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Clear communication around surgical device use is crucial to patient safety. We evaluated the utility of the Device Briefing Tool (DBT) as an adjunct to the Surgical Safety Checklist.A nonrandomized, controlled pilot of the DBT was conducted with surgical teams at an academic referral center. Intervention departments used the DBT in all cases involving a surgical device for 10 wk. Utility, relative advantage, and implementation effectiveness were evaluated via surveys. Trained observers assessed adherence and team performance using the Oxford NOTECHS system.Of 113 individuals surveyed, 91 responded. Most respondents rated the DBT as moderately to extremely useful. Utility was greatest for complex devices (89%) and new devices (88%). Advantages included insight into the team's familiarity with devices (70%) and improved teamwork and communication (68%). Users found it unrealistic to review all device instructional materials (54%). Free text responses suggested that the DBT heightened awareness of deficiencies in device familiarity and training but lacked a clear mechanism to correct them. DBT adherence was 82%. NOTECHS scores in intervention departments improved over the course of the study but did not significantly differ from comparator departments.The DBT was rated highly by both surgeons and nurses. Adherence was high and we found no evidence of "checklist fatigue." Centers interested in implementing the DBT should focus on devices that are complex or new to any surgical team member. Guidance for correcting deficiencies identified by the DBT will be provided in future iterations of the tool.

In February 2022, Massachusetts rescinded a statewide universal masking policy in public schools, and many Massachusetts school districts lifted masking requirements during the subsequent weeks. In the greater Boston area, only two school districts - the Boston and neighboring Chelsea districts - sustained masking requirements through June 2022. The staggered lifting of masking requirements provided an opportunity to examine the effect of universal masking policies on the incidence of coronavirus disease 2019 (Covid-19) in schools.We used a difference-in-differences analysis for staggered policy implementation to compare the incidence of Covid-19 among students and staff in school districts in the greater Boston area that lifted masking requirements with the incidence in districts that sustained masking requirements during the 2021-2022 school year. Characteristics of the school districts were also compared.Before the statewide masking policy was rescinded, trends in the incidence of Covid-19 were similar across school districts. During the 15 weeks after the statewide masking policy was rescinded, the lifting of masking requirements was associated with an additional 44.9 cases per 1000 students and staff (95% confidence interval, 32.6 to 57.1), which corresponded to an estimated 11,901 cases and to 29.4% of the cases in all districts during that time. Districts that chose to sustain masking requirements longer tended to have school buildings that were older and in worse condition and to have more students per classroom than districts that chose to lift masking requirements earlier. In addition, these districts had higher percentages of low-income students, students with disabilities, and students who were English-language learners, as well as higher percentages of Black and Latinx students and staff. Our results support universal masking as an important strategy for reducing Covid-19 incidence in schools and loss of in-person school days. As such, we believe that universal masking may be especially useful for mitigating effects of structural racism in schools, including potential deepening of educational inequities.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 the statewide masking policy was rescinded.

We developed an agent-based model using a trial emulation approach to quantify effect measure modification of spillover effects of pre-exposure prophylaxis (PrEP) for HIV among men who have sex with men (MSM) in the Atlanta-Sandy Springs-Roswell metropolitan area, Georgia. PrEP may impact not only the individual prescribed, but also their partners and beyond, known as spillover. We simulated a two-stage randomised trial with eligible components (≥3 agents with ≥1 HIV+ agent) first randomised to intervention or control (no PrEP). Within intervention components, agents were randomised to PrEP with coverage of 70%, providing insight into a high PrEP coverage strategy. We evaluated effect modification by component-level characteristics and estimated spillover effects on HIV incidence using an extension of randomisation-based estimators. We observed an attenuation of the spillover effect when agents were in components with a higher prevalence of either drug use or bridging potential (if an agent acts as a mediator between ≥2 connected groups of agents). The estimated spillover effects were larger in magnitude among components with either higher HIV prevalence or greater density (number of existing partnerships compared to all possible partnerships). Consideration of effect modification is important when evaluating the spillover of PrEP among MSM.

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 (with a high false negative rate) 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 0.88% (95% uncertainty interval 0.86%-0.89%) with rapid antigen testing. When considering an optimal scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6%-62.8%). 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.

