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J Gen Intern Med. Oct 2002; 17(10): 756–765.
PMCID: PMC1495120

A Prospective Study of Predictors of Adherence to Combination Antiretroviral Medication

Carol E Golin, MD,1 Honghu Liu, PhD,2,6 Ron D Hays, PhD,2,6 Loren G Miller, MD, MPH,3,6 C Keith Beck, MD,3 Jeanette Ickovics, PhD,4 Andrew H Kaplan, MD,5 and Neil S Wenger, MD, MPH2,6

Abstract

OBJECTIVE

Adherence to complex antiretroviral therapy (ART) is critical for HIV treatment but difficult to achieve. The development of interventions to improve adherence requires detailed information regarding barriers to adherence. However, short follow-up and inadequate adherence measures have hampered such determinations. We sought to assess predictors of long-term (up to 1 year) adherence to newly initiated combination ART using an accurate, objective adherence measure.

DESIGN

A prospective cohort study of 140 HIV-infected patients at a county hospital HIV clinic during the year following initiation of a new highly active ART regimen.

MEASURES AND MAIN RESULTS

We measured adherence every 4 weeks, computing a composite score from electronic medication bottle caps, pill count and self-report. We evaluated patient demographic, biomedical, and psychosocial characteristics, features of the regimen, and relationship with one's HIV provider as predictors of adherence over 48 weeks. On average, subjects took 71% of prescribed doses with over 95% of patients achieving suboptimal (<95%) adherence. In multivariate analyses, African-American ethnicity, lower income and education, alcohol use, higher dose frequency, and fewer adherence aids (e.g., pillboxes, timers) were independently associated with worse adherence. After adjusting for demographic and clinical factors, those actively using drugs took 59% of doses versus 72% for nonusers, and those drinking alcohol took 66% of doses versus 74% for nondrinkers. Patients with more antiretroviral doses per day adhered less well. Participants using no adherence aids took 68% of doses versus 76% for those in the upper quartile of number of adherence aids used.

CONCLUSIONS

Nearly all patients' adherence levels were suboptimal, demonstrating the critical need for programs to assist patients with medication taking. Interventions that assess and treat substance abuse and incorporate adherence aids may be particularly helpful and warrant further study.

Keywords: adherence, antiretroviral, medication compliance, HIV

Available treatment for HIV can dramatically suppress viral load, enhance CD4 counts and decrease morbidity and mortality related to HIV infection.16 If antiretroviral medications are not taken as prescribed, treatment failure may ensue.721 Nonadherence is widely viewed as a risk factor for drug-resistant virus, which can be transmitted through unsafe sexual and drug use practices.8 It appears that patients must ingest at least 90% to 95% of their prescribed doses consistently to maintain virologic success.7,9 Although patients taking antiretrovirals generally achieve higher levels of adherence than do patients on other chronic medical therapies,7,21 the regimens are complex and lifelong; not surprisingly, a large proportion of patients are unable to achieve the targeted levels of adherence.13,2125 Therefore, interventions to facilitate patients' adherence to antiretroviral medications are critical to optimal HIV care.

Development of successful interventions to improve adherence requires a detailed understanding of the numerous factors that influence patients' medication taking. Identified correlates of adherence are often grouped into several broad categories: characteristics of the patient,26 features of the regimen,27 aspects of the clinical interaction,28 features of the illness, and socioenvironmental factors.29 Studies that have assessed adherence to antiretroviral therapy (ART) have identified salient factors in each of these categories.22,23,3056 Unfortunately, many reports have been limited by a cross-sectional design, the use of self-report measures or both.7,13,3048 Several studies assessed only patients' self-reported reasons for nonadherence, rather than testing for associations between these factors and actual adherence.36,37,45,47,48

We designed a longitudinal, cohort study to address some of the unresolved questions related to the influence of various factors on adherence to ART. We prospectively measured hypothesized predictors of ART adherence and followed patients for a prolonged period of time (up to 48 weeks). Then we used a carefully constructed measure of adherence that has been shown to be significantly predictive of virologic outcomes.21 We derived the following hypotheses from the existing literature and tested them in this study:

  1. Patient Factors: We hypothesize that patients who have more-positive attitudes toward ART,22,31,37,38,45 greater self-efficacy toward adherence,44,38 and higher literacy levels42 will be more adherent with ART. We expect patients who are active substance abusers22,31,33,38,46,49 or who report lower emotional well-being7,33 to be less adherent.
  2. Regimen Factors: We expect that patients receiving more complex antiretroviral regimens22,27,31,49,52 and regimens that fit less well with the other daily activities35,38,40,41,44,48,52,53 will be less adherent. We also expect that use of adherence aids (such as pillboxes, medication timers, etc.)31 will be associated with better adherence.
  3. Features of the Clinical Interaction: We expect patients with greater continuity of care, satisfaction with medical care, and trust in their provider to be more adherent,5765
  4. Social/Environmental Factors: We expect patients with more social support to be more adherent.40,45,46,54,55

METHODS

Subjects

All patients were enrolled in the ADEPT (Adherence and Efficacy to Protease inhibitor Therapy) study, a prospective observational investigation of medication adherence in HIV.21 From February of 1998 through April of 1999, we enrolled HIV-infected patients attending a public hospital–affiliated HIV care clinic who spoke English or Spanish and who were newly initiating a protease inhibitor (PI) or a non-nucleoside reverse transcriptase inhibitor (NNRTI). Participants were followed for 48 weeks after initiation of the new regimen. Sixty percent of eligible subjects enrolled in the trial. For this analysis, we examined all patients with adherence data available for at least 2 four-week periods.

