The study objective was to evaluate whether the formation and funding of team science-guided Integrated Clinical Research Ensembles (ICREs) enhance individual faculty productivity, measured by publication and impact factor adjusted citation rates. The setting was a multi-institutional NIH Clinical and Translational Science Award-supported hub.

Monthly faculty publication and impact factor adjusted citation rates were analyzed using data extracted from the hub-managed Faculty Collaboration Database (FCD). The FCD imports indexed publications for all faculty members across four academic institutions, drawing from PubMed and faculty curriculum vitae. Monthly publication counts were modeled using Poisson regression, fitted using generalized estimating equations to account for clustering of observed monthly publication rates of individual faculty. Publication rates were compared before and after ICRE formation and funding, and between faculty in and outside ICREs.

Before joining ensemble teams, ICRE faculty had an 87% higher monthly publication rate than non-ICRE faculty. As ICREs were funded, the monthly publication rate increased an average 72% compared to baseline levels and future citation rates determined by journal impact factors increased by 150%.

Faculty publication and citation rates significantly increased following ICRE funding, demonstrating the potential of structured team science models to boost academic productivity and influence. Faculty inclined to participate in team science through formalized ICREs were already among the more productive faculty.

Clinical and Translational Research (CTR) requires a team-based approach, with successful teams engaging in skilled management and use of information. Yet we know little about the ways that Translational Teams (TTs) engage with information across the lifecycle of CTR projects. This qualitative study explored the challenges that information management imposes on the conduct of team-based CTR.

We conducted interviews with ten members of TTs at University of Wisconsin. Interviews were transcribed and thematic analysis was conducted.

TTs’ piecemeal and reactive approaches to information management created conflict within the team and slowed scientific progress. The lack of cohesive information management strategies made it more difficult for teams to develop strong team processes like communication, scientific coordination, and project management. While TTs’ research was hindered by the institutional challenges of interdisciplinary team information sharing, TTs who had developed shared approaches to information management that foregrounded transparency, accountability, and trust, described substantial benefits to their teamwork.

We propose a new model for the Science of Team Science field – a Translational Team Science Hierarchy of Needs – that suggests interventions should be targeted at the appropriate stage of team development in order to maximize a team’s scientific potential.

In Michigan, the COVID-19 pandemic severely impacted Black and Latinx communities. These communities experienced higher rates of exposure, hospitalizations, and deaths compared to Whites. We examine the impact of the pandemic and reasons for the higher burden on communities of color from the perspectives of Black and Latinx community members across four Michigan counties and discuss recommendations to better prepare for future public health emergencies.

Using a community-based participatory research approach, we conducted semi-structured interviews (n = 40) with Black and Latinx individuals across the four counties. Interviews focused on knowledge related to the pandemic, the impact of the pandemic on their lives, sources of information, attitudes toward vaccination and participation in vaccine trials, and perspectives on the pandemic’s higher impact on communities of color.

Participants reported overwhelming effects of the pandemic in terms of worsened physical and mental health, financial difficulties, and lifestyle changes. They also reported some unexpected positive effects. They expressed awareness of the disproportionate burden among Black and Latinx populations and attributed this to a wide range of disparities in Social Determinants of Health. These included racism and systemic inequities, lack of access to information and language support, cultural practices, medical mistrust, and varied individual responses to the pandemic.

Examining perspectives and experiences of those most impacted by the pandemic is essential for preparing for and effectively responding to public health emergencies in the future. Public health messaging and crisis response strategies must acknowledge the concerns and cultural needs of underrepresented populations.

Academic health systems and their investigators are challenged to systematically assure clinical research regulatory compliance. This challenge is heightened in the emerging era of centralized single Institutional Review Boards for multicenter studies, which rely on monitoring programs at each participating site.

To describe the development, implementation, and outcome measurement of an institution-wide paired training curriculum and internal monitoring program for clinical research regulatory compliance.

Standard operating procedures (SOPs) were developed to facilitate investigator and research professional adherence to institutional policies, federal guidelines, and international standards. An SOP training curriculum was developed and implemented institution-wide. An internal monitoring program was launched, utilizing risk-based monitoring plans of pre-specified frequency and intensity, assessed upon Institutional Review Boards approval of each prospective study. Monitoring plans were executed according to an additional SOP on internal monitoring, with monitoring findings captured in a REDCap database.

We observed few major violations across 3 key domains of clinical research conduct and demonstrated a meaningful decrease in the rates of nonmajor violations in each, over the course of 2 years.

The paired training curriculum and monitoring program is a successful institution-wide clinical research regulatory compliance model that will continue to be refined.

