Opioid agonist treatment scale-up and the initiation of injection drug use: A dynamic modeling analysis

English version

Autoři: Charles Marks ^aff001; Annick Borquez ^aff003; Sonia Jain ^aff004; Xiaoying Sun ^aff004; Steffanie A. Strathdee ^aff003; Richard S. Garfein ^aff003; M-J Milloy ^aff005; Kora DeBeck ^aff005; Javier A. Cepeda ^aff003; Dan Werb ^aff003; Natasha K. Martin ^aff003
Působiště autorů: SDSU-UCSD Joint Doctoral Program in Interdisciplinary Research on Substance Use, San Diego, California, United States of America ^aff001; The School of Social Work, San Diego State University, San Diego, California, United States of America ^aff002; Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, California, United States of America ^aff003; Biostatistics Research Center, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California, United States of America ^aff004; British Columbia Centre on Substance Use, Vancouver, Canada ^aff005; Department of Medicine, University of British Columbia, Vancouver, Canada ^aff006; School of Public Policy, Simon Fraser University, Vancouver, Canada ^aff007; Population Health Sciences, University of Bristol, Bristol, United Kingdom ^aff008
Vyšlo v časopise: Opioid agonist treatment scale-up and the initiation of injection drug use: A dynamic modeling analysis. PLoS Med 16(11): e32767. doi:10.1371/journal.pmed.1002973
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1002973

Souhrn

Background

Injection drug use (IDU) is associated with multiple health harms. The vast majority of IDU initiation events (in which injection-naïve persons first adopt IDU) are assisted by a person who injects drugs (PWID), and as such, IDU could be considered as a dynamic behavioral transmission process. Data suggest that opioid agonist treatment (OAT) enrollment is associated with a reduced likelihood of assisting with IDU initiation. We assessed the association between recent OAT enrollment and assisting IDU initiation across several North American settings and used dynamic modeling to project the potential population-level impact of OAT scale-up within the PWID population on IDU initiation.

Methods and findings

We employed data from a prospective multicohort study of PWID in 3 settings (Vancouver, Canada [n = 1,737]; San Diego, United States [n = 346]; and Tijuana, Mexico [n = 532]) from 2014 to 2017. Site-specific modified Poisson regression models were constructed to assess the association between recent (past 6 month) OAT enrollment and history of ever having assisted an IDU initiation with recently assisting IDU initiation. Findings were then pooled using linear mixed-effects techniques. A dynamic transmission model of IDU among the general population was developed, stratified by known factors associated with assisting IDU initiation and relevant drug use behaviors. The model was parameterized to a generic North American setting (approximately 1% PWID) and used to estimate the impact of increasing OAT coverage among PWID from baseline (approximately 21%) to 40%, 50%, and 60% on annual IDU initiation incidence and corresponding PWID population size across a decade. From Vancouver, San Diego, and Tijuana, respectively, 4.5%, 5.2%, and 4.3% of participants reported recently assisting an IDU initiation, and 49.4%, 19.7%, and 2.1% reported recent enrollment in OAT. Recent OAT enrollment was significantly associated with a 45% lower likelihood of providing recent IDU initiation assistance among PWID (relative risk [RR] 0.55 [95% CI 0.36–0.84], p = 0.006) compared to those not recently on OAT. Our dynamic model predicts a baseline mean of 1,067 (2.5%–97.5% interval [95% I 490–2,082]) annual IDU initiations per 1,000,000 individuals, of which 886 (95% I 406–1,750) are assisted by PWID. Based on our observed statistical associations, our dynamic model predicts that increasing OAT coverage from approximately 21% to 40%, 50%, or 60% among PWID could reduce annual IDU initiations by 11.5% (95% I 2.4–21.7), 17.3% (95% I 5.6–29.4), and 22.8% (95% I 8.1–36.8) and reduce the PWID population size by 5.4% (95% I 0.1–12.0), 8.2% (95% I 2.2–16.9), and 10.9% (95% I 3.2–21.8) relative to baseline, respectively, in a decade. Less impact occurs when the protective effect of OAT is diminished, when a greater proportion of IDU initiations are unassisted by PWID, and when average IDU career length is longer. The study’s main limitations are uncertainty in the causal pathway between OAT enrollment and assisting with IDU initiation and the use of a simplified model of IDU initiation.

