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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Geriatr Psychiatry. Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2679810
NIHMSID: NIHMS110444

Aging, Neurocognition, and Medication Adherence in HIV Infection

Abstract

Objective

To evaluate the hypothesis that poor adherence to highly active antiretroviral treatment (HAART) would be more strongly related to cognitive impairment among older than among younger HIV-seropositive adults.

Setting and Participants

A volunteer sample of 431 HIV-infected adult patients prescribed self-administered HAART was recruited from community agencies and university-affiliated infectious disease clinics in the Los Angeles area.

Measurements

Neurocognitive measures included tests of attention, information processing speed, learning/memory, verbal fluency, motor functioning, and executive functioning. Medication adherence was measured using microchip-embedded pill bottle caps (Medication Event Monitoring System) and self-report. Latent/structural analysis techniques were used to evaluate factor models of cognition and adherence.

Results

Mean adherence rates were higher among older (≥50 years) than younger (<50 years) HIV-positive adults. However, latent/structural modeling demonstrated that neurocognitive impairment was associated with poorer medication adherence among older participants only. When cognitive subdomains were examined individually, executive functioning, motor functioning, and processing speed were most strongly related to adherence in this age group. CD4 count and drug problems were also more strongly associated with adherence among older than younger adults.

Conclusions

Older HIV-positive individuals with neurocognitive impairment or drug problems are at increased risk of suboptimal adherence to medication. Likewise, older adults may be especially vulnerable to immunological and neurocognitive dysfunction under conditions of suboptimal HAART adherence. These findings highlight the importance of optimizing medication adherence rates and evaluating neurocognition in the growing population of older HIV-infected patients.

Keywords: HIV, AIDS, aging, cognition, medication adherence, executive functions

HIV infection can result in significant neurocognitive dysfunction, most prominently in the domains of motor functioning, attention, processing speed, executive functioning, and memory.1,2 This profile of neurocognitive impairment has been interpreted as reflecting a primarily frontal-subcortical pathogenesis and tends to increase in severity with progression from nonsymptomatic to AIDS phases of HIV illness.2 Advances in the treatment of HIV infection, such as the advent of highly active antiretroviral treatment (HAART), have been shown to greatly improve markers of immune function, with positive consequences downstream. In addition to increased CD4 count and reduced HIV viral load,3,4 HAART can bolster patients’ motor functioning, processing speed, memory, and general cognition.57

However, a number of factors are likely to moderate the neuroprotective effects of HAART. For example, consistently high levels of adherence are needed to modulate certain aspects of immune functioning (e.g., antigen-specific responses and cytokine production) and successfully reconstitute or maintain desirable levels of immune functioning.8,9 Unfortunately, objective measures show that only 50%–60% of HIV-infected patients achieve adequate adherence to their medications.8,10 In addition, different antiretroviral medications have been shown to vary considerably in their ability to penetrate the central nervous system (CNS) and reduce CNS HIV viral load.11 Interestingly, recent studies have also found increased permeability of the blood-brain barrier in older adults.12,13 To the degree that these changes serve to promote the penetration of medications into the CNS, the relative neuroprotection afforded by proper HAART adherence may increase with advancing age.

Patient age is also a potent predictor of medication adherence in HIV-positive populations, with individuals older than 50 years of age demonstrating considerably better adherence overall than those younger than 50 years of age.14 Nevertheless, recent evidence suggests that older adults are at a substantially increased risk for HIV-associated cognitive decline and dementia relative to their younger counterparts.1517 This issue continues to grow in importance with the advancing age of the HIV/AIDS patient population; according to the most recent statistics from the Centers for Disease Control and Prevention, approximately 24% of HIV-positive individuals are now aged 50 or older.18

Thus far, mechanisms responsible for older adults’ increased susceptibility to HIV-associated cognitive impairment have not been clearly identified. Further study of factors related to cognitive morbidity in HIV is greatly needed to provide a basis for effective prevention and intervention in this rapidly evolving patient population. In the current study, possible age differences in the relationship between neurocognitive functioning and medication adherence were examined in HIV-positive individuals. It was hypothesized that poor adherence would be more strongly related to cognitive impairment among older than among younger adults.

