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If You Build It, Will They Use It? Preferences for Antiretroviral Therapy (ART) Adherence Monitoring Among People Who Inject Drugs (PWID) in Kazakhstan

  • Alissa DavisEmail author
  • Lyailya Sarsembayeva
  • Valeriy Gulyaev
  • Sholpan Primbetova
  • Assel Terlikbayeva
  • Gaukhar Mergenova
  • Robert H. Remien
Original Paper
  • 3 Downloads

Abstract

Adherence to antiretroviral therapy (ART) is an important predictor of long-term treatment success and is associated with optimal individual and public health outcomes. Novel technologies, such as electronic monitoring devices (EMDs) or pharmacokinetic testing, provide more objective measures of ART adherence than traditional measures of adherence (e.g., self-report) and may facilitate improved adherence through the provision of patient feedback. This study examines preferences for ART adherence monitoring among people who inject drugs (PWID) in Kazakhstan. In-depth interviews were conducted with 20 HIV-positive PWID, 18 of their intimate partners, and 7 AIDS Center healthcare providers in Almaty, Kazakhstan. Results indicated that patients varied in their preferences of which strategies would be most effective and acceptable to use in monitoring their adherence. Overall, patients were highly enthusiastic about the potential use of pharmacokinetic testing. Many participants supported the use of EMDs, though some were concerned about having their adherence tracked. Other participants thought reminders through text messaging or smart phone applications would be helpful, though several had concerns about confidentiality and others worried about technological difficulties operating a smart phone. Future studies should evaluate the feasibility and impact of providing quantitative drug levels as feedback for ART adherence using biomarkers of longer-term ART exposure, (i.e., hair sampling or dried blood spot testing).

Keywords

Antiretroviral therapy adherence People who inject drugs Drug monitoring Kazakhstan 

Notes

Acknowledgements

We would like to thank the individuals that participated in this study.

Funding

This study was supported by funding from the HIV Center for Clinical and Behavioral Studies at Columbia University and the New York State Psychiatric Institute through the National Institute of Mental Health (P30MH043520). Dr. Davis also received support from the National Institute of Mental Health (T32MH019139) and the National Institute of Drug Abuse (K01DA044853-01A1).

Compliance with Ethical Standards

Conflict of Interest

Alissa Davis declares that she has no conflict of interest. Lyailya Sarsembayeva declares that she has no conflict of interest. Valeriy Gulyaev declares that he has no conflict of interest. Sholpan Primbetova declares that she has no conflict of interest. Assel Terlikbayeva declares that she has no conflict of interest. Gaukhar Mergenova declares that she has no conflict of interest. Robert H. Remien declares that he has no conflict of interest.

