The value of Source Data Verification (SDV) has been a common theme in the applied Clinical Translational Science literature. Yet, few published assessments of SDV quality exist even though they are needed to design risk-based and reduced monitoring schemes. This review was conducted to identify reports of SDV quality, with a specific focus on accuracy.

A scoping review was conducted of the SDV and clinical trial monitoring literature to identify articles addressing SDV quality. Articles were systematically screened and summarized in terms of research design, SDV context, and reported measures.

The review found significant heterogeneity in underlying SDV methods, domains of SDV quality measured, the outcomes assessed, and the levels at which they were reported. This variability precluded comparison or pooling of results across the articles. No absolute measures of SDV accuracy were identified.

A definitive and comprehensive characterization of SDV process accuracy was not found. Reducing the SDV without understanding the risk of critical findings going undetected, i.e., SDV sensitivity, is counter to recommendations in Good Clinical Practice and the principles of Quality by Design. Reference estimates (or methods to obtain estimates) of SDV accuracy are needed to confidently design risk-based, reduced SDV processes for clinical studies.

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.

The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.

As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Social determinants of health (SDOH) can contribute to disparities that negatively impact health outcomes and healthcare utilization. Comprehensive screening is frequently overlooked during inpatient clinical care. This pilot aimed to evaluate the capturability of a multi-domain SDOH screening tool during hospitalization, as well as correlation of SDOH needs to readmissions.

The Protocol for Responding to and Assessing Patients’ Assets, Risks and Experiences (PRAPARE) screening tool was implemented on admission with adult inpatients at an academic tertiary medical center in central Pennsylvania. A total of 80 patients were screened over an 8-week period using the PRAPARE tool.

43.7% of participants were identified as having at least one SDOH need and 21.2% were identified as having two or more needs. Of the participants identified as having at least one SDOH need through PRAPARE screening, 42.5% experienced a readmission within 30 days, compared to 15% readmissions among participants with no identified SDOH needs. For each additional SDOH need a patient had, the odds they experienced a readmission increased by 2.2 times.

The study findings suggest that utilization of the PRAPARE screening tool has the ability to capture significant SDOH needs among hospitalized patients. This study also suggests that higher SDOH needs correlate to increased odds of experiencing a hospital readmission.

There are two main schools of thought about statistical inference: frequentist and Bayesian. The frequentist approach relies solely on available data for predictions, while the Bayesian approach incorporates both data and prior knowledge about the event of interest. Bayesian methods were developed hundreds of years ago; however, they were rarely used due to computational challenges and conflicts between the two schools of thought. Recent advances in computational capabilities and a shift toward leveraging prior knowledge for inferences have led to increased use of Bayesian methods.

Many biostatisticians with expertise in frequentist approaches lack the skills to apply Bayesian techniques. To address this gap, four faculty experts in Bayesian modeling at the University of Michigan developed a practical, customized workshop series. The training, tailored to accommodate the schedules of full-time staff, focused on immersive, project-based learning rather than traditional lecture-based methods. Surveys were conducted to assess the impact of the program.

All 20 participants completed the program and when surveyed reported an increased understanding of Bayesian theory and greater confidence in using these techniques. Capstone projects demonstrated participants’ ability to apply Bayesian methodology. The workshop not only enhanced the participants’ skills but also positioned them to readily apply Bayesian techniques in their work.

Accommodating the schedules of full-time biostatistical staff enabled full participation. The immersive project-based learning approach resulted in building skills and increasing confidence among staff statisticians who were unfamiliar with Bayesian methods and their practical applications.

Persistence in physician-scientist careers has been suboptimal, particularly among women. There is a gender gap in self-confidence in medicine. We measured the impact of our physician-scientist training programs on trainee’s confidence in professional, personal, and scientific competencies, using a survey measuring self-rated confidence in 36 competencies across two timepoints.

