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.

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.

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.

Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.

Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.

CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features.

This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.

Pragmatic trials aim to speed translation to practice by integrating study procedures in routine care settings. This study evaluated implementation outcomes related to clinician and patient recruitment and participation in a trial of community paramedicine (CP) and presents successes and challenges of maintaining pragmatic study features.

Adults in the pre-hospital setting, emergency department (ED), or hospital being considered for referral to the ED/hospital or continued hospitalization for intermediate-level care were randomized 1:1 to CP care or usual care. Referral and enrollment data were tracked administratively, and patient characteristics were abstracted from the electronic health record (EHR). Enrolled patients completed baseline surveys, and a subset of intervention patients were interviewed. All CPs and a sample of clinicians and administrators were invited to complete a survey and interview.

Between January 2022 and February 2023, 240 enrolled patients (42% rural) completed surveys, and 22 completed an interview; 63 staff completed surveys and 20 completed an interview. Ninety-three clinicians in 27 departments made at least one referral. Factors related to referrals included program awareness and understanding the CP practice scope. Most patients were enrolled in the hospital, but characteristics were similar to the primary care population and included older and medically complex patients. Challenges to achieving representativeness included limited EHR infrastructure, constraints related to patient consenting, and clinician concerns about patient randomization disrupting preferred care.

Future pragmatic trials in busy clinical settings may benefit from regulatory policies and EHR capabilities that allow for real-world study conduct and representative participation. Trial registration: NCT05232799.

The US Food and Drug Administration (FDA) is responsible for assessing safety (risks) and effectiveness (benefits) of new drug products using the data provided in a Sponsor’s new drug product marketing application before they can be marketed. The FDA forms cross-disciplinary review teams to conduct these assessments. Recently, the FDA began implementing more interdisciplinary approaches to its assessments, reducing redundancy in review processes and documentation by increasing team integration around review issues.

Through a phenomenological descriptive comparative case study, the impact of FDA’s new interdisciplinary approach on review team integration was compared with its traditional multidisciplinary review approach.

We identified collaborative integration occurring in one FDA review team using the new interdisciplinary review and another team using the traditional review and then modeled and analyzed the collaborative, cross-disciplinary integration in each case using an input-process-output (IPO) model drawn from the Science-of-Team-Science (SciTS).

This study provides a systematic method for understanding and visualizing integration in each type of review previously and presently used at FDA and illustrates how the new interdisciplinary approach can ensure more integration than more traditional approaches previously used. In addition, our study suggests that an IPO model of integration can characterize how effectively FDA review teams are integrating around issues and assist in the evaluation of differences in integration between FDA’s new interdisciplinary review and the existing multidisciplinary approach. The approach used here is a new application of SciTS scholarship in a unique sector, and it also serves as an example for measuring review team effectiveness.

Older adults have a high disease burden but are often underrepresented in research studies due to recruitment and retention obstacles, among others. Geriatric research specialists have identified solutions to these challenges and designed frameworks to help other researchers. Our team utilized three frameworks to create an interactive webinar series aimed to educate research team members on Age-Friendly practices.

We recruited 40 non-aging-trained research team members to participate in a six-session, real-time webinar series from October to November 2022. Sessions were comprised of 20–30 minute didactics and 30–40 minute group discussions. Participants completed pre- and post-program surveys, commitment to change forms, and post-webinar session surveys. Responses were examined for strengths and areas for improvement. Wilcoxon signed-rank tests assessed differences in confidence scores.

Self-reported confidence scores improved after the webinar series. Most participants provided positive feedback and high likeliness to use what they learned and recommend the webinar to others. The strengths were practical tips, applicable tools, and real-world examples. The major area for improvement was information on industry-sponsored trials. The commitment to change responses varied from pledging to use more inclusive language to adapting materials to improve the consent process.

This interactive Age-Friendly Research webinar series was feasible and well received by participants. We created an Age-Friendly Research community fostering commitment to change clinical and translational research to be more inclusive of older adults. Future work will include more information on industry-sponsored trials and expand to other research centers.

In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.