Secondary Logo

Journal Logo

Clinical Science

High Medication Possession Ratios Associated With Greater Risk of Virologic Failure Among Youth Compared With Adults in a Nigerian Cohort

Ahonkhai, Aimalohi A. MD, MPH*,†,‡,§,║; Banigbe, Bolanle MD, MPH; Adeola, Juliet BSc, MBA; Bassett, Ingrid V. MD, MPH‡,§,║,#; Idigbe, Ifeoma MSc**; Okonkwo, Prosper MD, FMCPH; Freedberg, Kenneth A. MD, MSc‡,§,║,#,††,‡‡; Regan, Susan PhD§,#,‡‡; Losina, Elena PhD§,║,§§,║║

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: July 1, 2018 - Volume 78 - Issue 3 - p 322-328
doi: 10.1097/QAI.0000000000001670

Abstract

INTRODUCTION

Scale-up of antiretroviral therapy (ART) has led to unprecedented progress in the global AIDS response, but adolescents and young adults (AYA, aged 15–25 years) have not benefitted fully from these gains.1 Marked disparities have been described between the health outcomes of HIV-infected AYA and older adults.2 In fact, in an era from 2005 to 2012, when AIDS-related deaths declined by 30% for adults, they increased by 50% in this high-risk group.3 Adolescence and young adulthood is a unique time of development often characterized by increased risk-taking and poor abstract thinking, both of which may negatively impact health behaviors.4,5 Likely in part as a result of these dynamics, HIV-infected youth have been found to have poorer attendance at clinical visits and adherence to life-saving ART, culminating in greater risk of virologic failure (VF) and death.6–9

Optimal adherence to ART is central to effective HIV treatment for all patients, including AYA.10 Early identification of patients poorly adherent to ART may be an important health system indicator, to guide patient-level interventions to improve adherence, to decrease HIV drug resistance in the community, and to reduce viral transmission.11 Indeed, the World Health Organization has recommended that adherence be routinely monitored at the clinic level, with a target of at least 90% adherence to minimize the risk of HIV drug resistance.11 Nonetheless, there is no gold standard for measuring adherence, and in settings where routine viral load monitoring is not available, using additional markers of adherence is even more important.12,13 However, there are limited data to determine how existing measures perform in AYA relative to older adults.12,13

Several methods exist to assess adherence to ART.14 Subjective measures such as self-report are easy to operationalize and inexpensive, but tend to overestimate adherence.14 Pill count is also easy to measure and inexpensive, but overestimates adherence.15 Electronic monitoring is expensive and difficult to implement clinically.12 Pharmacy adherence measures are objective, easy to operationalize, and inexpensive when patients use one central pharmacy for all drug pick-ups.15 This is often the case in resource-limited settings, and allows for use of pharmacy-based adherence measures as a potential medication adherence screening tool.15 The most popular of these is medication possession ratio (MPR).15 MPR reflects the proportion of patients' medication doses retrieved from pharmacy. Low MPRs, especially <80%, have been associated with increased risk of VF and death.16–18

Our objective was to assess the relationship between MPR and VF among AYA (aged 15–25 years) compared with older adults (aged >25 years) on ART in a multisite, comprehensive HIV treatment program in Nigeria.

METHODS

Setting

The study was conducted at the AIDS Prevention Initiative in Nigeria (APIN). APIN is an implementing partner of the President's Emergency Plan for AIDS Relief (PEPFAR) and administers a network of HIV treatment centers in Nigeria. APIN has provided HIV care and treatment services to over 150,000 people living with HIV since 2000. During the study period (2009–2012), APIN oversaw 36 comprehensive treatment centers in 9 of Nigeria's 36 states. APIN-supported comprehensive sites provide care for adults and children; prevention of mother-to-child transmission services; laboratory services including viral load, CD4, and routine safety and monitoring laboratories; and pharmacy services for ART dispensing. The study was conducted at 10 of these comprehensive clinics with infrastructure to administer on-site viral load testing. First-line ART regimens at these sites included 2 efavirenz (tenofovir/lamivudine/efavirenz; abacavir/lamivudine/efavirenz) and 2 nevirapine (zidovudine/lamivudine/nevirapine; abacavir/lamivudine/nevirapine) based regimens. HIV RNA samples were processed using Cobas AmpliPrep/Cobas TaqMan assays. Both internal (quality control kits to check for outlier samples) and external (twice yearly quality assessment panels from AFRIQUALAB) quality control measures were routinely conducted.