Results were analyzed for the full survey and for thematic subscales identified through exploratory factor analysis (EFA). A mixed effects linear model and a difference in differences (DID) design were used to assess the differential impact of the programing by gender and career level.

Analysis included 100 MD-PhD or MD-only medical student or resident/fellow trainees enrolled between 2020 and 2023. Five subscales were identified through EFA; career sustainability, science productivity, grant management, goal setting, and goal alignment (Cronbach’s alpha 0.85–0.94). Overall, mean scores increased significantly for all five subscales. Women significantly increased their confidence levels in all five areas, whereas men increased only in science productivity and grant management. Mixed effects models showed significant increases over time for women compared to men in career sustainability and goal alignment. Residents and fellows had greater increases than medical students across all subscales.

Physician-scientist trainees fellows increased their confidence in personal, professional, and scientific skills during training. Training had a greater impact on women than men in building confidence in sustaining careers and aligning their goals with professional and institutional priorities. The magnitude of increased confidence among residents and fellows exceeded that in medical students.

Leveraging the National COVID-19 Cohort Collaborative (N3C), a nationally sampled electronic health records repository, we explored associations between individual-level social determinants of health (SDoH) and COVID-19-related hospitalizations among racialized minority people with human immunodeficiency virus (HIV) (PWH), who have been historically adversely affected by SDoH.

We retrospectively studied PWH and people without HIV (PWoH) using N3C data from January 2020 to November 2023. We evaluated SDoH variables across three domains in the Healthy People 2030 framework: (1) healthcare access, (2) economic stability, and (3) social cohesion with our primary outcome, COVID-19-related hospitalization. We conducted hierarchically nested additive and adjusted mixed-effects logistic regression models, stratifying by HIV status and race/ethnicity groups, accounting for age, sex, comorbidities, and data partners.

Our analytic sample included 280,441 individuals from 24 data partner sites, where 3,291 (1.17%) were PWH, with racialized minority PWH having higher proportions of adverse SDoH exposures than racialized minority PWoH. COVID-19-related hospitalizations occurred in 11.23% of all individuals (9.17% among PWH, 11.26% among PWoH). In our initial additive modeling, we observed that all three SDoH domains were significantly associated with hospitalizations, even with progressive adjustments (adjusted odds ratios [aOR] range 1.36–1.97). Subsequently, our HIV-stratified analyses indicated economic instability was associated with hospitalization in both PWH and PWoH (aOR range 1.35–1.48). Lastly, our fully adjusted, race/ethnicity-stratified analysis, indicated access to healthcare issues was associated with hospitalization across various racialized groups (aOR range 1.36–2.00).

Our study underscores the importance of assessing individual-level SDoH variables to unravel the complex interplay of these factors for racialized minority groups.

To better understand and prevent research errors, we conducted a first-of-its-kind scoping review of clinical and translational research articles that were retracted because of problems in data capture, management, and/or analysis.

The scoping review followed a preregistered protocol and used retraction notices from the Retraction Watch Database in relevant subject areas, excluding gross misconduct. Abstracts of original articles published between January 1, 2011 and January 31, 2020 were reviewed to determine if articles were related to clinical and translational research. We reviewed retraction notices and associated full texts to obtain information on who retracted the article, types of errors, authors, data types, study design, software, and data availability.

After reviewing 1,266 abstracts, we reviewed 884 associated retraction notices and 786 full-text articles. Authors initiated the retraction over half the time (58%). Nearly half of retraction notices (42%) described problems generating or acquiring data, and 28% described problems with preparing or analyzing data. Among the full texts that we reviewed: 77% were human research; 29% were animal research; and 6% were systematic reviews or meta-analyses. Most articles collected data de novo (77%), but only 5% described the methods used for data capture and management, and only 11% described data availability. Over one-third of articles (38%) did not specify the statistical software used.

Authors may improve scientific research by reporting methods for data capture and statistical software. Journals, editors, and reviewers should advocate for this documentation. Journals may help the scientific record self-correct by requiring detailed, transparent retraction notices.