METHODS

Participants

Participants consisted of 431 HIV-infected adults who were prescribed self-administered HAART at the time of participation. Recruitment was conducted from community agencies in the Los Angeles area that specialize in providing services to HIV-infected individuals and using fliers posted in infectious disease clinics at university-affiliated medical centers. HIV status was confirmed with enzyme-linked immunosorbent assay and Western blot. Demographic and health characteristics of the sample are presented in Table 1.

Table 1
Demographic and Health Characteristics by Participant Group

Measures

Adherence to HAART medication over a mean of 30.84 (SD: 5.60) days was determined through a combination of self-report and objective measures. Objective measurement of adherence was achieved via the Medication Event Monitoring System (MEMS), which uses a pressure-activated microprocessor in the medication bottle cap that automatically records the date, time, and duration of bottle opening. These data were later downloaded from the bottle cap using a personal computer. Priority for the single medication chosen for MEMS monitoring was given to patients’ medications in the following order: protease inhibitors (monitored for 43.9% of participants), nucleoside reverse transcriptase inhibitors (monitored for 35.3%); nonnucleoside reverse transcriptase inhibitors (monitored for 17.2%); nucleotide reverse transcriptase inhibitors (monitored for 1.9%); and other classes of medication (monitored for 1.9%). MEMS adherence was calculated as percent of doses taken relative to the total number of doses prescribed. To maximize MEMS data accuracy, participants were instructed to open the MEMS cap only when they are taking a dose, to refill the bottle at a time when they ordinarily took a dose, and not to use pill organizers or take “pocket doses” (e.g., removing multiple pills at a time for later use). In addition, to account for bottle openings which were not likely to be related to an actual dosing event, the following corrections were made to MEMS data: number of MEMS cap openings were truncated to the total number of prescribed doses per day; only one MEMS cap opening was counted per 2-hour period, and MEMS cap openings performed by the research team for administrative purposes were removed from the data. Self-report of adherence was determined in three ways, including participants’ report of the number of doses of the MEMS-tracked medication missed during the last month (30-day self-report); participants’ report of whether they missed a dose of their MEMS medication the previous day (1-day self-report); and participants’ report of subjective degree of overall medication adherence, as determined by the Medical Outcome Scale questionnaire total score (qualitative self-report).

All participants completed one of two similar fixed neuropsychological test batteries (Battery A [N = 185] and Battery B [N = 246]; see Appendix). Raw test scores were converted to demographically corrected t scores using published normative data. Individual test t scores were grouped into one of six cognitive domains (attention, information processing speed, learning/memory, verbal fluency, motor functioning, and executive functioning) and averaged to establish domain t scores for use in primary data analyses. Supporting the psychometric equivalence of the two test batteries, confirmatory factor analyses demonstrated no significant differences in cognitive domain factor structure.

APPENDIX
Neuropsychological Tests by Domain, with Normative Source Utilized

Total score from the Beck Depression Inventory (BDI-II19) was used as an index of depressive symptomology. Substance use modules from the Structured Clinical Interview for DSMIV 20 were administered to determine whether participants currently met diagnostic criteria for alcohol or substance abuse or dependence. Ordinal alcohol and drug problems variables were also computed from Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic data such that abuse was coded as 1, dependence was coded as 2, and no diagnosis was coded as 0.

Procedure

All procedures were approved by institutional review board panels at UCLA and the West Los Angeles VA Medical Center. After providing written informed consent, participants completed a detailed demographic questionnaire and structured psychiatric interview followed by a fixed battery of neuropsychological tests. Trained psychometrists conducted all neuropsychological testing under the supervision of a board-certified neuropsychologist (CHH). Psychiatric interviewing was conducted under the supervision of a licensed clinical psychologist (SAC). Participants then received instruction in how to use the MEMS caps and were scheduled to return approximately 1 month later. At the follow-up visit, participants completed self-report measures of adherence, and MEMS caps were collected for data download. Participants received payment of $80.00 for participating in this portion of the study.

