Objective A positive association between delay discounting and substance use has been documented; substance users tend to discount future rewards more than non-users. However, studies detailing the responsiveness of delay discounting to interventions are lacking, and few have examined how any behavioral intervention affects delay discounting and whether these effects moderate changes in substance abuse. This study assesses the effectiveness of a money management intervention, Advisor-Teller Money Manager (ATM), in reducing delay discounting over time and the relationship of these effects to changes in cocaine use. Method Ninety psychiatric patients with histories of cocaine and/or alcohol use were randomly assigned to 36-weeks of ATM treatment or to a minimal-attention control condition. Delay discounting and cocaine use were measured throughout the intervention with a 52-week follow up measure of cocaine use. Analyses were conducted of (a) the effect of ATM on slopes of delay discounting and cocaine abstinence and (b) the relationship between change in delay discounting and change in cocaine abstinence. Results The ATM intervention was associated with significantly less delay discounting and less cocaine use over time relative to controls. Increases in delay discounting were associated with decreased abstinence from cocaine. Conclusions ATM treatment decreased delay discounting rates and these effects extended to cocaine use. Concrete conceptualizations of future events, as occur in financial planning, with higher perceived probability may account for higher valuation of future rewards in counseled patients.

More than 1,000 Illinois schools are implementing schoolwide positive behavior support (SWPBS) to enhance outcomes for students and staff. Consequently, Illinois established layered support structures to facilitate scaling up SWPBS. This paper describes the development of this infrastructure and presents the results of HLM analyses exploring the effects of implementing SWPBS, with and without fidelity across time, on student behavior and academic outcomes (office discipline referrals, suspensions, and state-wide test scores in reading and math) for a sample of 428 Illinois schools implementing SWPBS. Results indicate that (a) most schools implemented with fidelity and maintained or improved student performance across time and (b) implementation fidelity was associated with improved social outcomes and academic outcomes in math. Study limitations and implications are discussed.

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To determine the association between individual substances of abuse and antiretroviral adherence, analyses require a large sample assessed using electronic data monitoring (EDM). In this analysis, EDM data from 1636 participants in 12 U.S. adherence-focused studies were analyzed to determine the associations between recent use of various substances and adherence during the preceding four weeks. In bivariate analyses comparing adherence among patients who had used a specific substance to those who had not, adherence was significantly lower among those who had recently used cocaine, other stimulants or heroin but not among those who had used cannabis or alcohol. In multivariate analyses controlling for sociodemographics, amount of alcohol use and recent use of any alcohol, cocaine, other stimulants and heroin each was significantly negatively associated with adherence. The significant associations of cocaine, other stimulants, heroin, and alcohol use with adherence suggest that these are important substances to target with adherence-focused interventions.

Objectives: Among opioid use disorder (OUD)-treating providers, to characterize adaptations used to provide medications for OUD (MOUD) and factors associated with desire to continue virtual visits post-COVID-19 pandemic. Methods: In a national electronic survey of OUD-treating prescribers (July-August 2020), analyses restricted to X-waivered buprenorphine prescribers providing outpatient, longitudinal care for adults with OUD, quantitative and qualitative analyses of survey items and free text responses were conducted. Results: Among 797 respondents, 49% were men, 57% ≥50 years, 76% White, 68% physicians. Respondents widely used virtual visits to continue prescribing existing MOUD regimens (79%), provide behavioral healthcare (71%), and initiate new MOUD prescriptions (49%). Most prescribers preferred to continue/expand use of virtual visits after COVID-19. In multivariable models, factors associated with preference to continue/expand virtual visits to initiate MOUD postpandemic were treating a moderate number of patients prepandemic (aOR = 1.67; 95%[CI] = 1.06,2.62) and practicing in an urban setting (aOR = 2.17; 95%[CI] = 1.48,3.18). Prescribing buprenorphine prepandemic (aOR = 2.06; 95%[CI] = 1.11,3.82) and working in an academic medical center (aOR = 2.47; 95%[CI] = 1.30,4.68) were associated with preference to continue/expand use of virtual visits to continue MOUD postpandemic. Prescribing naltrexone extended-release injection prepandemic was associated with preference to continue/expand virtual visits to initiate and continue MOUD (aOR = 1.51; 95%[CI] = 1.10,2.07; aOR = 1.74; 95%[CI] = 1.19,2.54). Qualitative findings suggest that providers appreciated virtual visits due to convenience and patient accessibility, but were concerned about liability and technological barriers. Conclusions: Surveyed prescribers widely used virtual visits to provide MOUD with overall positive experiences. Future studies should evaluate the impact of virtual visits on MOUD access and retention and clinical outcomes.

When modeling multilevel data, it is important to accurately represent the interdependence of observations within clusters. Ignoring data clustering may result in parameter misestimation. However, it is not well established to what degree parameter estimates are affected by model misspecification when applying missing data techniques (MDTs) to incomplete multilevel data. We compare the performance of three MDTs with incomplete hierarchical data. We consider the impact of imputation model misspecification on the quality of parameter estimates by employing multiple imputation under assumptions of a normal model (MI/NM) with two-level cross-sectional data when values are missing at random on the dependent variable at rates of 10%, 30%, and 50%. Five criteria are used to compare estimates from MI/NM to estimates from MI assuming a linear mixed model (MI/LMM) and maximum likelihood estimation to the same incomplete data sets. With 10% missing data (MD), techniques performed similarly for fixed-effects estimates, but variance components were biased with MI/NM. Effects of model misspecification worsened at higher rates of MD, with the hierarchical structure of the data markedly underrepresented by biased variance component estimates. MI/LMM and maximum likelihood provided generally accurate and unbiased parameter estimates but performance was negatively affected by increased rates of MD.

Prospective memory is defined as the ability to "remember to remember" something at a future time despite intervening distractions and may be particularly important in remembering to take prescribed medication among people infected with HIV. Ninety-seven HIV-positive participants in a clinical trial had their adherence measured by electronic pillcaps and were administered neuropsychological screening tests and the memory for intentions screening test (MIST). Factor analysis of the MIST and other neuropsychological measures identified four factors. Two were derived from MIST subscales and accounted for approximately 50% of the variance in cognitive functioning. Only one factor was significantly correlated with adherence, and this was a MIST factor. In this preliminary study, the MIST assessed a memory function that (a) could be distinguished from traditional retrospective recall and executive functioning and (b) was correlated with antiretroviral adherence.

Although structural equation modeling (SEM) is one of the most comprehensive and flexible approaches to data analysis currently available, it is nonetheless prone to researcher misuse and misconceptions. This article offers a brief overview of the unique capabilities of SEM and discusses common sources of user error in drawing conclusions from these analyses. We make recommendations to guide proper analytical practices and appropriate inferences and provide references for more advanced study.Structural equation modeling (SEM) refers to a family of techniques that utilize the analysis of covariances to explore relationships among a set of variables (Kline, 1998). SEM allows researchers to test the congruity of hypothesized models with their sample data (Breckler, 1990). Due to its versatility and flexibility, SEM represents one of the most comprehensive approaches to research design and data analysis available to social and behavioral scientists (Hoyle, 1995). SEM offers many advantages over traditional data analytic techniques, yet it is not a panacea capable of fixing flaws in theory, research design, sampling, or measurement. This article provides a short discussion of the advantages of SEM, followed by descriptions of several common errors of inference made when using SEM techniques. It is our hope that this exposition will help readers to become more savvy and critical consumers and producers of research using SEM.