As the world emerges from the COVID-19 pandemic Build Back Better has become the mantra. Important, but we need to Build Back Fairer. The levels of social, environmental, and economic inequality in society are damaging health and wellbeing. Taking action to reduce health inequalities is a matter of social justice. In developing strategies for tackling health inequalities, we need to confront the social gradient in health, not just the difference between the worst off and everybody else. Inequalities in mortality from COVID-19 and these rising health inequalities as a result of social and economic impacts, have made such action even more important.

BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. PURPOSE: The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs. MATERIALS AND METHODS: Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis. RESULTS: For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively. CONCLUSION: AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Framework: Healthcare project finance (PF) involves long-term structural investments in hospitals, typically within a publicCprivate partnership (PPP) Banks represent the third major stakeholder, supporting the private player Within this well-known framework, digital platforms represent a new virtual stakeholder, operating as a bridging node that incorporates information, and eases transactions The relationships among the stakeholders are re-engineered around the platform and may be expressed with network theory patterns, even considering its multilayer extensions Justification: As these investments are highly leveraged, especially during the construction phase, bankability represents a major sustainability concern Objective: The research question is focused on the savings deriving from the introduction of networked digital platforms, and on their impact on bankability, shaping a new PPP model Methodology: The study is conducted through (a) an economicCfinancial sensitivity analysis where digital savings impact on key PF parameters, including bankability;(b) a mathematical interpretation, based on network theory, where the stakeholders of two ecosystemsrespectively, without and with a digital platformare compared Results: The creation of a value-adding pie anticipates its partitioning among the value co-creating stakeholders This study represents an advance in the field, showing how technological innovation may improve the overall bankability and the value creation of leveraged infrastructural investments, even beyond the healthcare industry

While the novel covid19 disease caused by sar-cov-2 corona virus has proved a serious threat to mankind it being a pandemic, the rate at which technology in low resource income countries like Uganda has been used to predict the spread and impact of the disease in their economies has not been strongly employed. This paper presents a an excel model and desktop application software developed using open source python programming tools for carrying out risk analysis and prediction of demographics for covid19 disease. Prediction results for both models clearly stated using epidemiological curve, these results can vary based on the force of infection which varies based on government measures and actions. With a certain degree of certainty of the potential impact of the disease on low resource countries, it will foster proper planning and strategical methods to properly manage the pandemic

OBJECTIVE: An emerging outbreak of a novel coronavirus, COVID-19, has now been detected in at least 211 countries worldwide. Given this pandemic situation, robust risk communication is urgently needed, particularly in affected countries. Therefore, this study explored the potential use of Google Trends (GT) to monitor public restlessness toward COVID-19 infection in Taiwan. METHODS: We retrieved GT data for the specific locations and subregions in Taiwan nationwide using defined search terms related to the coronavirus, handwashing, and face masks. RESULTS: Searches related to COVID-19 and face masks in Taiwan rapidly increased following the announcements of Taiwan's first imported case and reached a peak as locally acquired cases were reported. However, searches for handwashing gradually increased during the period of face-mask shortage. Moreover, high to moderate correlations between Google relative search volumes (RSVs) and COVID-19 cases were found in Taipei (lag-3), New Taipei (lag-2), Taoyuan (lag-2), Tainan (lag-1), Taichung (lag0), and Kaohsiung (lag0). CONCLUSION: In response to the ongoing outbreak, our results demonstrated that GT could potentially define the proper timing and location for practicing appropriate risk communication strategies for affected populations.

BACKGROUND: It is not definitively known if people with HIV (PWH) are more likely to be SARS-CoV-2 tested or test positive than people without HIV (PWoH). We describe SARS-CoV-2 testing and positivity in 6 large geographically and demographically diverse cohorts of PWH and PWoH in the United States. SETTING: The Corona-Infectious-Virus Epidemiology Team (CIVET) comprises five clinical cohorts within a health system (Kaiser Permanente Northern California, Oakland, CA; Kaiser Permanente Mid-Atlantic States, Rockville, MD; University of North Carolina Health, Chapel Hill, NC; Vanderbilt University Medical Center, Nashville, TN; Veterans Aging Cohort Study) and one interval cohort (MACS/WIHS Combined Cohort Study). METHODS: We calculated the proportion of patients SARS-CoV-2 tested and the test positivity proportion by HIV status from March 1 to December 31, 2020. RESULTS: The cohorts ranged in size from 1,675 to 31,304 PWH and 1,430 to 3,742,604 PWoH. The proportion of PWH who were tested for SARS-CoV-2 (19.6%-40.5% across sites) was significantly higher than PWoH (14.8%-29.4%) in the clinical cohorts. However, among those tested, the proportion of patients with positive SARS-CoV-2 tests was comparable by HIV status; the difference in proportion of SARS-CoV-2 positivity ranged from 4.7% lower to 1.4% higher. CONCLUSION: Although PWH had higher testing proportions compared with PWoH, we did not find evidence of increased positivity in 6 large, diverse populations across the United States. Ongoing monitoring of testing, positivity, and COVID-19 related outcomes in PWH are needed given availability, response, and durability of COVID-19 vaccines; emergence of SARS-CoV-2 variants; and latest therapeutic options.

