The "Why" behind including "Y" in your imputation model.

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.

Randomized controlled trial: Quantifying the impact of disclosing uncertainty on adherence to hypothetical health recommendations.

We conducted a randomized controlled trial to assess whether disclosing elements of uncertainty in an initial public health statement will change the likelihood that participants will accept new, different advice that arises as more evidence is uncovered. Proportional odds models were fit, stratified by the baseline likelihood to agree with the final advice. 298 participants were randomized to the treatment arm and 298 in the control arm. Among participants who were more likely to agree with the final recommendation at baseline, those who were initially shown uncertainty had a 46% lower odds of being more likely to agree with the final recommendation compared to those who were not (OR: 0.54, 95% CI: 0.27-1.03). Among participants who were less likely to agree with the final recommendation at baseline, those who were initially shown uncertainty have 1.61 times the odds of being more likely to agree with the final recommendation compared to those who were not (OR: 1.61, 95% CI: 1.15-2.25). This has implications for public health leaders when assessing how to communicate a recommendation, suggesting communicating uncertainty influences whether someone will adhere to a future recommendation.

The Expert Next Door: Interactions With Friends and Family During the COVID-19 Pandemic.

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.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Will Podcasting and Social Media Replace Journals and Traditional Science Communication? No, but...

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.

Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study.

BACKGROUND: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. METHODS AND FINDINGS: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. CONCLUSIONS: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

Quantifying Uncertainty in Mechanistic Models of Infectious Disease.

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.

Concerns About the Special Article on Hydroxychloroquine and Azithromycin in High-Risk Outpatients With COVID-19.

In May 2020, the Journal published an opinion piece by a member of the Editorial Board, in which the author reviewed several papers and argued that using hydroxychloroquine (HCQ) + azithromycin (AZ) early to treat symptomatic coronavirus disease 2019 (COVID-19) cases in high-risk patients should be broadly applied. As members of the Journal's Editorial Board, we are strongly supportive of open debate in science, which is essential even on highly contentious issues. However, we must also be thorough in our examination of the facts and open to changing our minds when new information arises. In this commentary, we document several important errors in the manuscript, review the literature presented, and demonstrate why it is not of sufficient quality to support scale up of HCQ + AZ, and then discuss the literature that has been generated since the publication, which also does not support use of this therapy. Unfortunately, the current scientific evidence does not support HCQ + AZ as an effective treatment for COVID-19, if it ever did, and even suggests many risks. Continuing to push the view that it is an essential treatment in the face of this evidence is irresponsible and harmful to the many people already suffering from infection.

Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding.

Concerns over ACE inhibitor or ARB use to treat hypertension during COVID-19 remain unresolved. Although studies using more robust methodologies provided some clarity, sources of bias persist and it remains critical to quickly address this question. In this review, we discuss pernicious sources of bias using a causal model framework, including time-varying confounder, collider, information, and time-dependent bias, in the context of recently published studies. We discuss causal inference methodologies that can address these issues, including causal diagrams, time-to-event analyses, sensitivity analyses, and marginal structural modeling. We discuss effect modification and we propose a role for causal mediation analysis to estimate indirect effects via mediating factors, especially components of the renin--angiotensin system. Thorough knowledge of these sources of bias and the appropriate methodologies to address them is crucial when evaluating observational studies to inform patient management decisions regarding whether ACE inhibitors or ARBs are associated with greater risk from COVID-19.

Mental health conditions and the risk of chronic opioid therapy among patients with rheumatoid arthritis: a retrospective veterans affairs cohort study.

OBJECTIVE: Patients with rheumatoid arthritis (RA) often receive opioid analgesics for pain management. We examined the association between mental health conditions and the risk of chronic opioid therapy. METHODS: A retrospective cohort of veterans with RA initiating opioid use was assembled using Veterans Health Administration databases (2001-2012). Mental health conditions included anxiety (N = 1108, 12.9%), depression (N = 1912, 22.2%), bipolar disease (N = 131, 1.5%), and post-traumatic stress disorder (N = 768, 8.9%) and were identified by ICD coded diagnoses and use of specific medications. Cohort members were followed from opioid initiation through chronic opioid therapy, defined as the continuous availability of opioids for at least 90 days. Multivariable Cox proportional hazard regression models assessed the association between mental health conditions and chronic opioid therapy accounting for relevant covariates. Subgroup analyses examined whether the strength of the observed association varied by the duration of the initial opioid prescription. RESULTS: We identified 14,767 patients with RA with 22,452 episodes of opioid use initiation. Mental health conditions were identified in 8607 (38.3%) patients. Compared with patients without mental health conditions, patients with mental health conditions have a higher risk of developing chronic opioid therapy (469.3 vs 378.1 per 1000 person-years, adjusted hazard ratio [aHR] 1.18, 95% CI 1.09, 1.29). The increased risk was highest for those with a history of opioid use disorder (aHR 1.94, 95% CI 1.09, 3.46) and also elevated for those with other substance use disorders (aHR 1.35, 95% CI 1.05, 1.73). Duration of the initial opioid prescription was independently associated with chronic opioid therapy, regardless of the estimated opioid daily dose. CONCLUSIONS: History of mental health conditions and duration of the initial opioid prescription were associated with an increased risk of chronic opioid therapy among patients with RA.Key Points• Approximately a third of patients with RA are exposed to opioid analgesics.• Patients with RA and history of mental health disease, especially substance use disorders, who initiate opioid use have an increased risk of chronic opioid therapy.• This study provides insight in an underrepresented population of mainly male patients with RA.

