Patient preferences using telehealth during COVID19 pandemic in four Victorian tertiary hospital services

Introduction: To measure patients’ evaluation of telehealth, preferences for telehealth versus in-person appointments, and potential cost savings by patient characteristics.
Methods: A cross-sectional online survey (including patient and appointment characteristics, telehealth evaluation, preferences for care and costs) of adult patients using video telehealth in four metropolitan tertiary hospital services in Melbourne, Victoria.
Results and discussions: A total of 1045 patients (44 years – IQR 29-59) participated with an overall response rate of 9.2%. For 98.7% patients telehealth was convenient, 96.4% stated that it saved time, 95.9% found telehealth acceptable to receive care and 97.0% found that telehealth improved their access to care. Most (62.6%) preferred in-person consultations, although 86.9% agreed that telehealth was equivalent to an in-person consultation. Those in regional and rural areas were less likely to prefer in-person consultations. Patients attending for medical reasons were less likely to prefer in-person consultation compared to patients with surgical reasons. Patient preference to telehealth were independent of level of education, appointment type, self-rated health status and socio-economic status. Patients saved an average of $120.9 (SD $93.0) per appointment, with greater cost savings for patients from low and middle socioeconomic areas and regional or rural areas. Telehealth video consultations were largely evaluated positively with most patients considering the service to be as good as in-person. Understanding patient preference is critical to consider when implementing telehealth as mainstream across hospital health services.

Health and safety risks faced by delivery riders during the <em>Covid</em>-<em>19</em> pandemic

Objectives: Delivery riders have been front-line workers throughout the pandemic but little is known about their own health and safety during this time. This study explores the health and safety issues facing delivery riders in Ho Chi Minh City, Vietnam, during the Covid-19 pandemic, in particular during the second lockdown (May-October 2021).
Method: A web-based survey of more than 800 riders was conducted in August-September 2021. Following descriptive statistics, four logit models were fitted to examine the factors associated with (a) sanitizing one’s hands, (b) using a face shield, (c) contracting a new health issue, and (d) engaging in riskier traffic behaviors during the lockdown.
Results: The riders who were less consistent in adopting health and safety measures tended to be male, older, less-educated, and vaccinated. Also, they were under greater financial pressure and had suffered a larger loss of income during the pandemic. To recover the loss, they worked longer hours and felt under more intense pressure at work. The job pressure, long working hours, and financial burdens led many drivers to adopt risky traffic behaviors, such as speeding. Conversely, where the companies and co-workers were more supportive, riders tended to adopt health prevention measures more often. Fear of Covid-19 also acted as a facilitator. Job and financial pressure combined with the fear of contracting the virus contributed to the occurrence of new heath issues during the pandemic. Again, support from the company and co-workers helped to reduce the risk of new health problems emerging.
Conclusion: In Ho Chi Minh City and other Global South megacities that employ tens of thousands of riders, ensuring their health and safety is important to support both private businesses and public health. Overall, companies should assume a much larger responsibility here.

Combating the infodemic: <em>COVID</em>-<em>19</em> induced fake news recognition in social media networks

COVID-19 has caused havoc globally due to its transmission pace among the inhabitants and prolific rise in the number of people contracting the disease worldwide. As a result, the number of people seeking information about the epidemic via Internet media has increased. The impact of the hysteria that has prevailed makes people believe and share everything related to illness without questioning its truthfulness. As a result, it has amplified the misinformation spread on social media networks about the disease. Today, there is an immediate need to restrict disseminating false news, even more than ever before.
This paper presents an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification. From the observation, we found that the proposed early fusion-based method outperforms models that work on single embeddings. We also conducted detailed studies using several machine learning and deep learning models to classify misinformation on social media platforms relevant to COVID-19. To facilitate our work, we have utilized the dataset of “CONSTRAINT shared task 2021”. Our research has shown that language and ensemble models are well adapted to this role, with a 97% accuracy..

Short- and long-term outcome and predictors in an international cohort of patients with neuro <em>COVID</em>-<em>19</em>

Background: Despite the increasing number of reports on the spectrum of neurological manifestations of COVID-19 (neuro-COVID), few studies have assessed short and long-term outcome of the disease.
Methods: This is a cohort study enrolling adult patients with neuro-COVID seen in neurological consultation. Data were collected prospectively or retrospectively in the EAN NEuro-covid ReGistrY. The outcome at discharge was measured using the modified Rankin Scale (mRS) and defined as: “stable/improved” if mRS score was equal or lower than pre-morbid score; “worse” if the score was higher than pre-morbid score. Status at 6 months was also recorded. Demographic and clinical variables were assessed as predictors of outcome at discharge and 6 months.
Results: From July 2020 to March 2021, 971 patients from 19 countries were included. 810 (83.4%) were hospitalized. 432 (53.3%) were discharged with worse functional status. Older age, stupor/coma, stroke and ICU admission were predictors of worse outcome at discharge. 132 (16.3%) died in hospital. Older age, cancer, cardiovascular complications, refractory shock, stupor/coma and ICU admission were associated with death. 262 were followed for 6 months. Acute stroke or ataxia, ICU admission and degree of functional impairment at discharge were predictors of worse outcome. 65/221 hospitalized patients (29.4%) and 10/32 non-hospitalized patients (24.4%) experienced persisting neurological symptoms/signs. 10/262 patients (3.8%) developed new neurological complaints during the 6 months of follow-up.
Conclusions: Neuro-COVID is a severe disease associated with worse functional status at discharge, particularly in older subjects and those with comorbidities and acute complications of infection.


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Misidentification of the <em>SARS</em>-<em>CoV</em>-<em>2</em> Mu variant using commercial mutation screening assays

Detection of mutations by multiplex real-time RT-PCR is a widely used method for the screening of SARS-CoV-2 variants, but this method has several limitations. We describe three cases in which a Mu strain containing the mutation K417N was initially misclassified as the Beta variant.
We recommend the detection of P681H to distinguish between these two variants. Our experience highlights the importance of keeping track of new variants and mutations in order to adapt the current workflows.

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