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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.

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

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.

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.

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.

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 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.

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 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

More than 1.6 million Americans have been infected with SARS-CoV-2 and >10 times that number carry antibodies to it. High-risk patients presenting with progressing symptomatic disease have only hospitalization treatment with its high mortality. An outpatient treatment that prevents hospitalization is desperately needed. Two candidate medications have been widely discussed: remdesivir, and hydroxychloroquine+azithromycin. Remdesivir has shown mild effectiveness in hospitalized inpatients, but no trials have been registered in outpatients. Hydroxychloroquine+azithromycin has been widely misrepresented in both clinical reports and public media, and outpatient trials results are not expected until September. Early outpatient illness is very different than later hospitalized florid disease and the treatments differ. Evidence about use of hydroxychloroquine alone, or of hydroxychloroquine+azithromycin in inpatients, is irrelevant concerning efficacy of the pair in early high-risk outpatient disease. Five studies, including two controlled clinical trials, have demonstrated significant major outpatient treatment efficacy. Hydroxychloroquine+azithromycin has been used as standard-of-care in more than 300,000 older adults with multicomorbidities, with estimated proportion diagnosed with cardiac arrhythmias attributable to the medications 47/100,000 users, of which estimated mortality is

Background: Clinicians and patients may be more interested in per-protocol effect estimates than intention-to-treat effect estimates from randomized trials. However, per-protocol effect estimates may be biased due to insufficient adjustment for prognostic factors that predict adherence. Adjustment for this bias is possible when appropriate methods, such as inverse probability weighting, are used. But, when adherence is measured as a continuous variable, constructing these weights can be challenging. Methods: In the placebo arm of the Lipid Research Clinics Coronary Primary Prevention Trial, we estimated the 7-year cumulative incidence of coronary heart disease under 100% adherence and 0% adherence to placebo. We used dose-response discrete-hazards models with inverse probability weighting to adjust for pre- and post-randomization covariates. We considered several continuous distributions for constructing the inverse probability weights. Results: The risk difference estimate for 100% adherence compared with 0% adherence ranged from −7.7 to −6.1 percentage points without adjustment for baseline and post-baseline covariates, and ranged from −1.8 to 2.2 percentage points with adjustment using inverse probability weights, depending on the dose-response model and inverse probability weight distribution used. Conclusions: Methods which appropriately adjust for time-varying post-randomization variables can explain away much of the bias in the “effect” of adherence to placebo. When considering continuous adherence, investigators should consider several models as estimates may be sensitive to the model chosen.

Background Estimating the strength of causal effects is an important component of epidemiologic research, and causal graphs provide a key tool for optimizing the validity of these effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, including directed acyclic graphs, to assess and describe causal assumptions, and translate these assumptions into appropriate statistical analysis plans, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. Objective We sought to understand this gap by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. Methods We conducted an anonymous survey of self-identified epidemiology and health researchers via Twitter and via the Society of Epidemiologic Research membership listserv. The survey was conducted using Qualtrics and asked a series of multiple choice and open-ended questions about causal graphs. Results In total, 439 responses were collected. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them ‘sometimes’, ‘often’, or ‘always’ in their research. Almost three quarters of respondents had received formal training on causal graphs (typically causal directed acyclic graphs). Having received training appeared to improve comprehension of the underlying assumptions of causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Of the participants who did not use DAGs, many expressed that trainings, either in-person or online, would be useful resources to help them use causal graphs more often in their research. Conclusion Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.

Preexposure prophylaxis (PrEP) for prevention of human immunodeficiency virus (HIV) infection may benefit not only the person who uses it but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, Georgia, from 2015 to 2017. Model components (subsets of agents connected through partnerships in a sexual network but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or the control group; then, within intervention components, eligible agents were randomized to receive or not receive PrEP. We calculated direct and disseminated (indirect) effects using randomization-based estimators and report corresponding 95% simulation intervals across scenarios ranging from 10% coverage in the intervention components to 90% coverage. A population of 11,245 agents was simulated, with an average of 1,551 components identified. When comparing agents randomized to no PrEP in 70% coverage components with control agents, there was a 15% disseminated risk reduction in HIV incidence (risk ratio = 0.85, 95% simulation interval: 0.65, 1.05). Persons not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful for evaluating possible direct and disseminated effects of HIV prevention modalities in sexual networks.

