Private Practice, Private Insurance, and Private Pay Mental Health Services: An Understudied Area in Implementation Science.

The private practice setting is understudied. Private practice includes settings in which mental health providers are unaffiliated with healthcare and hospital systems. Private practices may accept insurance (private and sometimes public) or no insurance (private pay). Increasing attention to this setting is critical to facilitating equitable access to mental health services, especially given enduring mental health workforce shortages and service waitlists. Further, there have been recent federal government calls to increase mental health and physical healthcare parity and to reduce out-of-pocket patient costs. Implementation science theories, models, frameworks, and methods can help illuminate determinants of private practice service availability and quality (e.g., evidence-based intervention delivery with fidelity), guide evaluation of implementation outcomes such as cost and acceptability of interventions to patients, and identify strategies to mitigate barriers to high-quality, affordable private practice services. This article suggests research questions to begin filling the private practice research gap using an implementation determinants framework - the Consolidated Framework for Implementation Research (CFIR) 2.0. Research questions are proposed across CFIR domains: outer context (e.g., policies impacting whether private practices accept insurance); individuals involved (e.g., provider professional experiences; direct-to-consumer marketing impacts on evidence-based intervention demand); innovation characteristics (e.g., appropriateness for private practice); inner context (e.g., organizational characteristics); and implementation processes (e.g., innovation sustainability). The illustrative research questions aim to begin a conversation amongst researchers and funders. Bringing an implementation science lens to the private practice context has the potential to improve the quality and affordability of mental health care for many.

Methodological Strategies for Prospective Harmonization of Studies: Application to 10 Distinct Outcomes Studies of Preventive Interventions Targeting Opioid Misuse.

The Helping to End Addiction Long-Term (HEAL) Prevention Cooperative (HPC) is rapidly developing 10 distinct evidence-based interventions for implementation in a variety of settings to prevent opioid misuse and opioid use disorder. One HPC objective is to compare intervention impacts on opioid misuse initiation, escalation, severity, and disorder and identify whether any HPC interventions are more effective than others for types of individuals. It provides a rare opportunity to prospectively harmonize measures across distinct outcomes studies. This paper describes the needs, opportunities, strategies, and processes that were used to harmonize HPC data. They are illustrated with a strategy to measure opioid use that spans the spectrum of opioid use experiences (termed involvement) and is composed of common "anchor items" ranging from initiation to symptoms of opioid use disorder. The limitations and opportunities anticipated from this approach to data harmonization are reviewed. Lastly, implications for future research cooperatives and the broader HEAL data ecosystem are discussed.

Re-envisioning, Retooling, and Rebuilding Prevention Science Methods to Address Structural and Systemic Racism and Promote Health Equity.

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.

Exploring Clusters of Risk and Association With Child Maltreatment in North Carolina Counties.

BACKGROUND: Decision makers face challenges in estimating local risk for child maltreatment and how best to prioritize which factors to intervene upon. METHODS: Using US Census and survey data for all US counties (N = 3141), we derived US county profiles characterized by the severity of child maltreatment risk factors observed at the county level, such as parental health, health care access, and economic distress. We estimated how five child maltreatment outcomes would vary across the profiles for North Carolina counties (n = 100): total maltreatment reports (including unsubstantiated and substantiated), substantiated neglect, substantiated abuse, whether services were received, and reported child's race/ethnicity. RESULTS: We derived three profiles of county-level child maltreatment risk: high, moderate, and low risk, denoting that predicted risk factors means within profiles were all high, moderate, or low levels compared to counties in other profiles. One risk factor did not follow this pattern: the drug overdose death rate. It was highest in the moderate-risk profile instead of the high-risk profile, as would have been consistent with other factor levels. Moderate-risk counties had the highest predicted rate of child maltreatment reports, with over 20 more reports per 10,000 residents compared to low-risk counties (95% CI, 1.38, 38.86). LIMITATIONS: We included only factors for which aggregate, county-level estimates were available, thus limiting inclusion of all relevant factors. CONCLUSIONS: Results suggest the need for increased family-based services and interventions that reduce risk factors such as economic distress and drug overdose deaths. We discuss the implications for tailoring county efforts to prevent child maltreatment.

Cost-Effectiveness of Multigene Pharmacogenetic Testing in Patients With Acute Coronary Syndrome After Percutaneous Coronary Intervention.

OBJECTIVE: To evaluate the cost-effectiveness of multigene testing (CYP2C19, SLCO1B1, CYP2C9, VKORC1) compared with single-gene 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.

Developing a Multi-Criteria Decision Analysis Tool to Support the Adoption of Evidence-Based Child Maltreatment Prevention Programs.

