BACKGROUND:Primary care patients with depression or anxiety are at higher risk for cardiovascular disease. Those who engage in integrated mental health treatment may also improve their cardiovascular health. METHODS:We conducted secondary analysis of electronic health record-extracted data related to a pragmatic, implementation trial of the collaborative care model for depression and anxiety (CoCM; NCT04321876). Primary care patients with elevated depressive/anxiety symptoms (N = 3252) in 11 primary care clinics were classified as CoCM Patients (n = 718), Not Referred to CoCM (n = 1348), or Not Engaged in CoCM (n = 1459). Cardiovascular health measures included blood pressure (BP; mmHg), total cholesterol (mg/dL), HbA1c (%), and body mass index (BMI; kg/m2). Primary analyses were linear regressions evaluating associations of CoCM treatment (vs. Not Referred, vs. Not Engaged) with changes in cardiovascular health, adjusted for demographics, cardiovascular diagnoses, and medications. RESULTS:At baseline, CoCM Patients had poorer cardiovascular health than Not Referred (higher systolic BP, total cholesterol, BMI) and better cardiovascular health than Not Engaged (lower total cholesterol, HbA1c). CoCM Patients, vs. Not Referred, had small decreases in total cholesterol (B = -0.44, 95 % CI: -0.72, -0.17) and increases in BMI (B = 0.18, 95 % CI: 0.03, 0.34). CoCM Patients did not differ from Not Engaged patients on cardiovascular health outcomes (all ps > 0.05). DISCUSSION:Differences in cardiovascular health profiles were evident prior to treatment. Patients who bengaged in CoCM treatment, compared to patients not referred to CoCM, evidenced small improvements in total cholesterol and increases in BMI. Widespread implementation of integrated mental health treatment may have implications for population cardiovascular health.

This study sought to compare implementation and effectiveness outcomes for two delivery strategies of a digital HIV prevention intervention: community-based organization (CBO) versus direct-to-consumer (DTC). A type III hybrid effectiveness–implementation trial compared two implementation strategies of Keep It Up! (KIU! ) for young men who have sex with men (YMSM) (n = 2124). Data were collected at baseline and 12-weeks in 2019–2023. DTC enrolled more participants, but CBO recruited more Black (11.7 https://kiu.northwestern.edu/ . Trial registration: NCT03896776 (date of registration: 04/01/2019).

There is a dearth of research exploring sleep difficulties/satisfaction among ethnically-diverse younger cannabis consumers - a vulnerable population with increasing cannabis use. We examined self-reported sleep difficulties by cannabis use (CU) and Hispanic origin. Data are from 18-to-35-year-olds in the Herbal Heart Study cohort (N=200). Cannabis was self-reported, confirmed via urine, and categorized as low-to-moderate (< 20-days/month) and high-frequency (>20-days/month). Self-reported difficulties were falling (DFA) and staying asleep (DSA) over the past month; “poor sleep” included those with either. Sleep satisfaction was measured via WHO Quality of Life and classified as satisfied, neutral, or unsatisfied. Differences by CU and ethnicity were assessed using Chi-squared/Fisher exact tests. Of participants (mean age: 25.2 years, SD=4.8; 65.0% female, 54.5% Hispanic), 63.0% were CUs; 74.6% of which were high-frequency CUs. Overall, 23.0% reported DFA, 16.5% DSA, 29.0% poor sleep and 23.0% reported sleep dissatisfaction. DSA was more prevalent among CUs vs non-CUs (20.6% vs. 9.5%, p = 0.04), particularly among Hispanics (24.0% vs. 8.6%, p = 0.049). Frequent CUs reported higher DSA than non-frequent CUs (26.0% vs. 3.1%, p < 0.01). There were no differences found by sex. Frequent cannabis consumers had higher prevalence difficulty staying asleep, particularly among Hispanic/Latinos. These findings underscore the need in further investigating the relationship between frequent cannabis use, sleep disturbances, and racial/ethnic disparities. R01HL153467; T37MD008647; T32HL166609

