Journal of Gerontological Nursing

Feature Article 

Considering the Impact of Aging When Caring For and Treating Adults With HIV

Judy Frain, PhD, RN


The current study investigates how age impacts factors associated with successfully managing HIV. One hundred thirty adults with HIV were recruited for the study. Participants were divided into two groups, those age ≥50 and those age <50. Cognitive impairment and depressive symptoms were seen at higher rates in older adults, and the severity of depressive symptoms was also higher in older adults. Depressive symptoms impacted cognitive function to a higher degree in older adults compared to younger adults (r = −0.293, p = 0.018 vs. r = −0.109, p = 0.387). Polypharmacy was a greater concern in older adults, with 88% having polypharmacy compared with 60% of younger adults. Similarly, the prevalence of comorbidities was more than double in older adults compared to younger adults. Factors associated with aging complicate management of HIV. Gaining insight into the challenges of caring for this population will furnish nurses with information necessary to provide the best possible care for this growing population. [Journal of Gerontological Nursing, 46(4), 31–40.]


The current study investigates how age impacts factors associated with successfully managing HIV. One hundred thirty adults with HIV were recruited for the study. Participants were divided into two groups, those age ≥50 and those age <50. Cognitive impairment and depressive symptoms were seen at higher rates in older adults, and the severity of depressive symptoms was also higher in older adults. Depressive symptoms impacted cognitive function to a higher degree in older adults compared to younger adults (r = −0.293, p = 0.018 vs. r = −0.109, p = 0.387). Polypharmacy was a greater concern in older adults, with 88% having polypharmacy compared with 60% of younger adults. Similarly, the prevalence of comorbidities was more than double in older adults compared to younger adults. Factors associated with aging complicate management of HIV. Gaining insight into the challenges of caring for this population will furnish nurses with information necessary to provide the best possible care for this growing population. [Journal of Gerontological Nursing, 46(4), 31–40.]

Since the start of the HIV epidemic, tremendous advances have been made in the care and treatment of persons living with this disease. These successes have transformed HIV from a fatal to a chronic disease and have also contributed to the demographic shift now seen in the HIV population. The majority of new infections are still found in younger adults, but now more than one half of all persons with HIV are older than 50 (Centers for Disease Control and Prevention, 2017), and it is expected that this aging population will continue to grow.

As is true with many other chronic diseases, aging has led to an increase in the number of comorbid conditions. Comorbidities complicate HIV treatment, decrease quality of life, increase morbidity and mortality, and increase the cost of health care (Cahill & Valadéz, 2013; Rodriguez-Penney et al., 2013). Several studies have found that the number of comorbidities experienced by people with HIV was greater when compared to HIV-negative adults, including those with other chronic conditions (Maciel et al., 2018; Mayer et al., 2018; Ruzicka et al., 2019). Although the number of comorbid conditions was greater in persons with HIV, the types of comorbidities are typical of aging adults in general, with hypertension, lipid/metabolism, respiratory, and psychological disorders common in persons with and without HIV, suggesting that aging, rather than HIV, is responsible (Kong et al., 2019; Serrão et al., 2019; Smith & Wrobel, 2014).

One corollary of the high number of comorbidities is the increase in pill burden for many adults with HIV (Moore et al., 2015). In a recent study, overall prevalence of polypharmacy (five or more medications) was 25% among persons with HIV, compared to 18.7% in HIV-negative adults. Significantly, polypharmacy was approximately 50% in adults 50 and older with HIV (Ware et al., 2018). In another study, polypharmacy was an issue for 66% of older adults with HIV, and 48% of younger adults with HIV, compared to just 13% of HIV-negative adults. Even when HIV medications were excluded from pill counts, 30% of older and 14% of younger adults with HIV had polypharmacy (Halloran et al., 2019).

Due to its importance in suppressing the virus, adherence to antiretroviral therapy (ART) remains a vital component in the successful management of HIV. Common advice from providers has been that patients need to take their HIV medications correctly 95% of the time to achieve and maintain viral suppression. Although newer drugs may be more forgiving of missed doses, non-adherence remains a primary cause of virologic failure for persons with HIV (Denison et al., 2015; Dunn et al., 2018; Glass et al., 2015). Studies have shown polypharmacy and multicomorbidities, along with other factors, contributed to non-adherence (Bogart et al., 2016; Cantudo-Cuenca et al., 2014; Corless et al., 2017; Manzano-García et al., 2018).

Depression is one of the most common mental health comorbidities found in persons with HIV. The prevalence of depression in adults with HIV is more than three times that of the general adult population (Brody et al., 2018; Do et al., 2014; Nanni et al., 2015). Depressive disorders have been associated with faster HIV progression, increased morbidity and mortality, slower immune response, reduced adherence to ART, and a decrease in cognitive function and quality of life (Gonzalez et al., 2011; Wagner et al., 2011). Depressive symptoms have been shown to negatively impact HIV self-management, including daily health practices, and were positively correlated with perceived stress (Webel et al., 2016). Studies are mixed as to the effect of aging on the prevalence of depression in adults with HIV; however, there is some evidence that depressive symptoms have a greater impact on quality of life and health outcomes as this population ages (Millar et al., 2017; Thomas et al., 2009).

The connection between psychiatric symptoms and cognitive function has been examined in previous HIV studies with mixed results. Evidence from studies of older adults with HIV found that although psychiatric symptom burden was high, it did not result in an increase in HIV-associated neurocognitive disorders, and comorbid psychiatric symptoms were not associated with cognitive impairment (Bourgeois et al., 2019; Milanini et al., 2017). However, in other studies that included older and younger adults with HIV, results indicated that depressive symptoms impact cognitive function (Laverick et al., 2017; Rubin & Maki, 2019; Schouten et al., 2016).

