"No Escalation of Treatment" Designations: A Multi-institutional Exploratory Qualitative Study.

BACKGROUND: No Escalation of Treatment (NoET) designations are used in ICUs internationally to limit treatment for critically ill patients. However, they are the subject of debate in the literature and have not been qualitatively studied. RESEARCH QUESTION: How do physicians understand and perceive NoET designations, especially regarding their usefulness and associated challenges? What mechanisms do hospitals provide to facilitate the use of NoET designations? STUDY DESIGN AND METHODS: Qualitative study at seven US hospitals, employing semistructured interviews with 30 physicians and review of relevant institutional records (eg, hospital policies, screenshots of ordering menus in the electronic health record). RESULTS: At all hospitals, participants reported the use of NoET designations, which were understood to mean that providers should withhold new or higher-intensity interventions ("escalations") but not withdraw ongoing interventions. Three hospitals provided a specific mechanism for designating a patient as NoET (eg, a DNR/Do Not Escalate code status order); at the remaining hospitals, a variety of informal methods (eg, verbal hand-offs) were used. We identified five functions of NoET designations: (1) Defining an intermediate point of treatment limitation, (2) helping physicians navigate prearrest clinical decompensations, (3) helping surrogate decision-makers transition toward comfort care, (4) preventing patient harm from invasive measures, and (5) conserving critical care resources. Across hospitals, participants reported implementation challenges related to the ambiguity in meaning of NoET designations. INTERPRETATION: Despite ongoing debate, NoET designations are used in a varied sample of hospitals and are perceived as having multiple functions, suggesting they may fulfill an important need in the care of critically ill patients, especially at the end of life. The use of NoET designations can be improved through the implementation of a formal mechanism that encourages consistency across providers and clarifies the meaning of "escalation" for each patient.

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research.

DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs.

Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG). All branch DAGs share a common ruleset, and must 1) change the root DAG, 2) be a valid DAG, and either 3a) change the minimally sufficient adjustment set or 3b) change the number of frontdoor paths. Branch DAGs comprise a list of assumptions which must be justified as negligible. We define two types of branch DAGs: exclusion branch DAGs add a single- or bidirectional pathway between two nodes in the root DAG (e.g., direct pathways and colliders), while misdirection branch DAGs represent alternative pathways that could be drawn between objects (e.g., creating a collider by reversing the direction of causation for a controlled confounder). The DAGWOOD framework 1) organizes causal model assumptions, 2) reinforces best DAG practices, 3) provides a framework for evaluation of causal models, and 4) can be used for generating causal models.

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on 26 November 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-sectional), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigour to be actionable by policy-makers. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Much ado about something: a response to "COVID-19: underpowered randomised trials, or no randomised trials?"

Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak trials or not having a trial at all. In a recent article, Dr. Atle Fretheim argues that statistically underpowered studies are still valuable, particularly in conjunction with other similar studies in meta-analysis in the context of the DANMASK-19 trial, asking "Surely, some trial evidence must be better than no trial evidence?" However, informative trials are not always feasible, and feasible trials are not always informative. In some cases, even a well-conducted but weakly designed and/or underpowered trial such as DANMASK-19 may be uninformative or worse, both individually and in a body of literature. Meta-analysis, for example, can only resolve issues of statistical power if there is a reasonable expectation of compatible well-designed trials. Uninformative designs may also invite misinformation. Here, we make the case that-when considering informativeness, ethics, and opportunity costs in addition to statistical power-"nothing" is often the better choice.

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

INTRODUCTION: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature. METHODS: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes. After searching PubMed for peer-reviewed articles published on November 26, 2020 or earlier and screening, all studies were reviewed by three reviewers first independently and then to consensus. The review tool was based on previously developed and released review guidance for COVID-19 policy impact evaluation, assessing what impact evaluation method was used, graphical display of outcomes data, functional form for the outcomes, timing between policy and impact, concurrent changes to the outcomes, and an overall rating. RESULTS: After 102 articles were identified as potentially meeting inclusion criteria, we identified 36 published articles that evaluated the quantitative impact of COVID-19 policies on direct COVID-19 outcomes. The majority (n=23/36) of studies in our sample examined the impact of stay-at-home requirements. Nine studies were set aside because the study design was considered inappropriate for COVID-19 policy impact evaluation (n=8 pre/post; n=1 cross-section), and 27 articles were given a full consensus assessment. 20/27 met criteria for graphical display of data, 5/27 for functional form, 19/27 for timing between policy implementation and impact, and only 3/27 for concurrent changes to the outcomes. Only 1/27 studies passed all of the above checks, and 4/27 were rated as overall appropriate. Including the 9 studies set aside, reviewers found that only four of the 36 identified published and peer-reviewed health policy impact evaluation studies passed a set of key design checks for identifying the causal impact of policies on COVID-19 outcomes. DISCUSSION: The reviewed literature directly evaluating the impact of COVID-19 policies largely failed to meet key design criteria for inference of sufficient rigor to be actionable by policymakers. This was largely driven by the circumstances under which policies were passed making it difficult to attribute changes in COVID-19 outcomes to particular policies. More reliable evidence review is needed to both identify and produce policy-actionable evidence, alongside the recognition that actionable evidence is often unlikely to be feasible.

