In the United States, from the start of the COVID-19 pandemic to December 31, 2020, 341,199 deaths and more than 19,663,976 infections were recorded. Recent literature establishes that communities with poverty-related health problems, such as obesity, cardiovascular disease, diabetes, and hypertension, are more susceptible to mortality from SARS-CoV-2 infection. Additionally, controversial public health policies implemented by the nation's political leaders have highlighted the socioeconomic inequalities of minorities. Therefore, through multivariate correlational analysis using machine learning techniques and structural equations, we measure whether social determinants are associated with increased infection and death from COVID-19 disease. The PLS least squares regression analysis allowed identifying a significant impact between social determinants and COVID-19 disease through a predictive value of R2 = .916, \b{eta} = .836, p =. 000 (t-value = 66,137) shows that for each unit of increase in social determinants, COVID-19 disease increases by 91.6%. The clustering index used for correlational analysis generated a new data set comprising three groups: C1 Republicans, C2 and C3 Democrats from California, New York, Texas, and Florida. This analysis made it possible to identify the poverty variable as the main risk factor related to the high rates of infection in Republican states and a high positive correlation between the population not insured with a medical plan and high levels of virus contagion in the states of group C3. These findings explain the argument that poverty and lack of economic security put the public or private health system at risk and calamity.
翻译:在美国,从COVID-19大流行开始到2020年12月31日,记录了341 199人死亡和超过19 663 976人感染,最近文献证实,有与贫穷有关的健康问题的社区,如肥胖、心血管疾病、糖尿病和高血压等,更容易因SARS-COV-2感染而死亡;此外,国家政治领导人执行的有争议的公共卫生政策突出了少数群体的社会经济不平等,因此,通过使用机器学习技术和结构方程式进行多变量相关分析,我们衡量社会决定因素是否与COVID-19疾病感染和死亡增加有关。 PLS最低的回归分析通过预测值R2=916, 糖尿病和高血压等确定社会决定因素和死亡率之间的重大影响。 C1、C2和C3的相对风险分析显示,在C州和Cexaxal的相对贫困比率很高,C2和CFRODR的相对风险很高。