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1.
PLoS Global Public Health ; 2(6), 2022.
Article in English | CAB Abstracts | ID: covidwho-2021490

ABSTRACT

The COVID-19 pandemic has placed the use of evidence for policy-making high up on the international agenda. To fight the pandemic, Governments around the world have publicly stressed the need to draw on evidence by engaging scientific advisors and advisory bodies [1]. Furthermore, the increased demand for evidence has led to a global push for innovative solutions such as the scaling-up of living evidence syntheses [2]. At the same time, COVID-19 revealed fatal structural and systemic weaknesses in the production and use of evidence-flaws which have cost lives [3]. In many cases, institutional mechanisms and capacities to systematically mobilize and contextualize the best available evidence for rapid decision-making were missing [4]. As a consequence, policy-makers, practitioners and citizens alike were confronted with a deluge of competing claims and misinformation, severely limiting suitable decisionmaking and taking action [5]. The related surge of vaccine hesitancy has disproportionally impacted ethnic minorities and deprived communities, with the lowest vaccine uptake, worryingly, to be seen among the most vulnerable people-the older, the more clinically vulnerable, and those living in the most deprived areas-worsening pre-existing disparities in vaccine use, health inequalities and socio-economic marginalization [6, 7]. To assess different institutional responses in terms of the evidence-policy-society nexus and to learn lessons on how to build equity-centred, agile and responsive evidence-informed decision- making mechanisms, WHO convened its first Global Evidence-to-Policy Summit [8] in late 2021. The Summit, organized by the newly created Evidence to Policy Unit at WHO headquarters in collaboration with the corresponding teams in WHO regional offices, brought together more than 2,500 policy-makers, knowledge brokers, health actors, civil society representatives and researchers from around the world.

2.
New England Journal of Medicine ; 387(9):e24, 2022.
Article in English | MEDLINE | ID: covidwho-2016966
3.
Journal of primary care & community health ; 13:21501319221119692, 2022.
Article in English | MEDLINE | ID: covidwho-2009334

ABSTRACT

BACKGROUND: Deployment of telehealth has been touted as a means of reducing health disparities in underserved groups. However, efforts to reduce regulatory barriers have not been associated with greater telehealth uptake. The goal of this study was to examine engagement with technology among low-income people of color living in Newark, New Jersey. METHODS: Using surveys and focus groups, we examined study participants' daily use of technology (eg, Internet) and comfort with telehealth services (eg, use of teleconferencing for medication refills) before and after COVID-related social distancing mandates went into effect. RESULTS: Use of technology was significantly lower in the pre-COVID period. However, prior months' use of technology had a weak but significant correlation with comfort with telehealth (r = .243, P = .005) in bivariate analyses and was the only significant predictor in multivariate analyses. Analyses of focus group discussions confirmed that lack of experience with technology and distrust of the security and privacy of digital systems were the most important barriers to comfort with telehealth in our sample. CONCLUSION: Our study found that approximately 20% of people in this under-resourced community lacked access to basic technologies necessary for successful deployment of telehealth services. The study's timing provided an unexpected opportunity to compare experiences and attitudes relating to telehealth in 2 regulatory environments. Although uptake of telehealth services increased with the Federal governments' relaxation of regulatory barriers, there was not a similar increase in comfort with telehealth use. Investments in broadband access and equipment should be accompanied by educational programs to increase day-to-day use of and comfort with associated technologies which would improve consumer confidence in telehealth.

4.
INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION ; 14(3):3261-3265, 2022.
Article in English | Web of Science | ID: covidwho-1912150

ABSTRACT

It was just as we were about to enter the fifth year of the SDGs that we were made hostages to the virus that was finagling its way into our life. Many scholarly papers, reports, and investigations have highlighted the impact of Covid-19 on global governance. When the 2030 Agenda for Sustainable Development is fully implemented, this article aims to investigate the post-Covid world's potential for SDGs as a catalyst for minimising trade-off and maximising synergies. As part of the Indian government's effort to navigate this difficult time, the author uses a multidisciplinary approach to try and map the impact of Covid-19 on SDGs. Following a review of global economic trends, the article focuses on the impact of the crisis on India's economy, specifically. An effort has been made to highlight the importance of SDGs as a post-Covid recovery and development path in India.