Ethical Approval

This study received approval from institutional review boards at the New York State Psychiatric Institute, Columbia University, and the Kazakhstan School of Public Health. All procedures performed in studies involving human subjects were in accordance with the ethical standards of the institutional and/or National Research Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    UNAIDS. Global AIDS update. 2016.Google Scholar
  2. 2.
    Sarang A, Rhodes T, Sheon N. Systemic barriers accessing HIV treatment among people who inject drug in Russia: a qualitative study. Health Policy Plan. 2013;28:681–91.Google Scholar
  3. 3.
    Wolfe D. Paradoxes in antiretroviral treatment for injecting drug users: access, adherence and structural barriers in Asia and the former Soviet Union. Int J Drug Policy. 2007;18:246–54.Google Scholar
  4. 4.
    Republican AIDS Center. HIV care cascade among people living with HIV in Kazakhstan. 2017.Google Scholar
  5. 5.
    Davis A, McCrimmon T, Dasgupta A, et al. Individual, social, and structural factors affecting antiretroviral therapy adherence among HIV-positive people who inject drugs in Kazakhstan. Int J Drug Policy. 2018;62:43–50.Google Scholar
  6. 6.
    Kazakhstan Republican AIDS Center. Clinical Protocol: 2017. Kazakhstan Republican AIDS Center; 2017.Google Scholar
  7. 7.
    UNAIDS. 90-90-90: An ambitious treatment target to help end the AIDS epidemic. Geneva: UNAIDS; 2014.Google Scholar
  8. 8.
    Castillo-Mancilla J, Searls K, Caraway P, et al. Tenofovir diphosphate in dried blood spots as an objective measure of adherence in HIV-infected women. AIDS Res Hum Retroviruses. 2015;31(4):428–32.Google Scholar
  9. 9.
    Gaiter J, Johnson W, Taylor E, et al. Sisters empowered, sisters aware: three strategies to recruit African American women for HIV testing. AIDS Educ Prev. 2013;25(3):190–202.Google Scholar
  10. 10.
    Tabb Z, Mmbaga B, Gandhi M, et al. Association of self-reported adherence and antiretroviral drug concentrations in hair among youth with virologic failure in Tanzania. Open Forum Infect Dis. 2017;4(Suppl 1):S663–4.Google Scholar
  11. 11.
    Haberer J, Robbins G, Ybarra M, et al. Real-time electronic adherence monitoring is feasible, comparable to unannounced pill counts, and acceptable. AIDS Behav. 2012;16(2):375–82.Google Scholar
  12. 12.
    Haberer J, Kiwanuka J, Nansera D, et al. Realtime adherence monitoring of antiretroviral therapy among HIV-infected adults and children in rural Uganda. AIDS. 2013;27(3):2166–8.Google Scholar
  13. 13.
    DeSilva M, Gifford A, Bonawitz R, et al. Real-time electronic drug monitoring for HIV-positive adolescents: promising acceptability and feasibility in China. J AIDS Clin Res. 2016;7:586.Google Scholar
  14. 14.
    DeSilva M, Gifford A, Keyi X, et al. Feasibility and acceptability of a real-time adherence device among HIV-positive IDU Patients in China. AIDS Res Treatm. 2013;2013:957862.Google Scholar
  15. 15.
    Haberer J, Sabin L, Amico K, et al. Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendations. J Int AIDS Soc. 2017;20(1):21371.Google Scholar
  16. 16.
    Montgomery E, Mensch B, Musara P, et al. Misreporting of product adherence in the MTN-003/VOICE trial for HIV prevention in Africa: participants’ explanations for dishonesty. AIDS Behav. 2017;21(2):481–91.Google Scholar
  17. 17.
    Levine A, Hinkin C, Marion S, et al. Adherence to antiretroviral medications in HIV: differences in data collected via self-report and electronic monitoring. Health Psychol. 2006;25:329–35.Google Scholar
  18. 18.
    Pearson C, Simoni J, Hoff P, et al. Assessing antiretroviral adherence via electronic drug monitoring and self-report: an examination of key methodological issues. AIDS Behav. 2007;11:161–73.Google Scholar
  19. 19.
    de Boer I, Prins J, Sprangers MA, et al. Using different calculations of pharmacy refill adherence to predict virological failure among HIV-infected patients. J Acquir Immune Defic Syndr. 2010;55:635–40.Google Scholar
  20. 20.
    Grossberg R, Gross R. Use of pharmacy refill data as a measure of antiretroviral adherence. Curr HIV/AIDS Rep. 2007;4:187–91.Google Scholar
  21. 21.
    Bonner K, Mezochow A, Roberts T, Ford N, Cohn J. Viral load monitoring as a tool to reinforce adherence: a systematic review. J Acquir Immune Defic Syndr. 2013;64(1):74–8.Google Scholar
  22. 22.
    Bangsberg D, Mills E. Long-term adherence to antiretoviral therapy in resource-limited settings: a bitter pill to swallow. Antiviral Therapy. 2013;18(1):25–8.Google Scholar
  23. 23.
    Ford N, Darder M, Spelman T, Maclean E, Boulle A. Early adherence to antiretroviral medication as a predictor of long-term HIV virological suppression: Five-year follow up of an observational cohort. PLoS ONE. 2010;5(5):e10460.Google Scholar
  24. 24.
    Mills E, Nachega J, Buchan I, et al. Adherence to antiretroviral therapy in sub-Saharan Africa and North America: a meta-analysis. JAMA. 2006;296(6):679–90.Google Scholar
  25. 25.
    Bangsberg D. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis. 2006;43(7):939–41.Google Scholar
  26. 26.
    Rosenblum M, Deeks S, van der Laan M, Bangsberg D. The risk of virologic failure decreases with duration of HIV suppression, at greater than 50% adherence to antiretroviral therapy. PLoS ONE. 2009;4(9):e7196.Google Scholar