Study Design

We conducted a retrospective cohort study of AYA (aged 15–25 years) and older adults (aged >25 years) initiated on ART at APIN sites between January 2009 and December 2012. Patients who defaulted from care (eg, who had ≥6 months between the last clinic encounter and the expected 12-month visit) were excluded from the analysis. We reviewed patient clinic, pharmacy, and laboratory visits in the first 12 months after ART initiation. The exposure of interest was MPR, defined as the number of daily doses of ART dispensed divided by the total number of days on ART, and was measured 12 months after ART initiation. MPR was determined from pharmacy records. MPR was categorized as optimal (>94%), suboptimal (80%–94%), and poor (<80%) based on existing literature associating MPR with risk of VF.16 The outcome of interest was VF, defined as HIV RNA >1000 copies per milliliter. In Nigeria, HIV RNA testing is recommended on a 6-month basis. Given that not all sites routinely obtained HIV RNA measurements according to this schedule, we assessed the first HIV RNA at least 6 months, but no more than 18 months, after ART initiation (12 ± 6 months) for the study outcome.

Analysis

Our aim was to determine the association between MPR category and risk of VF and compare it between AYA and older adults and potential confounders, of the relationship between MPR and VF, in addition to other covariates significantly associated with VF including sex (M/F), employment status (employed vs. unemployed vs. student), baseline CD4 count (<100/μL vs. 101-350/μL vs. >350/μL vs. missing), and visit pattern (missed visits vs. attended all visits). We used χ2 tests to compare the proportion of AYA vs. older adults with VF in each MPR category.

In the first year on ART, 13 clinic encounters are recommended by Nigeria's national HIV treatment guidelines: 5 for clinician visits, medication pick-up, and laboratory testing, 1 for clinician visit alone, and 7 for ART pick-up alone. We defined visit attendance based on clinic (and not pharmacy) visits so that the MPR measurement, which relies on pharmacy encounters, was not biased. Patients were defined as most compliant if they attended at least 3 of 5 visits in months 0–3 on ART, and at least 2 of 3 visits in months 3–12 on ART. Patients were defined as least compliant if they attended 0–2 of 5 visits in months 0–3 on ART, or 0–1 of 3 visits required in months 3–12 on ART. We summarized the type of clinic encounters (clinician + drug pick-up vs. clinician alone vs. drug pick-up alone) attended by AYA and older adults in the 1st year on ART. We used t tests to assess for differences in the mean number of visits attended for each age group.

We used generalized linear models with Poisson distribution and log-link function to assess the rate of VF by age and MPR categories. Patient-level variables that were statistically significant in bivariate analysis (P < 0.10) were included in the multivariate model. We accounted for potential site-level clustering by adjusting for site-level characteristics including geographic location (urban vs. rural), clinic ownership (public vs. private), and level of care (secondary vs. tertiary facility). Site-level variables remained in the model to adjust for potential site-level clustering. To examine whether the relationship between MPR category and rate of VF was modified by age group, we included an interaction term between age and MPR in the multivariate model. We used the most parsimonious multivariate model, removing covariates that did not exhibit statistically significant association with the outcome (VF) at the level of P < 0.05. In addition, we built separate multivariate models for each MPR category to quantify the rate of VF for AYA compared with adults at optimal, suboptimal, and poor MPR levels.

We conducted sensitivity analysis on the rate of VF by MPR categories using different cut-offs for MPR categories, given data showing a relationship between “overadherence” and VF. For this analysis, we categorized MPR into 4 categories: superlative MPR (≥110%), optimal MPR (95%–109%), suboptimal MPR (80%–94%), and poor MPR (<80%).

IRB Approval

We obtained Institutional Review Board approval from Partners HealthCare (Protocol no. 2013P000219) and Harvard T.H. Chan School of Public Health in Boston, MA, USA, the Nigerian Institute for Medical Research (Protocol no. 12/212) in Lagos, Nigeria, and Vanderbilt University Medical Center (Protocol no. 161779).

