Predictors of COVID-19 Vaccination Among EMS Personnel.

INTRODUCTION: Unvaccinated emergency medical services (EMS) personnel are at increased risk of contracting coronavirus disease 2019 (COVID-19) and potentially transmitting the virus to their families, coworkers, and patients. Effective vaccines for the severe acute respiratory syndrome coronavirus 2 virus exist; however, vaccination rates among EMS professionals remain largely unknown. Consequently, we sought to document vaccination rates of EMS professionals and identify predictors of vaccination uptake. METHODS: We conducted a cross-sectional survey of North Carolina EMS professionals after the COVID-19 vaccines were widely available. The survey assessed vaccination status as well as beliefs regarding COVID-19 illness and vaccine effectiveness. Prediction of vaccine uptake was modeled using logistic regression. RESULTS: A total of 860 EMS professionals completed the survey, of whom 74.7% reported receiving the COVID-19 vaccination. Most respondents believed that COVID-19 is a serious threat to the population, that they are personally at higher risk of infection, that vaccine side effects are outweighed by illness prevention, and the vaccine is safe and effective. Despite this, only 18.7% supported mandatory vaccination for EMS professionals. Statistically significant differences were observed between the vaccinated and unvaccinated groups regarding vaccine safety and effectiveness, recall of employer vaccine recommendation, perceived risk of infection, degree of threat to the population, and trust in government to take actions to limit the spread of disease. Unvaccinated respondents cited reasons such as belief in personal health and natural immunity as protectors against infection, concerns about vaccine safety and effectiveness, inadequate vaccine knowledge, and lack of an employer mandate for declining the vaccine. Predictors of vaccination included belief in vaccine safety (odds ratio [OR] 5.5, P=<0.001) and effectiveness (OR 4.6, P=<0.001); importance of vaccination to protect patients (OR 15.5, P=<0.001); perceived personal risk of infection (OR 1.8, P=0.04); previous receipt of influenza vaccine (OR 2.5, P=0.003); and sufficient knowledge to make an informed decision about vaccination (OR 2.4, P=0.024). CONCLUSION: In this survey of EMS professionals, over a quarter remained unvaccinated for COVID-19. Given the identified predictors of vaccine acceptance, EMS systems should focus on countering misinformation through employee educational campaigns as well as on developing policies regarding workforce immunization requirements.

How does nudging the COVID-19 vaccine play out in people who are in doubt about vaccination?

In spite of the growing availability of COVID-19 vaccines, a substantial number of people is reluctant or uncertain about getting the vaccine. Nudges may improve vaccine uptake but it is unclear how this plays out with the experience of autonomous choice, decision competence, decision satisfaction, and being pressured to make a choice. In an online experiment among a representative sample (N = 884), we examined whether a social norm nudge or a default nudge (either or not transparent) was effective in steering the desired choice of making a hypothetical early vaccination appointment as compared to making a later appointment or no appointment. We also examined how both nudges affected autonomy and related downstream consequences. None of the nudges proved effective in making the desired choice of early vaccination and neither did they impact on downstream consequences. Rather, our results indicate that participants who were certain about their choice (i.e., opted for the earliest available vaccination opportunity or not getting vaccinated at all) reported higher levels of autonomy, competence and satisfaction than participants who did not know yet about vaccination or who postponed the moment of getting their vaccination. We conclude that the experience of autonomy and related downstream consequences is determined by having made up one's mind about vaccination, and is not affected by attempts to nudge the individual.

Clinical usefulness of the Patient-Generated Subjective Global Assessment and modified Glasgow Prognostic Score in decision making concerning the indication of enteral nutritional therapy in patients with incurable cancer receiving palliative care.

OBJECTIVES: This study aimed to identify the clinical usefulness of assessing nutritional status using validated tools for the indication of enteral nutrition for patients with incurable cancer in palliative care. METHODS: In this prospective cohort study, patients were assessed for nutritional risk using the Patient-Generated Subjective Global Assessment and for cancer cachexia (CC) using the modified Glasgow Prognostic Score upon enrollment and after ∼30 d. The outcome was stable or improved Karnofsky Performance Status. Logistic regression models were used, providing the odds ratio (OR) and 95% confidence interval (CI). RESULTS: A total of 180 patients participated. The only nutritional status parameter that was associated with function was CC. The less severe the CC, the more likely Karnofsky Performance Status was to remain stable or improve over 30 d (non-cachectic: OR = 1.95; 95% CI, 1.01-3.47; malnourished: OR = 1.06; 95% CI, 1.01-1.42). Furthermore, white skin color (OR = 1.79; 95% CI, 1.04-2.47), higher educational level (OR = 1.39; 95% CI, 1.13-2.78), and inadequate calorie intake (OR = 1.96; 95% CI, 1.02-2.81) were also associated with the outcome. CONCLUSIONS: Using the modified Glasgow Prognostic Score to identify the existence and severity of CC, which is associated with function, has the potential to help clinical decision making concerning the indication of enteral nutrition in patients with incurable cancer receiving palliative care.

