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Health disparities research in the United States over the past 2 decades has yielded considerable progress and contributed to a developing evidence base for interventions that tackle disparities in health status and access to care. However, health disparity interventions have focused primarily on individual and interpersonal factors, which are often limited in their ability to yield sustained improvements. Health disparities emerge and persist through complex mechanisms that include socioeconomic, environmental, and system-level factors. To accelerate the reduction of health disparities and yield enduring health outcomes requires broader approaches that intervene upon these structural determinants. Although an increasing number of innovative programs and policies have been deployed to address structural determinants, few explicitly focused on their impact on minority health and health disparities. Rigorously evaluated, evidence-based structural interventions are needed to address multilevel structural determinants that systemically lead to and perpetuate social and health inequities. This article highlights examples of structural interventions that have yielded health benefits, discusses challenges and opportunities for accelerating improvements in minority health, and proposes recommendations to foster the development of structural interventions likely to advance health disparities research.

Multilevel interventions can be uniquely effective at addressing minority health and health disparities, but they pose substantial methodological, data analytic, and assessment challenges that must be considered when designing and applying interventions and assessment. To facilitate the adoption of multilevel interventions to reduce health disparities, we outline areas of need in filling existing operational challenges to the design and assessment of multilevel interventions. We discuss areas of development that address overarching constructs inherent in multilevel interventions, with a particular focus on their application to minority health and health disparities. Our approach will prove useful to researchers, as it allows them to integrate information related to health disparities research into the framework of broader constructs with which they are familiar. We urge researchers to prioritize building transdisciplinary teams and the skills needed to overcome the challenges in designing and assessing multilevel interventions, as even small contributions can accelerate progress toward improving minority health and reducing health disparities. To make substantial progress, however, a concerted and strategic effort, including work to advance analytic techniques and measures, is needed.

Many evidence-based interventions (EBIs) have been developed to prevent or treat major health conditions. However, many EBIs have exhibited limited adoption, reach, and sustainability when implemented in diverse community settings. This limitation is especially pronounced in low-resource settings that serve health disparity populations. Often, practitioners identify problems with existing EBIs originally developed and tested with populations different from their target population and introduce needed adaptations to make the intervention more suitable. Although some EBIs have been extensively adapted for diverse populations and evaluated, most local adaptations to improve fit for health disparity populations are not well documented or evaluated. As a result, empirical evidence is often lacking regarding the potential effectiveness of specific adaptations practitioners may be considering. We advocate an expansion in the emphasis of adaptation research from researcher-led interventions to research that informs practitioner-led adaptations. By presenting a research vision and strategies needed to build this area of science, we aim to inform research that facilitates successful adaptation and equitable implementation and delivery of EBIs that reduce health disparities.

The combination of network theory and network psychometric methods has opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, simply estimating a network model is not sufficient for determining which symptoms might be most effective to intervene upon, nor is it sufficient for determining the potential efficacy of any given intervention. In this paper, we attempt to remedy this gap by introducing fundamental concepts of control theory to both psychometricians and applied psychologists. We introduce two controllability statistics to the psychometric literature, average and modal controllability, to facilitate selecting the best set of intervention targets. Following this introduction, we show how intervention scientists can probe the effects of both theoretical and empirical interventions on networks derived from real data and demonstrate how simulations can account for intervention cost and the desire to reduce specific symptoms. Every step is based on rich clinical EMA data from a sample of subjects undergoing treatment for complicated grief, with a focus on the outcome suicidal ideation. All methods are implemented in an open-source R package netcontrol, and complete code for replicating the analyses in this manuscript are available online.

The digital divide related to consumer information technologies (CITs) has diminished, thus increasing the potential to use CITs to overcome barriers of access to health interventions as well as to deliver interventions situated in the context of daily lives. However, the evidence base regarding the use and impact of CIT-enabled interventions in health disparity populations lags behind that for the general population. Literature and case examples are summarized to demonstrate the use of mHealth, telehealth, and social media as behavioral intervention platforms in health disparity populations, identify challenges to achieving their use, describe strategies for overcoming the challenges, and recommend future directions. The evidence base is emerging. However, challenges in design, implementation, and evaluation must be addressed for the promise to be fulfilled. Future directions include (1) improved design methods, (2) enhanced research reporting, (3) advancement of multilevel interventions, (4) rigorous evaluation, (5) efforts to address privacy concerns, and (6) inclusive design and implementation decisions.

