Diversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone

Utilizing a Virtual Reality Matrix in Medical Education

Virtual reality (VR) is an emerging technology that has demonstrated incredible value within medical education. However, medical institutions adopting VR as a learning tool need to ensure that the immersive technology product they pick possesses standard usability criteria. The current literature is limited in defining what specific criteria institutions should look for, or how to select between various VR products. Since there have been little to no algorithms available to the medical education community to aid in this process, a reproducible matrix has been developed to evaluate multiple VR platforms at once which can help identify the best option for medical education programs. The matrix is a 10-point scoring system that includes what the research team considered to be the 10 most important factors when selecting a VR product for medical education. The scores of any two or more VR products can be quantitatively compared. Therefore, the matrix is to be used as a methodological framework to help objectively select the highest-rated immersive technology platform. The research team involved in the development of the matrix consisted of an associate dean for simulation and technology, a director of simulation and technology, and eight medical students.

Association of estimated pulse wave velocity with cognitive function in a multiethnic diverse population: The Northern Manhattan Study

INTRODUCTION

Arterial stiffness is linked to age-related cognitive dysfunction. Estimated pulse wave velocity (ePWV) is associated with cerebrovascular disease. We sought to determine whether ePWV was associated with cognition in a multiethnic population.

METHODS

We included 1257 participants enrolled in a Northern Manhattan Study magnetic resonance imaging MRI-cognitive study (mean age 64 ± 8 years, 61% women, 67% Hispanic, 18% non-Hispanic Black, 15% non-Hispanic white) and analyzed cognitive performance at two time points, at enrollment and on an average 5.0 ± 0.6 years later. ePWV was calculated using baseline age and blood pressure. Cognition and cognitive change scores were regressed on ePWV in multivariable linear regression models.

RESULTS

In adjusted models, ePWV (mean 11 ± 2 m/s) was significantly associated with cognition (b = -0.100, 95% CI, -0.120, -0.080) and cognitive change over time (b = -0.063, 95% CI, -0.082, -0.045). Effect modification by race and sex was found.

DISCUSSION

In this multiethnic population, the associations of ePWV with cognitive performance underline the role of vascular stiffness in age-related cognitive decline.

HIGHLIGHTS

ePWV is a modest but independent predictor of cognitive function and cognitive decline among older individuals. After adjustment, the ePWV measure was inversely associated with performance and decline in global cognition, processing speed, episodic memory, executive function, and semantic memory. After adjustment, modification of the association between ePWV and change in episodic memory and executive function by race and ethnicity was suggested by a significant interaction term. The association between ePWV and episodic memory decline was stronger in females.

Simplifying informed consent as a universal precaution

One barrier to participating in clinical research is that patients with low literacy skills (1 in 5 US adults) may struggle to understand the informed consent document (ICD). Writing consents using health literacy and plain language guidelines including simplified syntax and semantics can increase understandability and facilitate inclusivity of research populations with literacy challenges. Our study aim was to evaluate a simplified ICD for understandability while considering factors known to relate to comprehension (reading skills and working memory). We performed an on-line survey of 192 adults ages 18-77 in Georgia. Participants performed significantly better on the simplified ICD test. We built an additional model with all version x measure interactions (i.e., age, sex, race, urbanicity, GMVT, WM). This model did not significantly improve model fit, F < 1.00, suggesting that individual differences did not moderate the effect of simplification. Our findings suggest that using plain language and simplified syntax and semantics in ICD as a universal precaution may reduce cognitive reading burden for adults regardless of differences in reading skill or working memory. Increasing understandability in ICD may help improve targets for clinical trial enrollment.

Integrating Diversity, Equity, and Inclusion into Preclinical, Clinical, and Public Health Mathematical Models

Mathematical modelling applied to preclinical, clinical, and public health research is critical for our understanding of a multitude of biological principles. Biology is fundamentally heterogeneous, and mathematical modelling must meet the challenge of variability head on to ensure the principles of diversity, equity, and inclusion (DEI) are integrated into quantitative analyses. Here we provide a follow-up perspective on the DEI plenary session held at the 2023 Society for Mathematical Biology Annual Meeting to discuss key issues for the increased integration of DEI in mathematical modelling in biology.