Critical care staff are frequently required to respond to stressful scenarios. The way staff counter organisational challenge may be influenced by their underlying personality type, preferred style of cognitive processing and previous clinical experience. Our objective was to explore the personality types of a sample of critical care workers, and the potential relationship of this with cognitive processing. This was achieved through a qualitative interview study in which participants were presented with difficult but realistic scenarios pertaining to staffing. Data on individual's personality were captured using the '16 Personality Factor' assessment, a tool that produces scores for 16 different elements of an individual's personality. The existence of perfectionist and pragmatic cognitive processing styles were identified as one theme emerging from a prior analysis of these interview transcripts. We aimed to validate this, explore our ability to categorise individuals into groups based upon their cognitive processing. We identified that some individuals strongly tended to either a perfectionist or pragmatic style of cognitive processing for the majority of their decisions; however most adapted their style of processing according to the nature of the decision. Overall participants generally demonstrated average scores for all 16 personality factors tested. However, we observed that some factors tended to higher scores than others, indicating a pattern within the personalities of our sample cohort. Whilst a small sample size, our data suggests that individuals working within the same critical care environment may have clear differences in their approach to problem solving as a consequence of both their personality type and preferred style of cognitive processing. Thus there may be individuals within this environment who would benefit from increased support to minimise their risk of cognitive dissonance and stress in times of challenge.

Background The survival difference between adherers and non-adherers to placebo in the Coronary Drug Project has been used to support the thesis that adherence adjustment in randomized trials is not generally possible and, therefore, that only intention-to-treat analyses should be trusted. We previously demonstrated that adherence adjustment can be validly conducted in the Coronary Drug Project using a simplistic approach. Here, we re-analyze the data using an approach that takes full advantage of recent methodological developments. Methods We used inverse-probability weighted hazards models to estimate the 5-year survival and mortality risk when individuals in the placebo arm of the Coronary Drug Project adhere to at least 80% of the drug continuously or never during the 5-year follow-up period. Results Adjustment for post-randomization covariates resulted in 5-year mortality risk difference estimates ranging from − 0.7 (95% confidence intervals (CI), − 12.2, 10.7) to 4.5 (95% CI, − 6.3, 15.3) percentage points. Conclusions Our analysis confirms that appropriate adjustment for post-randomization predictors of adherence largely removes the association between adherence to placebo and mortality originally described in this trial. Trial registration ClinicalTrials.gov, Identifier: NCT00000482. Registered retrospectively on 27 October 1999. Electronic supplementary material The online version of this article (10.1186/s13063-018-2519-5) contains supplementary material, which is available to authorized users.

OBJECTIVES: The aims of this study were to 1) examine individual professionals’ perceptions of staffing risks and safe staffing in intensive care and 2) identify and examine the cognitive processes that underlie these perceptions. DESIGN: Qualitative case study methodology with nurses, doctors, and physiotherapists. SETTING: Three mixed medical and surgical adult ICUs, each on a separate hospital site within a 1,200-bed academic, tertiary London hospital group. SUBJECTS: Forty-four ICU team members of diverse professional backgrounds and seniority. INTERVENTIONS: None. MAIN RESULTS: Four themes (individual, team, unit, and organizational) were identified. Individual care provision was influenced by the pragmatist versus perfectionist stance of individuals and team dynamics by the concept of an “A” team and interdisciplinary tensions. Perceptions of safety hinged around the importance of achieving a “dynamic balance” influenced by the burden of prevailing circumstances and the clinical status of patients. Organizationally, professionals’ risk perceptions affected their willingness to take personal responsibility for interactions beyond the unit. CONCLUSIONS: This study drew on cognitive research, specifically theories of cognitive dissonance, psychological safety, and situational awareness to explain how professionals’ cognitive processes impacted on ICU behaviors. Our results may have implications for relationships, management, and leadership in ICU. First, patient care delivery may be affected by professionals’ perfectionist or pragmatic approach. Perfectionists’ team role may be compromised and they may experience cognitive dissonance and subsequent isolation/stress. Second, psychological safety in a team may be improved within the confines of a perceived “A” team but diminished by interdisciplinary tensions. Third, counter intuitively, higher “situational” awareness for some individuals increased their stress and anxiety. Finally, our results suggest that professionals have varying concepts of where their personal responsibility to minimize risk begins and ends, which we have termed “risk horizons” and that these horizons may affect their behavior both within and beyond the unit.

