01 May, 2023

The changing mix of gay bar subtypes after COVID-19 restrictions in the United States, 2017-2023 -

What was the impact on gay bars of COVID-19 occupancy restrictions? Gay bars had been closing steadily for 30 years before public health authorities shuttered all bars and restaurants in March 2020. Trends are drawn from historic data from printed business guides were supplemented with two national censuses of online business listings for LGBTQ+ bars. Results show a rebound from a nadir of 470 gay bars in Spring 2021 to 803 two years later. Not all bars rebounded: bars serving mostly/only cisgender men plummeted in their share from 44.6% of all gay bars to only 24.2% in Spring 2023. Bars serving men’s kink communities also declined, from 8.5% to 6.6% of all gay bars. Bars serving men and women increased from 44.2% to more than 65.6% of all gay bars, by far the largest gay bar subtype. Lesbian bars, a small share of all gay bars, nevertheless nearly doubled from 15 to 29 establishments to 3.6% of the total. Bars serving people of color experienced a small decline in their share 2019-23. The COVID-19 pandemic marked a dramatic change in the gender composition of gay bars and a slowing rate of decline.

🖺 Full Text HTML: The changing mix of gay bar subtypes after COVID-19 restrictions in the United States, 2017-2023

In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission -

Many hospitals have stopped or are considering stopping universal admission testing for SARS-CoV-2. We discuss reasons why admission testing should still be part of a layered system to prevent hospital-acquired SARS-CoV-2 infections during times of significant community transmission. These include the morbidity of SARS-CoV-2 in vulnerable patients, the predominant contribution of presymptomatic and asymptomatic people to transmission, the high rate of transmission between patients in shared rooms, and the data suggesting surveillance testing is associated with fewer nosocomial infections. Preferences of diverse patient populations, particularly the hardest hit communities, should be surveyed and used to inform prevention measures. Hospitals’ ethical responsibility to protect patients from serious infections should predominate over concerns about costs, labor, and inconvenience. We call for more rigorous data on the incidence and morbidity of nosocomial SARS-CoV-2 infections and more research to help determine when to start, stop, and restart universal admission testing and other prevention measures.

🖺 Full Text HTML: In Support of Universal Admission Testing for SARS-CoV-2 During Significant Community Transmission

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data -

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance (DA) patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because DA patterns are often of small effect size. Here we present a DA-testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous DA algorithm that uses Louvain for clustering, as well as local neighborhood-based DA-testing methods, demonstrating that ELVAR improves the sensitivity to detect DA-shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent DA-testing. ELVAR is available as an open-source R-package.

🖺 Full Text HTML: Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data

Cohort Profile: A longitudinal Victorian COVID-19 cohort (COVID PROFILE) -

Purpose: The COVID PROFILE cohort is a longitudinal clinical study based in Victoria Australia, which was established to understand immunity to SARS-CoV-2 in a low transmission population setting and to identify immunological markers of long-term immunity and immune-dysregulation after both infection and vaccination. Additionally, this cohort was established as a biobank resource for researchers to address other health-related immunological questions. Participants: We enrolled 178 adult community members, including household contacts, who had either recovered from a SARS-CoV-2 infection or were SARS-CoV-2 naive. Only participants 18 years of age or older and, in the case of female participants, non-pregnant women at the time of enrollment were included in the study. Detailed COVID-19 clinical data, vaccination status, medical history and demographics was collected. Findings to date: At enrollment, we found that 87.8% of COVID-19 recovered individuals were seropositive with detectable levels of anti-SARS-CoV-2 IgG antibodies. Seronegative COVID-19 recovered individuals included asymptomatic individuals or participants that were enrolled more than 12 months after their COVID-19 diagnosis. Except for one individual who was seropositive at baseline despite a previous SARS-CoV-2 PCR negative diagnosis, all household contacts and other community members enrolled as SARS-CoV-2 PCR negative, were seronegative for all SARS-CoV-2-specific antibodies tested. The infection rate (re-infection or new infection) during 24 months of the study was 42.7%, as determined by either rapid antigen tests, PCRs or serology screens. Of the SARS-CoV-2 recovered participants, 32.6% reported ongoing symptoms at enrollment of which 47% had already experienced ongoing symptoms for more than 12 weeks. Future Plans: COVID PROFILE will be used to comprehensively understand temporal immunity to SARS-CoV-2 and COVID-19 vaccines and to understand the impact of host immunological composition on such immunity and symptom severity. Additionally, studies focusing on understanding immunity following breakthrough infections and immunological risk factors that contribute towards development of long COVID are planned.