Strategies for improving diversity, equity, and inclusion in cardiovascular research: a primer

This paper aims to empower cardiovascular (CV) researchers by promoting diversity, equity, and inclusion (DE&I) principles throughout the research cycle. It defines DE&I and introduces practical strategies for implementation in recruitment, retention, and team dynamics within CV research. Evidence-based approaches supporting underrepresented populations' participation are outlined for each research phase. Emphasizing the significance of inclusive research environments, the paper offers guidance and resources. We invite CV researchers to actively embrace DE&I principles, enhancing research relevance and addressing longstanding CV health disparities.

More than Just a Number: Perspectives from Black Male Participants on Community-Based Interventions and Clinical Trials to Address Cardiovascular Health Disparities

BACKGROUND

Black Americans remain significantly underrepresented and understudied in research. Community-based interventions have been increasingly recognized as an effective model for reckoning with clinical trial participation challenges amongst underrepresented groups, yet a paucity of studies implement this approach. The present study sought to gain insight into Black male participants' perception of clinical trials before and after participating in a community-based team lifestyle intervention in the United States.

METHODS

Black Impact, a 24-week community-based lifestyle intervention, applied the American Heart Association's Life's Simple 7 (LS7) framework to assess changes in the cardiovascular health of seventy-four Black male participants partaking in weekly team-based physical activities and LS7-themed education and having their social needs addressed. A subset of twenty participants completed an exit survey via one of three semi-structured focus groups aimed at understanding the feasibility of interventions, including their perceptions of participating in clinical trials. Data were transcribed verbatim and analyzed using a content analysis, which involved systematically identifying, coding, categorizing, and interpreting the primary patterns of the data.

RESULTS

The participants reported a positive change in their perceptions of clinical trials based on their experience with a community-based lifestyle intervention. Three prominent themes regarding their perceptions of clinical trials prior to the intervention were as follows: (1) History of medical abuse; (2) Lack of diversity amongst research teams and participants; and (3) A positive experience with racially concordant research teams. Three themes noted to influence changes in their perception of clinical trials based on their participation in Black Impact were as follows: (1) Building trust with the research team; (2) Increasing awareness about clinical trials; and (3) Motivating participation through community engagement efforts.

CONCLUSIONS

Improved perceptions of participating in clinical trials were achieved after participation in a community-based intervention. This intervention may provide a framework by which to facilitate clinical trial participation among Black men, which must be made a priority so that Black men are "more than just a number" and no longer "receiving the short end of the stick".

Sex, gender, sexual orientation, and more: Sexual diversity in Alzheimer's research needs a new lens to achieve inclusive research and generalizable results

Diversity, Equity, and Inclusion (DEI) efforts in Alzheimer's disease and related dementia (ADRD) research are guiding the adoption of two-step self-report questions that capture research participants' identity based on categories of sex, sexual orientation, and gender identity. The intent is to facilitate inclusion and representation of sexual and gender minoritized (SGM) communities in ADRD research. The data from using these questions are on a collision course with another National Institute of Aging initiative, which is aimed at understanding sex differences in ADRD mechanisms. Here, we critically analyze the goals and methods of the two initiatives. We propose that, in addition to being SGM focused, DEI efforts are needed to expand how scientists consider and measure sexual diversity itself.

Highlights

Sex, sexual orientation, and gender identity (SSOGI) will be asked in ADRD studies.SSOGI data will expand representation of research participant identities.SSOGI data are on a collision course with sex differences research.Both emphasize sexual diversity (SD) largely as SSOGI identity categories.Diversity, Equity, and Inclusion efforts must develop SD methods in ADRD research.

