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Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide. Over the past couple of years and with the surge of the COVID-19 pandemic, mortality from CVDs has been slightly overshadowed by those due to COVID-19, although it was during the peak of the pandemic. In the present study, patients with CVDs (CVDs; n = 41,883) were analyzed to determine which comorbidities had the largest impact on overall patient mortality due to their association with both diseases (n = 3,637). Obesity, hypertension, and diabetes worsen health in patients diagnosed positive for COVID-19. Hence, they were included in the overview of all patients with CVD. Our findings showed that 1,697 deaths were attributable to diabetes (p < 0.001) and 987 deaths to obesity (p < 0.001). Lastly, 2,499 deaths were attributable to hypertension (p < 0.001). Using logistic regression modeling, we found that diabetes (OR: 1.744, p < 0.001) and hypertension (OR: 2.179, p < 0.001) significantly affected the mortality rate of patients. Hence, having a CVD diagnosis, with hypertension and/or diabetes, seems to increase the likelihood of complications, leading to death in patients diagnosed positive for COVID-19.

Because of COVID-19, many social interactions have been continued through electronic means. This has been especially true for the educational experience, where professors and students have strived to move to the virtual classroom. The data sets presented here examined how academic professionals have dealt with the stress of the move, how they have strived to become more prepared and eventually more resilient to this change. Using an online platform, we obtained data at the end of the academic period during November and December 2020. The data analyzed three major areas: teaching anxiety and preparedness, resilience, and overall stress, by applying a modified version of the adapted COVID-Stress Scales. We statistically analyzed our data looking for important correlations, to help better understand the challenges educators have met though the pandemic, and how this can assist policy-makers in managing stress and looking for ways to enhance resilience in academic professionals.

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.