🖺 Full Text HTML: Cohort Profile: A longitudinal Victorian COVID-19 cohort (COVID PROFILE)

Population immunity of natural infection, primary-series vaccination, and booster vaccination in Qatar during the COVID-19 pandemic: An observational study -

Background: Waning of natural infection protection and vaccine protection highlight the need to evaluate changes in population immunity over time. Population immunity of previous SARS-CoV-2 infection or of COVID-19 vaccination are defined, respectively, as the overall protection against reinfection or against breakthrough infection at a given point in time in a given population. Methods: We estimated these population immunities in Qatar population between July 1, 2020 and November 30, 2022, to discern generic features of the epidemiology of SARS-CoV-2. Effectiveness of previous infection, mRNA primary-series vaccination, and mRNA booster (third-dose) vaccination in preventing infection were estimated, month by month, using matched, test-negative, case-control studies. Findings: Previous-infection effectiveness against reinfection was strong before emergence of Omicron, but declined with time after a wave and rebounded after a new wave. Effectiveness dropped immediately after Omicron emergence from 88.3% (95% CI: 84.8-91.0%) in November 2021 to 51.0% (95% CI: 48.3-53.6%) in December 2021. Primary-series effectiveness against infection was 84.0% (95% CI: 83.0-85.0%) in April 2021, soon after introduction of vaccination, before waning gradually to 52.7% (95% CI: 46.5-58.2%) by November of 2021. Effectiveness declined linearly by ~1 percentage point every 5 days. After Omicron emergence, effectiveness dropped suddenly from 52.7% (95% CI: 46.5-58.2%) in November 2021 to negligible levels in December 2021. Booster effectiveness dropped immediately after Omicron emergence from 83.0% (95% CI: 65.6 -91.6%) in November 2021 to 32.9% (95% CI: 26.7-38.5%) in December 2021, and continued to decline thereafter. Effectiveness of previous infection and vaccination against severe, critical, or fatal COVID-19 were generally >80% throughout the study duration. Interpretation: High population immunity may not be sustained beyond a year. This creates fertile grounds for repeated waves of infection to occur, but these waves may increasingly exhibit a benign pattern of infection. Funding: The Biomedical Research Program and the Biostatistics, Epidemiology, and the Biomathematics Research Core, both at Weill Cornell Medicine-Qatar, Ministry of Public Health, Hamad Medical Corporation, Sidra Medicine, Qatar Genome Programme, Qatar University Biomedical Research Center, and Qatar University Internal Grant ID QUCG-CAS-23/24-114.

🖺 Full Text HTML: Population immunity of natural infection, primary-series vaccination, and booster vaccination in Qatar during the COVID-19 pandemic: An observational study

The epidemiological impact of digital and manual contact tracing on the SARS-CoV-2 epidemic in the Netherlands: empirical evidence. -

Background. The Dutch government introduced the CoronaMelder smartphone application for digital contact tracing (DCT) to complement manual contact tracing (MCT) by Public Health Services (PHS) during the 2020-2022 SARS-CoV-2 epidemic. Modelling studies showed great potential but empirical evidence of DCT and MCT impact is scarce. Methods. We determined reasons for testing, and mean exposure-testing intervals by reason for testing, using routine data from PHS Amsterdam (1 December 2020 to 31 May 2021) and data from two SARS-CoV-2 rapid diagnostic test accuracy studies at other PHS sites in the Netherlands (14 December 2020 to 18 June 2021). Throughout the study periods, notification of DCT-identified contacts was via PHS contact-tracers, and self-testing was not yet widely available. Results. The most commonly reported reason for testing was having symptoms. In asymptomatic individuals, it was having been warned by an index case. Only around 2% and 2-5% of all tests took place after DCT or MCT notification, respectively. About 20-36% of those who had received a DCT or MCT notification had symptoms at the time of test request. Test positivity after a DCT notification was significantly lower, and exposure-test intervals after a DCT or MCT notification were longer, than for the above-mentioned other reasons for testing. Conclusions. Our data suggest that the impact of DCT and MCT on the SARS-CoV-2 epidemic in the Netherlands was limited. However, DCT impact might be enlarged if app use coverage is improved, contact-tracers are eliminated from the digital notification process to minimise delays, and DCT is combined with self-testing.

🖺 Full Text HTML: The epidemiological impact of digital and manual contact tracing on the SARS-CoV-2 epidemic in the Netherlands: empirical evidence.

