Download Adherence to Antiretroviral Therapy by Human Immunodeficiency

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Adherence to Antiretroviral Therapy by Human Immunodeficiency
Virus– Infected Patients
Barbara J. Turnera
Division of General Medicine, University of Pennsylvania,
Philadelphia, Pennsylvania
Poor adherence to antiretroviral treatment regimens has serious consequences for human immunodeficiency virus– infected patients, including failure to prevent viral replication and an
increased risk of developing viral resistance. Recent data suggest that the level of medication adherence required for optimal treatment effectiveness is extremely high. Treatment adherence can
be measured by use of a variety of methods, including patients’ self-reports, pharmacy-based approaches, pill counts, and electronic monitoring. However, these measures of adherence have
different strengths and weaknesses in regard to practical application and identifying deficient
adherence. All patients receiving antiretroviral therapy require support to insure a high level of treatment adherence, but the evidence about effective interventions is limited. Emerging evidence suggests adherence interventions should employ a multidisciplinary effort involving health care providers,
social support networks, family, and friends. Although such programs will require a substantial
investment in terms of time and energy, the rewards associated with optimal treatment adherence
are worth the effort.
Introduction
While antiretroviral therapy has improved dramatically the
clinical status of many patients with human immunodeficiency
virus (HIV) infection, attention is increasingly focusing on the
role of treatment adherence to this therapy. Evidence shows
that poor adherence to antiretroviral treatment regimens has
serious consequences for HIV-infected patients, including failure to prevent viral replication, an increased likelihood of developing viral resistance, the development of clinical complications, and shortened survival [1– 3]. The central role of
adherence to antiretroviral therapy to achieve successful treatment of HIV has prompted a flurry of research into adherence
and increased clinician interest in attempting to address adherence issues in the context of ongoing care. This review offers
a discussion and comparison of various measures of treatment
adherence, an examination of the association of adherence
level with virologic success and other outcomes, a description
of barriers to and predictors of treatment adherence, and a commentary on strategies to improve treatment adherence.
Measures of Treatment Adherence
Treatment adherence can be measured by using data obtained
from patients, providers, pill bottles, pharmacy records, electronic devices, biochemical assays, or combinations of these
a
The author receives research support from Ortho Biotech.
Reprints or correspondence: Dr. Barbara J. Turner, University of Pennsylvania, Division of General Medicine, 1122 Blockley Hall, 423 Guardian Dr.,
Philadelphia, PA 19104–6021 ([email protected]).
The Journal of Infectious Diseases
2002;185(Suppl 2):S143–51
q 2002 by the Infectious Diseases Society of America. All rights reserved.
1058-4838/2002/18510S-0008$03.00
sources. Patients may judge their own adherence through interviews, questionnaires, or written documentation (e.g., diaries).
Self-reported adherence measures can be supplemented by a
pills identification test (PIT) [4]. The PIT asks patients to examine a board displaying two similar pills for each antiretroviral drug
and to identify which they have been taking. Correct scores on
the PIT have been shown to be associated with treatment adherence [4].
Health care professionals themselves can estimate the level of
patient adherence. Alternatively, health care providers or their assistants can count the number of pills remaining in a medication
container. Pharmacy records offer an additional source of information that can be used to evaluate the regularity with which patients obtain drugs. Electronic monitoring devices, such as the
Medication Events Monitoring System (MEMS; Aprex Corporation) and the eDEM Monitor (AARDEX Corporation), represent
more elaborate means of measuring treatment adherence. These
devices are pill bottles with caps that contain an electronic chip
that records each time the bottle is opened; researchers or providers can download the data periodically from the chip and identify patterns of adherence. Biochemical measures of adherence,
such as therapeutic drug monitoring or other laboratory markers
(e.g., changes in corpuscular volume), also have been used to evaluate treatment adherence.
Regardless of the method used to measure treatment adherence, a definition of an adequate level of adherence must be established. Earlier studies of antiretroviral adherence used a cutoff
point for adherence that was derived from the literature on the
level of adherence (e.g., >80%) [5, 6] necessary to achieve successful tuberculosis prophylaxis [7]. Numerous studies subsequently have shown that an even higher level of adherence
(e.g., > 95%) is necessary for durable suppression of HIV-1
virus load [1– 3]. However, the cutoff depends on the measure
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that was used. The following section comments on the strengths
and weaknesses of several of these measures of adherence and
findings regarding reasonable cut points to use in assessing adherence by use of various measures.
