Advertisement

AIDS and Behavior

, Volume 19, Issue 11, pp 2057–2068 | Cite as

Novel Approaches for Visualizing and Analyzing Dose-Timing Data from Electronic Drug Monitors, or “How the ‘Broken Window’ Theory Pertains to ART Adherence”

  • Christopher J. Gill
  • Mary Bachman DeSilva
  • Davidson H. Hamer
  • Xu Keyi
  • Ira B. Wilson
  • Lora Sabin
Original Paper

Abstract

Adherence to antiretroviral medications is usually expressed in terms of the proportion of doses taken. However, the timing of doses taken may also be an important dimension to overall adherence. Little is known about whether patients who mistime doses are also more likely to skip doses. Using data from the completed Adherence for Life randomized controlled trial, we created visual and statistical models to capture and analyze dose timing data collected longitudinally with electronic drug monitors (EDM). From scatter plots depicting dose time versus calendar date, we identified dominant patterns of dose taking and calculated key features [slope of line over calendar date; residual mean standard error (RMSE)]. Each was assessed for its ability to categorize subjects with ‘sub-optimal’ (<95 % of doses taken) using area under the receiver operating characteristic (AROC) curve analysis. Sixty eight subjects contributed EDM data, with ~300 to 400 observations/subject. While regression line slopes did not predict ‘sub-optimal’ adherence (AROC 0.51, 95 % CI 0.26–0.75), the variability in dose timing (RMSE) was strongly predictive (AROC 0.79, 95 % CI 0.62–0.97). Compared with the lowest quartile of RMSE (minimal dose time variability), each successive quartile roughly doubled the odds of ‘sub-optimal’ adherence (OR 2.1, 95 % CI 1.3–3.4). Patterns of dose timing and mistiming are strongly related to overall adherence behavior. Notably, individuals who skip doses are more likely to mistime doses, with the degree of risk positively correlated with the extent of dose timing variability.

Keywords

Electronic drug monitors Dose timing Adherence Statistical methods EDM 

Notes

Acknowledgments

We wish to thank Ka Lai Poon for her assistance in generating the library of subject-by-subject scatter plots for this analysis. The AFL study was supported by a cooperative agreement (GHS-A-00-03-00030-00) between Boston University and the Office of Health and Nutrition of the United States Agency for International Development (USAID), with additional support from the World Health Organization (WHO) and CDC-GAP/China. Dr. Gill’s work was supported by NIH/NIAID K23 AI 62208. This study was supported by a cooperative agreement (GHS-A-00-03-00030-00) between Boston University and the Office of Health and Nutrition of the United States Agency for International Development (USAID), with additional support from the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention.

Conflict of interest

All authors declare no conflicts of interest.

Supplementary material

10461_2015_1065_MOESM1_ESM.docx (2.1 mb)
Supplementary material 1 (DOCX 2193 kb). A. SAS codes used to generate the uni- and bi-modal scatter plots. B. Individual subject uni-modal scatter plots. C. Individual subject bi-modal scatter plots