Migrant healthcare workers experiences and mobility patterns in a global health crisis: a qualitative study with Romania physicians working in Germany -

Introduction. Attention to the healthcare workforce has increased, yet comprehensive information on migrant healthcare workers is missing. This study focuses on migrant healthcare workers experiences and explores mobility patterns in the middle of a global health crisis, aiming to explore the capacity for circular migration and support effective equitable healthcare workforce policy. Methods. Romanian physicians in Germany served as an empirical case study. We applied a qualitative explorative approach; interviews (n=21) were collected from mid of September to early November 2022 and content analysis was performed. Results. Migrant physicians showed strong resilience during the COVID-19 crisis and rarely complained. Commitment to high professional standards and career development were major pull factors towards Germany, while perceptions of limited career choices, nepotism and corruption in Romania caused strong push mechanisms. We identified two major mobility patterns that may support circular migration policies: well-integrated physicians with a wish to give something back to their home country, and mobile cosmopolitan physicians who flexibly balance career opportunities and personal/family interests. Conclusions. Health policy must establish systematic monitoring of the migrant HCWF including actor-centred approaches, support integration in destination countries as well as health system development in sending countries, and invest in evidence-based circular migration policy.

🖺 Full Text HTML: Migrant healthcare workers experiences and mobility patterns in a global health crisis: a qualitative study with Romania physicians working in Germany

Mortality Classification of Hospitalized COVID-19 Patients in Zambia Using Machine Learning -

The Corona Virus Disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included: decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), naïve bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best performing model was the XGB which had accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann-Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients’ hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness and decongest health facilities in Zambia and other countries with similar settings.

🖺 Full Text HTML: Mortality Classification of Hospitalized COVID-19 Patients in Zambia Using Machine Learning

Discovery of GS-5245 (Obeldesivir), an Oral Prodrug of Nucleoside GS-441524 that Exhibits Antiviral Efficacy in SARS-CoV-2 Infected African Green Monkeys -

Remdesivir 1 is an amidate prodrug that releases the monophosphate of nucleoside GS-441524 (2) into lung cells thereby forming the bioactive triphosphate 2-NTP. 2-NTP, an analog of ATP, inhibits the SARS-CoV-2 RNA-dependent RNA polymerase replication and transcription of viral RNA. Strong clinical results for 1 have prompted interest in oral approaches to generate 2-NTP. Here we describe the discovery of a 5’-isobutyryl ester prodrug of 2 (GS-5245, Obeldesivir, 3) that has low cellular cytotoxicity and three to seven-fold improved oral delivery of 2 in monkeys. Prodrug 3 is cleaved pre-systemically to provide high systemic exposures of 2 that overcome its less efficient metabolism to 2-NTP leading to strong SARS-CoV-2 antiviral efficacy in an African green monkey infection model. Exposure-based SARS-CoV-2 efficacy relationships resulted in an estimated clinical dose of 350-400 mg twice-daily. Importantly, all SARS-CoV-2 variants remain susceptible to 2 which supports development of 3 as a promising COVID-19 treatment.

🖺 Full Text HTML: Discovery of GS-5245 (Obeldesivir), an Oral Prodrug of Nucleoside GS-441524 that Exhibits Antiviral Efficacy in SARS-CoV-2 Infected African Green Monkeys

Impact of the COVID-19 Pandemic, Stratified by Transfer and COVID-19 Infection Status, on Inpatient Mortality in ST-Elevation Myocardial Infarction Patients, Using a Nationally Representative Database -

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has impacted various aspects of healthcare, including the management of ST-elevation myocardial infarction (STEMI) patients. Our study investigates the in-hospital outcomes and the impact of transfer and COVID-19 infection status on mortality in STEMI patients. Methods: We conducted a retrospective cohort study to compare the inpatient outcomes of STEMI patients in 2020 with STEMI patients from 2016 to 2019 using the National Inpatient Sample database. We performed 1:1 greedy nearest neighbor matching and utilized logistic regression to compare mortality. Results: In our matched cohort, there was no difference in overall mortality between STEMI patients in 2020 and those from 2016 to 2019 (OR 1.00, 95% CI: 0.94-1.05; p = 0.87). When stratified by COVID-19 infection status, regularly admitted STEMI patients with concurrent COVID-19 infection in 2020 had 2.11 times higher odds of inpatient mortality compared to regularly admitted STEMI patients from 2016 to 2019 (OR 2.11, 95% CI: 1.55-2.87; p < 0.001). STEMI acute care transfers with concurrent COVID-19 infection in 2020 had 3.17 times higher odds of inpatient mortality than those from 2016 to 2019 (OR 3.17, 95% CI: 1.83-5.50; p < 0.001). STEMI non-acute care transfers with concurrent COVID-19 infection in 2020 had 5.13 times higher odds of inpatient mortality than those from 2016 to 2019 (OR 5.13, 95% CI: 1.87-14.06; p = 0.001). Conclusion: The COVID-19 pandemic exacerbated many longstanding disparities within our healthcare system. Moving forward, it is crucial to engage in further discussions addressing the national physician shortage, the patient transfer system and healthcare in underserved regions.

