Transdisciplinary (TD) research and community-based participatory research (CBPR) represent promising investigative approaches to ameliorate health disparities. Public investments in team-based TD research to address multifactorial public health problems have increased over the last two decades. Similarly, recognition that community participation in research and social action is essential to promoting health equity is reflected in increased prioritization of community engagement in research and practice. Yet, models that describe and guide the combined TD and CBPR approach are lacking.

We utilized a qualitative, convergent parallel case study design that included document reviews and one-on-one interviews to assess how investigators from the Centers for Population Health and Health Disparities (CPHHD) initiative integrated TD team science and CBPR in their work, and what they perceived as the impact of that work on health equity.

Twenty-five CPHHD investigators and National Institutes of Health program staff participated in a one-on-one interview. Document and interview data informed the development of an iterative conceptual model of TD CBPR comprising five domains: problem focus, contexts, collaboration and partnership, outcomes, and societal impact of TD CBPR.

TD team science and CBPR are integrally related; combining principles of both can facilitate more efficient, equitable progress toward team outcomes, improved population health, and increased health equity. This model could assist researchers and public health practitioners in designing community-relevant, scientifically rigorous research with practical implications for improving health and quality of life among marginalized populations.

Although dissemination and implementation (D&I) science is a growing field, many health researchers with relevant D&I expertise do not self-identify as D&I researchers. The goal of this work was to analyze the distribution, clustering, and recognition of D&I expertise in an academic institution.

A snowball survey was administered to investigators at University of Rochester with experience and/or interest in D&I research. The respondents were asked to identify their level of D&I expertise and to nominate others who were experienced and/or active in D&I research. We used social network analysis to examine nomination networks.

Sixty-eight participants provided information about their D&I expertise. Thirty-eight percent of the survey respondents self-identified as D&I researchers, 24% as conducting D&I under different labels, and 38% were familiar with D&I concepts. D&I researchers were, on average, the most central actors in the network (nominated most by other survey participants) and had the highest within-group density, indicating wide recognition by colleagues and among themselves. Researchers who applied D&I under different labels had the highest within-group reciprocity (25%), and the highest between-group reciprocity (29%) with researchers familiar with D&I. Participants significantly tended to nominate peers within their departments and within their expertise categories.

Identifying and engaging unrecognized clusters of expertise related to D&I research may provide opportunities for mutual learning and dialog and will be critical to bridging across departmental and topic area silos and building capacity for D&I in academic settings.

The process of identifying and connecting with clinical trial study teams can be challenging and difficult for members of the public. The national volunteer community registry, ResearchMatch, and the public clinical trials search tool, Trials Today, work in tandem to bridge this connection by providing a streamlined process for potential participants to identify clinical trials which may be of interest.

Building on the existing infrastructure of ResearchMatch and Trials Today, we created a mechanism by which the public can request that their basic contact information (e.g., email/phone) be securely shared with any actively recruiting clinical trial, including trials with no existing relationship with ResearchMatch.

Within the first 2 years of use (July 2019–July 2021), ResearchMatch Volunteers sent 12,251 requests to study teams. On average, 20% of these requests were accepted by the study teams.

The utilization of this tool indicates that there is active interest among members of the public to independently contact study teams about trials of interest. Additionally, research teams unaffiliated with ResearchMatch are willing to at minimum accept contact information. This allows ResearchMatch to successfully serve as a medium, connecting members of the public with actively recruiting trials.

Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms. The noisy nature of the data and the large size of the network make this a very challenging task. Complex approaches handle the stochastic nature of the data but can only do this for small networks; simpler, linear models generate large networks but with less reliability.

We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge. We cluster the experimental data and learn an interaction network for each cluster, which are merged using the interaction network for the representative genes selected for each cluster.

We generated an interaction atlas for 337 human pathways yielding a network of 11,454 genes with 17,777 edges. Simulated gene expression data from this atlas formed the basis for reconstruction. Based on the area under the curve of the precision-recall curve, the proposed approach outperformed the baseline (random classifier) by ∼15-fold and conventional methods by ∼5–17-fold. The performance of the proposed workflow is significantly linked to the accuracy of the clustering step that tries to identify the modularity of the underlying biological mechanisms.

