Evaluating alcohol and marijuana use among emerging adult males via analysis of text messages

Background: Advances in social technologies offer new tools for large scale data collection and analysis of peer influence and social networks on substance use attitudes and behaviors. Objective: The objective of this study was to determine if text message content can predict alcohol and marijuana use attitudes and behaviors. Methods: Text messages from 91 males ages 18–25 were monitored over a period of 6 months and examined for content related to alcohol and marijuana. Self-report data indicating alcohol and marijuana use attitudes and behaviors were used to determine relationships between text message content, social network structure, and substance use attitudes and behaviors. Results: In total, 23,173 text messages were analyzed with 166 text messages including alcohol related terms and 195 text messages including drug related terms. Individuals who sent text messages related to alcohol use were more likely to have problem alcohol use and positive attitudes toward alcohol use, and individuals who sent text messages related to marijuana use reported higher frequency of marijuana use and more positive attitudes toward marijuana use. Individuals with multiple daily marijuana use were in positions that had less control over network structure. Conclusions: The results of this study indicate that monitoring text message content and social network structure among emerging adult males can potentially predict alcohol and marijuana use attitudes and behaviors. Text message content analysis is a novel technique increasing our understanding of the role of peer influence and social network on substance use attitudes and behaviors.     

ASO Author Reflections: Perioperative Antibiotic Prophylaxis for Mastectomy—Too Much or Too Little

Annals of Surgical Oncology -     

Multi‐scale network regression for brain‐phenotype associations

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051) , we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.     

Development of a COVID-19 alternate care site from ground zero: A nursing perspective

The novel coronavirus disease SARS-CoV-2 (COVID-19) outbreak rapidly generated an unprecedented global, national, and state public health crisis with the need to rapidly develop alternate care sites (ACS) to care for COVID-19 patients within an overburdened health care system. A hospital care model ACS to increase the health care capacity, provide care for mild to moderately symptomatic patients, and offer local self-sustainment for a surge of patients was developed in Memphis, Tennessee located in Shelby County. We completed a temporary conversion of a large unused newspaper publication building to a health care facility for COVID-19 patients. Developing an ACS from ground zero was met with many challenges, and throughout the process important lessons were learned. With the goal to complete the building conversion within a 28-day timeframe, collaboration among the numerous governmental, health care, and private agencies was critical and nursing leadership was key to this process. The purpose of this paper is to describe the development of a COVID-19 ACS in Memphis, TN, which has a large at-risk population with limited access to health care. Specifically, we will discuss the strong leadership role of nursing faculty, key challenges, and lessons learned, as well as provide checklists and models for others in similar circumstances.