccination data from NHS records, we applied hierarchical clustering to group districts by vaccination coverage into low- and high-coverage clusters. A CatBoost classifier was then trained to predict districts' vaccination clusters using their GDSC data. Finally, the SHapley Additive exPlanations (SHAP) method was used to interpret the predictors' importance. The classifier achieved high accuracies of 92.1, 90.6, and 86.3 in predicting districts' vaccination clusters for the years 2021-2022, 2022-2023, and 2023-2024, respectively. SHAP revealed that geographic, cultural, and demographic variables, particularly rurality, English language proficiency, the percentage of foreign-born residents, and ethnic composition, were the most influential predictors of vaccination coverage, whereas socioeconomic variables, such as deprivation and employment, consistently showed lower importance, especially in 2023-2024. Surprisingly, rural districts were significantly more likely to have higher vaccination rates. Additionally, districts with lower vaccination coverage had higher populations whose first language was not English, who were born outside the UK, or who were from ethnic minority groups.

Problem

• Identifying key predictors of childhood vaccination disparities in England
• Analyzing geographic, demographic, socioeconomic, and cultural factors' impact
• Using machine learning to predict and explain vaccination coverage patterns

Innovation

• Longitudinal machine learning analysis of vaccination data
• Hierarchical clustering for district coverage grouping
• SHAP method for predictor importance interpretation