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The COVID-19 pandemic has catapulted scientific conversations and scientific divisions into the public consciousness. Epidemiology and economics have long operated in distinct silos, but the COVID-19 pandemic presents a complex and cross-disciplinary problem that impacts all facets of society. Many economists have recognized this and want to engage in efforts to mitigate and control the pandemic, but others seem more interested in attacking epidemiology than attacking the virus. As an epidemiologist, I call upon economists to join with us in combating COVID-19 and in preventing future pandemics. In this essay, I attempt to provide some insight for economists into how epidemiology works, where it doesn't work, and the much-needed answers that economists can help us obtain. I hope this will spur economists towards an epidemic-related economics that can provide a blueprint for a healthy economy and population.

Summary What does it mean to control for confounding, and when do we actually need to do it? To answer this, we need a well-defined research question, driven by the goal of the study. For descriptive goals, we explain that confounding adjustment is often not just unnecessary but can be harmful.

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.

Among school districts in the greater Boston area, the lifting of masking requirements was associated with an additional 44.9 Covid-19 cases per 1000 students and staff during the 15 weeks after a statewide masking policy was rescinded.

Scientists across disciplines, policymakers, and journalists have voiced frustration at the unprecedented polarization and misinformation around coronavirus disease 2019 (COVID-19) pandemic. Several false dichotomies have been used to polarize debates while oversimplifying complex issues. In this comprehensive narrative review, we deconstruct six common COVID-19 false dichotomies, address the evidence on these topics, identify insights relevant to effective pandemic responses, and highlight knowledge gaps and uncertainties. The topics of this review are: 1) Health and lives vs. economy and livelihoods, 2) Indefinite lockdown vs. unlimited reopening, 3) Symptomatic vs. asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, 4) Droplet vs. aerosol transmission of SARS-CoV-2, 5) Masks for all vs. no masking, and 6) SARS-CoV-2 reinfection vs. no reinfection. We discuss the importance of multidisciplinary integration (health, social, and physical sciences), multilayered approaches to reducing risk (“Emmentaler cheese model”), harm reduction, smart masking, relaxation of interventions, and context-sensitive policymaking for COVID-19 response plans. We also address the challenges in understanding the broad clinical presentation of COVID-19, SARS-CoV-2 transmission, and SARS-CoV-2 reinfection. These key issues of science and public health policy have been presented as false dichotomies during the pandemic. However, they are hardly binary, simple, or uniform, and therefore should not be framed as polar extremes. We urge a nuanced understanding of the science and caution against black-or-white messaging, all-or-nothing guidance, and one-size-fits-all approaches. There is a need for meaningful public health communication and science-informed policies that recognize shades of gray, uncertainties, local context, and social determinants of health.

To inform training program development and curricular initiatives, quantitative descriptions of the disciplinary training of research teams publishing in top-tier clinical and epidemiological journals are needed. Our objective was to assess whether interdisciplinary academic training and teamwork of authors publishing original research in 15 top-tier journals varied by year of publication (2000/2010/2020), type of journal (epidemiological/general clinical/specialty clinical), corresponding author gender, and time since the corresponding author completed formal training relative to the article publication date (<5/[greater than or equal to]5 years). We invited corresponding authors of original research articles to participate in an online survey (n = 103; response rate = 8.3% of 1240 invited authors). In bivariate analyses, year of publication, type of journal, gender, and recency of training were not significantly associated with interdisciplinary team composition, whether a co-author with epidemiological or biostatistical training was involved in any research stage (design/analysis/interpretation/reporting), or with participants' confidence in their own or their co-authors epidemiological or biostatistical expertise (p > 0.05 for each comparison). Exceptions were participants with more recent epidemiological training all had co-author(s) with epidemiological training contribute to study design and interpretation, and participants who published in 2020 were more likely to report being extremely confident in their epidemiological abilities. This study was the first to quantify interdisciplinary training among research teams publishing in epidemiological and clinical journals. Our quantitative results show research published in top-tier journals generally represents interdisciplinary teamwork and that interdisciplinary training may provide publication type options. Our qualitative results show researchers view interdisciplinary training favorably.

Healthcare teams are expected to deliver high quality and safe clinical care, a goal facilitated by an environment of psychological safety. We hypothesised that an individual’s personality would influence psychological safety, perceived stressors in the clinical environment and confer a suitability for different professional roles. Staff members were recruited from the Emergency or Critical Care Departments of one National Health Service Trust. Qualitative interviews explored participants’ experiences of personality, incorporating quantitative surveys to evaluate psychological safety and perceived stressors. The 16 Primary Factor Assessment provided a quantitative measure of personality. Participants demonstrated midrange scores for most personality traits, highlighting an ability to adapt to changing environments and requirements. There was a signal that different personality traits predominated between the two professional groups, and that certain traits were significantly associated with higher psychological safety and certain perceived stressors. Personality was described as having a strong influence on teamwork, the working environment and leadership ability. Our analysis highlights that personality can influence team dynamics and the suitability of individuals for certain clinical roles. Understanding the heterogeneity of personalities of team members and their likely responses to challenge may help leaders to support staff in times of challenge and improve team cohesiveness.

RationalePhysical rehabilitation (encompassing early mobilisation) of the critically ill patient is recognised best practice; however, further work is needed to explore the patients’ experience of rehabilitation qualitatively; a better understanding may facilitate implementation of early rehabilitation and elucidate the journey of survivorship.ObjectivesTo explore patient experience of physical rehabilitation from critical illness during and after a stay on intensive care unit (ICU).DesignExploratory grounded theory study using semistructured interviews.SettingAdult medical/surgical ICU of a London teaching hospital.ParticipantsA purposive sample of ICU survivors with intensive care unit acquired weakness (ICUAW) and an ICU length of stay of >72 hours.AnalysisData analysis followed a four-stage constant comparison technique: open coding, axial coding, selective coding and model development, with the aim of reaching thematic saturation. Peer debriefing and triangulation through a patient support group were carried out to ensure credibility.Main resultsFifteen people were interviewed (with four relatives in attendance). The early rehabilitation period was characterised by episodic memory loss, hallucinations, weakness and fatigue, making early rehabilitation arduous and difficult to recall. Participants craved a paternalised approach to care in the early days of ICU.The central idea that emerged from this study was recalibration of the self. This is driven by a lost sense of self, with loss of autonomy and competence; dehumanised elements of care may contribute to this. Participants described a fractured life narrative due to episodic memory loss, meaning that patients were shocked on awakening from sedation by the discrepancy between their physical form and cognitive representation of themselves.ConclusionsRecovery from ICUAW is a complex process that often begins with survivors exploring and adapting to a new body, followed by a period of recovering autonomy. Rehabilitation plays a key role in this recalibration period, helping survivors to reconstruct a desirable future.

The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-naïve primary hypertensive patients without co-morbidities (aged 18–55, n = 73), and matched normotensive controls (n = 79) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24 h ambulatory monitoring. Vascular phenotyping included SphygmoCor® measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT™-2000® provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing ‘arterially stiffened’, ‘vaso-protected’, and ‘non-dipper’ patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2’s propensity for asymptomatic transmission, raise the question “how reliable was contact tracing for COVID-19 in the United States”? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.