Background The wide variety of dissemination and implementation designs now being used to evaluate and improve health systems and outcomes warrants review of the scope, features, and limitations of these designs. Methods This paper is one product of a design workgroup formed in 2013 by the National Institutes of Health to address dissemination and implementation research, and whose members represented diverse methodologic backgrounds, content focus areas, and health sectors. These experts integrated their collective knowledge on dissemination and implementation designs with searches of published evaluations strategies. Results This paper emphasizes randomized and non-randomized designs for the traditional translational research continuum or pipeline, which builds on existing efficacy and effectiveness trials to examine how one or more evidence-based clinical/prevention interventions are adopted, scaled up, and sustained in community or service delivery systems. We also mention other designs, including hybrid designs that combine effectiveness and implementation research, quality improvement designs for local knowledge, and designs that use simulation modeling.
This paper presents findings from an overview of meta-analyses of the effects of prevention and promotion programs to prevent mental health, substance use and conduct problems. The review of 48 meta-analyses found small but significant effects to reduce depression, anxiety, anti-social behavior and substance use. Further, the effects are sustained over time. Meta-analyses often found that the effects were heterogeneous. A conceptual model is proposed to guide the study of moderators of program effects in future meta-analyses and methodological issues in synthesizing findings across preventive interventions are discussed.
BackgroundMuch is to be learned about what implementation strategies are the most beneficial to communities attempting to adopt evidence-based practices. This paper presents outcomes from a randomized implementation trial of Multidimensional Treatment Foster Care (MTFC) in child public service systems in California and Ohio, including child welfare, juvenile justice, and mental health.MethodsFifty-one counties were assigned randomly to one of two different implementation strategies (Community Development Teams (CDT) or independent county implementation strategy (IND)) across four cohorts after being matched on county characteristics. We compared these two strategies on implementation process, quality, and milestone achievements using the Stages of Implementation Completion (SIC) (Implement Sci 6(1):1–8, 2011).ResultsA composite score for each county, combining the final implementation stage attained, the number of families served, and quality of implementation, was used as the primary outcome. No significant difference between CDT and IND was found for the composite measure. Additional analyses showed that there was no evidence that CDT increased the proportion of counties that started-up programs (i.e., placed at least one family in MTFC). For counties that did implement MTFC, those in the CDT condition served over twice as many youth during the study period as did IND. Of the counties that successfully achieved program start-up, those in the CDT condition completed the implementation process more thoroughly, as measured by the SIC. We found no significant differences by implementation condition on the time it took for first placement, achieving competency, or number of stages completed.ConclusionsThis trial did not lead to higher rates of implementation or faster implementation but did provide evidence for more robust implementation in the CDT condition compared to IND implementation once the first family received MTFC services. This trial was successful from a design perspective in that no counties dropped out, even though this study took place during an economic recession. We believe that this methodologic approach of measurement utilizing the SIC, which is comprised of the three dimensions of quality, quantity, and timing, is appropriate for a wide range of implementation and translational studies.Trial registrationTrial ID: NCT00880126 (ClinicalTrials.gov).Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-014-0134-8) contains supplementary material, which is available to authorized users.
This paper presents the first findings of an integrative data analysis of individual-level data from 19 adolescent depression prevention trials (n = 5210) involving nine distinct interventions across 2 years post-randomization. In separate papers, several interventions have been found to decrease the risk of depressive disorders or elevated depressive/internalizing symptoms among youth. One type of intervention specifically targets youth without a depressive disorder who are at risk due to elevated depressive symptoms and/or having a parent with a depressive disorder. A second type of intervention targets two broad domains: prevention of problem behaviors, which we define as drug use/abuse, sexual risk behaviors, conduct disorder, or other externalizing problems, and general mental health. Most of these latter interventions improve parenting or family factors. We examined the shared and unique effects of these interventions by level of baseline youth depressive symptoms, sociodemographic characteristics of the youth (age, sex, parent education, and family income), type of intervention, and mode of intervention delivery to the youth, parent(s), or both. We harmonized eight different measures of depression utilized across these trials and used growth models to evaluate intervention impact over 2 years. We found a significant overall effect of these interventions on reducing depressive symptoms over 2 years and a stronger impact among those interventions that targeted depression specifically rather than problem behaviors or general mental health, especially when baseline symptoms were high. Implications for improving population-level impact are discussed.
