Conceptualizing and Measuring Trust, Mistrust, and Distrust: Implications for Advancing Health Equity and Building Trustworthiness.

Trust is vital to public confidence in health and science, yet there is no consensus on the most useful way to conceptualize, define, measure, or intervene on trust and its related constructs (e.g., mistrust, distrust, and trustworthiness). In this review, we synthesize literature from this wide-ranging field that has conceptual roots in racism, marginalization, and other forms of oppression. We summarize key definitions and conceptual frameworks and offer guidance to scholars aiming to measure these constructs. We also review how trust-related constructs are associated with health outcomes, describe interventions in this field, and provide recommendations for building trust and institutional trustworthiness and advancing health equity. We ultimately call for future efforts to focus on improving the trustworthiness of public health professionals, scientists, health care providers, and systems instead of aiming to increase trust in these entities as they currently exist and behave.

All of Us participant perspectives on the return of value in research.

PURPOSE: To understand participant preferences for receiving specific types of research information, whether information preferences vary across sociodemographic groups, and the types of health providers participants could access to understand returned information. METHODS: All of Us Research Program participants completed a value of returning research information survey. Stratified sampling was implemented to enhance participant diversity and avoid noncoverage. We used weighted multivariable logistic regression to evaluate associations between the most valuable information types, access to providers, and sociodemographic variables. RESULTS: Participants (N = 20,405) were diverse in their race/ethnicity (eg, 52% were White, 18% were Hispanic/Latino or Spanish, 3% were Asian, and 20% were Black or African American). Most participants (78.6%) valued information about their risk of serious genetic diseases with available treatment. Primary care physicians, specialists, and genetic counselors were the top providers that participants could access for help understanding returned information. Information preferences and provider access varied across sociodemographic groups. For example, as income levels increased, the odds of placing value on genetic results indicating risk of serious disease with available treatment increased when compared with the lowest income levels (P value < .001). CONCLUSION: Although genetic information was most valuable to participants, preferences about specific information types varied across sociodemographic groups.

Relative predictive value of sociodemographic factors for chronic diseases among All of Us participants: a descriptive analysis.

BACKGROUND: Although sociodemographic characteristics are associated with health disparities, the relative predictive value of different social and demographic factors remains largely unknown. This study aimed to describe the sociodemographic characteristics of All of Us participants and evaluate the predictive value of each factor for chronic diseases associated with high morbidity and mortality. METHODS: We performed a cross-sectional analysis using de-identified survey data from the All of Us Research Program, which has collected social, demographic, and health information from adults living in the United States since May 2018. Sociodemographic data included self-reported age, sex, gender, sexual orientation, race/ethnicity, income, education, health insurance, primary care provider (PCP) status, and health literacy scores. We analyzed the self-reported prevalence of hypertension, coronary artery disease, any cancer, skin cancer, lung disease, diabetes, obesity, and chronic kidney disease. Finally, we assessed the relative importance of each sociodemographic factor for predicting each chronic disease using the adequacy index for each predictor from logistic regression. RESULTS: Among the 372,050 participants in this analysis, the median age was 53 years, 59.8% reported female sex, and the most common racial/ethnic categories were White (54.0%), Black (19.9%), and Hispanic/Latino (16.7%). Participants who identified as Asian, Middle Eastern/North African, and White were the most likely to report annual incomes greater than $200,000, advanced degrees, and employer or union insurance, while participants who identified as Black, Hispanic, and Native Hawaiian/Pacific Islander were the most likely to report annual incomes less than $10,000, less than a high school education, and Medicaid insurance. We found that age was most predictive of hypertension, coronary artery disease, any cancer, skin cancer, diabetes, obesity, and chronic kidney disease. Insurance type was most predictive of lung disease. Notably, no two health conditions had the same order of importance for sociodemographic factors. CONCLUSIONS: Age was the best predictor for the assessed chronic diseases, but the relative predictive value of income, education, health insurance, PCP status, race/ethnicity, and sexual orientation was highly variable across health conditions. Identifying the sociodemographic groups with the largest disparities in a specific disease can guide future interventions to promote health equity.

Patient Lung Cancer Screening Decisions and Environmental and Psychosocial Factors.