From the Document: Media reports and public company disclosures have indicated that some 'larger' businesses, nonprofits, and corporate groups have received PPP [Paycheck Protection Program] loan amounts, in excess of the average or most commonly sought loan amounts This has raised questions as to whether a small number of borrowers are disproportionately benefiting from the PPP and preventing a broader range of smaller borrowers from obtaining PPP loans This Insight describes [1] the distribution of PPP loans, by approval amount;[2] existing tools (including the SBA's [Small Business Administration] 'affiliation rules') and executive branch actions taken to address perceived 'abuses' of larger, eligible borrowers and corporate groups;and [3] oversight options for Congress Legislative oversight;Coronavirus Aid, Relief, and Economic Security (CARES) Act;Loans

In response to the increasing burden of recent health emergencies and disasters, the World Health Organization (WHO) and its partners established the WHO thematic platform for health emergency and disaster risk management research network (health EDRM RN) in 2016, with the purposes of promoting global research collaboration among various stakeholders and enhancing research activities that generate evidence to manage health risks associated with all types of emergencies and disasters. With the strong support and involvement of all WHO regional offices, the health EDRM RN now works with more than 200 global experts and partners to implement its purposes. The 1st and 2nd Core Group Meetings of the health EDRM RN were held on 17-18 October 2019 and 27 November 2020, respectively, to discuss the development of a global research agenda that the health EDRM RN will focus on facilitating, promoting, synthesizing and implementing, taking into account the emergence of the coronavirus disease 2019 (COVID-19) (health EDRM RN research agenda). A focus of the meetings was the establishment of an online platform to share information and knowledge, including the databases that the health EDRM RN accumulates (WHO health EDRM knowledge hub). This paper presents a summary of the discussion results of the meetings.

The paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can be an important advantage in transforming a destination into a safer destination in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought to the spotlight the issue of overcrowded attractions inside a destination at certain moments. The method proposed in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented aims to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating the social distancing measures imposed by Covid-19.

As the medical landscape changes daily with the coronavirus disease (COVID-19) pandemic, clinical researchers are caught off-guard and are forced to make decisions on research visits in their ongoing clinical trials. Although there is some guidance from local and national organizations, the principal investigator (PI) is ultimately responsible for determining the risk-benefit ratio of conducting, rescheduling, or cancelling each research visit. The PI should take into consideration the ethical principles of research, local/national guidance, the community risk of the pandemic in their locale, staffing strain, and the risk involved to each participant, to ultimately decide on the course of action. While balancing the rights and protection of the human subject, we seldom examine patients' views and opinions about their scheduled research visit(s). This article discusses the ethical principles of beneficence and autonomy in helping the decision-making process. We discuss ways to weigh-in local and national guidance, staffing strain, and institutional support into the decision-making process and outline potential changes needed for regulatory bodies depending on the decision. Further, we discuss the need to weigh-in the individual risk-benefit ratio for each participant and present a decision tree to navigate this complex process. Finally, we examine participant and caregiver perspectives on their fears, sense of preparedness, and factors that they consider before deciding whether to keep or postpone the research appointments. This entry also provides PIs ways to support their research participants in both scenarios, including provision of psychological support.

Black joy is a distinctive part of the Black experience. Amid the wakes of the tragic losses of George Floyd and Breonna Taylor, and the resurgence of the Black Lives Matter Movements, in tandem with the rise of the global COVID-19 pandemic, I photograph Black millennial experiences and the site of the White City Estate in Shepherd's Bush, West London as a visual artist, writer and geographer.

Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyse steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.

We appreciate the thoughtful suggestions provided by Bansal and colleagues regarding the American College of Rheumatology (ACR) Guidance for the Management of Rheumatic Disease in Adult Patients During the COVID-19 Pandemic [1]. While there are noteworthy strengths to the ACR-led effort, we recognize that the guidance generated is not comprehensive, as it does not address all of the potential scenarios or treatments relevant to the day-to-day management of rheumatic disease (RD) patients.