Ethical issues in using ambient intelligence in health-care settings.

Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.

Variation in the design of Do Not Resuscitate orders and other code status options: a multi-institutional qualitative study.

BACKGROUND: US hospitals typically provide a set of code status options that includes Full Code and Do Not Resuscitate (DNR) but often includes additional options. Although US hospitals differ in the design of code status options, this variation and its impacts have not been empirically studied. DESIGN AND METHODS: Multi-institutional qualitative study at 7 US hospitals selected for variability in geographical location, type of institution and design of code status options. We triangulated across three data sources (policy documents, code status ordering menus and in-depth physician interviews) to characterise the code status options available at each hospital. Using inductive qualitative methods, we investigated design differences in hospital code status options and the perceived impacts of these differences. RESULTS: The code status options at each hospital varied widely with regard to the number of code status options, the names and definitions of code status options, and the formatting and capabilities of code status ordering menus. DNR orders were named and defined differently at each hospital studied. We identified five key design characteristics that impact the function of a code status order. Each hospital's code status options were unique with respect to these characteristics, indicating that code status plays differing roles in each hospital. Physician participants perceived that the design of code status options shapes communication and decision-making practices about resuscitation and life-sustaining treatments, especially at the end of life. We identified four potential mechanisms through which this may occur: framing conversations, prompting decisions, shaping inferences and creating categories. CONCLUSIONS: There are substantive differences in the design of hospital code status options that may contribute to known variability in end-of-life care and treatment intensity among US hospitals. Our framework can be used to design hospital code status options or evaluate their function.

Digital Contact Tracing, Privacy, and Public Health.

Digital contact tracing, in combination with widespread testing, has been a focal point for many plans to "reopen" economies while containing the spread of Covid-19. Most digital contact tracing projects in the United States and Europe have prioritized privacy protections in the form of local storage of data on smartphones and the deidentification of information. However, in the prioritization of privacy in this narrow form, there is not sufficient attention given to weighing ethical trade-offs within the context of a public health pandemic or to the need to evaluate safety and effectiveness of software-based technology applied to public health.

Ventilator Triage Policies During the COVID-19 Pandemic at U.S. Hospitals Associated With Members of the Association of Bioethics Program Directors.

BACKGROUND: The coronavirus disease 2019 pandemic has or threatens to overwhelm health care systems. Many institutions are developing ventilator triage policies. OBJECTIVE: To characterize the development of ventilator triage policies and compare policy content. DESIGN: Survey and mixed-methods content analysis. SETTING: North American hospitals associated with members of the Association of Bioethics Program Directors. PARTICIPANTS: Program directors. MEASUREMENTS: Characteristics of institutions and policies, including triage criteria and triage committee membership. RESULTS: Sixty-seven program directors responded (response rate, 91.8%); 36 (53.7%) hospitals did not yet have a policy, and 7 (10.4%) hospitals' policies could not be shared. The 29 institutions providing policies were relatively evenly distributed among the 4 U.S. geographic regions (range, 5 to 9 policies per region). Among the 26 unique policies analyzed, 3 (11.3%) were produced by state health departments. The most frequently cited triage criteria were benefit (25 policies [96.2%]), need (14 [53.8%]), age (13 [50.0%]), conservation of resources (10 [38.5%]), and lottery (9 [34.6%]). Twenty-one (80.8%) policies use scoring systems, and 20 of these (95.2%) use a version of the Sequential Organ Failure Assessment score. Among the policies that specify the triage team's composition (23 [88.5%]), all require or recommend a physician member, 20 (87.0%) a nurse, 16 (69.6%) an ethicist, 8 (34.8%) a chaplain, and 8 (34.8%) a respiratory therapist. Thirteen (50.0% of all policies) require or recommend that those making triage decisions not be involved in direct patient care, but only 2 (7.7%) require that their decisions be blinded to ethically irrelevant considerations. LIMITATION: The results may not be generalizable to institutions without academic bioethics programs. CONCLUSION: Over one half of respondents did not have ventilator triage policies. Policies have substantial heterogeneity, and many omit guidance on fair implementation. PRIMARY FUNDING SOURCE: None.