Conclusions

In addition to its known benefits on preventing HIV, hepatitis C virus (HCV), and overdose among PWID, our modeling suggests that OAT scale-up may also reduce the number of IDU initiations and PWID population size.

Klíčová slova:

Cannabis – Death rates – Drug research and development – Drug therapy – Drug users – HIV prevention – Opioids – Recreational drug use

Zdroje

1. Degenhardt L, Peacock A, Colledge S, Leung J, Grebely J, Vickerman P, 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: e1192–e1207. doi: 10.1016/S2214-109X(17)30375-3 29074409

2. Banerjee G, Edelman EJ, Barry DT, Becker WC, Cerdá M, Crystal S, et al. Non-medical use of prescription opioids is associated with heroin initiation among US veterans: a prospective cohort study. Addiction. 2016;111: 2021–2031. doi: 10.1111/add.13491 27552496

3. Jalal H, Buchanich JM, Roberts MS, Balmert LC, Zhang K, Burke DS. Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016. Science. 2018;361. doi: 10.1126/science.aau1184

4. Novak SP, Kral AH. Comparing Injection and Non-Injection Routes of Administration for Heroin, Methamphetamine, and Cocaine Users in the United States. J Addict Dis. 2011;30: 248–257. doi: 10.1080/10550887.2011.581989 21745047

5. Vlahov David; Fuller CM; Ompad DC; Galea S; Des Jarleis D. Updating the Infection Risk Reduction Hierarchy: Preventing Transition into Injection. J Urban Heal Bull New York Acad Med. 2004;81: 14–19. doi: 10.1093/jurban/jth083

6. Werb D, Garfein R, Kerr T, Davidson P, Roux P, Jauffret-Roustide M, et al. A socio-structural approach to preventing injection drug use initiation: rationale for the PRIMER study. Harm Reduct J. 2016;13. doi: 10.1186/s12954-016-0103-4

7. Bluthenthal RN, Wenger L, Chu D, Lorvick J, Quinn B, Thing JP, et al. Factors associated with being asked to initiate someone into injection drug use. Drug Alcohol Depend. 2015;149: 252–258. doi: 10.1016/j.drugalcdep.2015.02.011 25735468

8. Morris MD, Brouwer KC, Lozada RM, Gallardo M, Vera A, Strathdee SA. “Injection First”: A Unique Group of Injection Drug Users in Tijuana, Mexico. Am J Addict. 2012;21: 23–30. doi: 10.1111/j.1521-0391.2011.00194.x 22211343

9. Jauffret-Roustide M, Le Strat Y, Couturier E, Thierry D, Rondy M, Quaglia M, et al. A national cross-sectional study among drug-users in France: epidemiology of HCV and highlight on practical and statistical aspects of the design. BMC Infect Dis. 2009;9. doi: 10.1186/1471-2334-9-113

10. Werb D, Kerr T, Buxton J, Shoveller J, Richardson C, Montaner J, et al. Crystal methamphetamine and initiation of injection drug use among street-involved youth in a Canadian setting. Can Med Assoc J. 2013;185: 1569–1575. doi: 10.1503/cmaj.130295

11. Kermode M, Longleng V, Singh B, Hocking J, Langkham B, Crofts N. My first time: initiation into injecting drug use in Manipur and Nagaland, north-east India. Harm Reduct J. 2007;4. doi: 10.1186/1477-7517-4-19

12. Day CA, Ross J, Dietze P, Dolan K. Initiation to heroin injecting among heroin users in Sydney, Australia: cross sectional survey. Harm Reduct J. 2005;2. doi: 10.1186/1477-7517-2-2

