Long-term trends in adherence to antiretroviral therapy from start of HAART
Cambiano, Valentinaa; Lampe, Fiona Ca; Rodger, Alison Ja,b; Smith, Colette Ja; Geretti, Anna Mb; Lodwick, Rebecca Ka; Puradiredja, Dewi Ia; Johnson, Margaretb; Swaden, Leonieb; Phillips, Andrew Na
aHIV Epidemiology & Biostatistics Group, Research Department of Infection & Population Health, UCL Medical School, UK
bDepartment of HIV, Royal Free Hospital NHS Trust, London, UK.
Received 14 October, 2009
Revised 14 January, 2010
Accepted 2 February, 2010
Correspondence to Valentina Cambiano, HIV Epidemiology & Biostatistics Group, Research Department of Infection & Population Health, UCL Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK. E-mail: firstname.lastname@example.org
Objective: People on antiretroviral therapy are likely to be required to maintain good adherence throughout their lives. We aimed to investigate long-term trends in highly active antiretroviral therapy (HAART) adherence to identify the main predictors and to evaluate whether participants experience periods of low adherence (≤60%).
Methods: Participants in the Royal Free Clinic Cohort were followed from the date of start of HAART until the end of the last recorded ART prescription or death. Follow-up was divided into 6-month periods, and for each period, a value of adherence, measured as the percentage of days in the 6-month period covered by a valid prescription for at least three antiretroviral drugs, was calculated.
Results: Patients were assessed for drug coverage adherence for a median of 4.5 years [inter-quartile range (IQR) 2.4–7.2; maximum 9 years] covering a period up to 13 years from start of HAART. There was evidence of a slight increase in adherence over time [adjusted odds ratio (OR) of >95% adherence = 1.02 per year; 95% confidence interval (CI) 1.01–1.04; P = 0.0053]. Independent predictors of adherence were age, demographic group, calendar year period, drug regimen and previous virologic failures. The overall rate of at least one period of low adherence was 0.12 per person-year, but this rate decrease markedly over time to 0.01 in 2007/2008.
Conclusion: Adherence, as measured by drug coverage, does not decrease on average over more than a decade from start of HAART. This is encouraging, because it shows that patients could potentially maintain viral suppression for many years.
The introduction of antiretroviral therapy (ART) has produced a remarkable decrease in HIV morbidity and mortality in the last 15 years [1,2]. Currently, most HIV-positive patients who start ART achieve a HIV viral load of less than 50 copies/ml and a reconstitution in CD4 cell count [3,4]. Thus, maintaining long-term viral load suppression and avoiding development of ART resistance strains is now a key issue. The crucial factor determining achievement of these goals is the level of adherence to ART. It has been shown that adherence is fundamental to achieving viral suppression [5–7], avoiding viral rebound [7–9], increasing levels of CD4 cell counts [7,10] and minimizing the development of both drug resistance [11,12] and the risk of death [13–15]. As HIV cannot as yet be eradicated, it is likely that people on ART will need to take antiretroviral drugs for the foreseeable future. Therefore, it is essential to understand whether patients are able to maintain adherence to ART over time and identify the main factors that influence this trend.
Most [6,16–19], but not all [20–22], prospective studies that have investigated trends in adherence reported that adherence to ART declines over time. However, these studies have a short follow-up time (<30 months), moderate sample size (<500, some <100), and most rely on patient self-reported adherence [18,20,21].
Adherence is a complex dynamic phenomenon influenced by sociodemographic and psychological characteristics of the patient, treatment regimen, patient–provider relationship , and clinical setting . Among demographic characteristics, those found to be associated with lower adherence are female sex , younger age [21,22,25], black ethnicity [16,22,26–28], and lower education . Apart from these, there are several social factors that have been demonstrated to be linked to lower adherence: being an injection drug user (IDU) [5,22,26], active substance abuse [16,25], and lack of social support [29,30]. There are also many psychological factors that have been shown to negatively influence adherence to ART such as low perceived self-efficacy (personal confidence in the ability to adhere to medications) for ART use [18,27], negative attitude toward taking medication , having neurocognitive impairment , depression , and psychiatric illness [28,31].
Differences in adherence have been observed according to drug regimen. Drug toxicity [29,32], complexity of the treatment , number, and type of medication prescribed [22,28] have been shown to be, with some exceptions , negatively associated with adherence. Advanced disease stage, in particular, history of prior opportunistic infection , has also been found to be associated with higher adherence, suggesting that worsening disease severity motivates patients to adhere.