RESULTS

Cohort Description

There were 27,445 patients who initiated ART at the 10 APIN sites during the study period. Patients who defaulted from care (n = 8495) were excluded from the analysis. An additional 6066 patients were missing 12-month viral load and were also excluded. We compared patient (sex, education, and CD4 count) and site (facility level, facility sector, and geographic setting) characteristics between AYA and adults who were excluded from and retained in the analysis. The excluded patient group looked similar to the analysis cohort regarding sex distribution (88% vs. 88% for AYA, 62% vs. 63% for adults). In both age groups, a higher proportion of patients who were retained in the analysis reported having any education (79% vs. 74% for AYA, 85% vs. 77% for adults). There was no substantial difference in the proportion of patients with the lowest CD4 counts (<100 cells/μL) who were retained in vs. excluded from the analysis for either age category (22% vs. 21% for AYA, 29% vs. 28% for adults); and a higher proportion of those with the highest CD4 counts (>350 cells/μL) were excluded from the analysis (20% vs 13% for AYA, 16% vs 9% for adults) compared with those retained. There were no substantial differences in the facility types (primary vs. secondary vs. tertiary or faith-based vs. private vs. public) or geographic setting (rural vs. semi-urban vs. urban) among patients who were retained in or excluded from the analysis (see Supplemental Digital Content Table 1, http://links.lww.com/QAI/B132). We did not find statistically significant difference between the proportion of AYA (n = 725, 32%) and older adults (n = 5,341, 32%) with missing viral load data.

Among the 12,884 patients comprising the analysis cohort, there were 1508 AYA and 11,376 older adults. Twenty one percent of the cohort (n = 2014) had VF at 12 months, but more AYA had VF at 12 months than older adults (30% vs. 24%, P < 0.001) (Fig. 1). Overall, 74% of patients had optimal adherence as defined by MPR (MPR >94%), 16% had suboptimal adherence (90%–94%), and 9% had poor adherence (MPR <80%). Ninety-seven percent of ART pick-ups were for first-line ART. Fewer AYA had optimal visit attendance compared with older adults (57% vs. 62%, P < 0.001).

FIGURE 1.
FIGURE 1.:
Cohort description of the relationship between MPR and virologic outcome in 10 HIV care sites in Nigeria. VF: Virologic failure after 12 months on ART (HIV RNA >1000 copies/mL).

Baseline Characteristics

There were several differences in baseline demographics and clinical characteristics between AYA and older adults. AYA had a greater proportion of females than older adults (88% vs. 63%, P < 0.001), and AYA had a greater proportion of unemployed than older adults (31% vs. 15%, P < 0.001). Finally, AYA initiated ART with higher baseline CD4 counts (190/μL vs. 160/μL, P < 0.001) than older adults. The majority of AYA (88%) and adults (90%) reported heterosexual sex as the transmission risk factor (P = 0.003). Perinatal transmission was reported as the risk factor for a small minority of patients (<1% for both AYA and adults, P < 0.001) (Table 1).

Table 1:
Table 1::
Baseline Characteristics of a Cohort of AYA (15–25 years) and Adults (>25 years) Initiating ART in Nigeria

Age, MPR, and Risk of VF

In bivariate analysis of patient-level variables, AYA had an increased rate of VF compared with older adults [respiratory rate (RR) 1.25; 95% confidence interval (CI): 1.13 to 1.38]. Patients with optimal (MPR >94%, RR 0.42; 95% CI: 0.38 to 0.46) or suboptimal adherence (MPR 80%–94%, RR 0.56; 95% CI: 0.50 to 0.63) had a decreased rate of VF compared with patients with poor adherence as defined by MPR (MPR <80%). We found that several factors independently associated with the rate of VF. Females had an increased rate of VF at 12 months compared with males (RR 1.13; 95% CI: 1.04 to 1.21), and this cohort notably did not include women who were initiating ART for the prevention of mother-to-child transmission; and educated patients had a decreased rate of VF compared with patients with no education (RR 0.92; 95% CI: 0.84 to 1.01). There was a U-shaped relationship between baseline CD4 count and rate of VF. Compared with patients with baseline CD4 <100/μL, those with CD4 101-350/μL (RR 0.76; 95% CI: 0.70 to 0.82) had a decreased rate of VF but not those with higher or missing CD4 counts. Patients with optimal visit attendance had a decreased rate of VF (RR 0.84; 95% CI: 0.78 to 0.90) compared with patients with poor visit attendance. Finally, patients attending tertiary facilities (RR 0.81; 95% CI: 0.70 to 0.92) and public facilities (RR 0.87; 95% CI: 0.79 to 0.95) had a decreased risk of VF relative to secondary and faith-based clinics, respectively. The former likely had a wider variety of multidisciplinary services to support comprehensive HIV care (Table 2).

