Community health workers and promotoras (CHW/Ps) have a fundamental role in facilitating research with communities. However, no national standard training exists as part of the CHW/P job role. We developed and evaluated a culturally- and linguistically tailored online research best practices course for CHW/Ps to meet this gap.

After the research best practices course was developed, we advertised the opportunity to CHW/Ps nationwide to complete the training online in English or Spanish. Following course completion, CHW/Ps received an online survey to rate their skills in community-engaged research and their perceptions of the course using Likert scales of agreement. A qualitative content analysis was conducted on open-ended response data.

104 CHW/Ps completed the English or Spanish course (n = 52 for each language; mean age 42 years SD ± 12); 88% of individuals identified as female and 56% identified as Hispanic, Latino, or Spaniard. 96%–100% of respondents reported improvement in various skills. Nearly all CHW/Ps (97%) agreed the course was relevant to their work, and 96% felt the training was useful. Qualitative themes related to working more effectively as a result of training included enhanced skills, increased resources, and building bridges between communities and researchers.

The CHW/P research best practices course was rated as useful and relevant by CHW/Ps, particularly for communicating about research with community members. This course can be a professional development resource for CHW/Ps and could serve as the foundation for a national standardized training on their role related to research best practices.

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records.

This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models.

A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, p < 0.01) and gradient boosted machine (0.68, p = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category.

Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.

SARS-CoV-2 has been found in the heart of COVID-19 patients. It is unclear how the virus passes from the upper respiratory tract to the myocardium. We hypothesized that SARS-CoV-2 is present in the blood of COVID-19 infected patients, spreading to other organs such as heart.

We targeted two viroporins, Orf3a and E, in SARS-CoV-2. Orf3a and E form non-voltage-gated ion channels. A combined fluorescence potassium ion assay with three channel modulators (4-aminopyridine, emodin-Orf3a channel blocker, and gliclazide-E channel blocker) was developed to detect SARS-CoV-2 Orf3a/E channel activity. In blood samples, we subtracted the fluorescence signals in the absence and presence of emodin/gliclazide to detect Orf3a and E channel activity.

In lentivirus-spiked samples, we detected significant channel activity of Orf3a/E based on increase in fluorescence induced by 4-aminopyridine, and this increase in fluorescence was inhibited by emodin and gliclazide. In 18 antigen/PCR-positive samples, our test results found 15 are positive, demonstrating 83.3% concordance. In 24 antigen/PCR-negative samples, our test results found 21 are negative, showing 87.5% concordance.

We developed a cell-free test that can detect Orf3a/E channel activity of SARS-CoV-2 in blood samples from COVID-19-infected individuals, confirming a hypothesis that the virus spreads to the heart via blood circulation.

Housing instability is a social determinant of health associated with multiple negative health outcomes including substance use disorders (SUDs). Real-world evidence of housing instability is needed to improve translational research on populations with SUDs.

We identified evidence of housing instability by leveraging structured diagnosis codes and unstructured clinical data from electronic health records of 20,556 patients from 2017 to 2021. We applied natural language processing with named-entity recognition and pattern matching to unstructured clinical notes with free-text documentation. Additionally, we analyzed semi-structured addresses containing explicit or implicit housing-related labels. We assessed agreement on identification methods by having three experts review of 300 records.

Diagnostic codes only identified 58.5% of the population identifiable as having housing instability, whereas 41.5% are identifiable from addresses only (7.1%), clinical notes only (30.4%), or both (4.0%). Reviewers unanimously agreed on 79.7% of cases reviewed; a Fleiss’ Kappa score of 0.35 suggested fair agreement yet emphasized the difficulty of analyzing patients having ambiguous housing situations. Among those with poisoning episodes related to stimulants or opioids, diagnosis codes were only able to identify 63.9% of those with housing instability.

All three data sources yield valid evidence of housing instability; each has their own inherent practical use and limitations. Translational researchers requiring comprehensive real-world evidence of housing instability should optimize and implement use of structured and unstructured data. Understanding the role of housing instability and temporary housing facilities is salient in populations with SUDs.

Rural cancer survivors have worse outcomes than their urban counterparts. To improve outcomes, it is essential that rural survivors participate in research, yet they are underrepresented in cancer research. The aim of this study was to assess urban-rural differences in participation in a cancer survivorship survey and differences in mode of participation (mail, online, or phone) by rurality and age.

We developed a survivorship needs assessment survey and invited cancer survivors to participate by mail, online, or phone. We compared participation between rural and urban invitees and examined differences in mode of participation by rurality and age.

A quarter (25.47%) of invited rural patients and 27.84% of invited urban patients participated in the survivorship study. The probability of participation by urban survivors was approximately 1.09 times higher than for rural survivors (χ2(1) = 4.31, p = 0.038). Rural survivors were more likely to participate by mail (average difference [Rural-Urban] = 9.64%, p < 0.001), while urban survivors were more likely to participate online (average difference [Urban-Rural] = 8.77%, p < 0.001). As participant age increased, the likelihood of survey participation by mail increased (1.16% per year of age, p < 0.001) while the probability of participating online decreased by 1.20% per year of age (p < 0.001).

To ensure equitable access to research for rural and older cancer survivors, researchers should design studies with a range of participation modes. Non-digital methods, such as mailed paper surveys, appear to promote participation among rural and older survivors.

All IN for Health is a well-established community-academic partnership dedicated to helping improve the lives of Indiana residents by increasing health research literacy and promoting health resources, as well as opportunities to participate in research. It is sponsored by the Indiana Clinical and Translational Science Institute (I-CTSI). The study’s purpose was to measure trust in biomedical research and healthcare organizations among research volunteers.