Data Collection

Overview

Information was collected from patients at baseline and every 4 weeks for up to 48 weeks. A study nurse interviewed patients face-to-face at baseline, week 8, week 24, and study exit. During these interviews, a standardized questionnaire was administered to assess self-reported adherence, all current medications, barriers to adherence, and reasons for missed doses. In addition, chart review was conducted at baseline and at study exit using a standardized instrument to assess disease severity and to confirm information obtained from patients regarding their complete medication regimen.

Measurement of Adherence

Adherence was assessed by combining 3 measures of adherence: Medication Event Monitoring System (MEMS) cap data, pill count, and self-reported adherence. Adherence was computed as the actual number of doses taken divided by the number of doses prescribed over a 4-week period and expressed as a percentage.21 Upon patient enrollment, the study nurse placed on the bottle of the patient's newly initiated PI medication a pill bottle cap containing a microchip that records each instance of bottle opening. If 2 PIs were started, each was fitted with a MEMS cap. For patients started on a non-PI or NNRTI-containing regimen, the most frequently dosed antiretroviral was measured. Every 4 weeks, at a follow-up visit, the study nurse downloaded information from the MEMS cap to a medication database and replaced the cap on the appropriate bottle. The study nurse also counted the patients' remaining ART pills. Self-reported adherence was assessed at baseline, week 8, week 24, and exit interview by asking patients: “Many people don't take their medication perfectly all the time. Over the past 7 days, how many times did you miss a dose of {Medication X}?” Responses were confirmed by a secondary question. Patients also were asked whether they had any medication changes since the last visit and whether they had used a pillbox. This information was used in the computation of a composite adherence score (CAS).21

The composite adherence score, described in detail elsewhere,21 was based primarily on MEMS data, with the use of pill count and then interview data (each calibrated to the MEMS metric) when MEMS data were missing or inaccurate. To identify inaccuracies, all MEMS data were carefully reviewed along with other information collected from the patient (use of pillboxes, changes in medications, discontinuation of medication) and qualitative notes from study nurses about unusual use of the MEMS cap (such as regular use of “pocket doses,” medication-sharing, use of liquid medication, and loss or damage of caps or bottles). The majority of CAS measures were based on MEMS data (61%). Where MEMS data were determined to be inaccurate or missing, calibrated pill count data were used (37%). In the 2% of cases in which neither accurate MEMS data nor pill counts were available, we based the CAS on calibrated self-report data. Of note, correlations between MEMS data and pill count were 0.46, between MEMS data and interview were 0.38, and between pill count and interview were 0.62. For this analysis, a patient's adherence was summed over all 48 weeks.

Measurement of Potential Determinants of Adherence and Covariates

At baseline, patients were interviewed to assess the following: 1) patient demographic, clinical, and psychosocial characteristics; 2) regimen characteristics; 3) features of the clinical interaction; and 4) socioenvironmental factors.

Patient Factors

Patients were asked about demographics (age, gender, race/ethnicity, acculturation level if Hispanic, education, income level, work status, number of children and relationship status), clinical characteristics (duration of antiretroviral treatment), physical and mental health,66,67 source of infection, and current alcohol intake and drug use,38 as well as psychosocial factors (therapy, self-efficacy, active coping style,68 and literacy69). Acculturation was measured using a modification of the Marin Acculturation scale.70 In addition, highest viral load and lowest CD4 count were determined by chart review. To assess patients' beliefs about ART (perceived treatment utility, perceived susceptibility, and perceived medication efficacy), we adapted for HIV the health beliefs subscale of the Adherence Determinants Questionnaire.71 Self-efficacy was assessed with a 1-item medication-specific question that used a visual analog scale: “On a scale of 0 to 10, where 0 = not at all sure and 10 = very sure, how sure are you that you will be able to take all of {medication X} exactly as directed over the next 30 days?”

Regimen Factors

Patients were asked how their ART regimens fit with their lifestyle: “How often will taking HIV medications fit into your daily activities in the next 30 days?” Regimen complexity was measured as: 1) the total number of antiretroviral and non-antiretroviral pills that the patient was prescribed to take each day; 2) the total number of antiretroviral medications the patient was prescribed to take each day; and 3) the number of daily doses of the most frequently dosed antiretroviral medication, referred to as “dose frequency.” We identified whether patients received medication through a drug trial and whether they used any of 6 adherence aids (medication list, timer, calendar, pillbox, taking medications with meals, or other) to help them remember to take their antiretroviral medication.

Features of the Clinical Interaction

To assess features of the clinical interaction, we used 4 scales. Continuity of care was assessed by asking, “How often do you see the same doctor or nurse practitioner in this clinic?” with 5 response options ranging from none to all of the time. Our measure of satisfaction with health care in the clinic used the 0 to 10 global rating scale from the Consumer Assessment of Health Plans Study.72 Trust in the provider was assessed using a previously published measure (Cronbach's α 0.86).73,74 Provider adherence counseling behaviors were assessed using a 10-item scale that asked patients whether their provider had done each of 10 adherence counseling behaviors (Cronbach's α 0.68).

