A comparison of clinical outcomes between vaccinated and vaccine-naive patients of COVID-19, in four tertiary care hospitals of Kerala, South India

The problem considered: This multi-centric study analyzed data of COVID-19 patients and compared differences in symptomatology, management, and outcomes between vaccinated and vaccine-naive patients. Methods: All COVID-19 positive individuals treated as an in-or out-patient from the 1stMarch to 15th May 2021 in four selected study sites were considered for the study. Treatment details, symptoms, and clinical course were obtained from hospital records. Chi-square was used to test the association of socio-demographic and treatment variables with the vaccination status and binary logistic regression were used to obtain the odds ratio with a 95% confidence interval. Results: The analysis was of 1446 patients after exclusion of 156 with missing data of which males were 57.3% and females 42.7%. 346 were vaccinated; 189 received one dose and 157 both doses. Hospitalization was more in vaccinated (38.2% vs 27.4%); ICU admissions were less in vaccinated (3.5% vs 7.1%). More vaccinated were symptomatic (OR = 1.5); half less likely to be on non-invasive ventilation (OR = 0.5) while vaccine naive patients had 4.21 times the risk of death. Conclusion: Severe infection, duration of hospital stays, need for ventilation and death were significantly less among vaccinated when compared with vaccine naive patients

Get screened: a pragmatic randomized controlled trial to increase mammography and colorectal cancer screening in a large, safety net practice

Abstract Background Most randomized controlled trials of interventions designed to promote cancer screening, particularly those targeting poor and minority patients, enroll selected patients. Relatively little is known about the benefits of these interventions among unselected patients. Methods/Design "Get Screened" is an American Cancer Society-sponsored randomized controlled trial designed to promote mammography and colorectal cancer screening in a primary care practice serving low-income patients. Eligible patients who are past due for mammography or colorectal cancer screening are entered into a tracking registry and randomly assigned to early or delayed intervention. This 6-month intervention is multimodal, involving patient prompts, clinician prompts, and outreach. At the time of the patient visit, eligible patients receive a low-literacy patient education tool. At the same time, clinicians receive a prompt to remind them to order the test and, when appropriate, a tool designed to simplify colorectal cancer screening decision-making. Patient outreach consists of personalized letters, automated telephone reminders, assistance with scheduling, and linkage of uninsured patients to the local National Breast and Cervical Cancer Early Detection program. Interventions are repeated for patients who fail to respond to early interventions. We will compare rates of screening between randomized groups, as well as planned secondary analyses of minority patients and uninsured patients. Data from the pilot phase show that this multimodal intervention triples rates of cancer screening (adjusted odds ratio 3.63; 95% CI 2.35 - 5.61). Discussion This study protocol is designed to assess a multimodal approach to promotion of breast and colorectal cancer screening among underserved patients. We hypothesize that a multimodal approach will significantly improve cancer screening rates. The trial was registered at Clinical Trials.gov NCT00818857http://deepblue.lib.umich.edu/bitstream/2027.42/78264/1/1472-6963-10-280.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78264/2/1472-6963-10-280.pdfPeer Reviewe

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.publishedVersionPeer reviewe