ObjectivesTo determine how self-reported level of exposure to patients with novel coronavirus 2019 (COVID-19) affected the perceived safety, training and well-being of residents and fellows.MethodsWe administered an anonymous, voluntary, web-based survey to a convenience sample of trainees worldwide. The survey was distributed by email and social media posts from April 20th to May 11th, 2020. Respondents were asked to estimate the number of patients with COVID-19 they cared for in March and April 2020 (0, 1–30, 31–60, >60). Survey questions addressed (1) safety and access to personal protective equipment (PPE), (2) training and professional development and (3) well-being and burnout.ResultsSurveys were completed by 1420 trainees (73% residents, 27% fellows), most commonly from the USA (n=670), China (n=150), Saudi Arabia (n=76) and Taiwan (n=75). Trainees who cared for a greater number of patients with COVID-19 were more likely to report limited access to PPE and COVID-19 testing and more likely to test positive for COVID-19. Compared with trainees who did not take care of patients with COVID-19 , those who took care of 1–30 patients (adjusted OR [AOR] 1.80, 95% CI 1.29 to 2.51), 31–60 patients (AOR 3.30, 95% CI 1.86 to 5.88) and >60 patients (AOR 4.03, 95% CI 2.12 to 7.63) were increasingly more likely to report burnout. Trainees were very concerned about the negative effects on training opportunities and professional development irrespective of the number of patients with COVID-19 they cared for.ConclusionExposure to patients with COVID-19 is significantly associated with higher burnout rates in physician trainees.

BACKGROUND: Birth cohort screening is recommended for hepatitis C virus (HCV) and underserved populations are disproportionally affected by HCV. Little is known about the influence of race on the HCV care continuum in this population. OBJECTIVE: To assess the cascade of HCV care in a large racially diverse and underserved birth cohort. DESIGN: Retrospective cohort study using electronic medical record data abstracted until August 31, 2017. PATIENTS: 34,810 patients born between 1945 and 1965 engaged in primary care between October 1, 2014, and October 31, 2016, within the safety-net clinics of the San Francisco Health Network. MAIN MEASURES: Rate of hepatitis C testing, hepatitis C treatment, and response to therapy. RESULTS: Cohort characteristics were as follows: median age 59 years, 57.6% male, 25.5% White (20.6% Black, 17.7% Latino, 33.0% Asian/Pacific Islander (API), 2% other), and 32.6% preferred a non-English language. 99.7% had an HCV test (95.4% HCV antibody, 4.3% HCVRNA alone). Among HCV antibody-positive patients (N = 4587), 22.9% were not tested for confirmatory HCVRNA. Among viremic patients (N = 3673), 20.8% initiated HCV therapy, 90.6% achieved sustained virologic response (SVR) and 8.1% did not have a SVR test. HCV screening and treatment were highest in APIs (98.7 and 34.7% respectively; p < 0.001). Blacks had the highest chronic HCV rate (22.2%; p < 0.001). Latinos had the lowest SVR rate (81.3%; p = 0.01). On multivariable analysis, API race (vs White, OR 1.20; p = 0.001), presence of HIV co-infection (OR 1.58; p = 0.02), presence of chronic kidney disease (OR 0.47; p < 0.001), English (vs non-English) as preferred language (OR 0.54; p = 0.002), ALT (OR 0.39 per doubling; p < 0.001), and HCVRNA (OR 0.83 per 10-fold increase; p < 0.001) were associated with HCV treatment. CONCLUSIONS: Despite near-universal screening, gaps in active HCV confirmation, treatment, and verification of cure were identified and influenced by race. Tailored interventions to engage and treat diverse and underserved populations with HCV infection are needed.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.