Impact of COVID-19 on Rocky Vista University medical students' mental health: A cross-sectional survey.

Background: The COVID-19 pandemic brought immense changes to medical school curriculums world-wide, such as the widespread adoption of virtual learning. We sought to better understand the impact on medical students' mental health at Rocky Vista University College of Osteopathic Medicine, Parker, CO, United States. This study assessed the impact the pandemic had on anxiety and depression levels of medical students. It also assessed the impact of several domains on student mental health during the pandemic and how various sub-groups within the studied population were affected. Methods: A cross-sectional survey was sent to students through an online anonymous google survey in May to June 2021, centered around the 7-item questionnaire used to screen for Generalized Anxiety Disorder GAD-7, 9-item questionnaire used to screen for depression PHQ-9, and self-designed questions to assess the personal impact of the pandemic. Data obtained were screened for error and analyzed with significance value of p < 0.05. Results: A total of 152 responses were received (25.5% response rate). Of these, 64.1% identified as female, 75.8% were white, 50.3% were between ages 21-25, and 77.8% were first year medical students. During the pandemic, 79.6% of respondents felt more anxious and 65.1% felt more depressed. 67.8% of students reported feeling social isolation amidst the pandemic. Students living with friends were more likely to see a therapist for depression during the pandemic (p = 0.0169) and prescribed an antidepressant (p = 0.0394). Females and students in relationships were more likely to score higher on GAD-7 (p = 0.0194) and (p = 0.0244), respectively. Conclusion: This study investigated the effect of the pandemic on medical students' mental health and the need to address this issue. Results suggest that the pandemic had a negative impact on medical student's mental health and that anxiety and depression levels worsened for pre-clinical medical students at Rocky Vista University. As such, it is imperative to incorporate additional resources to protect the well-being of medical students as they progress through their medical careers.

COVID-19 and mental health: A systematic review of international medical student surveys

Background The medical school curriculum has imposed psychological stressors on students worldwide, some of which may induce feelings of increased depression and anxiety. Meanwhile, the COVID-19 pandemic has exacerbated the feelings of stress, depression, and anxiety that a portion of medical students experience in their daily life. The aim of this systematic review is to gather concrete data from medical schools around the globe, and further evaluate how the COVID-19 pandemic has impacted medical students' mental health. Materials and methods Systematic search of data from PubMed, EMBASE, psycINFO, MEDLINE Complete, and Global Health for studies conducted between December 2019 and July 2021 was conducted. Data from 47 different surveys of medical students from various medical institutions throughout the world were included in this review. These surveys, administered in the form of questionnaires that utilized rating scales, measured anxiety, depression, and stress levels in medical students amidst the COVID-19 outbreak. Results The COVID-19 outbreak was found to have negative effects on medical students, most notably causing emotional and behavioral changes and detrimental mental health impacts. Higher levels of stress, depression, and anxiety have been found in medical students since the outbreak. Conclusion This systematic review highlights the sustained high prevalence of moderate depression, anxiety and stress among medical students during the COVID-19 pandemic. Appropriate support and research on which interventions could mitigate these risks is essential in order to ensure that future physicians are properly cared for, and ultimately have the adequate tools needed to provide high quality and empathetic care to future patients.

A national program for the implementation of lung Stereotactic Ablative Body Radiotherapy (SABR)