Table 2.
Table 2.:
Association Between MPR, Age Category, and Risk of VF in the First Year on ART in a Multisite Nigerian Cohort

In multivariate analysis, AYA status remained an independent risk factor for VF relative to older adults (RR 1.15; 95% CI: 1.04 to 1.27), whereas suboptimal and optimal MPR remained protective against VF relative to poor MPR (RR 0.57; 95% CI: 0.51–0.64 and RR 0.43; 95% CI: 0.39 to 0.49, respectively) even while adjusting for potential confounders and other significant covariates including sex, education, baseline CD4 count, visit attendance, and site-level variables (Table 2). In sensitivity analysis, when MPR was categorized into 4 groups, namely poor (MPR <80%), suboptimal (MPR 80%–94%), optimal (MPR 95%–109%), and superlative (MPR ≥110%), superlative MPR was associated with a decreased rate of VF relative to poor MPR (RR 0.37; 95% CI: 0.30–0.45) (Table 2).

Associations Among Age, MPR Category, and VF

When we added an interaction term between age and MPR categories to the most parsimonious multivariate model, we found that age modified the relationship between MPR and VF (P < 0.05) (Table 2). We therefore conducted additional stratified analyses, presenting results for AYA and older adults separately. AYA had a greater proportion of patients with VF than older adults in each MPR category, but these differences were only significant in those with optimal adherence as defined by MPR. Among patients with poor adherence (MPR <80%), 51% of AYA and 50% of adults had VF at 12 months (P = 0.97). Thirty-three percent of AYA and 28% of adults with suboptimal adherence (MPR 80%–94%) had VF at 12 months (P = 0.07). Finally, 26% of AYA with optimal adherence (MPR >94%) had VF at 12 months compared with 20% of older adults (P = 0.01) (Fig. 2). We quantified these rate differences between age groups with multivariate models built for each of the 3 MPR categories (adjusted for sex, baseline CD4 count, visit attendance, site level, site ownership, and site setting). Among those with poor and suboptimal MPR (MPR <80% and MPR 80%–94%, respectively), AYA did not have an increased rate of VF compared with older adults [accounting rate of return (aRR) 1.00; 95% CI: 0.80 to 1.25; aRR 1.13; 95% CI: 0.90 to 1.42]. However, among patients with optimal MPR (>94%), AYA had a 20% increased rate of VF compared with adults (aRR 1.20, P < 0.005) (Fig. 2).

FIGURE 2.
FIGURE 2.:
Proportion of patients with VF in each MPR category. **aRR 1.20 VF among AYA compared with adults with MPR >94% [adjusted for sex, CD4 count at baseline, visit attendance, site level (primary vs. secondary), private vs. public, and geographic setting: Urban vs. semi-urban, vs rural]. VF: Virologic failure after 12 months on ART (HIV RNA >1000 copies/mL).

Visit Patterns for AYA and Adults

Consistent with the recommended visit and ART pick-up schedule, more visits overall were for ART pick-up alone (mean 6, SD 3.1) than for clinician and ART pick-up (mean 4, SD 2.2) or clinician alone (mean 2, SD 1.7). There was no difference in average number of clinician (2 vs. 2, P = 0.05) or clinician + ART pick-up visits (4 vs. 4, P = 0.93), but AYA had fewer drug pick-up only contacts than older adults overall (5 vs. 6, P < 0.001). Within MPR categories, the difference in the number of drug pick-ups by age group was significant only for the MPR >94% group (5 for AYA vs. 6 for older adults, P < 0.0001).