The current study explores the impact of aging on successfully treating and managing HIV in adults age 50 and older. Age 50 was chosen because that is the age defined by the Centers for Disease Control and Prevention (Blanco et al., 2012) as older adult in the study of persons with HIV. How aging impacts psychosocial, cognitive, and quality of life measures is explored, in addition to aging's impact on medication adherence, as measured by a 3-day medication recall. This research fills a gap by giving health care providers and their patients information they can use to better understand how aging can impact successfully living with and managing HIV.


Design and Sample

A descriptive, correlational design was used in this study. Participants were recruited from an outpatient infectious disease clinic of an urban medical center in the Midwest, and from the AIDS Clinical Trials Unit (ACTU), which shares a building with the clinic. Persons were approached during their appointment to discuss the trial. Informational flyers were also placed in the waiting rooms of the clinic and clinical trials unit. Data were collected as part of a study assessing medication management in adults with HIV.

A convenience sample of 130 adults between ages 20 and 76 with HIV were enrolled and divided into two groups, those 50 and older, and those younger than 50. Inclusion criteria were having documented HIV, taking ART for at least 16 consecutive weeks prior to study entry, and ability to read and understand English. After giving informed consent, participants completed instruments that included the Montreal Cognitive Assessment (MoCA), Center for Epidemiologic Studies Depression Scale (CES-D), Self-Efficacy for Managing Chronic Disease Scale, and a medication adherence instrument. Data included demographic information; alcohol, drug, and tobacco history; current medications; current viral load and current and nadir CD4 count; years since HIV diagnosis; and additional comorbidities. HIV viral load and CD4 counts were documented in this study as measures of HIV. A low or undetectable viral load indicates ART is effectively controlling the HIV. When uncontrolled, HIV attacks the body's CD4 cells, causing a decrease in number and resulting in an increased risk for opportunistic infections. A normal CD4 count ranges from 500 to 1,500 cells/mm3 of blood. Generally, if CD4 counts >500 cells/mm3 can be maintained, the risk for opportunistic infections is decreased.


The study was approved by the University's Institutional Review Board. All participants provided written informed consent prior to completing any study-related activities. Study procedures took place in a quiet, private room conveniently located near the clinic and ACTU. Participants completed a demographic form, which included medical and social histories, the CES-D, the MoCA, the Self-Efficacy for Managing Chronic Disease Scale, and a medication management test. Trained research staff administered the MoCA and the medication management test and were available to assist if questions arose when participants were completing the other instruments. Medical records were also used to complete information on the demographic form, including number of comorbidities, medications, and CD4 counts and viral load. HIV health care providers were notified of participants scoring ≥16 on the CES-D, indicating clinically significant depressive symptomology.


Brevity and adaptability to the clinical setting were considerations when choosing the instruments for this study. The CES-D and MoCA have been used in previous HIV research and have been shown to be valid and reliable instruments (Nasreddine et al., 2005; Radloff, 1977). Conciseness and ease of administration make these instruments ideally suited for use in the clinical setting, where time and resources are often limited.

The CES-D is a 20-item, self-administered questionnaire used to measure depressive symptoms over the past 1 week (Radloff, 1977). A summary score is calculated, with total possible scores ranging from 0 to 60. Scoring for each item is on a 4-point scale ranging from 0 (rarely or none of the time) to 3 (most or all of the time). Responses are based on frequency of occurrence during the past 1 week. A higher score indicates a higher level of depressive symptomology. A score ≥16 indicates a clinically significant level of depressive symptomology. Less than 20% of the general population would be expected to score in this range. The CES-D has been used in many large-scale HIV research trials, and its reliability and validity have been well-established. Cronbach's alpha is 0.94 (Radloff, 1977). In a recent study focused on persons with HIV, Cronbach's alpha ranged from 0.92 to 0.94 (Mueses-Marín et al., 2019). Another study comparing men with HIV with uninfected men found specificity of 99.9% and sensitivity of 75% in the HIV group (Armstrong et al., 2019).

The MoCA is designed as a quick screening tool for assessing mild cognitive impairment (MCI) and is not recommended as a stand-alone diagnostic tool. It is a 30-item instrument that takes approximately 10 minutes to complete and can be administered with minimal training. The MoCA assesses different cognitive domains, including attention, concentration, executive function, memory, language, visuoconstructional skills, conceptual thinking, calculations, and orientation (Nasreddine et al., 2005). The highest possible score is 30, and a person scoring ≥26 is considered to have normal cognitive function. The MoCA has been used to measure HIV–associated MCI (Bourgeois et al., 2019; Overton et al., 2013). Overall reliability of this instrument has been shown to be 0.83 (Nasreddine et al., 2005). Studies focused on adults with HIV have reported sensitivities ranging from 59% to 85% (Hasbun et al., 2012).

The Self-Efficacy for Managing Chronic Disease Scale is a six-item scale that is designed to measure how confident a person is in managing symptoms of his/her disease. Responses can range from 1 (not at all) to 10 (totally confident). The scale score is the mean of the six items. When originally tested on a sample of 605 adults with chronic disease, the mean score was 5.17 (SD = 2.2), and reliability was 0.91 (Lorig et al., 2001).

Data Management and Analysis

Data were analyzed using SPSS version 22. Data were stored on a hard drive and backed up on a secured server. Hard copies of completed study questionnaires were stored in a locked file cabinet in a secured office. All stored data were de-identified. Descriptive statistics were used to summarize the demographic characteristics and other information from the demographic form. Independent t tests, analysis of variance (ANOVA), and regression analysis were used to analyze data.


