HIV and Injection Drug Use: New Approaches to HIV Prevention
Injection drug use has become a major public health problem. Its emerging significance is demonstrated globally in the dual HIV and HCV epidemics among people who inject drugs (PWID). Despite the advent of effective antivirals against HIV and HCV, PWID face multiple barriers to access and adherence to such treatments. Additionally, the lack of infrastructure for medication-assisted therapy for opioid addiction, inadequate treatment for underlying mental health disorders, and poor access to needle-syringe exchange programs and HIV pre-exposure prophylaxis pose grave challenges to control these epidemics. In this chapter, we focus on the impact of the global injection drug use epidemic as well as new approaches on HIV prevention and the HIV care continuum for people who inject drugs.
KeywordsHIV PWID Artificial intelligence Machine learning Injection drug use Care continuum Social media Phylodynamics Phylogenetic Digital technologies
Conflicts of Interest
The authors report no potential conflicts of interest.
- 1.World Health Organization (WHO). HIV/AIDS. https://www.who.int/news-room/fact-sheets/detail/hiv-aids. Published 2018. Updated July 19, 2018. Accessed 15 Apr 2019.
- 3.World Health Organization (WHO). HIV/AIDS: People who inject drugs. https://www.who.int/hiv/topics/idu/en/. Published 2019. Accessed 15 Apr 2019.
- 10.National Institute on Drug Abuse. Opioid overdose crisis. https://www.drugabuse.gov/drugs-abuse/opioids/opioid-overdose-crisis#nine. Published 2019. Updated January 2019. Accessed 15 Apr 2019.
- 12.Joint United Nations Programme on HIV/AIDS (UNAIDS). Miles to go. Closing gaps, breaking barriers, righting injustices. 2018. https://www.unaids.org/sites/default/files/media_asset/miles-to-go_en.pdf. Accessed 16 Apr 2019.
- 15.WHO. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis. Geneva: World Health Organization.Google Scholar
- 16.Joint United Nations Programme on HIV/AIDS (UNAIDS). Fast-track: ending the AIDS epidemic by 2030. 2014. https://www.unaids.org/sites/default/files/media_asset/JC2686_WAD2014report_en.pdf. Accessed 18 Apr 2019.
- 17.Centers for Disease Control (CDC). HIV among people who inject drugs. https://www.cdc.gov/hiv/group/hiv-idu.html. Published 2019. Updated March 15, 2019. Accessed 18 Apr 2019.
- 20.Miller WC, Hoffman IF, Hanscom BS, et al. A scalable, integrated intervention to engage people who inject drugs in HIV care and medication-assisted treatment (HPTN 074): a randomised, controlled phase 3 feasibility and efficacy study. Lancet (London, England). 2018;392(10149):747–59.CrossRefGoogle Scholar
- 23.Degenhardt LMB, Wirtz AL, Wolfe D, Kamarulzaman A, Carrieri MP, Strathdee SAM-SK, Kazatchkine M, Beyrer C. What has been achieved in HIV prevention, treatment and care for people who inject drugs, 2010–2012? A review of the six highest burden countries. Int J Drug Policy. 2014;25(1):8.CrossRefGoogle Scholar
- 36.International Telecommunication Union (ITU). ICT facts and figures 2017. 2017. https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf. Accessed 19 Apr 2019.
- 40.Lelutiu-Weinberger C, Pachankis JE, Gamarel KE, Surace A, Golub SA, Parsons JT. Feasibility, acceptability, and preliminary efficacy of a live-chat social media intervention to reduce HIV risk among Young men who have sex with men. AIDS Behav. 2015;19(7):1214–27.PubMedPubMedCentralCrossRefGoogle Scholar
- 45.Singh Y, Mars NNM. Applying machine learning to predict patient-specific current CD 4 cell count in order to determine the progression of human immunodeficiency virus (HIV) infection. Afr J Biotechnol. 2013;12(23):11.Google Scholar
- 46.Larder B, Wang D, Revell A. Application of artificial neural networks for decision support in medicine. Methods Mol Biol (Clifton, NJ). 2008;458:123–36.Google Scholar
- 51.Isabelle Guyon AE. An introduction to variable and feature selection. Mach Learn Res. 2003;3:25.Google Scholar