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Abstract Background It is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. There has been limited systematic evaluation of variation in U.S. local COVID-19-related policies. This study introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies. Methods In January-March 2021, we collected an initial wave of cross-sectional data from government and media websites for 171 counties in 7 states on 22 county-level COVID-19-related policies within 3 policy domains that are likely to affect health: (1) containment/closure, (2) economic support, and (3) public health. We characterized the presence and comprehensiveness of policies using univariate analyses. We also examined the correlation of policies with one another using bivariate Spearman’s correlations. Finally, we examined geographical variation in policies across and within states. Results There was substantial variation in the presence and comprehensiveness of county policies during January-March 2021. For containment and closure policies, the percent of counties with no restrictions ranged from 0% (for public events) to more than half for public transportation (67.8%), hair salons (52.6%), and religious gatherings (52.0%). For economic policies, 76.6% of counties had housing support, while 64.9% had utility relief. For public health policies, most were comprehensive, with 70.8% of counties having coordinated public information campaigns, and 66.7% requiring masks outside the home at all times. Correlations between containment and closure policies tended to be positive and moderate (i.e., coefficients 0.4–0.59). There was variation within and across states in the number and comprehensiveness of policies. Conclusions This study introduces the UCCP Database, presenting granular data on local governments’ responses to the COVID-19 pandemic. We documented substantial variation within and across states on a wide range of policies at a single point in time. By making these data publicly available, this study supports future research that can leverage this database to examine how policies contributed to and continue to influence pandemic-related health and socioeconomic outcomes and disparities. The UCCP database is available online and will include additional time points for 2020–2021 and additional counties nationwide.

BackgroundDirect-acting antiviral medications have the potential to eliminate the hepatitis C virus (HCV) epidemic among people who inject drugs; yet, suboptimal adherence remains a barrier. Directly observed treatment (DOT), an effective strategy for optimizing adherence, has been frequently implemented in opioid treatment programs but less commonly in community health settings due to the heavy burden of daily visits. An alternative is video-observed therapy (VOT), which uses mobile health technology to monitor adherence. VOT has not been widely studied among people who inject drugs with HCV. ObjectiveThis qualitative study, part of a larger implementation evaluation, investigates stakeholder perceptions and experiences with VOT in Project HERO (Hepatitis C Real Outcomes), a multisite pragmatic trial testing treatment delivery models for people who inject drugs with HCV. Our goal was to understand the potential barriers and facilitators to the implementation of the VOT technology. MethodsQualitative interviews were conducted with 27 Project HERO study staff and 7 patients. Interviews focused on perceptions and experiences with the VOT app and barriers and facilitators to implementation. Team meeting minutes over the first 2 years of the project were transcribed. A coding system was developed and applied to the data. We summarized thematic data and compared participant perceptions to generate a close understanding of the data. ResultsFrequent barriers to VOT included mechanical failure, stolen or lost phones, and a steep learning curve for participants and study staff. In sites with older and less technically skilled participants, staff found it difficult to implement the VOT app. Research staff found that the routine monitoring of app use led to closer engagement with participants. This was both a benefit and a potential threat to the validity of this pragmatic trial. Patient participants reported mixed experiences. ConclusionsVOT may be a useful alternative to DOT for some patients, but it may not be feasible for all. Significant staff involvement may be required.

Abstract Background The United States needs to increase access to effective obesity prevention and treatment programming for impoverished youth at risk for health disparities. Although recommended, schools have difficulty consistently implement evidence-based obesity programing. We report on the effectiveness of adding structured nutrition education and minimum physical activity (PA) requirements to standard middle school after-school programming. Methods Using a longitudinal pre-post study design, we evaluated program effectiveness at one year on target behaviors on students recruited during three consecutive school years (2016–2018). We used generalized linear (or logistic) mixed-effects modeling to determine: 1) impact on healthy weight and target healthy behavior attainment, and 2) whether target behavior improvement and weight change were associated with after-school program attendance. The seven target behaviors relate to eating healthy, physical activity, and sleep. Results Over the three years, a total of 76 students enrolled and completed one year of programming (62% Hispanic, 46% girls, 72% with BMI > 85th %ile, 49% with BMI > 95th %ile). Of students with BMI > 85th %ile, 44% maintained or decreased BMI Z-score. There were improvements (non-significant) in BMI Z-score and the adoption of four healthy eating behaviors: fruit, vegetables, sugar-free beverages, and unhealthy snack food. Students with higher after-school attendance (> 75%) had greater improvements (non-significant) in composite behavior scores, BMI Z-score, and in most target behaviors (5/7) than students with lower after-school attendance (

The efficient and effective movement of research into practice is acknowledged as crucial to improving population health and assuring return on investment in healthcare research. The National Center for Advancing Translational Science which sponsors Clinical and Translational Science Awards (CTSA) recognizes that dissemination and implementation (D&I) sciences have matured over the last 15 years and are central to its goals to shift academic health institutions to better align with this reality. In 2016, the CTSA Collaboration and Engagement Domain Task Force chartered a D&I Science Workgroup to explore the role of D&I sciences across the translational research spectrum. This special communication discusses the conceptual distinctions and purposes of dissemination, implementation, and translational sciences. We propose an integrated framework and provide real-world examples for articulating the role of D&I sciences within and across all of the translational research spectrum. The framework’s major proposition is that it situates D&I sciences as targeted “sub-sciences” of translational science to be used by CTSAs, and others, to identify and investigate coherent strategies for more routinely and proactively accelerating research translation. The framework highlights the importance of D&I thought leaders in extending D&I principles to all research stages.

