Using Telehealth through the Mobile Channel to Reduce Health Inequality and Disparity: A Real-World Study Examining Patients with Type 2 Diabetes Mellitus

Background and Aims: COVID-19 led to digital acceleration, raising alarms that minorities (Black/Hispanic) would be left further behind. Did patients with type 2 diabetes (PwT2D) who rely on routine care change their use of health IT resources? Methods: Using longitudinal patient portal usage data of 55,548 PwT2Dfroman urban hospital in the U.S.,we examinedmobile-vsdesktop internet access before-and-after COVID-19. We constructed threemodels using the panel dataset: pooled Ordinary Least Squares (OLS), random effect (RE), and fixed effect (FE). Result(s): The interaction of COVID-PeriodxMinority across the three models (OLS/RE/FE) was significant and showed racial disparity is increasing for desktop use (beta= -0.052/-0.053/-0.054) and decreasing for mobile use (beta = 0.026/0.025/0.025). COVID- 19 has reduced the gap by 34% (0.025/0.073) according to the RE model. Table 1 shows that racial disparity shrinkage is largely driven by the use of mobile communication. Conclusion(s): COVID-19 is a natural experiment providing the opportunity to investigate whether accelerated digitization impacted health inequality and disparity among PwT2D. The effect is mostly driven by mobile device access and cannot be explained by pre-COVID-19 trends. First, COVID-19 has been cited as a ''great magnifier'' of pre-existing racial inequality in health;however, telehealth can become a ''great equalizer'' for reducing inequity. Second, in the U.S., much effort in combating the digital divide has focused on the broadband connectivity gap;the transformative potential of mobile health is overlooked. Third, the lack of access to patient portals has disadvantaged PwT2D minorities;so long as they have access, they can ''catch up.'' NIH Award 5UL1TR001425-03.

Chronic noncommunicable diseases and COVID-19: How they both interact

This white paper will summarize the key topics, outcomes, and recommendations from the Canada-India Healthcare Summit 2021 COVID-19 Pandemic Response and Initiatives sessions held on May 20-21, 2021. In particular, the authors have focused their attention on topics on the effect of COVID-19 on noncommunicable diseases, depression, research on substance abuse, and post COVID-19 pain management. The authors have developed a better understanding of these conditions' interplay with COVID-19 infection. The paper also deals with important topics around the effects of NCD on COVID-19 and vice versa, as well as key considerations around research and development, innovation, policy, and finally, summarizes the ways forward in which Canada and India could collaborate strategically. We also include key points raised during the summit.

COVID-19: Cardiac and Neurological Complications among Ontario Visible Minorities: Chinese and South Asians

Background: This is a population-based retrospective study of cardiac and neurological complications of COVID-19 among Ontario Chinese and South Asians. Methods: From January 1, 2020 to September 30, 2020 using the last name algorithm to identify Ontario Chinese and South Asians who were tested positive by PCR for COVID-19, their demographics, cardiac, and neurological complications including hospitalization and emergency visit rates were analyzed compared to the general population. Results: Chinese (N = 1,186) with COVID-19 were found to be older (mean age 50.7 years) compared to the general population (N = 42,547) (mean age 47.6 years) (p < 0.001), while South Asians (N = 3,459) were younger (age of 42.1 years) (p < 0.001). For neurological complications, the 30-day crude rate for Chinese was 160/ 10,000 (p < 0.001);South Asians was 40/10,000 (p = 0.526), and general population was 48/10,000. The 30-day all-cause mortality rate was significantly higher for Chinese at 8.1% vs 5.0% for the general population (p < 0.001), while it was lower in South Asians at 2.1% (p < 0.001). Conclusions: Chinese and South Asians in Ontario with COVID-19 during the first wave of the pandemic were found to have a significant difference in their demographics, cardiac, and neurological outcomes. .

Deepfake Detection for Fake Images with Facemasks

Hyper-realistic face image generation and manipulation have given rise to numerous unethical social issues, e.g., invasion of privacy, threat of security, and malicious political maneuvering, which resulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of the COVID-19. In this paper, we thoroughly evaluate the performance of state-of-The-Art deepfake detection models on the deepfakes with the facemask. Our result shows that fake facial images with facemask can deceive well-known deepfake detection models, thereby evading the real-world security systems. © 2022 ACM.

Interpret with caution: An evaluation of the commercial AusDiagnostics versus in-house developed assays for the detection of SARS-CoV-2 virus.

INTRODUCTION: There is limited data on the analytical performance of commercial nucleic acid tests (NATs) for laboratory confirmation of COVID-19 infection. METHODS: Nasopharyngeal, combined nose and throat swabs, nasopharyngeal aspirates and sputum was collected from persons with suspected SARS-CoV-2 infection, serial dilutions of SARS-CoV-2 viral cultures and synthetic positive controls (gBlocks, Integrated DNA Technologies) were tested using i) AusDiagnostics assay (AusDiagnostics Pty Ltd); ii) in-house developed assays targeting the E and RdRp genes; iii) multiplex PCR assay targeting endemic respiratory viruses. Discrepant SARS-CoV-2 results were resolved by testing the N, ORF1b, ORF1ab and M genes. RESULTS: Of 52 clinical samples collected from 50 persons tested, respiratory viruses were detected in 22 samples (42 %), including SARS CoV-2 (n = 5), rhinovirus (n = 7), enterovirus (n = 5), influenza B (n = 4), hMPV (n = 5), influenza A (n = 2), PIV-2 (n = 1), RSV (n = 2), CoV-NL63 (n = 1) and CoV-229E (n = 1). SARS-CoV-2 was detected in four additional samples by the AusDiagnostics assay. Using the in-house assays as the "gold standard", the sensitivity, specificity, positive and negative predictive values of the AusDiagnostics assay was 100 %, 92.16 %, 55.56 % and 100 % respectively. The Ct values of the real-time in-house-developed PCR assay targeting the E gene was significantly lower than the corresponding RdRp gene assay when applied to clinical samples, viral culture and positive controls (mean 21.75 vs 28.1, p = 0.0031). CONCLUSIONS: The AusDiagnostics assay is not specific for the detection SARS-CoV-2. Any positive results should be confirmed using another NAT or sequencing. The case definition used to investigate persons with suspected COVID-19 infection is not specific.