Evaluating the Impact of the Positive Choices Intervention on Substance Use, Psychological, and Care Engagement Outcomes Relevant to Current National HIV Prevention Goals

The HIV epidemic in the United States continues to be a significant public health problem, with approximately 50,000 new infections occurring each year. National public health priorities have shifted in recent years towards targeted HIV prevention efforts among people living with HIV/AIDS (PLWHA) that include: increasing engagement in and retention in care, improving HIV treatment adherence, and increasing screening for and treatment of substance use and psychological difficulties. This study evaluated the efficacy of Positive Choices (PC), a brief, care-based, theory-driven, 3-session counseling intervention for newly HIV-diagnosed men who have sex with men (MSM), in the context of current national HIV prevention priorities. The study involved secondary analysis of data from a preliminary efficacy trial of the PC intervention (n=102). Descriptive statistics examined baseline substance use, psychological characteristics and strategies, and care engagement and HIV-related biological outcomes. Generalized Estimating Equations (GEE) examined longitudinal changes in these variables by study condition. Results indicated that PC improved adherence to HIV treatment, but increased use of illicit drugs, specifically amyl nitrates and other stimulant drugs; additionally, moderation analyses indicated differences in patterns of change over time in viral load by baseline depression status. Implications of the findings and suggestions for future research are discussed.

Can a Broader Education Narrow the Gap? Evidence on Non-Academic Features of Schooling

Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.