Analysis

Latent/structural modeling techniques were used to test all primary theoretical models. This multivariate approach pools the shared variance of multiple measures for each latent factor to maximize construct-relevant variance, thereby excluding variance unique to individual measures (including error) and maximizing reliability. Extreme outliers (z > 4.0 and more than 0.5 SD from next score) were truncated to within 0.5 SD of the next nearest score to prevent the undue influence of single scores on linear models and reduce Type I and Type II error. Expectation maximization was used to impute missing data for all latent/structural analyses (1.12% of all data points). The AMOS 7.0 statistical package, using maximum likelihood estimation, was used for all confirmatory factor analysis (CFA) and latent/structural modeling with the following fit indices reported: 1) Pearson χ2 for comparisons of absolute fit between nested models; 2) Comparative Fit Index for which values 0.95 or greater are considered good fit; and 3) root mean square error of approximation for which values 0.08 or less are acceptable and 0.05 or less are considered good fit. In consideration of multivariate nonnormality of the data (determined by Mardia’s normalized estimate), bias-corrected confidence intervals were generated using a 500-sample maximum likelihood bootstrapping technique as a secondary check on the accuracy of latent/structural model parameter estimates. These confidence intervals were consistent in all instances with interpretations of results described below. The SPSS 15.0 statistical package was used for all other analyses.

RESULTS

Descriptive Statistics

As shown in Table 1, younger (age <50 years) and older (age ≥50 years) participant groups were not significantly different in terms of gender, race/ethnicity, education, mean estimated verbal intelligence quotient, most recent CD4 count, months on HAART, number of total medications (HAART and non-HAART), mean BDI-II total score, or prevalence of alcohol dependence. However, more individuals in the younger group met criteria for AIDS and current drug dependence.

Proportions of medication classes tracked by MEMS (protease inhibitor, nucleoside reverse transcriptase inhibitor, nucleotide reverse transcriptase inhibitor, and nonnucleoside reverse transcriptase inhibitor) were not significantly different between the age groups (χ2(3) = 0.845, p = 0.84). Likewise, the proportion of younger versus older adults on once-daily (younger: 92, 26.14%; older: 11, 13.92%), twice-daily (younger: 240, 68.18%; older: 62, 78.48%), and three-times-daily (younger: 20, 5.68%; older: 6, 7.59%) dosing schedules for these medications was not significantly different (χ2(2) = 5.40, p = 0.07).

Greater variability for MEMS and 30-day self-report measures was found among younger than among older adults (Levene’s F [1, 429] = 15.58, p <0.001, and Levene’s F [1, 414] = 13.99, p <0.001, respectively). As shown in Table 2, older adults demonstrated significantly better adherence than younger adults on these measures when equal variances were not assumed. No significant differences were found between age groups for qualitative self-report of adherence. The proportion of individuals reporting a missed dose in 1-day self-report was also not significantly different between younger (12.68%) and older (9.09%) participants, χ2(1) = 0.77, p = 0.38.

Table 2
Descriptive Statistics and t Tests of Adherence to Medication

Independent-samples t tests for each of the six demographically corrected cognitive domain t scores (presented in Table 3) demonstrated no significant between-group mean differences. However, greater variability was found in the older group for learning/memory (Levene’s F [1, 429] = 4.17, p = 0.04) and verbal fluency (Levene’s F [1, 429] = 47.49, p <0.001).

Table 3
Descriptive Statistics and t Tests of Cognitive Domains

CFA of Cognition and Adherence

A CFA was conducted for cognitive and adherence variables so that comparisons of these constructs could be made between the younger and older age groups. CFA models consisted of two correlated latent factors: “Cognition“ and “Adherence.” Indicators of Cognition included standardized t scores of processing speed, memory, attention, executive functioning, verbal fluency, and motor functioning (tests for each domain are identified in the Appendix). Indicators of Adherence included MEMS adherence, qualitative self-report, 30-day self-report, and 1-day self-report. CFA models were first evaluated separately for the younger and older age groups (Models A and B). Subsequently, data from both groups were modeled simultaneously (Model C0).

