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The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.
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Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Objectives: The Covid-19 pandemic highlighted the importance of considering Social Determinants of Health (SDoH) in healthcare research. Administrative claims databases are widely used for research, but often lack SDoH data or sufficient transparency in how these data were obtained. This study describes innovative methods for integrating SDoH data with administrative claims to facilitate health equity research. Method(s): The HealthCore Integrated Research Database (HIRD) contains medical and pharmacy claims from a large, national US payer starting in 2006 and includes commercial (Comm), Medicare Advantage (MCare), and Medicaid (MCaid) populations. The HIRD includes individually identifiable information, which was used for linking with SDoH data from the following sources: national neighborhood-level data from the American Community Survey, the Food Access Research Atlas, and the National Center for Health Statistics' urbanicity classification;and member-level data on race/ethnicity from enrollment files, medical records, self-attestation, and imputation algorithms. We examined SDoH metrics for members enrolled as of 05-July-2022 and compared them to the respective US national data using descriptive statistics. We also examined telehealth utilization in 2022. Result(s): SDoH data were available for ~95% of currently active members in the HIRD (Comm/MCare/MCaid 12.5m/1m/7.6m). Socioeconomic characteristics at the neighborhood-level differed by membership type and vs. national data: % of members with at least a high-school education (90/88/84 vs. 87);median family income ($98k/$76k/$70k vs. $82k);% of members living in low-income low-food-access tracts (9/14/18 vs. 13);urban (57/52/47 vs. 61). At the member-level, the % of White Non-Hispanics, Black Non-Hispanics, Asian Non-Hispanics, and Hispanics were 61/6/5/6 (Comm), 76/12/2/2 (MCare), and 45/26/5/19 (MCaid). Imputation contributed 15-60% of race/ethnicity values across membership types. Telehealth utilization increased with socioeconomic status. Conclusion(s): We successfully integrated SDoH data from a variety of sources with administrative claims. SDoH characteristics differed by type of insurance coverage and were associated with differences in telehealth utilization.Copyright © 2023
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Background/Objectives: Li-Fraumeni Syndrome (LFS) is a rare hereditary cancer predisposition syndrome characterized by high lifetime risks for multiple primary malignancies. Although most individuals with LFS inherit a pathogenic TP53 variant from a parent, approximately 20% have de novo variants with no suggestive family cancer history. This may result in an LFS experience distinct from individuals with affected relatives. This multi-case study report examines the unique psychosocial experiences of three young adults with de novo TP53 variants. Method(s): The National Cancer Institute's LFS study (NCT01443468) recruited adolescents and young adults (AYAs;aged 15-39 years) with LFS for qualitative interviews. Three participants had a de novo TP53 variant and a personal cancer history. An interprofessional team analyzed interview data using extended case study and narrative methods. Result(s): De novo participants lacked familiarity with LFS to situate a cancer diagnosis, interpret genetic test results, or adjust to chronic cancer risk. Communicating with and receiving support from family was challenged by their lack of common experience. De novo participants experienced socioemotional isolation, which was amplified during the COVID-19 pandemic. To cope, they sought support in online rare disease communities or through mental health providers. Conclusion(s): Individuals with de novo variants may lack familial guides and familiar providers to address disease management and uncertainty. Specialty health and mental health providers may support de novo patients across hereditary cancer syndromes by validating their uncertainties and connecting them with diseasespecific patient advocacy groups that support adjustment to chronic cancer risk.
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The world is becoming a place where the number of emergencies and humanitarian crises is increasing rapidly due to economic inequality and the gap between developed and underdeveloped countries, as well as climate changes leading to disruption of the natural balance and development of natural disasters. The most vulnerable groups of the population including women and children always are affected by disasters. The younger the child, the more vulnerable he/she is, especially if not naturally fed or having a mother or parents. Various humanitarian organizations have been involved in a number of crises, with the World Health Organization and UNICEF and other United Nations-related organizations leading the way. In the care of mothers, infants and young children, most important is to ensure appropriate nutrition because otherwise it can result in life-threatening health conditions. The lack of protection, support and promotion of natural nutrition (breastfeeding) and its disruption and undermining by uncritical and uncontrolled donations and distribution of infant formula are the biggest challenge due to the lack of information of mothers, those who provide support in emergencies from both governmental and non-governmental sector, without cross-sectoral cooperation, thus causing uncoordinated and sometimes harmful interventions. Therefore, it is recommended that governments issue guidelines on infant and young child nutrition prior to the occurrence of an emergency, and crisis management regulations in which the issue of infant and young child nutrition will be given due consideration.Copyright © 2020 Croatian Paediatric Society. All rights reserved.