DISCUSSION

Although there is no gold standard for the measurement of medication adherence, pharmacy-based measures are considered to be robust correlates of adherence to ART that consistently predict patient outcomes.12 Our study of MPR among patients initiating ART in Nigeria illustrates the capacity of the APIN network to effectively administer ART to patients who are retained in care, with nearly three-fourths of all patients having MPR values greater than 94%. In addition, we found a strong, dose-dependent correlation between MPR category and risk of VF in the first year on ART. We observed discordance, however, between ART pick-up and virologic control in a subset of AYA patients. Overall, about 1 in 4 patients with optimal adherence (MPR >94%) had VF, and this risk was 20% higher in AYA than in older adults.

Despite a strong relationship between MPR category and viral load outcomes in our analysis, the correlation was not perfect. About half of those with poor MPR (<80%), one-third of those with suboptimal MPR (80%–94%), and one-fourth of those with optimal MPR (>94%) still had VF. Higher rates of VF are expected with poor MPR, but we did observe notable rates of VF among those with optimal MPR that were higher than reported in other cohorts in Sub-Saharan Africa. One analysis of adults living with HIV in Abidjan, Côte d'Ivoire, reported detectable viremia in 9% of patients with MPR >94%; another in Tanzania reported detectable viral load in 10% of those with high MPRs.17,19 This was less than half of the rate of viremia in the adults, and one-third the rate of viremia in the AYA in our cohort. These studies are difficult to compare directly, given different approaches to categorizing MPR, and different thresholds used for viremia. There may be sociocultural factors contributing to these cohort-level differences as well.

Our analysis underscores important age-related differences in MPR and risk of VF in apparently adherent patients. In contrast to other studies that have identified overadherence (defined by MPR >100%) as a risk factor for VF (due to pill dumping and social desirability bias), “overadherence” in our cohort (defined by MPR >109%) was protective against VF.20,21 Nonetheless, more than 1 in 4 AYA with optimal MPR (>94%) experienced VF in the 1st year on ART, suggesting a tension between medication pick-up and medication-taking behavior. Such discordance might be explained by the overwhelming role played by both perceived and experienced stigma and the fear of unwanted disclosure in the lives of AYA living with HIV.22–24 Qualitative studies of AYA living with HIV have consistently identified stigma and fear of disclosure as central obstacles to adherence, and have described how HIV is typically managed in isolation, within the immediate family unit, and within the home.22–25 As such, few youth were comfortable taking ART outside the home, so that not being at home at the time they were expected to take ART was a very common reason for missed doses.22–25

We also observed interesting differences in the types of clinic encounters (clinic, laboratory, and pharmacy) most common for AYA compared with older adults. Overall, AYA had fewer encounters for ART pick-up alone than adults (5 vs. 6 visits), but there was no difference in attendance at combined visits or clinic visits alone in the 1st year on ART. Although the magnitude of the difference is not large, other studies have found a strong linear correlation between the number of missed visits in the first year on ART and long-term mortality.26,27 Data from US cohorts also suggest that positive provider relationships are associated with medication adherence and engagement in care.28,29 Interaction with their providers might have served as an incentive for engagement in care for youth. However, many youth still seemed to have particular challenge with encounters for the pharmacy alone, even among those who otherwise seem to be compliant with their care. There are several factors that might disproportionately impact medication pick-up for youth as they transition from dependence to autonomy, including declining caregiver accompaniment to clinic and transportation barriers.30–32 Lack of health care and financial autonomy, coupled with monthly clinic visits required for ART pick-up, may combine to create major obstacles to frequent pharmacy encounters for some youth.

There may be important threats to the validity of MPR as an adherence measure that are more apparent in different environments or patient subgroups. Adherence is composed of several discrete behaviors (medication retrieval, medication ingestion according to appropriate dosing intervals, and observation of appropriate dietary requirements) that are difficult to incorporate into one measure.12 This phenomenon, called “construct underrepresentation,” occurs when a measure fails to assess important elements of the construct at hand (adherence).33 As we have described in this cohort, medication possession may not always correlate with medication ingestion, or with virologic control. Although MPR measures medication retrieval from the pharmacy, and this is presumed to correlate with medication ingestion, our analysis highlights that “construct underrepresentation” may be a more important threat to validity when MPR is applied to youth with apparent optimal adherence. In addition, although MPR in our analysis reflects medication retrieval over several months, adherence behaviors may vary widely within that period and directly impact virologic outcomes. Unstructured treatment interruptions are common after ART initiation, and longer treatment interruptions are associated with a greater risk of VF, especially early after ART initiation.34–37