Socioenvironmental Factors

Access to medication was assessed using a 5-item scale that was adapted for medication use from an existing access to medical care measure for patients with HIV (Cronbach's α 0.74).75 A 3-item social support scale was adapted from the Medical Outcomes Study (Cronbach's α 0.71).76 We measured stress using 4 items adapted from a 14-item scale (Cronbach's α 0.65) and active coping style using 3 items adapted from an existing coping scale.77

Reasons for Missing Doses

At exit, among those patients who self-reported any history of nonadherence, we assessed reasons for nonadherence by asking whether they had ever missed any antiretroviral medication for each of 14 possible reasons listed (seeTable 2). These reasons were selected because they were the most common causes for missed antiretroviral medication reported in a focus group of HIV-infected men conducted to help in designing the survey instrument.

Table 2
Patient-reported Reasons for Missing Doses (N = 71)

Statistical Analyses

We first assessed each patient's mean adherence to the initiated PI or NNRTI over the course of the study. We used descriptive statistics to assess patients' demographic, clinical, psychosocial, regimen, provider interaction, and socioenvironmental characteristics. Missing data on predictors of adherence were imputed, grouped by age and gender, using the hot deck imputation procedure in STATA 6.0 (Stata Corp., College Station, Tex). Values were imputed for the following variables (number of missing values in parentheses): living with a partner with HIV (8), working (9), acculturation (5), duration of diagnosis (25), duration of time on an antiretroviral (19), alcohol use (2), drug use (3), fit with lifestyle (29), number of adherence aids used (15), antiretroviral attitude (PI is worth taking) (8), other attitudes (2), self-efficacy (22), continuity (14), provider adherence counseling behaviors (33), trust in the provider (3), satisfaction with medical care (5), access to care (35), social support (2), income (47), literacy (34), and active coping style (1). We then performed bivariate analyses of the associations of hypothesized predictor variables with adherence to PI or NNRTI using t test, χ2, Wilcoxon rank sum, and analysis of variance as appropriate. On the basis of our a priori model and incorporating variables related to adherence in the bivariate analyses (P < .15), we used forward stepwise regression to help select variables for a multivariate model. We excluded acculturation from the final model because of its multicollinearity with ethnicity. The final model selected included only factors that were associated with adherence at P < .10 in the model. We used Predicted Residual Sum of Squares (PRESS), a method that combines model estimation and validation into a single step.78 The final multivariate model had the lowest PRESS compared with 53 other plausible models (8 intermediate models from the forward stepwise regression, 9 models with 1 more predictor added to the final model, and 36 models with 2 more predictors added to the final model). Goodness of model fitting was evaluated using adjusted R2. Adjusted means were computed for significant predictors of adherence. Holding other values in the model constant at their mean level, the final model was used to predict adherence levels for each category of the categorical predictors of adherence and for the upper and lower quartile of continuous predictors of adherence.

RESULTS

Sample Characteristics and Adherence Levels

Of the 140 patients enrolled in ADEPT, 117 had their adherence measured for at least 2 four-week periods. Data from a total of 1,030 four-week periods from these 117 patients were available for this analysis. Median follow-up for patients in this sample was 40 weeks. Compared to the 23 without adherence data, the 117 patients in this study sample were somewhat older (37.7 vs 33.9 years; P = .03) and had more total daily ART doses (13.3 vs 9.8). All other demographic, clinical, and regimen features, including duration of time on ART and daily dose frequency, were not significantly different.

The mean age of the subjects was 38 years. Eighty percent of subjects were male, 47% were Hispanic, 26% African American, and 16% white. Subjects were largely poor (63% reported an annual income ≤$10,000), with 35% having less than a high school education. On average, subjects had received an average of 24 months of ART (range, 1 to 120 months), and 40% had participated in a study that supplied HIV medication. The mean highest viral load was 422,429 copies/mL, and the mean CD4 count nadir was 148. Seventy-five percent of patients reported seeing the same provider most or all of the time. Additional sample characteristics are displayed in Table 1. On average, patients took 71.3% of their prescribed PI or NNRTI doses over the 48-week study period (SD, 18.1%; median 73.0%; range, 4.8% to 96.6%).

Table 1
Sample Characteristics and Their Bivariate Associations with ART Adherence over the 12 Months Following Initiation of a HAART Regimen

Patient Beliefs and Self-efficacy Regarding Antiretroviral Medications

At study baseline, about 80% of patients felt that protease inhibitors were definitely or probably worth taking and agreed that antiretroviral medications helped people to live longer. However, only 73% agreed that antiretroviral medications improved the quality of people's lives. Seventy-seven percent did not agree with the statement “you could fight off HIV without medication.” Eighty-five percent of patients agreed that if they did not take antiretrovirals exactly as instructed, their HIV could become resistant. Patients' perceived self-efficacy to take their antiretroviral medication was 9.38 on a scale of 1 to 10 (SD, 1.42; median, 10; range, 1 to 10) (Table 1).

Self-reported Reasons for Missing Doses

Among the 71 patients who, at exit, reported having ever missed a dose of ART, being too busy or forgetting (62%), being away from home (59%), or having a change in their daily routine (42%) were the most commonly cited of 17 possible reasons for missing. Being asleep (38%) and running out of medication (20%) were also commonly reported reasons. Less frequently, patients reported that having too many pills (15%), or being confused about dosage instructions (8%) or drug toxicity (12%) lead them to miss doses (Table 2).