Comparative Effectiveness of Two Collagen-containing Dressings: Oxidized Regenerated Cellulose (ORC)/Collagen/Silver-ORC Dressing Versus Ovine Collagen Extracellular Matrix.

INTRODUCTION: Chronic wounds are characterized by impaired tissue physiology that stalls healing. The prevalence of wound chronicity presents challenges in wound management and health care cost-containment. OBJECTIVE: This comparative effectiveness study evaluates the value proposition of 2 collagen-containing wound dressings - oxidized regenerated cellulose (ORC)/collagen/silver-ORC dressing and ovine collagen extracellular matrix (ECM) - in matched cohorts of patients undergoing treatment for diabetic foot ulcers (DFUs). MATERIALS AND METHODS: Data extracted from the US Wound Registry identified DFUs treated with either dressing and included wounds with complete data records (n = 3230). Thirty-seven variables were considered in propensity score matching to develop a case-matched cohort of 844 DFUs (n = 422 DFUs/group). RESULTS: The ORC/collagen/silver-ORC dressing group yielded a significantly higher percentage of DFUs that healed or improved (82% vs. 74.6%; P = .0096). The ovine collagen ECM dressing group yielded a significantly higher percentage of DFUs that worsened (15.2% vs. 23.9%; P = .0013). The ORC/collagen/silver-ORC dressing group demonstrated a higher percentage of DFUs that attained 75% to 100% granulation at zero depth at 4, 8, 12, and 16 weeks. Median time to 75% to 100% granulation was 42 days for the ORC/collagen/silver-ORC dressing group versus 60 days for the ovine collagen ECM dressing group (P = .0109). CONCLUSIONS: According to this comparative effectiveness study using real world data, ORC/collagen/silver-ORC dressing appears to afford improved healing and reduced time to granulation relative to ovine collagen ECM dressing.

Caring for Critically Ill Patients with the ABCDEF Bundle: Results of the ICU Liberation Collaborative in Over 15,000 Adults.

OBJECTIVE: Decades-old, common ICU practices including deep sedation, immobilization, and limited family access are being challenged. We endeavoured to evaluate the relationship between ABCDEF bundle performance and patient-centered outcomes in critical care. DESIGN: Prospective, multicenter, cohort study from a national quality improvement collaborative. SETTING: 68 academic, community, and federal ICUs collected data during a 20-month period. PATIENTS: 15,226 adults with at least one ICU day. INTERVENTIONS: We defined ABCDEF bundle performance (our main exposure) in two ways: 1) complete performance (patient received every eligible bundle element on any given day) and 2) proportional performance (percentage of eligible bundle elements performed on any given day). We explored the association between complete and proportional ABCDEF bundle performance and three sets of outcomes: patient-related (mortality, ICU and hospital discharge), symptom-related (mechanical ventilation, coma, delirium, pain, restraint use), and system-related (ICU readmission, discharge destination). All models were adjusted for a minimum of 18 a priori determined potential confounders. MEASUREMENTS AND RESULTS: Complete ABCDEF bundle performance was associated with lower likelihood of seven outcomes: hospital death within 7 days (adjusted hazard ratio, 0.32; CI, 0.17-0.62), next-day mechanical ventilation (adjusted odds ratio [AOR], 0.28; CI, 0.22-0.36), coma (AOR, 0.35; CI, 0.22-0.56), delirium (AOR, 0.60; CI, 0.49-0.72), physical restraint use (AOR, 0.37; CI, 0.30-0.46), ICU readmission (AOR, 0.54; CI, 0.37-0.79), and discharge to a facility other than home (AOR, 0.64; CI, 0.51-0.80). There was a consistent dose-response relationship between higher proportional bundle performance and improvements in each of the above-mentioned clinical outcomes (all p < 0.002). Significant pain was more frequently reported as bundle performance proportionally increased (p = 0.0001). CONCLUSIONS: ABCDEF bundle performance showed significant and clinically meaningful improvements in outcomes including survival, mechanical ventilation use, coma, delirium, restraint-free care, ICU readmissions, and post-ICU discharge disposition.

Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses.

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value-a second-generation p-value (pδ)-that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.

Comparative Safety of Sulfonylurea and Metformin Monotherapy on the Risk of Heart Failure: A Cohort Study.