Comparison of Approaches to Measure Treatment
Adherence
Currently available approaches to estimate treatment adherence all have distinctive benefits and drawbacks. The following
approaches are discussed below: patients’ self-reports, health
care provider estimates, pill counts, pharmacy-based measures,
electronic monitors, and biologic/laboratory markers.
Estimates of treatment adherence from patients’ self-reports
are less complex to obtain than other methods. Because patients
may have trouble recalling their adherence over longer periods
of time, one commonly used measure relies on self-report over
each of the past 4 days. However, additional questions may be
necessary to ask about weekends, which tend to be a difficult
time for treatment adherence. Many studies ask about adherence
over longer time periods, examining patient’s “gestalt” about
their qualitative level of adherence rather than a specific recollection of missing a medication. All forms of self-report inevitably overestimate adherence compared with other treatment
adherence measures [3, 8, 9]. Even patients who acknowledge
having missed doses appear to overestimate adherence relative
to other measures [10]. The patient’s wish to please his or her
health care provider (i.e., social desirability bias) may contribute to these findings. Nonetheless, patients who admit to having
problems with adherence tend also to have poorer adherence on
other measures [11]. Of note, self-acknowledged nonadherent
patients appear to be responsive to interventions and represent
an important group to identify [11].
To increase the validity of self-reported adherence, a preamble
is needed before asking adherence questions to reassure patients
that the information will not be held against them and that problems with adherence are nearly universal. Audio computer-assisted self-interviewing is an option that has been shown to encourage more honest answers from patients on sensitive topics
[12, 13]. For example, a computerized version of the Risk Assessment Battery for intravenous drug users increased reports
of current substance abuse [14]. A patient diary offers a less
complicated and less expensive alternative to computerized
methods but has the liability of being easily neglected or lost
and may still not reflect honest answers.
Treatment adherence can also be measured by having the
health care provider or a designee count the number of pills remaining in the bottle [1]. Unfortunately, pill counts are time-consuming and determining the date when the patient commenced
the current prescription(s) can be difficult, especially when patients combine all of their medications in one bottle. Although
pill count is less subjective than self-reporting, inaccuracies
occur when patients remove pills from their bottles without tak-
JID 2002;185 (Suppl 2)
ing them (“pill dumping”) to appear more adherent when counts
occur. This phenomenon was observed over 4 decades ago: “That
there is a further large field for inquiry is suggested by the many
tablets that can be found scattered in the flowerbeds surrounding
some of our hospitals” [15]. Unannounced pill counts have been
adopted to limit this behavior. However, patients may regard
pill counts—announced or unannounced—as unacceptably intrusive or threatening. Patients may also forget their pill bottles,
separate pills into weekly organizers, or use different formulations of the same medication that may appear to be different medications. Pill counts are more useful in a research setting where
more structured prescribing of medications occurs.
Pharmacy-based measures that use health insurance claims
data offer a much less intrusive way than pill counts to monitor
adherence. This type of analysis assumes that patients use the
same pharmacy or that all pharmacies used by the patient bill
for medications to the same payer source (e.g., Medicaid). Pharmacy measures examine the rate of refilling medications over a
period of more than 2 months [16]. This measure is based on a
straightforward premise that when a patient does not receive
timely refills of a drug from the pharmacy, he or she is either
not taking medication between refills or is missing doses such
that a given prescription lasts longer than it should. The strengths
of this approach are that it is not susceptible to reporting bias or
tampering and offers population-based information. However,
patients may receive free samples from their doctor’s office or
other sources that would be missed in this analysis. Further,
paid pharmacy claims suggest but cannot prove that patients
picked up and took their prescription. Pharmacy claims or refill
adherence measures have been used in numerous studies of persons with chronic diseases, including those with HIV infection
[17– 19].