13. Mittal ML, Jain S, Sun S, DeBeck K, Milloy MJ, Hayashi K, et al. Opioid agonist treatment and the process of injection drug use initiation. Drug Alcohol Depend. 2019;197: 354–360. doi: 10.1016/j.drugalcdep.2018.12.018 30922483

14. Rafful C, Melo J, Medina-Mora ME, Rangel G, Sun X, Jain S, et al. Cross-Border Migration and Initiation of Others into Drug Injecting in Tijuana, Mexico. Drug Alcohol Rev. 2018;37: S277–S284. doi: 10.1111/dar.12630 29168262

15. Hunt N, Stillwell G, Taylor C, Griffiths P. Evaluation of a Brief Intervention to Prevent Initiation into Injecting. Drugs Educ Prev Policy. 1998;5: 185–194. doi: 10.3109/09687639809006684

16. Mittal ML, Vashishtha D, Sun S, Jain S, Cuevas-Mota J, Garfein R, et al. History of medication-assisted treatment and its association with initiating others into injection drug use in San Diego, CA. Subst Abuse Treat Prev Policy. 2017;12. doi: 10.1186/s13011-017-0126-1

17. Larney S, Peacock A, Leung J, Colledge S, Hickman M, Vickerman P, 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: e1208–e1220. doi: 10.1016/S2214-109X(17)30373-X 29074410

18. Gowing L, Farrell MF, Bornemann R, Sullivan LE, Ali R. Oral substitution treatment of injecting opioid users for prevention of HIV infection. Cochrane Database Syst Rev. 2011; doi: 10.1002/14651858.CD004145.pub4

19. Karki P, Shrestha R, Huedo-Medina TB, Copenhaver M. The Impact of Methadone Maintenance Treatment on HIV Risk Behaviors among High-Risk Injection Drug Users: A Systematic Review. Evidence-based Med public Heal. 2017;2: 139–148. doi: 10.1016/j.physbeh.2017.03.040

20. Ickowicz S, Wood E, Dong H, Nguyen P, Small W, Kerr T, et al. Association between public injecting and drug-related harm among HIV-positive people who use injection drugs in a Canadian setting: A longitudinal analysis. Drug Alcohol Depend. 2017;180: 33–38. doi: 10.1016/j.drugalcdep.2017.07.016 28865390

21. Guise A, Horyniak D, Melo J, McNeil R, Werb D. The experience of initiating injection drug use and its social context: a qualitative systematic review and thematic synthesis. Addiction. 2017;112: 2098–2111. doi: 10.1111/add.13957 28734128

22. Fraser H, Vellozzi C, Hoerger TJ, Evans JL, Kral AH, Havens J, et al. Scaling-up Hepatitis C Prevention and Treatment Interventions for Achieving Elimination in the United States–a Rural and Urban Comparison. Am J Epidemiol. 2019;21205: 2007–2015. doi: 10.1093/aje/kwz097

23. Borquez A, Beletsky L, Nosyk B, Strathdee SA, Madrazo A, Abramovitz D, et al. The effect of public health-oriented drug law reform on HIV incidence in people who inject drugs in Tijuana, Mexico: an epidemic modelling study. Lancet Public Heal. 2018;3: e429–e437. doi: 10.1016/S2468-2667(18)30097-5

24. Cepeda JA, Eritsyan K, Vickerman P, Lyubimova A, Shegay M, Odinokova V, et al. Potential impact of implementing and scaling up harm reduction and antiretroviral therapy on HIV prevalence and mortality and overdose deaths among people who inject drugs in two Russian cities: a modelling study. Lancet HIV. 2018;5: e578–e587. doi: 10.1016/S2352-3018(18)30168-1 30033374

25. Martin NK, Vickerman P, Grebely J, Hellard M, Hutchinson SJ, Lima VD, et al. Hepatitis C Virus Treatment for Prevention Among People Who Inject Drugs: Modeling Treatment Scale-Up in the Age of Direct-Acting Antivirals. Hepatology. 2013;58: 1598–1609. doi: 10.1002/hep.26431 23553643

