ABSTRACT
Given the enormous global impact of the COVID-19 pandemic, outbreaks of highly pathogenic avian influenza in Canada, and manifold other zoonotic pathogen activity, there is a pressing need for a deeper understanding of the human-animal-environment interface and the intersecting biological, ecological, and societal factors contributing to the emergence, spread, and impact of zoonotic diseases. We aim to apply a One Health approach to pressing issues related to emerging zoonoses, and propose a functional framework of interconnected but distinct groups of recommendations around strategy and governance, technical leadership (operations), equity, education and research for a One Health approach and Action Plan for Canada. Change is desperately needed, beginning by reorienting our approach to health and recalibrating our perspectives to restore balance with the natural world in a rapid and sustainable fashion. In Canada, a major paradigm shift in how we think about health is required. All of society must recognize the intrinsic value of all living species and the importance of the health of humans, other animals, and ecosystems to health for all. © 2022 Authors: Mubareka, Amuasi, Banerjee, Carabin, Jack, Jardine, Jaroszewicz, Keefe, Kotwa, Kutz, McGregor, Mease, Nicholson, Nowak, Reed, Saint-Charles, Simonienko, Weese, Parmley, and The Crown.
ABSTRACT
INTRODUCTION: Several state-based and single center studies have demonstrated evidence of higher COVID-19 exposure rates, infection rates, and worse morbidity and mortality outcomes among minorities. Furthermore, challenges with vaccine access, hesitancy, distrust of the medical system further influenced who was protected from COVID-19.This study combines databases to conduct a multisite study across diverse states during the pandemic. METHOD(S): We conducted an ancillary study using the VIRUS (Viral Infection and Respiratory illness Universal Study) registry data supplemented by electronic medical record data from Mayo Clinic enterprise to assess demographics and outcomes among hospitalized patients with severe COVID-19. We included hospitalized adult patients admitted in five participating sites between April 2020 and January 2022 including academic hospitals in MN, AZ, and FL and two community hospitals in MN and WI. Selfidentified race and ethnicity data was categorized as White, Black, Asian, and Other;Hispanic and non-Hispanic. Other baseline characteristics, disease severity, and vaccination status were included in the analyses. The primary outcome was hospital mortality, the secondary outcomes were length of stay and healthcare utilization. Multivariable regression models were developed to analyze the interactions of relevant variables to predict outcomes. RESULT(S): 6904 patients were included. 3398 (57.8%) were male and 86.9% White,3.6% Black,3.3% Asian,6.2% Other. The mean age of Whites was 64.9 years v.53.8, 58, 52.8 respectively (p< 0.0005). Whites had higher Charlson comorbidity scores-5.2 v.4.0,3.6,3.0 respectively (p< 0.005). Vaccination rates were low in cohort, but higher among Whites 11.2% v.5.4%,4.6%,5.0% respectively (p< 0.0005). Mortality outcomes between different racial groups did not differ (p=0.41). Non-Hispanics were older than Hispanics- mean age 64.5 years v.53 (p< 0.005) and had higher Charlson comorbidity scores-5.2 v.3.4 (p< 0.005) Vaccination rates among non-Hispanics were 10.7 v 3.4% (p< 0.005)). Mortality outcomes between ethnic groups did not differ(p=0.86). Mortality outcomes between vaccinated and unvaccinated patients did not differ (p=0.9). CONCLUSION(S): Despite differences in risk factors between demographic groups, outcomes did not differ significantly in this cohort.
ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) global pandemic required a rapid and effective response. This included ethical and legally appropriate sharing of data. The European Commission (EC) called upon the Research Data Alliance (RDA) to recruit experts worldwide to quickly develop recommendations and guidelines for COVID-related data sharing. Purpose: The purpose of the present work was to explore how the RDA succeeded in engaging the participation of its community of scientists in a rapid response to the EC request. Methods: A survey questionnaire was developed and distributed among RDA COVID-19 work group members. A mixed-methods approach was used for analysis of the survey data. Results: The three constructs of radical collaboration (inclusiveness, distributed digital practices, productive and sustainable collaboration) were found to be well supported in both the quantitative and qualitative analyses of the survey data. Other social factors, such as motivation and group identity were also found to be important to the success of this extreme collaborative effort. Conclusions: Recommendations and suggestions for future work were formulated for consideration by the RDA to strengthen effective expert collaboration and interdisciplinary efforts. © 2021 Pickering B et al.
ABSTRACT
To use technology or engage with research or medical treatment typically requires user consent: agreeing to terms of use with technology or services, or providing informed consent for research participation, for clinical trials and medical intervention, or as one legal basis for processing personal data. Introducing AI technologies, where explainability and trustworthiness are focus items for both government guidelines and responsible technologists, imposes additional challenges. Understanding enough of the technology to be able to make an informed decision, or consent, is essential but involves an acceptance of uncertain outcomes. Further, the contribution of AIenabled technologies not least during the COVID-19 pandemic raises ethical concerns about the governance associated with their development and deployment. Using three typical scenarios— contact tracing, big data analytics and research during public emergencies—this paper explores a trustbased alternative to consent. Unlike existing consent-based mechanisms, this approach sees consent as a typical behavioural response to perceived contextual characteristics. Decisions to engage derive from the assumption that all relevant stakeholders including research participants will negotiate on an ongoing basis. Accepting dynamic negotiation between the main stakeholders as proposed here introduces a specifically socio–psychological perspective into the debate about human responses to artificial intelligence. This trust-based consent process leads to a set of recommendations for the ethical use of advanced technologies as well as for the ethical review of applied research projects. © 2021 by the author. Licensee MDPI, Basel, Switzerland.