Journal of General Internal Medicine

, Volume 26, Issue 12, pp 1391–1393 | Cite as

The Importance of Social Ties in Sustaining Medication Adherence in Resource-Limited Settings

Editorials

Ten years ago the late John Eisenberg, along with Elaine Powers, elaborated a conceptual framework to explain unachieved potential for high-quality care delivery in the US, analogous to the voltage drops that occur as an electrical current flows through a series of resistors.1 Using an adapted version of their framework, one can appreciate that a mix of public health and health care strategies will be needed to address the growing burden of cardiovascular disease in resource-limited settings. Many countries' efforts to reduce cardiovascular risk at the population level are hampered by unaffordable (or unavailable) health insurance schemes; fragmented, acute care-oriented health care systems; and weak primary care infrastructures.2,3 Even if these sizable voltage drops are addressed, potential quality will be further dissipated through another series of resistors: patients must seek evaluation for treatment4; health care workers must be consistently available5 to provide patients with accurate diagnostic assessment,6 either through clinical examination or blood testing; appropriate treatment must be prescribed,7 whether pharmacologic or non-pharmacologic (e.g., lifestyle change); and patients must adhere to prescribed treatment.8

Maximizing treatment adherence is the final step needed to translate potential access to health care into improved outcomes. Adherence consists of two related constructs: (1) persistence, or the duration of time from initiation to discontinuation of therapy; and (2) dose-taking execution, or the proportion of doses taken as prescribed prior to discontinuation.9,10 In high-income countries, suboptimal dose-taking behavior by persons with different types of chronic illnesses is well-documented.11 In resource-limited settings, however, less is known about dose-taking execution by persons with chronic illnesses other than HIV. In this issue of the Journal, Bowry et al.12 provide the first systematic review and meta-analysis of dose-taking behavior in the setting of cardiovascular medication use in the emerging and developing economies of Africa, Asia, and Central and South America. Across 76 studies, 57.5% of participants were classified as adherent. To place this finding in context, a previously published meta-analysis of 129 cardiovascular medication adherence studies conducted in high-income countries estimated that 76.6% of participants were classified as adherent.11

Dose-taking execution is typically measured on a continuous scale as the average percentage of pills prescribed for a specified time window actually ingested. However, the meta-analytic findings of Bowry et al.12 were based on the proportion of persons classified as adherent (e.g., "regular medication use," "≥85% of pills taken"). Data on the average proportion of doses taken were presumably unavailable. Their experience was consistent with that of DiMatteo,11 who reported that data on average doses taken were only available for 3% of the 569 studies included in her meta-analysis.

The practice of dichotomizing data on dose-taking execution to classify persons as adherent or non-adherent has a long history in clinical research and has been characterized pejoratively as being "patently devoid of pharmacodynamic content."13(p.587) The choice of cutoff may be arbitrary (>66%),14 less arbitrarily based on natural breaks in the data (≥90%),15 or empirically derived from the clinical response to treatment (≥95%).16 When continuous variables are dichotomized in this fashion and used in primary analyses—rather than, for example, in secondary analyses to enhance exposition of the primary findings—valuable distributional information is lost. Subsequent analyses using the dichotomized variables as predictors17, 18, 19 (i.e., to assess the effect of dose-taking execution on clinical outcomes) or as outcomes20,21 (i.e., to identify facilitators of or barriers to adherence) tend to suffer from loss of efficiency and various biases.