Expertise in evidence-based medicine: a tale of three models.

BACKGROUND: Expertise has been a contentious concept in Evidence-Based Medicine (EBM). Especially in the early days of the movement, expertise was taken to be exactly what EBM was rebelling against-the authoritarian pronouncements about "best" interventions dutifully learned in medical schools, sometimes with dire consequences. Since then, some proponents of EBM have tried various ways of reincorporating the idea of expertise into EBM, with mixed results. However, questions remain. Is expertise evidence? If not, what is it good for, if anything? METHODS: In this article, I describe and analyze the three historical models of expertise integration in EBM and discuss the difficulties in putting each into practice. I also examine accounts of expertise from disciplines outside of medicine, including philosophy, sociology, psychology, and science and technology studies to see if these accounts can strengthen and clarify what EBM has to say about expertise. RESULTS: Of the accounts of expertise discussed here, the Collins and Evans account can do most to clarify the concept of expertise in EBM. CONCLUSIONS: With some additional clarification from EBM proper, theoretical resources from other disciplines might augment the current EBM account of expertise.

'What the patient wants': an investigation of the methods of ascertaining patient values in evidence-based medicine and values-based practice.

Evidence-Based Medicine (EBM), Values-Based Practice (VBP) and Person-Centered Healthcare (PCH) are all concerned with the values in play in the clinical encounter. However, these recent movements are not in agreement about how to discover these relevant values. In some parts of EBM textbooks, the prescribed method for discovering values is through social science research on the average values in a particular population. VBP by contrast always investigates the individually held values of the different stakeholders in the particular clinical encounter, although the account has some other difficulties. I argue that although average values for populations might be very useful in informing questions of resource distribution and policy making, their use cannot replace the individual solicitation of patient (and other stakeholder) values in the clinical encounter. Because of the inconsistency of the EBM stance on values, the incompatibility of some versions of the EBM treatment of values with PCH, and EBM's attempt to transplant research methods from science into the realm of values, I must recommend the use of the VBP account of values discovery.

Stoic beliefs and health: development and preliminary validation of the Pathak-Wieten Stoicism Ideology Scale.

INTRODUCTION: We developed and validated a new parsimonious scale to measure stoic beliefs. Key domains of stoicism are imperviousness to strong emotions, indifference to death, taciturnity and self-sufficiency. In the context of illness and disease, a personal ideology of stoicism may create an internal resistance to objective needs, which can lead to negative consequences. Stoicism has been linked to help-seeking delays, inadequate pain treatment, caregiver strain and suicide after economic stress. METHODS: During 2013-2014, 390 adults aged 18+ years completed a brief anonymous paper questionnaire containing the preliminary 24-item Pathak-Wieten Stoicism Ideology Scale (PW-SIS). Confirmatory factor analysis (CFA) was used to test an a priori multidomain theoretical model. Content validity and response distributions were examined. Sociodemographic predictors of strong endorsement of stoicism were explored with logistic regression. RESULTS: The final PW-SIS contains four conceptual domains and 12 items. CFA showed very good model fit: root mean square error of approximation (RMSEA)=0.05 (95% CI 0.04 to 0.07), goodness-of-fit index=0.96 and Tucker-Lewis Index=0.93. Cronbach's alpha was 0.78 and ranged from 0.64 to 0.71 for the subscales. Content validity analysis showed a statistically significant trend, with respondents who reported trying to be a stoic 'all of the time' having the highest PW-SIS scores. Men were over two times as likely as women to fall into the top quartile of responses (OR=2.30, 95% CI 1.44 to 3.68, P<0.001). ORs showing stronger endorsement of stoicism by Hispanics, Blacks and biracial persons were not statistically significant. DISCUSSION: The PW-SIS is a valid and theoretically coherent scale which is brief and practical for integration into a wide range of health behaviour and outcomes research studies.