Decision-makers need to consider a range of factors when selecting evidence-based programs (EBPs) for implementation, which can be especially challenging when addressing complex issues such as child maltreatment prevention. Multi-criteria decision analysis (MCDA) frameworks and tools are useful for evaluating such complex decisions. We describe the development and testing of the first MCDA tool to compare EBPs for child neglect prevention. To develop the tool, we engaged stakeholders (n = 8) to define the problem and identify 13 criteria and associated weights. In a pilot study, we tested the MCDA tool with decision-makers (n = 11) who were asked to rank three evidence-based child neglect prevention interventions both with and without the tool. The MCDA's weighted sum intervention ranking differed from the ranking without the tool in the majority of the sample (55%). Decision-makers provided guidance on criteria that should be clarified or added, resulting in 16 criteria in an iterated tool. The most frequent criterion suggestions related to community acceptance of the intervention, health equity, implementation supports, and sustainability. Decision-maker feedback guided user interface refinements. The MCDA tool was generally well accepted by decision-makers due to their trust in the stakeholder engagement process. More research is needed to understand the acceptability of MCDA approaches in additional contexts and whether EBPs adopted with decision support have different population health impacts compared with EBPs adopted without support. MCDA tools could facilitate evidence-based responses to federal policy and funding opportunities such as the Families First Preventive Services Act.

Effects of the Mental Health Parity and Addiction Equity Act on Specialty Outpatient Behavioral Health Spending and Utilization.

BACKGROUND: For decades, insurance plans in the United States have applied more restrictive treatment limits and higher cost-sharing burdens for mental health and substance use treatments compared to physical health treatments. The Mental Health Parity and Addiction Equity Act (MHPAEA) required health plans that offer mental health and substance use benefits to offer them at parity with physical health benefits starting in January 2010. AIMS OF THE STUDY: To determine the effect of MHPAEA on out-of-pocket spending and utilization of outpatient specialty behavioral health services. METHODS: The proportion of individuals with at least one outpatient specialty behavioral health visit, the average number of visits among those with any behavioral health visit, and the proportion of behavioral health spending paid out-of-pocket were obtained from the nationally-representative Medical Expenditure Panel Survey (MEPS) for the years 2006 to 2013. Difference-in-differences models were estimated comparing individuals with employer-sponsored insurance to those with Medicaid, Medicare, or who were uninsured. RESULTS: Out-of-pocket share of spending was lowest among Medicaid (2.0%) and highest among the uninsured (22%), followed by the employer group (13%). Individuals in Medicaid had the highest proportion of any behavioral health visit (11%) and the uninsured had the lowest (2.4%). Among those with any behavioral health visits, the average number of visits was similar across groups. Our primary and sensitivity analyses suggest MHPAEA did not lead to changes in utilization or spending on specialty outpatient behavioral visits for individuals with employer-sponsored insurance compared to other groups. DISCUSSION: Potential reasons for MHPAEA's apparent lack of effect are that health plans were already at parity before the law's passage, that many health plans continue to be out of compliance with the law, that concurrent changes in plans' cost-sharing blunted the law's effects, and that other barriers to behavioral health service use continue to limit utilization. While our study cannot provide direct evidence of these mechanisms, we review existing evidence in support of each of them. Our study had several limitations. We cannot test definitively whether the difference-in-differences assumption was violated or fully control for time-varying differences between groups. We attempt to address this by using multiple control groups and presenting evidence of parallel trends before MHPAEA implementation. Second, because our data do not have state identifiers, we cannot control for which states had existing mental health parity laws. Third, a nationally representative analysis may mask substantial heterogeneity for affected subgroups. IMPLICATIONS FOR HEALTH POLICIES: We find no evidence MHPAEA substantially affected behavioral health utilization or out-of-pocket spending. Federal parity legislation alone is likely insufficient to address barriers to behavioral health affordability and access.

Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis.

Individual participant data (IPD) meta-analysis is a meta-analysis in which the individual-level data for each study are obtained and used for synthesis. A common challenge in IPD meta-analysis is when variables of interest are measured differently in different studies. The term harmonization has been coined to describe the procedure of placing variables on the same scale in order to permit pooling of data from a large number of studies. Using data from an IPD meta-analysis of 19 adolescent depression trials, we describe a multiple imputation approach for harmonizing 10 depression measures across the 19 trials by treating those depression measures that were not used in a study as missing data. We then apply diagnostics to address the fit of our imputation model. Even after reducing the scale of our application, we were still unable to produce accurate imputations of the missing values. We describe those features of the data that made it difficult to harmonize the depression measures and provide some guidelines for using multiple imputation for harmonization in IPD meta-analysis.

