Associations between cancer history, social distancing behaviors, and loneliness in adults during the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, enforced social distancing initiatives have highlighted differences in social distancing practices and the resulting loneliness in various populations. The objective of this study was to examine how cancer history and social distancing practices relate to loneliness during COVID-19. METHODS AND FINDINGS: Participants from previous studies (N = 32,989) with permission to be re-contacted were invited to complete a survey online, by phone, or by mail between June and November 2020. Linear and logistic regression models were used to determine the associations between cancer history, social distancing, and loneliness. RESULTS: Among the included participants (n = 5729), the average age was 56.7 years, 35.6% were male, 89.4% were White, and 54.9% had a cancer history (n = 3147). Individuals with a cancer history were more likely to not contact people outside of their household (49.0% vs. 41.9%, p<0.01), but were less likely to feel lonely (35.8% vs. 45.3%, p<0.0001) compared to those without a cancer history. Higher adherence to social distancing behaviors was associated with higher odds of loneliness among individuals with (OR = 1.27, 95% CI: 1.17-1.38) and without a cancer history (OR = 1.15, 95% CI: 1.06-1.25). CONCLUSIONS: Findings from this study can inform efforts to support the mental health of individuals susceptible to loneliness during the COVID-19 pandemic.

An adversarial training framework for mitigating algorithmic biases in clinical machine learning.

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.

Privacy-Aware Early Detection of COVID-19 Through Adversarial Training.

Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect potential cases of coronavirus using routine clinical data (blood tests, and vital signs measurements). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this study, two machine learning techniques that aim to predict a patient's COVID-19 status are examined. Using adversarial training, robust deep learning architectures are explored with the aim to protect attributes related to demographic information about the patients. The two models examined in this work are intended to preserve sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets from the Oxford University Hospitals (OUH), Bedfordshire Hospitals NHS Foundation Trust (BH), University Hospitals Birmingham NHS Foundation Trust (UHB), and Portsmouth Hospitals University NHS Trust (PUH), two neural networks are trained and evaluated. These networks predict PCR test results using information from basic laboratory blood tests, and vital signs collected from a patient upon arrival to the hospital. The level of privacy each one of the models can provide is assessed and the efficacy and robustness of the proposed architectures are compared with a relevant baseline. One of the main contributions in this work is the particular focus on the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks. The results on hold-out test set and external validation confirmed that there was no impact on the generalisibility of the model using adversarial learning.

Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening.

As patient health information is highly regulated due to privacy concerns, most machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. Different approaches have been introduced for developing models across multiple clinical sites, however less attention has been given to adopting ready-made models in new settings. We introduce three methods to do this-(1) applying a ready-made model "as-is" (2); readjusting the decision threshold on the model's output using site-specific data and (3); finetuning the model using site-specific data via transfer learning. Using a case study of COVID-19 diagnosis across four NHS Hospital Trusts, we show that all methods achieve clinically-effective performances (NPV > 0.959), with transfer learning achieving the best results (mean AUROCs between 0.870 and 0.925). Our models demonstrate that site-specific customization improves predictive performance when compared to other ready-made approaches.

Seroprevalence of anti-SARS-CoV-2 antibodies in a cohort of New York City metro blood donors using multiple SARS-CoV-2 serological assays: Implications for controlling the epidemic and "Reopening".