Despite the availability of preventive interventions to address mental health and drug use among Hispanic adolescents, few are implemented in real-world settings. Favorable attitudes towards evidence-based practices and a better implementation climate can facilitate the successful execution of interventions in real-word settings to ameliorate health disparities among Hispanic youth. The purpose of this study was to investigate how implementation climate influences attitudes toward a mental health and drug use preventive intervention for Hispanic families at the individual and clinic level. Participants included 73 clinic personnel from 18 primary care clinics that were part of an effectiveness-implementation hybrid Type 1 study in South Florida. Clinic personnel completed the Implementation Climate Scale and Evidence-based Practice Attitude Scale. Using hierarchal linear modeling, we examined: (1) whether individual differences in implementation climate were associated with individual attitudes towards an evidence-based practice within clinics, and (2) whether clinic-level differences in mean implementation climate were associated with clinic-level differences in attitudes towards an evidence-based practice. At the individual level, there was a significant positive relationship between individual implementation climate and attitudes toward the evidence-based practice. Implementation climate varied significantly among individuals. At the clinic level, clinics with higher average implementation climate did not show significantly different average attitudes towards the mental health and drug use preventive intervention. Understanding implementation climate and attitudes toward evidence-based practices can inform tailored implementation strategies for the unique needs of primary care settings to address drug use and mental health disparities among Hispanic youth.

Rollout designs, which include stepped wedge designs, are defined by staggered implementation of new or alternative programs or services. Critiques of stepped wedge and other rollout designs have raised concerns regarding the confounding of true implementation or program effects with unrelated, global changes in service delivery, with some recommending they only be used when traditional parallel-group designs are not practicable. However, rollout designs may sometimes be more suitable than traditional parallel group designs for ethical, scientific, or practical reasons. As investigators involved in several recent rollout trials, we define and provide rationale for and examples of stepped wedge and the larger class of rollout designs, in which all participating units receive a new program or service implementation. Staged implementation in a rollout design may be necessary when denying, rather than delaying, implementation of a known effective service is ethically unacceptable. Scientifically, stepped wedge has increased statistical power relative to an equivalent parallel group design, and some rollout designs have the capability to compare different phases of implementation and sustainment. A rollout design may be practically necessary either because of limited resources and other logistical challenges or community requirements that no site serve as a control. Examples of completed and ongoing rollout trials illustrate how these ethical, scientific, and practical considerations influenced trial designs. Stepped wedge and other rollout trial designs may be well suited to evaluation of implementation strategies or policy changes. In implementation trials, rollout designs may be necessary for practical reasons, may be required for ethical reasons, and may be preferred for scientific reasons. We summarize when such rollout designs have advantages and drawbacks.

Youth internalizing symptoms (i.e., depression and anxiety), suicide ideation and attempts have been rising in recent years, including among Hispanics. Disparities in mental healthcare are concerning and require intervention, ideally prevention or early intervention. Familias Unidas is a culturally-syntonic, family-centered intervention effective in reducing youth drug use and sexual risk, with evidence of unanticipated effects on internalizing symptoms. This paper describes the systematic process used to adapt the eHealth version of the Familias Unidas intervention to more directly address internalizing symptoms and suicide risk in preparation for an effectiveness-implementation hybrid trial for youth with elevated internalizing symptoms, a history of suicide ideation/attempts, or poor parent-youth communication. The resulting eHealth Familias Unidas Mental Health intervention is described. Guided by a 4-phase framework, the steps in the adaptation process involved: assessment of the community and intervention delivery setting (pediatric primary care clinics); integration of previous intervention research, including intervention mechanisms of action; and expert and community consultation via focus groups. Focus group analyses showed that youth and parents perceived that the intervention was helpful. Their feedback was categorized into themes that were used to directly target mental health by addressing technology use, parent mental health, and social support. Effective and scalable preventive interventions are needed to address mental health disparities. The systematic adaptation process described in this paper is an efficient approach to expanding interventions while maintaining known, empirical and theoretical mechanisms of action. Findings from the ongoing effectiveness-implementation trial will be critical.

Offering patients medications for opioid use disorder (MOUD) is the standard of care for opioid use disorder (OUD), but an estimated 75%-90% of people with OUD who could benefit from MOUD do not receive medication. Payment policy, defined as public and private payers' approaches to covering and reimbursing providers for MOUD, is 1 contributor to this treatment gap. We conducted a policy analysis and qualitative interviews (n = 21) and surveys (n = 31) with US MOUD payment policy experts to characterize MOUD insurance coverage across major categories of US insurers and identify opportunities for reform and innovation. Traditional Medicare, Medicare Advantage, and Medicaid all provide coverage for at least 1 formulation of buprenorphine, naltrexone, and methadone for OUD. Private insurance coverage varies by carrier and by plan, with methadone most likely to be excluded. The experts interviewed cautioned against rigid reimbursement models that force patients into one-size-fits-all care and endorsed future development and adoption of value-based MOUD payment models. More than 70% of experts surveyed reported that Medicare, Medicaid, and private insurers should increase payment for office- and opioid treatment program-based MOUD. Validation of MOUD performance metrics is needed to support future value-based initiatives.