Bivariate Associations between Independent Variables and Adherence

In bivariate analyses of patient factors and adherence, patients who were younger, had lower income, and who had lower educational attainment were less likely to adhere. African-American patients had significantly lower levels of adherence compared with Hispanic patients (mean adherence 62.8% vs 71.6%; P < .01). Patients who drank no alcohol in the last 30 days took 74.6% of their doses compared with 65.5% for those who drank alcohol (P = .008). In addition, patients who received medications in a drug study missed fewer doses (76.1%) than those not enrolled in a drug study (68.0%; P = .012). Patient gender, whether they were in a committed relationship, whether they were working, number of children, literacy level, self-efficacy, other clinical characteristics and beliefs about antiretrovirals were not associated with adherence. (Table 1)

The total number of pills and the number of antiretroviral medications were not significantly associated with adherence. The fit of the regimen with the patient's lifestyle also was not related to adherence. However, a greater dose frequency was associated with lower adherence levels (P = .006). The number of adherence aids used by the patient was weakly related with adherence.

The number of provider adherence counseling behaviors as reported by the patient was not associated with adherence. Although patients' satisfaction with their health care and continuity of care were not associated with adherence, trust in the provider was directly related to subsequent adherence (P = .03).

No socioenvironmental factor was found to be statistically significantly related to adherence, although there was a weak correlation between self-reported access to antiretroviral medication and adherence levels (P = .10).

Multivariate Results

In the final multivariate model, African-American ethnicity, lower income, lower education, greater alcohol use and active drug use, higher dose frequency, and the use of more medication reminders were independently associated with adherence (Table 3). Patient age and whether the patient had received HIV medication in a clinical trial were not significant predictors of adherence in this model. When we added use of medication reminders at 6 months to the final model, this variable was not associated with adherence and did not change the other parameter estimates in the model, so this was not included in the final model. Using the final model, we predicted adherence levels for each category of the significant categorical predictors of adherence and for the upper and lower quartile of continuous predictors of adherence while holding other values in the model constant at their mean level. Accordingly, those actively using drugs were predicted to take 59% of doses versus 72% for nonusers. Alcohol users were predicted to take 66% of ART doses, compared to 74% for nondrinkers. Patients with dosing regimens in the top quartile of frequency took 67% of prescribed pills versus 72% adherence for those with less-frequent dosing. Those using no adherence aids were predicted to take 67.5% of doses versus 76% adherence among the top quartile of adherence aid users.

Table 3
Independent Predictors of Adherence to a PI or NNRTI

DISCUSSION

We conducted a prospective cohort study among patients initiating a new highly active ART regimen to assess their objectively measured antiretroviral adherence during the 48 weeks following initiation of therapy. This study goes beyond prior work by evaluating adherence prospectively over a long time period among patients at the time of initiation of a new combination antiretroviral regimen. On average, patients attending this public hospital–affiliated clinic took about 71% of their prescribed doses. This is consistent with other studies showing that patients on combination ART miss fewer pills than do most patients on other chronic medical therapies.7 However, this adherence level is lower than that required to prevent treatment failure.7 In fact, 96% of patients in this sample took less than the 95% of prescribed doses probably necessary for long-term success.7,21 These data underscore the extraordinary need that exists for interventions to facilitate patient adherence to antiretrovirals.

To help inform such interventions, we tested an a priori conceptual model of hypothesized determinants of adherence to identify factors affecting ART adherence. In multivariate analyses, African-American ethnicity, lower income and education, alcohol use, active drug use, greater dose frequency, and the use of no adherence aids were independently associated with worse adherence.

The relationship between substance abuse and adherence appears to be complex. Patients who drank alcohol were significantly less adherent. Current active drug use was also associated with suboptimal adherence. At the same time, there was no association between adherence and a history of prior drug use. Some studies have shown that any history of intravenous drug use is associated with worse adherence,31,46 while others have found that recovered intravenous drug users demonstrate increased adherence to ART.33,49 Our results are consistent with studies showing that active substance abuse is the important predictor of ART adherence.22,31,33,38,46,49 These findings underscore the need for ongoing assessment of substance abuse and concurrent alcohol and drug counseling for patients on antiretroviral therapy. Use of alcohol and drugs needs to be talked about as a part of in-depth discussions about antiretroviral medication taking.

Patients who used more adherence aids were more adherent. This finding is interesting, and to our knowledge, this relationship has been noted in only 1 prior study.31 We cannot assume a causal relationship between aids such as pillboxes and calendars and adherence, yet such reminder systems may represent important intervention options. Preliminary reports of the impact of reminders on adherence have had mixed results. In a pilot study of 55 patients, only those who received monetary reinforcement in addition to reminders and MEMS feedback were more adherent than controls.79 In contrast, in preliminary data from another randomized trial of an on-line paging system, patients receiving paged medication reminders improved their adherence significantly more than controls over 4 weeks.80 In qualitative studies, HIV-positive patients reported the usefulness of technological adherence aids, but many patients were unaware that such aids existed.56 The incorporation of these aids into clinical practice may be warranted, given their association with adherence in this prospective study. Including standardized patient education about adherence aids during ART initiation is a practical way to introduce patients to these potentially valuable interventions. Further studies are needed to assess the long-term effects of medication reminder systems and to compare the efficacy of different types of reminders to improve ART adherence. Of note, because the use of MEMS precludes pillbox use, the relationship between pillbox use and adherence could be confounded by the measurement technique used. To determine whether this is the case requires more intensive study with a trial focused on types of adherence adjuncts. If such studies show that pillboxes are associated with better adherence than MEMS, then for both ethical and clinical reasons, clinical trials should not preclude pillbox use in favor of MEMS.

