This paper reports on a novel measure, attitudes toward genomics and precision medicine (AGPM), which evaluates attitudes toward activities such as genetic testing, collecting information on lifestyle, and genome editing – activities necessary to achieve the goals of precision medicine.

The AGPM will be useful for researchers who want to explore attitudes toward genomics and precision medicine. The association of concerns about precision medicine activities with demographic variables such as religion and politics, as well as higher levels of education, suggests that further education on genomic and precision activities alone is unlikely to shift AGPM scores significantly.

We wrote items to represent psychological and health benefits of precision medicine activities, and concerns about privacy, social justice, harm to embryos, and interfering with nature. We validated the measure through factor analysis of its structure, and testing associations with trust in the health information system and demographic variables such as age, sex, education, and religion.

The AGPM had excellent alpha reliability (.92) and demonstrated good convergent validity with existing measures. Variables most strongly associated with higher levels of concern with precision medicine activities included: regular religious practice, republican political leanings, and higher levels of education.

Given the reach and influence of social media, the National Children’s Study Vanguard Study evaluated the feasibility, acceptability, and cost of using social media to support participant retention.

We describe a social media experiment designed to assess the impact of social media on participant retention, discuss several key considerations for integrating social media into longitudinal research, and review factors that may influence engagement in research-related social media.

User participation varied but was most active when at launch. During the short life of the private online community, a total of 39 participants joined. General enthusiasm about the prospect of the online community was indicated. There were many lessons learned throughout the process in areas such as privacy, security, and Institutional Review Board clearance. These are described in detail.

The opportunity to engage participants in longitudinal research using online social networks is enticing; however, more research is needed to consider the feasibility of their use in an ongoing manner. Recommendations are presented for future research seeking to use social media to improve retention in longitudinal research.

Research coordinators (RCs) are vital to the clinical research enterprise, ensuring research is conducted ethically, results are scientifically sound and human research subjects are protected. Given the absence of a prior systematic inquiry, we sought to understand the ways in which Clinical & Translational Science Award-funded academic health centers support RCs.

Our survey asked questions about existing coordinator networks and where none existed, collected data on the characterization of resources and services provided to support RCs (outside of salary).

Findings show 4 mechanisms support RCs: identification of the workforce, professionalization of the position, dissemination of information, and offering of services.

An academic health center that wishes to deliver research training and services to their RC workforce will need identification and communication structures in place before supportive activities can be effectively implemented.

Coronavirus Disease 2019 (COVID-19) instigated a flurry of clinical research activity. The unprecedented pace with which trials were launched left an early void in data standardization, limiting the potential for subsequent data pooling. To facilitate data standardization across emerging studies, the National Heart, Lung, and Blood Institute (NHLBI) charged two groups with harmonizing data collection, and these groups collaborated to create a concise set of COVID-19 Common Data Elements (CDEs) for clinical research.

Our iterative approach followed three guiding principles: 1) draw from existing multi-center COVID-19 clinical trials as precedents, 2) incorporate existing data elements and data standards whenever possible, and 3) alignment to data standards that facilitate data sharing and regulatory submission. We also supported rapid implementation of the CDEs in NHLBI-funded studies and iteratively refined the CDEs based on feedback from those study teams

The NHLBI COVID-19 CDEs are publicly available and being used for current COVID-19 clinical trials. CDEs are organized into domains, and each data element is classified within a three-tiered prioritization system. The CDE manual is hosted publicly at https://nhlbi-connects.org/common_data_elements with an accompanying data dictionary and implementation guidance.

The NHLBI COVID-19 CDEs are designed to aid data harmonization across studies to achieve the benefits of pooled analyses. We found that organizing CDE development around our three guiding principles focused our efforts and allowed us to adapt as COVID-19 knowledge advanced. As these CDEs continue to evolve, they could be generalized for use in other acute respiratory illnesses.