Purpose or Objective Following completion of an evaluation program of SABR for primary lung cancer and metachronous extracranial oligometastatic disease, NHS England & NHS Improvement funded a SABR expansion program to increase SABR provision to 50 radiotherapy centres. With reduced access to surgery due to the impact of the COVID-19 pandemic, efforts were focussed on implementing SABR for lung primary and oligometastatic disease in the first instance. Materials and Methods The program consisted of 3 elements, delivered by multi-disciplinary SABR experts from the SABR Consortium and the National Radiotherapy Trials Quality Assurance (RTTQA) Group: Education;Mentorship of RT centres new to SABR by those with extensive experience;QA to ensure the safe and consistent implementation of the technique. A contouring workshop for radiation oncologists was developed by the SABR Consortium Steering Committee. In addition, educational sessions were provided within the SABR Consortium Online Conference, offered free of charge to enable wide access to education and professional development. The Committee produced a Guide for SABR Mentorship, setting out a consistent framework under which mentorship would proceed (Table 1). Mentoring and local protocols followed implementation guidance from the UK SABR Consortium Guidelines. In parallel, RTTQA developed a comprehensive RT QA program (Table 2). Two radiation oncologists and one treatment platform were assessed per hospital. Credentialed clinicians then provided cascade training and education within their departments, formally documenting peer reviews through standard templates provided by RTTQA. (Table Presented) (Table Presented) Results 54 radiation oncologists participated in the contouring workshop. The conference had 1335 registrants, 65% of whom were UK multi-professionals. 15 experienced centres were invited to mentor 24 new SABR sites. Mentors were assigned by equipment for planning and delivery. The mentoring framework was adjusted to accommodate visiting restrictions due to the pandemic and performed remotely through email and video-conferencing instead. The RT QA program commenced in June 2020 with circulation of the facility questionnaire, collecting data on equipment, technique, intention to treat and expected level of mentoring. Responses were used to tailor mentoring and inform what components of the QA program were required for each centre. Contouring submissions were received from 46 radiation oncologists and planning benchmarks were completed by all 24 centres, 22 of which underwent the dosimetry audit. All assessments were reported through standard templates to ensure consistent feedback. Conclusion The implementation of lung SABR was completed in June 2021. Collaboration amongst all stakeholders ensured centres were trained and supported to safely deliver high-quality SABR for lung primary and oligometastatic disease. The education, mentoring and QA program proved effective and has now been rolled out to other oligometastatic sites.

The impact of broadening COVID-19 testing eligibility on communities of color - A lesson for overcoming structural barriers to health care.

CDC guidelines for COVID-19 testing in March 2020 did not prioritize underserved communities. We present the effect that expanding COVID-19 testing had for residents of the predominantly Hispanic city of Chelsea, MA, which had the highest case rate in the state. Results were compared to another city with similar demographics, Lynn, MA, where testing eligibility remained unchanged. Institutional data were used to identify outpatient visits for influenza-like illness or COVID-19 exposure, COVID-19 tests, and hospitalizations for confirmed COVID-19 between 3/30/2020-4/28/2020. Multivariable logistic regressions were used to compare outcomes before and after the change in testing eligibility occurred on 4/13/2020. A total of 3,060 patients were included, 1,374 Chelsea residents and 1,686 Lynn residents. After guidelines changed, Chelsea residents were more likely to present as outpatients (adjusted odds ratio [AOR] 4.2, p < 0.001) and less likely to be hospitalized (AOR 0.2, p < 0.001). They were more likely to be tested (AOR 8.8, p < 0.001), but less likely to test positive (AOR 0.6, p = 0.05). Lynn residents were also more likely to be tested after 4/13/2020 (AOR 1.9, p < 0.001), but no significant differences in visit acuity or test positivity were observed. This study demonstrates how broadening testing eligibility for one highly affected, predominantly Hispanic community was associated with an increase in outpatient presentations and a concomitant decrease in test positivity and hospitalizations. These results highlight the impact of improved access to care on utilization of services among underserved communities, a lesson that is especially crucial as we continue to grapple with the COVID pandemic.

A Logistic Growth Model with Logistically Varying Carrying Capacity for Covid-19 Deaths Using Data from Ontario, Canada

We develop a logistic growth model, with its carrying capacity parameter allowed to also vary logistically, for modeling the cumulative number of deaths due to Covid-19 in the province of Ontario. Although this is an empirical model, it incorporates conceptual elements that support the framework required for modeling Covid-19 or more generally, any infectious disease, where hospitalizations are required in management of the disease. By nature, the logistic growth model is deterministic, and so we induce stochasticity through incorporating the variability that is observed in modeling the daily counts and propose a tool that can be used to quantify the behavior of the disease within a short time period. The stochasticity in the daily number of deaths is modeled using a negative binomial distribution. We discuss shifts in the trend of the cumulative number of deaths that could be used to assess effectiveness of public health interventions. © 2022, Springer Nature Switzerland AG.

