In this treatment-experienced population of HIV-infected women in the United States, we found that single-tablet regimen use was associated with significant improvements in adherence and virologic suppression. We also found suggestive evidence that it may also improve overall QOL and reduce the incidence of clinical events such as AIDS-defining illness and death. These associations were consistent based on 2 complementary approaches: a nested cohort study that compared periods of single-tablet ART use with periods of multiple-tablet ART use among similar individuals, and a case-crossover study that limited assessment to women who recently switched regimens. Our approach allowed us to make generalizations about the effectiveness of single-tablet regimen use in real-world conditions that are not restricted to those found in clinical trials.
Use of single-tablet formulations reached only about 20% of this group through 2013, which is lower than previously reported in some other settings.8 This level may reflect the clinical history of this treatment-experienced cohort, with some women having been enrolled in the study for up to 19 years. Switching to a single-tablet regimen may not have been considered a priority to their providers if they were already stable on their current regimen, if they already developed resistance to one of the components of the available single-tablet regimens, or if they were planning to become pregnant. In contrast, the few women who were ART-naive before initiating therapy during the study period started on a single-tablet regimen 48% of the time.
Continued monitoring of the effects of emerging adherence strategies is warranted to strengthen the evidence base, especially as the health care environment continues to evolve in the United States and internationally. For example, it has been postulated that as generic versions of individual components of single-tablet regimens become available, some individuals may switch back to multiple-tablet formulations due to their anticipated lower cost, particularly in resource-poor settings.43 The potential effects of such a change on adherence outcomes in our population will be important to follow over time. Future work should also follow the outcomes of previously ART-naive women initiating single-tablet regimens, including younger women currently being recruited to join the WIHS as new participants.
Our study has limitations. One limitation is that ART adherence is based on self-report rather than medication event monitoring systems or unannounced pill counts.17 However, self-report has been shown to have comparable validity with other more expensive monitoring systems44,45 and is recommended for routine adherence monitoring in patients despite the potential for reporting bias.46 Other limitations relate to our measurement of adherence. Assessing adherence at 6-month intervals only captures behaviors only in a broad sense. Use of 95% adherence as the outcome of interest, which is based on older studies of unboosted PIs,27 may be too conservative for more recent regimens that may not require levels of adherence as high47 and therefore may mask additional benefits of single-tablet regimens. Despite these drawbacks, the uniformity and regularity of adherence assessment over the 8-year study period improves the robustness of our findings. We grouped all multiple-tablet regimens together to compare these collectively with single-tablet regimens, but this makes it difficult to distinguish between benefits derived from the dosing schedule versus the regimen components.46 Finally, it is possible that the switching effects that we found in the case-crossover study are a transient consequence of counseling, and future work should also examine the sustainability of these improvements.
Despite these limitations, our study has several strengths. We report trends in single-tablet regimen use and extend known inferences on their effectiveness to HIV-infected women in the United States, a growing population with less available research addressing their unique circumstances. Our data come from a well-established prospective study population that is demographically representative of the national female HIV-infected population. The WIHS's detailed longitudinal data on health behaviors, medical history, and medication use were instrumental in being able to create balanced groups to minimize the possibility of confounding by indication (although residual confounding remains possible). Finally, the WIHS follows many women who either continue to participate in the study past childbearing age or have undergone sterilization procedures, and therefore it is particularly suitable to examine single-tablet regimen use among such women who can safely be prescribed EFV despite its contraindications in terms of teratogenicity.
Adherence has been described as “a set of interacting behaviors informed by individual, social, and environmental forces”.48 Our study found that about 85% of ART-treated women in the WIHS in 2013 were adherent at the 95% level, but only 50% were adherent at the 100% level, even with the availability of single-tablet regimens. Thus, the “overlapping, combination approaches” to HIV prevention advocated by the US National HIV/AIDS Strategy also apply to adherence.49 Some examples of additional evidence-based approaches that may be relevant to our study population include those that involve self-management tools and individual- and group-level education and counseling.46 Although the simplification of treatment regimens has contributed to improved adherence and virologic suppression in women, it is just 1 component of a multifaceted strategy to be able to truly maximize the therapeutic benefits of ART.