26. Mackintosh DR, Stewart GT. A mathematical model of a heroin epidemic: implications for control policies. J Epidemiol Community Heal. 1979;33: 299–304. doi: 10.1136/jech.33.4.299

27. Caulkins JP, Tragler G, Wallner D. Optimal timing of use reduction vs. harm reduction in a drug epidemic model. Int J Drug Policy. 2009;20: 480–487. doi: 10.1016/j.drugpo.2009.02.010 19361975

28. Behrens DA, Caulkins JP, Tragler G, Haunschmied JL, Feichtinger G. A dynamic model of drug initiation: implications for treatment and drug control. Math Biosci. 1999;159: 1–20. doi: 10.1016/s0025-5564(99)00016-4 10361802

29. Almeder C, Caulkins JP, Feichtinger G, Tragler G. An age-structured single-state drug initiation model—cycles of drug epidemics and optimal prevention programs. Socioecon Plann Sci. 2004;38: 91–109. doi: 10.1016/S0038-0121(03)00030-2

30. Nyabadza F, Mukwembi S, Rodrigues BG. A graph theoretical perspective of a drug abuse epidemic model. Phys A Stat Mech its Appl. 2011;390: 1723–1732. doi: 10.1016/j.physa.2011.01.014

31. Everingham Susan; Rydell CP. Modeling the Demand for Cocaine. Santa Monica; 1994.

32. Santoro M, Triolo L, Rossi C. Drug user dynamics: A compartmental model of drug users for scenario analyses. Drugs Educ Prev Policy. 2013;20: 184–194. doi: 10.3109/09687637.2012.750274

33. Rossi C. The role of dynamic modelling in drug abuse epidemiology. Bull Narc. 2002;54: 33–44.

34. Wakeland W, Nielsen A, Geissert P. Dynamic Model of Nonmedical Opioid Use Trajectories and Potential Policy Interventions. Am J Drug Alcohol Abuse. 2015;41: 508–518. doi: 10.3109/00952990.2015.1043435 25982491

35. Wakeland W, Nielsen A, Schmidt TD, McCarty D, Webster LR, Fitzgerald J, et al. Modeling the Impact of Simulated Educational Interventions on the Use and Abuse of Pharmaceutical Opioids in the United States. Heal Educ Behav. 2013;40: 74S–86S. doi: 10.1177/1090198113492767

36. Behrens DA, Caulkins JP, Tragler G, Feichtinger G. Optimal Control of Drug Epidemics: Prevent and Treat—But Not at the Same Time? Manage Sci. 2000;46: 333–347. doi: 10.1287/mnsc.46.3.333.12068

37. Caulkins JP, Dietze P, Ritter A. Dynamic compartmental model of trends in Australian drug use. Health Care Manag Sci. 2007;10: 151–162. doi: 10.1007/s10729-007-9012-0 17608056

38. Rossi C. Operational models for epidemics of problematic drug use: the Mover–Stayer approach to heterogeneity. Socioecon Plann Sci. 2004;38: 73–90. doi: 10.1016/S0038-0121(03)00029-6

39. White SR, Hutchinson SJ, Taylor A, Bird SM. Modeling the Initiation of Others Into Injection Drug Use, Using Data From 2,500 Injectors Surveyed in Scotland During 2008–2009. Am J Epidemiol. 2015;181: 771–780. doi: 10.1093/aje/kwu345 25787265

40. De Angelis Daniela; Matthew Hickman; Yang S. Estimating Long-term Trends in the Incidence and Prevalence of Opiate Use/Injecting Drug Use and the Number of Former Users: Back-Calculation Methods and Opiate Overdose Deaths. Am J Epidemiol. 2004;160: 994–1004. doi: 10.1093/aje/kwh306 15522856