Bowry et al.12 did not report on persistence to treatment, as this was beyond the scope of their study. Persistence deserves additional scrutiny by researchers studying chronic illness care in emerging and developing economies for several reasons. First, dose-taking execution and persistence may not necessarily coincide. One can sputter along indefinitely with a set of medications yet execute the prescribed regimen poorly, taking doses with variable punctuality or omitting a large proportion of doses entirely. Conversely, one can take all doses in a timely fashion without persisting with the regimen for the entire prescribed duration of treatment. Second, studies of adherence to pharmacological treatment for chronic illnesses have documented declining dose-taking execution22 and persistence23 over time, and this bodes poorly for the long-term success of treatment scale-up for persons with uncurable but manageable chronic illnesses who face a lifetime of pill-taking. Third, depending on the pharmacokinetic properties of the specific medication under consideration, a high degree of punctuality in average dose-taking execution can still result in poor outcomes if the rare episodes of non-persistence (described variously as drug holidays,9 treatment interruptions,24 or non-permissible gaps10) are sequentially concentrated in time.24,25

Comparing the findings of Bowry et al.12 to those of DiMatteo11 might lead one to conclude that dose-taking execution of cardiovascular medication regimens is worse among patients living in emerging and developing economies, and that the primary barriers they face are cognitive in nature. This is notably different from what has been reported in the HIV literature. Dose-taking execution of HIV antiretroviral therapy has been shown to be at least as good, or possibly better, among persons living with HIV/AIDS in sub-Saharan Africa compared to those living in the US and Canada.26 And, while barriers like inadequate knowledge, side effects, and regimen complexity adversely affect adherence in any setting, studies of HIV antiretroviral therapy adherence suggest that structural and economic barriers may be more relevant factors in resource-limited settings.27, 28, 29

These puzzling discrepancies might be explained by the ways in which the social dynamics of treatment for these chronic illnesses differ in resource-limited settings. People living with HIV/AIDS in sub-Saharan Africa often initiate HIV antiretroviral therapy at advanced stages of illness characterized by severe debilitation.30 By the time they have initiated treatment, they (and their caregivers) have long been removed from contributing to the income-generating and food-producing activities taking place within their network of social ties.31 Treatment often leads to a rapid and profound return to productive functional status (described as the "Lazarus effect"32,33) and a renewed ability to contribute economically to their social networks.34 Their social networks, in turn, are motivated to help them overcome structural and economic barriers to adherence so that their economic contributions can be sustained.28 In the case of medications aimed at reducing cardiovascular risk in resource-limited settings, we hypothesize that treatment adherence, functional status, and social ties are not as tightly linked. The same structural and economic barriers exist,2 but cardiovascular medications are primarily aimed at preventing future loss of function rather than restoring lost function. As such, it may be more difficult for patients taking cardiovascular medications to draw on their social ties for assistance in adhering to treatment.

The increasing burden of cardiovascular disease in sub-Saharan Africa2,3 and in other emerging and developing economies35 militates for the expanded use of interventions to improve chronic illness care in these settings.36 Some strategies might be adapted from those already used in treatment scale-up for persons living with HIV/AIDS,37 but more research to develop innovative interventions to optimize adherence to medications used to reduce cardiovascular risk are sorely needed. Bowry et al.12 show that we have a long way to go.

Notes

Acknowledgements

The authors acknowledge funding from the following sources: U.S. National Institutes of Health R01 MH-054907 (Bangsberg), K24 MH-087227 (Bangsberg), and the Robert Wood Johnson Foundation Health & Society Scholars Program (Tsai).

Conflicts of Interest

None disclosed.