The authors would like to thank X. Xue, PhD, for statistical advice and the WIHS participants for their time and commitment. Data in this manuscript were collected by the WIHS Collaborative Study Group with centers (Principal Investigators) at New York City/Bronx Consortium (K. Anastos); Brooklyn, NY (H. Minkoff); Washington, DC, Metropolitan Consortium (M. Young); The Connie Wofsy Study Consortium of Northern California (R. Greenblatt); Los Angeles County/Southern California Consortium (A. Levine); Chicago Consortium (M. Cohen); Data Coordinating Center (S. Gange).
The WIHS Collaborative Study Group
New York City/Bronx Consortium: Montefiore Medical Center (K. Anastos, MD, Principal Investigator; A. Cajigas, MD; E. Robison, PhD; and R. Wright, MD); University of California Davis (H. Burger, MD, PhD; and B. Weiser, MD); Albert Einstein College of Medicine (R. Kaplan, PhD; and M. Keller, MD); Weill Medical College of Cornell University (M. Glesby, MD); Rutgers (D. Hoover, PhD); and Community Advisor (N. Ramos-Santiago).
Brooklyn, NY: State University of New York Health Science Center at Brooklyn (H. Minkoff, MD, Principal Investigator; D. Gustafson, PhD, Co-Principal Investigator; M. Augenbraun, MD; H. Crystal, MD; J. DeHovitz, MD, MPH; H. Durkin, PhD; S. Holman, RN, MS; J. Lazar, MD; M. Nowakowski, PhD; R. Schwartz, PhD; D. Seifer, MD; A. Sharma, MD, MS; and T. Wilson, PhD).
Washington, DC, Metropolitan Consortium: Georgetown University Medical Center (M. Young, MD, Principal Investigator; and L. Goparaju, PhD); George Washington University Medical Center (S. Silver, DA); Whitman-Walker Clinic (K. Sathasivam, MD); Montgomery County Health Department (C. Jordan, RN, MPH); Inova Health System of Northern Virginia (D. Wheeler, MD; and B. Lawrence, BS); and Community Advisors (K. Kelsey and K. Moore).
The Connie Wofsy Study Consortium of Northern California: University of California, San Francisco (R. Greenblatt, MD, Principal Investigator; P. Bacchetti, PhD; D. Cohan, MD, MPH; N. Hessol, MSPH; P. Tien, MD); Alameda County Medical Center (H. Edelstein, MD); and Alta Bates Medical Center (C. Borkert, MD); Community Advisor (N. Rodriguez).
Los Angeles County/Southern California Consortium: Keck School of Medicine, University of Southern California and Los Angeles County and USC Medical Center (A. M. Levine, MD, Principal Investigator; Y. Barranday, BA; M. Nowicki, PhD; L. Pearce, PhD; J. Richardson, DrPH); the Santa Barbara County Department of Health Services (E. Downing, MD); University of Hawaii (C. Shikuma, MD); and Community Advisor (E. Sanchez).
Chicago Consortium: Cook County Hospital (M. H. Cohen, MD, Principal Investigator; A. French, MD; K. M. Weber, BSN); University of Illinois at Chicago (R. Hershow, MD); Rush Presbyterian-St. Luke's Medical Center (B. Sha, MD); Northwestern Memorial Hospital (S. Cohn, MD); and Community Advisor (M. Santiago).
Data Coordinating Center: Johns Hopkins Bloomberg School of Public Health (S. Gange, PhD, Principal Investigator; E. Golub, PhD, MPH, Co-Principal Investigator; A. Abraham, PhD; C. Alden, BA; K. Althoff, PhD, MPH; L. Benning, MS; C. Cox, PhD; G. D'Souza, PhD; L. Jacobson, ScD; B. Lau, PhD; S. Modur, PhD; A. Muñoz, PhD; C. Pierce, MHS; A. Platt, BS; M. Schneider, MS; E. Seaberg, PhD, MPH; G. Springer, MLA; S. Su, ScD; E. Wentz, MA; W. Yoo, BS; and J. Zhang, MS).
National Institute of Health: National Institute of Allergy and Infectious Diseases (G. Sharp, DrPH; C. Williams, PhD); Eunice Kennedy Shriver National Institute of Child Health and Human Development (K. Ryan, PhD; H. Watts, MD); National Institute of Drug Abuse (K. Davenny, MPH; R. Jenkins, PhD); and National Cancer Institute (G. Dominguez, PhD).