References

  1. 1.
    Liu H, Golin CE, Miller LG, Hays RD, Beck CK, Sanandaji S, et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med. 2001;134(10):968–77.CrossRefPubMedGoogle Scholar
  2. 2.
    Gross R, Bilker WB, Friedman HM, Strom BL. Effect of adherence to newly initiated antiretroviral therapy on plasma viral load. Aids. 2001;15(16):2109–17.CrossRefPubMedGoogle Scholar
  3. 3.
    Wagner GJ. Predictors of antiretroviral adherence as measured by self-report, electronic monitoring, and medication diaries. AIDS Patient Care STDS. 2002;16(12):599–608.CrossRefPubMedGoogle Scholar
  4. 4.
    Wilson IB, Tchetgen E, Spiegelman D. Patterns of adherence with antiretroviral medications: an examination of between-medication differences. J Acquir Immune Defic Syndr. 2001;28(3):259–63.CrossRefPubMedGoogle Scholar
  5. 5.
    Knafl GJ, Bova CA, Fennie KP, O’Malley JP, Dieckhaus KD, Williams AB. An analysis of electronically monitored adherence to antiretroviral medications. AIDS Behav. 2010;14(4):755–68.PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Knafl GJ, Fennie KP, Bova C, Dieckhaus K, Williams AB. Electronic monitoring device event modelling on an individual-subject basis using adaptive Poisson regression. Stat Med. 2004;23(5):783–801.CrossRefPubMedGoogle Scholar
  7. 7.
    Ferguson NM, Donnelly CA, Hooper J, Ghani AC, Fraser C, Bartley LM, et al. Adherence to antiretroviral therapy and its impact on clinical outcome in HIV-infected patients. J R Soc Interface. 2005;2(4):349–63.PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Wilson JQ, Kelling GL. Broken Windows: the police and neighborhood safety. Atlantic 1982.Google Scholar
  9. 9.
    Bachman Desilva M, Gifford AL, Keyi X, Li Z, Feng C, Brooks M, et al. Feasibility and acceptability of a real-time adherence device among HIV-positive IDU patients in China. AIDS Res Treat 2013;2013:957862.Google Scholar
  10. 10.
    Haberer JE, Robbins GK, Ybarra M, Monk A, Ragland K, Weiser SD, et al. Real-time electronic adherence monitoring is feasible, comparable to unannounced pill counts, and acceptable. AIDS Behav. 2012;16(2):375–82.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Haberer JE, Kiwanuka J, Nansera D, Muzoora C, Hunt PW, So J, et al. Realtime adherence monitoring of antiretroviral therapy among hiv-infected adults and children in rural uganda. Aids. 2013;27(13):2166–8.CrossRefPubMedGoogle Scholar
  12. 12.
    Haberer JE, Kahane J, Kigozi I, Emenyonu N, Hunt P, Martin J, et al. Real-time adherence monitoring for HIV antiretroviral therapy. AIDS Behav. 2010;14(6):1340–6.PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Sabin LL, Desilva MB, Hamer DH, Keyi X, Yue Y, Wen F, et al. Barriers to adherence to antiretroviral medications among patients living with HIV in southern China: a qualitative study. AIDS Care. 2008;20(10):1242–50.PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Sabin LL, DeSilva MB, Hamer DH, Xu K, Zhang J, Li T, et al. Using electronic drug monitor feedback to improve adherence to antiretroviral therapy among HIV-positive patients in China. AIDS Behav. 2010;14(3):580–9.PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Wools-Kaloustian K, Kimaiyo S, Diero L, Siika A, Sidle J, Yiannoutsos CT, et al. Viability and effectiveness of large-scale HIV treatment initiatives in sub-Saharan Africa: experience from western Kenya. Aids. 2006;20(1):41–8.CrossRefPubMedGoogle Scholar
  16. 16.
    Nachega JB, Hislop M, Dowdy DW, Chaisson RE, Regensberg L, Maartens G. Adherence to nonnucleoside reverse transcriptase inhibitor-based HIV therapy and virologic outcomes. Ann Intern Med. 2007;146(8):564–73.CrossRefPubMedGoogle Scholar
  17. 17.
    Bangsberg DR. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis. 2006;43(7):939–41.CrossRefPubMedGoogle Scholar
  18. 18.
    Mocroft A, Horban A, Clumeck N, Stellbrink HJ, Monforte ADA, Zilmer K, et al. Comparison of single and boosted protease inhibitor versus nonnucleoside reverse transcriptase inhibitor-containing cART regimens in antiretroviral-naive patients starting cART after January 1, 2000. HIV Clin Trials. 2006;7(6):271–84.CrossRefPubMedGoogle Scholar
  19. 19.
    Weiser SD, Guzman D, Riley ED, Clark R, Bangsberg DR. Higher rates of viral suppression with nonnucleoside reverse transcriptase inhibitors compared to single protease inhibitors are not explained by better adherence. HIV Clin Trials. 2004;5(5):278–87.CrossRefPubMedGoogle Scholar
  20. 20.
    Price MA, Wallis CL, Lakhi S, Karita E, Kamali A, Anzala O, et al. Transmitted HIV type 1 drug resistance among individuals with recent HIV infection in East and Southern Africa. AIDS Res Hum Retrovir. 2011;27(1):5–12.PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.
    Phanuphak P, Sirivichayakul S, Jiamsakul A, Sungkanuparph S, Kumarasamy N, Lee MP, et al. Transmitted drug resistance and antiretroviral treatment outcomes in non-subtype B HIV-1-infected patients in South East Asia. J Acquir Immune Defic Syndr. 2014;66(1):74–9.PubMedCentralCrossRefPubMedGoogle Scholar
  22. 22.
    Mantovani NP, Azevedo RG, Rabelato JT, Sanabani S, Diaz RS, Komninakis SV. Analysis of transmitted resistance to raltegravir and selective pressure among HIV-1-infected patients on a failing HAART in Sao Paulo, Brazil. J Clin Microbiol. 2012;50(6):2122–5.PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
    Sherr L, Lampe F, Norwood S, Leake Date H, Harding R, Johnson M, et al. Adherence to antiretroviral treatment in patients with HIV in the UK: a study of complexity. AIDS Care. 2008;20(4):442–8.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Christopher J. Gill
    • 1
    • 2
  • Mary Bachman DeSilva
    • 1
    • 2
  • Davidson H. Hamer
    • 1
    • 2
    • 3
  • Xu Keyi
    • 4
  • Ira B. Wilson
    • 5
  • Lora Sabin
    • 1
    • 2
  1. 1.Department of Global HealthBoston University School of Public HealthBostonUSA
  2. 2.Center for Global Health and Development (CGHD)Boston UniversityBostonUSA
  3. 3.Infectious Diseases Section, Department of MedicineBoston Medical CenterBostonUSA
  4. 4.Department of STDs and DermatologyDitan HospitalBeijingChina
  5. 5.Department of Health Services, Policy and PracticeBrown UniversityProvidenceUSA

Personalised recommendations