  27. 27.
    Lima V, Bangsberg D, Harrigan P, et al. Risk of viral failure declines with duration of suppression on highly active antiretroviral therapy irrespective of adherence level. J Acquir Immune Defic Syndr. 2010;55(4):460–5.Google Scholar
  28. 28.
    Parienti J, Das-Douglas M, Massari V, et al. Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels. PLoS ONE. 2008;3(7):e2783.Google Scholar
  29. 29.
    Ncaca L, Kranzer K, Orrell C. Treatment interruption and variation in tablet taking behaviour result in viral failure: a case-control study from Cape Town, South Africa. PLoS ONE. 2011;6(8):e23088.Google Scholar
  30. 30.
    Haberer J, Kahane J, Kigozi I, et al. Real-time adherence monitoring for HIV antiretroviral therapy. AIDS Behav. 2010;14(6):1340–6.Google Scholar
  31. 31.
    Castillo-Mancilla J, Bushman L, Meditz A, et al. Emtricitabine-triphosphate in dried blood spots (DBS) as a marker of recent dosing. In: 22nd Conference on Retroviruses and Opportunistic Infections; February 25, 2015; Seattle, WA2015.Google Scholar
  32. 32.
    Liu A, Yang Q, Huan Y, et al. Strong relationship between oral dose and tenofovir hair levels in a randomized trial: hair as a potential adherence measure for pre-exposure prophylaxis (PrEP). PLoS ONE. 2014;9(1):e83736.Google Scholar
  33. 33.
    Henny K, Wilkes A, McDonald C, Denson D, Neumann M. A rapid review of eHealth interventions addressing the continuum of HIV care (2007–2017). AIDS Behav. 2018;22(1):43–63.Google Scholar
  34. 34.
    Mbuagbaw L, van der Kop M, Lester R, et al. Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials. BMJ Open. 2013;3(12):e003950.Google Scholar
  35. 35.
    Finitsis D, Pellowski J, Johnson B. Text message intervention designs to promote adherence to antiretroviral therapy (ART): a meta-analysis of randomized controlled trials. PLoS ONE. 2014;9(2):e88166.Google Scholar
  36. 36.
    Moore D, Pasipanodya E, Umlauf A, et al. Individualized texting for adherence building (iTAB) for methamphetamine users living with HIV: a pilot randomized clinical trial. Drug Alcohol Depend. 2018;189:154–60.Google Scholar
  37. 37.
    Sabin L, DeSilva M, Gill C, et al. Improving adherence to antiretroviral therapy with triggered real-time text message reminders: the China adherence through technology study. J Acquir Immune Defic Syndr. 2015;69(5):551–9.Google Scholar
  38. 38.
    Orrell C, Cohen K, Mauff K, et al. A randomized controlled trial of real-time electronic adherence monitoring with text message dosing reminders in people starting first-line antiretroviral therapy. J Acquir Immune Defic Syndr. 2015;70(5):495–502.Google Scholar
  39. 39.
    Haberer J, Musiimenta A, Atukunda E, et al. Short message service (SMS) reminders and real-time adherence monitoring improve antiretroviral therapy adhrence in rural Uganda. AIDS. 2016;30(8):1295–300.Google Scholar
  40. 40.
    Haberer J, Musinguzi N, Tsai A, et al. Real-time electronic adherence monitoring plus follow-up improves adherence compared with standard electronic adherence monitoring. AIDS. 2017;31(1):169–71.Google Scholar
  41. 41.
    Sabin L, DeSilva M, Hamer D, et al. Using electronic drug monitor feedback to improve adherence to antiretroviral therapy among HIV-positive patients in China. AIDS Behav. 2010;14:580–9.Google Scholar
  42. 42.
    Castillo-Mancilla J, Zheng J, Rower J, et al. Tenofovir, emtricitabine, and tenofovir diphosphate in dried blood spots for determining recent and cumulative drug exposure. AIDS Res Hum Retroviruses. 2013;29(2):384–90.Google Scholar
  43. 43.
    Beumer J, Bosman I, Maes R. Hair as a biological specimen for therapeutic drug monitoring. Int J Clin Pract. 2001;55:353–7.Google Scholar
  44. 44.
    Guest G, MacQueen K, Namey E. Applied thematic analysis. Thousand Oaks: SAGE; 2011.Google Scholar
  45. 45.
    Martin C, Upvall M. A Mobile phone HIV medication adherence intervention: acceptability and feasibility study. J Assoc Nurses AIDS Care. 2016;27(6):804–16.Google Scholar
  46. 46.
    Siedner M, Haberer J, Bwana M, Ware N, Bangsberg D. High acceptability for cell phone text messages to improve communication of laboratory results with HIV-infected patients in rural Uganda: a cross-sectional survey study. BMC Med Inform Decis Mak. 2012;12:56.Google Scholar
  47. 47.
    Sabin L, Mansfield L, DeSilva M, et al. Why it worked: participants’ insights into an mHealth antiretroviral therapy adherence intervention in China. Open AIDS J. 2018;12:20–37.Google Scholar
  48. 48.
    Anderson P, Glidden D, Liu A, et al. Emtricitabine-tenofovir concentrations and pre-exposure prophylaxis effiacy in men who have sex with men. Sci Transl Med. 2012;4(151):151ra25.Google Scholar
  49. 49.
    Seewoodharry M, Maconachie G, Gillies C, Gottlob I, McLean R. The effects of feedback on adherence to treatment: a systematic review and meta-analysis of RCTs. Am J Prev Med. 2017;53(2):232–40.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Social WorkColumbia UniversityNew YorkUSA
  2. 2.Columbia University Global Health Research Center of Central AsiaAlmatyKazakhstan
  3. 3.Division of Gender, Sexuality & Health, HIV Center, New York State Psychiatric InstituteColumbia University Medical CenterNew YorkUSA

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