41. Sánchez-Niubò A, Aalen OO, Domingo-Salvany A, Amundsen EJ, Fortiana J, Røysland K. A multi-state model to estimate incidence of heroin use. BMC Med Res Methodol. 2013;13. doi: 10.1186/1471-2288-13-4

42. Robertson AM, Garfein RS, Wagner KD, Mehta SR, Magis-Rodriguez C, Cuevas-Mota J, et al. Evaluating the impact of Mexico’s drug policy reforms on people who inject drugs in Tijuana, B.C., Mexico, and San Diego, CA, United States: a binational mixed methods research agenda. Harm Reduct J. 2014;11. doi: 10.1186/1477-7517-11-4

43. Cheng T, Small W, Nosova E, Hogg B, Hayashi K, Kerr T, et al. Nonmedical prescription opioid use and illegal drug use: initiation trajectory and related risks among people who use illegal drugs in Vancouver, Canada. BMC Res Notes. 2018;11. doi: 10.1186/s13104-018-3152-9

44. Prangnell A, Dong H, Daly P, Milloy MJ, Kerr T, Hayashi K. Declining rates of health problems associated with crack smoking during the expansion of crack pipe distribution in Vancouver, Canada. BMC Public Health. 2017;17. doi: 10.1186/s12889-017-4099-9

45. Wenger LD, Lopez AM, Kral AH, Bluthenthal RN. Moral ambivalence and the decision to initiate others into injection drug use: A qualitative study in two California cities. Int J Drug Policy. 2016;37: 42–51. doi: 10.1016/j.drugpo.2016.07.008 27572714

46. Melo JS, Garfein RS, Hayashi K, Milloy MJ, DeBeck K, Sun S, et al. Do law enforcement interactions reduce the initiation of injection drug use? An investigation in three North American settings. Drug Alcohol Depend. 2018;182: 67–73. doi: 10.1016/j.drugalcdep.2017.10.009 29169035

47. Zou G. A Modified Poisson Regression Approach to Prospective Studies with Binary Data. Am J Epidemiol. 2004;159: 702–706. doi: 10.1093/aje/kwh090 15033648

48. Blettner M, Sauerbrei W, Schlehofer B, Scheuchenpflug T, Friedenreich C. Traditional reviews, meta-analyses and pooled analyses in epidemiology. Int J Epidemiol. 1999;28: 1–9. doi: 10.1093/ije/28.1.1 10195657

49. Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36. doi: 10.18637/jss.v036.i03

50. Young AM, Havens JR. Transition from first illicit drug use to first injection drug use among rural Appalachian drug users: a cross-sectional comparison and retrospective survival analysis. Addiction. 2012;107: 587–596. doi: 10.1111/j.1360-0443.2011.03635.x 21883604

51. Trenz RC, Scherer M, Harrell P, Zur J, Sinha A, Latimer W. Early Onset of Drug and Polysubstance Use as Predictors of Injection Drug Use Among Adult Drug Users. Addict Behav. 2012;37: 367–372. doi: 10.1016/j.addbeh.2011.11.011 22172686

52. Reddon H, DeBeck K, Socias ME, Dong H, Wood E, Montaner J, et al. Cannabis use is associated with lower rates of initiation of injection drug use among street-involved youth: A longitudinal analysis. Drug Alcohol Rev. 2018;37: 421–428. doi: 10.1111/dar.12667 29430806

53. Xia Y, Seaman S, Hickman M, Macleod J, Robertson R, Copeland L, et al. Factors affecting repeated cessations of injecting drug use and relapses during the entire injecting career among the Edinburgh Addiction Cohort. Drug Alcohol Depend. 2015;151: 76–83. doi: 10.1016/j.drugalcdep.2015.03.005 25869544

54. Nosyk B, Anglin MD, Brecht M-L, Lima VD, Hser Y-I. Characterizing Durations of Heroin Abstinence in the California Civil Addict Program: Results From a 33-Year Observational Cohort Study. Am J Epidemiol. 2013;177: 675–682. doi: 10.1093/aje/kws284 23445901