Finally, important factors linked to adherence are beliefs in effectiveness and toxicity of ART, concerns about the impact of ART on self-identity, the possibility that taking treatment might lead to disclosure of the individual's HIV status [29,30,33], and the ability to understand the relationship between adherence and medication resistance [27,34].
In this paper, we aim to assess whether adherence changes over time from start of HAART and to investigate which factors are associated with adherence to ART, based on data on drug prescriptions, among 2060 patients seen at a large urban clinic.
Patients and methods
Our analysis has been conducted on the Royal Free HIV Cohort, an observational cohort of HIV-infected individuals attending the Royal Free Hampstead NHS Trust, London, UK. Data collected on all patients includes demographics, CD4 cell counts, plasma viral load levels, other laboratory findings, AIDS diagnoses, ART start and stop dates, recorded by the clinicians, reasons for stopping all drugs, and date and durations of drugs prescriptions. Generally, HIV-infected patients receiving ART are seen by a physician approximately every 3–4 months and they can collect for free in the same clinic the drugs prescribed by the clinician. First prescriptions are usually only for 1 month and patients are seen more frequently – often monthly – when they start HAART until the viral load is undetectable. Clinical data are audited against case notes every 12 months by a trained research assistant .
Adherence was assessed for each ‘6-month period’ (‘period’) starting from each patient's first recorded ART prescription after the start of HAART until the date of death (in which case, the period including the date of death was excluded), last clinic visit or until November 2008. Adherence was evaluated by means of dispensed prescriptions as the percentage of days covered by prescription(s) for at least three drugs. We assessed the drug coverage over 6 months, because it is a more clinically relevant timeframe than longer periods such as 12 months  and to accommodate different prescription refill patterns. Moreover, although adherence to one drug is strongly correlated to adherence to other drugs , differential adherence is possible . Therefore, it was decided to consider the drug coverage on at least three drugs, instead of evaluating it on the basis of one drug alone. This adherence measure has been shown to predict the probability of viral load rebound in people with viral load less than 50 copies/ml on HAART in our clinic population .
Patients who had been or still were on HAART (regimen composed by at least three antiretroviral drugs), with data available on treatment and prescriptions, and who experienced at least two valid 6-month periods were included in the analysis.
A ‘period’ was excluded from analyses if there was a gap after a prescription's end to the next prescription longer than 3 months (to reduce the likelihood of missing prescription data or receipt of drugs from other sources), the duration of the prescription (i.e., amount of drug) was missing, unless this did not result in any gap in drug coverage, or the end of the period was more than 2 weeks after the end of the final (at the time of the analysis) recorded prescription.
Because of the highly left-skewed distribution of adherence (Fig. 1), it was used as a continuous variable only for descriptive purposes but for all other analyses it was dichotomized, according to whether drug coverage was greater than 95% or not for the period of interest. As each patient could contribute more than one period of adherence to the analysis, odds ratios (OR) and 95% confidence intervals (CIs) were estimated using generalized estimating equations (GEEs), assuming an autoregressive variance–covariance matrix between ‘periods’ in the same individuals.
The following variables were investigated for their association with adherence: age (centered at mean age 41, per 10 years increase), sex, ethnicity, and risk group [as separated variables and combined into one variable: demographic group (black women, nonblack women, black heterosexual men, nonblack heterosexual men, IDUs men, black homosexual men, nonblack homosexual men)], calendar year of ‘6-month period’, previous virologic failures (defined as viral load >500 copies/ml after 4 months continuously on a specific ART drug), previous rebound (defined as a viral load >200 copies/ml), and current drug regimen (characterized by the ‘third’ drug in regimen). This latter was defined as ‘other’ unless it was composed of at least two nucleoside reverse transcriptase inhibitors (NRTIs) [zidovuline (ZDV), ddC, ddI, d4T, lamivudine (3TC), abacavir (ABC), lodenosine, emtricitabine (FTC), tenofovir (TDF), and adefovir] and one ART drug among the list below. The only exception was trizivir, a specific regimen composed of ZDV, 3TC, and ABC. On the basis of the third drug, regimens were classified as unboosted nelfinavir (NFV), ritonavir-boosted saquinavir (SAQ/r), ritonavir-boosted lopinavir (LPV/r), ritonavir-boosted atazanavir (ATV/r), efavirenz (EFV), or nevirapine (NVP).
Among regimens not included in the list above, we distinguished among others ‘other, boosted protease inhibitors and other, unboosted protease inhibitor’ [these both could not contain nonnucleoside reverse transcriptase inhibitors (NNRTIs)]. These HAART regimens were chosen because they were the most common in our sample (at least 500 periods).