5.
Topics in Antiviral Medicine ; 30(1 SUPPL):38-39, 2022.
Article in English | EMBASE | ID: covidwho-1880187

ABSTRACT

Background: Cardiopulmonary symptoms and reduced exercise capacity can persist after SARS-CoV-2 infection. Mechanisms of post-acute sequelae of COVID-19 ("PASC" or "Long COVID") remain poorly understood. We hypothesized that systemic inflammation would be associated with reduced exercise capacity and pericardial/myocardial inflammation. Methods: As part of a COVID recovery cohort (NCT04362150) we assessed symptoms, biomarkers, and echocardiograms in adults >2 months after PCR-confirmed SARS-CoV-2 infection. In a subset, we performed cardiac magnetic resonance imaging (CMR), ambulatory rhythm monitoring (RM), and cardiopulmonary exercise testing (CPET) >12 months after acute infection. Associations between symptoms and oxygen consumption (VO2), cardiopulmonary parameters and biomarkers were evaluated using linear and logistic regression with adjustment for age, sex, BMI, and time since infection. Results: We studied 120 participants (median age 51, 42% female, and 47% had cardiopulmonary symptoms at median 7 months after acute infection). Elevated hsCRP was associated with symptoms (OR 1.32 per doubling, 95%CI 1.01-1.73, p=0.04). No differences in echocardiographic indices were found except for presence of pericardial effusions among those with symptoms (p=0.04). Of the subset (n=33) who underwent CMR at a median 17 months, all had normal cardiac function (LVEF 53-76%), 9 (27%) had pericardial effusions and none had findings suggestive of prior myocarditis. There were no differences on RM by symptoms. On CPET, 33% had reduced exercise capacity (peak VO2 <85% predicted). Individuals with symptoms had lower peak VO2 compared to those reporting recovery (28.4 vs 21.4 ml/kg/min, p=0.04, Figure). Elevated hsCRP was independently associated with lower peak VO2 after adjustment (-9.8 ml/kg/min per doubling, 95%CI-17.0 to-2.5;p=0.01, Figure). The predominant mechanism of reduced peak VO2 was chronotropic incompetence (HR 19% lower than predicted, 95%CI 11-26%;p<0.0001, Figure). Chronotropic incompetence on CPET correlated with lower peak HR during ambulatory RM (p<0.001). Conclusion: Persistent systemic inflammation (hsCRP) is associated with pericardial effusions and reduced exercise capacity > 1 year after acute SARS-CoV-2 infection. This finding appears to be driven mainly by chronotropic incompetence rather than respiratory compromise, cardiac pump dysfunction, or deconditioning. Evaluation of therapeutic strategies to target inflammation and/or chronotropy to alleviate PASC is urgently needed.

6.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-338328

ABSTRACT

BACKGROUND: Mechanisms underlying persistent cardiopulmonary symptoms following SARS-CoV-2 infection (post-acute sequelae of COVID-19 "PASC" or "Long COVID") remain unclear. The purpose of this study was to elucidate the pathophysiology of cardiopulmonary PASC using multimodality cardiovascular imaging including cardiopulmonary exercise testing (CPET), cardiac magnetic resonance imaging (CMR) and ambulatory rhythm monitoring. METHODS: We performed CMR, CPET, and ambulatory rhythm monitoring among adults > 1 year after PCR-confirmed SARS-CoV-2 infection in the UCSF Long-Term Impact of Infection with Novel Coronavirus cohort (LIINC;NCT04362150 ) and correlated findings with previously measured biomarkers. We used logistic regression to estimate associations with PASC symptoms (dyspnea, chest pain, palpitations, and fatigue) adjusted for confounders and linear regression to estimate differences between those with and without symptoms adjusted for confounders. RESULTS: Out of 120 participants in the cohort, 46 participants (unselected for symptom status) had at least one advanced cardiac test performed at median 17 months following initial SARS-CoV-2 infection. Median age was 52 (IQR 42-61), 18 (39%) were female, and 6 (13%) were hospitalized for severe acute infection. On CMR (n=39), higher extracellular volume was associated with symptoms, but no evidence of late-gadolinium enhancement or differences in T1 or T2 mapping were demonstrated. We did not find arrhythmias on ambulatory monitoring. In contrast, on CPET (n=39), 13/23 (57%) with cardiopulmonary symptoms or fatigue had reduced exercise capacity (peak VO 2 <85% predicted) compared to 2/16 (13%) without symptoms (p=0.008). The adjusted difference in peak VO 2 was 5.9 ml/kg/min lower (-9.6 to -2.3;p=0.002) or -21% predicted (-35 to -7;p=0.006) among those with symptoms. Chronotropic incompetence was the primary abnormality among 9/15 (60%) with reduced peak VO 2 . Adjusted heart rate reserve <80% was associated with reduced exercise capacity (OR 15.6, 95%CI 1.30-187;p=0.03). Inflammatory markers (hsCRP, IL-6, TNF-alpha) and SARS-CoV-2 antibody levels measured early in PASC were negatively correlated with peak VO 2 more than 1 year later. CONCLUSIONS: Cardiopulmonary symptoms and elevated inflammatory markers present early in PASC are associated with objectively reduced exercise capacity measured on cardiopulmonary exercise testing more than 1 year following COVID-19. Chronotropic incompetence may explain reduced exercise capacity among some individuals with PASC. Clinical Perspective: What is New?Elevated inflammatory markers in early post-acute COVID-19 are associated with reduced exercise capacity more than 1 year later.Impaired chronotropic response to exercise is associated with reduced exercise capacity and cardiopulmonary symptoms more than 1 year after SARS-CoV-2 infection.Findings on ambulatory rhythm monitoring point to perturbed autonomic function, while cardiac MRI findings argue against myocardial dysfunction and myocarditis. Clinical Implications: Cardiopulmonary testing to identify etiologies of persistent symptoms in post-acute sequalae of COVID-19 or "Long COVID" should be performed in a manner that allows for assessment of heart rate response to exercise. Therapeutic trials of anti-inflammatory and exercise strategies in PASC are urgently needed and should include assessment of symptoms and objective testing with cardiopulmonary exercise testing.