We provide an interaction atlas generation workflow optimizing the algorithm/parameter selection. The proposed approach integrates external knowledge in the reconstruction of the interactome using probabilistic graphs. Network characterization and understanding long-range effects in interaction atlases provide means for comparative analysis with implications in biomarker discovery and therapeutic approaches. The proposed workflow is freely available at http://otulab.unl.edu/atlas.

Applications for and receipt of external research grants are important indicators of scientific productivity, and ones that the Clinical and Translational Science Awards (CTSA) Program is intended to support.

We investigate the association between the receipt of support services from one CTSA-supported hub and investigator productivity as measured by individual investigator grant submissions and grant awards at a Research I public university over an 8-year period. Negative binomial regression models are used to evaluate associations between individual grant productivity (in 2017, 2018, and 2019) and receipt of support services during the preceding 2 years.

Controlling for prior levels of productivity, service receipt is found to be predictive of both grants submitted and grants awarded to investigators in each model examined. Analyses also found some evidence of faculty rank and race/ethnic differences in the effects of Center for Clinical and Translational Science (CCTS) service use on subsequent grant productivity.

Further work is needed to understand the relationship between clinical and translational services and other measures of productivity.

The COVID-19 pandemic significantly changed the lives of millions of people in the USA, preventing them from continuing their regular lifestyles. This study examined the manifestation of “Covibesity” in the patient population of the Virginia Commonwealth University (VCU) Health System and explored the effects of the distraction caused by the pandemic in the management of diabetes disease.

This project analyzed body mass index (BMI) rates of the general adult patient population at the VCU Health System during the COVID-19 pandemic compared to prior years. The project also investigated the changes in the severity of diabetes cases treated at the VCU Health System by comparing HbA1c laboratory results and the number of diabetes-related emergency department (ED) visits before and during the pandemic. The results were stratified by age, gender, and race to examine subpopulations.

The mean BMI for the general patient population increased from 2018 to 2019 but decreased in 2020. The mean HbA1c measurements for the diabetic patient population increased from 2018 to 2020, while the number of ED visits declined in 2020 for the same population. When stratified by race, the trends in the outcomes largely reflected those of the overall mean. The African American population had a higher mean BMI, HbA1c, and number of ED visits than other races, but showed the same temporal behavior to the overall mean.

Identifying the most effective ways to support career development of early stage investigators in clinical and translational science should yield benefits for the biomedical research community. Institutions with Clinical and Translational Science Awards (CTSA) offer KL2 programs to facilitate career development; however, the sustained impact has not been widely assessed.

A survey comprised of quantitative and qualitative questions was sent to 2144 individuals that had previously received support through CTSA KL2 mechanisms. The 547 responses were analyzed with identifying information redacted.

Respondents held MD (47%), PhD (36%), and MD/PhD (13%) degrees. After KL2 support was completed, physicians’ time was divided 50% to research and 30% to patient care, whereas PhD respondents devoted 70% time to research. Funded research effort averaged 60% for the cohort. Respondents were satisfied with their career progression. More than 95% thought their current job was meaningful. Two-thirds felt confident or very confident in their ability to sustain a career in clinical and translational research. Factors cited as contributing to career success included protected time, mentoring, and collaborations.

This first large systematic survey of KL2 alumni provides valuable insight into the group’s perceptions of the program and outcome information. Former scholars are largely satisfied with their career choice and direction, national recognition of their expertise, and impact of their work. Importantly, they identified training activities that contributed to success. Our results and future analysis of the survey data should inform the framework for developing platforms to launch sustaining careers of translational scientists.

This research examined the perspective of the Huntington’s disease (HD) community regarding the use of predictive biomarkers as endpoints for regulatory approval of therapeutics to prevent or delay the onset of clinical HD in asymptomatic mutation carriers.