In order to ensure that the results were unlikely to be affected by selective loss to follow-up, the distribution of the change in drug coverage since last ‘period’ was explored (Fig. 3b). Additionally, we investigated the association between time-dependent drug coverage and loss to follow-up by means of a multivariate Cox model. A patient was considered lost to follow-up if his/her last HIV-RNA viral load was measured before July 2006, in people not known to have died, or more than 2 years before the date of death in people who had died (n = 68).
Finally, we were interested in evaluating whether patients experienced at least one period of low adherence (≤60%), in identifying the main predictors and in understanding whether this was a single period or whether these patients experienced longer periods of low adherence. It is important to understand how average adherence changes over time, but lack of adherence even for a short time may be enough to develop resistance and failure, so it is important to understand how many people experience at least one period of low adherence (≤60%). For this purpose, time to the first period with low adherence was estimated using Kaplan–Meier curve. We also investigated predictors of a period of adherence less than 60% using a Cox model.
Analyses were performed using SAS software (version 9.1; SAS Institute, Cary, North Carolina, USA). All tests of significance used P less than 0.05 as the threshold of statistical significance.
Among HIV-infected patients enrolled in the Royal Free HIV Cohort by May 2008, 2060 patients who had been or still were on HAART with more than one period of prescription data available were included in the analysis. Patients initiated HAART with median (IQR) start date of November 2000 (February 1998 to November 2003), but prescriptions have been regularly recorded starting from July 1999, so the periods eligible for the analysis occurred between July 1999 and May 2008; the median (IQR) time from start of HAART to the first ‘period’ was 4 months (0 days to 27 months). Among eligible patients, 25% (506/2060) had started ART treatment with less than three antiretroviral drugs, with median (IQR) start date of May 1996 (September 1993 to May 1997) 1.6 (median) years before starting HAART (IQR = 0.6–4.3). Patients were assessed for drug coverage adherence for a median of 4.5 years (IQR 2.4–7.2; maximum 9 years), and up to a median of 5.1 years from the start of HAART (IQR 2.5–8.5; maximum 13 years). The included patients contributed a total of 18 930 ‘periods’, of which 16 545 (87%) were eligible for inclusion. Reasons for excluding ‘periods’ were a gap between a prescription's end and the next prescription longer than 3 months in 82% of cases, the end of the ‘period’ was more than 2 weeks after the end of the final (at the time of the analysis) recorded prescription in 17% of cases and a missing value of the duration (amount of drug) of the prescription, that resulted in a gap in drug coverage, in less than 1% of cases.
Characteristics of patients included in the analysis are shown in Table 1. In the majority of ‘periods’, the patient was man (78%), white (66%), with viral load suppression (79%), a CD4 cell count more than 350 cells/μl (67%) or between 200 and 350 cells/μl (21%) and had not experienced any previous virologic failures (57%). For 5% of periods, patients were on only NRTI, 10% on unboosted protease inhibitor-containing regimen (no NNRTI), 40% on ritonavir-boosted protease inhibitor-containing regimen (no NNRTI), 35% on NNRTI-containing regimen (no protease inhibitor), and the remaining 10% on other regimens. Thirty-two percent of these ‘periods’ occurred, respectively, in 1999–2002 and 2005–2006, 26% in 2003–2004, and the remaining 11% in 2007–2008. The median time since start of HAART and the median age at the beginning of the ‘period’ were, respectively, 3.7 years (IQR 1.9–6.2) and 41 years (IQR 36–46).
Among ‘periods’ starting within 6 months of the start of HAART, the median drug coverage was 92% (IQR 79–99%) (Fig. 2). Considering the change in adherence compared with the patient's first eligible ‘period’ (Fig. 3a) or the previous ‘period’ (Fig. 3b), the same picture emerges of stability over time, with approximately equal number showing positive and negative changes.
In univariate analysis, adherence showed a significantly positive trend over time since start of HAART (OR of >95% adherence = 1.02 per year; 95% CI 1.01–1.03; P = 0.0010), suggesting a small average increase in the odds of more than 95% adherence with increasing time.
