识别加拿大未接种疫苗的个人：一个预测模型
Identifying Unvaccinated Individuals in Canada: A Predictive Model
摘要：
最近，媒体和公共卫生官员越来越意识到反疫苗情绪的上升。疫苗接种对免疫个体以及通过群体免疫对公众具有许多健康益处。鉴于人们选择不接种常规疫苗导致的免疫可预防疾病的增加，我们确定加拿大的健康数据可以识别60岁以上选择不接种疫苗的人（80.1%阴性预测值）和60岁以下最近接种疫苗的人（96.4%阳性预测值）。使用2009-2014年加拿大社区健康调查（CCHS），概率模型确定了与流感疫苗接种结果最常见相关的变量。在1，381个变量中，选择了47个具有最显著边际效应的变量，包括疾病的存在（例如糖尿病和癌症），行为特征（例如吸烟和运动），接触医疗系统（例如个人是否定期检查）和一个人的生活状况（例如家中有年幼的孩子）。然后，这些变量用于生成随机森林分类模型，在2009-2013数据集上进行训练，并在2014数据集上进行测试。我们在两个最终模型之间实现了87.8%的总体准确率，每个模型使用25个分类树，有界深度为20个节点，从所有47个变量中随机选择。有了这两项拟议的政策，可以利用这一模型来有效地分配疫苗接种促进工作。此外，它可以应用于未来的调查，只需要3.6%的变量在CCHS成功的预测。
英文摘要：
Recently, the media and public health officials have become increasingly aware of the rise in anti-vaccine sentiment. Vaccinations have numerous health benefits for immunized individuals as well as for the general public through herd immunity. Given the rise in immunization-preventable diseases, a consequence of people opting out of their routine vaccinations, we determined that Canadian health data can identify individuals over the age of 60 who chose not to get vaccinated (80.1% negative predictive value) and individuals under the age of 60 who have recently been vaccinated (96.4% positive predictive value). Using the 2009-2014 Canadian Community Health Surveys (CCHS), a probit model identified the variables that were most commonly associated with flu vaccination outcomes. Of 1,381 variables, 47 with the most significant marginal effects were selected, including the presence of diseases (e.g. diabetes and cancer), behavioral characteristics (e.g. smoking and exercise), exposure to the medical system (e.g. whether the individual gets a regular check-up), and a person's living situation (e.g. having young children in the household). These variables were then used to generate a Random Forest classification model, trained on the 2009-2013 dataset, and tested on the 2014 dataset. We achieved an overall accuracy of 87.8% between the two final models, each using 25 classification trees with bounded depth of 20 nodes, randomly selecting from all 47 variables. With the two proposed policies, this model can be leveraged to efficiently allocate vaccination promotion efforts. Additionally, it can be applied to future surveys, only requiring 3.6% of the variables in the CCHS for successful prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