IntroductionThe study objective was to elucidate the relationship between social vulnerability and COVID-19 impacts in Philadelphia over a 2.5-year period, between June 2020 and December 2022.MethodsUsing publicly available COVID-19 case, test, hospitalization, and mortality data for Philadelphia (June 7, 2020-December 31, 2022) and area-level social vulnerability data, we compared the incidence, test positivity, hospitalization, and mortality rates in high and low vulnerability neighborhoods of Philadelphia, characterized as scoring above or below the national median score on the social vulnerability index. We used linear mixed effects models to test the association between social vulnerability and COVID-19 incidence, test positivity, hospitalization, and mortality rates, adjusting for time and age distribution.Results90.4% of Philadelphians (n= 1,430,153) live in neighborhoods classified as socially vulnerable, based on scoring above the national median score on the social vulnerability index. COVID-19 incidence, hospitalization, and mortality rates were significantly elevated in the more vulnerable communities, withp< 0.05,p< 0.005, andp< 0.001, respectively. The relative risks of COVID-19-related incidence, hospitalization, and death, comparing the more vulnerable neighborhoods to the less vulnerable neighborhoods, were 1.11 (95%CI: 1.10-1.12), 2.07 (95%CI: 1.93-2.20), and 2.06 (95%CI: 1.78-2.38), respectively. Thus, between June 7, 2020 and December 31, 2022, 32,573 COVID-19 cases, 9,409 hospitalizations, and 1,967 deaths would have been avoided in Philadelphia’s more vulnerable communities had they experienced the same rates of incidence, hospitalization, and death as the less vulnerable Philadelphia communities.ConclusionsThese results highlight the disparate morbidity and mortality experienced by people living in more vulnerable neighborhoods in a large US city. Importantly, our findings illustrate the importance of designing public health policies and interventions with an equity-driven approach, with greater resources and more intensive prevention strategies applied in socially vulnerable communities.

The coronavirus disease 2019 (COVID-19) pandemic thrust the field of public health into the spotlight. For many epidemiologists, biostatisticians, and other public health professionals, this caused the professional aspects of our lives to collide with the personal, as friends and family reached out with concerns and questions. Learning how to navigate this space was new for many of us and required refining our communication style depending on context, setting, and audience. Some of us took to social media, utilizing our existing personal accounts to share information after sorting through and summarizing the rapidly emerging literature to keep loved ones safe. However, those in our lives sometimes asked unanswerable questions, or began distancing themselves when we suggested more stringent guidance than they had hoped for, causing additional stress during an already traumatic time. We often had to remind ourselves that we were also individuals experiencing this pandemic and that our time-intensive efforts were meaningful, relevant, and impactful. As this pandemic and other public health crises continue, we encourage members of our discipline to consider how we can best use shared lessons from this period and to recognize that our professional knowledge, when used in our personal lives, can promote, protect, and bolster confidence in public health.

BackgroundIn February 2022, following the rescinding of a Massachusetts statewide school masking mandate, only two cities (Boston and neighboring Chelsea) out of 79 school districts in the greater-Boston area, maintained masking requirements in K-12 schools. This provided an opportunity to examine the impact of removing masking on COVID-19 case rates among students and staff in the public-school setting.MethodsWe used difference-in-differences for staggered policy adoption to compare incidence of COVID-19 cases among students and staff in greater-Boston area school districts that lifted masking requirements to those that had not yet lifted masking requirements during the 2021-2022 school year.ResultsBefore the statewide school masking policy was lifted, there was no statistically significant difference in case rate trajectories between school districts. However, weekly and cumulative case rates were significantly higher in students and staff in school districts that removed masking requirements, compared to districts that had not yet lifted requirements. We estimate that lifting of school masking requirements was associated with an additional 44.9 (95% CI: 32.6, 57.1) COVID-19 cases per 1,000 students and staff over the 15 weeks since the lifting of the statewide school masking requirement, representing nearly 30% of all cases observed in schools during that time. School districts that sustained masking requirements for longer periods tended to have older school buildings in poorer condition, more crowded classrooms, higher proportion of low income and English learning students and students with disabilities, and a higher proportion of Black and Latinx students and staff.ConclusionsMasking is a relatively low-cost but effective intervention that can protect students and staff from substantial illness and loss of in-person days in school. Despite compelling evidence that masking significantly reduces the spread of SARS-CoV-2, political will and public adherence to masking has waned. Our study confirms that universal masking requirements can benefit all students and staff, and therefore represents an important strategy to mitigate the impacts of structural racism, ensure health equity, and to avoid potential deepening of educational inequities.