Importance: Screening for lung cancer using low-dose computed tomography is associated with reduced lung cancer-specific mortality, but uptake is low in the US; understanding how patients make decisions to engage with lung cancer screening is critical for increasing uptake. Prior research has focused on individual-level psychosocial factors, but environmental factors (eg, historical contexts that include experiencing racism) and modifying factors-those that can be changed to make it easier or harder to undergo screening-also likely affect screening decisions. Objective: To investigate environmental, psychosocial, and modifying factors influencing lung cancer screening decision-making and develop a conceptual framework depicting relationships between these factors. Design, Setting, and Participants: This multimethod qualitative study was conducted from December 2021 to June 2022 using virtual semistructured interviews and 4 focus groups (3-4 participants per group). All participants met US Preventive Services Task Force eligibility criteria for lung cancer screening (ie, age 50-80 years, at least a 20 pack-year smoking history, and either currently smoke or quit within the past 15 years). Screening-eligible US participants were recruited using an online panel. Main Outcomes and Measures: Key factors influencing screening decisions (eg, knowledge, beliefs, barriers, and facilitators) were the main outcome. A theory-informed, iterative inductive-deductive approach was applied to analyze data and develop a conceptual framework summarizing results. Results: Among 34 total participants (interviews, 20 [59%]; focus groups, 14 [41%]), mean (SD) age was 59.1 (4.8) years and 20 (59%) identified as female. Half had a household income below $20 000 (17 [50%]). Participants emphasized historical and present-day racism as critical factors contributing to mistrust of health care practitioners and avoidance of medical procedures like screening. Participants reported that other factors, such as public transportation availability, also influenced decisions. Additionally, participants described psychosocial processes involved in decisions, such as perceived screening benefits, lung cancer risk appraisal, and fear of a cancer diagnosis or harmful encounters with practitioners. In addition, participants identified modifying factors (eg, insurance coverage) that could make receiving screening easier or harder. Conclusions and Relevance: In this qualitative study of patient lung cancer screening decisions, environmental, psychosocial, and modifying factors influenced screening decisions. The findings suggest that systems-level interventions, such as those that help practitioners understand and discuss patients' prior negative health care experiences, are needed to promote effective screening decision-making.

Impact of financial compensation on enrollment and participation in a remote, mobile-app based research study.

Background: There is no consensus on how to determine appropriate financial compensation for research recruitment. Selecting incentive amounts that are reasonable and respectful, without undue inducement, remains challenging. Previously, we demonstrated that incentive amount significantly impacts participants' willingness to complete various hypothetical research activities. Here we further explore this relationship in a mock decentralized study. Methods: Adult ResearchMatch volunteers were invited to join a prospective study where interested individuals were given an opportunity to view details for a study along with participation requirements, then offered a randomly generated compensation amount between $0 and $50 to enroll and participate. Individuals agreeing to participate were then asked to complete tasks using a remote mobile application (MyCap), for two weeks. Tasks included a weekly survey, a daily gratitude journal and daily phone tapping task. Results: Willingness to participate was 85% across all incentive levels but not significantly impacted by amount. Task completion appeared to increase as a function of compensation until a plateau at $25. While participants described the study as low burden and reported that compensation was moderately important to their decision to join, only 31% completed all study tasks. Conclusion: While offering compensation in this study did not have a strong effect on enrollment rate, this work provides insight into participant motivation when joining and participating in studies employing mobile applications.

Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

OBJECTIVE: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. MATERIALS AND METHODS: We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. RESULTS: ChatGPT achieved 95.9% (95% CI, 92.1-97.9) accuracy and 96.2% (92.4-98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9-93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9-85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch's technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform's recruitment mechanism. DISCUSSION AND CONCLUSION: Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Development, implementation, and dissemination of operational innovations across the trial innovation network.

Improving the quality and conduct of multi-center clinical trials is essential to the generation of generalizable knowledge about the safety and efficacy of healthcare treatments. Despite significant effort and expense, many clinical trials are unsuccessful. The National Center for Advancing Translational Science launched the Trial Innovation Network to address critical roadblocks in multi-center trials by leveraging existing infrastructure and developing operational innovations. We provide an overview of the roadblocks that led to opportunities for operational innovation, our work to develop, define, and map innovations across the network, and how we implemented and disseminated mature innovations.

Recruitment and retention for chronic pain clinical trials: a narrative review.

Opioid misuse is at a crisis level. In response to this epidemic, the National Institutes of Health has funded $945 million in research through the Helping to End Addiction Long-term (HEAL) Pain Management Initiative, including funding to the Vanderbilt Recruitment Innovation Center (RIC) to strategize methods to catalyze participant recruitment. The RIC, recognizing the challenges presented to clinical researchers in recruiting individuals experiencing pain, conducted a review of evidence in the literature on successful participant recruitment methods for chronic pain trials, in preparation for supporting the HEAL Pain trials. Study design as it affects recruitment was reviewed, with issues such as sufficient sample size, impact of placebo, pain symptom instability, and cohort characterization being identified as problems. Potential solutions found in the literature include targeted electronic health record phenotyping, use of alternative study designs, and greater clinician education and involvement. For retention, the literature reports successful strategies that include maintaining a supportive staff, allowing virtual study visits, and providing treatment flexibility within the trial. Community input on study design to identify potential obstacles to recruitment and retention was found to help investigators avoid pitfalls and enhance trust, especially when recruiting underrepresented minority populations. Our report concludes with a description of generalizable resources the RIC has developed or adapted to enhance recruitment and retention in the HEAL Pain studies. These resources include, among others, a Recruitment and Retention Plan Template, a Competing Trials Tool, and MyCap, a mobile research application that interfaces with Research Electronic Data Capture (REDCap).