7.
Modern Pathology ; 35(SUPPL 2):1006-1007, 2022.
Article in English | EMBASE | ID: covidwho-1857652

ABSTRACT

Background: COVID-19 pandemic has caused more than 4.7 million deaths worldwide to date and still continues globally unabated. Numerous studies have linked the mortality in COVID-19 to aggressive immune response and cytokine storm. However, little is known about the cytokine profiles of individual immune cells that are directly involved in tissue damage. Here we investigate intracellular cytokines in individual T and NK cells of COVID-19 patients. Design: We studied 50 blood samples from 22 COVID-19 patients, 4 with mild, 6 moderate and 12 severe disease. There were 6 healthy controls. We performed high-dimensional 30-color spectral flow cytometry to characterize the immune cell subsets. For cytokine study, cells were stimulated for 6 hours, and stained for surface antigens and intracellular cytokines (IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17a, IL21, INFg, GnzB, TNFa, and GMCSF). Data ware acquired on FACSymphony 50-parameter analyzer and analysis performed using FlowJo. Results: Our studies revealed significant differences in lymphocyte cytokine profiles between COVID+ and healthy controls (Fig 1). CD4+ and CD8+ T-cells exhibited increased percentages of IL2+ and IFNg+ cells, indicating a shift towards Th1 reaction. Granzyme B is highly upregulated in all T and NK cell subsets, demonstrating highly armed cytotoxic cells in COVID patients. The most prominent changes were noted in NK cells, 7 cytokines were highly expressed, most are proinflammatory cytokines. Of particular interest are IL-21 and GMCSF, both are known to play important roles in inflammatory cell recruitment, activation and renewal, which can lead to augmented tissue inflammation and injury. These changes were already evident in patients with mild disease, but there is heightened cytokine production in severe cases. Conclusions: Using high-dimensional flow cytometry we demonstrated for the first time significantly increased production of multiple proinflammatory cytokines and cytotoxic molecules in individual T and NK cells of COVID-19 patients. NK cells are most drastically activated. It is conceivable that when recruited to the target tissue such as lung, these highly primed cells will play a major role in tissue injury and ultimately organ failure via their direct cytotoxicity and cytokine secretion. This is consistent with previous reports of increased NK cells in the COVID lungs. Analysis of NK cell cytokine profiles may serve to predict disease progression, and reveal new targets for immune-therapy for severe COVID patients. (Table Presented).