Background Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation. Objectives We aimed to i) estimate the degree to which causality was implied by both the language linking exposures to outcomes and by action recommendations in the high-profile health literature, ii) examine disconnects between language and recommendations, iii) identify which linking phrases were most common, and iv) generate estimates by which these phrases imply causality. Methods We identified 18 of the most prominent general medical/public health/epidemiology journals, and searched and screened for articles published from 2010 to 2019 that investigated exposure/outcome pairs until we reached 65 non-RCT articles per journal (n=1,170). Two independent reviewers and an arbitrating reviewer rated the degree to which they believed causality had been implied by the language in abstracts based on written guidance. Reviewers then rated causal implications of linking words in isolation. For comparison, additional review was performed for full texts and for a secondary sample of RCTs. Results Reviewers rated the causal implication of the sentence and phrase linking the exposure and outcome as None (i.e., makes no causal implication) in 13.8%, Weak in 34.2%, Moderate in 33.2%, and Strong in 18.7% of abstracts. Reviewers identified an action recommendation in 34.2% of abstracts. Of these action recommendations, reviewers rated the causal implications as None in 5.3%, Weak in 19.0%, Moderate in 42.8% and Strong in 33.0% of cases. The implied causality of action recommendations was often higher than the implied causality of linking sentences (44.5%) or commensurate (40.3%), with 15.3% being weaker. The most common linking word root identified in abstracts was “associate” (n=535/1,170; 45.7%) (e.g. “association,” “associated,” etc). There were only 16 (1.4%) abstracts using “cause” in the linking or modifying phrases. Reviewer ratings for causal implications of word roots were highly heterogeneous, including those commonly considered non-causal. Discussion We found substantial disconnects between causal implications used to link an exposure to an outcome and the action implications made. This undercuts common assumptions about what words are often considered non-causal and that policing them eliminates causal implications. We recommend that instead of policing words, editors, researchers, and communicators should increase efforts at making research questions, as well as the potential of studies to answer them, more transparent. Summary box

Methods for estimating optimal treatment strategies typically assume unlimited access to resources. However, when a health system has resource constraints, such as limited funds, access to medication, or monitoring capabilities, medical decisions must account for competition between individuals in resource usage. The problem of incorporating resource constraints into optimal treatment strategies has been solved for point exposures (1), that is, treatment strategies entailing a decision at just one time point. However, attempts to directly generalize the point exposure solution to dynamic time-varying treatment strategies run into complications. We sidestep these complications by targeting the optimal strategy within a clinically defined subclass. Our approach is to employ dynamic marginal structural models to estimate (counterfactual) resource usage under the class of candidate treatment strategies and solve a constrained optimization problem to choose the optimal strategy for which expected resource usage is within acceptable limits. We apply this method to determine the optimal dynamic monitoring strategy for people living with HIV when resource limits on monitoring exist using observational data from the HIV-CAUSAL Collaboration.

Directed acyclic graphs (DAGs) are a visual tool used in causal inference to guide which variables to include in statistical models in research. A primary feature of DAGs is their acyclic nature, which for causal DAGs means variables cannot have direct or indirect effects on their own parent variables occurring before them in time. This is often taken to mean that DAGs cannot display “feedback loops” where two variables inform each other over time, and thus that the utility of DAGs for displaying complex biological relationship is limited. These “feedback loops,” hereafter referred to as bidirectional effects, are common in epidemiological, biological, and psychological research and do pose a major challenge for researchers aiming to incorporate DAGs into their research process. However, this challenge does not arise from a violation of the underlying temporality of causation but rather from a need to clearly specify how causal relationships evolve over time. In this article, we explain how to approach bidirectional relationships in DAGs and discuss considerations and strategies for using DAGs when bidirectional effects are present in research areas.

Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key “no interference” assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.

Many psychological researchers use some form of a visual diagram in their research processes. Model diagrams used with structural equation models (SEMs) and causal directed acyclic graphs (DAGs) can guide causal-inference research. SEM diagrams and DAGs share visual similarities, often leading researchers familiar with one to wonder how the other differs. This article is intended to serve as a guide for researchers in the psychological sciences and psychiatric epidemiology on the distinctions between these methods. We offer high-level overviews of SEMs and causal DAGs using a guiding example. We then compare and contrast the two methodologies and describe when each would be used. In brief, SEM diagrams are both a conceptual and statistical tool in which a model is drawn and then tested, whereas causal DAGs are exclusively conceptual tools used to help guide researchers in developing an analytic strategy and interpreting results. Causal DAGs are explicitly tools for causal inference, whereas the results of a SEM are only sometimes interpreted causally. A DAG may be thought of as a “qualitative schematic” for some SEMs, whereas SEMs may be thought of as an “algebraic system” for a causal DAG. As psychology begins to adopt more causal-modeling concepts and psychiatric epidemiology begins to adopt more latent-variable concepts, the ability of researchers to understand and possibly combine both of these tools is valuable. Using an applied example, we provide sample analyses, code, and write-ups for both SEM and causal DAG approaches.

