International Vaccination Center at Santa Cruz de Tenerife, Spanish Ministry of Health, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain.
University of La Laguna, Department of Economics, Spain; University of La Laguna, Research Center of Social Inequality and Governance (CEDESOG), Spain; University of La Laguna, IUDR, Spain.
Travel Med Infect Dis. 2023 Jul-Aug;54:102607. doi: 10.1016/j.tmaid.2023.102607. Epub 2023 Jun 22.
The reactivation of international travel in 2021 has created a new scenario in which the profile of the traveler to medium-high health risk areas may well have changed. However, few studies have analyzed this new profile since the reopening of borders in that year.
We designed an ad hoc questionnaire that was administered face-to-face by our medical team during appointments with 330 travelers in the second half of 2021. Information was collected on the following topics: sociodemographic and socioeconomic status; type of travel and previous travel experience; health status and risk perception (of COVID-19 and tropical infectious diseases). Using all features simultaneously, an unsupervised machine learning approach (k-means) is implemented to characterize groups of travelers. Pairwise chi-squared tests were performed to identify key features that showed statistically significant differences between clusters.
The travelers were clustered into seven groups. We associated the clusters with different intensities of perceived risk of acquiring COVID-19 and tropical infectious diseases on the trip. The perceived risk of both diseases was low in the group "middle or lower middle class young inexperienced male tourist" but high in the group "middle or lower middle-class young with children inexperienced business traveler".
Broadening our knowledge of the profiles of travelers to intermediate-high health risk areas would help to tailor the health advice provided by practitioners to their characteristics and type of travel. In a changing health context, the k-means approach supposes a flexible statistical method that calculates travelers' profiles and can be easily adapted to process new information.
2021 年国际旅行的重新开放创造了一个新的情景，前往中高健康风险地区的旅行者的特征可能已经发生了变化。然而，自那年边境重新开放以来，很少有研究分析这种新的特征。
我们设计了一个专门的问卷，由我们的医疗团队在 2021 年下半年的 330 名旅行者预约时进行面对面的调查。收集的信息包括以下主题：社会人口学和社会经济学地位；旅行类型和以往旅行经验；健康状况和风险认知（COVID-19 和热带传染病）。使用所有特征同时，实施无监督机器学习方法（k-均值）来描述旅行者群体。进行两两卡方检验，以确定显示聚类之间存在统计学显著差异的关键特征。
旅行者被分为七个群体。我们将这些群体与旅行中对 COVID-19 和热带传染病的感知风险的不同强度联系起来。在“中产阶级或中下阶层年轻无经验的男性游客”群体中，对两种疾病的感知风险较低，但在“中产阶级或中下阶层年轻有子女无经验的商务旅行者”群体中，对两种疾病的感知风险较高。
拓宽对前往中高健康风险地区旅行者特征的了解，有助于根据旅行者的特点和旅行类型为他们提供量身定制的健康建议。在不断变化的健康环境中，k-均值方法是一种灵活的统计方法，可以计算旅行者的特征，并可以轻松适应处理新信息。