8.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333548

ABSTRACT

Some patients infected with Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) develop severe pneumonia and the acute respiratory distress syndrome (ARDS) [1]. Distinct clinical features in these patients have led to speculation that the immune response to virus in the SARS-CoV-2-infected alveolus differs from other types of pneumonia [2]. We collected bronchoalveolar lavage fluid samples from 86 patients with SARS-CoV-2-induced respiratory failure and 252 patients with known or suspected pneumonia from other pathogens and subjected them to flow cytometry and bulk transcriptomic profiling. We performed single cell RNA-Seq in 5 bronchoalveolar lavage fluid samples collected from patients with severe COVID-19 within 48 hours of intubation. In the majority of patients with SARS-CoV-2 infection at the onset of mechanical ventilation, the alveolar space is persistently enriched in alveolar macrophages and T cells without neutrophilia. Bulk and single cell transcriptomic profiling suggest SARS-CoV-2 infects alveolar macrophages that respond by recruiting T cells. These T cells release interferon-gamma to induce inflammatory cytokine release from alveolar macrophages and further promote T cell recruitment. Our results suggest SARS-CoV-2 causes a slowly unfolding, spatially-limited alveolitis in which alveolar macrophages harboring SARS-CoV-2 transcripts and T cells form a positive feedback loop that drives progressive alveolar inflammation. This manuscript is accompanied by an online resource: https://www.nupulmonary.org/covid-19/. ONE SENTENCE SUMMARY: SARS-CoV-2-infected alveolar macrophages form positive feedback loops with T cells in patients with severe COVID-19.

10.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-330674

ABSTRACT

Objective : To investigate the effect of interview format changes (in-person to virtual, one-to-one to multiple-to-one) necessitated by the COVID-19 travel restrictions on candidate ranking variabilities. Method : In 2018/2019, the glaucoma fellowship interviews were conducted in-person and one-to-one, whereas in 2020, interviews were virtual and multiple (interviewers)-to-one (candidate). We compared ranking ranges of interviewers within the same virtual room (WSR) and not within the same virtual room (NWSR) to assess the effect of this change on ranking variabilities. We also compared ranking categories ("accept," "alternate," and "pass") agreements between in-person and virtual interviews to assess the effect of this change on ranking variabilities. Results : NWSR and WSR mean rankings differed by 1.33 (95% confidence interval difference 0.61 to 2.04, p = 0.0003), with WSR interviewers having less variability than NWSR pairs. The variability of in-person interviews and later virtual interviews showed no differences (weighted Kappa statistic 0.086 for 2018, 0.158 for 2019, and 0.101 for 2020;p < 0.05 for all years). The overall least attractive candidate has the lowest variability;the most attractive candidate has the second lowest variability. Conclusion: Grouping interviewers decreased ranking variabilities, while a change from in-person to virtual interview format did not increase the ranking variabilities.

13.
Bioscience Biotechnology Research Communications ; 14(3):1376-1380, 2021.
Article in English | Web of Science | ID: covidwho-1504856

ABSTRACT

The Corona Virus Disease 2019 (COVID-19) is an acute virus creating respiratory disease and gastro intestine disease in humans. The outbreak of novel corona virus (COVID-19) has brought serious impact on all counties around the world. Spread of COVID-19 was controlled by countries through restricted movement, self-hygiene practices and social distancing. Despite all the efforts made by the governments, this pandemic brought serious effect on economy and environment. The impacts of COVID-19 on air, water and waste management were assessed and were observed that air and water quality has improved due to lockdown but the management of waste is a serious issue. This article describes the results of study performed on the environmental effects particularly in air and water by assessing the environmental conditions before and after the outbreak of pandemic COVID-19. The study results yields that the purity of air and water has been improved during the pandemic period when compared with the period before the outbreak of COVID-19 virus. Waste generated from self-quarantine houses, hospitals and self-hygiene practices followed by people has posed an enormous effect on waste management sector. Disposal of infectious waste along with municipal solid waste has created threat to people handling the waste and the environment. Based on the environmental analysis performed on air, water and waste management, solid guidelines has been provided in treating the waste management effectively. This article recommends the need for improving the waste treatment methodology and the significances of policy framework to face pandemic situation in future. This study improves the hope that, implementation of proposed guidelines will improve the purity level of environment and management of biomedical wastes effectively.

