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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: AIDS Behav. 2021 Sep 22;26(4):1163–1172. doi: 10.1007/s10461-021-03469-5

Subjective Cognitive Complaints: Predictors and Health Outcomes in People Living with HIV

Vaughn E Bryant 1,2,4, Robert A Fieo 1, Andrew J Fiore 1, Veronica L Richards 1, Eric C Porges 2, Renessa Williams 3, Huiyin Lu 1, Zhi Zhou 1, Robert L Cook 1
PMCID: PMC8938306  NIHMSID: NIHMS1784675  PMID: 34550502

Abstract

There is a paucity of research on the prevalence of subjective cognitive complaints in people living with human immunodeficiency virus, along with the predictors and outcomes related to these complaints. We assessed demographics, substance use and psychiatric predictors, and HIV-related outcomes associated with subjective cognitive complaint items from the Cognitive Difficulties Scale. The sample consisted of 889 people living with HIV in the survey-based Florida Cohort. Results of multivariable regression models indicated that age (45–54), hazardous alcohol consumption, more frequent marijuana use and psychiatric symptoms (depression, anxiety, PTSD) were significant predictors of subjective cognitive complaints. Subjective cognitive complaints were associated with lower adherence to antiretroviral therapy in bivariate analyses, but this relationship was no longer significant after controlling for depression, race, alcohol and drug use. Further research into the relationship between depressive and subjective cognitive complaints may provide additional avenues for intervention.

Keywords: HIV, Subjective cognitive complaints, Depression, ART, Adherence

Introduction

Subjective cognitive complaints (SCC) can be defined as “cognitive complaints of people who may or may not have deficits in objective testing [1].” There is a paucity of research on SCC and their utility as screening tools across medical settings. While some researchers have questioned the utility of SCC [2], as a group, those with cognitive complaints tend to perform worse on neuropsychological tests than the general population [3]. Individuals with SCC are at increased risk of dementia [4, 5] and SCC may be an early clinical marker that precedes detectable objective cognitive impairment. There is evidence to suggest that SCC are associated with biomarkers in the earliest stages of Alzheimer’s, namely, beta amyloid [6, 7]. A dual-coupling model (pathway of change from objective impairment to subjective complaints, or subjective to objective cognition impairment) has also been reported [8]. Finally, SCC have been incorporated into the diagnostic criteria of mild cognitive impairment (MCI)—the hypothesized intermediate stage between normal and pathological cognitive aging.

Early studies of SCC [9] primarily focused on memory complaints in geriatric populations. However, their utility is no less relevant in the area of human immunodeficiency virus (HIV) research. Cognitive disorders are relatively common among people living with HIV (PLWH), with some estimates suggesting that nearly half of PLWH exhibit evidence of cognitive impairment [10]. These estimates will likely be compounded by the success of antiretroviral therapy (ART) that has served to extend the life expectancy of PLWH, thereby introducing a cognitive-aging element to HIV infection. The prevalence of cognitive impairment in HIV has prompted the use of terminology specific to HIV and cognitive impairment. HIV-associated neurocognitive disorders (HAND) reflect a staging of neurocognitive impairment that ranges from asymptomatic neurocognitive impairment (ANI), to mild neurocognitive disorder (MND), to the most severe, HIV associated dementia (HAD; [11]).

Cognitive symptoms in HIV are often not monitored in a uniform manner, and formal neurocognitive testing is often not practical in primary care clinical settings. There is no widely accepted screening tool for cognitive impairment in this population and impairment is often only detected at more severe levels when daily activities are disrupted [12]. As the HIV-positive population ages, structured subjective cognitive assessment may be beneficial in identifying the early signs of cognitive impairment, assessing who is most at risk, and to broaden the window for earlier interventions. However, it is also important to determine if other factors are associated with SCC beyond objective cognition. For example, previous studies suggest that mood symptoms are associated with SCC [13-17] and objective cognition [18, 19] in PLWH. Furthermore, other factors that affect cognitive function in this population are important to consider, including substance use [20-23] and demographic factors [24, 25]. Finally, it is unclear to what extent SCC relate to critical HIV health-related outcomes, such as ART adherence. Thus, ART adherence is a major outcome of interest, given its functional significance. This study aims to identify: (1) the frequency of SCC in a large cohort of PLWH, (2) the association of psychiatric symptoms, substance use, and demographics with SCC and (3) the association of psychiatric symptoms and SCC with HIV-health-related outcomes. We hypothesize that greater frequency of SCC will be associated with worse ART adherence among a sample of PLWH.