Decision-making requires choosing from treatments on the basis of correctly estimated outcome distributions under each treatment. In the absence of randomized trials, 2 possible approaches are the parametric g-formula and agent-based models (ABMs). The g-formula has been used exclusively to estimate effects in the population from which data were collected, whereas ABMs are commonly used to estimate effects in multiple populations, necessitating stronger assumptions. Here, we describe potential biases that arise when ABM assumptions do not hold. To do so, we estimated 12-month mortality risk in simulated populations differing in prevalence of an unknown common cause of mortality and a time-varying confounder. The ABM and g-formula correctly estimated mortality and causal effects when all inputs were from the target population. However, whenever any inputs came from another population, the ABM gave biased estimates of mortality-and often of causal effects even when the true effect was null. In the absence of unmeasured confounding and model misspecification, both methods produce valid causal inferences for a given population when all inputs are from that population. However, ABMs may result in bias when extrapolated to populations that differ on the distribution of unmeasured outcome determinants, even when the causal network linking variables is identical.

Frozen shoulder might be a complication or a presenting symptom of cancer. We examined the risk of a cancer diagnosis after an incident diagnosis of frozen shoulder.We used prospectively collected data from Danish registries to identify patients with frozen shoulder during 1995-2013 and followed them for the development of cancer.We observed 2572 incident cancers among 29 098 frozen shoulder patients. The expected number of incident cancers in the general population was 2434. The 6-month cumulative incidence of any cancer was 0.70%, corresponding to a standardised incidence ratio (SIR) of 1.38 (95% confidence interval (CI): 1.19-1.58). Risk increases were highest for lung cancer (SIR=2.19, 95% CI: 1.48-3.13), breast cancer (SIR=1.51, 95% CI: 1.02-2.15), and non-Hodgkin's lymphoma (SIR=2.28, 95% CI: 1.09-4.20). The cumulative incidence of any cancer during the remainder of follow-up (6 months to a maximum 18.9 years) was 24.8% with an SIR of 1.04 (95% CI: 1.00-1.08).Frozen shoulder might be an early predictor for a subsequent cancer diagnosis.

Background Randomized trials are considered the gold standard for making inferences about the causal effects of treatments. However, when protocol deviations occur, the baseline randomization of the trial is no longer sufficient to ensure unbiased estimation of the per-protocol effect: post-randomization, time-varying confounders must be sufficiently measured and adjusted for in the analysis. Given the historical emphasis on intention-to-treat effects in randomized trials, measurement of post-randomization confounders is typically infrequent. This may induce bias in estimates of the per-protocol effect, even using methods such as inverse probability weighting, which appropriately account for time-varying confounders affected by past treatment. Methods/design In order to concretely illustrate the potential magnitude of bias due to infrequent measurement of time-varying covariates, we simulated data from a very large trial with a survival outcome and time-varying confounding affected by past treatment. We generated the data such that the true underlying per-protocol effect is null and under varying degrees of confounding (strong, moderate, weak). In the simulated data, we estimated per-protocol survival curves and associated contrasts using inverse probability weighting under monthly measurement of the time-varying covariates (which constituted complete measurement in our simulation), yearly measurement, as well as 3- and 6-month intervals. Results Using inverse probability weighting, we were able to recover the true null under the complete measurement scenario no matter the strength of confounding. Under yearly measurement intervals, the estimate of the per-protocol effect diverged from the null; inverse probability weighted estimates of the per-protocol 5-year risk ratio based on yearly measurement were 1.19, 1.12, and 1.03 under strong, moderate, and weak confounding, respectively. Bias decreased with measurement interval length. Under all scenarios, inverse probability weighted estimators were considerably less biased than a naive estimator that ignored time-varying confounding completely. Conclusions Bias that arises from interval measurement designs highlights the need for planning in the design of randomized trials for collection of time-varying covariate data. This may come from more frequent in-person measurement or external sources (e.g., electronic medical record data). Such planning will provide improved estimates of the per-protocol effect through the use of methods that appropriately adjust for time-varying confounders. Electronic supplementary material The online version of this article (10.1186/s13063-019-3577-z) contains supplementary material, which is available to authorized users.