After adjusting for the variables found significant in univariate analysis, there remained a tendency for adherence to increase with time from start of HAART (OR = 1.02; 95% CI 1.01–1.04; P = 0.0053) (Table 1). Factors found to be independent predictors of lower adherence were being a black heterosexual man (OR of >95% adherence = 0.82; 95% CI 0.70–0.96; P = 0.0153), earlier calendar year for ‘period’ (P = 0.0003), and having previously experienced one to three (OR = 0.81; 95% CI 0.73–0.90; P < 0.0001) or more than three virologic failures (OR = 0.63; 95% CI 0.55–0.72; P < 0.0001) compared with none. Factors found associated with higher adherence were being older (OR per 10 year increase = 1.12; 95% CI 1.06–1.18; P < 0.0001), being a black woman (OR = 1.13; 95% CI 1.00–1.28; P = 0.0495), and among different drug regimens, being on SAQ/r (OR = 1.21; 95% CI 1.00–1.45; P = 0.0441), ATV/r (OR = 1.23; 95% CI 1.02–1.47; P = 0.0280) or other regimen (constituted by those regimens without protease inhibitors not included in the above categories) (OR = 1.17; 95% CI 1.00–1.37; P = 0.0459) compared with EFV.
These results should not be affected by lost to follow-up or by a higher proportion of treatment interruptions in less-adherent patients, because the percentage of patients on HAART over time since start of HAART is stably high at 90–100% (Fig. 4).
We also considered the rate of loss to follow-up and the association between adherence and this rate in order to understand whether the positive adherence trend over time could be partly due to selection bias. The overall rate of loss to follow-up was 0.022 per person-year. We found that poorer adherence was associated with greater risk of being lost to follow-up. Compared with those fully adherent, those with less than 60% of drug coverage were characterized by a hazard ratio of 5.80 (95% CI 3.68–9.15), those with drug coverage included between 60 and 80% by a hazard ratio of 5.40 (95% CI 3.57–8.17), those with drug coverage between 80 and 95% by a hazard ratio of 4.32 (95% CI 2.88–6.48), and those with drug coverage between 95 and 99% by a hazard ratio of 3.63 (95% CI 2.23–5.90).
Finally, we focused on the proportion of patients who experienced at least one period of low adherence (≤60%). The median time to a ‘period’ with 60% adherence or less was 4 years since start of HAART. In particular, the probability of experiencing a period of low adherence by 1 year was 0.17, by 3 years 0.45, by 5 years 0.61, by 7 years 0.73, and by 10 years 0.85.
Forty-eight percent of the patients experienced at least one ‘period’ characterized by low adherence and the overall rate was 0.12 per person-year. Factors found to be independent predictors of a ‘period’ with adherence lower than 60% were generally the same as those of adherence less than 95%, with the exception that there was a strong effect of calendar year, with a decreasing rate of periods of adherence less than 60% in more recent calendar years (rate of period <60% per 1 person-year for 1999–2002: 0.315; for 2003–2004: 0.197; for 2005–2006: 0.123; and for 2007–2008: 0.013).
Sixty-one percent of those who experienced at least one period of low adherence, experienced only one consecutive period of low adherence, and among those who experienced more than one, 72% did not experience consecutive periods of low adherence.
In this analysis based on up to 9 years of prescription data, we found no evidence of a decrease in average levels of adherence to HAART over time, and even some evidence of a small increase. Although adherence levels in patients can vary from one ‘period’ to the next, we found approximately equal proportions with increasing and decreasing adherence at any point in time (Fig. 3b), against a broadly stable and very high (>90%) proportion of patients under clinic follow-up who had started HAART remaining on HAART over time (Fig. 4). These encouraging observations have implications for our understanding of the likelihood that patients will be able to maintain sufficient levels of adherence for a lifetime. There was evidence that adherence levels have improved over calendar time, so this provides further reason for optimism in this regard. We also identified some factors associated with poorer adherence, in particular younger age, being a black man with heterosexually acquired HIV, having experienced previous virologic failures, and being on certain regimens. The association found between some ART regimens and poorer adherence must be interpreted with caution. Choice of specific drugs for a patient depends on calendar time and on the patient's characteristics (e.g., anticipated compliance and tolerance issues), so differences in adherence could reflect these confounding factors rather that represent real differences between drugs in ease of use.
In general, we observed high average levels of adherence, which is consistent with the very high proportion of patients in the clinic with viral load less than 50 copies/ml [3,38]. However, a high proportion of patients (48%) experienced at least one period of low adherence (≤60%). This is an issue because a short period of low adherence may be sufficient to develop resistance and failure. Therefore, if our data truly reflect the fact that most people experience a marked reduction in adherence at some point, then this represents a threat to maintenance of long-term suppression. It suggests that monitoring of drug prescription pickup could be used in clinics to flag-up possible issues with periods of reduced adherence. However, we have previously observed that in patients with viral load suppression, the risk of viral rebound with adherence less than 60% according to our measure is below 5%. It is also worth noting that only 39% of people experiencing adherence less than 60% had more than one period of low adherence and among these 72% have no consecutive periods characterized by 60% drug coverage or less. Additionally, in some cases, the period of less than 60% drug coverage will not coincide with a real drop in adherence of such magnitude, this is the case for example if a prescription was unrecorded or if drug had been accumulated from previous prescriptions. Thus, we may have overestimated the frequency with which such periods of low adherence occur. Finally, it is reassuring that periods characterized by low adherence have become far less common in recent years.