1. Bangsberg DR, Perry S, Charlebois ED, et al.. Non-adherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS. 2001;15:1181–1183.
2. Nachega JB, Hislop M, Dowdy DW, et al.. Adherence to nonnucleoside reverse transcriptase inhibitor-based HIV therapy and virologic outcomes. Ann Intern Med. 2007;146:564–573.
3. Gonzalez JS, Batchelder AW, Psaros C, et al.. Depression and HIV/AIDS treatment nonadherence: a review and meta-analysis. J Acquir Immune Defic Syndr. 2011;58:181–187.
4. Lucas GM. Substance abuse, adherence with antiretroviral therapy, and clinical outcomes among HIV-infected individuals. Life Sci. 2011;88:948–952.
5. Lazo M, Gange SJ, Wilson TE, et al.. Patterns and predictors of changes in adherence to highly active antiretroviral therapy: longitudinal study of men and women. Clin Infect Dis. 2007;45:1377–1385.
6. Beer L, Heffelfinger J, Frazier E, et al.. Use of and adherence to antiretroviral therapy in a large U.S. sample of HIV-infected adults in care, 2007-2008. Open AIDS J. 2012;6:213–223.
7. Simoni JM, Huh D, Wilson IB, et al.. Racial/Ethnic disparities in ART adherence in the United States: findings from the MACH14 study. J Acquir Immune Defic Syndr. 2012;60:466–472.
8. Sax PE, Meyers JL, Mugavero M, et al.. Adherence to antiretroviral treatment and correlation with risk of hospitalization among commercially insured HIV patients in the United States. PLoS One. 2012;7:e31591.
9. Parienti JJ, Bangsberg DR, Verdon R, et al.. Better adherence with once-daily antiretroviral regimens: a meta-analysis. Clin Infect Dis. 2009;48:484–488.
10. Horberg MA, Klein DB. An update on the use of Atripla in the treatment of HIV in the United States. HIV AIDS (Auckl). 2010;2:135–140.
11. De Clercq E. Where rilpivirine meets with tenofovir, the start of a new anti-HIV drug combination era. Biochem Pharmacol. 2012;84:241–248.
12. Marchand C. The elvitegravir Quad pill: the first once-daily dual-target anti-HIV tablet. Expert Opin Investig Drugs. 2012;21:901–904.
15. Dejesus E, Young B, Morales-Ramirez JO, et al.. Simplification of antiretroviral therapy to a single-tablet regimen consisting of efavirenz, emtricitabine, and tenofovir disoproxil fumarate versus unmodified antiretroviral therapy in virologically suppressed HIV-1-infected patients. J Acquir Immune Defic Syndr. 2009;51:163–174.
16. Hodder SL, Mounzer K, Dejesus E, et al.. Patient-reported outcomes in virologically suppressed, HIV-1-Infected subjects after switching to a simplified, single-tablet regimen of efavirenz, emtricitabine, and tenofovir DF. AIDS Patient Care STDS. 2010;24:87–96.
17. Bangsberg DR, Ragland K, Monk A, et al.. A single tablet regimen is associated with higher adherence and viral suppression than multiple tablet regimens in HIV+ homeless and marginally housed people. AIDS. 2010;24:2835–2840.
18. Puskas CM, Forrest JI, Parashar S, et al.. Women and vulnerability to HAART non-adherence: a literature review of treatment adherence by gender from 2000 to 2011. Curr HIV/AIDS Rep. 2011;8:277–287.
19. Silverberg MJ, Gore ME, French AL, et al.. Prevalence of clinical symptoms associated with highly active antiretroviral therapy in the Women's Interagency HIV Study. Clin Infect Dis. 2004;39:717–724.
20. Phillips KD, Moneyham L, Murdaugh C, et al.. Sleep disturbance and depression as barriers to adherence. Clin Nurs Res. 2005;14:273–293.
21. Merenstein D, Schneider MF, Cox C, et al.. Association of child care burden and household composition with adherence to highly active antiretroviral therapy in the Women's Interagency HIV Study. AIDS Patient Care STDS. 2009;23:289–296.