Methods

Participants

The sample consisted of 932 PLWH enrolled in the survey-based Florida Cohort project, which assessed demographics, substance use, health behaviors, and HIV-related outcomes [26]. The Florida Cohort was conducted in accordance with policies of all site Institutional Review Boards. Participants who did not complete the full set of SCC questions were excluded. Additionally, participants who were diagnosed within 1 year of this study were excluded because of the potential confounding acute effects related to HIV viral load, whether or not they had been treated with ART and other psychosocial factors, such as stigma and more acute distress. The focus of this study was on individuals with more chronic HIV infection. The final sample size used in this study included 889 PLWH. Data from this study sample were collected from 2014 to 2018.

Measures

Subjective Cognitive Complaints

SCC score was measured with a commonly used general screening question, ‘Are you currently experiencing thinking or memory problems?’ [THINKING & MEMORY], and four items from the Cognitive Difficulties Scale (CDS, [27]). The CDS items included: ‘I put down things (glasses, keys, wallet, papers) and have trouble finding them’ [TROUBLE FINDING THINGS]; ‘I forget right away what people say to me’ [FORGET WHAT IS SAID]; ‘When walking or riding, I forget how I had gotten from one place to another’ [HOW I GOT SOMEWHERE], and ‘I forget to pay bills, record checks, or mail letters’ [FORGOT TO PAY BILLS]. All five questions required the participant to rate how often they currently experience cognitive difficulties in everyday life using a 5-point scale (from “never” = 0 to “very often” = 4). These 5 SCC items were summed to form a total score, ranged 0 to 20; A Cronbach coefficient α (α = 0.85) was calculated to measure the internal consistency of these five cognitive items. Exploratory factor analysis using principal components analysis (PCA) was used to identify whether the brief SCC scale was sufficiently unidimensional or contained subscales, with approximation of simple structure as the criterion for accepting a factor solution. In terms of extraction, we selected Eigenvalues greater than 1.0. PCA analysis, revealing a unidimensional questionnaire for subjective memory complaints, with all items correlating with the principal component at ≥ 0.75. The loadings were as follows: THINKING & MEMORY = 0.81; TROUBLE FINDING THINGS = 0.78; FORGET WHAT IS SAID = 0.83; HOW I GOT SOMEWHERE = 0.75; and FORGOT TO PAY BILLS = 0.75. This latent unidimensional construct appears most closely aligned with subjective concerns related to memory, accounting for 62% of the total variance in subjective cognition.

Covariates

Demographic variables, including age, sex at birth, self-identified race, and education, were included in the analyses. A three-level variable for CD4 count was created from the most recent CD4 tests in the past year. Depression and anxiety symptoms were assessed using the Personal Health Questionnaire Depression Scale (PHQ-8; [28]) and the Generalized Anxiety Disorder 7-item scale (GAD-7; [29]), respectively. Both depression and anxiety scores were categorized into one of four groups: none/minimal (1 to 4 points), mild (5 to 9 points), moderate (10 to 14 points), or moderately severe/severe (15 or more points) [29]. Finally, posttraumatic stress disorder symptoms were measured through the Primary Care PTSD Screen (PC-PTSD; [30]). Previous reporting suggests that endorsing ≥ 3 symptoms may be clinically relevant.

Hazardous drinking, marijuana use, and other substance use were also assessed. Hazardous drinking was assessed using the Alcohol Use Disorders Identification Test-Concise (AUDIT-C) [31], which assesses the frequency of drinking, the average number of drinks per occasion, and the frequency of exceeding hazardous drinking threshold, which depends upon sex. For women, hazardous drinking was defined as consuming more than 7 drinks per week in the past year, or consuming 4 or more drinks per occasion in the past month [32-34]. For men, hazardous drinking was defined as consuming more than 14 drinks per week in past year or consuming 5 or more drinks per occasion in past month. Non-hazardous drinking was assigned to those who drank but did not qualify for hazardous drinking. Participants were considered non-drinkers if they reported no past-year drinking or had never had alcoholic drinks before.

Participants drug use was measured using a medical history questionnaire developed by the study investigators and other team members [26]. Individuals were considered regular users of marijuana if they reported use 4 or more times per week in the past 3 months, which is a definition of regular use that has been used in previous literature and may be more strongly associated with health outcomes [35, 36]. If a participant reported marijuana use ranging from once in the past 3 months to once a month but less than 4 times a week, then they were considered an occasional user. Participants were defined as non-users if they did not use marijuana in the past 3 months or had never used marijuana before. Marijuana use was assigned to be “unreported” if the participants skipped or refused to answer marijuana use questions. Other drug use was a binary variable that represented using other illicit drugs besides marijuana in the past 12 months, which included self-reported crack, cocaine, ecstasy, sedatives, opioids, and/or injection heroin or stimulant use.

