Abstract
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.
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References
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.
Degenhardt L, Peacock A, Colledge S, et al. Global prevalence of injecting drug use and sociodemographic characteristics and prevalence of HIV, HBV, and HCV in people who inject drugs: a multistage systematic review. Lancet Glob Health. 2017;5(12):e1192–207.
World Health Organization (WHO). HIV/AIDS: People who inject drugs. https://www.who.int/hiv/topics/idu/en/. Published 2019. Accessed 15 Apr 2019.
Shapshak P, Kangueane P, Fujimura RK, et al. Editorial neuroAIDS review. AIDS (London, England). 2011;25(2):123–41.
Tyagi M, Bukrinsky M, Simon GL. Mechanisms of HIV transcriptional regulation by drugs of abuse. Curr HIV Res. 2016;14(5):442–54.
Tyagi M, Weber J, Bukrinsky M, Simon GL. The effects of cocaine on HIV transcription. J Neurovirol. 2016;22(3):261–74.
Csete J, Kamarulzaman A, Kazatchkine M, et al. Public health and international drug policy. Lancet (London, England). 2016;387(10026):1427–80.
Cicero TJ, Ellis MS, Surratt HL, Kurtz SP. The changing face of heroin use in the United States: a retrospective analysis of the past 50 years. JAMA Psychiat. 2014;71(7):821–6.
Carlson RG, Nahhas RW, Martins SS, Daniulaityte R. Predictors of transition to heroin use among initially non-opioid dependent illicit pharmaceutical opioid users: a natural history study. Drug Alcohol Depend. 2016;160:127–34.
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.
Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and opioid-involved overdose deaths – United States, 2013–2017. MMWR Morb Mortal Wkly Rep. 2018;67(5152):1419–27.
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.
Campbell EM, Jia H, Shankar A, et al. Detailed transmission network analysis of a large opiate-driven outbreak of HIV infection in the United States. J Infect Dis. 2017;216(9):1053–62.
Lundgren JD, Babiker AG, Gordin F, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;373(9):795–807.
WHO. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis. Geneva: World Health Organization.
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.
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.
Escudero DJ, Lurie MN, Mayer KH, et al. The risk of HIV transmission at each step of the HIV care continuum among people who inject drugs: a modeling study. BMC Public Health. 2017;17(1):614.
Metsch L, Philbin MM, Parish C, Shiu K, Frimpong JA, Giangle M. HIV testing, care, and treatment among women who use drugs from a global perspective: progress and challenges. J Acquir Immune Defic Syndr. 2015;69(Suppl 2):S162–8.
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.
Bachireddy C, Weisberg DF, Altice FL. Balancing access and safety in prescribing opioid agonist therapy to prevent HIV transmission. Addiction. 2015;110(12):1869–71.
Bruce RD, Govindasamy S, Sylla L, Haddad MS, Kamarulzaman A, Altice FL. Case series of buprenorphine injectors in Kuala Lumpur, Malaysia. Am J Drug Alcohol Abuse. 2008;34(4):511–7.
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.
Larney S, Peacock A, Leung J, et al. Global, regional, and country-level coverage of interventions to prevent and manage HIV and hepatitis C among people who inject drugs: a systematic review. Lancet Glob Health. 2017;5(12):e1208–20.
Smith MK, Graham M, Latkin CA, Go VL. Using contact patterns to inform HIV interventions in persons who inject drugs in Northern Vietnam. J Acquir Immune Defic Syndr. 2018;78(1):1–8.
Tempalski B, Cooper HLF, Kelley ME, et al. Identifying which place characteristics are associated with the odds of recent HIV testing in a large sample of people who inject drugs in 19 US metropolitan areas. AIDS Behav. 2019;23(2):318–35.
Smith DK, Van Handel M, Grey J. Estimates of adults with indications for HIV pre-exposure prophylaxis by jurisdiction, transmission risk group, and race/ethnicity, United States, 2015. Ann Epidemiol. 2018;28:850–857.e9.
Roth A, Tran N, Piecara B, Welles S, Shinefeld J, Brady K. Factors associated with awareness of pre-exposure prophylaxis for HIV among persons who inject drugs in Philadelphia: national HIV behavioral surveillance, 2015. AIDS Behav. 2019;23(7):1833–40.
Bednasz CJ, Venuto CS, Ma Q, Morse GD. Pharmacokinetic considerations for combining antiretroviral therapy, direct-acting antiviral agents for hepatitis C virus, and addiction treatment medications. Clin Pharmacol Drug Dev. 2017;6(2):135–9.