An online, choice-based conjoint survey was shared with HD community members including untested at-risk individuals, presymptomatic mutation carriers, and symptomatic individuals. Across 15 scenarios, participants chose among two proposed therapies with differing degrees of biomarker improvement and side effects or a third option of no treatment.

Two hundred and thirty-eight responses were received. Attributes reflecting biomarker efficacy (e.g., prevention of brain atrophy on magnetic resonance imaging, reduced mutant huntingtin, or reduced inflammation biomarkers) had 3- to 7-fold greater importance than attributes representing side effects (e.g., increased risk of heart disease, cancer, and stroke over 20 years) and were more influential in directing choice of treatments. Reduction in mutant huntingtin protein was the most valued attribute overall. Multinomial logit model simulations based on survey responses demonstrated high interest among respondents (87–99% of the population) for drugs that might prevent or delay HD solely based upon biomarker evidence, even at the risk of serious side effects.

These results indicate a strong desire among members of the HD community for preventive therapeutics and a willingness to accept significant side effects, even before the drug has been shown to definitively delay disease onset if the drug improves biomarker evidence of HD progression. Preferences of the HD community should inform regulatory policies for approving preventive therapies.

Sustainment refers to continued intervention delivery over time, while continuing to produce intended outcomes, often with ongoing adaptations, which are purposeful changes to the design or delivery of an intervention to improve its fit or effectiveness. The Hispanic Kidney Transplant Program (HKTP), a complex, culturally competent intervention, was implemented in two transplant programs to reduce disparities in Hispanic/Latinx living donor kidney transplant rates. This study longitudinally examined the influence of adaptations on HKTP sustainment.

Qualitative interviews, learning collaborative calls, and telephone meetings with physicians, administrators, and staff (n = 55) were conducted over three years of implementation to identify HKTP adaptations. The Framework for Reporting Adaptations and Modifications-Expanded was used to classify adaptation types and frequency, which were compared across sites over time.

Across sites, more adaptations were made in the first year (n = 47), then fell and plateaued in the two remaining years (n = 35). Adaptations at Site-A were consistent across years (2017: n = 18, 2018: n = 17, 2019: n = 14), while Site-B made considerably fewer adaptations after the first year (2017: n = 29, 2018: n = 18, 2019: n = 21). Both sites proportionally made mostly skipping (32%), adding (20%), tweaking (20%), and substituting (16%) adaptation types. Skipping- and substituting-type adaptations were made due to institutional structural characteristics and lack of available resources, respectively. However, Site-A’s greater proportion of skipping-type adaptations was attributed to greater system complexity, and Site-B’s greater proportion of adding-type adaptation was attributed to the egalitarian team-based culture.

Our findings can help prepare implementers to expect certain context-specific adaptations and preemptively avoid those that hinder sustainment.

Although one of the fastest-growing populations in the USA, Latinx individuals remain underrepresented in research. In this study, we aimed to identify how Latina/Latinx participants of the Environment, Leiomyomas, Latinas, and Adiposity Study (ELLAS) learned about the research study and what motivated them to participate.

Using a standardized survey tool, bilingual staff interviewed participants and asked them, 1) how they heard about ELLAS and 2) to identify and rank their top three reasons for participating in ELLAS.

“Word of mouth” through a friend or relative was the most common method of learning about ELLAS (49.0%), followed by a “community outreach event” (29.3%). The three most common reasons for participating in ELLAS were “to learn more about women’s health” (83.3%), “to receive a free health assessment” (79.4%), and “to contribute to scientific knowledge” (59.5%). Correlation between demographic and socioeconomic characteristics and participant responses indicated that there are different reasons for participation based on these factors.

Community engagement and word of mouth are vital to the successful recruitment of Latina/Latinx participants to research studies. Latinx participants are most motivated to participate by health benefits and health education, as well as altruistic aspects of research studies. Therefore, establishing mutually beneficial relationships within Latinx communities and appealing to motivations for research participation with close attention to the demographics of participants can both expand and allow for targeted recruitment efforts for this underrepresented group in research studies.