Dose frequency was related to adherence, although the total number of pills and the total number of antiretrovirals prescribed was not. This supports prior studies demonstrating the importance of the number of times per day medications must be taken, although not all of these studies also assessed the number of medications taken.22,27 More frequent dosing may lead to missing doses because patients have difficulty with the middle of the day dose.56 The impact of dosing complexity on adherence can guide clinicians in selecting medication regimens and delineates a role for adherence aids to help to remind patients of midday doses. In addition, the fact that dose frequency is the only aspect of regimen complexity that affected adherence may have important implications for the development of combination pills, particularly if these medications are taken more frequently and/or are more expensive.

Lower educational achievement and lower income each were independently associated with having lower adherence. The relationship of lower socioeconomic status with ART adherence has been identified in other studies34,44,50 but not consistently. This strong association does not appear to be mediated by access to care or literacy, neither of which were related to adherence in this study. Literacy, found to be related to adherence in other studies,42 may have been compromised in this evaluation because of the large number of imputed values. The finding that lower education level is associated with worse adherence is consistent with the fact that understanding of treatment recommendations is necessary for adherence.

The finding that after controlling for other sociodemographic features, African-American patients were less adherent than others has also been noted in some studies34,44,50 but not others.22,41,42 Attempts to understand the mediators of the association between African-American ethnicity and nonadherence were unsuccessful in this study. We evaluated patients' beliefs about antiretrovirals, their trust in the provider, and their access to care. Post-hoc analyses indicate that there was no correlation between African-American ethnicity and beliefs about the medication. Further, there was no association between ethnicity and trust in the provider or access to care. It may be that because we measured trust in the physician only and access to medications specifically, we did not assess the exact beliefs that might explain these differences. Further studies to understand the mediators of nonadherence in relation to ethnicity are needed.

Several factors hypothesized to be associated with antiretroviral adherence were not. Patients who reported that their provider performed more adherence counseling and those with more-positive beliefs about the medications and more social support were no more adherent than other patients. However, adherence counseling was measured by patient report and may not accurately reflect provider behavior. At the same time, the trend toward greater adherence among patients with more trust in their provider and who were in a drug study suggests that the contact and rapport with the medical provider may play a role in influencing adherence. The vast majority of patients had positive beliefs about their medication, including high self-efficacy to take the medications. The minimal variation in responses to these questions may explain the lack of association between beliefs and adherence. Alternatively, some of the nonsignificant associations between predictors and the adherence measure could be due to limitations of the measures.

It is interesting that the reasons that patients gave for missing doses differed from those identified in comparative analyses. Although patients reported that factors related to fitting the regimen into their lifestyle (such as being busy, having a change in routine, being asleep) were important reasons for missing doses, perceptions of the medication fit with their routine was not associated with objectively measured adherence. This is in contrast to studies of self-reported adherence.44 Hence, patients' perceptions of how well the regimen fits into their lifestyle may be more related to perceived adherence than to actual adherence.

The findings of this study must be interpreted in light of its limitations. Because it was conducted at a single site, the findings may not be generalizable to dissimilar clinical settings. In addition, this is an observational study, and the associations found cannot be assumed to be causal. However, the prospective design does reduce temporal ambiguity, and our multivariate analyses reduce confounding bias. Although the method we used to measure adherence allowed us to exclude MEMS data that were likely to be invalid (such as with the use of pillboxes), we may have missed some episodes in which patients took more than 1 dose out of their bottle at a time. Missing such episodes would result in a slight underestimate of adherence, but failure to adjust for these errors is extremely unlikely to change the findings of the study. Further, we did not assess predictors of different patterns of adherence, which may also be related to virologic outcomes. Finally, our sample size may have prevented us from detecting some relationships.

In summary, consistent with other studies, the vast majority of patients in this longitudinal study need interventions to improve adherence. Interventions are needed that attend to the needs of low-income, low-education patients. We also confirm other studies that underscore the need for ongoing assessment and treatment of substance abuse in concert with antiretroviral therapy. In addition, data reported here suggest a new finding: interventions that include technological aids and other reminders to help patients take their doses may be particularly useful and warrant further study. Finally, more forgiving, less-frequently dosed medications are needed to help patients on ART adhere and maintain virologic success.

Acknowledgments

This study was supported by NIH RO1 AI41413. We thank Victor Gonzalez for technical assistance. Drs. Miller, Hays, and Wenger were supported in part by a Center for AIDS Research (CFAR) grant from the National Institute of Health (AI 28697).

