ABSTRACT
Introduction: COVID-19 is an acute viral infection that mainly affects the respiratory system leading to mortality. Therefore, positive COVID-19 patients may require intensive care unit (ICU) admission in severe cases. Many factors are thought to exacerbate the symptoms of COVID-19 resulting in increased mortality, smoking, hypertension and type 2 diabetes mellitus (T2DM) are on the top of these factors. Objective(s): This study was designed to detect the strength of association between death rate among COVID-19 ICU admitted patients and being smokers, type 2 diabetes mellitus (T2DM), or hypertension. Material(s) and Method(s): A cross-sectional study was conducted. A sample of 302 patients included all COVID-19 patients admitted to the ICU of the central hospital in Amman, Jordan, in July 2021. Result(s) and Conclusion(s): Of the total 302 patients, 171 were smokers. the death rate among smokers (67.25%) was significantly higher than (53.43%) among non-smokers X2= 5.966, p=0.0145. We found that 118 cases had T2DM. the death rate among patient with T2DM (62.71%) was insignificantly higher than (60.32%) among non-diabetic patients X2= 0.172, p=0.67. Of the 130 COVID-19 patients with hypertension, the death rate was (70.76%) significantly higher than (54.1%) among those without hypertension X2= 8.70, p=0.0031. Moreover, by using the OR and 95% CI. Interestingly, we found that smokers were almost two times significantly more prone to death than nonsmokers (OR=1.79, 95%CI:1.12 - 2.86, p=0.015). Also, patients with hypertension were two times significantly more prone to death than normotensive patients, (OR=2.06, 95% CI: 1.27 - 3.33, p=0.0034). On the other hand, T2DM showed an insignificant risk factor (OR=1.11) for death. 95% CI: 0.687- 1.78, p=0.6780. Smoking and hypertension act as significant risk factors to increase mortality in COVID-19 patients.
ABSTRACT
This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.