Our study has several limitations. First, baseline resistance testing was not available in this cohort; so, we could not exclude patients with transmitted resistance who would not have been expected to achieve virologic suppression on routine first-line ART. In this retrospective analysis, the timing between the MPR assessment and HIV RNA was driven by the availability of the HIV RNA value, which may have occurred at the end of the MPR assessment or after the MPR assessment. In addition, nontrivial rates of loss to follow-up along with missing viral load data could have introduced bias into the analysis. We were reassured that the proportion of patients with missing viral load did not differ between AYA and older adults. We found that patients with higher baseline CD4 and less education were overrepresented in the excluded patient population. These factors were not significant predictors of VF in our multivariate model, which is consistent with other data from the region.38–40 Despite these concerns, our study also has important strengths. The data are derived from a multisite cohort from clinics varying in size, geographic location, and sector affiliation (public vs. private) within Nigeria, thus improving the generalizability of our findings.

In Sub-Saharan Africa, more than half of patients on ART do not have access to routine viral load testing. This number may be further reduced as global HIV support is reduced and programs have cut back on the use of laboratory tests and other services.41,42 Consistent ART pick-up from the pharmacy, especially in a setting where monthly ART pick-up is routine, may lead clinicians to assume a compliant pattern of health behaviors, with minimal or low risk of VF. Our study of patients initiating ART in Nigeria, however, underscores that more than 1 in 4 AYA with optimal adherence as assessed by MPR still developed VF. This risk was 20% greater in AYA than older adults, supporting disparate performance of MPR as a marker of virologic response across age groups, especially among those with the highest MPRs. Our findings support continued investigation for robust correlates of adherence, especially among youth. The findings also underscore the importance of removing obstacles to care, including a consideration of decreasing the frequency of visits required for ART pick-up, continued efforts to combat HIV-related stigma in communities at large, promoting clinic and home, peer-based social support to support adherence, investing the role of digital solutions to support adherence for youth in resource-limited settings (such as mHealth interventions), testing novel medication formulations such as long-acting ART, and formalizing youth-based models of care.43–48