The Relationship of Trust and Research Engagement (RTRE) survey was developed utilizing 3 validated scales. The RTRE consisted of 36 items in a 5-point Likert scale with three open-text questions. We conducted 3 focus groups with a total of 24 individuals ahead of the survey’s launch. Recruitment was done through the All IN for Health newsletter. The survey was administered in the summer of 2022.

Six hundred and sixty-three individuals participated in the survey. Forty-one percent agreed that doctors do medical research for selfish reasons. Moreover, 50% disagree that patients get the same medical treatment regardless of race/ethnicity. Sixty-seven percent think it is safe to participate in medical research, yet 79% had never been asked to participate. Ten percent believe that researchers select minorities for their most dangerous studies and expose minoritized groups to diseases.

The utilization of tools to measure trust will facilitate participant recruitment and will assist institutions and investigators alike in accountability. It is imperative, we work toward understanding our communities’ trust in medical research, assessing our own trustworthiness, and critically reflect on the authenticity of our efforts.

The avoidance of asthma triggers, like tobacco smoke, facilitates asthma management. Reliance upon caregiver report of their child’s environmental tobacco smoke (ETS) exposure may result in information bias and impaired asthma management. This analysis aimed to characterize the chronicity of ETS exposure, assess the validity of caregiver report of ETS exposure, and investigate the relationship between ETS exposure and asthma attack.

A secondary data analysis was performed on data from a longitudinal study of 162 children aged 7–12 years with asthma living in federally subsidized housing in three US cities (Boston, Cincinnati, and New Orleans). Data were collected at three time points over 1 year.

Over 90% of children were exposed to ETS (≥0.25 ng/ml of urine cotinine (UC)). Exposure was consistent over 1 year. Questionnaire data had a sensitivity of 28–34% using UC ≥0.25 ng/ml as the gold standard. High ETS exposure (UC ≥ 30 ng/ml) was significantly associated with asthma attack (aOR 2.97, 0.93–9.52, p = 0.07). Lower levels (UC 0.25–30 ng/ml) were not statistically significant (aOR 1.76, 0.71– 4.38, p = 0.22). No association was found using caregiver-reported ETS exposure.

Relying on questionnaire data to assess children’s exposure to tobacco smoke may lead to substantial information bias. For children with asthma, incorrect characterization may substantially impact asthma morbidity.

Although classical randomized clinical trials (RCTs) are the gold standard for proof of drug efficacy, randomized discontinuation trials (RDTs), sometimes called “enriched” trials, are used increasingly, especially in psychiatric maintenance studies.

A narrative review of two decades of experience with RDTs.

RDTs in psychiatric maintenance trials tend to use a dependent variable as a predictor: treatment response. Treatment responders are assessed for treatment response. This tautology in the logic of RDTs renders them invalid, since the predictor and the outcome are the same variable. Although RDTs can be designed to avoid this tautologous state of affairs, like using independent predictors of outcomes, such is not the case with psychiatric maintenance studies

Further, purported benefits of RDTs regarding feasibility were found to be questionable. Specifically, RDTs do not enhance statistical power in many settings, and, because of high dropout rates, produce results of questionable validity. Any claimed benefits come with notably reduced generalizability.

RDTs appear to be scientifically invalid as used in psychiatric maintenance designs. Their purported feasibility benefits are not seen in actual trials for psychotropic drugs. There is warrant for changes in federal policy regarding marketing indications for maintenance efficacy using the RDT design.

Large, transdisciplinary research consortia have increasingly been called upon to address complex and challenging health problems. The National Institutes of Health’s (NIH) Environmental influences on Child Health Outcomes (ECHO) Program developed multisite collaboration strategies to promote impactful collaborative observational research on child health. Team science and implementation science offer theoretical and methodological structure to answer questions about the strategies that facilitate successful consortia. We sought to characterize the elements and conditions that influence the implementation of a complex, interdisciplinary longitudinal research program, ECHO.

Informed by the Practical, Robust, Implementation and Sustainability Model, our ethnographic research included semi-structured interviews with internal stakeholders and program evaluation metrics. We conducted template and matrix analysis and triangulated the qualitative and quantitative data to understand the implementation of ECHO.

Between February and May 2022, we conducted 24 virtual interviews with representatives from ECHO components. The main cross-cutting topics that emerged from thematic analysis were collaboration and team science; communication and decision-making; data processes and harmonization; and diversity, equity, and inclusion. Both the qualitative and secondary quantitative evaluation data provided insights into the reach, adoption, implementation, and effectiveness of the program.

A large, multidisciplinary research consortium such as ECHO has produced conceptual, instrumental, capacity building, and connectivity impact for internal and external stakeholders. Facilitators included infrastructure that supported collaboration and learning, alignment of data processes, and harmonization. Opportunities for enhanced impact include multidisciplinary, multimethod communication strategies, and alignment of research priorities.

Recruiting and retaining racial/ethnic minorities in research remains a significant challenge, often due to mistrust in clinical research and cultural misconceptions related to specific conditions. Despite the anonymity provided by technology-based intervention studies, difficulties in participant recruitment and retention in these studies remain. This paper addresses practical issues in recruiting and retaining Asian American breast cancer survivors with pain and depressive symptoms in a technology-based intervention study.

To identify practical issues in participant recruitment and retention, a content analysis was conducted on all recorded materials, including research diaries of individual research team members, weekly team meeting minutes, and research team members’ posts on Microsoft Teams.

Analysis identified six practical issues: (a) strict inclusion/exclusion criteria; (b) multiple stigmas associated with cancer, depressive symptoms, and pain; (c) lack of interest in research participation; (d) closed Asian American communities/groups; (e) frequent technological issues; and (f) potential unauthentic cases.

Addressing these recruitment and retention issues can inform the design of future culturally tailored, technology-based intervention studies for racial and ethnic minority populations.

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.