BACKGROUND:The Collaborative Care Model (CoCM) is an evidence-based mental health treatment in primary care. A greater understanding of the determinants of successful CoCM implementation, particularly the characteristics of multi-level implementers, is needed. METHODS:This study was a process evaluation of the Collaborative Behavioral Health Program (CBHP) study (NCT04321876) in which CoCM was implemented in 11 primary care practices. CBHP implementation included screening for depression and anxiety, referral to CBHP, and treatment with behavioral care managers (BCMs). Interviews were conducted 4- and 15-months post-implementation with BCMs, practice managers, and practice champions (primary care clinicians). We used framework-guided rapid qualitative analysis with the Consolidated Framework for Implementation Research, Version 2.0, focused on the Individuals domain, to analyze response data. These data represented the roles of Mid-Level Leaders (practice managers), Implementation Team Members (clinicians, support staff), Innovation Deliverers (BCMs), and Innovation Recipients (primary care/CBHP patients) and their characteristics (i.e., Need, Capability, Opportunity, Motivation). RESULTS:Mid-level leaders (practice managers) were enthusiastic about CBHP (Motivation), appreciated integrating mental health services into primary care (Need), and had time to assist clinicians (Opportunity). Although CBHP lessened the burden for implementation team members (clinicians, staff; Need), some were hesitant to reallocate patient care (Motivation). Innovation deliverers (BCMs) were eager to deliver CBHP (Motivation) and confident in assisting patients (Capability); their opportunity to deliver CBHP could be limited by clinician referrals (Opportunity). Although CBHP alleviated barriers for innovation recipients (patients; Need), it was difficult to secure services for those with severe conditions (Capability) and certain insurance types (Opportunity). CONCLUSIONS:Overall, respondents favored sustaining CoCM and highlighted the positive impacts on the practice, health care team, and patients. Participants emphasized the benefits of integrating mental health services into primary care and how CBHP lessened the burden on clinicians while providing patients with comprehensive care. Barriers to CBHP implementation included ensuring appropriate patient referrals, providing treatment for patients with higher-level needs, and incentivizing clinician engagement. Future CoCM implementation should include strategies focused on education and training, encouraging clinician buy-in, and preparing referral paths for patients with more severe conditions or diverse needs. TRIAL REGISTRATION:ClinicalTrials.gov(NCT04321876). Registered: March 25,2020. Retrospectively registered.

Implementation strategies can help support the adoption and implementation of health interventions that are appropriate for a local context and acceptable to decision makers and community members. Implementation strategies should be designed to handle the complexity of the multi-level, dynamic contexts in which interventions are implemented. Systems science theories and methods explicitly attend to complexity and can be valuable for specifying implementation strategies. Group Model Building (GMB) combines research partner engagement strategies with systems science to support researchers’ and partners’ learning about complex problems and to identify solutions through consensus. This paper specifies how GMB can operationalize implementation strategies — methods for supporting evidence implementation in real-world practice — and describes how GMB can aid in selecting and tailoring both health interventions and implementation strategies. A case study in child maltreatment prevention planning is provided to illustrate how GMB was used to specify the “actions” — strategy activities — for three implementation strategies (conduct local consensus discussions; build a coalition; model and simulate change) during the earliest implementation phases, with the goal of supporting intervention selection decisions. Examples are provided of generalizable research products that can be produced concurrently through GMB, in addition to contextually-driven implementation support. Participants (n = 8) were engaged over four sessions using tailored GMB activities. Participants generated a qualitative system dynamics model that described their theory of change for how to prevent child maltreatment in their communities. This theory of change reflected a dynamic understanding of the interconnected determinants of child maltreatment. GMB was acceptable to participants and resulted in products that could be used for implementation planning (e.g., to model and simulate change) and future research. GMB fostered trust and idea sharing between participants. GMB can facilitate learning about which outcomes are (or are not) impacted by interventions, which resources and approaches are required for quality implementation (e.g., implementation strategies), and tradeoffs in outcomes and resources between interventions. GMB also provides a structured, effective process to generate a shared implementation vision amongst participants. Lessons learned include methods for developing trust with and between participants, and the need for researchers to tailor GMB actions for participant and project needs.