Projections of the stage of the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) pandemic and local, regional and national public health policies to limit coronavirus spread as well as "reopen" cities and states, are best informed by serum neutralizing antibody titers measured by reproducible, high throughput, and statically credible antibody (Ab) assays. To date, a myriad of Ab tests, both available and FDA authorized for emergency, has led to confusion rather than insight per se. The present study reports the results of a rapid, point-in-time 1,000-person cohort study using serial blood donors in the New York City metropolitan area (NYC) using multiple serological tests, including enzyme-linked immunosorbent assays (ELISAs) and high throughput serological assays (HTSAs). These were then tested and associated with assays for neutralizing Ab (NAb). Of the 1,000 NYC blood donor samples in late June and early July 2020, 12.1% and 10.9% were seropositive using the Ortho Total Ig and the Abbott IgG HTSA assays, respectively. These serological assays correlated with neutralization activity specific to SARS-CoV-2. The data reported herein suggest that seroconversion in this population occurred in approximately 1 in 8 blood donors from the beginning of the pandemic in NYC (considered March 1, 2020). These findings deviate with an earlier seroprevalence study in NYC showing 13.7% positivity. Collectively however, these data demonstrate that a low number of individuals have serologic evidence of infection during this "first wave" and suggest that the notion of "herd immunity" at rates of ~60% or higher are not near. Furthermore, the data presented herein show that the nature of the Ab-based immunity is not invariably associated with the development of NAb. While the blood donor population may not mimic precisely the NYC population as a whole, rapid assessment of seroprevalence in this cohort and serial reassessment could aid public health decision making.

Outcomes of a NYC Public Hospital System Low-Threshold Tele-Buprenorphine Bridge Clinic at 1 Year.

Background: This study evaluated clinical outcomes of a low barrier tele-buprenorphine bridge program for NYC residents with opioid use disorder (OUD) at 1 year during the coronavirus disease 2019 (COVID-19) pandemic. Methods and materials: This retrospective analysis of the NYC Health + Hospitals (NYC H + H) Virtual Buprenorphine Clinic registry assessed baseline demographic and clinical characteristics, rates of referrals to community treatment, and induction-related adverse events among city residents with OUD, from March 2020 to the end of March 2021. Results: The program enrolled 199 patients, of whom 62.3% were provided same-day visits (n = 124). Patients were enrolled in the program for a median of 14 days (range 0-130 days). Referrals sources included hospital and clinic staff (n = 83, 47.7%), word of mouth (n = 30, 17.2%), and correctional health or reentry services (n = 30, 17.2%). Induction-related adverse events were mostly limited to precipitated withdrawal symptoms (n = 21, 5%). Roughly half of patients were referred to community treatment (n = 109, 54.8%) and of those 51.4% (n = 56/109) completed at least one visit in community treatment. Discussion: Our experience indicates that a low threshold tele-buprenorphine bridge program in place of a safe and feasible approach to facilitating entry in community treatment for underserved people who use opioids in a large metropolitan area.

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening.

BACKGROUND: Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12-24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. METHODS: We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). FINDINGS: 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858-0·881, 95% CI 0·838-0·912, for CURIAL-Lab and 0·836-0·854, 0·814-0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5-85·7, for CURIAL-Lab and 83·5%, 81·8-85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9-71·8) for CURIAL-Lab and 63·6% (63·1-64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7-62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6-88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4-91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32-64), 16 min (26·3%) sooner than with LFDs (61 min, 37-99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9-97·8), specificity of 85·4% (81·3-88·7), and negative predictive value of 99·7% (98·2-99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR. INTERPRETATION: Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Early innovations in opioid use disorder treatment and harm reduction during the COVID-19 pandemic: a scoping review.