Best Practices for the Design of COVID-19 Dashboards.

Since 2020, health informaticians have developed and enhanced public-facing COVID-19 dashboards worldwide. The improvement of dashboards implemented by health informaticians will ultimately benefit the public in making better healthcare decisions and improve population-level healthcare outcomes. The authors evaluated 100 US city, county, and state government COVID-19 health dashboards and identified the top 10 best practices to be considered when creating a public health dashboard. These features include 1) easy navigation, 2) high usability, 3) use of adjustable thresholds, 4) use of diverse chart selection, 5) compliance with the Americans with Disabilities Act, 6) use of charts with tabulated data, 7) incorporated user feedback, 8) simplicity of design, 9) adding clear descriptions for charts, and 10) comparison data with other entities. To support their findings, the authors also conducted a survey of 118 randomly selected individuals in six states and the District of Columbia that supports these top 10 best practices for the design of health dashboards.

Decision-Making in Sport During a Pandemic.

OBJECTIVE: The COVID-19 pandemic has affected all elements of global society, and sport is not exempt. Many sporting events have been either postponed or canceled, and national sporting organizations have had to make highly complex decisions in the face of scientific uncertainty and risk. This article applies these lessons to the world of sport with the goal of assisting sporting organizations to make sound and reasoned decisions during a pandemic. DATA SOURCES: A narrative approach using both academic literature sources and live examples from the authors' experience. We use Daniels and Sabin's accountability for reasonableness framework to facilitate decision-making in the face of such uncertainty. MAIN RESULTS: Decision-making in the context of uncertainty has the potential to create conflict and disengagement from key stakeholders. Evidence from recent pandemics has illustrated that an ethical approach to decision-making results in reasoned decision-making and confers a legitimacy to decisions that ultimately supports engagement and satisfaction from stakeholders. CONCLUSIONS: The incorporation of ethical considerations into risk assessment and management when making complex decisions, which incorporate high levels of uncertainty, will assist sporting organizations have positive outcomes.

Zoom and its Discontents: Group Decision Making in Pediatric Cardiology in the Time of COVID (and Beyond) : Aurthors.

The emergence of Covid-19 has led to change within hospital-based healthcare. An example, has been to reconfigure clinical decision making meetings from traditional in-person (Face-to-face, FtF) to online video-conferencing (VC) format inorder to decrease contagion risk. Despite its widespread uptake, there is minimal empirical data evaluating this format. This narrative review considers the implications on medical decision-making when clinicians communicate remotely via Microsoft Teams. The discussion is informed by the psychological literature and by commentary obtained from a survey of paediatric cardiac clinicians who participated in clinical meetings when video-conferencing was first introduced. Whist video-conferencing can optimize clinician presence, this is potentially offset by compromises in current imaging quality, the group discussion, information sharing and decision quality. Implementing a shift from face-to-face to VC within the group decision-making process requires an appreciation of the changed environment, appropriate adaptations and the implemention of new technology solutions. Meanwhile, healthcare should carefully consider the potential implications of clinical decision making using online video conferencing, be prepared to adapt and evaluate prior to a shift away from face-to-face formats.

Decisional needs assessment for patient-centred pain care in Canada: the DECIDE-PAIN study protocol.

INTRODUCTION: The 2021 Action Plan for Pain from the Canadian Pain Task Force advocates for patient-centred pain care at all levels of healthcare across provinces. Shared decision-making is the crux of patient-centred care. Implementing the action plan will require innovative shared decision-making interventions, specifically following the disruption of chronic pain care during the COVID-19 pandemic. The first step in this endeavour is to assess current decisional needs (ie, decisions most important to them) of Canadians with chronic pain across their care pathways. METHODS AND ANALYSIS: DesignGrounded in patient-oriented research approaches, we will perform an online population-based survey across the ten Canadian provinces. We will report methods and data following the CROSS reporting guidelines.SamplingThe Léger Marketing company will administer the online population-based survey to its representative panel of 500 000 Canadians to recruit 1646 adults (age ≥18 years old) with chronic pain according to the definition by the International Association for the Study of Pain (eg, pain ≥12 weeks). ContentBased on the Ottawa Decision Support Framework, the self-administered survey has been codesigned with patients and contain six core domains: (1) healthcare services, consultation and postpandemic needs, (2) difficult decisions experienced, (3) decisional conflict, (4) decisional regret, (5) decisional needs and (6) sociodemographic characteristics. We will use several strategies such as random sampling to improve survey quality. AnalysisWe will perform descriptive statistical analysis. We will identify factors associated with clinically significant decisional conflict and decision regret using multivariate analyses. ETHICS AND DISSEMINATION: Ethics was approved by the Research Ethics Board at the Research Centre of the Centre Hospitalier Universitaire de Sherbrooke (project #2022-4645). We will codesign knowledge mobilisation products with research patient partners (eg, graphical summaries and videos). Results will be disseminated via peer-reviewed journals and national and international conferences to inform the development of innovative shared decision-making interventions for Canadians with chronic pain.