Physicians’ estimates of the level of patient adherence to treatment have been shown to be very inaccurate. In a study by Paterson et al. [20], physicians and nurses were asked to predict .80%
treatment adherence on an individual basis. Their predictions
were only slightly better than a coin toss; treatment adherence
was predicted incorrectly by physicians for 41% of patients,
while nurses’ predictions were incorrect 30% of the time. In a
study comparing unannounced pill counts with physicians’ estimates of and patient self-reports of treatment adherence, providers’ estimates explained only 26% of the variation of pill count
adherence, while patients’ estimates explained 72% [21]. The
sensitivity and specificity of estimates of nonadherence (defined
as , 80% of pills taken according to pill count) were 72% and
95%, respectively, for patient interview but only 40% and
85%, respectively, for provider estimates [21].
Electronic monitors offer a more objective measure of treatment adherence than do self-reports. These monitors also offer
the best way to assess adherence to taking pills on a particular
dosing schedule (i.e., daily intervals). However, the cost of these
devices limits their use in non-research settings, and the caps
can malfunction or get lost. Patients often refuse to participate
JID 2002;185 (Suppl 2)
HIV Infection and Treatment Adherence
when the study uses a large bottle with a MEMS cap instead of a
pillbox that can help them organize and remember their medications [22]. Pillboxes are not currently equipped with electronic
devices. In addition, decanting (removing .1 dose at a time) or
loosening the caps to make it easier to remove pills has been
shown to be a common problem in studies using electronic monitors. A study of antiretroviral adherence in 64 predominantly
white male subjects found that . 40% routinely decanted their
medications before joining the study, and one-third of these “decanters” continued to do so during the study [22]. Such behavior
may explain why electronic monitors may underestimate the level
of adherence.
Biologic and laboratory markers have been suggested as alternative ways to measure treatment adherence. In a study comparing plasma drug assays with patients’ self-reported adherence to
antiretroviral therapy, plasma concentrations of protease inhibitor drugs below the assay limit of quantitation were related to
patient self-reported adherence in the previous day [23]. However, plasma drug levels reflect only recent treatment adherence
(e.g., within the previous 24 h) and cannot be used to monitor
many drugs. It is also possible to gauge treatment adherence by
monitoring biologic changes associated with medications, such
as an increase in mean corpuscular volume associated with zidovudine treatment. However, these changes are only marginally sensitive and give no information about patterns of missed doses.
Regrettably, we do not currently have an optimal measure of
adherence. As noted in the following section, work is being conducted on understanding the association of these various adherence measures alone or in combination with clinically important
outcomes.
Association of Level of Adherence with Virologic Success
and Other Outcomes
Studies using a single measure of treatment adherence. Patients’ self-reports result in higher estimates of treatment adher-
Figure 1.
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ence, while measures using electronic measures, such as MEMS
caps, typically find much lower rates of adherence [3, 24] . Nevertheless, self-reported level of adherence to antiretroviral therapy
over 1 week has been correlated with virus load reduction. Among
133 HIV-infected patients, 28% reported , 80% adherence
(poor), 23% reported 80%– 99% adherence (fair), and 50% reported 100% adherence (excellent) [2]. The mean decreases in
HIV-1 concentration (log10 copies/mL) from the highest level
previously achieved were greater for each successive adherence
level: 1.3, 1.6, and 2.0 log10 copies/mL, respectively. Similarly,
self-reported treatment adherence was correlated with virus load
and CD4 cell count in a study of 173 patients who completed
questionnaires at 2 months (n ¼ 164) and 6 months (n ¼ 119)
[6]. Patients who reported , 80% adherence had an increase in
virus load of 0.2 log10 copies/mL and a decrease in CD4 cells
of 19 £ 106 , while patients who reported 100% adherence had
a decrease in virus load of 1.1 log10 copies/mL and an increase
in CD4 cells of 72 £ 106 [6]. As shown in figure 1, this study
showed a linear decrement in the proportion of patients with suppressed virus load by successively lower levels of adherence.
The association of a high-level adherence with virologic success has also been reported in research using pharmacy records.
In a study of 483 patients, adherence was defined by consistently
refilling prescriptions for more than 4 consecutive months after
initiating highly active antiretroviral therapy [18]. Adherent patients experienced a significant reduction in virus load (P , :01)
as well as a significant increase in the number of CD4 cells (P ,
:01) and CD8 cells (P ¼ :005). Patients who were nonadherent
also experienced a significant reduction in virus load (P ,
:05) but did not have a significant reduction in CD4 or CD8
cells [18]. Pharmacy-based adherence was evaluated in nearly
900 patients (766 men, 120 women) at the British Columbia
Centre for Excellence in HIV/AIDS Drug Treatment Program
[25]. As shown in figure 2, a significant linear trend toward improved virologic load was found across categories of adherence
(P ¼ :001).
