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.

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.

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.

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.

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.

Prevention Adaptation of an Evidence-Based Treatment for Parents Involved With Child Welfare Who Use Substances.

Background: Parental substance use, especially opioid misuse and/or methamphetamine use, is a key driver for recent increases in family involvement with child welfare and foster care placements in the United States. There is an urgent need for programs that prevent parental substance use disorders, yet few prevention programs exist that target parents' unique needs and strengths. Adapting evidence-based treatment approaches for prevention might be an efficient, effective way to address this gap. The current study informed the rigorous adaptation of an evidence-based treatment that supports families involved with child welfare due to substance use, Families Actively Improving Relationships (FAIR), to a prevention-oriented intervention: "PRE-FAIR." FAIR entails four treatment domains: substance use, parenting, mental health, and ancillary services (e.g., housing, medical care, and food). FAIR significantly improved parenting and reduced parental substance use in three rigorous treatment trials, but FAIR's effectiveness in preventing the initiation or escalation of opioid misuse and/or methamphetamine use is untested. To inform adaptation, particular attention was paid to operationalizing strategies underlying a key hypothesized mediator of successful parent outcomes-engagement. Methods: Graduated FAIR parents (n = 9) and FAIR administrators, clinical supervisors, and clinicians (n = 11) participated in semi-structured interviews. Content analysis was used to identify key variables driving FAIR engagement and parent outcomes. Causal loop diagramming, a qualitative systems science method, was employed to operationalize emergent themes, and describe how causal links between key variables interrelated dynamically over time. Results: Themes reinforced the value of FAIR's treatment domains for supporting parent's sobriety and parenting skills within a prevention orientation. Ancillary supports and strong relationships were particularly crucial for helping parents cope with stressors leading to substance use. Five engagement strategies were identified as essential to parent success: 24/7 clinician availability, in-person clinician advocacy, in-home delivery, strengths-based interactions, and urinalysis. Implications for PRE-FAIR engagement strategies and dosage were identified. Discussion: Traditional qualitative analyses and qualitative analyses based in systems science can inform rigorous adaptations of evidence-based treatment programs for prevention. Future research will explore additional required, fidelity-consistent prevention adaptations to FAIR, and the impact of PRE-FAIR on parental substance use and child welfare case outcomes.

Rapid-cycle systems modeling to support evidence-informed decision-making during system-wide implementation.

BACKGROUND: To "model and simulate change" is an accepted strategy to support implementation at scale. Much like a power analysis can inform decisions about study design, simulation models offer an analytic strategy to synthesize evidence that informs decisions regarding implementation of evidence-based interventions. However, simulation modeling is under-utilized in implementation science. To realize the potential of simulation modeling as an implementation strategy, additional methods are required to assist stakeholders to use models to examine underlying assumptions, consider alternative strategies, and anticipate downstream consequences of implementation. To this end, we propose Rapid-cycle Systems Modeling (RCSM)-a form of group modeling designed to promote engagement with evidence to support implementation. To demonstrate its utility, we provide an illustrative case study with mid-level administrators developing system-wide interventions that aim to identify and treat trauma among children entering foster care. METHODS: RCSM is an iterative method that includes three steps per cycle: (1) identify and prioritize stakeholder questions, (2) develop or refine a simulation model, and (3) engage in dialogue regarding model relevance, insights, and utility for implementation. For the case study, 31 key informants were engaged in step 1, a prior simulation model was adapted for step 2, and six member-checking group interviews (n = 16) were conducted for step 3. RESULTS: Step 1 engaged qualitative methods to identify and prioritize stakeholder questions, specifically identifying a set of inter-related decisions to promote implementing trauma-informed screening. In step 2, the research team created a presentation to communicate key findings from the simulation model that addressed decisions about programmatic reach, optimal screening thresholds to balance demand for treatment with supply, capacity to start-up and sustain screening, and availability of downstream capacity to provide treatment for those with indicated need. In step 3, member-checking group interviews with stakeholders documented the relevance of the model results to implementation decisions, insight regarding opportunities to improve system performance, and potential to inform conversations regarding anticipated implications of implementation choices. CONCLUSIONS: By embedding simulation modeling in a process of stakeholder engagement, RCSM offers guidance to realize the potential of modeling not only as an analytic strategy, but also as an implementation strategy.

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.

Opioid misuse as a coping behavior for unmet mental health needs among U.S. adults.