Table 1 summarizes participant demographic data. All participants were seen in care at least once within the past 1 year and were prescribed ART. Thus, this was a relatively healthy group, with three quarters of participants having undetectable viral loads (n = 97), and a mean CD4 count of 558 cells/mm3, with counts ranging from 5 to 1,649 cells/mm3. CD4 counts were similar between groups, with older adults having a mean CD4 count of 549 (SD = 291) cells/mm3, and younger adults 567 (SD = 318) cells/mm3. Viral load, however, differed significantly between groups. Whereas 81.5% of older adults had an undetectable viral load, only 67.7% of younger adults were undetectable. The median viral load was undetectable for both groups; however, the mean for older adults was 1,303 (SD = 7,507) cells/mm3, whereas the mean for younger adults was 6,600 (SD = 27,956) cells/mm3, primarily due to approximately 10% of younger adults having viral loads >10,000 cells/mm3, compared to <2% of older adults.

Participant Demographics

Table 1:

Participant Demographics

Cognitive function did not differ significantly between groups. The mean overall score for all participants was 24.23 (SD = 3.67). Older adults had a mean score on the MoCA of 23.62 (SD = 4.04) compared to a slightly higher score of 24.85 (SD = 3.17) in the younger group. Overall, 36% (n = 47) of participants were classified as having normal cognitive function and 64% (n = 83) had scores suggesting MCI. Separately, 66% (n = 43) of older adults and 61% (n = 40) of younger adults exhibited MCI.

Depressive symptoms were common in both groups, with 55 (42%) participants exhibiting clinically significant depressive symptoms (≥16 on the CES-D). Higher scores indicate a greater number of depressive symptoms overall. There was no significant difference in depressive symptoms between older and younger adults. Forty-three percent of older adults and 42% of younger adults experienced depressive symptoms classified as clinically significant. The mean score on the CES-D was 15.85 (SD = 12.8) for older adults and 14.98 (SD = 10.2) for younger adults. Differences were noted in the severity of depressive symptoms, with older adults experiencing severe depressive symptoms (CES-D >24) at a higher rate than younger adults (i.e., 23% compared to 14%, respectively).

Self-efficacy for managing chronic disease was high for older and younger participants. In participants 50 and older, the mean score was 7.67 (SD = 2.1), and in the younger group, the mean was 7.83 (SD = 1.9). Both groups scored significantly higher than the original testing group, where >600 patients with chronic disease had a mean score of 5.17 (SD = 2.2) (Lorig et al., 2001).

Multiple regression analysis was used in each group to test predictors of depressive symptoms (Table 2). The results of the regression indicated that self-efficacy in managing symptoms and education explained 43% of the variance in older adults (R2 = 0.43, F[2,62] = 23.45, p < 0.001), considered to be a moderate to large effect size. Self-efficacy and education significantly predicted depressive symptoms (β = −0.534, p < 0.001 and β = −0.296, p = 0.004, respectively). However, in younger adults, these same variables explained only 11% of the variance (R2 = 0.11, F[2,62] = 3.75, p = 0.029), which was considered to be a small to negligible effect size. In the younger age group, education level remained a significant predictor (β = −0.290, p < 0.02); however, self-efficacy was no longer significant (β = −0.120, p = 0.324).

Depressive Symptoms: Model Summaries for Older and Younger Adults

Table 2:

Depressive Symptoms: Model Summaries for Older and Younger Adults

The correlation between depressive symptoms and cognitive function was significant for older adults (r = −0.293, p = 0.018), indicating that as depressive symptom scores increased, cognitive function scores decreased. This correlation was not significant in younger adults (r = −0.109, p = 0.387). Cognitive function and depressive symptoms were predictors of medication management ability in older adults (R2 = 0.45, p < 0.001), which was considered a moderate to large effect size. Cognitive function and depressive symptoms also predicted medication management in younger adults, although the effect size was small (R2 = 0.27, p < 0.001). Table 3 provides more information about correlations found in the current study. Correlations, and the strength of those correlations, differed between older and younger adults.

Correlations Among Variables: Older and Younger Adults

Table 3:

Correlations Among Variables: Older and Younger Adults

Even greater differences were seen when examining comorbidities. Older adults had a mean number of comorbidities more than twice that of persons younger than 50 (4.55 [SD = 2.61] vs. 2.14 [SD = 1.78], p < 0.001). Number of medications was also approximately twice as high for older adults compared to younger adults (mean = 6.4 [SD = 3.9] vs. mean = 3.4 [SD = 2.7], p < 0.001).


The current study found a number of similarities between older and younger adults with HIV; however, significant differences were noted that could impact how care is provided for older adults. Depressive symptoms remain a common problem for many adults with HIV, but the impact of depressive symptoms may be greater for older adults. A stronger correlation was found between depressive symptoms and cognitive function in older adults than in younger adults, and depressive symptoms, along with cognitive function, were stronger predictors of medication management in older adults than younger adults. Approximately 42% of participants had clinically significant levels of depressive symptomology. These rates are similar to rates found in previous studies that measured depression in persons with HIV. Significantly, these rates are approximately three times the rates found in the general adult population (Bhatia & Munjal, 2014). It is also noteworthy that an additional 22% of current participants scored at a subclinical level of depressive symptomology. Prior research has found that there is an incremental relationship between depressive symptomology and treatment non-adherence, and that this incremental relationship has been identified in high and low levels of depression severity, indicating even at subclinical levels depressive symptoms can affect treatment adherence (Magidson et al., 2015; Uthman et al., 2014).

