To examine the impact of nonmedical use of prescription drugs (NMUPD) during sexual activity on the frequency of condom use among a sample of college students. Students attending a large Midwestern University ( = 4284) during April 2015. Retrospective cross-sectional analysis of survey data using logistic regression. Respondents and/or their sexual partners who engaged in NMUPD during sexual activity were significantly less likely to use condoms during 75% or more of past 12-month sexual encounters compared to respondents who had not engaged in lifetime and past 12-month NMUPD. Although not statistically significant, trends suggest that respondents who engaged in NMUPD during sexual activity may be less likely to use condoms than those who engaged in lifetime or past 12-month NMUPD but not during sexual activity. Findings suggest a need for specific strategies for reducing risk behaviors related to prescription drugs and sexual activity.

Download full-text PDF

Source
http://dx.doi.org/10.1080/07448481.2018.1486843DOI Listing

Publication Analysis

Top Keywords

sexual activity
24
nmupd sexual
16
prescription drugs
12
nonmedical prescription
8
sexual
8
drugs sexual
8
condom sample
8
sample college
8
college students
8
engaged nmupd
8

Similar Publications

The Future of HIV: Challenges in meeting the 2030 Ending the HIV Epidemic in the U.S. (EHE) reduction goal.

AIDS

January 2025

Center for Biomedical Modeling, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA.

Objectives: To predict the burden of HIV in the United States (US) nationally and by region, transmission type, and race/ethnicity through 2030.

Methods: Using publicly available data from the CDC NCHHSTP AtlasPlus dashboard, we generated 11-year prospective forecasts of incident HIV diagnoses nationally and by region (South, non-South), race/ethnicity (White, Hispanic/Latino, Black/African American), and transmission type (Injection-Drug Use, Male-to-Male Sexual Contact (MMSC), and Heterosexual Contact (HSC)). We employed weighted (W) and unweighted (UW) n-sub-epidemic ensemble models, calibrated using 12 years of historical data (2008-2019), and forecasted trends for 2020-2030.

View Article and Find Full Text PDF

This review examined research to identify longitudinal predictors of adolescent sexual behavior outcomes. These predictors hold promise as potential outcomes for teen pregnancy prevention program evaluations when measuring sexual behavior outcomes is infeasible or theoretically, methodologically, or developmentally inappropriate. We conducted a systematic review using a prespecified search strategy and processes consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

View Article and Find Full Text PDF

A Qualitative Study of First HIV Test Experiences Among Sexual and Gender Minority Adolescents.

Sex Res Social Policy

December 2024

Institute for Sexual and Gender Minority Health and Wellbeing, Northwestern University, 625 N. Michigan Avenue, Suite 1400, Chicago, IL 60611, USA.

Introduction: This qualitative study examined adolescents' first experiences with HIV testing.

Methods: Data were collected from April 2018 to October 2020 as part of an HIV prevention and sex education intervention; SGM adolescents ( = 175) answered open-ended questions regarding experiences with their first HIV test and advice for other adolescents seeking HIV testing. Data were analyzed through inductive content analysis.

View Article and Find Full Text PDF

Ben Wa balls are often used for sexual pleasure and pelvic floor exercise. However, their use can lead to complications, including retention within the vagina. We present a case of a 64-year-old female, status post-hysterectomy 20 years prior, who experienced the loss of a Ben Wa ball during sexual activity.

View Article and Find Full Text PDF

A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG Boost are applied and evaluated on the Demographic and Health Surveys Program (DHSP), with thirteen features ultimately selected as the most predictive features. Classification and generalization experiments are conducted to test the accuracy, F1-score, precision, and area under the curve (AUC) performance of these four algorithms.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!