14.
Chest ; 160(4):A556-A557, 2021.
Article in English | EMBASE | ID: covidwho-1458383

ABSTRACT

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Viral Respiratory illnesses such as Covid-19 and Influenza pose significant health challenges worldwide. There are more than 150M confirmed cases of Covid-19 with a reported 3.15M deaths (as of April, 2021). The WHO reports there to be ~ 1 billion influenza cases and 290-650K influenza-associated deaths annually. A signature feature of these illnesses is an early infection period that, if insufficiently recognized and controlled early, can lead to viral spread and avoidable morbidity/mortality. The need for personalized, remote care tools that facilitate early detection and triage of viral illness has never been greater. To address this gap, we developed an institutional software, Vironix, that uses machine-learned (ML) prediction models to enable real-time risk stratification and decision support for global organizations. METHODS: ML models were trained on clinical characteristic data from East and South Asia, Western Europe, and USA. Algorithms take an input of symptom, profile, biometric, and exposure data and return an assessment of disease severity. Covid-19 algorithms were validated on computer generated patient vigenttes and deployed in the Vironix web app among 22 participants in a small business commercial pilot for member self-screening. Members conducted daily health assessments and received personalized decision support while organization managers received work-from-home recommendations and compliant symptom monitoring without seeing member health data. For influenza, Vironix ML algorithms were tested on a dataset (with a 90/10 train test split) collected from one academic and two community emergency rooms from March 2014 to July 2017 (Hong et al.). RESULTS: ML-predictions showed 87.6% accuracy, 85.5% sensitivity, and 87.8% specificity in identifying severe Covid-19 presentations in an out-of-sample validation set of 5,000 patient cases. After 4-months pilot use, Vironix issued 14 stay-at-home and 10 healthcare escalation recommendations while maintaining 30-day and 7-day user retention of 66% and 72%, greatly exceeding common app adoption rates. ML predictions for the Influenza data set showed 67.8% accuracy, 71.7% sensitivity, and 65.4% specificity in identifying admissible or dischargeable presentations of influenza in an out-of-sample validation set of 56,000 patient cases. CONCLUSIONS: Covid-19 ML-severity assessments showed strong accuracy, sensitivity, and specificity in identifying severe clinical presentations. The deployed web-app showed high adoption with members receiving relevant decision support. Flu algorithm performance could be bolstered by inclusion of biometric features. Additional controlled trials could be conducted to establish validated markers of health improvement and early illness detection resulting from Vironix use. The overall methodology for mapping clinical characteristic data into patient scenarios for training ML classifiers of health deterioration is generalizable for a variety of potential software and hardware deployments across disease spaces. CLINICAL IMPLICATIONS: The technology detailed in this study represents a potential low cost, scalable, hardware/software agnostic, global solution for early detection and intervention on infectious respiratory illness. These solutions can be integrated into remote care and institutional wellness workflows to support public health initiatives. DISCLOSURES: No relevant relationships by Anna Berryman, source=Web Response No relevant relationships by Shreyas Iyer, source=Web Response No relevant relationships by Vinay Konda, source=Web Response Advisory Committee Member relationship with ABMRCC Please note: $1-$1000 by Chris Landon, source=Web Response, value=Consulting fee Removed 04/28/2021 by Chris Landon, source=Web Response Consultant relationship with ABM Respiratory Please note: 11/20 - date Added 04/30/2021 by Chris Landon, source=Web Response, value=Consulting fee no disclosure on file for Nicholas Mark;No relevant relationships by James Morrill, source=Web R sponse No relevant relationships by Sriram Ramanathan, source=Web Response Owner/Founder relationship with Vironix Health, Inc Please note: 05/2020 - Present Added 04/28/2021 by Sumanth Swaminathan, source=Web Response, value=Ownership interest Owner/Founder relationship with Vironix Health Please note: 04/2020-Now Added 05/10/2021 by Botros Toro, source=Web Response, value=Ownership interest Consultant relationship with Vironix Please note: 2019-present Added 04/28/2021 by Nicholas Wysham, source=Web Response, value=Ownership interest

15.
Nephrology ; 26:54-54, 2021.
Article in English | Web of Science | ID: covidwho-1381776
16.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277141

ABSTRACT

RATIONALE The Covid-19 pandemic has posed a serious, ongoing global health challenge. The United States has been the worst affected, with more than 11M confirmed cases and 246K deaths (as of November 2020). Two primary and persisting concerns are the continued necessity for shutdown/isolation and the possibility of singular waves of rapid virus spread that could overwhelm global healthcare systems, resulting in preventable mortality and substantial economic burden. While vaccines are being developed and disseminated, the need for remote patient care has never been more critical. To that end, we developed a Covid-19 remote triage software, Vironix, which uses machine-learning algorithms to enable real-time risk stratification and decision support for users. This remote management approach has significant potential to increase safety, improve health outcomes, and stem virus spread as organizations reopen. METHODS Vironix uses personalized machine-learning algorithms trained off clinical characteristic data from the EU, East Asia, and the USA in tandem with prescribed guidelines from the CDC, WHO, and Zhejiang University's handbook on Covid-19 prevention. Clinical characteristics of thousands of patients in the literature were mapped into patient vignettes using Bayesian inference. Subsequent stacked, ensemble decision tree classifiers were trained on these vignettes to classify severity of presenting symptoms and signs. Crucially, the algorithm continuously learns from ongoing use of the application, strengthening decisions, and adapting decision boundaries based on inputted information. Vironix was deployed using a user-friendly API, allowing users to easily screen themselves and obtain remote decision support through a variety of devices (mobile apps, computers, health monitors, etc).RESULTS Algorithm performance was assessed based on its binary classification performance in an out-of-sample test set including severe and nonsevere labels. Vironix correctly assigned the severity classes with an accuracy of 87.6%. Vironix further demonstrated superior specificity (87.8%) and sensitivity (85.5%) in identifying positive (severe) presentations of Covid-19. The algorithms, deployed behind the Vironix Web Application, have been invoked by tens of thousands of users around the world. CONCLUSION 1. The Vironix approach is a highly novel, generalizable methodology for mapping clinical characteristic data into patient scenarios for the purpose of training machine-learning prediction models to detect health deterioration due to viral illness. 2. Vironix exhibits excellent accuracy, sensitivity, and specificity in identifying and triaging clinical presentations of Covid-19 and the most appropriate level of medical urgency. 3. Algorithms continuously learn and improve decision boundaries as individual user input increases. .