Sociocultural and moral narratives influencing the decision to vaccinate among rheumatic disease patients: a qualitative study.

INTRODUCTION/OBJECTIVES: Vaccination is a process that involves individual, social, and ethical aspects, beyond public governance of vaccines or vaccination as a public health concern. The aim of this study is to describe the sociocultural and moral narratives that influence the decision to vaccinate in general and to vaccinate against COVID-19 specifically, among patients at the rheumatology units of two hospitals. METHODS: Qualitative study involving individual semi-structured interviews following an interview guide. We conducted a thematic analysis using the ATLAS.ti software, with further triangulation to verify concordance and aid in the interpretation of the data from a medical anthropology framework and using a narrative ethics approach to gain insight into the participants' underlying moral values. RESULTS: We interviewed 37 patients in total, along with 3 rheumatologists. Five core themes emerged from the analysis to understand the decision to vaccinate: (1) information about vaccines and disease, (2) perceived risk-benefit of vaccination, (3) the physician-patient relationship, (4) governance of vaccination programs, (5) attitudes towards vaccines. Individual and family experiences with vaccination are diverse depending on the type of vaccine. The COVID-19 vaccine, as a new medical technology, is met with more controversy leading to hesitancy. CONCLUSIONS: The decision to vaccinate among Mexican rheumatic disease patients can sometimes involve doubt and distrust, especially for those with a lupus diagnosis, but ultimately there is acceptance in most cases. Though patients make and value autonomous decisions, there is a collective process involving sociocultural and ethical aspects. Key points • The complexity of vaccine decision-making is better identified through a narrative, qualitative approach like the one used in this study, as opposed to solely quantitative approaches • Sociocultural and moral perspectives of vaccination shape decision-making and, therefore, highlight the importance of including patients in the development of effective clinical practice guidelines as well as ethically justified public policy • Sociohistorical context and personal experiences of immunization influence vaccine decision-making much more than access to biomedical information about vaccines, showing that approaches based on the information deficit model are inadequate to fight vaccine hesitancy.

The wisdom in the story: Clarifying assumptions about radical uncertainty and reasonableness in narrative judgment.

Human lives are radically uncertain. Making sense of such uncertainties is the hallmark of wisdom. Sense-making requires narratives, putting them in the center stage of human everyday decision-making. Yet what if radical uncertainty is a narrative itself? Moreover, do laypeople always consider such narratives irrational? Here we pose these questions to enrich a theory of choice under uncertainty.

Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach.

BACKGROUND: Vaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. METHODS: We adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. RESULTS: The cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. CONCLUSIONS: The MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence. *ISPOR - The Professional Society for Health Economics and Outcomes Research.

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning.

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

Decision making under deep uncertainty for pandemic policy planning.

Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on 'prepare, monitor, and adapt', enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.

Decision-Making about COVID-19 Vaccines among Health Care Workers and Their Adolescent Children.

Health care workers promote COVID-19 vaccination for adolescent patients, and as parents, may influence their own children to get vaccinated. We conducted virtual, semi-structured qualitative interviews with vaccinated health care workers and their adolescent children to explore their decision-making process for COVID-19 vaccination. In total, 21 health care workers (physicians, nurses, and medical staff) and their adolescent children (N = 17) participated in interviews. The following three themes described parent-adolescent decision-making for COVID-19 vaccination: (1) family anticipation and hesitation about COVID-19 vaccine approval; (2) parents' or adolescents' choice: the decision maker for adolescent COVID-19 vaccination; and (3) leveraging one's vaccination status to encourage others to get vaccinated. Nurses encouraged adolescent autonomy in decisions for COVID-19 vaccination while physicians viewed vaccination as the parent's decision. Health care workers and their adolescent children used role-modeling to motivate unvaccinated peers and may model their decision-making process for adolescent COVID-19 vaccination with their own children to support their patients' and parents' vaccine decisions.

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision making.

BACKGROUND: The COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future. METHODS: Using archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance. RESULTS: Based on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations. CONCLUSIONS: Ensemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.