Department of Industrial Engineering, Tel Aviv University, 55 Haim Levanon St, Tel Aviv, Israel.
MaccabiTech Institute of Research and Innovation, 4 Kaufmann St, Tel Aviv, Israel.
BMC Public Health. 2020 Feb 12;20(1):222. doi: 10.1186/s12889-020-8327-3.
Seasonal influenza vaccination coverage remains suboptimal in most developed countries, despite longstanding recommendations of public health organizations. The individual's decision regarding vaccination is located at the core of non-adherence. We analyzed large-scale data to identify personal and social behavioral patterns for influenza vaccination uptake, and develop a model to predict vaccination decision of individuals in an upcoming influenza season.
We analyzed primary data from the electronic medical records of a retrospective cohort of 250,000 individuals between the years 2007 and 2017, collected from 137 clinics. Individuals were randomly sampled from the database of Maccabi Healthcare Services. Maccabi's clients are representative of the Israeli population, reflect all demographic, ethnic, and socioeconomic groups and levels. We used several machine-learning models to predict whether a patient would get vaccinated in the future. Models' performance was evaluated based on the area under the ROC curve.
The vaccination decision of an individual can be explained in two dimensions, Personal and social. The personal dimension is strongly shaped by a "default" behavior, such as vaccination timing in previous seasons and general health consumption, but can also be affected by temporal factors such as respiratory illness in the prior year. In the social dimension, a patient is more likely to become vaccinated in a given season if at least one member of his family also became vaccinated in the same season. Vaccination uptake was highly assertive with age, socioeconomic score, and geographic location. An XGBoost-based predictive model achieved an ROC-AUC score of 0.91 with accuracy and recall rates of 90% on the test set. Prediction relied mainly on the patient's individual and household vaccination status in the past, age, number of encounters with the healthcare system, number of prescribed medications, and indicators of chronic illnesses.
Our ability to make an excellent prediction of the patient's decision sets a major step toward personalized influenza vaccination campaigns, and will help shape the next generation of targeted vaccination efforts.
尽管公共卫生组织长期以来一直建议接种季节性流感疫苗，但在大多数发达国家，疫苗接种覆盖率仍然不理想。个人对疫苗接种的决定是不遵守规定的核心。我们分析了大规模数据，以确定个人和社会行为模式，以提高流感疫苗接种率，并开发一种模型来预测个人在即将到来的流感季节的疫苗接种决策。
我们分析了来自 2007 年至 2017 年 137 家诊所的 25 万名个体的电子病历中的原始数据。个体是从 Maccabi 医疗保健服务的数据库中随机抽取的。Maccabi 的客户代表了以色列人口，反映了所有的人口统计学、种族和社会经济群体和水平。我们使用了几种机器学习模型来预测患者未来是否会接种疫苗。根据 ROC 曲线下的面积评估模型的性能。
个体的疫苗接种决策可以用两个维度来解释，个人和社会。个人维度主要由“默认”行为决定，例如以前季节的接种时间和一般健康消费，但也会受到前一年呼吸道疾病等时间因素的影响。在社会维度，如果患者的至少一个家庭成员在同一季节也接种了疫苗，他更有可能在特定季节接种疫苗。疫苗接种率随着年龄、社会经济评分和地理位置的增加而显著增加。基于 XGBoost 的预测模型在测试集上的 ROC-AUC 评分为 0.91，准确率和召回率为 90%。预测主要依赖于患者过去的个人和家庭疫苗接种状况、年龄、与医疗保健系统的接触次数、开处方的药物数量以及慢性病指标。
我们能够对患者的决策进行出色的预测，这是迈向个性化流感疫苗接种运动的重要一步，并将有助于塑造下一代有针对性的疫苗接种工作。
