COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy.

Here we present a method called COVIDNearTerm to “forecast” hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT).

We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%.

COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations.

Hyperbilirubinemia is common in the neonatal period; however, delayed diagnosis or inadequate treatment can cause irreparable damage to the neonates. We aimed to evaluate the efficacy of oral fenofibrate for hyperbilirubinemia in term neonates.

This single-blind randomized controlled trial included 86 term neonates aged 3–7 days, with birth weight ≥2500 g, admitted to Bandar Abbas Children’s Hospital, Bandar Abbas Iran, from July 23, 2019, to July 22, 2020. The fenofibrate group received 10 mg/kg oral fenofibrate and phototherapy, while controls only received phototherapy. Serum total bilirubin was measured at 24 and 48 h and at the time of discharge. Hospital length of stay was also noted.

The two study groups were comparable regarding age, gender, gestational age, birth weight, and baseline total serum bilirubin levels. Serum total bilirubin levels at 48 h (P < 0.001) and at discharge (P < 0.001) were significantly lower in the fenofibrate group compared to controls. Although hospital length of stay was lower in the fenofibrate group compared to controls, the difference was not statistically significant (P = 0.612). Fenofibrate was more effective on the reduction of serum bilirubin in neonates aged 3–4.5 days starting at the 24th hour. Moreover, it was more effective in female neonates compared to males starting at the 48th hour.

A single dose of oral fenofibrate reduced total serum bilirubin in term neonates with hyperbilirubinemia without any side effects; however, this effect was more prominent after 48 h.

The Great CTSA Team Science Contest (GTSC) was developed to discover how Clinical and Translational Science Award (CTSA) hubs promote and support team science [1]. The purpose of this study was a secondary qualitative analysis of the GTSC submissions to better understand the diversity of team science initiatives across the CTSA consortium.

Secondary qualitative analysis of the GTSC data addressed the following research questions, which defined the top-level coding: (1) What CTSA component sponsored it? (2) Who was the team doing the work? (3) Who were the intended beneficiaries? (4) What was the intended outcome? (5) What strategies did they use? (6) What translational science (TS) stage was addressed? (7) How do they align with the NCATS team science strategic goals? (8) How do the CTSA’s team science efforts align with the National Academies Research Council (NRC) recommendations for enhancing the effectiveness of team science?

The GTSC received 170 submissions from 45 unique CTSA hubs. Qualitative analysis revealed a great variety of team science strategies for virtually all team science stakeholders. In addition to strategies to promote team science, results show successful examples that focus on outcomes and illustrate ways of measuring success.

The GTSC shows that the CTSA consortium is involved in an extremely diverse array of team science activities, which align well with both the NRC recommendations for enhancing the effectiveness of team science and the NCATS strategic goals for team science. Future research should evaluate the efficacy of team science strategies.

Arkansans have some of the worst breast cancer mortality to incidence ratios in the United States (5th for Blacks, 4th for Whites, 7th overall). Screening mammography allows for early detection and significant reductions in mortality, yet not all women have access to these life-saving services. Utilization in Arkansas is well below the national average, and the number of FDA-approved screening facilities has decreased by 38% since 2001. Spatial accessibility plays an important role in whether women receive screenings.

We use constrained optimization models within a geographic information system (GIS) to probabilistically allocate women to nearby screening facilities, accounting for facility capacity and patient travel time. We examine accessibility results by rurality derived from rural–urban commuting area (RUCA) codes.

Under most models, screening capacity is insufficient to meet theoretical demand given travel constraints. Approximately 80% of Arkansan women live within 30 minutes of a screening facility, most of which are located in urban and suburban areas. The majority of unallocated demand was in Small towns and Rural areas.

Geographic disparities in screening mammography accessibility exist across Arkansas, but women living in Rural areas have particularly poor spatial access. Mobile mammography clinics can remove patient travel time constraints to help meet rural demand. More broadly, optimization models and GIS can be applied to many studies of healthcare accessibility in rural populations.

Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors.

This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets.

The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index.

The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching.

Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.

With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist.

HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions?

This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected.

This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.

UMHealthResearch is the University of Michigan’s digital health research recruitment platform. It allows health researchers to connect efficiently with potentially eligible volunteers.

In 2013, the UMHealthResearch team strategically adapted a consumer behavior model, the buying funnel, to create the Digital Health Research Participation Funnel. The Digital Health Research Participation Funnel was then used to design a more active way for potential participants to volunteer for research studies through UMHealthResearch.

In the 5 years before the redesign (2007–2012), an average of 1844 new accounts were created every year, whereas in the completed years after the redesign (2013–2016) the annual average improved to 3906, an increase of 111%.

Although a randomized design was not possible in this instance, these preintervention and postintervention data suggest that the focus on user experience is an effective strategy for improving web-based research recruitment platforms.

To quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.

Over 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.

Staffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.

Understanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics.

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.

Diversity, equity, and inclusion (DEI) in clinical and translational science (CTS) are paramount to driving innovation and increasing health equity. One important area for improving diversity is among trainees in CTS programs. This paper reports on findings from a special session at the November 2020 Clinical and Translational Science Award (CTSA) national program meeting that focused on advancing diversity and inclusion within CTS training programs.

Using qualitative content analysis, we identified approaches brought forth to increase DEI in KL2 career development and other training programs aimed at early-stage CTS investigators, beyond the six strategies put forth to guide the breakout session (prioritizing representation, building partnerships, making it personal, designing program structure, improving through feedback, and winning endorsement). We used an inductive qualitative content analysis approach to identify themes from a transcript of the panel of KL2 program leaders centered on DEI in training programs.

We identified four themes for advancing DEI within CTS training programs: 1) institutional buy-in; 2) proactive recruitment efforts; 3) an equitable application process; and 4) high-quality, diverse mentorship.

Implementing these strategies in CTS and other training programs will be an important step for advancing DEI. However, processes need to be established to evaluate the implementation and effectiveness of these strategies through continuous quality improvement, a key component of the CTSA program. Training programs within the CTSA are well-positioned to be leaders in this critical effort to increase the diversity of the scientific workforce.