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.

翻译：儿童疫苗接种是公共卫生的基石，然而英格兰各地的疫苗接种覆盖率仍存在差异。这些差异是由地理、人口、社会经济和文化（GDSC）等多重因素复杂交互作用形成的。以往的研究大多依赖横截面数据和传统统计方法，孤立地评估单个或有限变量集。此类方法可能难以捕捉疫苗接种的动态性和多变量本质。本文对2021年至2024年间英格兰150个地区的儿童疫苗接种覆盖率进行了纵向机器学习分析。利用来自NHS记录的疫苗接种数据，我们采用层次聚类法将各地区按疫苗接种覆盖率划分为低覆盖率和高覆盖率聚类。随后训练CatBoost分类器，使用各地区的GDSC数据预测其疫苗接种聚类。最后，采用SHapley Additive exPlanations（SHAP）方法解释预测变量的重要性。该分类器在预测2021-2022、2022-2023和2023-2024年度各地区疫苗接种聚类时，分别达到了92.1%、90.6%和86.3%的高准确率。SHAP分析显示，地理、文化和人口变量——特别是乡村化程度、英语熟练度、外国出生居民比例和种族构成——是疫苗接种覆盖率最具影响力的预测因素；而社会经济变量（如贫困程度和就业状况）的重要性始终较低，在2023-2024年度尤为明显。令人惊讶的是，乡村地区显著更可能具有更高的疫苗接种率。此外，疫苗接种覆盖率较低的地区，其母语非英语、非英国出生或来自少数族裔群体的人口比例更高。