The demand for researchers to share their data has increased dramatically in recent years. There is a need to replicate and confirm scientific findings to bolster confidence in many research areas. Data sharing also serves the critical function of allowing synthesis of findings across trials. As innovative statistical methods have helped resolve barriers to synthesis analyses, data sharing and synthesis can help answer research questions that cannot be answered by individual trials alone. However, the sharing of data among researchers remains challenging and infrequent. This article aims to (a) increase support for data sharing and synthesis collaborations among researchers to advance scientific knowledge and (b) provide a model for establishing these collaborations using the example of the ongoing National Institute of Mental Health’s Collaborative Data Synthesis on Adolescent Depression Trials. This study brings together datasets from existing prevention and treatment trials in adolescent depression, as well as researchers and stakeholders, to answer questions about “for whom interventions work” and “by what pathways interventions have their effects.” This is critical to improving interventions, including increasing knowledge about intervention efficacy among minority populations, or what we call “scientific equity.” The collaborative model described is relevant to fields with research questions that can only be addressed by synthesizing individual-level data.
Careful fidelity monitoring and feedback are critical to implementing effective interventions. A wide range of procedures exist to assess fidelity; most are derived from observational assessments (Schoenwald et al, 2013). However, these fidelity measures are resource intensive for research teams in efficacy/effectiveness trials, and are often unattainable or unmanageable for the host organization to rate when the program is implemented on a large scale. We present a first step towards automated processing of linguistic patterns in fidelity monitoring of a behavioral intervention using an innovative mixed methods approach to fidelity assessment that uses rule-based, computational linguistics to overcome major resource burdens. Data come from an effectiveness trial of the Familias Unidas intervention, an evidence-based, family-centered preventive intervention found to be efficacious in reducing conduct problems, substance use and HIV sexual risk behaviors among Hispanic youth. This computational approach focuses on “joining,” which measures the quality of the working alliance of the facilitator with the family. Quantitative assessments of reliability are provided. Kappa scores between a human rater and a machine rater for the new method for measuring joining reached .83. Early findings suggest that this approach can reduce the high cost of fidelity measurement and the time delay between fidelity assessment and feedback to facilitators; it also has the potential for improving the quality of intervention fidelity ratings.
CONTEXT: More than 4 decades of research indicate that parenting interventions are effective at preventing and treating mental, emotional, and behavioral disorders in children and adolescents. Pediatric primary care is a viable setting for delivery of these interventions.OBJECTIVE: Previous meta-analyses have shown that behavioral interventions in primary care can improve clinical outcomes, but few reviews have been focused specifically on the implementation of parenting interventions in primary care. We aimed to fill this gap.DATA SOURCES: We reviewed 6532 unique peer-reviewed articles published in PubMed, the Cumulative Index to Nursing and Allied Health Literature, and PsycInfo.STUDY SELECTION: Articles were included if at least part of the intervention was delivered in or through primary care; parenting was targeted; and child-specific mental, emotional, and behavioral health outcomes were reported.DATA EXTRACTION: Articles were reviewed in Covidence by 2 trained coders, with a third coder arbitrating discrepancies.RESULTS: In our review of 40 studies, most studies were coded as a primary. Few researchers collected implementation outcomes, particularly those at the service delivery system level.LIMITATIONS: Including only published articles could have resulted in underrepresentation of implementation-related data.CONCLUSIONS: Parenting interventions delivered and implemented with fidelity in pediatric primary care could result in positive and equitable impacts on mental, emotional, and behavioral health outcomes for both parents and their children. Future research on the implementation strategies that can support adoption and sustained delivery of parenting interventions in primary care is needed if the field is to achieve population-level impact.
To evaluate the cost-effectiveness of multigene testing (CYP2C19, SLCO1B1, CYP2C9, VKORC1) compared with singlegene testing (CYP2C19) and standard of care (no genotyping) in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) from Medicare's perspective.Methods: A hybrid decision tree/Markov model was developed to simulate patients post-PCI for ACS requiring antiplatelet therapy (CYP2C19 to guide antiplatelet selection), statin therapy (SLCO1B1 to guide statin selection), and anticoagulant therapy in those that develop atrial fibrillation (CYP2C9/VKORC1 to guide warfarin dose) over 12 months, 24 months, and lifetime. The primary outcome was cost (2016 US dollar) per quality-adjusted life years (QALYs) gained. Costs and QALYs were discounted at 3% per year. Probabilistic sensitivity analysis (PSA) varied input parameters (event probabilities, prescription costs, event costs, health-state utilities) to estimate changes in the cost per QALY gained.Results: Base-case-discounted results indicated that the cost per QALY gained was $59 876, $33 512, and $3780 at 12 months, 24 months, and lifetime, respectively, for multigene testing compared with standard of care. Single-gene testing was dominated by multigene testing at all time horizons. PSA-discounted results indicated that, at the $50 000/QALY gained willingness-to-pay threshold, multigene testing had the highest probability of cost-effectiveness in the majority of simulations at 24 months (61%) and over the lifetime (81%).Conclusions: On the basis of projected simulations, multigene testing for Medicare patients post-PCI for ACS has a higher probability of being cost-effective over 24 months and the lifetime compared with single-gene testing and standard of care and could help optimize medication prescribing to improve patient outcomes.
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