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Background Implementing treatments and interventions with demonstrated effectiveness is critical for improving patient health outcomes at a reduced cost. When an evidence-based intervention (EBI) is implemented with fidelity in a setting that is very similar to the setting wherein it was previously found to be effective, it is reasonable to anticipate similar benefits of that EBI. However, one goal of implementation science is to expand the use of EBIs as broadly as is feasible and appropriate in order to foster the greatest public health impact. When implementing an EBI in a novel setting, or targeting novel populations, one must consider whether there is sufficient justification that the EBI would have similar benefits to those found in earlier trials. Discussion In this paper, we introduce a new concept for implementation called “scaling-out” when EBIs are adapted either to new populations or new delivery systems, or both. Using existing external validity theories and multilevel mediation modeling, we provide a logical framework for determining what new empirical evidence is required for an intervention to retain its evidence-based standard in this new context. The motivating questions are whether scale-out can reasonably be expected to produce population-level effectiveness as found in previous studies, and what additional empirical evaluations would be necessary to test for this short of an entirely new effectiveness trial. We present evaluation options for assessing whether scaling-out results in the ultimate health outcome of interest. Conclusion In scaling to health or service delivery systems or population/community contexts that are different from the setting where the EBI was originally tested, there are situations where a shorter timeframe of translation is possible. We argue that implementation of an EBI in a moderately different setting or with a different population can sometimes “borrow strength” from evidence of impact in a prior effectiveness trial. The collection of additional empirical data is deemed necessary by the nature and degree of adaptations to the EBI and the context. Our argument in this paper is conceptual, and we propose formal empirical tests of mediational equivalence in a follow-up paper.

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.

Objectives. We examined the effectiveness of the Sources of Strength suicide prevention program in enhancing protective factors among peer leaders trained to conduct schoolwide messaging and among the full population of high school students. Methods. Eighteen high schools—6 metropolitan and 12 rural—were randomly assigned to immediate intervention or the wait-list control. Surveys were administered at baseline and 4 months after program implementation to 453 peer leaders in all schools and to 2675 students selected as representative of the 12 rural schools. Results. Training improved the peer leaders' adaptive norms regarding suicide, their connectedness to adults, and their school engagement, with the largest gains for those entering with the least adaptive norms. Trained peer leaders in larger schools were 4 times as likely as were untrained peer leaders to refer a suicidal friend to an adult. Among students, the intervention increased perceptions of adult support for suicidal youths and the acceptability of seeking help. Perception of adult support increased most in students with a history of suicidal ideation. Conclusions. Sources of Strength is the first suicide prevention program involving peer leaders to enhance protective factors associated with reducing suicide at the school population level.

Our objective is to improve local decision-making for strategies to end the HIV epidemic using the newly developed Levers of HIV agent-based model (ABM). Agent-based models use computer simulations that incorporate heterogeneity in individual behaviors and interactions, allow emergence of systemic behaviors, and extrapolate into the future. The Levers of HIV model (LHM) uses Chicago neighborhood demographics, data on sex-risk behaviors and sexual networks, and data on the prevention and care cascades, to model local dynamics. It models the impact of changes in local preexposure prophylaxis (PrEP) and antiretroviral treatment (ART) (ie, levers) for meeting Illinois’ goal of “Getting to Zero” (GTZ) —reducing by 90% new HIV infections among men who have sex with men (MSM) by 2030. We simulate a 15-year period (2016-2030) for 2304 distinct scenarios based on 6 levers related to HIV treatment and prevention: (1) linkage to PrEP for those testing negative, (2) linkage to ART for those living with HIV, (3) adherence to PrEP, (4) viral suppression by means of ART, (5) PrEP retention, and (6) ART retention. Using tree-based methods, we identify the best scenarios at achieving a 90% HIV infection reduction by 2030. The optimal scenario consisted of the highest levels of ART retention and PrEP adherence, next to highest levels of PrEP retention, and moderate levels of PrEP linkage, achieved 90% reduction by 2030 in 58% of simulations. We used Bayesian posterior predictive distributions based on our simulated results to determine the likelihood of attaining 90% HIV infection reduction using the most recent Chicago Department of Public Health surveillance data and found that projections of the current rate of decline (2016-2019) would not achieve the 90% (p = 0.0006) reduction target for 2030. Our results suggest that increases are needed at all steps of the PrEP cascade, combined with increases in retention in HIV care, to approach 90% reduction in new HIV diagnoses by 2030. These findings show how simulation modeling with local data can guide policy makers to identify and invest in efficient care models to achieve long-term local goals of ending the HIV epidemic.