Cognitive impairment was also prevalent, with 64% of participants scoring in the MCI range, with a mean MoCA score of 24.23 (SD = 3.67). These results are similar to a recent study that found 63.8% of participants scoring at the impairment level (mean = 25.4 [SD = 2.7]), and similar to the current study, found these results independent of age (Herrmann et al., 2019).

The current study also identified a correlation between depressive symptoms and cognitive function in older adults. It is understood that cognition plays an important role in medication adherence, and that adherence is the primary predictor of effectively managing HIV. In addition, research has shown that adherence to ART leads to undetectable viral loads, which can significantly reduce the risk of HIV transmission (Yah, 2017). Thus, early identification and implementation of treatment for depressive symptoms may increase medication adherence for older adults, leading not only to an improved state of health for individual patients, but also a decrease in risk for new infections. Although pharmacological treatment may be considered, counseling, support groups, and cognitive-behavioral therapy are also options for patients coping with depressive symptoms.

Interestingly, although current CD4 counts were similar between groups, nadir CD4 counts were significantly lower for older adults compared to younger adults (mean = 162 [SD = 147.68] cells/mm3 vs. 261.35 [SD = 218.15] cells/mm3, respectively, p = 0.003). There is evidence that nadir CD4 count is predictive of neurological outcomes, making it important for health care providers to be aware of the health history of their patients, including historical CD4 counts (McCombe et al., 2013; Valcour et al., 2006). The differences in nadir CD4 counts between groups are a reflection of increased HIV testing in younger adults, and historical changes in recommendations of when to start ART (Thompson et al., 2012). Unfortunately, these counts also reflect the reality that older adults are still getting diagnosed much later in the disease process, where their CD4 count has already dropped to a dangerous level (Roberson, 2018).

Although the number of medications and number of comorbidities were significantly higher for older adults than for younger adults in the current study, the only significant correlation was with self-efficacy in the younger group (r = −0.48, p < 0.001), indicating that as the number of medications increased, self-efficacy decreased. The same correlation was not significant for older adults (r = −0.159, p = 0.21), indicating that the number of medications taken did not affect self-efficacy.

Self-efficacy for managing chronic disease symptoms was high for older and younger adults. It was hypothesized that this was due to the sample having lived with HIV for many years (mean = 15.74 [SD = 7.93] years). Self-efficacy was negatively correlated with number of other diagnoses in older and younger adults, indicating that as the number of diagnoses increased, self-efficacy decreased. This finding may signal that as comorbidities make managing HIV more complicated, there is less confidence that individuals can successfully manage their disease.

Identifying depressive symptoms early can increase treatment options and optimize treatment strategies. The importance of talking with and educating patients with HIV about the signs and symptoms of depression cannot be overstated. Adults with HIV are living every day with the stigma of this diagnosis. The aversion to stacking a mental health diagnosis, with its own perceived stigma, on top of an HIV diagnosis may lead these patients to ignore or minimize depressive symptoms, muting meaningful discussion and erecting an impenetrable barrier to diagnosis and treatment. Health care providers who initiate conversations about and screenings for depression at the initial visit and continue those practices as a routine part of each health care visit may decrease the stigma surrounding mental health and lay the groundwork for the early recognition and discussion of depressive symptoms in persons with HIV, particularly older adults who are less likely to discuss psychological issues with their provider.

MCI was also a problem for the majority of older adults in the current study, with 66% exhibiting MCI as measured by the MoCA (score <26), possibly leading to problems managing medications. As patients are now seen as infrequently as once per year, poor medication management can have a detrimental effect on morbidity and quality of life. Early detection of cognitive changes offers the opportunity to intervene with strategies to improve adherence prior to experiencing the negative consequences of missed medications. Offering suggestions such as pill boxes, alarms, or help in setting up pills, could benefit patients who may be at risk for poor adherence.

Recognizing the difficulties of managing multiple chronic conditions and the medications used to treat them, providers must be vigilant to any medication changes that could impact HIV medications. Single pill ART options, when available, can decrease the risks associated with polypharmacy, a common problem for older adults with HIV.

Poor healthy lifestyle choices were found for older and younger adults. Tobacco use was prevalent in older and younger adults with HIV, with 77% of older adults and 75% of younger adults being past or current smokers. Correlations between smoking and cognitive function, depressive symptoms, and medication management were all seen in older adults, but not in younger adults. Encouraging healthy choices, such as smoking cessation, could provide benefits beyond the health benefits of not smoking. Alcohol and drug use were also prevalent in both groups (Table 1). However, the only correlation found was the negative correlation between drug use and depressive symptoms (Table 3).

Implications and Conclusion

Providing comprehensive, integrative care for the health of body and mind offers persons with HIV a better quality of life and a better chance for longevity. Early identification of at-risk patients using objective measures such as the MoCA and CES-D can help identify problems before they become clinically significant. These are quick, objective assessment tools that can be used to detect small changes in cognition and depressive symptoms that may not be apparent using subjective measures. Implementing depression and cognition assessments as a regular part of every health care visit affords health care professionals the opportunity to open a dialogue with their patients, discussing the emotional and mental health aspects of living with HIV, and about implementing effective treatment and coping strategies to combat the adverse effects of these comorbidities. There have been tremendous advances in treatment and care of persons with HIV over the past three decades, yet health care providers face new challenges in caring for older adults with HIV. Holistic care can best be achieved by focusing on both the physical and psychological symptoms of HIV.

Finally, when caring for adults with HIV, nurses should keep in mind that age 50 is considered the marker for identifying “older” in persons with HIV (Blanco et al., 2012). Nurses should be alert for comorbidities, polypharmacy issues, and other cognitive problems that typically arise decades later in patients without HIV. This awareness can help guide the health care visit and will contribute to improved care, increased patient satisfaction, and better outcomes.