IntroductionGlobally, overdose deaths increased near the beginning of the COVID-19 pandemic, which created availability and access barriers to addiction and social services. Especially in times of a crisis like a pandemic, local exposures, service availability and access, and system responses have major influence on people who use drugs. For policy makers to be effective, an understanding at the local level is needed.MethodsThis retrospective epidemiologic study from 2019 through 2021 compares immediate and 20-months changes in overdose deaths from the pandemic start to 16 months before its arrival in Pinellas County, FL We examine toxicologic death records of 1,701 overdoses to identify relations with interdiction, and service delivery.ResultsThere was an immediate 49% increase (95% CI 23–82%, p < 0.0001) in overdose deaths in the first month following the first COVID deaths. Immediate increases were found for deaths involving alcohol (171%), heroin (108%), fentanyl (78%), amphetamines (55%), and cocaine (45%). Overdose deaths remained 27% higher (CI 4–55%, p = 0.015) than before the pandemic through 2021.Abrupt service reductions occurred when the pandemic began: in-clinic methadone treatment dropped by two-thirds, counseling by 38%, opioid seizures by 29%, and drug arrests by 56%. Emergency transport for overdose and naloxone distributions increased at the pandemic onset (12%, 93%, respectively) and remained higher through 2021 (15%, 377%,). Regression results indicate that lower drug seizures predicted higher overdoses, and increased 911 transports predicted higher overdoses. The proportion of excess overdose deaths to excess non-COVID deaths after the pandemic relative to the year before was 0.28 in Pinellas County, larger than 75% of other US counties.ConclusionsService and interdiction interruptions likely contributed to overdose death increases during the pandemic. Relaxing restrictions on medical treatment for opioid addiction and public health interventions could have immediate and long-lasting effects when a major disruption, such as a pandemic, occurs. County level data dashboards comprised of overdose toxicology, and interdiction and service data, can help explain changes in overdose deaths. As a next step in predicting which policies and practices will best reduce local overdoses, we propose using simulation modeling with agent-based models to examine complex interacting systems.

South Asian Americans bear a high burden of atherosclerotic cardiovascular disease (ASCVD), but little is known about the sustainability of evidence-based interventions (EBI) to prevent ASCVD in this population. Using community-based participatory research, we previously developed and implemented the South Asian Healthy Lifestyle Intervention (SAHELI), a culturally-adapted EBI targeting diet, physical activity, and stress management. In this study, we use the Integrated Sustainability Framework to investigate multisectoral partners’ perceptions of organizational factors influencing SAHELI sustainability and strategies for ensuring sustainability. From 2022 to 2023, we conducted a mixed-methods study (quant- > QUAL) with 17 SAHELI partners in the Chicago area. Partners’ settings included: community organization, school district, public health department, and healthcare system. Descriptive statistics summarized quantitative results. Two coders used a hybrid thematic analysis approach to identify qualitative themes. Qualitative and quantitative data were integrated and analyzed using mixed methods. Surveys (score range 1–5: higher scores indicate facilitators; lower scores indicate barriers) indicated SAHELI sustainability facilitators to be its “responsiveness to community values and needs” (mean = 4.9). Barriers were “financial support” (mean = 3.5), “infrastructure/capacity to support sustainment” (mean = 4.2), and “implementation leadership” (mean = 4.3). Qualitative findings confirmed quantitative findings that SAHELI provided culturally-tailored cardiovascular health education responsive to the needs of the South Asian American community, increased attention to health issues, and transformed perceptions of research among community members. Qualitative findings expanded upon quantitative findings, showing that the organizational fit of SAHELI was a facilitator to sustainability while competing priorities were barriers for partners from the public health department and health system. Partners from the public health department and health system discussed challenges in offering culturally-tailored programming exclusively for one targeted population. Sustainability strategies envisioned by partners included: transitioning SAHELI to a program delivered by community members; integrating components of SAHELI into other programs; and expanding SAHELI to other populations. Modifications made to SAHELI (i.e., virtual instead of in-person delivery) had both positive and negative implications for sustainability. This study identifies common sustainability barriers and facilitators across different sectors, as well as those specific to certain settings. Aligning health equity interventions with community needs and values, organizational activities, and local context and resources is critical for sustainability. Challenges also arise from balancing the needs of specific populations against providing programming for broader audiences.