Incorporating real-world data using “big data” analysis in healthcare are useful to extract specific information for healthcare delivery system improvement. All-cause mortality is an essential measure to enhance patient safety in clinical trial research, especially for underrepresented pediatric participants.

This study aimed to determine the associations between pediatric mortality and patient-specific factors using the Healthcare Cost and Utilization Project (HCUP) database.

Data from the 2019 the HCUP Kids’ Inpatient Database (KID) were used to conduct a logistic regression analysis to determine associations between pediatric patients’ the chance of survival and their demographic and socioeconomic background, discharge records, and hospital information.

Total number of diagnoses (OR = 0.84), total number of procedures (OR = 0.86), length of stay (OR = 1.04), age intervals greater than 1 year (OR > 1.0), transfer into the hospital from a different acute care (OR = 0.34), major diagnoses of multiple significant trauma (OR = 0.03) or hepatobiliary system and pancreas (OR = 0.10), region of hospital – west and midwest (OR > 1.0), and medium or larger hospital bed size (OR > 1.0) were all significantly associated with the chance of survival for patients participating in pediatric clinical trials (p < 0.05).

Real-world clinical trial data analysis showed the potential improvement area including reallocating trial resources to promote trial quality and safe participation for pediatric patients. Pediatric trials need tools that are developed using user-centered design approaches to satisfy the unique needs and requirements of pediatric patients and their caregivers. Safe intrahospital transfer procedures and active dissemination of successful trial best practices are crucial to trial management, adherence, quality, and safety.

There is increasing pressure on the federal research budget and shifting public opinions about the value of the academic enterprise. We must develop and apply metrics that demonstrate the broad benefits of research for health and society. The Translational Science Benefits Model (TSBM) measures the impact of large-scale translational science initiatives, such as the National Cancer Institute’s Cancer Moonshot. TSBM provides the scaffolding to illustrate how science has real-world health impacts. We propose an expansion of the TSBM to explicitly include implementation-focused outcomes.

TSBM includes four categories of benefits, including (1) clinical and medical, (2) community and public health, (3) economic, and (4) policy and legislative. Implementation science outcomes serve as a precursor to the model’s established domains of impact and can help to sharpen focus on the translational steps needed to achieve a broad range of impacts. We provide several examples of studies that illustrate these implementation outcomes and other clinical and community benefits.

It is important to consider a broad range of scientific impacts and the conditions that are necessary to achieve them. The expansion of the TSBM to include implementation science outcomes may help to accelerate the cancer community’s ability to achieve the goal of preventing 4 million cancer deaths by 2047.

As posthumous data use policy within the broader scope of navigating postmortem data privacy is a procedurally complex landscape, our study addresses this by exploring patterns in individuals’ willingness to donate data with health researchers after death and developing practical recommendations.

An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch (www.researchmatch.org), a national health research registry. Descriptive statistics were used to observe trends in, and multinomial logistic regression analyses were conducted at a 95% confidence interval to determine the association between, willingness to donate some, all, or no data after death with researchers based on the participants’ demographics (education level, age range, duration of using online medical websites, and annual frequency of getting ill).

Of 399 responses, most participants were willing to donate health data (electronic medical record data [67%], prescription history data [63%], genetic data [54%], and fitness tracker data [53%]) after death. Among 397 respondents, we identified that individuals were more likely to donate some data after death (vs. no data) if they had longer duration of using online medical websites (adjusted relative risk ratio = 1.22, p= 0.04, 95% CI: 1.01 to 1.48). No additional significant findings were observed between willingness to donate all, some, or none of their data after death and other demographic factors.

Engaging patients in online medical websites may be one potential mechanism to encourage or inspire individuals to participate in posthumous data donation for health research purposes.

Individuals who play video games on computers and cellphones may have better psychomotor skills. It is unknown whether simulated driving performance varies between individuals who play video games more per week compared to individuals who play less. This study investigates whether initial simulated driving performance differs between high and low gamers during a brief (e.g., 10 minutes) driving simulation.

Data for this study were collected at baseline during enrollment for a randomized clinical trial (n = 40). Participants playing video games for > 10 hours/week were categorized as the high gaming group; others were in the low gaming group. Each participant drove the same simulation on the STISIM M1000 simulator, which recorded various driving performance metrics (e.g., driving out of lane and time to initial collision). Data between the groups were compared using Cox proportional hazards and analysis of covariance regression models.

The average age of participants was 21 ± 2.7 years and 48% were male. After adjusting for age, sex, and miles driven per week, the high gaming group spent a mean 4% less time driving out of lane compared to the low gaming group (β = –4.03, SD = 1.32, p ≤ 0.05). No other differences were observed between groups for any other outcome.