Behavioral medicine scientists, practitioners, and educators can engage in evidence-based science communication strategies to amplify the science and combat misinformation. Such efforts are critical to protect public health during crises such as the COVID-19 pandemic and to promote overall well-being.

We aimed to study the effects of hypothetical interventions on systolic blood pressure (SBP) and smoking on risk of stroke and dementia using data from 15 years of follow-up in the Rotterdam Study. We used data from 4930 individuals, aged 55–80 years, with no prior history of stroke, dementia or cognitive impairment, followed for 15 years within the Rotterdam Study, a population-based cohort. We defined the following sustained interventions on SBP: (1) maintaining SBP below 120 mmHg, (2) maintaining SBP below 140 mmHg, (3) reducing SBP by 10% if above 140 mmHg, (4) reducing SBP by 20% if above 140 mmHg, and a combined intervention of quitting smoking with each of these SBP-lowering strategies. We considered incident stroke and incident dementia diagnoses as outcomes. We applied the parametric g-formula to adjust for baseline and time-varying confounding. The observed 15-year risk for stroke was 10.7%. Compared to no specified intervention (i.e., the “natural course”), all interventions that involved reducing SBP were associated with a stroke risk reduction of about 10% (e.g., reducing SBP by 20% if above 140 mmHg risk ratio: 0.89; 95% CI 0.76, 1). Jointly intervening on SBP and smoking status further decreased the risk of stroke (e.g., risk ratio: 0.83; 95% CI 0.71, 0.94). None of the specified interventions were associated with a substantive change in dementia risk. Our study suggests that a joint intervention on SBP and smoking cessation during later life may reduce stroke risk, while the potential for reducing dementia risk were not observed. Electronic supplementary material The online version of this article (10.1007/s10654-020-00694-5) contains supplementary material, which is available to authorized users. A Spanish version of the manuscript is provided as supplementary material.

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

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.

Keeping schools open without permitting COVID-19 spread has been complicated by conflicting messages around the role of children and schools in fueling the pandemic. Here, we describe methodological limitations of research minimizing SARS-CoV-2 transmission in schools, and we review evidence for safely operating schools while reducing overall SARS-CoV-2 transmission.

When reporting results from randomized experiments, researchers often choose to present a per-protocol effect in addition to an intention-to-treat effect. However, these per-protocol effects are often described retrospectively, for example, comparing outcomes among individuals who adhered to their assigned treatment strategy throughout the study. This retrospective definition of a per-protocol effect is often confounded and cannot be interpreted causally because it encounters treatment-confounder feedback loops, where past confounders affect future treatment, and current treatment affects future confounders. Per-protocol effects estimated using this method are highly susceptible to the placebo paradox, also called the “healthy adherers” bias, where individuals who adhere to placebo appear to have better survival than those who don’t. This result is generally not due to a benefit of placebo, but rather is most often the result of uncontrolled confounding. Here, we aim to provide an overview to causal inference for survival outcomes with time-varying exposures for static interventions using inverse probability weighting. The basic concepts described here can also apply to other types of exposure strategies, although these may require additional design or analytic considerations. We provide a workshop guide with solutions manual, fully reproducible R, SAS, and Stata code, and a simulated dataset on a GitHub repository for the reader to explore.

BACKGROUND: The extent to which couples change their behaviors with increasing pregnancy attempt time is not well documented. METHODS: We examined change in selected behaviors over pregnancy attempt time in a North American preconception cohort study. Eligible females were ages 21-45 years and not using fertility treatment. Participants completed baseline and bimonthly follow-up questionnaires for up to 12 months or until pregnancy. RESULTS: Among 3,339 females attempting pregnancy for 0-1 cycles at enrollment, 250 contributed 12 months of follow-up without conceiving. Comparing behaviors at 12 months versus baseline, weighted for loss-to-follow-up, we observed small-to-moderate reductions in mean caffeine intake (-19.5 mg/day, CI = -32.7, -6.37), alcohol intake (-0.85 drinks/week, CI = -1.28, -0.43), marijuana use (-3.89 percentage points, CI = -7.33, 0.46), and vigorous exercise (-0.68 hours/week, CI = -1.05, -0.31), and a large increase in activities to improve conception chances (e.g., ovulation testing) (21.7 percentage points, CI = 14.8, 28.6). There was little change in mean cigarette smoking (-0.27 percentage points, CI = -1.58, 1.04), perceived stress scale score (-0.04 units, CI = -0.77, 0.69), or other factors (e.g., sugar-sweetened soda intake, moderate exercise, intercourse frequency, and multivitamin use), but some heterogeneity within subgroups (e.g., 31% increased and 32% decreased their perceived stress scores by ≥2 units; 14% reduced their smoking but none increased their smoking by ≥5 cigarettes/day). CONCLUSIONS: Although many behaviors changed with increasing pregnancy attempt time, mean changes tended to be modest for most variables. The largest differences were observed for the use of caffeine, alcohol, and marijuana, and methods to improve conception chances.