The historic momentum from national conversations on the roots and current impacts of racism in the USA presents an incredible window of opportunity for prevention scientists to revisit how common theories, measurement tools, methodologies, and interventions can be radically re-envisioned, retooled, and rebuilt to dismantle racism and promote equitable health for minoritized communities. Recognizing this opportunity, the NIH-funded Prevention Science and Methodology Group (PSMG) launched a series of presentations focused on the role of Prevention Science to address racism and discrimination guided by a commitment to social justice and health equity. The current manuscript aims to advance the field of Prevention Science by summarizing key issues raised during the series’ presentations and proposing concrete research priorities and steps that hold promise for promoting health equity by addressing systemic racism. Being anti-racist is an active practice for all of us, whether we identify as methodologists, interventionists, practitioners, funders, community members, or an intersection of these identities. We implore prevention scientists and methodologists to take on these conversations with us to promote science and practice that offers every life the right to live in a just and equitable world.

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo.MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach.ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption.ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Community violence and crime are significant public health problems with serious and lasting effects on young people, families, and communities. This violence and crime have significant ripple effects, affecting not just those who are directly physically injured, but also those who witness violent episodes, those who have friends or loved ones killed or injured, and those who must everyday navigate streets that they know have been frequent sites of serious violence and crime. The current study presents evidence of the impact that a data-driven, collective impact approach — the Communities that Care prevention system — can have on violence and crime outcomes within a large urban, high-burden community. Established as one of the national Youth Violence Prevention Centers (YVPC) funded by the Centers for Disease Control and Prevention, the Chicago Center for Youth Violence Prevention is among the first to implement the CTC approach in a large, urban community. The current study’s findings show reductions in violence (i.e., aggravated assaults and robberies) in the Bronzeville community, compared to similar communities in Chicago.

IntroductionLong-acting injectable (LAI) antipsychotic medications and clozapine are effective yet underutilized medical therapies in early intervention services. The purpose of this study was to conduct a pre-implementation evaluation of contextual determinants of early intervention programs to implement innovations optimizing LAI antipsychotic and clozapine use within a shared decision-making model.MethodsSemi-structured interviews explored barriers and facilitators to implementing LAI antipsychotics and clozapine in early intervention services. Participants were: prescribers (n = 2), non-prescribing clinicians (n = 5), administrators (n = 3), clients (n = 3), and caregivers (n = 3). Interviews were structured and analyzed using the Consolidated Framework for Implementation Research (CFIR 2.0).ResultsParticipants were supportive of using LAI antipsychotics, despite barriers (e.g., transportation, insurance coverage), while most were unfamiliar with clozapine (Innovation). Critical incidents (e.g., COVID-19) did not interfere with implementation, while barriers included lack of performance measures; stigma affecting willingness to take medication; and clozapine considered to be a “last resort” (Outer Setting). Treatment culture was described as client-centered and collaborative, and most participants indicated LAI antipsychotic use was compatible with clinic workflows, but some were in need of resources (e.g., individuals trained to administer LAI antipsychotics; Inner Setting). Participants on the healthcare team expressed confidence in their roles. Family education and collaborative decision-making were recommended to improve client/family engagement (Individuals). Participants related the importance of tracking medication compliance, addressing client concerns, and providing prescribers with updated guidelines on evidence-based treatment (Implementation Process).DiscussionResults may guide implementation strategy selection for future programs seeking to optimize the use of LAI antipsychotics and clozapine for early-phase schizophrenia, when appropriate.

Background: Social-emotional risk for subsequent behavioral health problems can be identified at toddler age, a period where prevention has a heightened impact. This study aimed to meaningfully engage pediatric clinicians, given the emphasis on health promotion and broad reach of primary care, to prepare an Implementation Research Logic Model to guide the implementation of a screening and referral process for toddlers with elevated social-emotional risk. Method: Using an adaptation of a previously published community partner engagement method, six pediatricians from community health centers (CHCs) comprised a Clinical Partner Work Group. The group was engaged in identifying determinants (barriers/facilitators), selecting and specifying strategies, strategy-determinant matching, a modified Delphi approach for strategy prioritization, and user-centered design methods. The data gathered from individual interviews, two group sessions, and a follow-up survey resulted in a completed Implementation Research Logic Model. Results: The Clinical Partner Work Group identified 16 determinants, including barriers (e.g., patient access to electronic devices) and facilitators (e.g., clinician buy-in). They then selected and specified 14 strategies, which were prioritized based on ratings of feasibility, effectiveness, and priority. The highest-rated strategies (e.g., integration of the screener into the electronic health record) provided coverage of all identified barriers and comprised the primary implementation strategy "package" to be used and tested. Conclusions: Clinical partners provided important context and insights for implementation strategy selection and specification to support the implementation of social-emotional risk screening and referral in pediatric primary care. The methodology described herein can improve partner engagement in implementation efforts and increase the likelihood of success.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