In recent years, there has been increasing discussion of the limitations of traditional randomized controlled trial (RCT) methodologies for the evaluation of eHealth and mHealth interventions, and in particular, the requirement that these interventions be locked down during evaluation. Locking down these interventions locks in defects and eliminates the opportunities for quality improvement and adaptation to the changing technological environment, often leading to validation of tools that are outdated by the time that trial results are published. Furthermore, because behavioral intervention technologies change frequently during real-world deployment, even if a tested intervention were deployed in the real world, its shelf life would be limited. We argue that RCTs will have greater scientific and public health value if they focus on the evaluation of intervention principles (rather than a specific locked-down version of the intervention), allowing for ongoing quality improvement modifications to the behavioral intervention technology based on the core intervention principles, while continuously improving the functionality and maintaining technological currency. This paper is an initial proposal of a framework and methodology for the conduct of trials of intervention principles (TIPs) aimed at minimizing the risks of in-trial changes to intervention technologies and maximizing the potential for knowledge acquisition. The focus on evaluation of intervention principles using clinical and usage outcomes has the potential to provide more generalizable and durable information than trials focused on a single intervention technology.

Background An increasingly large body of research is focused on designing and testing strategies to improve knowledge about how to embed evidence-based programs (EBP) into community settings. Development of strategies for overcoming barriers and increasing the effectiveness and pace of implementation is a high priority. Yet, there are few research tools that measure the implementation process itself. The Stages of Implementation Completion (SIC) is an observation-based measure that is used to track the time to achievement of key implementation milestones in an EBP being implemented in 51 counties in 53 sites (two counties have two sites) in two states in the United States. Methods The SIC was developed in the context of a randomized trial comparing the effectiveness of two implementation strategies: community development teams (experimental condition) and individualized implementation (control condition). Fifty-one counties were randomized to experimental or control conditions for implementation of multidimensional treatment foster care (MTFC), an alternative to group/residential care placement for children and adolescents. Progress through eight implementation stages was tracked by noting dates of completion of specific activities in each stage. Activities were tailored to the strategies for implementing the specific EBP. Results Preliminary data showed that several counties ceased progress during pre-implementation and that there was a high degree of variability among sites in the duration scores per stage and on the proportion of activities that were completed in each stage. Progress through activities and stages for three example counties is shown. Conclusions By assessing the attainment time of each stage and the proportion of activities completed, the SIC measure can be used to track and compare the effectiveness of various implementation strategies. Data from the SIC will provide sites with relevant information on the time and resources needed to implement MTFC during various phases of implementation. With some modifications, the SIC could be appropriate for use in evaluating implementation strategies in head-to-head randomized implementation trials and as a monitoring tool for rolling out other EBPs.

This article focuses on the rationale, design and methods of an effectiveness-implementation hybrid type I randomized trial of eHealth Familias Unidas Mental Health , a family-based, online delivered intervention for Hispanic families to prevent/reduce depressive and anxious symptoms, suicide ideation/behaviors, and drug use in Hispanic youth. Utilizing a rollout design with 18 pediatric primary care clinics and 468 families, this study addresses intervention effectiveness, implementation research questions, and intervention sustainment, to begin bridging the gap between research and practice in eliminating mental health and drug use disparities among Hispanic youth. Further, we will examine whether intervention effects are partially mediated by improved family communication and reduced externalizing behaviors, including drug use, and moderated by parental depression. Finally, we will explore whether the intervention’s impact on mental health and drug use, as well as sustainment of the intervention in clinics, varies by quality of implementation at clinic and clinician levels. Trail registration: ClinicalTrials.gov Identifier: NCT05426057 , First posted June 21, 2022.