Self-reported adherence and HIV-1 virus load suppression [6]. n ¼ 112.
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Figure 2.
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JID 2002;185 (Suppl 2)
Pharmacy-based adherence measure and HIV-1 virus load suppression [25]. n ¼ 886. HAART, highly active antiretroviral therapy.
Of 81 evaluable patients in a study using MEMS caps, a strong
association was observed between an HIV-1 virus load of , 400
copies/mL and adherence (P , :001; figure 3) and with increases
in CD4 cell count (P ¼ :006) [20]. In patients with a baseline HIV
RNA level of ,400 copies/mL, none of the 7 patients whose treatment adherence was >95% had detectable virus levels at the final
study visit, compared with 7 (41%) of 17 patients whose treatment
adherence was , 95% [20].
Studies using multiple measures of treatment adherence.
Several studies have compared treatment adherence findings from
self-report to those from electronic monitoring. Arnsten et al. [3]
reported that while 70% of subjects self-reported that their 1day adherence was >80%, only half as many (35%) had a similar
level of adherence using a MEMS measure over the same time
period [3]. Although both measures of treatment adherence in that
study were correlated with HIV load suppression (P < :001), the
association was stronger for the MEMS measure. The adjusted
odds of achieving a virus load of , 500 copies/mL was 8.2 if
self-reported adherence was .90% (95% confidence interval
[CI], 2.5– 27.0; P ¼ :0006), compared with 12.3 if MEMS adherence was . 90% (95% CI, 2.8– 52.6; P ¼ :0008) [3]. However, 16 (15%) of 99 eligible subjects refused to participate in
this study because they did not want to use the MEMS bottles.
Further, only 81% of the collected MEMS data were available
for analysis because of defective or lost caps or withdrawal
from the study [3].
Another study in an indigent population compared self-report
and unannounced pill counts with electronic medication monitoring [24]. Thirty-four indigent HIV-infected persons on a protease inhibitor for a median of 12 months were sampled from
Figure 3. Medication events monitoring system measured and HIV-1 virus load suppression [20]. n ¼ 91. HAART, highly active antiretroviral therapy.
JID 2002;185 (Suppl 2)
HIV Infection and Treatment Adherence
free-meal lines, low-income, single-room occupancy hotels, and
homeless shelters. The median treatment adherence was 89%
from self-report over 3 days, 73% on the basis of pill count during
a 2- to 4-week interval, and 67% from electronic monitoring over
3 days. Electronic monitoring (r ¼ 0:81, P , :0001) and pill
count (r ¼ 0:67, P , :0001) were more strongly correlated with
log virus load than was self-reported adherence (r ¼ 0:60, P ¼
:0002), but all showed strong linear relationships [24].
Liu et al. [26] have also shown that several measures for the
same patient result in different assessments of adherence and
predict virus load with various levels of success. In their study,
mean treatment adherence for the same group of persons monitored from 24 to 48 months, depending upon the measure, ranged
from 63% for a MEMS-based measure to 83% for pill count and
93% for interview. The level of adherence based on a composite
measure, using a combination of these three methods, was 76%.
These researchers used the MEMS measure as the “backbone”
of their composite measure. That combined measure had the
highest receiver-operating characteristic, indicating the best prediction of undetectable virus load at 6 months [26]. Therefore,
these comparisons show that MEMS-measured adherence performs best in relation to a clinical outcome, but a composite measure may be an even better solution—at least for researchers.
However, as a reassurance for clinicians, patient self-report is
also correlated with virus load suppression.
Effect on other outcomes. In addition to virus load, treatment adherence can affect other outcomes. For example, in the
study by Paterson et al. [20], patients with > 95% treatment adherence (n ¼ 23) had fewer hospitalization days than those with
lower treatment adherence rates (2.6 vs. 12.9 days per 1000 days
Figure 4.
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of follow-up, P ¼ :001; n ¼ 58). In addition, no opportunistic
infections or deaths occurred in patients who were >95% adherent to treatment [20].