REFERENCES

1. UNAIDS. Global AIDS Update; 2016. Available at: http://www.unaids.org/sites/default/files/media_asset/global-AIDS-update-2016_en.pdf. Accessed January 10, 2018.
2. WHO Adolescents: Health risks and solutions fact sheet. Available at: http://www.who.int/mediacentre/factsheets/fs345/en/. Accessed January 15, 2018.
3. UNAIDS. All In to #EndAdolescentAIDS; 2015. Available at: http://www.unaids.org/sites/default/files/media_asset/20150217_ALL_IN_brochure.pdf. Accessed January 22, 2018.
4. UNESCO. What do we mean by “youth?” Available from: http://www.unesco.org/new/en/social-and-human-sciences/themes/youth/youth-definition/. Accessed January 28, 2018.
5. Steinberg LA. Social neuroscience perspective on adolescent risk-taking. Dev Rev. 2008;28:78–106.
6. Lowenthal ED, Bakeera-Kitaka S, Marukutira T, et al. Perinatally acquired HIV infection in adolescents from sub-Saharan Africa: a review of emerging challenges. Lancet Infect Dis. 2014;14:627–639.
7. Ahonkhai AA, Banigbe B, Adeola J, et al. Age matters: increased risk of inconsistent HIV care and viremia among adolescents and young adults on antiretroviral therapy in Nigeria. J Adolesc Health. 2016;59:298–304.
8. Auld AF, Agolory SG, Shiraishi RW, et al. Antiretroviral therapy enrollment characteristics and outcomes among HIV-infected adolescents and young adults compared with older adults–seven African countries, 2004-2013. MMWR Morb Mortal wkly Rep. 2014;63:1097–1103.
9. Agwu AL, Siberry GK, Ellen J, et al. Predictors of highly active antiretroviral therapy utilization for behaviorally HIV-1-infected youth: impact of adult versus pediatric clinical care site. Journal of Adolescent Health. 2012;50:471–477.
10. Bangsberg DR, Hecht FM, Charlebois ED, et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS. 2000;14:357–366.
11. WHO. Meeting Report on Assessment of World Health Organization HIV Drug Resistance Early Warning Indicators; 2011. Available from: http://apps.who.int/iris/bitstream/10665/75186/1/9789241503945_eng.pdf?ua=1. Accessed January 14, 2018.
12. Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006;43(suppl 1):S79–S87.
13. Vreeman RC, Wiehe SE, Pearce EC, et al. A systematic review of pediatric adherence to antiretroviral therapy in low- and middle-income countries. Pediatr Infect Dis J. 2008;27:686–691.
14. Simoni JM, Kurth AE, Pearson CR, et al. Self-report measures of antiretroviral therapy adherence: a review with recommendations for HIV research and clinical management. AIDS Behav. 2006;10:227–245.
15. McMahon JH, Jordan MR, Kelley K, et al. Pharmacy adherence measures to assess adherence to antiretroviral therapy: review of the literature and implications for treatment monitoring. Clin Infect Dis. 2011;52:493–506.
16. Hong SY, Jerger L, Jonas A, et al. Medication possession ratio associated with short-term virologic response in individuals initiating antiretroviral therapy in Namibia. PLoS One. 2013;8:e56307.
17. Messou E, Chaix ML, Gabillard D, et al. Association between medication possession ratio, virologic failure and drug resistance in HIV-1-infected adults on antiretroviral therapy in Cote d'Ivoire. J Acquir Immune Defic Syndr. 2011;56:356–364.
18. Nachega JB, Hislop M, Dowdy DW, et al. Adherence to highly active antiretroviral therapy assessed by pharmacy claims predicts survival in HIV-infected South African adults. J Acquir Immune Defic Syndr. 2006;43:78–84.
19. Sangeda RZ, Mosha F, Prosperi M, et al. Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited settings. BMC Public Health. 2014;14:1035.
20. Rudd P, Byyny RL, Zachary V, et al. The natural history of medication compliance in a drug trial: limitations of pill counts. Clin Pharmacol Ther. 1989;46:169–176.
21. Okatch H, Beiter K, Eby J, et al. Brief report: apparent antiretroviral overadherence by pill count is associated with HIV treatment failure in adolescents. J Acquir Immune Defic Syndr. 2016;72:542–545.
22. Denison JA, Banda H, Dennis AC, et al. “The sky is the limit”: adhering to antiretroviral therapy and HIV self-management from the perspectives of adolescents living with HIV and their adult caregivers. J Int AIDS Soc. 2015;18:19358.
23. Nabukeera-Barungi N, Elyanu P, Asire B, et al. Adherence to antiretroviral therapy and retention in care for adolescents living with HIV from 10 districts in Uganda. BMC Infect Dis. 2015;15:520.
24. Abubakar A, Van de Vijver FJ, Fischer R, et al. 'Everyone has a secret they keep close to their hearts': challenges faced by adolescents living with HIV infection at the Kenyan coast. BMC Public Health. 2016;16:197.
