Automated Substance Use/Sexual Risk Reporting and HIV Test Acceptance Among Emergency Department Patients Aged 13-24 Years.
Ian David AronsonJingru ZhangSonali RajanLisa A MarschMona BugaighisMobolaji O IbitoyeLauren S ChernickDon C Des JarlaisPublished in: AIDS and behavior (2021)
Despite federal guidelines, many adolescents and emerging adults are not offered HIV testing by their healthcare providers. As such, many-including those who may be at high-risk for contracting HIV given their sexual and/or substance use risk-are not routinely tested. The current study examines sexual risk and substance use among emergency department patients aged 13-24 years (n = 147), who completed an automated screening as part of a tablet-based intervention designed to increase HIV testing. Twenty seven percent (n = 39) of participants chose to test for HIV after completing the tablet-based intervention. Among this sample, sexual risk was a significant independent predictor of HIV testing (χ2 = 16.50, p < 0.001). Problem substance use (e.g. trying but failing to quit) also predicted testing (χ2 = 7.43, p < 0.01). When considering these behaviors together, analyses indicated that the effect of problem substance use (ß = 0.648, p = 0.154) on testing is explained by sexual risk behavior (ß = 1.425, p < 0.01). The study's findings underscore the value of using routine automated risk screenings to collect sensitive data from emergency department patients, followed by computer-based HIV test offers for adolescent youth. Our research indicates tablet-based interventions can facilitate more accurate reporting of sexual behavior and substance use, and can also potentially increase HIV test uptake among those at risk.
Keyphrases
- hiv testing
- men who have sex with men
- emergency department
- hiv positive
- end stage renal disease
- mental health
- antiretroviral therapy
- healthcare
- human immunodeficiency virus
- ejection fraction
- hiv infected
- newly diagnosed
- chronic kidney disease
- hepatitis c virus
- hiv aids
- randomized controlled trial
- machine learning
- prognostic factors
- physical activity
- peritoneal dialysis
- clinical practice
- artificial intelligence
- health insurance