References

1. Markowitz M, Saag M, Powderly WG, et al. A preliminary study of ritonavir, an inhibitor of HIV-1 protease, to treat HIV-1 infection. N Engl J Med. 1995;333:1534–9. [PubMed]
2. Danner SA, Carr A, Leonard JM, et al. A short-term study of the safety, pharmacokinetics, and efficacy of ritonavir, an inhibitor of HIV-1 protease. European-Australian Collaborative Ritonavir Study Group. N Engl J Med. 1995;333:1528–33. [PubMed]
3. Collier AC, Coombs RW, Schoenfeld DA, et al. Treatment of human immunodeficiency virus infection with saquinavir, zidovudine and zalcitabine. N Engl J Med. 1996;334:1011–7. [PubMed]
4. Gulick RM, Mellors JW, Havlir D, et al. Treatment with indinavir, zidovudine, and lamivudine in adults with human immunodeficiency virus infection and prior antiretroviral therapy. N Engl J Med. 1997;337:734–9. [PubMed]
5. Mathez D, Truchis P, Gorin I, et al. Ritonavir, AZT, DDC, as a triple combination in AIDS patients. Presented at the Third Conference on Retroviruses and Opportunistic Infection, Washington, DC, February 1, 1996.
6. Gulick RM, Mellors JW, Havlir D, et al. Simultaneous vs.sequential initiation of therapy with indinavir, zidovudine, and lamivudine for HIV-1 infection: 100-week follow-up. JAMA. 1998;280:35–41. [PubMed]
7. Paterson L, 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. [PubMed]
8. 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;14:357–66. [PubMed]
9. Bangsberg DR, Perry S, Charlebois E, Clark R, Robertson M, Moss AR. Adherence to HAART predicts progression to AIDS. AIDS. 2001;15:1181–3. [PubMed]
10. Montessori V, Heath B, Yip R, et al. Predictors of adherence with triple combination antiretroviral therapy. Presented at the Seventh Conference on Retroviruses and Opportunistic Infection, January 30–February 2, 2000.
11. Carpenter CJ, Fischl MA, Hammer SM, et al. Antiretroviral therapy for HIV infection in 1998: updated recommendations of the International AIDS Society—USA Panel. JAMA. 1998;280:78–86. [PubMed]
12. Reichman LB. Compliance with zidovudine therapy. Ann Intern Med. 1990;113:332–3. [PubMed]
13. Chow R, Chin T, Fong IW, Bendayan R. Medication use patterns in HIV-positive patients. Can J Hosp Pharm. 1993;46:171–5. [PubMed]
14. Nobel H, Carmona A, Grau S, Pedro-Botet J, Diez A. Adherence and effectiveness of highly active antiretroviral therapy. Arch Intern Med. 1998;158:1953. [PubMed]
15. Condra JH, Schleif WA, Blahy OM, et al. In vivo emergence of HIV-1 variants resistant to multiple protease inhibitors. Nature. 1995;374:569–71. [PubMed]
16. el-Farrash MA, Kuroda MJ, Kitazaki T, et al. Generation and characterization of a human immunodeficiency virus type 1 (HIV-1) mutant resistant to an HIV-1 protease inhibitor. J Virol. 1994;68:233–9. [PMC free article] [PubMed]
17. Ho DD, Toyoshima T, Mo H, et al. Characterization of human immunodeficiency virus type 1 variants with increased resistance to a C2-symmetric protease inhibitor. J Virol. 1994;68:2016–20. [PMC free article] [PubMed]
18. Jacobsen H, Yasargil K, Winslow DL, et al. Characterization of human immunodeficiency virus type 1 mutants with decreased sensitivity to proteinase inhibitor Ro 31–8959. Virology. 1995;206:527–34. [PubMed]
19. Kaplan AH, Michael SF, Wehbie RS, et al. Selection of multiple HIV-1 variants with decreased sensitivity to an inhibitor of the viral protease. Proc Natl Acad Sci USA. 1994;91:5597–601. [PMC free article] [PubMed]
20. Lin Y, Lin X, Hong L, et al. Effect of point mutations on the kinetics and the inhibition of human immunodeficiency virus type 1 protease: relationship to drug resistance. Biochemistry. 1995;34:1143–52. [PubMed]
21. Liu HH, Golin CG, Miller L, et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med. 2001;134:968–77. [PubMed]
22. Eldred LJ, Wu AW, Chaisson RE, Moore RD. Adherence to antiretroviral and pneumocystis prophylaxis in HIV disease. J Acquir Immune Defic Syndr. 1998;18:117–25. [PubMed]
23. Kastrissios H, Suarez JR, Hammer S, Katzenstein D, Blaschke TF. The extent of non-adherence in a large AIDS clinical trial using plasma dideoxynucleoside concentrations as a marker. AIDS. 1998;12:2305–11. [PubMed]
24. Lucas GM, Chaisson RE, Moore RD. Highly active antiretroviral therapy in a large urban clinic: risk factors for virologic failure and adverse drug reactions. Ann Intern Med. 1999;131:81–7. [PubMed]
25. Deeks SG, Hecht FM, Swanson M, et al. HIV RNA and CD4 cell count response to protease inhibitor therapy in an urban AIDS clinic: response to both initial and salvage therapy. AIDS. 1999;13:F35–43. [PubMed]
26. Haynes RB, Taylor DW, Sackett DL. Compliance in Health Care. Baltimore, Md: Johns Hopkins University Press; 1979.
27. Cramer J, Mattson RH, Prevey ML, Scheer RD, Ouellette V. How often is medication taken as prescribed? A novel assessment technique. JAMA. 1989;261:3272–7. [PubMed]
