Bikaki Athina, Machiorlatti Michael, Clark Loren Cliff, Robledo Candace A, Kakadiaris Ioannis A
Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX 77204, USA.
Department of Population Health and Biostatistics, University of Texas at Rio Grande Valley, Harlingen, TX 78550, USA.
Vaccines (Basel). 2022 Aug 9;10(8):1282. doi: 10.3390/vaccines10081282.
Hispanic communities have been disproportionately affected by economic disparities. These inequalities have put Hispanics at an increased risk for preventable health conditions. In addition, the CDC reports Hispanics to have 1.5× COVID-19 infection rates and low vaccination rates. This study aims to identify the driving factors for COVID-19 vaccine hesitancy of Hispanic survey participants in the Rio Grande Valley. Our analysis used machine learning methods to identify significant associations between medical, economic, and social factors impacting the uptake and willingness to receive the COVID-19 vaccine. A combination of three classification methods (i.e., logistic regression, decision trees, and support vector machines) was used to classify observations based on the value of the targeted responses received and extract a robust subset of factors. Our analysis revealed different medical, economic, and social associations that correlate to other target population groups (i.e., males and females). According to the analysis performed on males, the Matthews correlation coefficient (MCC) value was 0.972. An MCC score of 0.805 was achieved by analyzing females, while the analysis of males and females achieved 0.797. Specifically, several medical, economic factors, and sociodemographic characteristics are more prevalent in vaccine-hesitant groups, such as asthma, hypertension, mental health problems, financial strain due to COVID-19, gender, lack of health insurance plans, and limited test availability.
西班牙裔社区受到经济差距的影响尤为严重。这些不平等使西班牙裔面临可预防健康状况的风险增加。此外,美国疾病控制与预防中心报告称,西班牙裔的新冠病毒感染率是其他群体的1.5倍,且疫苗接种率较低。本研究旨在确定里奥格兰德河谷地区西班牙裔调查参与者对新冠疫苗犹豫的驱动因素。我们的分析使用机器学习方法来确定影响新冠疫苗接种和接种意愿的医学、经济和社会因素之间的显著关联。结合三种分类方法(即逻辑回归、决策树和支持向量机),根据收到的目标反应值对观察结果进行分类,并提取出一组可靠的因素子集。我们的分析揭示了与其他目标人群组(即男性和女性)相关的不同医学、经济和社会关联。根据对男性进行的分析,马修斯相关系数(MCC)值为0.972。通过对女性的分析,MCC得分为0.805,而对男性和女性的综合分析得分为0.797。具体而言,一些医学、经济因素和社会人口特征在对疫苗犹豫的群体中更为普遍,如哮喘、高血压、心理健康问题、新冠疫情导致的经济压力、性别、缺乏医疗保险计划以及检测机会有限。