🖺 Full Text HTML: Impact of the COVID-19 Pandemic, Stratified by Transfer and COVID-19 Infection Status, on Inpatient Mortality in ST-Elevation Myocardial Infarction Patients, Using a Nationally Representative Database

Quantifying the Risk of General Health and Early COVID-19 Spread in Residential Buildings with Deep Learning and Expert-augmented Machine Learning -

Buildings9 built environment has been linked to their occupants9 health. It remains unclear whether those elements that predisposed its residents to poor general health before the two SARS pandemics also put residents at risk of contracting COVID-19 during early outbreaks. Relevant research to uncover the associations is essential, but there lacks a systematic examination of the relative contributions of different elements in one9s built environment and other non-environmental factors, singly or jointly. Hence, the current study developed a deep-learning approach with multiple input channels to capture the hierarchical relationships among an individual9s socioecology9s demographical, medical, behavioral, psychosocial, and built-environment levels. Our findings supported that 1) deep-learning models whose inputs were structured according to the hierarchy of one9s socioecology outperformed plain models with one-layered input in predicting one9s general health outcomes, with the model whose hierarchically structured input layers included one9s built environment performed best; 2) built-environment features were more important to general health compared to features of one9s sociodemographic and their health-related quality of life, behaviors, and service utilization; 3) a composite score representing built-environment features9 statistical importance to general health significantly predicted building-level COVID-19 case counts; and 4) building configurations derived from the expert-augmented learning of granular built-environment features that were of high importance to the general health were also linked to building-level COVID-19 case counts of external samples. Specific built environments put residents at risk for poor general health and COVID-19 infections. Our machine-learning approach can benefit future quantitative research on sick buildings, health surveillance, and housing design.

🖺 Full Text HTML: Quantifying the Risk of General Health and Early COVID-19 Spread in Residential Buildings with Deep Learning and Expert-augmented Machine Learning

Inequalities in Healthcare Use during the COVID-19 Pandemic -

The COVID-19 pandemic has led to severe reductions in non-COVID related healthcare use, but little is known whether this burden is shared equally across the population. This study investigates whether the reduction in administered care disproportionately affected certain sociodemographic strata, in particular marginalised groups. Using detailed medical claims data from the Dutch universal health care system and rich registry data that cover all residents in The Netherlands, we predict expected healthcare use based on pre-pandemic trends (2017- Feb 2020) and compare these expectations with observed healthcare use in 2020. Our findings reveal a substantial 10% decline in the number of weekly treated patients in 2020 relative to prior years. Furthermore, declines in healthcare use are unequally distributed and are more pronounced for individuals below the poverty line, females, the elderly, and foreign-born individuals, with cumulative relative risk ratios ranging from 1.09 to 1.22 higher than individuals above the poverty line, males, young, and native-born. These inequalities stem predominantly from declines in middle and low urgency procedures, and indicate that the pandemic has not only had an unequal toll in terms of the direct health burden of the pandemic, but has also had a differential impact on the use of non-COVID healthcare.

🖺 Full Text HTML: Inequalities in Healthcare Use during the COVID-19 Pandemic

Evaluation of the Pilot Wastewater Surveillance for SARS-CoV-2 in Norway, June 2022 - March 2023 -