28. Golin CE, DiMatteo MR, Gelberg L. The role of patient participation in the doctor visit. Implications for adherence to diabetes care. Diabetes Care. 1996;19:1153–64. [PubMed]
29. Glasgow RE, Strycker LA, Toobert DJ, Eakin E. A social-ecologic approach to assessing support for disease self-management: the Chronic Illness Resources Survey. J Behav Med. 2000;23:559–83. [PubMed]
30. Golin CE, DiMatteo MR, Meisler AW. Adherence in AIDS clinical trials: a framework for clinical research and clinical care. J Clin Epidemiol. 1997;50:385–91. [PubMed]
31. Samet JH, Libman H, Steger KA, et al. Compliance with zidovudine therapy in patients infected with human immunodeficiency virus, type 1: a cross-sectional study in a municipal hospital clinic. Am J Med. 1992;92:495–502. [PubMed]
32. Samuels JE, Hendrix J, Hilton M, Marantz PR, Sloan V, Small CB. Zidovudine therapy in an inner city population. J Acquir Immune Defic Syndr. 1990;3:877–83. [PubMed]
33. Broers B, Morabia A, Hirschel B. A cohort study of drug users' compliance with zidovudine treatment. Arch Intern Med. 1994;154:1121–7. [PubMed]
34. Muma RD, Ross MW, Parcel GS, Pollard RB. Zidovudine adherence among individuals with HIV infection. AIDS Care. 1995;7:439–47. [PubMed]
35. Weidle PJ, Ganera CE, Irwin KL, et al. Adherence to antiretroviral medications in an inner-city population. J Acquir Immune Defic Syndr. 1999;22:498–502. [PubMed]
36. Holzemer W, Henry S, Portillo CJ, Miramontes H. The Client Adherence Profiling-Intervention Tailoring (CAP-IT) intervention for enhancing adherence to HIV/AIDS medications: a pilot study. J Assoc Nurses AIDS Care. 2000;11:36–44. [PubMed]
37. Murphy D, Roberts K, Marelich W, Hoffman D. Barriers to antiretroviral adherence among HIV-Infected adults. AIDS Patient Care. 2000;14:47–58. [PubMed]
38. Chesney M, Ickovics J, Chambers D, et al. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. AIDS Care. 2000;12:255–66. [PubMed]
39. Murri R, Ammassari A, Gallicano K, et al. Patient-reported nonadherence to HAART is related to protease inhibitor levels. J Acquir Immune Defic Syndr. 2000;24:123–8. [PubMed]
40. Catz S, Kelly J, Bogart L, Benotsch E, McAuliffe T. Patterns, correlates, and barriers to medication adherence among persons prescribed new treatments for HIV disease. Health Psychol. 2000;19:124–33. [PubMed]
41. Holzemer WL, Corless IB, Nokes KM, et al. Predictors of self-reported adherence in persons living with HIV disease. Aids Patient Care STDS. 1999;13:185–97. [PubMed]
42. Kalichman SC, Ramachandran B, Catz S. Adherence to combination antiretroviral therapies in HIV patients of low health literacy. J Gen Intern Med. 1999;14:267–73. [PMC free article] [PubMed]
43. Tuldra A, Fumaz C, Ferrer MJ, et al. Prospective randomized two-arm controlled study to determine the efficacy of a specific intervention to improve long-term adherence to highly active antiretroviral therapy. J Acquir Immune Defic Syndr. 2000;25:221–8. [PubMed]
44. Gifford A, Bormann J, Shively M, Wright B, Richman D, Bozzette S. Predictors of self-reported adherence and plasma HIV concentration in patients on multidrug antiretroviral regimens. J Acquir Immune Defic Syndr. 2000;23:386–95. [PubMed]
45. Roberts KJ. Barriers to and facilitators of HIV-positive patients' adherence to antiretroviral treatment regimens. Aids Patient Care STDS. 2000;14:155–68. [PubMed]
46. Gordillo V, del Amo J, Soriano V, Gonzalez-Lahoz J. Sociodemographic and psychological variables influencing adherence to antiretroviral therapy. AIDS. 1999;13:1763–9. [PubMed]
47. Proctor VE, Tesfa A, Tompkins DC. Barriers to adherence to highly active antiretroviral therapy as expressed by people living with HIV/AIDS. Aids Patient Care STDS. 1999;13:535–44. [PubMed]
48. Meystre-Agustoni G, Dubois-Arber F, Cochand P, Telenti A. Antiretroviral therapies from the patient's perspective. AIDS Care. 2000;12:717–21. [PubMed]
49. Turner B, Newschaffer C, Zhang D, Cosler L, Hauck W. Antiretroviral use and pharmacy-based measurement of adherence in postpartum HIV infected women. Med Care. 2000;38:911–25. [PubMed]
50. Laine C, Newschaffer CJ, Zhang D, Cosler L, Hauck WW, Turner BJ. Adherence to antiretroviral therapy by pregnant women infected with human immunodeficiency virus: a pharmacy claims-based analysis. Obstet Gynecol. 2000;95:167–73. [PubMed]
51. Moatti JP, Carrieri MP, Spire B, Gastaut JA, Cassuto JP, Moreau J. Adherence to HAART in French HIV-infected injecting drug users: the contribution of buprenorphine drug maintenance treatment. The Manif 2000 study group. AIDS. 2000;14:151–5. [PubMed]
52. Ostrop N, Hallett K, Gill J. Long-term patient adherence to antiretroviral therapy. Ann Pharmacother. 2000;34:703–9. [PubMed]