CFA tests of invariance were then conducted, comparing absolute fit between nested versions of Model C by imposing successive constraints on the equality of measurement weights (C1), measurement intercepts (C2), and structural covariances (C3) between age groups. A significant change in fit between models C0 and C1 (Δχ2(8) = 29.99, p <0.001) demonstrated differences in factor structure between groups. Further examination demonstrated that the factor structure of cognition did not differ significantly between age groups, although 30-day and 1-day self-report were weaker indicators of medication adherence for older than for younger participants. In addition, as demonstrated by the change in fit between models C2 and C3(Δχ2(3) = 60.73, p <0.001), the covariance of cognition and adherence differed significantly between age groups. Fit statistics for selected latent models are shown in Table 4.

Table 4
Goodness-of-Fit Statistics for Latent Models of Cognition and Adherence

Relationship between Cognition and Adherence by Age Group

Model C0 was examined further to obtain estimates of the relationship between cognition and adherence. Although Cognition and Adherence were not significantly related in the younger group, r = 0.02, z = 0.23, p = 0.82, these latent factors were strongly related in the older group, r = 0.49, z = 2.57, p = 0.01. This model is represented in Figure 1.

FIGURE 1
Latent Model of Cognition and Medication Adherence Among Younger and Older HIV+ Adults

Additional models were tested to more closely examine cognition-adherence relationships. In Model D, the latent Adherence factor was permitted to covary freely with each of the six individual indicators of cognition. Adherence was not significantly related to any indicators of cognition in the younger group. However, in the older group, Adherence was related to executive functioning (r = 0.45, z = 2.50, p = 0.01), motor functioning (r = 0.46, z = 2.57, p = 0.01), and processing speed (r = 0.35, z = 2.09, p = 0.04). In Model E, the latent Cognition factor was permitted to covary freely with each of the three individual indicators of adherence. Cognition was not significantly related to any indicators of adherence in the younger group, but Cognition was significantly related to both MEMS adherence (r = 0.38, z = 2.85, p = 0.004) and qualitative self-report of adherence (r = 0.31, z = 2.40, p=0.02) in the older group.

Our findings of differential cognition-adherence relationships cannot be accounted for by the previously described between-group differences in variability, as adherence variability was lower in the group demonstrating stronger relationships (older adults). Likewise, the cognitive domains with greater variability among older adults (learning/memory and verbal fluency) were not individually related to adherence in either age group and, therefore, do not appear to be driving the overall cognition-adherence relationship.

Influence of Health Factors

Individual follow-up analyses were also conducted to evaluate and control for relevant health-related factors, including CD4 count, alcohol problems, drug problems, depression, hepatitis C, history of learning disabilities or neurological illness, and MEMS regimen complexity. Models were tested controlling for the potential influence of each of these variables on the relationship between Cognition and Adherence. In Model F, higher CD4 count was associated with better Adherence (β = 0.38, z = 2.53, p = 0.01), but only for individuals in the older group. In Model G, greater drug problems were also a strong predictor of poorer adherence in the older group (β = −0.59, z = −4.31, p <0.001). In Model I, increased depression (BDI-II score) was significantly related to poorer Adherence (β = −0.14, z = −2.29, p = 0.02) and Cognition (β = −0.14, z = −2.41, p = 0.02) in the younger group only. The magnitude of the depression-adherence relationship was similar among older adults (β = −0.25, z = −1.69, p = 0.09) but failed to reach statistical significance. In Model J, greater MEMS regimen complexity (dosing frequency) was significantly associated with poorer adherence in the younger group (β = −0.19, z = −3.22, p = 0.001). No other control variables were significantly associated with adherence or cognition in either age group. Notably, the cognition-adherence relationship was not meaningfully affected in any control analyses.

CONCLUSIONS

Previous research has demonstrated that older adults are particularly vulnerable to HIV-associated cognitive decline and dementia.1517 However, mechanisms for this vulnerability have not yet been clearly identified. In this study, medication adherence and neurocognitive functioning were examined in younger (age <50 years) and older (age ≥50 years) HIV-positive adults. Latent factor models demonstrated prominent age differences in the cognition-adherence relationship, such that neurocognitive impairment was strongly related to poorer adherence among older HIV-positive individuals, but not among their younger counterparts. Follow-up analyses demonstrated that factors such as alcohol problems, drug problems, depression, and regimen complexity could not account for these findings.