An association of treatment adherence (determined by use of
a pharmacy-based measure) with HIV disease progression and
death was demonstrated in a study of 950 patients (815 men, 135
women) who were antiretroviral naive and started triple therapy
with two nucleoside reverse transcriptase inhibitors (NRTI) and
a protease inhibitor or a non-NRTI [27]. In a multivariate analysis, the risk of death and/or progression to AIDS was 1.17 times
higher for each 10% decline in treatment adherence (95% CI,
1.08–1.28; P , :001).
Barriers to and Predictors of Treatment Adherence
Patients offer many diverse reasons for missing their medications. Gifford et al. [2] found that organizational difficulties
(e.g., too busy, forgot, away from home, change in routine) and
emotional issues were the most common reasons given for missed
doses (figure 4). Side effects of treatment were mentioned but
appeared to be less important. It should be noted that a large proportion of the patients included in this study were men (86%).
Several studies have been conducted on barriers to treatment
adherence among HIV-infected women. Among 520 women, depressive symptoms, adverse life events, and HIV-related stress
were significantly associated with nonadherence (P , :01 for
all parameters) [28]. In another study of 895 women, those with
poor adherence were more likely (P , :01) to have used intravenous drugs, to smoke, and to have a lower mean quality of life
[29]. In a study of . 200 women completing anonymous ques-
Reasons for poor antiretroviral treatment adherence by HIV-infected patients [2]
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Table 1. Predictors of antiretroviral treatment adherence in patients with HIV infection [31].
Characteristic
Age, years
> 50
35–50
18–34
Sex
Male
Female
Race/ethnicity
White
African American
Hispanic
Insurance
Medicare
Private
None
Medicaid
Percentage
JID 2002;185 (Suppl 2)
with interventions to improve adherence, all patients need support to help them with this onerous task.
P
, .0001
70
59
50
Approaches to Improving Treatment Adherence
.008
60
49
.0001
63
53
47
.01
64
62
55
40
tionnaires, the majority stated that care-giving commitments prevented them from adhering to their own treatment regimens [30].
Laine et al. [19] used Medicaid pharmacy claims data to evaluate
adherence in 682 pregnant HIV-infected women. The adjusted
odds ratio (AOR) for adherence (> 80% while on treatment)
by teenagers was nearly 70% lower than that for older women
(AOR, 0.34; 95% CI, 0.12–0.90) and 50% lower (P , :01) for
black or Hispanic women versus white women. A pharmacybased analysis of adherence to therapy in a cohort of 549 Medicaid-enrolled, post-partum women showed that those with . 1
child were . 50% less likely to adhere (AOR, 0.44; 95% CI,
0.20–0.95), whereas higher odds of adherence were observed for
former drug users (AOR, 2.40; 95% CI, 1.05–5.50) and women
who received HIV specialty care (AOR, 2.13; 95% CI, 10.5–
4.30) [17].
In a national interview study of thousands of HIV-infected
persons in care, Wenger et al. [31] found that younger individuals,
women, persons from minority groups, and patients without health
or Medicaid insurance were less likely to report good treatment
adherence (table 1). Other investigators have reported similar findings: Younger age, female sex, African-American descent, problem drinking, and intravenous drug use were also associated
with lower treatment adherence rates [2, 32– 34]. Differences
in adherence between sexes continue to be controversial, with
one large study finding similar adherence for women and men
[35]. Additional personal characteristics and sociobehavioral
factors have been related to treatment adherence (table 2) [2,
17, 32, 34].
Prescribing factors associated with better treatment adherence include convenience and an ability to incorporate the treatment regimen into a daily routine [2], fewer number of drugs (e.g.,
1 vs. >3 medications) in the regimen [17], and lower dosing frequency (e.g., two vs. three times a day) [20]. Although these
studies indicate groups who may need to be particularly targeted
One of the challenges in the management of HIV disease is
incorporating our knowledge of the factors affecting treatment
adherence into everyday practice. Because multiple factors may
need to be addressed, interventions aimed at improving treatment
adherence will require the implementation of a multifaceted approach involving physicians, nurses, social workers, family, and
friends. Unfortunately, few results on many research studies to
improve adherence to antiretroviral therapy are available to date.
A variety of approaches to improve adherence are summarized
in figure 5. Education regarding the importance of adherence to antiretroviral therapy should be a part of any management strategy.
