Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Amhara, Ethiopia
BMJ Open. 2024 Nov 14;14(11):e089764. doi: 10.1136/bmjopen-2024-089764.
Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from 2021 to 2023. The WHO has identified being unvaccinated against measles as a major factor driving this resurgence of cases and deaths. Consequently, this study aimed to apply robust machine learning algorithms to predict the key factors contributing to measles vaccination dropout.
This study utilised data from the 2016 Ethiopian Demographic and Health Survey to evaluate measles vaccination dropout. Eight supervised machine learning algorithms were implemented: eXtreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting, Support Vector Machine, Decision Tree, Naïve Bayes, K-Nearest Neighbours and Logistic Regression. Data preprocessing and model development were performed using R language V.4.2.1. The predictive models were evaluated using accuracy, precision, recall, F1-score and area under the curve (AUC). Unlike previous studies, this research utilised Shapley values to interpret individual predictions made by the top-performing machine learning model.
The XGBoost algorithm surpassed all classifiers in predicting measles vaccination dropout (Accuracy and AUC values of 73.9% and 0.813, respectively). The Shapley Beeswarm plot displayed how each feature influenced the best model's predictions. The model predicted that the younger mother's age, religion-Jehovah/Adventist, husband with no and mother with primary education, unemployment of the mother, residence in the Oromia and Somali regions, large family size and older paternal age have a strong positive impact on the measles vaccination dropout.
The measles dropout rate in the country exceeded the recommended threshold of <10%. To tackle this issue, targeted interventions are crucial. Public awareness campaigns, regular health education and partnerships with religious institutions and health extension workers should be implemented, particularly in the identified underprivileged regions. These measures can help reduce measles vaccination dropout rates and enhance overall coverage.
尽管在埃塞俄比亚已经有了一种安全有效的麻疹疫苗，但该国仍经历了反复且显著的麻疹疫情爆发，确诊病例数在 2021 年至 2023 年期间几乎增加了五倍。世界卫生组织已确定未接种麻疹疫苗是导致病例和死亡人数增加的主要因素。因此，本研究旨在应用强大的机器学习算法来预测导致麻疹疫苗接种失败的关键因素。
本研究利用了 2016 年埃塞俄比亚人口与健康调查的数据来评估麻疹疫苗接种失败情况。共实施了 8 种监督机器学习算法：极端梯度提升（XGBoost）、随机森林、梯度提升、支持向量机、决策树、朴素贝叶斯、K 近邻和逻辑回归。数据预处理和模型开发均使用 R 语言 V.4.2.1 完成。使用准确性、精度、召回率、F1 分数和曲线下面积（AUC）来评估预测模型。与以往的研究不同，本研究使用 Shapley 值来解释表现最佳的机器学习模型的个体预测。
XGBoost 算法在预测麻疹疫苗接种失败方面优于所有分类器（准确性和 AUC 值分别为 73.9%和 0.813）。Shapley Beeswarm 图显示了每个特征如何影响最佳模型的预测。该模型预测，母亲年龄较小、宗教为耶和华见证人/基督复临安息日会、丈夫无业、母亲接受过小学教育、母亲失业、居住在奥罗米亚和索马里地区、家庭规模较大以及父亲年龄较大，这些因素对麻疹疫苗接种失败有很强的正向影响。
该国的麻疹疫苗接种失败率超过了<10%的推荐阈值。为了解决这个问题，有针对性的干预措施至关重要。应开展公众宣传活动，定期进行健康教育，并与宗教机构和卫生推广工作者建立伙伴关系，特别是在确定的贫困地区。这些措施有助于降低麻疹疫苗接种失败率，提高整体覆盖率。