Previous studies have been limited in the length of time over which adherence has been assessed, with none to our knowledge covering more than 3 years. Although most have found a decreasing trend of adherence over time [6,16,18,19,39,40], this has not always been the case [20–22]. Our findings of stable adherence could result in part in improvements in tolerability of ART and clinical management, in part to proactive switching of drugs in patients with viral load suppression for reasons of toxicity  and increased patient awareness of the importance of adherence to long-term treatment success.
Among people still under follow-up, the proportion on ART is stably high. Thus, among patients remaining under clinic care, there is no tendency for patients to be more likely to be off-ART as time goes on. However, we did observe appreciable loss to follow-up and, further, we found that those with poorer adherence were more likely to be lost.
This could lead to a bias upward in our estimates of trends over time in adherence (Fig. 2) and within-patient trends from baseline in adherence (Fig. 3a), but should not, however, bias our estimates of within-patient changes from last period (Fig. 3b), which also shows no overall trend in adherence. Therefore, we do not consider that our observation of a nondecreasing trend in long-term adherence is explained by such bias. However, this could explain the tendency we observed for adherence to increase over time.
Despite the findings of our studies and others' [38,42–45] that viral load suppression can often be maintained with relatively low adherence on certain regimens, it is important to achieve and maintain a high level of adherence to minimize the risk of development of mutant strains of the virus , which can lead to viral load failure, and hence a need to switch drug regimen, and potential risk of transmission of drug resistance [12,47]. Nonadherent patients have higher concentrations of HIV RNA in their semen or cervical secretions, which may increase the risk of HIV transmission  and nonadherence accelerates the development of drug-resistant strains. In our previous study , we found that a shorter time with viral load stable below 50 copies/ml was an independent predictor of viral load failure (defined as a viral load >200 copies/ml), which is consistent with the stable adherence over time found in this study.
Consistent with our results, previous studies found that black men, mainly infected through heterosexual sex and often originating from outside the UK , were on average less likely to adhere than white patients [16,22,28]. This is likely to relate to socioeconomic and migration status, characterized by a more difficult access to care, and perhaps less access to information (including language issues). They tend to access medical care at a later stage and with more advanced disease than white patients  and hence start ART at lower CD4 cell counts with higher likelihood of associated opportunistic infections and the related drug interactions and toxicities that may impact on adherence. Older patients were associated with higher adherence compared with younger people [21,22,25,30] and this is also reflected in the fact that they are more likely to keep their HIV care-related appointments  and are more aware of the negative clinical effects of low adherence [22,51].
The main advantages of our study are the large number of patients analysed (n = 2060, with over 16 000 ‘periods’ covering over 8000 person-years of observation), the long follow-up with over 500 patients followed with prescription data up to more than 8.5 years from start of HAART, longer than previous studies, and the fact that we used an objective measure of adherence that is not subject to potential biases due to patient fatigue in adherence reporting. The fact that the adherence is measured by drug prescription coverage also has disadvantages, in that it does not measure actual drug taken. For example, it might be argued that some patients occasionally will obtain drugs from elsewhere. We considered it unlikely that this has occurred to any significant degree. Indeed, we are only including patients for whom regular prescriptions are made, other sources of drugs are not straightforward to access and it is difficult to see the purpose of seeking and using external supplies for the patient's own use if a regular free supply is on offer at our clinic. As mentioned, regularly receiving a new prescription to cover a period does not prove that drug is being taken and it is possible that some patients will accumulate drugs over time. This could result in overestimation of adherence, but it is likely to be small based on clinical experience and it seems unlikely that it would cause a systematic bias in long-terms trends over time. In other cases, we underestimated adherence because we assigned a drug coverage value of zero for patients for whom we have prescriptions on just two drugs. In any case, we think it is difficult to understand the reason why a patient would continue to visit his/her clinician asking for a prescription, if he/she still has many drugs left, and this is consistent with clinical experience. Therefore, it seems reasonable to assume that patients who consistently ask for repeat prescriptions are likely to generally be taking their drugs on a regular basis.