  • Armstrong, N. M., Surkan, P. J., Treisman, G. J., Sacktor, N. C., Irwin, M. R., Teplin, L. A., Stall, R. C., Jacobson, L. P. & Abraham, A. G. (2019). Optimal metrics for identifying long term patterns of depression in older HIV-infected and HIV-uninfected men who have sex with men. Aging & Mental Health, 23(4), 507–514 doi:10.1080/13607863.2017.1423037 [CrossRef] PMID:29424569
  • Bhatia, M. S. & Munjal, S. (2014). Prevalence of depression in people living with HIV/AIDS undergoing ART and factors associated with it. Journal of Clinical and Diagnostic Research: JCDR, 8(10), WC01–WC04. doi:10.7860/JCDR/2014/7725.4927 [CrossRef] PMID:25478433
  • Blanco, J. R., Jarrin, I., Vallejo, M., Berenguer, J., Solera, C., Rubio, R., Pulido, F., Asensi, V., del Amo, J. & Moreno, S. (2012). Definition of advanced age in HIV infection: Looking for an age cut-off. AIDS Research and Human Retroviruses, 28(9), 1000–1006 doi:10.1089/aid.2011.0377 [CrossRef]
  • Bogart, L. M., Wagner, G. J., Green, H. D. Jr.. , Mutchler, M. G., Klein, D. J., McDavitt, B., Lawrence, S. J. & Hilliard, C. L. (2016). Medical mistrust among social network members may contribute to antiretroviral treatment nonadherence in African Americans living with HIV. Social Science & Medicine, 164, 133–140 doi:10.1016/j.socscimed.2016.03.028 [CrossRef] PMID:27046475
  • Bourgeois, J. A., John, M., Zepf, R., Greene, M., Frankel, S. & Hessol, N. A. (2019). Functional deficits and other psychiatric associations with abnormal scores on the Montreal Cognitive Assessment (MoCA) in older HIV-infected patients. International Psychogeriatrics. Advance online publication. doi:10.1017/S1041610219000413 [CrossRef] PMID:31014404
  • Brody, D. J., Pratt, L. A. & Hughes, J. (2018). Prevalence of depression among adults aged 20 and over: United States, 2013–2016.
  • Cahill, S. & Valadéz, R. (2013). Growing older with HIV/AIDS: New public health challenges. American Journal of Public Health, 103(3), e7–e15 doi:10.2105/AJPH.2012.301161 [CrossRef] PMID:23327276
  • Cantudo-Cuenca, M. R., Jiménez-Galán, R., Almeida-Gonzalez, C. V. & Morillo-Verdugo, R. (2014). Concurrent use of comedications reduces adherence to antiretroviral therapy among HIV-infected patients. Journal of Managed Care & Specialty Pharmacy, 20(8), 844–850 doi:10.18553/jmcp.2014.20.8.844 [CrossRef] PMID:25062078
  • Centers for Disease Control and Prevention. (2017). HIV surveillance report, 2016 (vol 28).
  • Corless, I. B., Hoyt, A. J., Tyer-Viola, L., Sefcik, E., Kemppainen, J., Holzemer, W. L., Eller, L. S., Nokes, K., Phillips, J. C., Dawson-Rose, C., Rivero-Mendez, M., Iipinge, S., Chaiphibalsarisdi, P., Portillo, C. J., Chen, W. T., Webel, A. R., Brion, J., Johnson, M. O., Voss, J. & Nicholas, P. K. (2017). 90-90-90-Plus: Maintaining adherence to antiretroviral therapies. AIDS Patient Care and STDs, 31(5), 227–236 doi:10.1089/apc.2017.0009 [CrossRef] PMID:28514193
  • Denison, J. A., Koole, O., Tsui, S., Menten, J., Torpey, K., van Praag, E., Mukadi, Y. D., Colebunders, R., Auld, A. F., Agolory, S., Kaplan, J. E., Mulenga, M., Kwesigabo, G. P., Wabwire-Mangen, R. & Bangsberg, D. R. (2015). Incomplete adherence among treatment-experienced adults on antiretroviral therapy in Tanzania, Uganda and Zambia. AIDS (London, England), 29(3), 361–371 doi:10.1097/QAD.0000000000000543 [CrossRef] PMID:25686684
  • Do, A. N., Rosenberg, E. S., Sullivan, P. S., Beer, L., Strine, T. W., Schulden, J. D., Fagan, J. L., Freedman, M. S. & Skarbinski, J. (2014). Excess burden of depression among HIV-infected persons receiving medical care in the United States: Data from the medical monitoring project and the behavioral risk factor surveillance system. PLoS One, 9(3), e92842 doi:10.1371/journal.pone.0092842 [CrossRef] PMID:24663122
  • Dunn, K., Lafeuille, M. H., Jiao, X., Romdhani, H., Emond, B., Woodruff, K., Pesa, J., Tandon, N. & Lefebvre, P. (2018). Risk factors, health care resource utilization, and costs associated with nonadherence to anti-retrovirals in Medicaid-insured patients with HIV. Journal of Managed Care & Specialty Pharmacy, 24(10), 1040–1051 doi:10.18553/jmcp.2018.17507 [CrossRef] PMID:29877140
  • Glass, T. R., Sterne, J. A., Schneider, M. P., De Geest, S., Nicca, D., Furrer, H., Günthard, H. F., Bernasconi, E., Calmy, A., Rickenbach, M., Battegay, M. & Bucher, H. C. (2015). Self-reported nonadherence to antiretroviral therapy as a predictor of viral failure and mortality. AIDS (London, England), 29(16), 2195–2200 doi:10.1097/QAD.0000000000000782 [CrossRef] PMID:26544582
  • Gonzalez, J. S., Batchelder, A. W., Psaros, C. & Safren, S. A. (2011). Depression and HIV/AIDS treatment nonadherence: A review and meta-analysis. JAIDS Journal of Acquired Immune Deficiency Syndromes, 58(2), 181–187 doi:10.1097/QAI.0B013E31822D490A [CrossRef] PMID:21857529
  • Halloran, M. O., Boyle, C., Kehoe, B., Bagkeris, E., Mallon, P., Post, F. A., Vera, J., Williams, I., Anderson, J., Winston, A., Sachikonye, M., Sabin, C. & Boffito, M. (2019). Polypharmacy and drug-drug interactions in older and younger people living with HIV: The POPPY study. Antiviral Therapy, 24(3), 193–201 doi:10.3851/IMP3293 [CrossRef] PMID:30700636