55. Shah NG, Galai N, Celentano DD, Vlahov D, Strathdee SA. Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988–2000. Drug Alcohol Depend. 2006;83: 147–156. doi: 10.1016/j.drugalcdep.2005.11.007 16364568

56. Kimber J, Copeland L, Hickman M, Macleod J, McKenzie J, De Angelis D, et al. Survival and cessation in injecting drug users: prospective observational study of outcomes and effect of opiate substitution treatment. BMJ. 2010;341: c3172–c3172. doi: 10.1136/bmj.c3172 20595255

57. Termorshuizen F, Krol A, Prins M, Geskus R, van den Brink W, van Ameijden EJC. Prediction of relapse to frequent heroin use and the role of methadone prescription: An analysis of the Amsterdam Cohort Study among drug users. Drug Alcohol Depend. 2005;79: 231–240. doi: 10.1016/j.drugalcdep.2005.01.013 16002032

58. Sordo L, Barrio G, Bravo MJ, Indave BI, Degenhardt L, Wiessing L, et al. Mortality risk during and after opioid substitution treatment: systematic review and meta-analysis of cohort studies. BMJ. 2017;357: j1550. doi: 10.1136/bmj.j1550 28446428

59. Arias E, Xu J. United States Life Tables, 2015. Natl Vital Stat Rep. 2015;67. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_07-508.pdf. [cited 2019 May 1].

60. Mathers BM, Degenhardt L, Bucello C, Lemon J, Wiessing L, Hickman M. Mortality among people who inject drugs: a systematic review and meta-analysis. Bull World Health Organ. 2013;91: 102–123. doi: 10.2471/BLT.12.108282 23554523

61. Department of Health U.S. and Human Services; Substance Abuse and Menthal Health Services Administration; Center for Behavioral Health Statistics and Quality. National Survey on Drug Use and Health Public Use Data. [Internet]. 2018. Available from: https://datafiles.samhsa.gov/. [cited 2019 May 1].

62. Lipari RN, Ahrnsbrak RD, Pemberton MR, Porter JD. Risk and Protective Factors and Estimates of Substance Use Initiation: Results from the 2016 National Survey on Drug Use and Health [Internet]. CBHSQ Data Review. Rockville (MD): Substance Abuse and Mental Health Services Administration; 2017. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29431965. [cited 2019 May 1].

63. Genberg BL, Gange SJ, Go VF, Celentano DD, Kirk GD, Mehta SH. Trajectories of Injection Drug Use Over 20 Years (1988–2008) in Baltimore, Maryland. Am J Epidemiol. 2011;173: 829–836. doi: 10.1093/aje/kwq441 21320867

64. Saloner B, Daubresse M, Caleb Alexander G. Patterns of Buprenorphine-Naloxone Treatment for Opioid Use Disorder in a Multistate Population. Med Care. 2017;55: 669–676. doi: 10.1097/MLR.0000000000000727 28410339

65. Manhapra A, Agbese E, Leslie DL, Rosenheck RA. Three-Year Retention in Buprenorphine Treatment for Opioid Use Disorder Among Privately Insured Adults. Psychiatr Serv. 2018;69: 768–776. doi: 10.1176/appi.ps.201700363 29656707

66. Soetaert K, Petzoldt T, Setzer RW. Solving Differential Equations in R: Package deSolve. J Stat Softw. 2010;33. doi: 10.18637/jss.v033.i09

67. Elzhov T, Mullen K, Spiess A, Bolker B. Package “minpack. lm” [Internet]. 2016. Available from: https://cran.r-project.org/web/packages/minpack.lm/minpack.lm.pdf. [cited 2019 May 1].

