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Use of outpatient mental health services by U.S. children and adolescents increased between 1996 and 2012. Youths with more severe impairment were more likely to receive services, but youths with less severe impairment accounted for most of the absolute increase. In the past several years, there has been an increase in outpatient mental health treatment of children and adolescents in the United States. 1 – 3 Between 1995–1998 and 2007–2010, the number of mental health visits by young people to U.S. office-based physicians nearly doubled. 2 It is not known, however, whether the increase in outpatient mental health treatment has been driven primarily by an expansion in the care of youths with more severe mental health impairment or by an expansion in the care of those with less severe mental health impairment. Mental health impairment, which refers to the degree to which psychiatric . . .

Background Implementation strategies increase the adoption of evidence-based practices, but they require resources. Although information about implementation costs is critical for decision-makers with budget constraints, cost information is not typically reported in the literature. This is at least partly due to a need for clearly defined, standardized costing methods that can be integrated into implementation effectiveness evaluation efforts. Methods We present a pragmatic approach to systematically estimating detailed, specific resource use and costs of implementation strategies that combine time-driven activity-based costing (TDABC), a business accounting method based on process mapping and known for its practicality, with a leading implementation science framework developed by Proctor and colleagues, which guides specification and reporting of implementation strategies. We illustrate the application of this method using a case study with synthetic data. Results This step-by-step method produces a clear map of the implementation process by specifying the names, actions, actors, and temporality of each implementation strategy; determining the frequency and duration of each action associated with individual strategies; and assigning a dollar value to the resources that each action consumes. The method provides transparent and granular cost estimation, allowing a cost comparison of different implementation strategies. The resulting data allow researchers and stakeholders to understand how specific components of an implementation strategy influence its overall cost. Conclusion TDABC can serve as a pragmatic method for estimating resource use and costs associated with distinct implementation strategies and their individual components. Our use of the Proctor framework for the process mapping stage of the TDABC provides a way to incorporate cost estimation into implementation evaluation and may reduce the burden associated with economic evaluations in implementation science.

Background: Although growing concern has been expressed about premature medical mortality in persons with mental illness, limited data are available quantifying the extent and correlates of this problem using population-based, nationally representative samples. Methods: The study used data from the 1989 National Health Interview Survey mental health supplement, with mortality data through 2006 linked through the National Death Index (80,850 participants, 16,435 deaths). Multivariable models adjusting for demographic factors assessed the increased hazard of mortality adding socioeconomic status, healthcare variables, clinical factors first separately, and then together. Results: Persons with mental disorders died an average of 8.2 years younger than the rest of the population (P < 0.001). Adjusting for demographic factors, presence of a mental illness was associated with a significant risk of excess mortality, (hazard ratio = 2.06, 95% confidence interval = 1.71-2.40), with 95.4% of deaths owing to medical rather than unnatural causes. Adding socioeconomic variables to the model, the hazard ratio was 1.77 (P < 0.001); adding health system factors, it was 1.80 (P < 0.001)); adding baseline clinical characteristics, the hazard ratio was 1.32 (P < 0.001). After adding all the 3 groups of variables simultaneously, the association was reduced by 82% from baseline and became statistically nonsignificant (hazard ratio = 1.19, P = 0.053). Conclusions: The results of the study underscore the complex causes and high burden of medical mortality among persons with mental disorders in the United States. Efforts to address this public health problem will need to address the socioeconomic, healthcare, and clinical risk factors that underlie it.

This study examined differences by age in service use and associated expenditures during 2005 for Medicaid-enrolled children with autism spectrum disorders. Aging was associated with significantly higher use and costs for restrictive, institution-based care and lower use and costs for community-based therapeutic services. Total expenditures increased by 5 % with each year of age; by 23 % between 3–5 and 6–11 year olds, 23 % between 6–11 and 12–16, and 14 % between 12–16 and 17–20 year olds. Use of and expenditures for long-term care, psychiatric medications, case management, medication management, day treatment/partial hospitalization, and respite services increased with age; use of and expenditures for occupational/physical therapy, speech therapy, mental health services, diagnostic/assessment services, and family therapy declined.

The extent to which mental health services for youths embody system-of-care (SOC) principles is an important quality indicator. This study tested whether youth and family experiences of SOC principles varied depending on youths’ level of need after adjusting for sociodemographic and treatment factors. The relationship to caregiver-reported clinical outcomes was also examined. Using administrative data and cross-sectional surveys from a stratified random sample of 1124 caregivers of youths ages 5–20 within a statewide system, adjusted analyses indicated caregivers of youths with the most intensive needs were significantly less likely to report receiving care that embodied SOC principles, with deficits on six of nine items. Youths whose services embodied SOC principles experienced significantly greater improvement in caregiver-reported functioning even after adjusting for level of need. Results highlight disparities in SOC principles for youths with intensive needs and the need for policy and intervention development to improve care for this population.