53. Leider JM, Kalkut G. Understanding adherence to HIV medication. [letter] Ann Intern Med. 2000;132:418. [PubMed]
54. Sherbourne C, Hays RD, Ordway L, DiMatteo R, Kravitz RL. Antecendents of adherence to medical recommendations: results from the medical outcomes study. J Behav Med. 1992;15:447–67. [PubMed]
55. Glasgow RE. Social-environmental factors in diabetes. Barriers to diabetes self-care. In: Bradlely C, editor. Handbook of Psychology and Diabetes Research and Practice. Berkshire, UK: Hardwood Academic; 1994. pp. 335–49.
56. Golin CE, Isasi F, Breny Bontempi J, Eng G. Secret pills: HIV-positive patients' experiences taking antiretroviral therapy. AIDS Educ and Prev. 2002;14:317–28. [PubMed]
57. Anderson L. Health care communication and selected psychological adherence in diabetes management. Diabetes Care. 1990;13:66–7.
58. Rost K. The influence of patient participation on satisfaction and compliance. Diabetes Educ. 1989;15:134–8. [PubMed]
59. Street R, Piziak V, Carpentier W, et al. Provider-patient communication and metabolic control. Diabetes Care. 1993;16:714–21. [PubMed]
60. Kaplan SM, Greenfield S, Ware J. Assessing the effects of physician-patient interaction on the outcomes of chronic disease. Med Care. 1989;27:110–27. [PubMed]
61. Stewart M. What is a successful doctor-patient interview. A study of interactions and outcomes. Soc Sci Med. 1984;19:167–75. [PubMed]
62. Rost K, Carter WI. Introduction of information during the initial medical visit: consequences for patient follow-through with physician recommendations for medication. Soc Sci Med. 1989;28:315–21. [PubMed]
63. Greenfield S, Kaplan SH, Ware JE, Jr, Yano EM, Frank HJ. Patients' participation in medical care: effects on blood sugar control and quality of life in diabetes. J Gen Intern Med. 1988;3:448–57. [PubMed]
64. Korsch B, Gozzi E, Francis V. Gaps in doctor-patient communication. Doctor-patient interaction and patient satisfaction. Pediatrics. 1968;42:855–71. [PubMed]
65. Roter D, Hall J. Doctors Talking with Patients/Patients Talking with Doctors. Westport, Conn: Auburn House; 1993.
66. Hays RD, Cunningham WE, Sherbourne CD, et al. Health-related quality of life in patients with Human Immunodeficiency Virus Infection in the United States: Results from the HIV Cost and Services Utilization Study. Am J Med. 2000;108:714–22. [PubMed]
67. Ware JE, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34:220–33. [PubMed]
68. Fleishman JA, Sherbourne CD, Crystal S, et al. Coping, conflictual social interactions, social support and mood among HIV-infected persons. Am J Community Psychol. 2000;28:421–53. [PubMed]
69. Parker RM, Baker DW, Williams MV, Nurss JR. The test of functional health literacy in adults: a new instrument for measuring patients' literacy skills. J Gen Intern Med. 1995;10:537–41. [PubMed]
70. Marin G, Sabogal F, Marin B, et al. Development of a short acculturation scale for Hispanics. Hisp J Behav Sci. 1987;9:183–205.
71. DiMatteo MR, Hays RD, Gritz E, et al. Factors affecting patient adherence to cancer control regimens: development and validation of a multivariate assessment instrument. Psychol Assess. 1993;5:102–12.
72. Hays RD, Shaul JA, Williams VSL, et al. Psychometric properties of the CAHPSTM 1.0 Survey measures. Consumer Assessment of Health Plans Study. Med Care. 1999;37:MS22–31. [PubMed]
73. Kao AC, Green DC, Davis NA, Koplan JP, Cleary PD. Patients' trust in their physicians: effects of choice, continuity, and payment method. J Gen Intern Med. 1998;13:681–6. [PMC free article] [PubMed]
74. Kao AC, Green DC, Zaslavsky AM, Koplan JP, Cleary PD. The relationship between method of physician payment and patient trust. JAMA. 1998;280:1708–14. [PubMed]
75. Cunningham WE, Markson LE, Andersen RM, et al. Prevalence and predictors of highly active antiretroviral therapy use in patients with HIV infection in the united states. HCSUS Consortium. HIV Cost and Services Utilization. J Acquir Immune Defic Syndr. 2000;25:115–23. [PubMed]
76. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32:705–14. [PubMed]
77. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24:385–96. [PubMed]
78. Liu HH, Weiss RE, Jennrich RI, Wenger NS. PRESS model selection in repeated measures data. Computational Statistics and Data Analysis. 1999;30:169–84.
79. Rigsby MO, Rosen MI, Beauvais JE, et al. Cue-dose training with monetary reinforcement: pilot study of an antiretroviral adherence intervention. J Gen Intern Med. 2000;15:841–7. [PMC free article] [PubMed]
80. Safren S, Boswell S, Johnson W, Salomon L, Mayer K. Initial outcome of an online paging system (MEDIMom) to increase adherence to antiretroviral therapy. Presented at the Eighth Conference on Retroviruses and Opportunistic Infection, Chicago, February 2001.

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