68. Blower SM, Dowlatabadi H. Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: An HIV Model, as an Example. Int Stat Rev. 1994;62: 229. doi: 10.2307/1403510

69. Amato L, Davoli M, Minozzi S, Ferroni E, Ali R, Ferri M. Methadone at tapered doses for the management of opioid withdrawal. Cochrane Database Syst Rev. 2013; doi: 10.1002/14651858.CD003409.pub4

70. Mattick RP, Breen C, Kimber J, Davoli M. Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database Syst Rev. 2009; doi: 10.1002/14651858.CD002209.pub2

71. Brugal MT, Domingo-Salvany A, Puig R, Barrio G, García de Olalla P, de la Fuente L. Evaluating the impact of methadone maintenance programmes on mortality due to overdose and aids in a cohort of heroin users in Spain. Addiction. 2005;100: 981–989. doi: 10.1111/j.1360-0443.2005.01089.x 15955014

72. Tran BX, Ohinmaa A, Duong AT, Nguyen LT, Vu PX, Mills S, et al. Cost-effectiveness of integrating methadone maintenance and antiretroviral treatment for HIV-positive drug users in Vietnam’s injection-driven HIV epidemics. Drug Alcohol Depend. 2012;125: 260–266. doi: 10.1016/j.drugalcdep.2012.02.021 22436971

73. Zaric GS, Brandeau ML, Barnett PG. Methadone Maintenance and HIV Prevention: A Cost-Effectiveness Analysis. Manage Sci. 2000;46: 1013–1031. doi: 10.1287/mnsc.46.8.1013.12025

74. Barnett PG. The cost-effectiveness of methadone maintenance as a health care intervention. Addiction. 1999;94: 479–488. doi: 10.1046/j.1360-0443.1999.9444793.x 10605844

75. Platt L, Minozzi S, Reed J, Vickerman P, Hagan H, French C, et al. Needle syringe programmes and opioid substitution therapy for preventing hepatitis C transmission in people who inject drugs. Cochrane Database Syst Rev. 2017;2017. doi: 10.1002/14651858.CD012021.pub2

76. Vashishtha D, Mittal ML, Werb D. The North American opioid epidemic: current challenges and a call for treatment as prevention. Harm Reduct J. 2017;14. doi: 10.1186/s12954-017-0142-5

77. Alderks C. Trends in the use of methadone, buprenorphine, and extended-release naltrexone at substance abuse treatment facilities: 2003–2015 (Update) [Internet]. Rockville (MD); 2017. Available from: https://www.samhsa.gov/data/sites/default/files/report_3192/ShortReport-3192.pdf. [cited 2019 June 14]. 29200242

78. Ali S, Tahir B, Jabeen S, Malik M. Methadone Treatment of Opiate Addiction: A Systematic Review of Comparative Studies. Innov Clin Neurosci. 2017;14: 8–19. 29616150

79. Csete J. Criminal Justice Barriers to Treatment of Opioid Use Disorders in the United States: The Need for Public Health Advocacy. Am J Public Health. 2019;109: 419–422. doi: 10.2105/AJPH.2018.304852 30676805

80. Wakeman SE, Rich JD. Barriers to Medications for Addiction Treatment: How Stigma Kills. Subst Use Misuse. 2018;53: 330–333. doi: 10.1080/10826084.2017.1363238 28961017

81. Burns RM, Pacula RL, Bauhoff S, Gordon AJ, Hendrikson H, Leslie DL, et al. Policies Related to Opioid Agonist Therapy for Opioid Use Disorders: The Evolution of State Policies from 2004 to 2013. Subst Abus. 2016;37: 63–69. doi: 10.1080/08897077.2015.1080208 26566761

82. Christakis NA. Social networks and collateral health effects. BMJ. 2004;329: 184–185. doi: 10.1136/bmj.329.7459.184 15271805

83. Mattick RP, Breen C, Kimber J, Davoli M. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev. 2014;CD002207. doi: 10.1002/14651858.CD002207.pub4 24500948

84. Ferri M, Davoli M, Perucci CA. Heroin maintenance for chronic heroin-dependent individuals. Cochrane Database Syst Rev. 2011; 123–125. doi: 10.1002/14651858.CD003410.pub4