Background Theory and correlational research indicate organizational leadership and climate are important for successful implementation of evidence-based practices (EBPs) in healthcare settings; however, experimental evidence is lacking. We addressed this gap using data from the WISDOM (Working to Implement and Sustain Digital Outcome Measures) hybrid type III effectiveness-implementation trial. Primary outcomes from WISDOM indicated the Leadership and Organizational Change for Implementation (LOCI) strategy improved fidelity to measurement-based care (MBC) in youth mental health services. In this study, we tested LOCI’s hypothesized mechanisms of change, namely: (1) LOCI will improve implementation and transformational leadership, which in turn will (2) mediate LOCI’s effect on implementation climate, which in turn will (3) mediate LOCI’s effect on MBC fidelity. Methods Twenty-one outpatient mental health clinics serving youth were randomly assigned to LOCI plus MBC training and technical assistance or MBC training and technical assistance only. Clinicians rated their leaders’ implementation leadership, transformational leadership, and clinic implementation climate for MBC at five time points (baseline, 4-, 8-, 12-, and 18-months post-baseline). MBC fidelity was assessed using electronic metadata for youth outpatients who initiated treatment in the 12 months following MBC training. Hypotheses were tested using longitudinal mixed-effects models and multilevel mediation analyses. Results LOCI significantly improved implementation leadership and implementation climate from baseline to follow-up at 4-, 8-, 12-, and 18-month post-baseline (all p s < .01), producing large effects (range of ds  = 0.76 to 1.34). LOCI’s effects on transformational leadership were small at 4 months ( d  = 0.31, p  = .019) and nonsignificant thereafter ( p s > .05). LOCI’s improvement of clinic implementation climate from baseline to 12 months was mediated by improvement in implementation leadership from baseline to 4 months (proportion mediated [ p m ] = 0.82, p  = .004). Transformational leadership did not mediate LOCI’s effect on implementation climate ( p  = 0.136). Improvement in clinic implementation climate from baseline to 12 months mediated LOCI’s effect on MBC fidelity during the same period ( p m  = 0.71, p  = .045). Conclusions LOCI improved MBC fidelity in youth mental health services by improving clinic implementation climate, which was itself improved by increased implementation leadership. Fidelity to EBPs in healthcare settings can be improved by developing organizational leaders and strong implementation climates. Trial registration ClinicalTrials.gov identifier: NCT04096274. Registered September 18, 2019.

Estimating the latent dynamics underlying biological processes is a central problem in computational biology. State-space models with Gaussian statistics are widely used for estimation of such latent dynamics and have been successfully utilized in the analysis of biological data. Gaussian statistics, however, fail to capture several key features of the dynamics of biological processes (e.g., brain dynamics) such as abrupt state changes and exogenous processes that affect the states in a structured fashion. Although Gaussian mixture process noise models have been considered as an alternative to capture such effects, data-driven inference of their parameters is not well-established in the literature. The objective of this paper is to develop efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and to utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting. We develop an algorithm based on Expectation-Maximization to estimate the process noise parameters from state-space observations. We apply our algorithm to simulated and experimentally-recorded MEG data from auditory experiments in the cocktail party paradigm to estimate the underlying dynamic Temporal Response Functions (TRFs). Our simulation results show that the richer representation of the process noise as a Gaussian mixture significantly improves state estimation and capturing the heterogeneity of the TRF dynamics. Application to MEG data reveals improvements over existing TRF estimation techniques, and provides a reliable alternative to current approaches for probing neural dynamics in a cocktail party scenario, as well as attention decoding in emerging applications such as smart hearing aids. Our proposed methodology provides a framework for efficient inference of Gaussian mixture process noise models, with application to a wide range of biological data with underlying heterogeneous and latent dynamics.

Background Behavioral health organizations are characterized by multiple organizational climates, including molar climate, which encompasses clinicians’ shared perceptions of how the work environment impacts their personal well-being, and strategic implementation climate, which includes clinicians’ shared perceptions of the extent to which evidence-based practice implementation is expected, supported, and rewarded by the organization. Theory suggests these climates have joint, cross-level effects on clinicians’ implementation of evidence-based practice and that these effects may be long term (i.e., up to 2 years); however, no empirical studies have tested these relationships. We hypothesize that molar climate moderates implementation climate’s concurrent and long-term relationships with clinicians’ use of evidence-based practice such that strategic implementation climate will have its most positive effects when it is accompanied by a positive molar climate. Methods Hypotheses were tested using data collected from 235 clinicians in 20 behavioral health organizations. At baseline, clinicians reported on molar climate and implementation climate. At baseline and at a 2-year follow-up, all clinicians who were present in the organizations reported on their use of cognitive-behavioral psychotherapy techniques, an evidence-based practice for youth psychiatric disorders. Two-level mixed-effects regression models tested whether baseline molar climate and implementation climate interacted in predicting clinicians’ evidence-based practice use at baseline and at 2-year follow-up. Results In organizations with more positive molar climates at baseline, higher levels of implementation climate predicted increased evidence-based practice use among clinicians who were present at baseline and among clinicians who were present in the organizations at 2-year follow-up; however, in organizations with less positive molar climates, implementation climate was not related to clinicians’ use of evidence-based practice at either time point. Conclusions Optimizing clinicians’ implementation of evidence-based practice in behavioral health requires attention to both molar climate and strategic implementation climate. Strategies that focus exclusively on implementation climate may not be effective levers for behavior change if the organization does not also engender a positive molar climate. These findings have implications for the development of implementation theory and effective implementation strategies.