BACKGROUND: The COVID-19 pandemic has exerted a significant toll on the lives of people who use opioids (PWUOs). At the same time, more flexible regulations around provision of opioid use disorder (OUD) services have led to new opportunities for facilitating access to services for PWUOs. In the current scoping review, we describe new services and service modifications implemented by treatment and harm reduction programs serving PWUO, and discuss implications for policy and practice. METHODS: Literature searches were conducted within PubMed, LitCovid, Embase, and PsycInfo for English-language studies published in 2020 that describe a particular program, service, or intervention aimed at facilitating access to OUD treatment and/or harm reduction services during the COVID-19 pandemic. Abstracts were independently screened by two reviewers. Relevant studies were reviewed in full and those that met inclusion criteria underwent final data extraction and synthesis (n = 25). We used a narrative synthesis approach to identify major themes around key service modifications and innovations implemented across programs serving PWUO. RESULTS: Reviewed OUD treatment and harm reduction services spanned five continents and a range of settings from substance use treatment to street outreach programs. Innovative service modifications to adapt to COVID-19 circumstances primarily involved expanded use of telehealth services (e.g., telemedicine visits for buprenorphine, virtual individual or group therapy sessions, provision of donated or publicly available phones), increased take-home medication allowances for methadone and buprenorphine, expanded uptake of long-acting opioid medications (e.g. extended-release buprenorphine and naltrexone), home delivery of services (e.g. MOUD, naloxone and urine drug screening), outreach and makeshift services for delivering MOUD and naloxone, and provision of a safe supply of opioids. CONCLUSIONS: The COVID-19 pandemic has posed multiple challenges for PWUOs, while simultaneously accelerating innovations in policies, care models, and technologies to lower thresholds for life-saving treatment and harm reduction services. Such innovations highlight novel patient-centered and feasible approaches to mitigating OUD related harms. Further studies are needed to assess the long-term impact of these approaches and inform policies that improve access to care for PWUOs.

Low Seroprevalence of SARS-CoV-2 in Rhode Island blood donors during may 2020 as determined using multiple serological assay formats.

BACKGROUND: Epidemic projections and public health policies addressing Coronavirus disease (COVID)-19 have been implemented without data reporting on the seroconversion of the population since scalable antibody testing has only recently become available. METHODS: We measured the percentage of severe acute respiratory syndrome- Coronavirus-2 (SARS-CoV-2) seropositive individuals from 2008 blood donors drawn in the state of Rhode Island (RI). We utilized multiple antibody testing platforms, including lateral flow immunoassays (LFAs), enzyme-linked immunosorbent assays (ELISAs) and high throughput serological assays (HTSAs). To estimate seroprevalence, we utilized the Bayesian statistical method to adjust for sensitivity and specificity of the commercial tests used. RESULTS: We report than an estimated seropositive rate of RI blood donors of approximately 0.6% existed in April-May of 2020. Daily new case rates peaked in RI in late April 2020. We found HTSAs and LFAs were positively correlated with ELISA assays to detect antibodies specific to SARS-CoV-2 in blood donors. CONCLUSIONS: These data imply that seroconversion, and thus infection, is likely not widespread within this population. We conclude that IgG LFAs and HTSAs are suitable to conduct seroprevalence assays in random populations. More studies will be needed using validated serological tests to improve the precision and report the kinetic progression of seroprevalence estimates.

Treatment of Bronchopleural and Alveolopleural Fistulas in Acute Respiratory Distress Syndrome With Extracorporeal Membrane Oxygenation, a Case Series and Literature Review.

OBJECTIVES: To describe a ventilator and extracorporeal membrane oxygenation management strategy for patients with acute respiratory distress syndrome complicated by bronchopleural and alveolopleural fistula with air leaks. DESIGN SETTING AND PARTICIPANTS: Case series from 2019 to 2020. Single tertiary referral center-University of California, San Diego. Four patients with various etiologies of acute respiratory distress syndrome, including influenza, methicillin-resistant Staphylococcus aureus pneumonia, e-cigarette or vaping product use-associated lung injury, and coronavirus disease 2019, complicated by bronchopleural and alveolopleural fistula and chest tubes with air leaks. MEASUREMENTS AND MAIN RESULTS: Bronchopleural and alveolopleural fistula closure and survival to discharge. All four patients were placed on extracorporeal membrane oxygenation with ventilator settings even lower than Extracorporeal Life Support Organization guideline recommended ultraprotective lung ventilation. The patients bronchopleural and alveolopleural fistulas closed during extracorporeal membrane oxygenation and minimal ventilatory support. All four patients survived to discharge. CONCLUSIONS: In patients with acute respiratory distress syndrome and bronchopleural and alveolopleural fistula with persistent air leaks, the use of extracorporeal membrane oxygenation to allow for even lower ventilator settings than ultraprotective lung ventilation is safe and feasible to mediate bronchopleural and alveolopleural fistula healing.