Joint Modeling of Hospitalization and Mortality of Ontario Covid-19 Cases

Daily number of hospitalizations and deaths are key outcomes in quantifying the outbreak of infectious diseases. For the purposes of understanding the disease spread trend and the effect of observations from previous days, the time series approach for modeling the outcomes can be used to conduct a cointegration analysis to identify the long-run relationship between those multiple processes that are key to understanding trends such as hospitalization and death. As an alternative perspective, relationships between outcomes can be model through a shared latent stochastic error term;here, we propose a novel framework to study the underlying correlation between two time series processes through this method, called joint modeling. This framework is used for our Ontario Covid-19 study, where a cointegration analysis utilizes statistical tests to identify the long-run relationship between the daily number of new hospitalizations 6 days prior and the daily number of new deaths. Additionally, we show that a joint autoregressive model can provide a framework to model the underlying correlation between the processes. © 2022, Springer Nature Switzerland AG.

Nasal delivery of an IgM offers broad protection from SARS-CoV-2 variants.

Resistance represents a major challenge for antibody-based therapy for COVID-191-4. Here we engineered an immunoglobulin M (IgM) neutralizing antibody (IgM-14) to overcome the resistance encountered by immunoglobulin G (IgG)-based therapeutics. IgM-14 is over 230-fold more potent than its parental IgG-14 in neutralizing SARS-CoV-2. IgM-14 potently neutralizes the resistant virus raised by its corresponding IgG-14, three variants of concern-B.1.1.7 (Alpha, which first emerged in the UK), P.1 (Gamma, which first emerged in Brazil) and B.1.351 (Beta, which first emerged in South Africa)-and 21 other receptor-binding domain mutants, many of which are resistant to the IgG antibodies that have been authorized for emergency use. Although engineering IgG into IgM enhances antibody potency in general, selection of an optimal epitope is critical for identifying the most effective IgM that can overcome resistance. In mice, a single intranasal dose of IgM-14 at 0.044 mg per kg body weight confers prophylactic efficacy and a single dose at 0.4 mg per kg confers therapeutic efficacy against SARS-CoV-2. IgM-14, but not IgG-14, also confers potent therapeutic protection against the P.1 and B.1.351 variants. IgM-14 exhibits desirable pharmacokinetics and safety profiles when administered intranasally in rodents. Our results show that intranasal administration of an engineered IgM can improve efficacy, reduce resistance and simplify the prophylactic and therapeutic treatment of COVID-19.

High Seroprevalence of Anti-SARS-CoV-2 Antibodies in Chelsea, Massachusetts.

SARS-CoV-2 antibody testing allows quantitative determination of disease prevalence, which is especially important in high-risk communities. We performed anonymized convenience sampling of 200 currently asymptomatic residents of Chelsea, the epicenter of COVID-19 illness in Massachusetts, by BioMedomics SARS-CoV-2 combined IgM-IgG point-of-care lateral flow immunoassay. The seroprevalence was 31.5% (17.5% IgM+IgG+, 9.0% IgM+IgG-, and 5.0% IgM-IgG+). Of the 200 participants, 50.5% reported no symptoms in the preceding 4 weeks, of which 24.8% (25/101) were seropositive, and 60% of these were IgM+IgG-. These data are the highest seroprevalence rates observed to date and highlight the significant burden of asymptomatic infection.

Image quality assessment for closed-loop computer-assisted lung ultrasound (preprint)

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-oh-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient quality - as determined by the quality assessment module - the mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at point-of-care.