Diversity in Oncology Clinical Trials: Current Landscape for Industry-Sponsored Clinical Trials in Asia

INTRODUCTION

There has been a growing recognition on the importance of diversity in clinical trials. Existing research has highlighted a significant demographic imbalance. Amidst this renewed focus on diversity, it is crucial to acknowledge that Asia comprises over half of the world's population. Given the region's demographic significance, we sought to compare various characteristics and growth rates for trials with sites in Asia against those without any sites in Asia.

METHODS

We performed comprehensive analyses of industry-sponsored phase 2 and 3 oncology trials registered at Clinicaltrials.gov, using drugs or biologics as investigational agents and executed between 1 January 2018 and 31 December 2022. We applied the compound annual growth rate (CAGR) as an analytical tool to track the trial growth rates over this 5-year period.

RESULTS

We identified 894 industry-sponsored phase 2 and 3 cancer studies with available study location data. Out of these, 415 trials (46.42%) had study sites in Asia. Notably, these trials with sites in Asia were also more likely to be phase 3 trials (39.76% vs 6.47%, p < 0.001), include female and paediatric populations, and be randomised trials. Interestingly, lung and stomach cancers were more commonly studied in these trials, while myeloma was less commonly studied. The number of trial sites for liver cancer was not significantly higher for Asia, even though the incidence of the disease is much higher in this region. Despite an overall declining trend in the number of clinical trials in the last 5 years, we observed a transitional positive increase in the CAGR from 2020 to 2021 for trials with sites in Asia. However, East Asia, specifically China, exhibited a disproportionate overrepresentation in these trials.

CONCLUSIONS

There are notable characteristics of clinical trials with sites in Asia. Comprehending these disparities may aid in the strategic planning to enhance a balanced representation of ethnicities in trials.

The landscape of decentralized clinical trials (DCTs): focusing on the FDA and EMA guidance

Decentralized clinical trials (DCTs) consist of off-site trial-related procedures referred to as decentralized elements. We aimed to provide an overview of the landscape of DCTs by comparing regulatory guidance reports and analyzing decentralized elements from clinical trial registries. Two guidance reports on DCTs published by the U.S. Food and Drug Administration and the European Medicines Agencies were summarized and analyzed. Both guidance publications commonly emphasized an assessment of the appropriateness of decentralized elements along 2 axes: patient safety and data integrity. DCT cases were identified from ClinicalTrials.gov by searching with 6 keywords: decentralized, remote, mobile, digital, virtual, and hybrid. Cases where the keyword was used in a non-DCT context, such as digital flexor tendon, were excluded by means of natural language processing. A total of 4,874 trials were identified as DCT cases, with annual increases, especially after 2020. The most common keywords were 'mobile' and 'digital' (36.2% and 24.8%, respectively). Interventions in the DCT cases were analyzed by means of a network analysis. Behavioral and technological tokens were frequently combined, such as 'rehabilitation' and 'app.' Drugs were used in only 1.8% of the DCT cases. Of these, most drugs had been approved previously (96.8%) and were in oral formulation (67.2%). Most of the DCT cases identified in this study involved simple interventions and low-risk drugs. These characteristics were in accordance with the common recommendations in the DCT guidance publications.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

BACKGROUND

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

MAIN TEXT

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

CONCLUSIONS

Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review

Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

An Equity and Justice-Informed Ethical Framework to Guide Incidental Findings in Brain Imaging Research

The handling of incidental findings (IFs) in brain imaging studies has been a source of contention among scientists and bioethicists. A conceptual framework informed by diversity, equity, and inclusion (DEI) and distributive justice approaches, namely EUSTICE, is proposed for the ethical handling and reporting of IFs in brain imaging research. I argue that EUSTICE provides a systematic and inclusive approach to addressing the ethical conundrum around IF disclosure and managing IFs proportionately and sensitively in brain imaging research. The EUSTICE framework may have implications for the field of neurosciences or human studies broadly in guiding ethics of IFs in research.