Mental Health and Black Male Graduate Students.

The purpose of this study was to understand how masculinity and race impact mental health among Black male graduate students. A qualitative study using in-depth interviews recruited Black male graduate students enrolled at a private university in the southern United States. Data were collected over zoom and recorded. Interviews were transcribed and the data were analyzed for similar themes. Twenty-nine Black male graduate students 23 to 51 were recruited. Participants reported the three main elements that impacted their mental health were (1) expectations, (2) pressure, and (3) being strong. These findings suggest that colleges need to develop programming to help Black men learn how to handle racial discrimination in positive ways. Additionally, findings also highlight the need for culturally relevant mental health services that let Black men know seeking help is ok and is what men do.

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.

Background: Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods: We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results: A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions: Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.