Predictors of Academic Achievement and Failure Among Low-Income Urban African American Adolescents: An Ecological Perspective

Predictors of academic achievement among urban low-income African American adolescents have primarily been investigated by examining main effects, or limited interactions with conventional statistical techniques. This paper adds to the literature by examining the factors that influence academic outcomes among this population within an ecological systems framework. This allowed for a comprehensive understanding of how numerous protective and risk factors, across ecological settings, interact to influence academic outcomes. Optimal Data Analysis (ODA) was employed to create prediction models for mathematic and reading achievement. ODA allowed for the examination of a vast number of variables in one statistical model without increasing statistical error. In total, 111 variables across seven constructs (e.g., individual level characteristics, psychopathology, family structure, family functioning, social support, life stressors, and community/neighborhood) competed within the ODA model for the strongest predictor of academic outcomes (both success and failure). In addition, ODA allowed for the creation of the pathways towards academic outcomes by creating classification tree models. Data was collected among a final sample of 167 low-income fifth through eighth grade urban African American adolescents (53% females and 47% males). Parent report, self-report, and in vivo accounts of the adolescents\u27 daily experience (e.g., daily distress) were collected during a one week time frame. Contrary to expectation, family structure and family functioning variables were weaker predictors of academic outcomes than community variables. The strongest predictor of both mathematic and reading achievement was school factors (e.g., school socioeconomic status). In addition, common factors in the literature predictive of academic outcomes were not found to be associated with academic achievement in our sample (gender, family income). Several individual level characteristics\u27 (e.g., academic self-efficacy, social problems, and openness to other racial groups) main effects were predictive of academic achievement. When accounting for ecological contexts, family functioning variables were found to interact with each other and with each individual\u27s school to predict mathematic outcomes. For example, within the context of a low socioeconomic status (SES) school, increased time spent with family may be protective of academic outcomes, if the student\u27s family is not engaging in high-risk behaviors (e.g., high levels of parental alcohol consumption). With regard to reading achievement, individual level characteristics (e.g., delinquency, social problems, openness to other racial groups) were found to interact among each other in the context of specific school environments. The most consistent predictor of academic achievement was the SES of the school a student attended. Within the school context, characteristics at a Microsystem level were found to interact and either protect, or place students at greater risk for academic achievement

Predictors and Profiles of Antiretroviral Therapy Adherence Among African-American Adolescents and Young Adult Males Behaviorally-Infected with HIV: A Classification Tree Analysis Approach

Adherence to antiretroviral therapy is crucial for thwarting disease progression and reducing secondary transmission, yet HIV+ youth struggle with adherence. The highest rates of new HIV infections occur in young African American men (YAAM), thus understanding reasons for non-adherence in this group is critical. Reasons for non-adherence can be complex and multifactorial, and innovative methods of exploration are needed for advancing prevention and treatment efforts. A sample of 387 HIV+ YAAM who reported currently taking HIV medications were selected from a cross-sectional assessment of 2,226 HIV+ youth from sites within the Adolescent Trials Network for HIV/AIDS Interventions (ATN) from 2009-2012 (12-24 years-old, Median = 22.00, SD 2.06). Participants completed self-reported adherence, demographic, health, and psychosocial measures. Seventy-two theoretically relevant predictors of adherence underwent Optimal Data Analysis (ODA) to construct a classification tree which hierarchically maximizes the classification accuracy of 100% adherence. Sixty-two percent reported 100% adherence (no missing doses) over the past seven days. Frequency of cannabis use was the strongest predictor of adherence, yielding moderate effect strength sensitivity, ESS = 27.1, p \u3c 0.00. Among participants with infrequent cannabis use, 72% demonstrated full adherence, while only 45% of participants who used cannabis (monthly or more) demonstrated full adherence. Classification tree analysis (CTA) correctly classified 82.35% of those who were adherent and 64.85% of those who were non-adherent. The final CTA adherence model was strong (ESS = 49.12) identifying four pathways towards adherence and five pathways toward non-adherence. Participants most likely to be adherent were those less likely to have substance abuse issues and reported low levels of psychiatric distress (92.59% were adherent). This research demonstrates the impact of substance use and mental health on adherence among YAAM. Moreover, this analysis identifies complex and multiple profiles of adherence among HIV+ YAAM and suggests that targeted interventions may be most prudent