Individual-level simulation models are used to assess the effects of health interventions in complex settings. However, estimating valid causal effects using these models requires correct parametrization of the relationships between time-varying treatments, outcomes, and other variables in the causal structure. To parameterize these relationships, individual-level simulation models typically need estimates of the direct effects of treatment. However, direct effects of treatment are often not well- defined and therefore cannot be validly estimated from any data. In this paper, we explain the causal meaning of the parameters of individual-level simulation models as direct effects, describe why direct effects may be difficult to define unambiguously in some settings, and conclude with some suggestions for the design of individual-level simulation models in those settings.

Systematic reviews and meta-analyses are essential for drawing conclusions regarding etiologic associations between exposures or interventions and health outcomes. Observational studies comprise a substantive source of the evidence base. One major threat to their validity is residual confounding, which may occur when component studies adjust for different sets of confounders, fail to control for important confounders, or have classification errors resulting in only partial control of measured confounders. We present the confounder matrix-an approach for defining and summarizing adequate confounding control in systematic reviews of observational studies and incorporating this assessment into meta-analyses. First, an expert group reaches consensus regarding the core confounders that should be controlled and the best available method for their measurement. Second, a matrix graphically depicts how each component study accounted for each confounder. Third, the assessment of control adequacy informs quantitative synthesis. We illustrate the approach with studies of the association between short interpregnancy intervals and preterm birth. Our findings suggest that uncontrolled confounding, notably by reproductive history and sociodemographics, resulted in exaggerated estimates. Moreover, no studies adequately controlled for all core confounders, so we suspect residual confounding is present, even among studies with better control. The confounder matrix serves as an extension of previously published methodological guidance for observational research synthesis, enabling transparent reporting of confounding control and directly informing meta-analysis so that conclusions are drawn from the best available evidence. Widespread application could raise awareness about gaps across a body of work and allow for more valid inference with respect to confounder control.

Oral health care professionals are well positioned to contribute to the prevention of human papillomavirus (HPV)-related oropharyngeal and other HPV-related cancers through engaging patients in conversations about HPV vaccination. This scoping review evaluates evidence regarding oral health care provider knowledge of, and discussion related to, HPV prevention, transmission, and associated risks, including oropharyngeal cancer. This review outlines relevant barriers to, and facilitators of, this knowledge and discussion. In addition, to determine the potential population that could be reached by an oral health care provider for a conversation about HPV vaccination, this review evaluates the prevalence of HPV vaccination as well as dental visits in a US population.Four databases were systematically searched (MEDLINE [PubMed], EMBASE, APA PsycInfo, Cumulative Index to Nursing and Allied Health Literature). Studies written in English and conducted in the United States were eligible. Eligibility criteria were not restricted to publication year or oral health care provider type. Behavioral Risk Factor Surveillance System 2018 data were analyzed to evaluate the prevalence of HPV vaccination and dental visits among patients aged 18 through 49 years.After duplicate record removal and second-stage screening, 32 full-text articles were retrieved, and data were independently extracted by 2 reviewers. Twenty-four studies were included in this review. Knowledge regarding HPV prevalence, transmission, disease processes, and risks varied. In general, discussions related to HPV in dental settings were infrequent. Facilitators to improve knowledge and discussion included guidance from professional dental organizations, education, and communication skills. Behavioral Risk Factor Surveillance System data showed that most people who are not vaccinated have visited their oral health care providers in the past year, highlighting the potential role of oral health care providers regarding discussion of HPV and promoting awareness and acceptance of vaccination.This review indicates that discussions related to HPV were infrequent in the oral health care setting, which may be related to lack of knowledge and communication skills among oral health care professionals; however, evidence exists supporting the interest of oral health care professionals in improving vaccine uptake to prevent oropharyngeal cancer.