As the need to tackle complex clinical and societal problems rises, researchers are increasingly taking on a translational approach. This approach, which seeks to integrate theories, methodologies, and frameworks from various disciplines across a team of researchers, places emphasis on translation of findings in order to offer practical solutions to real-world problems. While translational research leads to a number of positive outcomes, there are also a multitude of barriers to conducting effective team science, such as effective coordination and communication across the organizational, disciplinary, and even geographic boundaries of science teams. Given these barriers to success, there is a significant need to establish team interventions that increase science team effectiveness as translational research becomes the new face of science. This review is intended to provide translational scientists with an understanding of barriers to effective team science and equip them with the necessary tools to overcome such barriers. We provide an overview of translational science teams, discuss barriers to science team effectiveness, demonstrate the lacking state of current interventions, and present recommendations for improving interventions in science teams by applying best practices from the teams and groups literature across the four phases of transdisciplinary research.

Despite the documented impact of racial/ethnic, socioeconomic, and other sociodemographic disparities on the nation's health in terms of lost productivity, health care costs, and excess mortality,1,2 health disparities research currently consitutes less than 10% of the budget of the National Institutes of Health.3 In light of these limited resources, it is critical for health disparities research to be as efficient as possible if it is to have an impact on disparities in real world service delivery and community settings. According to the traditional continuum of translational research,4 which spans from \"bench to community,\" much of health disparities intervention research falls within the later stages of this continuum, as many projects are conducted within real-world community and practice settings.

When intervention scientists plan a clinical trial of an intervention, they select an outcome metric that operationalizes their definition of intervention success. The outcome metric that is selected has important implications for which interventions are eventually supported for implementation at scale and, therefore, what health benefits (including how much benefit and for whom) are experienced in a population. Particularly when an intervention is to be implemented in a population that experiences a health disparity, the outcome metric that is selected can also have implications for equity. Some outcome metrics risk exacerbating an existing health disparity, while others may decrease disparities for some but have less effect for the larger population. In this study, we use a computer to simulate implementation of a hypothetical multilevel, multicomponent intervention to highlight the tradeoffs that can occur between outcome metrics that reflect different operationalizations of intervention success. In particular, we highlight tradeoffs between overall mean population benefit and the distribution of health benefits in the population, which has direct implications for equity. We suggest that simulations like the one we present can be useful in the planning of a clinical trial for a multilevel and/or multicomponent intervention, since simulated implementation at scale can illustrate potential consequences of candidate operationalization of intervention success, such that unintended consequences for equity can be avoided.

The Science of Team Science (SciTS) has generated a substantial body of work detailing characteristics of effective teams. However, that knowledge has not been widely translated into accessible, active, actionable, evidence-based interventions to help translational teams enhance their team functioning and outcomes. Over the past decade, the field of Implementation Science has rapidly developed methods and approaches to increase the translation of biomedical research findings into clinical care, providing a roadmap for mitigating the challenges of developing interventions while maximizing feasibility and utility. Here, we propose an approach to intervention development using constructs from two Implementation Science frameworks, Consolidated Framework for Implementation Research, and Reach, Effectiveness, Adoption, Implementation, and Maintenance, to extend the Wisconsin Interventions for Team Science framework described in Rolland et al. 2021. These Implementation Science constructs can help SciTS researchers design, build, test, and disseminate interventions that meet the needs of both adopters , the institutional leadership that decides whether to adopt an intervention, and implementers , those actually using the intervention. Systematically considering the impact of design decisions on feasibility and usability may lead to the design of interventions that can quickly move from prototype to pilot test to pragmatic trials to assess their impact.

Mayor advancements have been achieved in research on the cultural adaptation of prevention and treatment interventions that are conducted with diverse ethnocultural groups. This commentary addresses conceptual, ethical, contextual, and methodological issues related to cultural adaptations. The articles in this special issue represent a major contribution to the study of cultural adaptations in prevention science. We frame our analysis of fidelity to core intervention components using a conceptual approach that examines (a) the propositional model (theory of change), (b) the procedural model (theory of action, methods), and (c) the philosophical assumptions that undergird these models. Regarding ethics, we caution against imposing the norms, values, and world views of the Western dominant society onto vulnerable populations such as ethnocultural groups. Given that the assumption of universality in behavioral science has been questioned, and as randomized clinical trials (RCTs) seldom examine the ecological validity of evidence-based interventions and treatments (EBI/T), imposing such interventions onto ethnocultural groups is problematic since these interventions contain values, norms, beliefs, and worldviews that may be contrary to those held by many ethnocultural groups. Regarding methods, several innovative designs are discussed that serve as alternatives to the RCT and represent an important contribution to prevention science. Also, we discuss guidelines for conducting cultural adaptations. Finally, the articles in this special issue make a major contribution to the growing field of cultural adaptation of preventive interventions with ethnocultural groups and majority-world populations.