Clarke TK, Crist RC, Ang A, et al. Genetic variation in OPRD1 and the response to treatment for opioid dependence with buprenorphine in European-American females. Pharmacogenomics J. 2014;14(3):303–8.
Nasarruddin AM, Saifi RA, Othman S, Kamarulzaman A. Opening up the HIV epidemic: a review of HIV seropositive status disclosure among people who inject drugs. AIDS Care. 2017;29(5):533–40.
Thomson N, Moore T, Crofts N. Assessing the impact of harm reduction programs on law enforcement in Southeast Asia: a description of a regional research methodology. Harm Reduct J. 2012;9:23.
Altice FL, Azbel L, Stone J, et al. The perfect storm: incarceration and the high-risk environment perpetuating transmission of HIV, hepatitis C virus, and tuberculosis in Eastern Europe and Central Asia. Lancet (London, England). 2016;388(10050):1228–48.
Muessig KE, Nekkanti M, Bauermeister J, Bull S, Hightow-Weidman LB. A systematic review of recent smartphone, Internet and Web 2.0 interventions to address the HIV continuum of care. Curr HIV/AIDS Rep. 2015;12(1):173–90.
Guse K, Levine D, Martins S, et al. Interventions using new digital media to improve adolescent sexual health: a systematic review. J Adolesc Health. 2012;51(6):535–43.
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.
Taggart T, Grewe ME, Conserve DF, Gliwa C, Roman Isler M. Social media and HIV: a systematic review of uses of social media in HIV communication. J Med Internet Res. 2015;17(11):e248.
Latkin CA, Davey-Rothwell MA, Knowlton AR, Alexander KA, Williams CT, Boodram B. Social network approaches to recruitment, HIV prevention, medical care, and medication adherence. J Acquir Immune Defic Syndr (1999). 2013;63(Suppl 1):S54–8.
Bull SS, Levine DK, Black SR, Schmiege SJ, Santelli J. Social media-delivered sexual health intervention: a cluster randomized controlled trial. Am J Prev Med. 2012;43(5):467–74.
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.
Tso LS, Tang W, Li H, Yan HY, Tucker JD. Social media interventions to prevent HIV: a review of interventions and methodological considerations. Curr Opin Psychol. 2016;9:6–10.
Jaganath D, Gill HK, Cohen AC, Young SD. Harnessing Online Peer Education (HOPE): integrating C-POL and social media to train peer leaders in HIV prevention. AIDS Care. 2012;24(5):593–600.
Brenner BG, Ibanescu RI, Hardy I, Roger M. Genotypic and phylogenetic insights on prevention of the spread of HIV-1 and drug resistance in “real-world” settings. Viruses. 2017;10(1):10.
Bisaso KR, Anguzu GT, Karungi SA, Kiragga A, Castelnuovo B. A survey of machine learning applications in HIV clinical research and care. Comput Biol Med. 2017;91:366–71.
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.
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.
Li Y, Rapkin B. Classification and regression tree uncovered hierarchy of psychosocial determinants underlying quality-of-life response shift in HIV/AIDS. J Clin Epidemiol. 2009;62(11):1138–47.
Munoz-Moreno JA, Perez-Alvarez N, Munoz-Murillo A, et al. Classification models for neurocognitive impairment in HIV infection based on demographic and clinical variables. PLoS One. 2014;9(9):e107625.
Choi I, Chung AW, Suscovich TJ, et al. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees. PLoS Comput Biol. 2015;11(4):e1004185.
Pan Y, Liu H, Metsch LR, Feaster DJ. Factors associated with HIV testing among participants from substance use disorder treatment programs in the US: a machine learning approach. AIDS Behav. 2017;21(2):534–46.
Isabelle Guyon AE. An introduction to variable and feature selection. Mach Learn Res. 2003;3:25.
Muessig KE, Knudtson KA, Soni K, et al. “I didn’t tell you sooner because I didn’t know how to handle it myself”: developing a virtual reality program to support HIV-status disclosure decisions. Digit Cult Educ. 2018;10:22–48.
Oliveira A, Faria BM, Gaio AR, Reis LP. Data mining in HIV-AIDS surveillance system: application to Portuguese data. J Med Syst. 2017;41(4):51.
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Somboonwit, C., Vazquez, L., Menezes, L.J. (2019). HIV and Injection Drug Use: New Approaches to HIV Prevention. In: Shapshak, P., et al. Global Virology III: Virology in the 21st Century. Springer, Cham. https://doi.org/10.1007/978-3-030-29022-1_14
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DOI: https://doi.org/10.1007/978-3-030-29022-1_14
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