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Introduction: Cancer patients have a high risk of severe COVID-19 and complications from it. Although the COVID-19 pandemic has led to an increase in the conduction of clinical trials (CTs), there is a scarcity of data on CT participation among cancer patients. We aimed to describe the level of participation in a COVID-19 CT, willingness to participate, as well as trust in sources of information for CTs among persons with and without a previous cancer diagnosis in Puerto Rico. Method(s): Data collected from November 2021 to March 2022 from two cross-sectional studies were merged and used for analysis. Informed consent, telephone, face-to-face, and online interviews were conducted among participants >=18 years old living in Puerto Rico (n=987). Descriptive statistics and bivariate analysis (Fisher's exact text and chi-squared test) was done to describe the outcomes of interest, overall and by cancer status. Result(s): Mean age of participants was 41+/-15.5 years. Most participants were women (71.3%), with an educational level greater than high school (89.5%) and with an annual family income below $20,000 (75.1%). Overall, 4.4% of participants (n=43) reported history of cancer diagnosis. Only 1.8% of the population reported to have participated in a COVID-19 CT to receive either a treatment or vaccine;stratifying by cancer, none of the cancer patients had participated in a COVID-19 CT, and only 1.9% of non-cancer patients participated. While 37.0% of the participants indicated being very willing to sign up for a CT assessing COVID-19 treatment, willingness was higher in cancer patients (55.8%) than among participants without cancer (36.1%). Regarding trust in sources of information for CTs, the level of trust ("a great deal/a fair amount") was higher for their physicians (87.6%), researchers (87.0%), the National Institute of Health (86.7%), their local clinics (82.9%), and a university hospital (82.7%), while it was lower for a pharmaceutical company (64.0%), and for friend, relative, or community leader (37.6%);no differences were observed by cancer status. Conclusion(s): While participation in COVID-19 CTs was extremely low in the study population, the willingness to participate was higher among cancer patients. Education on CTs and their availability are necessary to increase participation in this understudied group. Such efforts will enhance the representation of Hispanic and vulnerable populations, such as cancer patients, on COVID-19 CTs, and thus proper generalizability of study findings in the future.
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Background: Tocilizumab, an interleukin-6 (IL-6) antagonist, is being evaluated for the management of covid-19 pneumonia. The objective of this study was to assess the effectiveness of Tocilizumab in severe covid-19 pneumonia. Method(s): This was a retrospective, observational, single centre study performed in 121 patients diagnosed with severe covid-19 pneumonia. 83 patients received standard of care treatment whereas 38 patients received tocilizumab along with standard of care. Tocilizumab was administered intravenously at 8mg/kg (upto a maximum of 800mg). The second dose of Tocilizumab was given 12 to 24 hours apart. The primary outcome measure was ICU related and hospital related mortality. The secondary outcome measures were change in clinical status of patients measured by WHO (World Health Organisation) 7 category ordinary scale, changes in interleukin-6 (IL-6) levels, secondary infections and duration of ICU stay. Result(s): Tocilizumab was administered between 3-27 days after the patient reported symptoms ( a median of 10.9 days ) and between the 1st to 3rd day of ICU admission (median of 2.1 days) . In Tocilizumab group, 16(42.1%) of 38 patients died in ICU whereas in standard of care group, 27(32.53%) of 83 patients died. The difference in clinical status assessed using WHO (World Health Organisation) 7 category ordinary scale at 28 days between Tocilizumab group and standard of care group was not statistically significant (odds ratio 1.35, 95% confidence interval 0.61 to 2.97, p = 0.44). Conclusion(s): Tocilizumab plus standard care was not superior to standard care alone in reducing mortality and improving clinical outcomes at day 28.Copyright © RJPT All right reserved.