Background: To match medical education requirements to the professional goals of clinician-scholars, quantitative descriptions of the disciplinary training of research teams publishing in top-tier clinical and epidemiological journals are needed. The objective was to assess whether academic training and teamwork of authors publishing original research in top-tier journals varied by year of publication (2000/2010/2020), type of journal (epidemiological/general clinical/specialty clinical), gender of the corresponding author, and time since the corresponding author completed formal training relative to the article publication date (Methods: The authors invited the corresponding author of 1240 original research articles published in three volumes of 15 top-tier journals to participate in an online survey. Participants reported demographic information and academic training history, co-authors’ training history, and confidence in their own and their co-authors’ ability to apply epidemiological and biostatistical concepts. Using linear regression, logistic regression, and chi-squared tests, the authors examined bivariate trends in training history and confidence by publication type, publication year, gender, and time between training and publication.Results: The response rate was 8.3% (n=103). 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 bivariate comparison). Exceptions were: (1) participants with more recent epidemiological training all had co-author(s) with epidemiological training who contributed to the design and interpretation of the research; and (2) participants who published in 2020 were more likely to report being extremely confident in their epidemiological abilities. Conclusions: This study was the first to quantify interdisciplinary training among research teams publishing in epidemiological and clinical journals. Researchers viewed training that prepares clinician-scholars for collaboration with epidemiologists and biostatisticians favorably.

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the “Missing Link Fallacy.”

ObjectivesThis study aimed to quantify the presence of psychological safety (defined as an environment ‘safe for interpersonal risk taking’) in critical care staff, exploring the ways in which this manifested.DesignQualitative interview study incorporating a short quantitative survey.SettingThree intensive care units within one National Health Service Trust in London.ParticipantsThirty participants were recruited from all levels of seniority and roles within the multidisciplinary team. A purposive sampling technique was used, with recruitment ceasing at the point of thematic saturation.InterventionsSemistructured interviews explored attitudes towards psychological safety and contained a quantitative assessment measuring the climate of psychological safety present.ResultsTwenty-eight participants agreed that it was easy to ask for help, with 20 agreeing it is safe to take a risk on the team, demonstrating a strong perception of psychological safety in this group.Our thematic analysis highlighted areas where the context influenced an individual’s psychological safety including personality, culture and leadership. Possible negative consequences of psychological safety included distraction and fatigue for the team leader. We demonstrated that speaking up can be influenced by motivations other than patient safety, such as undermining or self-promotion.ConclusionsOur data demonstrate reassuring levels of psychological safety within the participants studied. This allowed us to explore in depth the participant experience of working within a psychologically safe environment. We add to the current literature by uniquely demonstrating there can be negative consequences to a psychologically safe environment in the healthcare setting. We expand on the influence of context on psychological safety by developing a model, allowing leaders to identify which elements of context can be modified in order to promote speaking up. Team leaders can use these data to help foster a culture of openness, innovation and error prevention while minimising the risk of negative implications

SummaryWhat is already known about this topic?Community-level face mask use is encouraged as an important preventive measure against COVID-19 transmission, and evidence suggests that jurisdictions which implement face mask mandates see a subsequent decline in COVID incidence.What is added by this report?In the Greater Boston area when a face mask mandate is in effect, 95% of people observed were wearing some type of face covering. Most of which were wearing fabric/cloth coverings (51%) or single use surgical masks (40%). Of those wearing a face covering, 85% were appropriately fitted. Indoor locations have higher adherence of appropriately worn face masks, compared to outdoor locations.What are the implications for public health practice?Adherence with face mask mandates was very high, but many individuals wore fabric face masks with unknown filtration efficacy. In addition, it was common for individuals to mis-wear, adjust, or remove their masks. Public health policies requiring mask use should include messaging about appropriate type and best practices for use.

A large proportion of COVID-19 research has been focused on identifying markers of high-risk individuals. However, this research often fails to consider basic epidemiologic concepts to prevent bias in the design, selection, and analysis of observational data. One suspected marker of risk that has been repeatedly assessed is ABO blood type. Given the ease of measuring this biomarker, it is an appealing target for identifying high-risk individuals. However, this same ease of measurement makes associational research on ABO blood type and COVID prone to a range of common epidemiologic errors. We conducted a systematic review of studies assessing correlations between ABO blood type and COVID incidence, hospitalization, and mortality to determine the quality of evidence these studies provide and whether the overall evidence suggests ABO blood type could provide a useful indicator of COVID risk. We conclude that most existing studies are low quality and suffer from major methodological flaws. The few higher-quality studies which do exist find no association between ABO blood type and COVID outcomes. We conclude that there is no evidence to support the use of ABO blood type as a marker for COVID risk or severity.Key PointsThere is no sufficient evidence to conclude a biological relationship between ABO blood types and COVID-19 infection or severity.Biases of existing research could be avoided by careful study design.