Background: In many randomized controlled trials, patients and doctors are more interested in the per-protocol effect than in the intention-to-treat effect. However, valid estimation of the per-protocol effect generally requires adjustment for prognostic factors associated with adherence. These adherence adjustments have been strongly questioned in the clinical trials community, especially after 1980 when the Coronary Drug Project team found that adherers to placebo had lower 5-year mortality than non-adherers to placebo. Methods: We replicated the original Coronary Drug Project findings from 1980 and re-analyzed the Coronary Drug Project data using technical and conceptual developments that have become established since 1980. Specifically, we used logistic models for binary outcomes, decoupled the definition of adherence from loss to follow-up, and adjusted for pre-randomization covariates via standardization and for post-randomization covariates via inverse probability weighting. Results: The original Coronary Drug Project analysis reported a difference in 5-year mortality between adherers and non-adherers in the placebo arm of 9.4 percentage points. Using modern approaches, we found that this difference was reduced to 2.5 (95% confidence interval: −2.1 to 7.0). Conclusion: Valid estimation of per-protocol effects may be possible in randomized clinical trials when analysts use appropriate methods to adjust for post-randomization variables.

Objectives Pragmatic randomized trials are important tools for shared decision-making, but no guidance exists on patients' preferences for types of causal information. We aimed to assess preferences of patients and investigators toward causal effects in pragmatic randomized trials. Study Design and Setting We (a) held three focus groups with patients ( n = 23) in Boston, MA; (b) surveyed ( n = 12) and interviewed ( n = 5) investigators with experience conducting pragmatic trials; and (c) conducted a systematic literature review of pragmatic trials ( n = 63). Results Patients were distrustful of new-to-market medications unless substantially more effective than existing choices, preferred stratified absolute risks, and valued adherence-adjusted analyses when they expected to adhere. Investigators wanted both intention-to-treat and per-protocol effects but felt methods for estimating per-protocol effects were lacking. When estimating per-protocol effects, many pragmatic trials used inappropriate methods to adjust for adherence and loss to follow-up. Conclusion We made four recommendations for pragmatic trials to improve patient centeredness: (1) focus on superiority in effectiveness or safety, rather than noninferiority; (2) involve patients in specifying a priori subgroups; (3) report absolute measures of risk; and (4) complement intention-to-treat effect estimates with valid per-protocol effect estimates.

Individual-level simulation models are valuable tools for comparing the impact of clinical or public health interventions on population health and cost outcomes over time. However, a key challenge is ensuring that outcome estimates correctly reflect real-world impacts. Calibration to targets obtained from randomized trials may be insufficient if trials do not exist for populations, time periods, or interventions of interest. Observational data can provide a wider range of calibration targets but requires methods to adjust for treatment-confounder feedback. We propose the use of the parametric g-formula to estimate calibration targets and present a case-study to demonstrate its application.We used the parametric g-formula applied to data from the HIV-CAUSAL Collaboration to estimate calibration targets for 7-y risks of AIDS and/or death (AIDS/death), as defined by the Center for Disease Control and Prevention under 3 treatment initiation strategies. We compared these targets to projections from the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model for treatment-naïve individuals presenting to care in the following year ranges: 1996 to 1999, 2000 to 2002, or 2003 onwards.The parametric g-formula estimated a decreased risk of AIDS/death over time and with earlier treatment. The uncalibrated CEPAC model successfully reproduced targets obtained via the g-formula for baseline 1996 to 1999, but over-estimated calibration targets in contemporary populations and failed to reproduce time trends in AIDS/death risk. Calibration to g-formula targets improved CEPAC model fit for contemporary populations.Individual-level simulation models are developed based on best available information about disease processes in one or more populations of interest, but these processes can change over time or between populations. The parametric g-formula provides a method for using observational data to obtain valid calibration targets and enables updating of simulation model inputs when randomized trials are not available.

Background Workplace stakeholders report the identification and translation of relevant high quality research to inform workplace disability policy and practice is a challenge. The present study engaged academic and community stakeholders in conducting a best evidence-synthesis to identify non-modifiable risk and protective worker and workplace factors impacting work-related absence across a variety of health conditions. Objective To identify non-modifiable worker and workplace disability risk and protective factors impacting work-related absence across common health conditions. Methods The research team searched Medline, Embase, CINAHL, The Cochrane Library, PsycINFO, BusinessSource-Complete, and ABI/Inform from 2000 to 2011. Quantitative, qualitative, or mixed methods systematic reviews of work-focused population were considered for inclusion. Two or more reviewers independently reviewed articles for inclusion and methodological screening. Results The search strategy, including expert input and grey literature, led to the identification of 2,467 unique records. From this initial search, 2325 were eliminated by title or abstract review, 142 articles underwent comprehensive review to assess for inclusion, 26 systematic reviews met eligibility criteria for this synthesis. For non-modifiable worker and workplace factors we found consistent evidence across two or more health conditions for increased risk of disability in situations where workers experience lower education, older age, emotional distress, poor personal functioning, decreased physical functioning, psychological symptoms, overweight status, and greater sick leave history. Limitations Heterogeneity of existing literature due to differences in outcome measures, definitions and research designs limited ability to assess effect size and results reflect findings limited to English-language papers.