Preventing Youth Internalizing Symptoms Through the Familias Unidas Intervention: Examining Variation in Response.

Prevention programs that strengthen parenting and family functioning have been found to reduce poor behavioral outcomes in adolescents, including substance use, HIV risk, externalizing and internalizing problems. However, there is evidence that not all youth benefit similarly from these programs. Familias Unidas is a family-focused intervention designed to prevent substance use and sexual risk among Hispanic youth and has recently demonstrated unanticipated reductions in internalizing symptoms for some youth. This paper examines variation in intervention response for internalizing symptoms using individual-level data pooled across four distinct Familias Unidas trials: (1) 266 eighth grade students recruited from the general school population; (2) 160 ninth grade students from the general school population; (3) 213 adolescents with conduct, aggression, and/or attention problems; and (4) 242 adolescents with a delinquency history. Causal inference growth mixture modeling suggests a three-class model. The two largest classes represent youth with low (60 %) and medium (27 %) internalizing symptoms at baseline, and both intervention and control participants show reductions in internalizing symptoms. The third class (13 %) represents youth with high levels of baseline internalizing symptoms who remain at steady levels of internalizing symptoms when exposed to the intervention, but who experience an increase in symptoms under the control condition. Female gender, low baseline levels of parent-adolescent communication, and older age were associated with membership in the high-risk class. These synthesis analyses involving a large sample of youth with varying initial risk levels represent a further step toward strengthening our knowledge of preventive intervention response and improving preventive interventions.

Two-Year Impact of Prevention Programs on Adolescent Depression: an Integrative Data Analysis Approach.

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.

An Overview of Research and Evaluation Designs for Dissemination and Implementation.

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. This article is one product of a design workgroup that was 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. This article emphasizes randomized and nonrandomized 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.

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials.

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings.

Toward scientific equity for the prevention of depression and depressive symptoms in vulnerable youth.

Certain subgroups of youth are at high risk for depression and elevated depressive symptoms, and experience limited access to quality mental health care. Examples are socioeconomically disadvantaged, racial/ethnic minority, and sexual minority youth. Research shows that there are efficacious interventions to prevent youth depression and depressive symptoms. These preventive interventions have the potential to play a key role in addressing these mental health disparities by reducing youth risk factors and enhancing protective factors. However, there are comparatively few preventive interventions directed specifically to these vulnerable subgroups, and sample sizes of diverse subgroups in general prevention trials are often too low to assess whether preventive interventions work equally well for vulnerable youth compared to other youth. In this paper, we describe the importance and need for "scientific equity," or equality and fairness in the amount of scientific knowledge produced to understand the potential solutions to such health disparities. We highlight possible strategies for promoting scientific equity, including the following: increasing the number of prevention research participants from vulnerable subgroups, conducting more data synthesis analyses and implementation science research, disseminating preventive interventions that are efficacious for vulnerable youth, and increasing the diversity of the prevention science research workforce. These strategies can increase the availability of research evidence to determine the degree to which preventive interventions can help address mental health disparities. Although this paper utilizes the prevention of youth depression as an illustrative case example, the concepts are applicable to other health outcomes for which there are disparities, such as substance use and obesity.

Blending Qualitative and Computational Linguistics Methods for Fidelity Assessment: Experience with the Familias Unidas Preventive Intervention.

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 and Garland, Psycholog Assess 25:146-156, 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 0.83. 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.

Overview of meta-analyses of the prevention of mental health, substance use, and conduct problems.

This review 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 changes that reduce depression, anxiety, antisocial behavior, and substance use. Furthermore, the results were 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.

Who's "in the room where it happens"? A taxonomy and five-step methodology for identifying and characterizing policy actors.