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OnTrackNY is a nationally recognized Coordinated Specialty Care model disseminated across New York state for young people experiencing early non-affective psychosis. OnTrackNY is a network of 22 teams located in licensed outpatient clinics, serving over 2500 individuals. OnTrackNY offers medication management, case management, individual and group cognitive behaviourally oriented therapy, family support and psychoeducation, supported employment and education, and peer support services. Teams receive training for implementation through an intermediary organization called OnTrack Central. OnTrackNY was selected as a regional hub of the National Institute of Mental Health Early Psychosis Intervention Network (EPINET), a national learning healthcare system (LHS) for young adults with early psychosis. This symposium will present the different ways in which EPINET OnTrackNY implemented systematic communitybased participatory processes to ensure robust stakeholder involvement to improve the quality of OnTrackNY care. Florence will present results of an assessment of stakeholder feedback experiences used to develop strategies for assertive outreach and engagement of program participants, families and providers. Bello will present on mechanisms for integrating of co-creation principles to design, develop and execute quality improvement projects in EPINET OnTrackNY. Stefancic will present on quality improvement projects that used rapid cycle qualitative methods, tools, and strategies to build team capacity and flexibility to respond to an LHS. Montague will present adaptations to OnTrackNY services during the COVID-19 pandemic using an implementation science framework. Finally, Patel will lead a discussion on the implications of involving individuals with lived experiences in all phases of the process to maximize learning in an LHS.
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The COVID-19 pandemic has caused a myriad of socio-economic challenges spanning from job losses to food shortages in cities and towns because of imposed lockdowns. It has affected agricultural value chains across the world, but little has been done to quantify the impacts and determine the implications for policy and strategy frameworks. The extent to which COVID-19 has affected the traders and vendors of horticultural produce in a developing city remains unknown. This study applies the multi-methods approach to explore the impact COVID-19 has had on the vendors and traders of horticultural produce, with a focus on the city of Masvingo. Data was collected using semi-structured questionnaire survey administered amongst vendors and traders in Masvingo city, key informant interviews and document analysis. The results show that COVID-19 disrupted the horticultural value chains. The major impact was felt on the inbound and outbound logistics. This had consequent undesirable effects on vendors and traders' livelihoods exacerbated by COVID-19‘s social, economic and psychological knock-on effects which aggravated poverty and suffering amongst horticultural vendors and traders. The study observes the need for policy and strategic interventions to build robust value chain capacity for horticultural produce in a comprehensive manner. This would help to address the plight of the players involved and abrogate the spill-over effects associated with extreme vulnerability to COVID-19-induced poverty. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.
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Objective: To determine the diagnostic accuracy of elevated C reactive protein (CRP) and ferritin in predicting severe Covid-19 infection using the World Health Organization's (WHO) Covid-19 severity classification as gold standard. Study Design: Descriptive study. Place and Duration of Study: This study was conducted at the Pak Emirates Military Hospital, Rawalpindi, from January 1st 2021 till April 30th 2021. Ethical review committee's (ERC) approval was taken and good clinical practice guidelines were followed. Material(s) and Method(s): Baseline blood samples were sent to the hospital laboratory for the measurement of C reactive protein and ferritin levels. PCR was taken as gold standard for the diagnosis of Corona virus disease. Patients were classified into severe and non-severe categories using WHO classification of severity. Sensitivity, specificity, diagnostic accuracy, negative predictive value and positive predictive value were calculated for elevated CRP and ferritin. Result(s): There were 65 (57.5%) patients who had severe Covid-19 disease and 48 (42.5%) patients who had non-severe Covid-19 disease. Among the patients with severe Covid-19, 57 (87.7%) had elevated CRP levels, and 50 (76.9%) patients had elevated ferritin levels. Testing ferritin levels, against the severity of Covid-19 patients, there was a sensitivity of 76.9%, specificity of 79.2%, positive predictive value (PPV) of 83.3%, negative predictive value (NPV) of 71.7% and diagnostic accuracy of 77.8%. Testing CRP levels, there was a sensitivity of 87.7%, specificity of 85.4%, PPV of 89.1%, NPV of 83.6% and diagnostic accuracy of 86.7%. Conclusion(s): The results from our study show that CRP has a slightly improved diagnostic accuracy as compared to ferritin. However, both these markers have value in the prediction of severity of Covid-19 infection.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.