Access to patient medical data is critical to building a real-time data analytic pipeline for improving care providers’ ability to detect, diagnose, and prognosticate diseases. Critical congenital heart disease (CCHD) is a common group of neonatal life-threatening defects that must be promptly diagnosed to minimize morbidity and mortality. CCHD can be diagnosed both prenatally and postnatally. However, despite current screening practices involving oxygen saturation analysis, timely diagnosis is missed in approximately 900 infants with CCHD annually in the USA and can benefit from increased data processing capabilities. Adding non-invasive perfusion measurements to oxygen saturation data can improve the timeliness and fidelity of CCHD diagnostics. However, real-time monitoring and interpretation of non-invasive perfusion data are currently limited.

To address this challenge, we created a hardware and software architecture utilizing a Pi-top™ for collecting, visualizing, and storing dual oxygen saturation, perfusion indices, and photoplethysmography data. Data aggregation in our system is automated and all data files are coded with unique study identifiers to facilitate research purposes.

Using this system, we have collected data from 190 neonates, 130 presumably without and 60 with congenital heart disease, in total comprising 1665 min of information. From these data, we are able to extract non-invasive perfusion features such as perfusion index, radiofemoral delay, and slope of systolic rise or diastolic fall.

This data collection and waveform analysis is relatively inexpensive and can be used to enhance future CCHD screening algorithms.

Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test’s performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals.

This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported.

A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide.

The digital site-less approach employed in the “Test Us At Home” study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.

The extent to which Clinical and Translational Science Award (CTSA) programs offer publicly accessible online resources for training in community-engaged research (CEnR) core competencies is unknown. This study cataloged publicly accessible online CEnR resources from CTSAs and mapped resources to CEnR core competency domains.

Following a search and review of the current literature regarding CEnR competencies, CEnR core competency domains were identified and defined. A systematic review of publicly accessible online CEnR resources from all 64 current CTSAs was conducted between July 2018 and May 2019. Resource content was independently reviewed by two reviewers and scored for the inclusion of each CEnR core competency domain. Domain scores across all resources were assessed using descriptive statistics.

Eight CEnR core competency domains were identified. Overall, 214 CEnR resources publicly accessible online from 35 CTSAs were eligible for review. Scoring discrepancies for at least one domain within a resource initially occurred in 51% of resources. “CEnR methods” (50.5%) and “Knowledge and relationships with communities” (40.2%) were the most frequently addressed domains, while “CEnR program evaluation” (12.1%) and “Dissemination and advocacy” (11.2%) were the least frequently addressed domains. Additionally, challenges were noted in navigating CTSA websites to access CEnR resources, and CEnR competency nomenclature was not standardized.

Our findings guide CEnR stakeholders to identify publicly accessible online resources and gaps to address in CEnR resource development. Standardized nomenclature for CEnR competency is needed for effective CEnR resource classification. Uniform organization of CTSA websites may maximize navigability.

Prior studies indicate greater disease burden for obesity among rural compared with urban residents but no differences for mood disorder based on geographic location. Recent attention has focused on the need to examine regional rural–urban disparities in disease burden. We focused on mood disorders and obesity prevalence within three southeastern Minnesota counties served by the Mayo Clinic Center for Translational Science Award, in Rochester, Minnesota, as these were top priorities identified in community health needs assessments.

Cross-sectional study to assess the association of rural–urban locality on 5-year (2009–2014) prevalence of mood disorder and obesity obtained using the Rochester Epidemiological Project medical records linkage system, among subjects residing in three mixed rural–urban counties on April 1, 2014. Multivariable analyses adjusted for demographics, socioeconomic status using an individual housing-based measure, and counties.

The study cohort (percent rural location) included 91,202 (15%) for Olmsted, 10,197 (51%) in Dodge, and 10,184 (57%) in Wabasha counties. On multivariate analysis, 5-year prevalence of mood disorders and obesity was significantly greater for urban compared with rural residents, after adjusting for confounders; odds ratios (95% confidence intervals): 1.21 (1.17–1.26), P < 0.001, and 1.05 (1.01–1.10), P = 0.016, respectively. Observed effects were not modified in additional models adjusted for health care utilization (HCU; ≥1 general medical examination visit and flu vaccination).

Rural–urban health disparities for burden of mood disorders and obesity are independent of socioeconomic status and HCU in a Midwestern community. It is important to assess potential regional heterogeneity of rural–urban disparities on health outcomes.