22. Barkan SE, Melnick SL, Preston-Martin S, et al.. The Women's Interagency HIV Study. Epidemiology. 1998;9:117–125.
23. Bacon MC, von Wyl V, Alden C, et al.. The Women's Interagency HIV Study: an observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol. 2005;12:1013–1019.
25. Horberg M, Silverberg M, Hurley L, et al.. Influence of prior antiretroviral experience on adherence and responses to new highly active antiretroviral therapy regimens. AIDS Patient Care STDS. 2008;22:301–312.
26. Chesney MA, Ickovics JR, Chambers DB, et al.. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. AIDS Care. 2000;12:255–266.
27. Paterson DL, Swindells S, Mohr J, et al.. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133:21–30.
28. Saberi P, Johnson MO, McCulloch CE, et al.. Medication adherence: tailoring the analysis to the data. AIDS Behav. 2011;15:1447–1453.
29. Liu C, Weber K, Robison E, et al.. Assessing the effect of HAART on change in quality of life among HIV-infected women. AIDS Res Ther. 2006;3:6.
30. Bozzette SA, Hays RD, Berry SH, et al.. Derivation and properties of a brief health status assessment instrument for use in HIV disease. J Acquir Immune Defic Syndr Hum Retrovirol. 1995;8:253–265.
31. Centers for Disease Control and Prevention. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep. 1992;41:1–19.
32. Radloff LS. The CES-D Scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
33. Glynn RJ, Schneeweiss S, Sturmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol. 2006;98:253–259.
34. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 2010;25:1–21.
35. Hanley JA, Negassa A, Edwardes MD, et al.. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol. 2003;157:364–375.
36. Wang PS, Schneeweiss S, Glynn RJ, et al.. Use of the case-crossover design to study prolonged drug exposures and insidious outcomes. Ann Epidemiol. 2004;14:296–303.
37. Ho DE, Imai K, King G, et al.. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15:199–236.
38. Airoldi M, Zaccarelli M, Bisi L, et al.. One-pill once-a-day HAART: a simplification strategy that improves adherence and quality of life of HIV-infected subjects. Patient Prefer Adherence. 2010;4:115–125.
39. Maggiolo F, Ripamonti D, Arici C, et al.. Simpler regimens may enhance adherence to antiretrovirals in HIV-infected patients. HIV Clin Trials. 2002;3:371–378.
40. Althoff KN, Buchacz K, Hall HI, et al.. U.S. trends in antiretroviral therapy use, HIV RNA plasma viral loads, and CD4 T-lymphocyte cell counts among HIV-infected persons, 2000 to 2008. Ann Intern Med. 2012;157:325–335.
41. Mugavero MJ, Amico KR, Westfall AO, et al.. Early retention in HIV care and viral load suppression: implications for a test and treat approach to HIV prevention. J Acquir Immune Defic Syndr. 2012;59:86–93.
42. Mugavero MJ, Westfall AO, Zinski A, et al.. Measuring retention in HIV care: the elusive gold standard. J Acquir Immune Defic Syndr. 2012;61:574–580.
43. Llibre JM, Clotet B. Once-daily single-tablet regimens: a long and winding road to excellence in antiretroviral treatment. AIDS Rev. 2012;14:168–178.
44. Buscher A, Hartman C, Kallen MA, et al.. Validity of self-report measures in assessing antiretroviral adherence of newly diagnosed, HAART-naive, HIV patients. HIV Clin Trials. 2011;12:244–254.
45. Deschamps AE, De Geest S, Vandamme AM, et al.. Diagnostic value of different adherence measures using electronic monitoring and virologic failure as reference standards. AIDS Patient Care STDS. 2008;22:735–743.
46. Thompson MA, Mugavero MJ, Amico KR, et al.. Guidelines for improving entry into and retention in care and antiretroviral adherence for persons with HIV: evidence-based recommendations from an International Association of Physicians in AIDS Care panel. Ann Intern Med. 2012;156:817–833.
47. Kobin AB, Sheth NU. Levels of adherence required for virologic suppression among newer antiretroviral medications. Ann Pharmacother. 2011;45:372–379.
48. Steiner JF. Rethinking adherence. Ann Intern Med. 2012;157:580–585.