Objective Although some studies document that posttraumatic stress disorder (PTSD) increases suicide risk, other studies have produced the paradoxical finding that PTSD decreases suicide risk. We sought to understand methodologic biases that may explain these paradoxical findings through the use of directed acyclic graphs (DAGs). Method DAGs are causal diagrams that visually encode a researcher's assumptions about data generating mechanisms and assumed causal relations among variables. DAGs can connect theories to data and guide statistical choices made in study design and analysis. In this article, we describe DAGs and explain how they can be used to identify biases that may arise from inappropriate analytic decisions and data limitations. Results We define a particular form of bias, collider bias, that is a likely explanation for why studies have found a supposedly protective association of PTSD with suicide. This protective association is interpreted by some researchers as evidence that PTSD reduces the risk of suicide. Collider bias may occur through inappropriate adjustment for a psychiatric comorbidity, such as adjustment for variables that are affected by PTSD and share common causes with suicide. Conclusions We recommend that researchers collect longitudinal measurements of psychiatric comorbidities, which would help establish the temporal ordering of variables and avoid the biases discussed in this article. Furthermore, researchers could use DAGs to explore how results may be impacted by design and analytic decisions prior to execution. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

IntroductionPsychological safety is the shared belief that the team is safe for interpersonal risk taking. Its presence improves innovation and error prevention. This evidence synthesis had 3 objectives: explore the current literature regarding psychological safety, identify methods used in its assessment and investigate for evidence of consequences of a psychologically safe environment.MethodsWe searched multiple trial registries through December 2018. All studies addressing psychological safety within healthcare workers were included and reviewed for methodological limitations. A thematic analysis approach explored the presence of psychological safety. Content analysis was utilised to evaluate potential consequences.ResultsWe included 62 papers from 19 countries. The thematic analysis demonstrated high and low levels of psychological safety both at the individual level in study participants and across the studies themselves. There was heterogeneity in responses across all studies, limiting generalisable conclusions about the overall presence of psychological safety.A wide range of methods were used. Twenty-five used qualitative methodology, predominantly semi-structured interviews. Thirty quantitative or mixed method studies used surveys.Ten studies inferred that low psychological safety negatively impacted patient safety. Nine demonstrated a significant relationship between psychological safety and team outcomes.The thematic analysis allowed the development of concepts beyond the content of the original studies. This analytical process provided a wealth of information regarding facilitators and barriers to psychological safety and the development of a model demonstrating the influence of situational context.DiscussionThis evidence synthesis highlights that whilst there is a positive and demonstrable presence of psychological safety within healthcare workers worldwide, there is room for improvement. The variability in methods used demonstrates scope to harmonise this. We draw attention to potential consequences of both high and low psychological safety.We provide novel information about the influence of situational context on an individual’s psychological safety and offer more detail about the facilitators and barriers to psychological safety than seen in previous reviews. There is a risk of participation bias - centres involved in safety research may be more aligned to these ideals. The data in this synthesis are useful for institutions looking to improve psychological safety by providing a framework from which modifiable factors can be identified.

The digital world in which we live is changing rapidly. The evolving media environment is having a direct impact on traditional forms of communication and knowledge translation in public health and epidemiology. Openly accessible digital media can be used to reach a broader and more diverse audience of trainees, scientists, and the lay public than can traditional forms of scientific communication. The new digital landscape for delivering content is vast, and new platforms are continuously being added. In this article, we focus on several, including Twitter and podcasting, and discuss their relevance to epidemiology and science communication. We highlight 3 key reasons why we think epidemiologists should be engaging with these mediums: 1) science communication, 2) career advancement, and 3) development of a community and public service. Other positive and negative consequences of engaging in these forms of new media are also discussed. The authors of this commentary are all engaged in social media and podcasting for scientific communication, and we reflect on our experiences with these mediums as tools to advance the field of epidemiology.

