Increasing Access To Perinatal Mental Health Care: The Perinatal Psychiatry Access Program Model.

Perinatal psychiatry access programs offer a scalable approach to building the capacity of perinatal professionals to identify, assess, and treat mental health conditions. Little is known about access programs' implementation and the relative merits of differing approaches. We conducted surveys and semistructured interviews with access program staff and reviewed policy and procedure documents from the fifteen access programs that had been implemented in the United States as of March 2021, when the study was conducted. Since then, the number of access programs has grown to thirty state, regional, or national programs. Access programs implemented up to five program components, including telephone consultation with a perinatal psychiatry expert, one-time patient-facing consultation with a perinatal psychiatry expert, resource and referral to perinatal professionals or patients, trainings for perinatal professionals, and practice-level technical assistance. Characterizing population-based intervention models, such as perinatal psychiatry access programs, that address perinatal mental health conditions is a needed step toward evaluating and improving programs' implementation, reach, and effectiveness.

Simulation and minimization: technical advances for factorial experiments designed to optimize clinical interventions.

BACKGROUND: The Multiphase Optimization Strategy (MOST) is designed to maximize the impact of clinical healthcare interventions, which are typically multicomponent and increasingly complex. MOST often relies on factorial experiments to identify which components of an intervention are most effective, efficient, and scalable. When assigning participants to conditions in factorial experiments, researchers must be careful to select the assignment procedure that will result in balanced sample sizes and equivalence of covariates across conditions while maintaining unpredictability. METHODS: In the context of a MOST optimization trial with a 2x2x2x2 factorial design, we used computer simulation to empirically test five subject allocation procedures: simple randomization, stratified randomization with permuted blocks, maximum tolerated imbalance (MTI), minimal sufficient balance (MSB), and minimization. We compared these methods across the 16 study cells with respect to sample size balance, equivalence on key covariates, and unpredictability. Leveraging an existing dataset to compare these procedures, we conducted 250 computerized simulations using bootstrap samples of 304 participants. RESULTS: Simple randomization, the most unpredictable procedure, generated poor sample balance and equivalence of covariates across the 16 study cells. Stratified randomization with permuted blocks performed well on stratified variables but resulted in poor equivalence on other covariates and poor balance. MTI, MSB, and minimization had higher complexity and cost. MTI resulted in balance close to pre-specified thresholds and a higher degree of unpredictability, but poor equivalence of covariates. MSB had 19.7% deterministic allocations, poor sample balance and improved equivalence on only a few covariates. Minimization was most successful in achieving balanced sample sizes and equivalence across a large number of covariates, but resulted in 34% deterministic allocations. Small differences in proportion of correct guesses were found across the procedures. CONCLUSIONS: Based on the computer simulation results and priorities within the study context, minimization with a random element was selected for the planned research study. Minimization with a random element, as well as computer simulation to make an informed randomization procedure choice, are utilized infrequently in randomized experiments but represent important technical advances that researchers implementing multi-arm and factorial studies should consider.

Using the Multiphase Optimization Strategy (MOST) framework to test intervention delivery strategies: a study protocol.

BACKGROUND: Delivery of behavioral interventions is complex, as the majority of interventions consist of multiple components used either simultaneously, sequentially, or both. The importance of clearly delineating delivery strategies within these complex interventions-and furthermore understanding the impact of each strategy on effectiveness-has recently emerged as an important facet of intervention research. Yet, few methodologies exist to prospectively test the effectiveness of delivery strategies and how they impact implementation. In the current paper, we describe a study protocol for a large randomized controlled trial in which we will use the Multiphase Optimization Strategy (MOST), a novel framework developed to optimize interventions, i.e., to test the effectiveness of intervention delivery strategies using a factorial design. We apply this framework to delivery of Family Navigation (FN), an evidence-based care management strategy designed to reduce disparities and improve access to behavioral health services, and test four components related to its implementation. METHODS/DESIGN: The MOST framework contains three distinct phases: Preparation, Optimization, and Evaluation. The Preparation phase for this study occurred previously. The current study consists of the Optimization and Evaluation phases. Children aged 3-to-12 years old who are detected as "at-risk" for behavioral health disorders (n = 304) at a large, urban federally qualified community health center will be referred to a Family Partner-a bicultural, bilingual member of the community with training in behavioral health and systems navigation-who will perform FN. Families will then be randomized to one of 16 possible combinations of FN delivery strategies (2 × 2 × 2× 2 factorial design). The primary outcome measure will be achieving a family-centered goal related to behavioral health services within 90 days of randomization. Implementation data on the fidelity, acceptability, feasibility, and cost of each strategy will also be collected. Results from the primary and secondary outcomes will be reviewed by our team of stakeholders to optimize FN delivery for implementation and dissemination based on effectiveness, efficiency, and cost. DISCUSSION: In this protocol paper, we describe how the MOST framework can be used to improve intervention delivery. These methods will be useful for future studies testing intervention delivery strategies and their impact on implementation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03569449. Registered on 26 June 2018.

Computer-based attention training in the schools for children with attention deficit/hyperactivity disorder: a preliminary trial.

Objective. This study examined the efficacy of 2 computer-based training systems to teach children with attention deficit/hyperactivity disorder (ADHD) to attend more effectively. Design/methods. A total of 41 children with ADHD from 2 middle schools were randomly assigned to receive 2 sessions a week at school of either neurofeedback (NF) or attention training through a standard computer format (SCF), either immediately or after a 6-month wait (waitlist control group). Parents, children, and teachers completed questionnaires pre- and postintervention. Results. Primary parents in the NF condition reported significant (P < .05) change on Conners's Rating Scales-Revised (CRS-R) and Behavior Assessment Scales for Children (BASC) subscales; and in the SCF condition, they reported significant (P < .05) change on the CRS-R Inattention scale and ADHD index, the BASC Attention Problems Scale, and on the Behavioral Rating Inventory of Executive Functioning (BRIEF). Conclusion. This randomized control trial provides preliminary evidence of the effectiveness of computer-based interventions for ADHD and supports the feasibility of offering them in a school setting.