  • Hasbun, R., Eraso, J., Ramireddy, S., Wainwright, D. A., Salazar, L., Grimes, R., York, M. & Strutt, A. (2012). Screening for neurocognitive impairment in HIV individuals: The utility of the Montreal Cognitive Assessment test. Journal of AIDS & Clinical Research, 3(10), 186 doi:10.4172/2155-6113.1000186 [CrossRef] PMID:23956944
  • Herrmann, S., McKinnon, E., Skinner, M., Duracinsky, M., Chaney, R., Locke, V. & Mastaglia, F. (2019). Screening for HIV-associated neurocognitive impairment: Relevance of psychological factors and era of commencement of antiretroviral therapy. The Journal of the Association of Nurses in AIDS Care, 30(1), 42–50 doi:10.1097/JNC.0000000000000040 [CrossRef] PMID:30586348
  • Kong, A. M., Pozen, A., Anastos, K., Kelvin, E. A. & Nash, D. (2019). Non-HIV comorbid conditions and polypharmacy among people living with HIV age 65 or older compared with HIV-negative individuals age 65 or older in the United States: A retrospective claims-based analysis. AIDS Patient Care and STDs, 33(3), 93–103 doi:10.1089/apc.2018.0190 [CrossRef] PMID:30844304
  • Laverick, R., Haddow, L., Daskalopoulou, M., Lampe, F., Gilson, R., Speakman, A., Antinori, A., Bruun, T., Vassilenko, A., Collins, S. & Rodger, A. (2017). Self-reported decline in everyday function, cognitive symptoms, and cognitive function in people with HIV. Journal of Acquired Immune Deficiency Syndromes, 76(3), e74–e83 doi:10.1097/QAI.0000000000001468 [CrossRef] PMID:29016449
  • Lorig, K. R., Sobel, D. S., Ritter, P. L., Laurent, D. & Hobbs, M. (2001). Effect of a self-management program on patients with chronic disease. Effective Clinical Practice, 4(6), 256–262 PMID:11769298
  • Maciel, R. A., Klück, H. M., Durand, M. & Sprinz, E. (2018). Comorbidity is more common and occurs earlier in persons living with HIV than in HIV-uninfected matched controls, aged 50 years and older: A cross-sectional study. International Journal of Infectious Diseases, 70, 30–35 doi:10.1016/j.ijid.2018.02.009 [CrossRef] PMID:29476902
  • Magidson, J. F., Blashill, A. J., Safren, S. A. & Wagner, G. J. (2015). Depressive symptoms, lifestyle structure, and ART adherence among HIV-infected individuals: A longitudinal mediation analysis. AIDS and Behavior, 19(1), 34–40 doi:10.1007/s10461-014-0802-3 [CrossRef] PMID:24874725
  • Manzano-García, M., Pérez-Guerrero, C., Álvarez de Sotomayor Paz, M., Robustillo-Cortés, M. L. A., Almeida-González, C. V. & Morillo-Verdugo, R. (2018). Identification of the medication regimen complexity index as an associated factor of nonadherence to antiretroviral treatment in HIV positive patients. The Annals of Pharmacotherapy, 52(9), 862–867 doi:10.1177/1060028018766908 [CrossRef] PMID:29592537
  • Mayer, K. H., Loo, S., Crawford, P. M., Crane, H. M., Leo, M., DenOuden, P., Houlberg, M., Schmidt, M., Quach, T., Ruhs, S., Vandermeer, M., Grasso, C. & McBurnie, M. A. (2018). Excess clinical comorbidity among HIV-infected patients accessing primary care in US community health centers. Public Health Reports (Washington, D.C.), 133(1), 109–118. doi:10.1177/0033354917748670 [CrossRef] PMID:29262289
  • McCombe, J. A., Vivithanaporn, P., Gill, M. J. & Power, C. (2013). Predictors of symptomatic HIV-associated neurocognitive disorders in universal health care. HIV Medicine, 14(2), 99–107 doi:10.1111/j.1468-1293.2012.01043.x [CrossRef] PMID:22994556
  • Milanini, B., Catella, S., Perkovich, B., Esmaeili-Firidouni, P., Wendelken, L., Paul, R., Greene, M., Ketellw, R. & Valcour, V. (2017). Psychiatric symptom burden in older people living with HIV with and without cognitive impairment: The UCSF HIV over 60 cohort study. AIDS Care, 29(9), 1178–1185 doi:10.1080/09540121.2017.1281877 [CrossRef] PMID:28127989
  • Millar, B. M., Starks, T. J., Gurung, S. & Parsons, J. T. (2017). The impact of comorbidities, depression, and substance use problems on quality of life among older adults living with HIV. AIDS and Behavior, 21(6), 1684–1690 doi:10.1007/s10461-016-1613-5 [CrossRef] PMID:27864625
  • Moore, H. N., Mao, L. & Oramasionwu, C. U. (2015). Factors associated with polypharmacy and the prescription of multiple medications among persons living with HIV (PLWH) compared to non-PLWH. AIDS Care, 27(12), 1443–1448 doi:10.1080/09540121.2015.1109583 [CrossRef] PMID:26608408
  • Mueses-Marín, H., Montaño, D., Galindo, J., Alvarado-Llano, B. & Martínez-Cajas, J. (2019). Psychometric properties and validity of the Center for Epidemiological Studies Depression Scale (CES-D) in a population attending an HIV clinic in Cali, Colombia. Biomedica, 39(1), 33–45 doi:10.7705/biomedica.v39i1.3843 [CrossRef] PMID:31021545
  • Nanni, M. G., Caruso, R., Mitchell, A. J., Meggiolaro, E. & Grassi, L. (2015). Depression in HIV infected patients: A review. Current Psychiatry Reports, 17(1), 530 doi:10.1007/s11920-014-0530-4 [CrossRef] PMID:25413636
  • Nasreddine, Z. S., Phillips, N. A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., Cummings, J. L. & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699 doi:10.1111/j.1532-5415.2005.53221.x [CrossRef] PMID:15817019