Nearly every introductory epidemiology course begins with a focus on person, place, and time, the key components of descriptive epidemiology. And yet in our experience, introductory epidemiology courses were the last time we spent any significant amount of training time focused on descriptive epidemiology. This gave us the impression that descriptive epidemiology does not suffer from bias and is less impactful than causal epidemiology. Descriptive epidemiology may also suffer from a lack of prestige in academia and may be more difficult to fund. We believe this does a disservice to the field and slows progress towards goals of improving population health and ensuring equity in health. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak and subsequent coronavirus disease 2019 pandemic have highlighted the importance of descriptive epidemiology in responding to serious public health crises. In this commentary, we make the case for renewed focus on the importance of descriptive epidemiology in the epidemiology curriculum using SARS-CoV-2 as a motivating example. The framework for error we use in etiological research can be applied in descriptive research to focus on both systematic and random error. We use the current pandemic to illustrate differences between causal and descriptive epidemiology and areas where descriptive epidemiology can have an important impact.

Background: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. Objectives: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. Methods: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions prior to data analysis and exemplify the process for designing causal graphs to inform an etiologic study.Conclusion: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research prior to undergoing data collection and/or analysis.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in a concomitant deluge of medical, biological, and epidemiologic research. Clinicians are interested in incorporating the best new evidence-based practices when treating individuals with COVID-19 and instituting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission prevention protocols. However, without sufficient background knowledge, evaluating epidemiologic studies can be challenging, and failure to identify sources of bias could lead to poor treatment decisions. Here we provide a brief primer on key concepts and terms related to COVID-19 epidemiology to provide clinicians with a starting point for evaluating the emerging COVID-19 literature.

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Methods Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. Results A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). Conclusion There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

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.

Background: Methodological advances can be slow to spread within scientific communities. A number of barriers have been hypothesized, including lack of technical competency, bias towards existing methods, and relative degree of improvement attributable to a novel method. Although the relative contribution of these has been modeled, few empirical examples exist.Objective: We present a case study of attitudes towards adjustment for predictors of non-adherence in randomized clinical trials. We track attitudes by assessing the trajectory of citations of a highly influential 1980 anti-adjustment publication from the Coronary Drug Project (CDP) over time.Methods: We obtained copies of all publications citing that 1980 CDP paper. Articles were reviewed to determine why the authors cited the paper, and categorized by theme. Results: Despite recent methodological developments and re-analyses showing that the results are no longer true using modern statistical best practices, the 1980 CDP paper continues to be highly cited. Citations primarily rely on the paper as evidence against adherence adjustment, with little change in practices over time. Conclusion: Despite well-documented improvement using novel analytic methods, bias towards existing methods—in the form of a single highly influential publication—can have long lasting consequences on the uptake of new methods.

Background The Zinc for INflammation and Chronic disease in HIV (ZINC) trial randomized person who live with HIV (PLWH) who engage in heavy drinking to either daily zinc supplementation or placebo. The primary outcome was change in the Veterans Aging Cohort Study (VACS) index, a predictor of mortality, between baseline and 18 months. Because adherence and follow-up were suboptimal, the intention-to-treat analysis, which was not statistically significant, may have underestimated the effect of the zinc supplementation. Objective We estimated the per-protocol effect of zinc versus placebo in the ZINC trial (i.e., the effect that would have been observed if all participants had had high adherence and none was lost to follow-up). Methods Adherence was measured as the self-reported percentage of pills taken in the previous 6 weeks and assessed at all post-baseline visits. We used inverse probability weighting to estimate and compare the change in the VACS index at 18 months in the zinc and placebo groups, had all the trial participants had high adherence (i.e., cumulative adherence ≥80% at 18 months). To examine trends by level of adherence, we rerun the analyses using thresholds for high adherence of 70% and 90% of average self-reported pill coverage. Results The estimated (95% confidence interval) change in the VACS index was − 2.16 (− 8.07, 3.59) and 5.84 (0.73, 11.80) under high adherence and no loss to follow-up in the zinc and placebo groups, respectively. The per-protocol effect estimate of the mean difference in the change between the zinc and placebo groups was − 8.01 (− 16.42, 0.01), somewhat larger than the intention-to-treat effect difference in change (− 4.68 (− 9.62, 0.25)), but it was still not statistically significant. The mean difference in the change between individuals in the zinc and placebo groups was − 4.07 (− 11.5, 2.75) and −12.34 (− 20.14, −4.14) for high adherence defined as 70% and 90% of pill coverage, respectively. Conclusions Overall, high adherence to zinc was associated with a lower VACS score, but confidence intervals were wide and crossed 0. Further studies with a larger sample size are needed to quantify the benefits of zinc supplementation in this population. Trial registration ClinicalTrials.gov NCT01934803. Registered on August 30, 2013