HAART therapy has greatly reduced both morbidity and mortality in individuals infected with HIV.23–25
However, a major impediment to the success of therapy is the poor adherence encountered in subjects prescribed HAART.1,3,4,26,27
We specifically looked at personal barriers to adherence that included the following three areas: mental health/substance abuse, adherence self-efficacy and outcome expectancy regarding antiretroviral treatment, and structural barriers. It was hypothesized that these barriers may coexist in certain nonadherent subjects, defining a typology of nonadherence that might direct the development of interventions that impact on multiple barriers that coexist.
In creating the typology, we focused on nonadherent subjects. Of those subjects with low SE/low OE, a mental health disorder and at least one structural barrier, 68.8% were nonadherent. These findings, along with results identifying significant interactions among barriers, support the notion that a large percentage of youth with adherence issues face more than a single barrier and that these barriers occur together in nonadherent patients. This approach of assessing multiple barriers within a population may prove to be very useful for the development of future adherence interventions, as an individual who faces multiple barriers can be offered more than a single, unimodal intervention to intervene with non-adherence. As noted, our study was not designed to determine the predictors of adherence but rather to identify combinations of barriers present in nonadherent adolescents.
Adherence self-efficacy has been shown to positively correlate with adherence in patients prescribed HAART.3,4,28
In a recent review, adherence self-efficacy was consistently associated with HAART adherence.29
Outcome expectancy regarding antiretroviral treatment has also been shown to enhance adherence.12,30–32
In our study, adherence self-efficacy and outcome expectancy regarding antiretroviral treatment were independently associated with adherence, although adherence self efficacy had greater sensitivity as a predictor than outcome expectancy. Thus, use of these scales would allow identification of subjects with low SE/OE for antiretroviral treatment among subjects who are truly non-adherent. Our data support the inclusion of these variables in measures of personal barriers to adherence and support the use of interventions to enhance self-efficacy and outcome expectancy as it relates to adherence to HAART in this population. Cronbach α for outcome expectancy for antiretroviral treatment was lower than what is generally accepted for this type of measure. As outcome expectancy was independently associated with adherence, this scale was included in subsequent analyses. Clearly, refinement of this scale is warranted for use in this population.
Depression has been shown to negatively correlate with adherence in adolescents and adults.1,2,33
In our study, mood disorders were the most prevalent mental health diagnosis. However, the overall prevalence of mental health disorders was 38% and no category of disorder was associated with adherence. There are a number of possible reasons for this. First, we relied on a documented mental health diagnosis obtained through chart abstraction. The actual prevalence of mental health issues may actually be higher than what was found through chart review. Many studies that report mental health impacting adherence use direct measures of depressive symptoms. Thus, many youth who have mental health issues, such as depressive symptoms, may have been missed in gathering the data through these methods and a more immediate measure, that assesses anxiety and depression at nonclinical levels, may be more prudent in future studies. In addition, those youth who have a documented mental health disorder may be far more likely to be receiving care for this disorder, which may explain why there was no association between adherence and mental health disorders.
Substance use has also been associated with poorer adherence in subjects prescribed HAART.8,34,35
One limitation of this study was that we were unable to utilize the substance abuse data. The numbers of subjects who were found on chart review to have a formal substance abuse diagnosis was lower than expected based on prior literature. We therefore excluded this variable from the analysis, as the data were poorly recorded in the medical records reviewed. Documentation of subjects meeting criteria for a substance abuse disorder, as well as subjects with a high level of use but not meeting criteria for a disorder, would have been important to capture. As this study was not intended to predict adherence in subjects initiating HAART, but rather to describe youth already prescribed HAART and reporting nonadherence, at the study development phase it was decided that subject burden required for administration of additional measures to assess current substance abuse was not warranted. At that time we thought the chart data would be accessible and well-documented. However, such instruments would be needed when planning for the implementation of specific adherence interventions.
As low SE/OE for antiretroviral treatment was found in many nonadherent subjects and many of these subjects also had an additional structural barrier, interventions designed to enhance these personal characteristics would need to either exclude subjects with other barriers or address the other barriers as well in order to adequately assess the impact of the intervention. For example, if one were designing an intervention to address low OE and low SE in non-adherent adolescents, assessment for depression and structural barriers and inclusion of interventions to address these issues may potentially enhance the impact on adherence and may also enhance the durability of the effect. As noted, this should be addressed in future adherence research in adolescent populations. In addition, interventions that address a number of barriers simultaneously could be developed. For example, motivational interviewing has been found to not only have an impact on adherence, but to decrease other risk behaviors such a substance abuse.36
Such approaches may be preferable in an adolescent population since some youth may have limited capacity to deal with multiple interventions.
The issue of structural barriers to adherence is particularly significant in an adolescent population. In HIV-infected adolescents 12 to 18 years of age, data from the REACH Project revealed a number of important issues that could lead to structural barriers to adherence. At their baseline evaluation (females vs. males), 26% and 25% had no health insurance, 29% and 31% had dropped out of school, 27% and 27% reported being homeless at some time, and 24% and 27% had been in a detention facility.37
In a study of barriers to adherence in the same population, Murphy and colleagues found two factors most strongly associated with adherence versus nonadherence: medication-related adverse effects and complications in day to day routines.38
It is thus not surprising that we found that structural barriers have an impact on adherence. Given that this study was conducted at sites with comprehensive, multidisciplinary services, these day to day barriers continue to impact adherence in adolescents and young adults.
It is clear that there may be associations among personal barriers to adherence. In a recent study by Remien and colleagues,39
depressive symptomatology was assessed in HIV-positive women utilizing a stress and coping model. These investigators found that stress was a mediating factor for depressive symptoms and that adherence self-efficacy mediated the relation of psychosocial support to depression. Although beyond the scope of our study, it is clear that the relationships among these barriers leading to poor adherence are quite complex and deserve further study in the adolescent population.
There are a number of limitations to our study. First, our study was not designed to predict adherence, but rather to better describe the prevalence of certain personal barriers to adherence and how these personal barriers to adherence cluster in patients with self-reported poor adherence. Second, chart review for identifying subjects with mental health and substance abuse barriers appears to have under-reported these barriers in the population. Finally, we only evaluated personal barriers to adherence and did not address medication-related barriers or barriers related to the clinical system of care. With that said, our study did show that many youth face a number of barriers and was successful in suggesting an approach to designing adherence interventions in populations where many barriers may exist.