With the exception of percentage time driving out of lane, the number of hours gaming per week does not seem to impact an individual’s initial driving performance on a driving simulator. These findings may inform future driving simulation research methodology.

The Fibrosis-4 (FIB-4) index, a simple index that includes age, liver enzymes, and platelet count has been studied as a tool to identify patients at a risk of requiring mechanical ventilation due to its high negative predictive value. It is unknown if FIB-4 remains useful to predict the severity of respiratory disease requiring mechanical ventilation amongst new Coronavirus disease 2019 (COVID-19) variants and whether a relationship also exists between FIB-4 and 30-day mortality. The main objective was to determine if FIB-4 can predict mechanical ventilation requirements and 30-day mortality from COVID-19 across variants including Alpha, Delta, and Omicron.

This was a population-based, retrospective cohort analysis of 232,364 hospitalized patients in the National COVID-19 Cohort Collaborative between the age of 18–90 who tested positive for COVID-19 between April 27, 2020 and June 25, 2022. The primary outcome was association between FIB-4 and need for mechanical ventilation. Secondary measures included the association of FIB-4 with 30-day mortality.

A FIB-4 > 2.67 had 1.8 times higher odds of requiring mechanical ventilation across all variants of COVID-19 (OR 1.81; 95% CI: [1.76, 1.86]). The area under the ROC curve showed high diagnostic accuracy with values ranging between 0.79 (Omicron wave) and 0.97 (delta wave). Increased FIB-4 was associated with 30-day mortality across the variates.

The FIB-4 was consistently associated with both increased utilization of mechanical ventilation and 30-day mortality among COVID-19 patients across all waves in both adjusted and unadjusted models. This provides a simple tool for risk-stratification for front-line health care professionals.

Numerous efforts are focused on building the clinical and translational research (CTR) workforce. Approaches to evaluate CTR initiatives are varied, and efforts often rely on research project-level outcomes. This article applies an evaluation tool to capture individual-level data.

The study used a novel Researcher Investment Tool (RIT) to measure researchers’ experience as well as perceptions of institutional support, including an analysis based on researcher characteristics. The study also evaluated the RIT based on common measures, including a bibliometric indicator, investigator status, and percent time dedicated to research.

The RIT was administered to researchers who received funding or targeted research support from a CTR initiative. Mean scores were assessed by RIT section, domains/sub-domains, and for each item. Mean scores per section were compared across researcher characteristics using t-tests, and associations between common measures and average domain scores were tested using linear regression.

Thirty researchers completed all RIT items. RIT domain scores ranged from a high mean of 4.0 for the research skills domain to a low mean of 2.6 for researcher productivity and community engagement domains. Analysis of indicators of commonly used measures across domains suggest that researchers with a higher bibliometric score had more advanced research skills, service to profession, research productivity, and research collaboration (p < .05). New investigators had lower perceptions of institutional support (p < .05).

As an evaluation tool, the RIT captures individual-level data that may help to determine key areas of strength and opportunities for growth of a CTR program.

Community-acquired bacterial pneumonia (CABP) contributes significantly to mortality and healthcare costs worldwide. The use of guideline-concordant antibiotic therapy for CABP is associated with improved outcomes.

This was a retrospective cohort study of inpatients with CABP due to MRSA or P. aeruginosa in the All of Us database. The proportion of patients on guideline-concordant antibiotics or guideline-discordant antibiotics was compared within groups based upon patient age, sex, self-reported race, ethnicity, marital status, alcohol use, and tobacco use. Guideline concordance was determined using the 2019 IDSA/ATS CABP guidelines. Associations were further analyzed using multivariate logistic regression.

A total of 336 patients with CABP due to MRSA (152) or P. aeruginosa (184) were included. Guideline-concordant antibiotic therapy was prescribed to 70% of CABP-MRSA patients and for 57% of CABP-P. aeruginosa patients. Independently predictive factors of guideline-concordant antibiotic prescribing for CABP-P. aeruginosa patients were Non-Hispanic Black (NHB) vs. Non-Hispanic White (NHW) race (odds ratio = 0.30, 95% confidence interval = 0.12 – 0.75).

In the All of Us database, the majority of CABP-MRSA and CABP-P. aeruginosa patients were prescribed guideline-concordant antibiotic therapy. Race was independently predictive of guideline-concordant antibiotic therapy for patients with CABP-P. aeruginosa, but not CABP-MRSA. NHB patients were less likely to receive guideline-concordant antibiotic therapy than NHW patients when treated for CABP-P. aeruginosa.