In conclusion, our study suggests that, despite some variability over time, adherence to ART is generally high in routine practice and does not have a tendency to decline over even long periods, providing encouragement that maintenance of adherence for a lifetime may well be possible.
This work was funded in part by NEAT FP6/03757 (European AIDS Treatment Network, European Commission).
Author contributions: Instigation and maintenance of the patient database (all), data compilation and merging (V.C., F.L., C.S., R.L., A.P.), planning of the analysis (all), data checking with case notes (D.P.), clinical leadership of patient database (M.J.), clinical input to analyses planned (M.J., A.R., A.G.), planning of prescription data collection (L.S., M.J., A.P., F.L.), drafting of manuscript (V.C.), statistical analysis for manuscript (V.C.), supervision of statistical analysis (A.P., F.L.), critical comment on draft manuscript (all), final approval of manuscript (all).
Clinical: S. Bhagani, P. Byrne, A. Carroll, I. Cropley, Z. Cuthbertson, A. Dunleavy, A.M. Geretti, B. Heelan, M. Johnson, S. Kinloch-de Loes, M. Lipman, S. Madge, T. Mahungu, N. Marshall, D. Nair, B. Prinz, A. Rodger, L. Swaden, M. Tyrer, M. Youle.
Data management: C. Chaloner, J. Holloway, J. Puradiredja, S. Scott, R. Tsintas.
Biostatistics/Epidemiology: W. Bannister, L. Bansi, V. Cambiano, A. Cozzi-Lepri, Z. Fox, E. Harris, T. Hill, A. Kamara, F. Lampe, R. Lodwick, A. Mocroft, A. Phillips, J. Reekie, A. Rodger, C. Sabin, C. Smith.
Laboratory: E. Amoah, C. Booth, G. Clewley, A. Garcia Diaz, A.M. Geretti, B. Gregory, W. Labbett,J Libaste, F. Tahami, M. Thomas, Y. Zhong.
1. Hogg RS, Heath KV, Yip B, Craib KJ, O'Shaughnessy MV, Schechter MT, et al
. Improved survival among HIV-infected individuals following initiation of antiretroviral therapy. JAMA 1998; 279:450–454.
2. Palella FJ Jr, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al
. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998; 338:853–860.
3. Lampe FC, Smith CJ, Madge S, Kinloch-de LS, Tyrer M, Sabin CA, et al
. Success of clinical care for human immunodeficiency virus infection according to demographic group among sexually infected patients in a routine clinic population, 1999 to 2004. Arch Intern Med 2007; 167:692–700.
4. May MT, Sterne JA, Costagliola D, Sabin CA, Phillips AN, Justice AC, et al
. HIV treatment response and prognosis in Europe and North America in the first decade of highly active antiretroviral therapy: a collaborative analysis. Lancet 2006; 368:451–458.
5. Mannheimer S, Friedland G, Matts J, Child C, Chesney M. The consistency of adherence to antiretroviral therapy predicts biologic outcomes for human immunodeficiency virus-infected persons in clinical trials. Clin Infect Dis 2002; 34:1115–1121.
6. Liu H, Miller LG, Hays RD, Golin CE, Wu T, Wenger NS, et al
. Repeated measures longitudinal analyses of HIV virologic response as a function of percentage adherence, dose timing, genotypic sensitivity, and other factors. J Acquir Immune Defic Syndr 2006; 41:315–322.
7. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, et al
. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med 2000; 133:21–30.
8. Bangsberg DR, Charlebois ED, Grant RM, Holodniy M, Deeks SG, Perry S, et al
. High levels of adherence do not prevent accumulation of HIV drug resistance mutations. AIDS 2003; 17:1925–1932.
9. McNabb J, Ross JW, Abriola K, Turley C, Nightingale CH, Nicolau DP. Adherence to highly active antiretroviral therapy predicts virologic outcome at an inner-city human immunodeficiency virus clinic. Clin Infect Dis 2001; 33:700–705.
10. Wood E, Hogg RS, Yip B, Harrigan PR, O'Shaughnessy MV, Montaner JS. The impact of adherence on CD4 cell count responses among HIV-infected patients. J Acquir Immune Defic Syndr 2004; 35:261–268.
11. Mayers DL. Drug-resistant HIV-1: the virus strikes back. JAMA 1998; 279:2000–2002.
12. Wainberg MA, Friedland G. Public health implications of antiretroviral therapy and HIV drug resistance. JAMA 1998; 279:1977–1983.
13. Hogg RS, Heath K, Bangsberg D, Yip B, Press N, O'Shaughnessy MV, et al
. Intermittent use of triple-combination therapy is predictive of mortality at baseline and after 1 year of follow-up. AIDS 2002; 16:1051–1058.
