Follow-Up Care After Maltreatment: Sociodemographic Associations With Timeliness in a Southern State.

Children who experience maltreatment are at elevated risk of developing mental health difficulties. Even so, they often do not receive timely, evidenced-based mental health treatment, which may exacerbate the risk of poor outcomes. This study aims to describe the receipt of timely follow-up care after maltreatment in a southern state with known treatment shortages and aims to identify factors associated with timely follow-up care. We utilized a retrospective cohort design using 2014 Mississippi Medicaid administrative claims data for youth 0-18 years. Prevalence estimates and associations with definite and probable maltreatment (based on recorded age/injury combinations) during inpatient and outpatient healthcare encounters were evaluated. Rates of 30-day maltreatment follow-up with any medical or behavioral health provider were also assessed. Prevalence estimates of definite and probable maltreatment in the eligible study population (N = 324,752) were 0.53% and 3.8%, respectively. Only one-third of identified children received 30-day follow-up. Black and older children as well as children diagnosed with anxiety or depression were more likely to receive 30-day follow-up than younger children, white children, and children without anxiety or depression. Low rates of timely follow-up indicate the need for intentional workflow practices to increase the likelihood of follow-up.

Lessons learned: A neuroimaging research center's transition to open and reproducible science.

Human functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and tools to both manage this complexity as well as to enhance the rigor and reproducibility of neuroimaging science. We group these best practices under four categories: experiment pre-registration, FAIR data principles, reproducible neuroimaging analyses, and open science. While there is growing recognition of the need to implement these best practices there exists little practical guidance of how to accomplish this goal. In this work, we describe lessons learned from efforts to adopt these best practices within the Brain Imaging Research Center at the University of Arkansas for Medical Sciences over 4 years (July 2018-May 2022). We provide a brief summary of the four categories of best practices. We then describe our center's scientific workflow (from hypothesis formulation to result reporting) and detail how each element of this workflow maps onto these four categories. We also provide specific examples of practices or tools that support this mapping process. Finally, we offer a roadmap for the stepwise adoption of these practices, providing recommendations of why and what to do as well as a summary of cost-benefit tradeoffs for each step of the transition.

Predicting attrition in long-term residential substance use disorder treatment: A modifiable risk factors perspective.

Although numerous factors are associated with attrition in substance use disorder (SUD) treatment, many are unmodifiable and therefore difficult to target in efforts to improve treatment outcomes. The current study sought to identify the strongest and most modifiable predictors of attrition in long-term residential SUD treatment from myriad characteristics associated with treatment termination. Archival data were examined for 2,069 adults (74% male; 38% non-Hispanic White) who entered a long-term residential SUD treatment facility between January 2010 and June 2016. Program staff recorded clients' demographic, situational, substance use, and intake data at admission; discharge data were recorded at termination. To increase the likelihood our results were clinically meaningful, we randomly split our sample, ran 2 5-step hierarchical logistic regressions, and cross-validated our results. Across samples, we found younger age, having less than a high school education (Step 1), unstable living arrangements (Step 2), greater prior month use of primary substances, less prior month use of alcohol, and prior year needle use preceding treatment (Step 4), and longer recommended length of stay in treatment (Step 5) predicted attrition. To improve long-term residential SUD treatment completion, we propose treatment adaptations begin with the most modifiable predictors of attrition. Accordingly, the current data indicate initial focus should be placed on refurbishing the process through which recommended treatment durations are approached by providers. Subsequent focus should be placed on modifiable factors that present greater systemic challenges, followed by those that are unmodifiable but can be indirectly targeted by interventions tailored to specific underrepresented groups. (PsycInfo Database Record (c) 2020 APA, all rights reserved).