17.
Topics in Antiviral Medicine ; 29(1):7, 2021.
Article in English | EMBASE | ID: covidwho-1250762

ABSTRACT

The COVID-19 pandemic has infected more than 100 million people, killed more than 2.4 million, and had a major impact on the health system's ability to deliver essential health services. The impact of COVID-19 on other infectious diseases such as HIV and tuberculosis (TB) has been immense, particularly in low-resource settings with high HIV and TB burden. Ongoing TB data collection and analysis from 200 countries have shown reduced access to care in outpatient and inpatient facilities, impacting the entire care cascade, including prevention, with case detection rates dropping by over 50% in some endemic countries in 2020. By its negative impact on poverty and malnutrition, it is possible that TB incidence could actually increase, strengthening the argument for robust prevention measures. The pandemic has caused significant disruption to HIV programs by limiting access to life-saving antiretrovirals due to movement restrictions, local stockouts, and decrease in uptake of facility-based services. These disruptions are also expected to have reverted some of the progress made in preventing vertical transmission of HIV, resulting in increased numbers of paediatric HIV infections. Therefore, strengthening systems for the maintenance of HIV, TB/HIV, and TB services is an urgent need in many high-burden countries. Although COVID-19 has challenged TB and HIV programmes, it has also offered several lessons, including how we join forces, innovate, and accelerate research and development. Some examples are the use of digital tools for contact tracing, use of AI-based diagnostic algorithms, widespread sharing of genomic sequence data to track virus evolution and emergence of new variants, and large multisite clinical trials to test new therapeutics and vaccines. The development and evaluation of new TB and HIV treatments and vaccines should learn from the past year of accelerated development and explore new models of public-private partnership for the development of global public goods. Despite progress, vulnerable populations such as children and pregnant women continue to lag behind innovations for TB and HIV, and these groups need to be included in clinical trials much sooner. Finally, we need to expand and strengthen the integration of services within the primary healthcare platform, optimizing differentiated service delivery, community engagement and the use of digital technologies to reach those most at risk of TB and HIV with screening, prevention, diagnosis, and treatment.

18.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-8842

ABSTRACT

Objective : To investigate the effect of interview format changes (in-person to virtual, one-to-one to multiple-to-one) necessitated by the COVID-19 travel restrictions on candidate ranking variabilities. Method : In 2018/2019, the glaucoma fellowship interviews were conducted in-person and one-to-one, whereas in 2020, interviews were virtual and multiple (interviewers)-to-one (candidate). We compared ranking ranges of interviewers within the same virtual room (WSR) and not within the same virtual room (NWSR) to assess the effect of this change on ranking variabilities. We also compared ranking categories ("accept," "alternate," and "pass") agreements between in-person and virtual interviews to assess the effect of this change on ranking variabilities. Results : NWSR and WSR mean rankings differed by 1.33 (95% confidence interval difference 0.61 to 2.04, p = 0.0003), with WSR interviewers having less variability than NWSR pairs. The variability of in-person interviews and later virtual interviews showed no differences (weighted Kappa statistic 0.086 for 2018, 0.158 for 2019, and 0.101 for 2020;p < 0.05 for all years). The overall least attractive candidate has the lowest variability;the most attractive candidate has the second lowest variability. Conclusion: Grouping interviewers decreased ranking variabilities, while a change from in-person to virtual interview format did not increase the ranking variabilities.

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