BACKGROUND A challenge facing stakeholders is the identification and translation of relevant high quality research to inform policy and practice. This study engaged academic and community stakeholders in conducting a best evidence-synthesis to enhance knowledge use. OBJECTIVES To identify modifiable workplace disability risk and protective factors across common health conditions impacting work-related absence. METHODS We searched MEDLINE, Embase, CINHAL, The Cochrane Library, PsycINFO, BusinessSourceComplete, and ABI/Inform from 2000 to 2011. Systematic reviews that employed quantitative, qualitative, or mixed methods of work-focused population were considered for inclusion. Two or more independent reviewers reviewed titles only, titles and abstracts, and/or full articles when assessing eligibility for inclusion. Selected articles underwent methodological screening. RESULTS The search strategy, expert input and grey literature identified 2,467 unique records from which 142 full text articles underwent comprehensive review. Twenty-seven systematic reviews met eligibility criteria. Modifiable work factors found to have consistent evidence across two or more health conditions included lack of social support, increased physical demands at work, job strain, lack of supervisory support, increased psychological demands, low job satisfaction, low worker control of job, and poor leadership quality. CONCLUSIONS The active engagement of stakeholders led to greater understanding of relevance of the study findings for community stakeholders and appreciation of the mutual benefits of collaboration.

Introduction To summarize evidence on workplace-based work disability prevention (WDP) interventions in workers with common mental health conditions (CMHCs). Primary outcomes of interest were work absence duration and work functioning; secondary outcomes were quality of life, and economic costs. Methods We conducted a systematic literature search in 5 electronic databases (MEDLINE, EMBASE, CINAHL, PsychINFO, Web of Science) for studies published from 2007 to 2009. Two reviewers screened for studies: (1) Targeting workers with CMHCs absent from, or struggling at, work; (2) evaluating workplace-based WDP interventions; (3) assessing our primary outcome(s); and (4) with controlled trials. Quality assessment (using 29 criteria) was performed by two reviewers. Results Our search yielded 671 abstracts: 8 eligible studies and of sufficient quality. We identified three main intervention elements: (a) Facilitation of access to clinical treatment; (b) Workplace-based high-intensity psychological intervention; and (c) Facilitation of navigation through the disability management system. Moderate evidence was found that facilitation of treatment improved work functioning, quality of life and economic outcomes, with limited evidence for work absence duration. Moderate evidence was found that psychological interventions, primarily cognitive-behavioral therapy, improved work functioning, quality of life, and economic outcomes. Moderate evidence indicated that facilitation of navigation through the disability management system improved work absence duration. Conclusions Workplace-based interventions could improve work disability outcomes for workers with CMHCs. Facilitation of access to clinical treatment, and workplace-based high-intensity psychological intervention were most effective in improving work functioning and quality of life, and in reducing costs.

Meticillin-resistant Staphylococcus aureus (MRSA) infection in the UK receives intense media attention. The nature of coverage, political responses and solutions offered has been questioned and the relationship between health professionals, the media and government policy needs greater understanding. We identified 2880 articles on MRSA published in 12 UK newspapers between 1994 and 2005, compared with 21 articles in six major US newspapers. To investigate the relative influences and relationships further, 68 weeks of coverage from 1990 to 2004 were analysed. The dates were selected based on publication dates of the ten most frequently cited articles on MRSA according the ISI Web of Science portal of Department of Health press releases on MRSA since 1997. Within this period, 351 news articles were published with members of the public and politicians representing 60% of sources quoted. Scientific articles, even those with the highest number of citations, have negligible influence on newspaper coverage. Simple solutions quoted in the newspaper articles focused almost exclusively on cleaning. The UK press exhibits a high interest in MRSA compared with that of the USA. Healthcare workers, experts and professional bodies have criticised the nature of media reporting, but have had little influence or involvement in the press. This may facilitate journalists, celebrities, the public and politicians to drive these stories unchecked and allow politics to address only the simplistic solutions generated.

To be effective, infection prevention and control must be integrated into the complex and multiple interlinking systems within a hospital's management structure. Each of the systems must consider how activity associated with it can be optimised to minimise infection risk to patients. The components of an organisational structure to achieve these quality assurance and patient safety aims are discussed. The use of performance management tools in relation to infection control metrics is reviewed, and the use of hospital-acquired infection as a proxy indicator for deficiencies of system management is considered. Infection prevention and control cannot be the role and responsibility of a single individual or a small dedicated team; rather it should be a priority at all levels and integrated within all management systems, including the research and educational agendas.