Background: During the COVID-19 pandemic, wastewater-based surveillance gained great international interest as an additional tool to monitor SARS-CoV-2. In autumn 2021, the Norwegian Institute of Public Health decided to pilot a national wastewater surveillance (WS) system for SARS-CoV-2 and its variants between June 2022 and March 2023. We evaluated the system to assess if it met its objectives and its attribute-based performance. Methods: We adapted the available guidelines for evaluation of surveillance systems. The evaluation was carried out as a descriptive analysis and consisted of the following three steps: (i) description of the WS system, (ii) identification of users and stakeholders, and (iii) analysis of the system attributes and performance including sensitivity, specificity, timeliness, usefulness, representativeness, simplicity, flexibility, stability, and communication. Cross-correlation analysis was performed to assess the system ability to provide early warning signal of new wave of infections. Results: The pilot WS system was a national surveillance system using existing wastewater infrastructures from the largest Norwegian municipalities. We found that the system was sensitive, timely, useful, representative, simple, flexible, acceptable, and stable to follow the general trend of infection. Preliminary results indicate that the system could provide an early signal of changes in variant distribution. However, challenges may arise with: (i) specificity due to temporary fluctuations of RNA levels in wastewater, (ii) representativeness when downscaling, and (iii) flexibility and acceptability when upscaling the system due to limited resources and/or capacity. Conclusions: Our results showed that the pilot WS system met most of its surveillance objectives. The system was able to provide an early warning signal of 1-2 weeks, and the system was useful to monitor infections at population level and complement routine surveillance when individual testing activity was low. However, temporary fluctuations of WS values need to be carefully interpreted. To improve quality and efficiency, we recommend to standardise and validate methods for assessing trends of new waves of infection and variants, evaluate the WS system using a longer operational period particularly for new variants, and conduct prevalence studies in the population to calibrate the system and improve data interpretation.

🖺 Full Text HTML: Evaluation of the Pilot Wastewater Surveillance for SARS-CoV-2 in Norway, June 2022 - March 2023

Demographic Influence on the Effectiveness of England’s SARS-CoV-2 Policies -

Health data is key for the development of medicinal drugs, treatments, and policy-planning to control the spread of infectious diseases. However, the collection, curation, and interpretation of health data is often biased. This paper discusses England-wide impact of public policies to control the spread of SARS-CoV-2 (Covid-19) infections, based on the recorded per-capita infection cases between July 2020 to January 2023. The analysis presented herewith highlights the disparities across the upper local tier authorities, in the number of Covid-19 cases recorded in response to the policies. This paper further presents the correlation between the Covid-19 cases count and demographic factors, thus highlighting the key factors determining the effectiveness of the NHS policies, and therefore the need for incorporating demographic imbalance in the policy planning process. It is concluded that the upper local tier authorities comprise of three clusters of low, mid, and high prevalence of Covid-19 infections. Where the regions with high prevalence of Covid-19 cases are also the ones with higher proportions of Black/ Mixed racial groups, amongst a mid-range and low internal Migrations.

🖺 Full Text HTML: Demographic Influence on the Effectiveness of England’s SARS-CoV-2 Policies

The impact of atypical intrahospital transfers on patient outcomes: a mixed methods study -

Background: The architectural design of hospitals worldwide is centred around individual departments, which require the movement of patients between wards. However, patients do not always take the simplest route from admission to discharge, but can experience convoluted movement patterns, particularly when bed availability is low. Few studies have explored the impact of these rarer, atypical trajectories. Methods: Using a mixed-method explanatory sequential study design, we firstly used three continuous years of electronic health record data prior to the Covid-19 pandemic, from 55,152 patients admitted to a London hospital network to define the ward specialities by patient type using the Herfindahl-Hirschman index. We explored the impact of regular transfers between pairs of wards with shared specialities, atypical transfers between pairs of wards with no shared specialities and site transfers between pairs of wards in different hospital site locations, on length of stay, 30-day readmission and mortality. Secondly, to understand the possible reasons behind atypical transfers we conducted three focus groups and three interviews with site nurse practitioners and bed managers within the same hospital network. Results: We found that at least one atypical transfer was experienced by 12.9% of patients. Each atypical transfer is associated with a larger increase in length of stay, 2.84 days (95%CI: 2.56-3.12), compared to regular transfers, 1.92 days (95%CI: 1.82-2.03). No association was found between odds of mortality, or 30-day readmission and atypical transfers after adjusting for confounders. Atypical transfers appear to be driven by complex patient conditions, a lack of hospital capacity, the need to reach specific services and facilities, and more exceptionally, rare events such as major incidents. Conclusion: Our work provides an important first step in identifying unusual patient movement and its impacts on key patient outcomes using a system-wide, data-driven approach. The broader impact of moving patients between hospital wards, and possible downstream effects should be considered in hospital policy and service planning.

🖺 Full Text HTML: The impact of atypical intrahospital transfers on patient outcomes: a mixed methods study

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