BACKGROUND: Medications that impact insulin sensitivity or cause weight gain may increase heart failure risk. Our aim was to compare heart failure and cardiovascular death outcomes among patients initiating sulfonylureas for diabetes mellitus treatment versus metformin. METHODS AND RESULTS: National Veterans Health Administration databases were linked to Medicare, Medicaid, and National Death Index data. Veterans aged ≥18 years who initiated metformin or sulfonylureas between 2001 and 2011 and whose creatinine was <1.4 (females) or 1.5 mg/dL (males) were included. Each metformin patient was propensity score-matched to a sulfonylurea initiator. The outcome was hospitalization for acute decompensated heart failure as the primary reason for admission or a cardiovascular death. There were 126 867 and 79 192 new users of metformin and sulfonylurea, respectively. Propensity score matching yielded 65 986 per group. Median age was 66 years, and 97% of patients were male; hemoglobin A1c 6.9% (6.3, 7.7); body mass index 30.7 kg/m2 (27.4, 34.6); and 6% had heart failure history. There were 1236 events (1184 heart failure hospitalizations and 52 cardiovascular deaths) among sulfonylurea initiators and 1078 events (1043 heart failure hospitalizations and 35 cardiovascular deaths) among metformin initiators. There were 12.4 versus 8.9 events per 1000 person-years of use (adjusted hazard ratio 1.32, 95%CI 1.21, 1.43). The rate difference was 4 heart failure hospitalizations or cardiovascular deaths per 1000 users of sulfonylureas versus metformin annually. CONCLUSIONS: Predominantly male patients initiating treatment for diabetes mellitus with sulfonylurea had a higher risk of heart failure and cardiovascular death compared to similar patients initiating metformin.

Quantitative Evaluation of the Community Research Fellows Training Program.

CONTEXT: The community research fellows training (CRFT) program is a community-based participatory research (CBPR) initiative for the St. Louis area. This 15-week program, based on a Master in Public Health curriculum, was implemented by the Division of Public Health Sciences at Washington University School of Medicine and the Siteman Cancer Center. OBJECTIVES: We measure the knowledge gained by participants and evaluate participant and faculty satisfaction of the CRFT program both in terms of meeting learning objectives and actively engaging the community in the research process. PARTICIPANTS: We conducted analyses on 44 community members who participated in the CRFT program and completed the baseline and follow-up knowledge assessments. MAIN OUTCOME MEASURES: Knowledge gain is measured by a baseline and follow-up assessment given at the first and final session. Additionally, pre- and post-tests are given after the first 12 sessions. To measure satisfaction, program evaluations are completed by both the participants and faculty after each topic. Mid-way through the program, a mid-term evaluation was administered to assess the program's community engagement. We analyzed the results from the assessments, pre- and post-tests, and evaluations. RESULTS: The CRFT participants' knowledge increased at follow-up as compared with baseline on average by a 16.5 point difference (p < 0.0001). Post-test scores were higher than pre-test scores for 11 of the 12 sessions. Both participants and faculty enjoyed the training and rated all session well. CONCLUSION: The CRFT program was successful in increasing community knowledge, participant satisfaction, and faculty satisfaction. This success has enhanced the infrastructure for CBPR as well as led to CBPR pilot projects that address health disparities in the St. Louis Greater Metropolitan Area.

Is low health literacy associated with increased emergency department utilization and recidivism?

OBJECTIVES: The objective was to determine whether patients with low health literacy have higher emergency department (ED) utilization and higher ED recidivism than patients with adequate health literacy. METHODS: The study was conducted at an urban academic ED with more than 95,000 annual visits that is part of a 13-hospital health system, using electronic records that are captured in a central data repository. As part of a larger, cross-sectional, convenience sample study, health literacy testing was performed using the short test of functional health literacy in adults (S-TOFHLA) and standard test thresholds identifying those with inadequate, marginal, and adequate health literacy. The authors collected patients' demographic and clinical data, including items known to affect recidivism. This was a structured electronic record review directed at determining 1) the median number of total ED visits in this health system within a 2-year period and 2) the proportion of patients with each level of health literacy who had return visits within 3, 7, and 14 days of index visits. Descriptive data for demographics and ED returns are reported, stratified by health literacy level. The Mantel-Haenszel chi-square was used to test whether there is an association between health literacy and ED recidivism. A negative binomial multivariable model was performed to examine whether health literacy affects ED use, including variables significant at the 0.1 alpha level on bivariate analysis and retaining those significant at an alpha of 0.05 in the final model. RESULTS: Among 431 patients evaluated, 13.2% had inadequate, 10% had marginal, and 76.3% had adequate health literacy as identified by S-TOFHLA. Patients with inadequate health literacy had higher ED utilization compared to those with adequate health literacy (p = 0.03). Variables retained in the final model included S-TOFHLA score, number of medications, having a personal doctor, being a property owner, race, insurance, age, and simple comorbidity score. During the study period, 118 unique patients each made at least one return ED visit within a 14-day period. The proportion of patients with inadequate health literacy making at least one return visit was higher than that of patients with adequate health literacy at 14 days, but was not significantly higher within 3 or 7 days. CONCLUSIONS: In this single-center study, higher utilization of the ED by patients with inadequate health literacy when compared to those with adequate health literacy was observed. Patients with inadequate health literacy made a higher number of return visits at 14 days but not at 3 or 7 days.