The benefit of education, involving a series of 90-minute sessions
with a health educator, was observed in an 8-week health promotion program about medical adherence and sex- and drug-risk reduction [36]. HIV-infected men and women who participated in
this program reported subsequently using more strategies to adhere to their medication. This program also resulted in lower scores
on a depression scale for the participants in the health promotion
program at a 3-month follow-up [36]. In another study, patients
were given cue-dose training (i.e., using personalized cues to remember specific dosing times) and monetary reinforcement.
These patients showed initial improvement in treatment adherence, but this did not extend beyond the training period [37]. A
second intervention study included 116 patients who were randomized to either a psychological/educational intervention program or usual medical follow-up [38]. At week 48, 94% of the
intervention group versus 69% of controls achieved adherence
levels > 95% (P , :01).
Physicians and other members of the health care team (e.g.,
physician assistant, nurse, or pharmacist) should strive to establish
a good relationship with the patient and to maintain an “open-door”
policy by being honest about the potential side effects of treatment and offering assistance with the organizational skills necessary for good treatment adherence. A strong patient-provider
relationship and trust in the provider appear to improve adher-
Table 2. Sociobehavioral factors associated with treatment adherence in patients with HIV infection.
Factors
Adjusted
odds ratio
95% confidence
interval
Perceived self-efficacy to
take medicationsa
Multiple children
Illicit drug use
Depression (fatigue)
5.30
0.44
0.49, 0.50
0.53
2.4–11.8
0.20–0.95
0.30–0.78, 0.36–0.71
0.28–1.02
a
Confidence in one’s ability to adhere to medication schedules.
Reference
no.
[2]
[17]
[32, 34]
[32]
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HIV Infection and Treatment Adherence
Figure 5.
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Approaches to improve treatment adherence in patients with HIV infection
ence [39–41] . A study of 500 US HIV/AIDS providers in 1999
(57% response rate) examined ways that health care professionals are trying to improve treatment adherence [42]. All providers reported using at least one strategy to enhance treatment
adherence. The most commonly reported activities in the past
2 years included giving patients pillboxes, providing tailored
written instructions, and prescribing simpler treatment regimens.
Chesney [43] reviewed other approaches to improving treatment adherence, including tailoring medications to suit a patient’s
lifestyle. For example, specific dosing intervals may improve
treatment adherence for some patients. Reducing the number
of doses can help simplify treatment regimens and is especially
important when these regimens include more medications.
Patients can link certain daily activities with taking medication.
Directly observed therapy, in which a health care provider observes a patient taking medication, ensures adherence but is
expensive and labor intensive [43]. One study of incarcerated
patients found similar adherence levels for persons with directly
observed therapy to those who self-administered their drugs [41].
Last, the importance of social support, mental health, and
substance abuse cannot be overlooked. Patients with a good support system that includes family, friends, other patients who are
role models, and members of the health care team may be more
motivated to adhere to treatment. Longitudinal substance abuse
treatment and psychiatric care appeared to improve pharmacybased adherence to antiretroviral therapy in a study of several
thousand drug users [44].
Conclusions
Adherence to antiretroviral therapy has emerged as a critical
predictor of HIV treatment success. Adherence measured by
any of a variety of approaches is strongly associated with achieving an undetectable virus load. The level of adherence needed to
successfully treat HIV unfortunately appears to be nearly 100%.
Researchers and clinicians are still searching for a highly reliable,
inexpensive, and accessible measure of adherence. Although
self-report is more easily obtained than other measures, it has
relatively poor sensitivity (i.e., many false negatives) but good
specificity (i.e., few false positives). On the other hand, electronic measures appear to have better sensitivity but may have
poorer specificity because patients who adhere may be decanting extra doses but still taking the medication on time.
Despite the limitations of treatment adherence measures, an
effort to evaluate adherence must be made in the course of routine HIV care because it offers an opportunity to remind the patient of the critical role of strict pill-taking behaviors. In addition
to counseling, interventions to promote adherence, such as reminders, tailoring the regimen to the patient’s lifestyle, and addressing issues related to side effects, may improve adherence.
Patients should be assessed for depression and substance abuse,
as these factors can be targeted with effective interventions.
The complexity of adherence likely requires that providers use
all of these approaches to try to help HIV-infected patients
with the monumental task of taking medications according to
a specific schedule for the foreseeable future.
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