Research ObjectivesTo evaluate changes in researchers’ attitudes regarding researcher-clinician collaboration after colocation of work environments and implementation of team-focused strategies.DesignProspective, longitudinal, observational study using an online survey at three time-points: (T1) 1-2 months pre-, (T2) 7-9 months post-, and (T3) 2.5 years post-intervention.SettingUrban inpatient rehabilitation facility.ParticipantsResponders were ∼150 clinical and non-clinical principal investigators (PIs) and other research staff.InterventionsPhysical move to a new hospital and team-focused strategies to promote clinician-researcher collaborations.Main Outcome MeasuresAdapted Organizational Change Recipients’ Beliefs Scale (OCRBS) measured acceptance of the organizational transition. Additional questions measured perceived benefits of working in proximity and perceived ability to communicate with clinicians. Ordinal logistic regression evaluated change over time and differences among research roles.ResultsOverall response rates were excellent (65%) across all surveys (T1=146; T2=157; T3=179). The number of other research staff increased by 27% from T1 to T3, while the number of clinical and non-clinical PIs remained stable. Clinical PIs were more accepting of organizational changes than non-clinical PIs (OCRBS F=5.34, p=0.005). From T1 to T2, perceived benefits to researchers (OR: 0.49; 95%CI: 0.32, 0.75) and the organization (OR: 0.27, 95%CI: 0.2, 0.4) were less. However, perceived benefits to researchers increased from T1 to T3 (OR: 2.68; 95%CI: 1.8, 4.1). Clinical PIs initially perceived a greater benefit of the transition compared to non-clinical PIs (OR: 2.2; 95%CI 1.1-4.5). From T1 to T3, researchers reported greater eagerness to conduct research in a new area if a clinician identified it as important (OR: 2.4; 95%CI: 1.5, 3.8). Researchers perceived less effective communication with clinicians (OR 0.54 T3-T1; 95%CI: 0.34 – 0.88), without changes in frequency or ease of communication, or opportunity to learn from clinicians.ConclusionsResearcher attitudes regarding collaboration were less positive 7-9 months after the move but rebounded two years later. Both clinical and non-clinical PIs report benefits to working in proximity with clinicians.Author(s) DisclosuresThe authors declare salary from the organization studied with no other relevant conflicts of interest.

Abstract Background Mindfulness-based interventions have been shown to improve psychological outcomes including stress, anxiety, and depression in general population studies. However, effectiveness has not been sufficiently examined in racially and ethnically diverse community-based settings. We will evaluate the effectiveness and implementation of a mindfulness-based intervention on depressive symptoms among predominantly Black women at a Federally Qualified Health Center in a metropolitan city. Methods In this 2-armed, stratified, individually randomized group-treated controlled trial, 274 English-speaking participants with depressive symptoms ages 18–65 years old will be randomly assigned to (1) eight weekly, 90-min group sessions of a mindfulness-based intervention (M-Body), or (2) enhanced usual care. Exclusion criteria include suicidal ideation in 30 days prior to enrollment and regular (>4x/week) meditation practice. Study metrics will be assessed at baseline and 2, 4, and 6 months after baseline, through clinical interviews, self-report surveys, and stress biomarker data including blood pressure, heart rate, and stress related biomarkers. The primary study outcome is depressive symptom score after 6 months. Discussion If M-Body is found to be an effective intervention for adults with depressive symptoms, this accessible, scalable treatment will widely increase access to mental health treatment in underserved, racial/ethnic minority communities. Trial registration ClinicalTrials.gov NCT03620721. Registered on 8 August 2018.

The current special issue of Prevention Science indicates that momentum in using individual participant data (IPD) and integrative data analysis (IDA) to combine and synthesize findings in prevention science has accelerated over the past decade. In this commentary, we focus on two general themes involving methods for harmonizing measures and findings of effect heterogeneity. We describe methods for harmonization as retrospective psychometrics, requiring that we attend to the assumptions necessary for accurate measurement, but adjust our methods given the constraints of working with existing datasets that often involve different measures in different studies. We point to novel approaches for increasing confidence that semantic matching and empirical modeling used in these studies will yield accurate and valid measurements that can be combined in IDA. We also review findings about effect heterogeneity, emphasizing the importance of using etiologic and action theories to identify and evaluate sources of such effects. We note that all of the papers in this issue deserve careful attention, as they illustrate how prevention scientists are approaching the complexities of IDA and exploring novel methods for overcoming its challenges.