25. Mutwa PR, Van Nuil JI, Asiimwe-Kateera B, et al. Living situation affects adherence to combination antiretroviral therapy in HIV-infected adolescents in Rwanda: a qualitative study. PLoS One. 2013;8:e60073.
26. Horberg MA, Hurley LB, Silverberg MJ, et al. Missed office visits and risk of mortality among HIV-infected subjects in a large healthcare system in the United States. AIDS Patient Care STDS. 2013;27:442–449.
27. Mugavero MJ, Lin HY, Willig JH, et al. Missed visits and mortality among patients establishing initial outpatient HIV treatment. Clinical Infect Dis. 2009;48:248–256.
28. Bakken S, Holzemer WL, Brown MA, et al. Relationships between perception of engagement with health care provider and demographic characteristics, health status, and adherence to therapeutic regimen in persons with HIV/AIDS. AIDS Patient Care STDS. 2000;14:189–197.
29. Roberts KJ. Physician-patient relationships, patient satisfaction, and antiretroviral medication Adherence among HIV-infected adults attending a public health clinic. AIDS Patient Care STDS. 2002;16:43–50.
30. Syed ST, Gerber BS, Sharp LK. Traveling towards disease: transportation barriers to health care access. J Community Health. 2013;38:976–993.
31. Siedner MJ, Lankowski A, Tsai AC, et al. GPS-measured distance to clinic, but not self-reported transportation factors, are associated with missed HIV clinic visits in rural Uganda. AIDS. 2013;27:1503–1508.
32. Lowenthal ED, Marukutira T, Tshume O, et al. Parental absence from clinic predicts human immunodeficiency virus treatment failure in adolescents. JAMA Pediatr. 2015;169:498–500.
33. Hubley AM, Zumbo BD. A dialectic on validity: where we have been and where we are going. J Gen Psychol. 1996;123:207–215.
34. Kranzer K, Ford N. Unstructured treatment interruption of antiretroviral therapy in clinical practice: a systematic review. Trop Med Int Health. 2011;16:1297–1313.
35. Ahonkhai AA, Banigbe B, Adeola J, et al. High rates of unplanned interruptions from HIV care early after antiretroviral therapy initiation in Nigeria. BMC Infect Dis. 2015;15:397.
36. Haberer JE, Musinguzi N, Boum Y II, et al. Duration of antiretroviral therapy adherence interruption is associated with risk of virologic rebound as determined by real-time adherence monitoring in rural Uganda. J Acquir Immune Defic Syndr. 2015;70:386–392.
37. Bangsberg DR, Mills EJ. Long-term adherence to antiretroviral therapy in resource-limited settings: a bitter pill to swallow. Antivir Ther. 2013;18:25–28.
38. Fox MP, Cutsem GV, Giddy J, et al. Rates and predictors of failure of first-line antiretroviral therapy and switch to second-line ART in South Africa. J Acquir Immune Defic Syndr. 2012;60:428–437.
39. Kamya MR, Mayanja-Kizza H, Kambugu A, et al. Predictors of long-term viral failure among Ugandan children and adults treated with antiretroviral therapy. J Acquir Immune Defic Syndr. 2007;46:187–193.
40. Labhardt ND, Bader J, Ramoeletsi M, et al. Clinical and socio-demographic predictors for virologic failure in rural Southern Africa: preliminary findings from CART-1. J Int AIDS Soc. 2014;17(4 suppl 3):19666.
41. Katz IT, Bassett IV, Wright AA. PEPFAR in transition–implications for HIV care in South Africa. N Engl J Med. 2013;369:1385–1387.
42. UNAIDS. Global Report: UNAIDS Report on the Global AIDS Epidemic 2010; 2010. Available from: http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2010/20101123_globalreport_en.pdf. Accessed January 11, 2018.
43. Chan BT, Tsai AC, Siedner MJ. HIV treatment scale-up and HIV-related stigma in Sub-Saharan Africa: a longitudinal cross-country analysis. Am J Public Health. 2015;105:1581–1587.
44. Katz IT, Ryu AE, Onuegbu AG, et al. Impact of HIV-related stigma on treatment adherence: systematic review and meta-synthesis. J Int AIDS Soc. 2013;16(3 suppl 2):18640.
45. Kanters S, Park JJ, Chan K, et al. Use of peers to improve adherence to antiretroviral therapy: a global network meta-analysis. J Int AIDS Soc. 2016;19:21141.
46. Tanner AE, Philbin MM, Duval A, et al. Youth friendly” clinics: considerations for linking and engaging HIV-infected adolescents into care. AIDS Care. 2014;26:199–205.
47. Hightow-Weidman LB, Muessig KE, Bauermeister J, et al. Youth, technology, and HIV: recent advances and future directions. Curr HIV/AIDS Rep. 2015;12:500–515.
48. Garofalo R, Kuhns LM, Hotton A, et al. A randomized controlled trial of personalized text message reminders to promote medication adherence among HIV-positive adolescents and young adults. AIDS Behav. 2016;20:1049–1059.
Keywords:

adherence; medication possession ratio; MPR; youth; adolescent; young adult; virologic failure

Supplemental Digital Content

Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.