Background A challenge facing stakeholders is the identification and translation of relevant high quality research to inform policy and practice. This study engaged academic and community stakeholders in conducting a best evidence-synthesis to identify modifiable risk and protective worker factors across health conditions impacting work-related absence. Objectives To identify modifiable worker disability risk and protective factors across common health conditions impacting work-related absence. Methods We searched Medline, Embase, CINHAL, The Cochrane Library, PsycINFO, BusinessSourceComplete, and ABI/Inform from 2000 to 2011. Quantitative, qualitative, or mixed methods systematic reviews of work-focused population were considered for inclusion. Two or more reviewers independently reviewed articles for inclusion and methodological screening. Results The search strategy, expert input and grey literature identified 2,467 unique records. One hundred and forty-two full text articles underwent comprehensive review. Twenty-four systematic reviews met eligibility criteria. Modifiable worker factors found to have consistent evidence across two or more health conditions included emotional distress, negative enduring psychology/personality factors, negative health and disability perception, decreased physical activity, lack of family support, poor general health, increased functional disability, increased pain, increased fatigue and lack of motivation to return to work. Conclusions Systematic reviews are limited by availability of high quality studies, lack of consistency of methodological screening and reporting, and variability of outcome measures used.

Objective To examine the role of pain experiences in relation to work absence, within the context of other worker health factors and workplace factors among Canadian nurses with work-related musculoskeletal (MSK) injury. Methods Structural equation modeling was used on a sample of 941 employed, female, direct care nurses with at least one day of work absence due to a work-related MSK injury, from the cross-sectional 2005 National Survey of the Work and Health of Nurses. Results The final model suggests that pain severity and pain-related work interference mediate the impact of the following worker health and workplace factors on work absence duration: depression, back problems, age, unionization, workplace physical demands and low job control. The model accounted for 14 % of the variance in work absence duration and 46.6 % of the variance in pain-related work interference. Conclusions Our findings support a key role for pain severity and pain-related work interference in mediating the effects of workplace factors and worker health factors on work absence duration. Future interventions should explore reducing pain-related work interference through addressing workplace issues, such as providing modified work, reducing physical demands, and increasing job control.

As of June 22, 2011, influenza A/H5N1 has caused a reported 329 deaths and 562 cases in humans, typically attributed to contact with infected poultry. Influenza H5N1 has been described as seasonal. Although several studies have evaluated environmental risk factors for H5N1 in poultry, none have considered seasonality of H5N1 in humans. In addition, temperature and humidity are suspected to drive influenza in temperate regions, but drivers in the tropics are unknown, for H5N1 as well as other influenza viruses. An analysis was conducted to determine whether human H5N1 cases occur seasonally in association with changes in temperature, precipitation and humidity. Data analyzed were H5N1 human cases in Indonesia (n = 135) and Egypt (n = 50), from January 1, 2005 (Indonesia) or 2006 (Egypt) through May 1, 2008 obtained from WHO case reports, and average daily weather conditions obtained from NOAA's National Climatic Data Center. Fourier time series analysis was used to determine seasonality of cases and associations between weather conditions and human H5N1 incidence. Human H5N1 cases in Indonesia occurred with a period of 1.67 years/cycle (p

This study examines the impact of behavioural factors in the successful management of an organisation-wide Norovirus outbreak in a UK hospital.

We propose a feature vector approach to characterize the variation in large data sets of biological sequences. Each candidate sequence produces a single feature vector constructed with the number and location of amino acids or nucleic acids in the sequence. The feature vector characterizes the distance between the actual sequence and a model of a theoretical sequence based on the binomial and uniform distributions. This method is distinctive in that it does not rely on sequence alignment for determining protein relatedness, allowing the user to visualize the relationships within a set of proteins without making a priori assumptions about those proteins. We apply our method to two large families of proteins: protein kinase C, and globins, including hemoglobins and myoglobins. We interpret the high-dimensional feature vectors using principal components analysis and agglomerative hierarchical clustering. We find that the feature vector retains much of the information about the original sequence. By using principal component analysis to extract information from collections of feature vectors, we are able to quickly identify the nature of variation in a collection of proteins. Where collections are phylogenetically or functionally related, this is easily detected. Hierarchical agglomerative clustering provides a means of constructing cladograms from the feature vector output.