References

  1. 1.
    Eisenberg JM, Power EJ. Transforming insurance coverage into quality health care: voltage drops from potential to delivered quality. JAMA. 2000;284(16):2100–7.PubMedCrossRefGoogle Scholar
  2. 2.
    Beaglehole R, Epping-Jordan J, Patel V, et al. Improving the prevention and management of chronic disease in low-income and middle-income countries: a priority for primary health care. Lancet. 2008;372(9642):940–9.PubMedCrossRefGoogle Scholar
  3. 3.
    Chopra M, Lawn JE, Sanders D, et al. Achieving the health Millennium Development Goals for South Africa: challenges and priorities. Lancet. 2009;374(9694):1023–31.PubMedCrossRefGoogle Scholar
  4. 4.
    Filmer D. Fever and its treatment among the more and less poor in sub-Saharan Africa. Health Policy Plan. 2005;20(6):337–46.PubMedCrossRefGoogle Scholar
  5. 5.
    Chaudhury N, Hammer J, Kremer M, Muralidharan K, Rogers FH. Missing in action: teacher and health worker absence in developing countries. J Econ Perspect. 2006;20(1):91–116.PubMedCrossRefGoogle Scholar
  6. 6.
    Das J, Hammer J. Money for nothing: the dire straits of medical practice in Dehli, India. J Dev Econ. 2007;83(1):1–36.CrossRefGoogle Scholar
  7. 7.
    Banerjee AV, Deaton A, Duflo E. Health care delivery in rural Rajasthan. Econ Pol Weekly. 2004;39(9):944–9.Google Scholar
  8. 8.
    DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care. 2002;40(9):794–811.PubMedCrossRefGoogle Scholar
  9. 9.
    Urquhart J. The electronic medication event monitor. Lessons for pharmacotherapy. Clin Pharmacokinet. 1997;32(5):345–56.PubMedCrossRefGoogle Scholar
  10. 10.
    Bae JW, Guyer W, Grimm K, Altice FL. Medication persistence in the treatment of HIV infection: a review of the literature and implications for future clinical care and research. AIDS. 2011;25(3):279–90.PubMedCrossRefGoogle Scholar
  11. 11.
    DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research. Med Care. 2004;42(3):200–9.PubMedCrossRefGoogle Scholar
  12. 12.
    Bowry ADK, Shrank WH, Lee JL, Stedman M, Choudhry NK. A systematic review of adherence to cardiovascular medications in resource-limited settings. J Gen Intern Med. 2011; doi:10.1007/s11606-011-1825-3.PubMedGoogle Scholar
  13. 13.
    Urquhart J. Pharmionics: research on what patients do with prescription drugs. Pharmacoepidemiol Drug Saf. 2004;13(9):587–90.PubMedCrossRefGoogle Scholar
  14. 14.
    Park LC, Lipman RS. A comparison of patient dosage deviation reports with pill counts. Psychopharmacologia. 1964;6(4):299–302.PubMedCrossRefGoogle Scholar
  15. 15.
    Mohler MJ. Adherence to highly-active antiretroviral therapies in HIV-infected veterans. PhD dissertation. Tucson: University of Arizona; 1999.Google Scholar
  16. 16.
    Paterson DL, Swindells S, Mohr J, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133(1):21–30.PubMedGoogle Scholar
  17. 17.
    Irwin JR, McClelland GH. Negative consequences of dichotomizing continuous predictor variables. J Market Res. 2003;40(3):366–71.CrossRefGoogle Scholar
  18. 18.
    MacCallum RC, Zhang S, Preacher KJ, Rucker DD. On the practice of dichotomization of quantitative variables. Psychol Methods. 2002;7(1):19–40.PubMedCrossRefGoogle Scholar
  19. 19.
    Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127–41.PubMedCrossRefGoogle Scholar
  20. 20.
    Fedorov V, Mannino F, Zhang R. Consequences of dichotomization. Pharm Stat. 2009;8(1):50–61.PubMedCrossRefGoogle Scholar
  21. 21.
    Ragland DR. Dichotomizing continuous outcome variables: dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology. 1992;3(5):434–40.PubMedCrossRefGoogle Scholar
  22. 22.
    Byakika-Tusiime J, Crane J, Oyugi JH, et al. Longitudinal antiretroviral adherence in HIV + Ugandan parents and their children initiating HAART in the MTCT-Plus family treatment model: role of depression in declining adherence over time. AIDS Behav. 2009;13(Suppl 1):82–91.PubMedCrossRefGoogle Scholar