BACKGROUND: Self-medication theory posits that opioids may be misused to cope with mental or emotional distress, especially if distress is not treated. Opioid misuse may lead to opioid use disorder, overdose, and death. We estimated the risk of opioid misuse associated with unmet mental health treatment needs (UMHN). METHODS: We used 2015-2018 data on U.S. adults from the National Survey of Drug Use and Health (n = 165,767). UMHN was the perceived need for mental health services in the past year without service receipt. The primary dependent variable was past year prescription opioid misuse (POM). Secondary analyses estimated POM and/or heroin misuse risk. Logistic regressions estimated the predicted probability for POM, controlling for demographics, social factors, a major depressive episode (MDE), and overall health. We also tested whether the association of UMHN on the predicted probability of POM varied by MDE. RESULTS: UMHN was associated with a 4.6 percentage point higher predicted probability of POM (p < 0.001; 95 % CI 0.04-0.05) and a 4.9 percentage point higher predicted probability of POM and/or heroin (p < 0.001; 95 % CI 0.04-0.06) compared to adults who accessed mental health services. MDE was associated with a significantly greater risk of POM (p < 0.001). POM risk was greater for adults potentially experiencing social isolation. CONCLUSION: Targeted mental health support strategies may reduce the likelihood of POM initiation and episodes. Future research should investigate the relationship between unmet mental health needs and initiation of POM episodes.

Impact of COVID-19 on service delivery for an evidence-based behavioral treatment for families involved in the child welfare system.

The novel coronavirus, COVID-19, has dramatically impacted clinical service delivery, particularly substance use treatment. The Families Actively Improving Relationships (FAIR) program is an action-oriented, evidence-based behavioral treatment for opioid and methamphetamine disorders in parents involved in the child welfare (CW) system. A seven-clinician team operates out of a Medicaid-funded clinic, primarily delivering services in the community. Attending to underlying mechanisms of FAIR's intervention strategies that promote client engagement and clinical outcomes, FAIR rapidly adapted procedures in response to COVID-19-onset disruptions. This study analyzed administrative records and Medicaid claims data from January 2019 to July 2020, including 157 clients and 17,449 claims. Analyses considered COVID-19 presence as March-July 2020. The study examined changes in the frequency and reimbursement volume of FAIR service delivery pre- and postonset of COVID-19. Although average monthly reimbursement per clinician did not significantly decline, reimbursement per client significantly declined by 31% (pre: $1005 [$732]; post: $698 [$546], p < .001). Clinicians delivered services on significantly more days per month during COVID-19 (mean (sd) = 16.73 (6.33); 20.26 (7.24), t(127) = -2.70, p < .01). Average clinician caseload size was stable, as was the average monthly service receipt days for clients. Thus, this study attributes reductions in reimbursement per client when FAIR provided services remotely to the elimination of in-person billable services and reductions in session length, but not in frequency. Medicaid-funded clinics and community-based substance use treatment interventions such as FAIR can successfully sustain and implement substance use treatment practices with deliberate, rapid adaptation to ensure that families receive needed supports in the face of contextual crises.

A scoping review of the use of ethnographic approaches in implementation research and recommendations for reporting.

BACKGROUND: Researchers have argued for the value of ethnographic approaches to implementation science (IS). The contested meanings of ethnography pose challenges and possibilities to its use in IS. The goal of this study was to identify sources of commonality and variation, and to distill a set of recommendations for reporting ethnographic approaches in IS. METHODS: We included in our scoping review English-language academic journal articles meeting two criteria: (1) IS articles in the healthcare field and (2) articles that described their approach as ethnographic. In March 2019, we implemented our search criteria in four academic databases and one academic journal. Abstracts were screened for inclusion by at least two authors. We iteratively develop a codebook for full-text analysis and double-coded included articles. We summarized the findings and developed reporting recommendations through discussion. RESULTS: Of the 210 articles whose abstracts were screened, 73 were included in full-text analysis. The number of articles increased in recent years. Ethnographic approaches were used within a wide variety of theoretical approaches and research designs. Articles primarily described using interviews and observational methods as part of their ethnographic approaches, though numerous other methods were also employed. The most cited rationales for using ethnographic approaches were to capture context-specific phenomena, understand insiders' perspective, and study complex interactions. In reporting on ethnographic approaches, we recommend that researchers provide information on researcher training and position, reflect on researchers' positionality, describe observational methods in detail, and report results from all the methods used. CONCLUSION: The number of IS studies using ethnography has increased in recent years. Ethnography holds great potential for contributing further to IS, particularly to studying implementation strategy mechanisms and understanding complex adaptive systems.

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.

Parenting Interventions in Pediatric Primary Care: A Systematic Review.

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.

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.

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.

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.

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.