Learning Health Systems (LHS) iteratively implement and evaluate health improvement projects. Dissemination and implementation (D&I) science is the study of evidence-based practices in real-world settings, a critical tool for LHS. This paper explores intersections between LHS and D&I science in Clinical and Translational Science Awards (CTSAs) institutions and identifies critical components of collaboration. We conducted website scans of 34 CTSAs and their home institutions that had Dissemination, Implementation, and Knowledge Translation (DIKT) Workgroup members. We identified linkages between CTSAs and their institutions’ LHS. We interviewed six CTSA leaders experienced in LHS and D&I sciences. Nearly half of CTSAs identified an LHS structure on their websites, but only one-third indicates CTSA involvement in these efforts. Interviewees identified key components for successful integration of LHS and D&I sciences: leadership, infrastructure, balance between rigor and efficiency, and aligned incentives. The need for research integration in LHS, to improve evaluation and increase knowledge, is an emerging opportunity for D&I scientists and CTSAs. CTSAs that are engaged in D&I science can introduce and/or expand the role of D&I science in LHS. Collaboration between CTSAs and clinical leaders could result in strengthened relationships between clinical and research enterprises, effective and efficient health care delivery, and improved health.

Abstract Introduction: Dissemination and implementation (D&I) science is not a formal element of the Clinical Translational Science Award (CTSA) Program, and D&I science activities across the CTSA Consortium are largely unknown. Methods: The CTSA Dissemination, Implementation, and Knowledge Translation Working Group surveyed CTSA leaders to explore D&I science-related activities, barriers, and needed supports, then conducted univariate and qualitative analyses of the data. Results: Out of 67 CTSA leaders, 55.2% responded. CTSAs reported directly funding D&I programs (54.1%), training (51.4%), and projects (59.5%). Indirect support (e.g., promoted by CTSA without direct funding) for D&I activities was higher – programs (70.3%), training (64.9%), and projects (54.1%). Top barriers included funding (39.4%), limited D&I science faculty (30.3%), and lack of D&I science understanding (27.3%). Respondents (63.4%) noted the importance of D&I training and recommended coordination of D&I activities across CTSAs hubs (33.3%). Conclusion:These findings should guide CTSA leadership in efforts to raise awareness and advance the role of D&I science in improving population health.

Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations.

Stakeholder engagement is acknowledged as central to dissemination and implementation (D&I) of research that generates and answers new clinical and health service research questions. There is both benefit and risk in conducting stakeholder engagement. Done wrong, it can damage trust and adversely impact study results, outcomes, and reputations. Done correctly with sensitivity, inclusion, and respect, it can significantly facilitate improvements in research prioritization, communication, design, recruitment strategies, and ultimately provide results useful to improve population and individual health. There is a recognized science of stakeholder engagement, but a general lack of knowledge that matches its strategies and approaches to particular populations of interest based on history and characteristics. This article reviews stakeholder engagement, provides several examples of its application across the range of translational research, and recommends that Clinical Translational Science Awards, with their unique geographical, systems, and historical characteristics, actively participate in deepening our understanding of stakeholder engagement science and methods within implementation and dissemination research. These recommendations include (a) development of an inventory of successful stakeholder engagement strategies; (b) coordination and intentionally testing a variety of stakeholder engagement strategies; (c) tool kit development; and (d) identification of fundamental motivators and logic models for stakeholder engagement to help align stakeholders and researchers.

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.

Effective obesity prevention and treatment interventions are lacking in the United States, especially for impoverished minority youths at risk for health disparities, and especially in accessible community-based settings. We describe the launch and pilot implementation evaluation of the first year of the B’N Fit POWER initiative as a middle school-based comprehensive wellness program that integrates weight management programming into existing onsite preventive and clinical services. Consistent with the existing implementation science literature, we focused on both the organizational structures that facilitate communication and the development of trust among stakeholders, students, and families and the development of realistic and timely goals to implement and integrate all aspects of the program. New implementation and programming strategies were developed and tested to increase the proportion of students screened, support the linkage of students to care, and streamline the integration of program clinical and afterschool components into routine services already offered at the school. We report on our initial implementation activities using the Standards for Reporting Implementation Studies (StaRI) framework using hybrid outcomes combining the Reach element from the RE-AIM framework with a newly conceptualized Wellness Cascade.

Abstract An important challenge in comparative effectiveness research is the lack of infrastructure to support pragmatic clinical trials, which compare interventions in usual practice settings and subjects. These trials present challenges that differ from those of classical efficacy trials, which are conducted under ideal circumstances, in patients selected for their suitability, and with highly controlled protocols. In 2012, we launched a 1‐year learning network to identify high‐priority pragmatic clinical trials and to deploy research infrastructure through the NIH Clinical and Translational Science Awards Consortium that could be used to launch and sustain them. The network and infrastructure were initiated as a learning ground and shared resource for investigators and communities interested in developing pragmatic clinical trials. We followed a three‐stage process of developing the network, prioritizing proposed trials, and implementing learning exercises that culminated in a 1‐day network meeting at the end of the year. The year‐long project resulted in five recommendations related to developing the network, enhancing community engagement, addressing regulatory challenges, advancing information technology, and developing research methods. The recommendations can be implemented within 24 months and are designed to lead toward a sustained national infrastructure for pragmatic trials.