Recruiting underrepresented people and communities in research is essential for generalizable findings. Ensuring representative participants can be particularly challenging for practice-level dissemination and implementation trials. Novel use of real-world data about practices and the communities they serve could promote more equitable and inclusive recruitment.

We used a comprehensive primary care clinician and practice database, the Virginia All-Payers Claims Database, and the HealthLandscape Virginia mapping tool with community-level socio-ecological information to prospectively inform practice recruitment for a study to help primary care better screen and counsel for unhealthy alcohol use. Throughout recruitment, we measured how similar study practices were to primary care on average, mapped where practices’ patients lived, and iteratively adapted our recruitment strategies.

In response to practice and community data, we adapted our recruitment strategy three times; first leveraging relationships with residency graduates, then a health system and professional organization approach, followed by a community-targeted approach, and a concluding approach using all three approaches. We enrolled 76 practices whose patients live in 97.3% (1844 of 1907) of Virginia’s census tracts. Our overall patient sample had similar demographics to the state for race (21.7% vs 20.0% Black), ethnicity (9.5% vs 10.2% Hispanic), insurance status (6.4% vs 8.0% uninsured), and education (26.0% vs 32.5% high school graduate or less). Each practice recruitment approach uniquely included different communities and patients.

Data about primary care practices and the communities they serve can prospectively inform research recruitment of practices to yield more representative and inclusive patient cohorts for participation.

Efforts to address health disparities and achieve health equity are critically dependent on the development of a diverse research workforce. However, many researchers from underrepresented backgrounds face challenges in advancing their careers, securing independent funding, and finding the mentorship needed to expand their research.

Faculty from the University of Maryland at College Park and the University of Wisconsin-Madison developed and evaluated an intensive week-long research and career-development institute—the Health Equity Leadership Institute (HELI)—with the goal of increasing the number of underrepresented scholars who can sustain their ongoing commitment to health equity research.

In 2010-2016, HELI brought 145 diverse scholars (78% from an underrepresented background; 81% female) together to engage with each other and learn from supportive faculty. Overall, scholar feedback was highly positive on all survey items, with average agreement ratings of 4.45-4.84 based on a 5-point Likert scale. Eighty-five percent of scholars remain in academic positions. In the first three cohorts, 73% of HELI participants have been promoted and 23% have secured independent federal funding.

HELI includes an evidence-based curriculum to develop a diverse workforce for health equity research. For those institutions interested in implementing such an institute to develop and support underrepresented early stage investigators, a resource toolbox is provided.

Food and Drug Administration’s (FDA) Draft Guidance for Industry on pharmaceutical REMS (Risk Evaluation and Mitigation Strategies) assessment and survey methodology highlights physician knowledge–attitudes–behaviors (KAB) surveys as regulatory science tools. This mixed-methods evaluation advances regulatory science and the assessment of FDA REMS programs when using physician surveys. We: (1) reviewed published physician survey response rates; and (2) assessed response bias in a simulation study of secondary survey data using different accrual cut-off strategies.

A systematic literature review was conducted of US physician surveys (2000–2014) on pharmaceutical use (n = 75). Kruskal–Wallis tests were used to examine the relationships between response rates and survey design characteristics. The simulation was conducted using secondary data from a population-based physician KAB survey on diabetes risk management with antipsychotic use in Missouri Medicaid (n = 973 accrued over 30 weeks). Survey item responses were compared using Pearson’s chi-square tests for two faster completion simulations: Fixed Sample (n = 300) and Fixed Time (8 weeks).

Survey response rates ranged from 7% to 100% (median = 48%, IQR = 34%–68%). Surveys of targeted populations and surveys using member lists were associated with higher response rates (p = 0.02). In the simulation, 9 of 20 (45%) KAB items, including diabetes screening advocacy, differed significantly using the smaller Fixed Sample strategy (achieved in 12 days) versus full accrual. Fewer response differences were found using the Fixed Time strategy (2 of 20 [10%] items).

Published data on physician surveys report low response rates with most associated with the sample source selected. FDA REMS assessments should include formal evaluation of survey accrual and response bias.