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INTRO: Viruses, including SARS-CoV-2, which causes COVID-19, are constantly changing. These genetic changes (aka mutations) occur over time and can lead to the emergence of new variants that may have different characteristics. After the first SARS-CoV-2 genome was published in early 2020, scientists all over the world soon realized the immediate need to obtain as much genetic information from as many strains as possible. However, understanding the functional significance of the mutations harbored by a variant is important to assess its impact on transmissibility, disease severity, immune escape, and the effectiveness of vaccines and therapeutics. METHODS: Here in Canada, we have developed an interactive framework for visualizing and reporting mutations in SARS-CoV-2 variants. This framework is composed of three stand-alone yet connected components;an interactive visualization (COVID-MVP), a manually curated functional annotation database (pokay), and a genomic analysis workflow (nf-ncov-voc). Findings: COVID-MVP provides (i) an interactive heatmap to visualize and compare mutations in SARS-CoV-2 lineages classified across different VOCs, VOIs, and VUMs;(ii) mutation profiles including the type, impact, and contextual information;(iii) annotation of biological impacts for mutations where functional data is available in the literature;(iv) summarized information for each variant and/or lineage in the form of a surveillance report;and (v) the ability to upload raw genomic sequence(s) for rapid processing and annotating for real-time classification. DISCUSSION: This comprehensive comparison allows microbiologists and public health practitioners to better predict how the mutations in emerging variants will impact factors such as infection severity, vaccine resistance, hospitalization rates, etc. CONCLUSION: This framework is cloud-compatible & standalone, which makes it easier to integrate into other genomic surveillance tools as well. COVID-MVP is integrated into the Canadian VirusSeq data portal (https://virusseqdataportal.ca) - a national data hub for SARS-COV-2 genomic data. COVID-MVP is also used by the CanCOGeN and CoVaRR networks in national COVID-19 genomic surveillance.
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On March 11, 2020, the World Health Organization declared the novel coronavirus (COVID-19) outbreak a global pandemic. In April 2020, the Pregnancy and Infant Linked Outcomes Team (PILOT) was established within the Centers for Disease Control and Prevention's (CDC) COVID-19 response structure, specifically to focus on better understanding the impact of COVID-19 in pregnancy. A total of 71 CDC staff deployed to PILOT, collectively contributing more than 99,000 hours to the response over the course of the team's 25-month activation. PILOT led or collaborated on the publication of over 40 manuscripts, managed several clinical guidance documents, and coordinated and provided subject matter expertise to three funded research studies with academic partners. The team developed six CDC webpages, a toolkit for pregnant people and new parents, and disseminated scientific findings with over 350 social media posts on Facebook, Twitter, Snapchat, LinkedIn, and Instagram with nearly 77 million total impressions. In this, we will summarize the work of PILOT, and other parts of the CDC COVID-19 response, including teams focused on vaccine effectiveness and safety, and surveillance and research activities outside of the CDC. We will review several key contributions to our understanding of COVID-19 in pregnancy: (1) pregnant people are at greater risk of severe illness from COVID-19, including hospitalization, admission to an intensive care unit, and the need for mechanical ventilation, compared with nonpregnant women of reproductive age;(2) pregnant people with COVID-19 are more likely to experience complications that can affect their pregnancy and developing baby, including stillbirth and preterm delivery, compared to pregnant people without COVID-19;(3) there are no recognized maternal or fetal adverse effects of COVID-19 vaccines in pregnancy;and (4) COVID-19 vaccine during pregnancy is effective in preventing severe illness, hospitalization and death among pregnant people, as well as preventing severe illness in infants up to age six months.
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The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.