85. Larney S, Toson B, Burns L, Dolan K. Effect of prison-based opioid substitution treatment and post-release retention in treatment on risk of re-incarceration. Addiction. 2012;107: 372–380. doi: 10.1111/j.1360-0443.2011.03618.x 21851442

86. Larney S. Does opioid substitution treatment in prisons reduce injecting-related HIV risk behaviours? A systematic review. Addiction. 2010;105: 216–223. doi: 10.1111/j.1360-0443.2009.02826.x 20078480

87. Vickerman P, Martin N, Turner K, Hickman M. Can needle and syringe programmes and opiate substitution therapy achieve substantial reductions in hepatitis C virus prevalence? Model projections for different epidemic settings. Addiction. 2012;107: 1984–1995. doi: 10.1111/j.1360-0443.2012.03932.x 22564041

88. Martin NK, Hickman M, Hutchinson SJ, Goldberg DJ, Vickerman P. Combination Interventions to Prevent HCV Transmission Among People Who Inject Drugs: Modeling the Impact of Antiviral Treatment, Needle and Syringe Programs, and Opiate Substitution Therapy. Clin Infect Dis. 2013;57: S39–S45. doi: 10.1093/cid/cit296 23884064

89. Alistar SS, Owens DK, Brandeau ML. Effectiveness and Cost Effectiveness of Expanding Harm Reduction and Antiretroviral Therapy in a Mixed HIV Epidemic: A Modeling Analysis for Ukraine. Celentano DD, editor. PLoS Med. 2011;8: e1000423. doi: 10.1371/journal.pmed.1000423 21390264

90. Mabileau G, Scutelniciuc O, Tsereteli M, Konorazov I, Yelizaryeva A, Popovici S, et al. Intervention Packages to Reduce the Impact of HIV and HCV Infections Among People Who Inject Drugs in Eastern Europe and Central Asia: A Modeling and Cost-effectiveness Study. Open Forum Infect Dis. 2018;5. doi: 10.1093/ofid/ofy040

91. Marshall BDL, Friedman SR, Monteiro JFG, Paczkowski M, Tempalski B, Pouget ER, et al. Prevention And Treatment Produced Large Decreases In HIV Incidence In A Model Of People Who Inject Drugs. Health Aff. 2014;33: 401–409. doi: 10.1377/hlthaff.2013.0824

92. Roy E, Haley N, Leclerc P, Cédras L, Blais L, Boivin J-F. Drug Injection Among Street Youths in Montreal: Predictors of Initiation. J Urban Health. 2003;80: 92–105. doi: 10.1093/jurban/jtg092 12612099

93. Guise A, Melo J, Mittal ML, Rafful C, Cuevas-Mota J, Davidson P, et al. A fragmented code: The moral and structural context for providing assistance with injection drug use initiation in San Diego, USA. Int J Drug Policy. 2018;55: 51–60. doi: 10.1016/j.drugpo.2018.02.009 29524733

94. Harrison LD, Martin SS, Enev T, Harrington D. Comparing Drug Testing and Self-Report of Drug Use among Youths and Young Adults in the General Population. DHHS Publication No. SMA 07–4249, Methodology Series M-7. Rockville, MD; 2007.

95. Darke S. Self-report among injecting drug users: A review. Drug Alcohol Depend. 1998;51: 253–263. doi: 10.1016/s0376-8716(98)00028-3 9787998

96. Werb D, Jain S, Sun S, DeBeck K, Rafful C, Hayashi K. Assessing the risk of incarceration on time to first injection initiation assistance provision among a cohort of people who use drugs in Vancouver, Canada. 10th International AIDS Conference. Mexico City, Mexico; 2019.

97. Socías ME, Ahamad K. An urgent call to increase access to evidence-based opioid agonist therapy for prescription opioid use disorders. CMAJ. 2016;188: 1208–1209. doi: 10.1503/cmaj.160554 27821463