Abstract To examine long-term effects of early intervention services (EIS) for first-episode psychosis, we compared Heinrichs-Carpenter Quality of Life (QLS) and Positive and Negative Syndrome Scale (PANSS) scores and inpatient hospitalization days over 5 years with data from the site-randomized RAISE-ETP trial that compared the EIS NAVIGATE (17 sites; 223 participants) and community care (CC) (17 sites; 181 participants). Inclusion criteria were: age 15–40 years; DSM-IV diagnoses of schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, or psychotic disorder not otherwise specified; first psychotic episode; antipsychotic medication taken for ≤6 months. NAVIGATE-randomized participants could receive NAVIGATE from their study entry date until NAVIGATE ended when the last-enrolled NAVIGATE participant completed 2 years of treatment. Assessments occurred every 6 months. 61% of participants had assessments conducted ≥2 years; 31% at 5 years. Median follow-up length was CC 30 months and NAVIGATE 38 months. Primary analyses assumed data were not-missing-at-random (NMAR); sensitivity analyses assumed data were missing-at-random (MAR). MAR analyses found no significant treatment-by-time interactions for QLS or PANSS. NMAR analyses revealed that NAVIGATE was associated with a 13.14 (95%CI:6.92,19.37) unit QLS and 7.73 (95%CI:2.98,12.47) unit PANSS better improvement and 2.53 (95%CI:0.59,4.47) fewer inpatient days than CC (all comparisons significant). QLS and PANSS effect sizes were 0.856 and 0.70. NAVIGATE opportunity length (mean 33.8 (SD = 5.1) months) was not associated (P = .72) with QLS outcome; duration of untreated psychosis did not moderate (P = .32) differential QLS outcome. While conclusions are limited by the low rate of five-year follow-up, the data support long-term benefit of NAVIGATE compared to community care.

In 2019, the requisite biomedical and behavioral interventions to eliminate new HIV infections exist. “Ending the HIV Epidemic” now becomes primarily a challenge of will and implementation. This review maps the extent to which implementation research (IR) has been integrated into HIV research by reviewing the recent funding portfolio of the NIH. We searched NIH RePORTER for HIV and IR-related research projects funded from January 2013 to March 2018. The 4629 unique studies identified were screened using machine learning and manual methods. 216 abstracts met the eligibility criteria of HIV and IR. Key study characteristics were then abstracted. NIH currently funds HIV studies that are either formally IR (n = 109) or preparatory for IR (n = 107). Few (13%) projects mentioned a guiding implementation model, theory, or framework, and only 56% of all studies explicitly mentioned measuring an implementation outcome. Considering the study aims along an IR continuum, 18 (8%) studies examined barriers and facilitators, 43 (20%) developed implementation strategies, 46 (21%) piloted strategies, 73 (34%) tested a single strategy, and 35 (16%) compared strategies. A higher proportion of formal IR projects involved established interventions (e.g., integrated services) compared to newer interventions (e.g., pre-exposure prophylaxis). Prioritizing HIV-related IR in NIH and other federal funding opportunity announcements and expanded training in implementation science could have a substantial impact on ending the HIV pandemic. This review serves as a baseline by which to compare funding patterns and the sophistication of IR in HIV research over time.

Between January 2016 and June 2020, the Substance Abuse and Mental Health Services Administration rapidly distributed $7.5 billion in response to the U.S. opioid crisis. These funds are designed to increase access to medications for addiction treatment, reduce unmet treatment need, reduce overdose death rates, and provide and sustain effective prevention, treatment and recovery activities. It is unclear whether or not the services developed using these funds will be sustained beyond the start-up period. Based on 34 (64%) State Opioid Response (SOR) applications, we assessed the states’ sustainability plans focusing on potential funding sources, policies, and quality monitoring. We found variable commitment to sustainability across response plans with less than half the states adequately describing sustainability plans. States with higher proportions of opioid prescribing, opioid misuse, and poverty had somewhat higher scores on sustainment. A text mining/machine learning approach automatically rated sustainability in SOR applications with an 82% accuracy compared to human ratings. Because life saving evidence-based programs and services may be lost, intentional commitment to sustainment beyond the bolus of start-up funding is essential.