  23. 23.
    Cramer JA, Benedict A, Muszbek N, Keskinaslan A, Khan ZM. The significance of compliance and persistence in the treatment of diabetes, hypertension and dyslipidaemia: a review. Int J Clin Pract. 2008;62(1):76–87.PubMedCrossRefGoogle Scholar
  24. 24.
    Oyugi JH, Byakika-Tusiime J, Ragland K, et al. Treatment interruptions predict resistance in HIV-positive individuals purchasing fixed-dose combination antiretroviral therapy in Kampala, Uganda. AIDS. 2007;21(8):965–71.PubMedCrossRefGoogle Scholar
  25. 25.
    Parienti JJ, 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.PubMedCrossRefGoogle Scholar
  26. 26.
    Mills EJ, Nachega JB, Buchan I, et al. Adherence to antiretroviral therapy in sub-Saharan Africa and North America: a meta-analysis. JAMA. 2006;296(6):679–90.PubMedCrossRefGoogle Scholar
  27. 27.
    Mills EJ, Nachega JB, Bangsberg DR, et al. Adherence to HAART: a systematic review of developed and developing nation patient-reported barriers and facilitators. PLoS Med. 2006;3(11):e438.PubMedCrossRefGoogle Scholar
  28. 28.
    Ware NC, Idoko J, Kaaya S, et al. Explaining adherence success in sub-Saharan Africa: an ethnographic study. PLoS Med. 2009;6(1):e11.PubMedCrossRefGoogle Scholar
  29. 29.
    Tuller DM, Bangsberg DR, Senkungu J, Ware NC, Emenyonu N, Weiser SD. Transportation costs impede sustained adherence and access to HAART in a clinic population in southwestern Uganda: a qualitative study. AIDS Behav. 2010;14(4):778–84.PubMedCrossRefGoogle Scholar
  30. 30.
    Kigozi IM, Dobkin LM, Martin JN, et al. Late-disease stage at presentation to an HIV clinic in the era of free antiretroviral therapy in Sub-Saharan Africa. J Acquir Immune Defic Syndr. 2009;52(2):280–9.PubMedCrossRefGoogle Scholar
  31. 31.
    Hosegood V, Preston-Whyte E, Busza J, Moitse S, Timaeus IM. Revealing the full extent of households' experiences of HIV and AIDS in rural South Africa. Soc Sci Med. 2007;65(6):1249–59.PubMedCrossRefGoogle Scholar
  32. 32.
    Walton DA, Farmer PE, Lambert W, Leandre F, Koenig SP, Mukherjee JS. Integrated HIV prevention and care strengthens primary health care: lessons from rural Haiti. J Public Health Policy. 2004;25(2):137–58.PubMedCrossRefGoogle Scholar
  33. 33.
    Leland J. The end of AIDS. Newsweek. 1996;128(23):65–73.Google Scholar
  34. 34.
    Russell S, Seeley J. The transition to living with HIV as a chronic condition in rural Uganda: working to create order and control when on antiretroviral therapy. Soc Sci Med. 2010;70(3):375–82.PubMedCrossRefGoogle Scholar
  35. 35.
    Gaziano TA. Reducing the growing burden of cardiovascular disease in the developing world. Health Aff Millwood. 2007;26(1):13–24.PubMedCrossRefGoogle Scholar
  36. 36.
    Tsai AC, Morton SC, Mangione CM, Keeler EB. A meta-analysis of interventions to improve care for chronic illnesses. Am J Manag Care. 2005;11(8):478–88.PubMedGoogle Scholar
  37. 37.
    Rabkin M, El-Sadr WM. Why reinvent the wheel? Leveraging the lessons of HIV scale-up to confront non-communicable diseases. Glob Public Health. 2011;6(3):247–56.PubMedCrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2011

Authors and Affiliations

  1. 1.Robert Wood Johnson Health and Society Scholars ProgramHarvard UniversityCambridgeUSA
  2. 2.Mbarara University of Science and TechnologyMbararaUganda
  3. 3.Center for Global HealthMassachusetts General HospitalBostonUSA
  4. 4.Division of Global Health EquityBrigham and Women’s HospitalBostonUSA
  5. 5.Phillip T. and Susan M. Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard Medical SchoolBostonUSA
  6. 6.Harvard Medical SchoolBostonUSA

Personalised recommendations