Proper training on the preventive measures against COVID-19 among health-care workers is crucial for mitigating the spread of viral infection The present study evaluated the efficacy of a brief web-based module on the practice of hand hygiene and respiratory etiquette among respective health-care workers A comparative study was conducted with a total of 500 participants A self-reported questionnaire was used for both pre- and post-intervention evaluation The post-intervention assessment was conducted 1-2 weeks following the intervention The difference in the practice of hand hygiene and respiratory etiquettes during work hours was recorded We found that the intervention resulted in an evident difference in the use of alcohol-based hand sanitizer by the participating doctors before examining the patient Interns showed a much higher propensity to wash their hands for at least 20 s, relative to other health-care workers The difference between pre- and post-intervention handwashing for >5 times/day was 6 5% in females and 4 5% in males In short, the study was able to demonstrate that a web-based health education module is an effective tool for the education and promotion of preventative measures in hospital setups, which may ultimately aid in halting the spread of COVID-19 among health-care workers

BACKGROUND The number of female and black, Asian and minor ethnicity (BAME) healthcare professionals has significantly increased over the last few decades. While this highlights the National Health Service (NHS) workforce as diverse and inclusive, most senior managers and conference panellists remain mainly men from Caucasian backgrounds. METHODS We reviewed all publicly available data for major Royal College conferences in the UK from 2015 to 2019 to examine how many of the panellists were men or women and how many were Caucasian or BAME. RESULTS Our first finding was that publicly available data were available for only 20 out of 70 conferences (29%). At 60% (n=12) of conferences, there were a predominance of male speakers. The median percentage of female speakers remained between 35% and 46%. There were no all-male panels. At 20% (n=4) of conferences in the sample, there were no BAME speakers. The median percentage of BAME speakers remained between 9% and 18%. CONCLUSION Conference panels do not yet reflect the diversity of the NHS workforce. We all have a duty to promote inclusivity and diversity in medicine. One way to do this is via conferences, through appropriate actions by conference organisers, panellists and delegates.

The corticolimbic system (prefrontal cortices, amygdala, and hippocampus) integrates emotion with cognition and produces a behavioral output that is flexible based on the environmental circumstances. It also modulates pain, being implicated in pathophysiology of maladaptive pain. Because of the anatomic and function overlap between corticolimbic circuitry for pain and emotion, the pathophysiology for maladaptive pain conditions is extremely complex. Addressing environmental needs and underlying triggers is more important than pharmacotherapy when dealing with feline orofacial pain syndrome or feline hyperesthesia syndrome. By contrast, autoimmune limbic encephalitis requires prompt diagnosis and management with immunosuppression and seizure control.

OBJECTIVE To perform a systematic review of economic evaluations of enhanced recovery pathways (ERP) for colorectal surgery. BACKGROUND Although there is extensive literature investigating the clinical effectiveness of ERP, little is known regarding its cost-effectiveness. METHODS A systematic literature search identified all relevant articles published between 1997 and 2012 that performed an economic evaluation of ERP for colorectal surgery. Studies were included only if their ERP included all 5 of the key components (patient information, preservation of GI function, minimization of organ dysfunction, active pain control, and promotion of patient autonomy). Quality assessment was performed using the Consensus on Health Economic Criteria instrument (scored 0-19; high quality 12). Incremental cost-effectiveness ratios were calculated if sufficient data were provided, using difference in length of stay and overall complication rates as effectiveness measures. RESULTS Of a total of 263 unique records identified (253 from databases and 10 from other sources), 10 studies met our inclusion criteria and were included for full qualitative synthesis. Overall quality was poor (mean quality 7.8). Eight reported lower costs for ERP. The majority (8 of 10) of studies were performed from an institutional perspective and therefore did not include costs related to changes in productivity and other indirect costs (eg, caregiver burden). Five studies provided enough information to calculate ICERs, of which ERP was dominant (less costly and more effective) in all cases for reduction in length of stay and was dominant or potentially cost-effective in 4 and questionable (no difference in costs nor effectiveness) in 1 for reduction in overall complications. CONCLUSIONS The quality of the current evidence is limited but tends to support the cost-effectiveness of ERP. There is need for well-designed trials to determine the cost-effectiveness of ERP from both the institutional and societal perspectives.

PURPOSE: The purpose of this study was to evaluate pain, functional impairment, mental health, and daily activity in patients with end-stage hip and knee osteoarthritis (OA) during the COVID-19 lockdown. METHODS: The study included 63 patients, with hip or knee OA, who had been scheduled for arthroplasty that was postponed because of COVID-19. Patients were evaluated by telephone interviews during the first week after lockdown, in the fourth week, and again at the end of the lockdown. Patients rated their pain level on the basis of a visual analog scale (VAS) and completed WOMAC, SF-12 and Tegner activity scale (TAS) questionnaires. RESULTS: VAS and WOMAC scores increased significantly during lockdown, while physical activity significantly decreased. At the final evaluation, VAS and WOMAC showed a significant negative correlation with TAS. The SF-12 subscale scores showed a significant decrease of the physical component during the lockdown, while the mental component remained largely unchanged. Patients with knee OA showed a faster progress of pain compared to those with hip OA. 50 patients (79%) stated they wished to have arthroplasty as soon as possible. CONCLUSION: The COVID-19 lockdown had a significant impact on pain, joint function, physical function, and physical activity in patients with end-stage hip and knee OA. LEVEL OF EVIDENCE: II (Prospective cohort study).

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homesand the role these connections serve in spreading a highly contagious respiratory infectionis currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study periodeven after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a homes staff network connections and its centrality within the greater network strongly predict COVID-19 cases.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.