Although the cross-sectional nature of this study prevents direct interpretations of cause and effect, our results provide some support for the possibility that poor medication adherence may be especially likely to result in cognitive impairment among older adults. For example, findings indicated that adherence in this group was related to executive functioning, motor functioning, and processing speed, but not verbal fluency, attention, or even memory. Whereas it is relatively straightforward to envision ways in which executive dysfunction could cause poor adherence, potential mechanisms by which slowed speed of motor function, and information processing could affect adherence are somewhat less compelling. In addition, it is notable that executive and motor impairment are considered sentinel symptoms of emerging HIV-related subcortical/ basal ganglia dysfunction.1,2

In previous research, older adults have demonstrated weaker CD4 recovery after HAART21,22 and a stronger relationship between cerebrospinal fluid viral load and neurocognitive impairment.23 Consistent with these previous studies, our finding that CD4 count was significantly related to adherence only among older participants may reflect an age-related susceptibility to negative immunological effects of poor adherence. This relatively greater adherence-related immunological dysfunction might then be expected to result in increased neurocognitive dysfunction over time. Although we found no relationship between CD4 count and current cognitive ability (similar to a number of previous findings2426), this may reflect differences between the time course of HIV-related neurodegeneration, which is likely to be progressive and cumulative in nature, and biomarkers of immunocompetence, which may have a more fluctuating trajectory.

However, given that a wide range of individual cognitive domains have previously been linked to specific medication-taking skills and overall adherence,10,27,28 it appears most likely that the cognition-adherence relationship is in fact bidirectional. The increased strength of this relationship among older adults in this study may be related, in part, to a threshold effect whereby medication adherence begins to decline only when cognitive ability drops below a certain absolute level. Older participants in this study were not significantly more impaired than their younger counterparts, but neurocognitive impairment is defined in this context by deviation from age-corrected normative data. Absolute levels of cognitive performance, on the other hand, are well-documented to show regular declines with advancing age, even in the absence of identifiable neuropathology.29,30 Our findings of an age differential in the relationship between drug problems and adherence might, therefore, reflect a greater sensitivity among older adults to transient disruptions in cognition due to age-related reductions in brain reserve.

A number of factors distinguish the present study from others previously conducted in this area. First, our relatively large sample and latent/ structural techniques of data analysis increased effective statistical power and allowed us to examine neurocognition and medication adherence as psychometrically robust constructs. In addition, we included HIV-positive participants with and without substance use, psychiatric, or medical conditions (neurological and otherwise) in the interests of maximizing ecological validity and enhancing clinical applicability of our results. This approach is well-suited for obtaining a broad, macrolevel perspective, and our results are should generalize well to diverse HIV-positive populations.

Nevertheless, follow-up studies concentrating on more restricted issues will greatly enhance the practical applicability of our findings. For example, examination of ways in which cognitive decline may be related to the CNS penetration of individual HAART medications will be useful in identifying optimal treatment regimens for older HIV-infected patients. In addition, recent studies suggest that some adjunctive psychiatric medications may also play a role in reducing CNS HIV viral load and improving cognition.31 These issues were not addressed in this study and represent important areas for future research.

In conclusion, these findings provide support for a bidirectional relationship between adherence to medication and cognitive ability in older adults. Older HIV-positive adults seem to be especially vulnerable to neurocognitive dysfunction under conditions of suboptimal HAART adherence. Likewise, older individuals with neurocognitive impairment or drug problems are at increased risk of suboptimal adherence to medication. In contrast, younger HIV-positive individuals demonstrate relatively low levels of adherence overall, irrespective of level of neurocognitive function. These findings highlight the importance of implementing improved methods of optimizing medication adherence and evaluating neurocognition in the growing population of older HIV-infected patients. Additional research investigating cognition and medication adherence will be crucial in further refining age-related patterns and mechanisms of vulnerability in HIV/AIDS.

Acknowledgments

This work was supported by National Institutes of Health grants R01 MH58552 and R01 DA13799 and the National Institutes of Health Ruth L. Kirschstein National Research Service award T32 MH19535 (all to CHH).

Footnotes

Portions of this article were previously presented at the annual meeting of the International Neuropsychological Society, February 6–9, 2008, Waikoloa, HI.

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