14. Garcia de OP, Knobel H, Carmona A, Guelar A, Lopez-Colomes JL, Cayla JA. Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients. J Acquir Immune Defic Syndr 2002; 30:105–110.
15. Wood E, Hogg RS, Yip B, Harrigan PR, O'Shaughnessy MV, Montaner JS. Effect of medication adherence on survival of HIV-infected adults who start highly active antiretroviral therapy when the CD4+ cell count is 0.200 to 0.350 × 10(9) cells/L. Ann Intern Med 2003; 139:810–816.
16. Moss AR, Hahn JA, Perry S, Charlebois ED, Guzman D, Clark RA, et al
. Adherence to highly active antiretroviral therapy in the homeless population in San Francisco: a prospective study. Clin Infect Dis 2004; 39:1190–1198.
17. Lucas GM, Gebo KA, Chaisson RE, Moore RD. Longitudinal assessment of the effects of drug and alcohol abuse on HIV-1 treatment outcomes in an urban clinic. AIDS 2002; 16:767–774.
18. Godin G, Cote J, Naccache H, Lambert LD, Trottier S. Prediction of adherence to antiretroviral therapy: a one-year longitudinal study. AIDS Care 2005; 17:493–504.
19. Lima VD, Harrigan R, Bangsberg DR, Hogg RS, Gross R, Yip B, et al
. The combined effect of modern highly active antiretroviral therapy regimens and adherence on mortality over time. J Acquir Immune Defic Syndr 2009; 50:529–536.
20. Tesoriero J, French T, Weiss L, Waters M, Finkelstein R, Agins B. Stability of adherence to highly active antiretroviral therapy over time among clients enrolled in the treatment adherence demonstration project. J Acquir Immune Defic Syndr 2003; 33:484–493.
21. Carrieri P, Cailleton V, Le Moing V, Spire B, Dellamonica P, Bouvet E, et al. The dynamic of adherence to highly active antiretroviral therapy: results from the French National APROCO cohort. J Acquir Immune Defic Syndr
22. Halkitis P, Palamar J, Mukherjee P. Analysis of HIV medication adherence in relation to person and treatment characteristics using hierarchical linear modeling. AIDS Patient Care STDS 2008; 22:323–335.
23. Stone VE, Clarke J, Lovell J, Steger KA, Hirschhorn LR, Boswell S, et al
. HIV/AIDS patients' perspectives on adhering to regimens containing protease inhibitors. J Gen Intern Med 1998; 13:586–593.
24. Bangsberg DR, Perry S, Charlebois ED, Clark RA, Roberston M, Zolopa AR, et al
. Nonadherence to highly active antiretroviral therapy predicts progression to AIDS. AIDS 2001; 15:1181–1183.
25. Hinkin CH, Hardy DJ, Mason KI, Castellon SA, Durvasula RS, Lam MN, et al
. Medication adherence in HIV-infected adults: effect of patient age, cognitive status, and substance abuse. AIDS 2004; 18(Suppl 1):S19–S25.
26. Singh N, Squier C, Sivek C, Wagener M, Nguyen MH, Yu VL. Determinants of compliance with antiretroviral therapy in patients with human immunodeficiency virus: prospective assessment with implications for enhancing compliance. AIDS Care 1996; 8:261–269.
27. Gifford AL, Bormann JE, Shively MJ, Wright BC, Richman DD, Bozzette SA. Predictors of self-reported adherence and plasma HIV concentrations in patients on multidrug antiretroviral regimens. J Acquir Immune Defic Syndr 2000; 23:386–395.
28. Kleeberger CA, Phair JP, Strathdee SA, Detels R, Kingsley L, Jacobson LP. Determinants of heterogeneous adherence to HIV-antiretroviral therapies in the Multicenter AIDS Cohort Study. J Acquir Immune Defic Syndr 2001; 26:82–92.
29. Altice FL, Mostashari F, Friedland GH. Trust and the acceptance of and adherence to antiretroviral therapy. J Acquir Immune Defic Syndr 2001; 28:47–58.
30. Spire B, Duran S, Souville M, Leport C, Raffi F, Moatti JP. Adherence to highly active antiretroviral therapies (HAART) in HIV-infected patients: from a predictive to a dynamic approach. Soc Sci Med 2002; 54:1481–1496.
31. Gordillo V, del Amo J, Soriano V, Gonzalez-Lahoz J. Sociodemographic and psychological variables influencing adherence to antiretroviral therapy. AIDS 1999; 13:1763–1769.