BACKGROUND: Engaging policy actors in research design and execution is critical to increasing the practical relevance and real-world impact of policy-focused dissemination and implementation science. Identifying and selecting which policy actors to engage, particularly actors involved in "Big P" public policies such as laws, is distinct from traditional engaged research methods. This current study aimed to develop a transparent, structured method for iteratively identifying policy actors involved in key policy decisions-such as adopting evidence-based interventions at systems-scale-and to guide implementation study sampling and engagement approaches. A flexible policy actor taxonomy was developed to supplement existing methods and help identify policy developers, disseminators, implementers, enforcers, and influencers. METHODS: A five-step methodology for identifying policy actors to potentially engage in policy dissemination and implementation research was developed. Leveraging a recent federal policy as a case study-The Family First Prevention Services Act (FFPSA)-publicly available documentation (e.g., websites, reports) were searched, retrieved, and coded using content analysis to characterize the organizations and individual policy actors in the "room" during policy decisions. RESULTS: The five steps are as follows: (1) clarify the policy implementation phase(s) of interest, (2) identify relevant proverbial or actual policymaking "rooms," (3) identify and characterize organizations in the room, (4) identify and characterize policy actors in the "room," and (5) quantify (e.g., count actors across groups), summarize, and compare "rooms" to develop or select engagement approaches aligned with the "room" and actors. The use and outcomes of each step are exemplified through the FFPSA case study. CONCLUSIONS: The pragmatic and transparent policy actor identification steps presented here can guide researchers' methods for continuous sampling and successful policy actor engagement. Future work should explore the utility of the proposed methods for guiding selection and tailoring of engagement and implementation strategies (e.g., research-policy actor partnerships) to improve both "Big P" and "little p" (administrative guidelines, procedures) policymaking and implementation in global contexts.

A structured approach to applying systems analysis methods for examining implementation mechanisms.

BACKGROUND: It is challenging to identify and understand the specific mechanisms through which an implementation strategy affects implementation outcomes, as implementation happens in the context of complex, multi-level systems. These systems and the mechanisms within each level have their own dynamic environments that change frequently. For instance, sequencing may matter in that a mechanism may only be activated indirectly by a strategy through another mechanism. The dosage or strength of a mechanism may vary over time or across different health care system levels. To elucidate the mechanisms relevant to successful implementation amidst this complexity, systems analysis methods are needed to model and manage complexity. METHODS: The fields of systems engineering and systems science offer methods-which we refer to as systems analysis methods-to help explain the interdependent relationships between and within systems, as well as dynamic changes to systems over time. When applied to studying implementation mechanisms, systems analysis methods can help (i) better identify and manage unknown conditions that may or may not activate mechanisms (both expected mechanisms targeted by a strategy and unexpected mechanisms that the methods help detect) and (ii) flexibly guide strategy adaptations to address contextual influences that emerge after the strategy is selected and used. RESULTS: In this paper, we delineate a structured approach to applying systems analysis methods for examining implementation mechanisms. The approach includes explicit steps for selecting, tailoring, and evaluating an implementation strategy regarding the mechanisms that the strategy is initially hypothesized to activate, as well as additional mechanisms that are identified through the steps. We illustrate the approach using a case example. We then discuss the strengths and limitations of this approach, as well as when these steps might be most appropriate, and suggest work to further the contributions of systems analysis methods to implementation mechanisms research. CONCLUSIONS: Our approach to applying systems analysis methods can encourage more mechanisms research efforts to consider these methods and in turn fuel both (i) rigorous comparisons of these methods to alternative mechanisms research approaches and (ii) an active discourse across the field to better delineate when these methods are appropriate for advancing mechanisms-related knowledge.

Simulating the role of knowledge brokers in policy making in state agencies: An agent-based model.

OBJECTIVE: To model children's mental health policy making dynamics and simulate the impacts of knowledge broker interventions. DATA SOURCES: Primary data from surveys (n = 221) and interviews (n = 64) conducted in 2019-2021 with mental health agency (MHA) officials in state agencies. STUDY DESIGN: A prototype agent-based model (ABM) was developed using the PARTE (Properties, Actions, Rules, Time, Environment) framework and informed through primary data collection. In each simulation, a policy is randomly generated (salience weights: cost, contextual alignment, and strength of evidence) and discussed among agents. Agents are MHA officials and heterogenous in their properties (policy making power and network influence) and policy preferences (based on salience weights). Knowledge broker interventions add agents to the MHA social network who primarily focus on the policy's research evidence. DATA COLLECTION/EXTRACTION METHODS: A sequential explanatory mixed method approach was used. Descriptive and regression analyses were used for the survey data and directed content analysis was used to code interview data. Triangulated results informed ABM development. In the ABM, policy makers with various degrees of decision influence interact in a scale-free network before and after knowledge broker interventions. Over time, each decides to support or oppose a policy proposal based on policy salience weights and their own properties and interactions. The main outcome is an agency-level decision based on policy maker support. Each intervention and baseline simulation runs 250 times across 50 timesteps. PRINCIPAL FINDINGS: Surveys and interviews revealed that barriers to research use could be addressed by knowledge brokers. Simulations indicated that policy decision outcomes varied by policy making context within agencies. CONCLUSIONS: This is the first application of ABM to evidence-informed mental health policy making. Results suggest that the presence of knowledge brokers can: (1) influence consensus formation in MHAs, (2) accelerate policy decisions, and (3) increase the likelihood of evidence-informed policy adoption.