  • Overton, E. T., Azad, T. D., Parker, N., Demarco Shaw, D., Frain, J., Spitz, T., Westerhaus, E., Paul, R., Clifford, D. B. & Ances, B. M. (2013). The Alzheimer's Disease-8 and Montreal Cognitive Assessment as screening tools for neurocognitive impairment in HIV-infected persons. Journal of Neurovirology, 19(1), 109–116 doi:10.1007/s13365-012-0147-5 [CrossRef] PMID:23345074
  • Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 17 doi:10.1177/014662167700100306 [CrossRef]
  • Roberson, D. W. (2018). Meeting the HIV prevention needs of older adults. The Journal of the Association of Nurses in AIDS Care, 29(1), 126–129 doi:10.1016/j.jana.2017.08.004 [CrossRef] PMID:28893548
  • Rodriguez-Penney, A. T., Iudicello, J. E., Riggs, P. K., Doyle, K., Ellis, R. J., Letendre, S. L., Grant, I. & Woods, S. P. (2013). Comorbidities in persons infected with HIV: Increased burden with older age and negative effects on health-related quality of life. AIDS Patient Care and STDs, 27(1), 5–16 doi:10.1089/apc.2012.0329 [CrossRef] PMID:23305257
  • Rubin, L. H. & Maki, P. M. (2019). HIV, depression, and cognitive impairment in the era of effective antiretroviral therapy. Current HIV/AIDS Reports, 16(1), 82–95 doi:10.1007/s11904-019-00421-0 [CrossRef] PMID:30661180
  • Ruzicka, D. J., Imai, K., Takahashi, K. & Naito, T. (2019). Greater burden of chronic comorbidities and co-medications among people living with HIV versus people without HIV in Japan: A hospital claims database study. Journal of Infection and Chemotherapy, 25(2), 89–95 doi:10.1016/j.jiac.2018.10.006 [CrossRef] PMID:30396821
  • Schouten, J., Su, T., Wit, F. W., Kootstra, N. A., Caan, M. W., Geurtsen, G. J., Schmand, B. A., Stolte, I. G., Prins, M., Majoie, C. B., Portegies, P. & Reiss, P. (2016). Determinants of reduced cognitive performance in HIV-1-infected middle-aged men on combination antiretroviral therapy. AIDS (London, England), 30(7), 1027–1038 doi:10.1097/QAD.0000000000001017 [CrossRef] PMID:26752277
  • Serrão, R., Piñero, C., Velez, J., Coutinho, D., Maltez, F., Lino, S., Sarmento E Castro, R., Tavares, A. P., Pacheco, P., Lopes, M. J., Mansinho, K., Miranda, A. C., Neves, I., Correia de Abreau, R., Almeida, J. & Pássaro, L. (2019). Non-AIDS-related comorbidities in people living with HIV-1 aged 50 years and older: The AGING POSITIVE study. International Journal of Infectious Diseases, 79, 94–100 doi:10.1016/j.ijid.2018.10.011 [CrossRef] PMID:30529370
  • Smith, M. C. & Wrobel, J. P. (2014). Epidemiology and clinical impact of major comorbidities in patients with COPD. International Journal of Chronic Obstructive Pulmonary Disease, 9, 871–888 doi:10.2147/COPD.S49621 [CrossRef] PMID:25210449
  • Thomas, A. J., Gallagher, P., Robinson, L. J., Porter, R. J., Young, A. H., Ferrier, I. N. & O'Brien, J. T. (2009). A comparison of neurocognitive impairment in younger and older adults with major depression. Psychological Medicine, 39(5), 725–733 doi:10.1017/S0033291708004042 [CrossRef] PMID:18667097
  • Thompson, M. A., Aberg, J. A., Hoy, J. F., Telenti, A., Benson, C., Cahn, P., Eron, J. J., Günthard, H. F., Hammer, S. M., Reiss, P., Richman, D. D., Rizzardini, G., Thomas, D. L., Jacobsen, D. M. & Volberding, P. A. (2012). Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel. Journal of the American Medical Association, 308(4), 387–402 doi:10.1001/jama.2012.7961 [CrossRef] PMID:22820792
  • Uthman, O. A., Magidson, J. F., Safren, S. A. & Nachega, J. B. (2014). Depression and adherence to antiretroviral therapy in low-, middle- and high-income countries: A systematic review and meta-analysis. Current HIV/AIDS Reports, 11(3), 291–307 doi:10.1007/s11904-014-0220-1 [CrossRef] PMID:25038748
  • Valcour, V., Yee, P., Williams, A. E., Shiramizu, B., Watters, M., Selnes, O., Paul, R., Shikuma, C. & Sacktor, N. (2006). Lowest ever CD4 lymphocyte count (CD4 nadir) as a predictor of current cognitive and neurological status in human immunodeficiency virus type 1 infection—The Hawaii Aging with HIV Cohort. Journal of Neurovirology, 12(5), 387–391 doi:10.1080/13550280600915339 [CrossRef] PMID:17065131
  • Wagner, G. J., Goggin, K., Remien, R. H., Rosen, M. I., Simoni, J., Bangsberg, D. R. & Liu, H. (2011). A closer look at depression and its relationship to HIV antiretroviral adherence. Annals of Behavioral Medicine, 42(3), 352–360 doi:10.1007/s12160-011-9295-8 [CrossRef] PMID:21818528
  • Ware, D., Palella, F. J. Jr.. , Chew, K. W., Friedman, M. R., D'Souza, G., Ho, K. & Plankey, M. (2018). Prevalence and trends of polypharmacy among HIV-positive and -negative men in the Multicenter AIDS Cohort Study from 2004 to 2016. PLoS One, 13(9), e0203890 doi:10.1371/journal.pone.0203890 [CrossRef] PMID:30204807
  • Webel, A. R., Sattar, A., Schreiner, N., Kinley, B., Moore, S. M. & Salata, R. A. (2016). The impact of mental wellness on HIV self-management. The Journal of the Association of Nurses in AIDS Care, 27(4), 468–475 doi:10.1016/j.jana.2016.03.002 [CrossRef] PMID:27066751
  • Yah, C. S. (2017). Nurturing the continuum of HIV testing, treatment and prevention matrix cascade in reducing HIV transmission. Ethiopian Journal of Health Sciences, 27(6), 621–630 doi:10.4314/ejhs.v27i6.7 [CrossRef] PMID:29487471