32. Ickovics JR, Cameron A, Zackin R, Bassett R, Chesney M, Johnson VA, et al
. Consequences and determinants of adherence to antiretroviral medication: results from Adult AIDS Clinical Trials Group protocol 370. Antivir Ther 2002; 7:185–193.
33. Horne R, Cooper V, Gellaitry G, Date HL, Fisher M. Patients' perceptions of highly active antiretroviral therapy in relation to treatment uptake and adherence: the utility of the necessity-concerns framework. J Acquir Immune Defic Syndr 2007; 45:334–341.
34. Chesney MA. Factors affecting adherence to antiretroviral therapy. Clin Infect Dis 2000; 30(Suppl 2):S171–S176.
35. Sabin CA, Lampe FC, Chaloner C, Madge SJ, Lipman MC, Youle M, et al
. An audit of antiretroviral treatment use in HIV-infected patients in a London clinic: the limitations of observational databases when auditing antiretroviral treatment use. HIV Med 2003; 4:87–93.
36. Grossberg R, Zhang Y, Gross R. A time-to-prescription-refill measure of antiretroviral adherence predicted changes in viral load in HIV. J Clin Epidemiol 2004; 57:1107–1110.
37. Gardner EM, Burman WJ, Maravi ME, Davidson AJ. Selective drug taking during combination antiretroviral therapy in an unselected clinic population. J Acquir Immune Defic Syndr 2005; 40:294–300.
38. Cambiano V, Lampe FC, Rodger AJ, Smith CJ, Geretti AM, Lodwick R, et al. Use of a prescription-based measure of antiretroviral therapy adherence to predict viral rebound in HIV infected individuals with viral suppression. HIV Med
39. Gross R, Bilker WB, Friedman HM, Strom BL. Effect of adherence to newly initiated antiretroviral therapy on plasma viral load. AIDS 2001; 15:2109–2117.
40. Liu H, Golin CE, Miller LG, Hays RD, Beck CK, Sanandaji S, et al
. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med 2001; 134:968–977.
41. Lodwick RK, Smith CJ, Youle M, Lampe FC, Tyrer M, Bhagani S, et al
. Stability of antiretroviral regimens in patients with viral suppression. AIDS 2008; 22:1039–1046.
42. Bangsberg DR. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis 2006; 43:939–941.
43. Gulick RM. Adherence to antiretroviral therapy: how much is enough? Clin Infect Dis 2006; 43:942–944.
44. Shuter J, Sarlo JA, Kanmaz TJ, Rode RA, Zingman BS. HIV-infected patients receiving lopinavir/ritonavir-based antiretroviral therapy achieve high rates of virologic suppression despite adherence rates less than 95%. J Acquir Immune Defic Syndr 2007; 45:4–8.
45. Martin M, del Cacho E, Codina C, Tuset M, De Lazzari E, Mallolas J, et al
. Relationship between adherence level, type of the antiretroviral regimen, and plasma HIV type 1 RNA viral load: a prospective cohort study. AIDS Res Hum Retroviruses 2008; 24:1263–1268.
46. Pillay D. The emergence and epidemiology of resistance in the nucleoside-experienced HIV-infected population. Antivir Ther 2001; 6(Suppl 3):15–24.
47. Kozal MJ, Amico KR, Chiarella J, Cornman D, Fisher W, Fisher J, et al
. HIV drug resistance and HIV transmission risk behaviors among active injection drug users. J Acquir Immune Defic Syndr 2005; 40:106–109.
48. Barroso PF, Schechter M, Gupta P, Bressan C, Bomfim A, Harrison LH. Adherence to antiretroviral therapy and persistence of HIV RNA in semen. J Acquir Immune Defic Syndr 2003; 32:435–440.
49. The UK Collaborative Group for HIV and STI Surveillance. Mapping the issues: HIV and other sexually transmitted infections in the United Kingdom
. London: HPA Centre for Infections; 2005.
50. Israelski D, Gore-Felton C, Power R, Wood MJ, Koopman C. Sociodemographic characteristics associated with medical appointment adherence among HIV-seropositive patients seeking treatment in a county outpatient facility. Prev Med 2001; 33:470–475.
51. Wutoh AK, Brown CM, Kumoji EK, Daftary MS, Jones T, Barnes NA, et al
. Antiretroviral adherence and use of alternative therapies among older HIV-infected adults. J Natl Med Assoc 2001; 93:243–250.
This article has been cited 3 time(s).
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antiretroviral drugs; drug adherence; HAART; HIV infection; prescription; trend
© 2010 Lippincott Williams & Wilkins, Inc.
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