Participant Demographics

VariableOlder Group (n = 65)Younger Group (n = 65)Total (N = 130)
n (%)
Identifies as male51 (78)44 (68)95 (73)
Identifies as female14 (22)21 (32)35 (27)
Undetectable viral load55 (86)44 (68)99 (76)
Alcohol (current/past use)58 (89)60 (92)118 (91)
Tobacco (current/past use)50 (77)49 (76)99 (76)
Drugs (current/past use)35 (54)44 (68)79 (61)
Mean (SD) (Range)
Age (years)56.2 (6.01) (50 to 76)37.7 (8.02) (20 to 49)46.9 (11.67) (20 to 76)
Years of education12.88 (2.66) (8 to 20)12.95 (2.43) (8 to 22)12.9 (2.54) (8 to 22)
Nadir CD4 count (cells/mm3)161.51 (147.67) (0 to 600)261.36 (218.15) (0 to 1,289)211.4 (192.20) (0 to 1,289)
Current CD4 count (cells/mm3)548.94 (290.87) (5 to 1,417)566.75 (317.85) (8 to 1,649)558 (303.60) (5 to 1,649)
Years since diagnosis17.8 (7.62) (5 to 32)13.68 (6.6) (3 to 30)15.74 (7.93) (3 to 32)
No. of medications8.75 (4.13) (1 to 22)5.83 (3.17) (1 to 14)7.29 (3.95) (1 to 22)
No. of comorbidities4.55 (2.61) (0 to 12)2.15 (1.78) (0 to 7)3.35 (2.54) (0 to 12)
CES-D scorea15.89 (1.28) (0 to 50)14.98 (10.17) (0 to 41)15.44 (11.52) (0 to 50)
MoCA scoreb23.62 (4.04) (13 to 30)24.85 (3.17) (16 to 30)24.23 (3.67) (13 to 30)

Depressive Symptoms: Model Summaries for Older and Younger Adults

Regression Model 1 (ANOVAa) Summary for Older Adults
RR2Adj. R2Fdfp Value
ModelSum of SquaresdfMean SquareFp Value
Regression Model 1 (ANOVAa) Summary for Younger Adults
RR2Adj. R2Fdfp Value
ModelSum of SquaresdfMean SquareFp Value

Correlations Among Variables: Older and Younger Adults

Older Adult MeasuresAlcoholTobaccoDrugsComorbiditiesNon-HIV MedsCognitive FunctionDepressive SymptomsMedication TestSelf-Efficacy
Non-HIV meds0.09−**
Cognitive function−0.200.25*0.080.01−0.06
Depressive symptoms−0.09−0.37**−0.27*0.180.18−0.29*
Medication test—0.100.28*0.000.02−0.060.59**−0.38**
Time on meds0.110.34**0.08−0.060.01−−0.12
Younger Adult MeasuresAlcoholTobaccoDrugsComorbiditiesNon-HIV MedsCognitive FunctionDepressive SymptomsMedication TestSelf-Efficacy
Non-HIV meds0.24*−0.02−0.070.80**
Cognitive function−0.190.01−0.06−0.13−0.17
Depressive symptoms−0.09−0.18−−0.11
Medication test−−0.09−0.110.59**−0.25*
Time on meds0.19−**0.41**−0.16−0.05−0.13−0.17

Dr. Frain is Associate Professor, Goldfarb School of Nursing at Barnes-Jewish College, St. Louis, Missouri.

The author has disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Judy Frain, PhD, RN, Associate Professor, Goldfarb School of Nursing at Barnes-Jewish College, 4483 Duncan Avenue, St. Louis, MO 63110; e-mail:

Received: August 30, 2019
Accepted: November 11, 2019


Sign up to receive

Journal E-contents