Outcome Variables

The main outcome of interest was ART adherence. ART adherence was calculated using self-reported data from the baseline survey and categorized as < 95% adherence or ≥ 95% adherence, based on number of days a participant reported missing a dose in the past 30 days. Retention in care was defined as two or more documented viral load or CD4 labs, HIV medical visits, or HIV prescriptions at least 3 months apart, in the past 12 months.

Data Analysis

Non-parametric Kruskal–Wallis tests were employed to compare differences in group mean SCC score by potential risk factors. Additionally, a multivariate generalized linear model with Poisson family and identity link was used to predict SCC score using socio-demographic characteristics and health risk factors. Variable selection was conducted by checking the model’s goodness of fit and diagnosis of the link function. Finally, logistic regression was used to model binary HIV health outcomes using socio-demographic characteristics and health risk factors. The regression coefficients and corresponding 95% confidence intervals were calculated from the maximum likelihood estimates from the SAS output. Coefficients and confidence intervals were exponentiated and displayed as odds ratios. All data analyses were conducted in SAS 9.4.

Results

Demographics

A majority of participants were between the ages of 45–54 (40%) years-old, male (67%), and identified as Black (55%). Education was distributed relatively evenly, with about one-third of individuals identifying with each of the three categories: less than high school education, high school diploma or equivalent, or greater than a high school education. Almost half of the participants reported that they were unable to work or disabled. Substance use for this population appeared relatively common, with one-third of participants reporting drug use beyond that of marijuana or alcohol. Similarly, 34% of subjects reported “hazardous drinking”, and finally, 13% of participants reported regular marijuana use. With regard to psychiatric symptoms: one-third of subjects reported depressive symptoms beyond the range of mild; symptoms of anxiety, beyond mild, were endorsed for 30% of the sample, and finally, 28% of the sample reported PTSD symptoms above the clinical cut-off criteria.

Factors Associated with Subjective Cognitive Complaints

The frequencies of SCC items are displayed in Table 1. Item-level frequencies indicate that, TROUBLE FINDING THINGS was endorsed as the most problematic (76% of subjects indicated ‘some’ difficulty), followed by THINKING & MEMORY (67%), FORGET WHAT IS SAID (65%), FORGOT TO PAY BILLS (41%), and HOW I GOT SOMEWHERE (32%). SCC mean score differences by group (demographics; psychiatric; drug use; HIV health outcomes) are presented in Table 2. Respondents age 45–54 scored about one point higher in SCC (higher scores = more impairment) than all other ages. Respondents who hazardously drank had a significantly higher SCC score than those who reported having no drinks in the past year or no hazardous drinking. Those with no drinks in the past year and those with no hazardous drinking experienced similar levels of SCC. Respondents that regularly use marijuana experienced a significantly higher memory score than those who do not use regularly, those who do not use marijuana, or those did not respond. Finally, respondents who reported other drug use had a significantly higher memory score than those who did not report ‘other drug use’.

Table 1.

Frequency of subjective cognitive complaints

Category Frequency Percent
1. I am currently experiencing thinking or memory problems Never 247 32.9
Rarely 165 21.9
Sometimes 219 29.1
Often 64 8.5
Very often 57 7.6
Binary (never/some) 247/505 67a
2. I put down things (glasses, keys, wallet, purse, papers) and have trouble finding them Never 179 23.80
Rarely 186 24.73
Sometimes 238 31.65
Often 77 10.24
Very often 72 9.57
Binary (never/some) 179/573 76a
3. I forget right away what people say to me Never 264 35.11
Rarely 189 25.13
Sometimes 194 25.80
Often 62 8.24
Very often 43 5.72
Binary (never/some) 264/488 65a
4. When walking or driving, I forget how I've gotten from one place to another Never 515 68.48
Rarely 124 16.49
Sometimes 77 10.24
Often 18 2.39
Very often 18 2.39
Binary (never/some) 515/237 32a
5. I forget to pay bills, record checks, or mail letters Never 443 58.91
Rarely 144 19.15
Sometimes 117 15.56
Often 26 3.46
Very often 22 2.93
Binary (never/some) 443/309 41a
a

Those endorsing at least some difficulty

Table 2.

Relationships between demographic factors, substance use, and mental health with subjective cognitive complaints (SCC) in a sample of 889 persons with HIV infection

Characteristics Levels Frequency Mean SCCa
score
SD P valueb
Age 18–34 153 4.86 4.2 0.01
35–44 172 4.84 4.2
45–54 349 6.07 4.8
≥ 55 208 5.13 3.9
Gender Male 594 5.25 4.5 0.08
Female 288 5.69 4.3
Race Not Hispanic, White 192 5.88 4.4 0.16
Hispanic 173 5.72 4.6
Not Hispanic, Black 484 5.09 4.3
Not Hispanic, other 33 5.42 4.9
Education < High school 298 5.57 4.4 0.51
High school diploma 266 5.56 4.7
> High school 317 5.10 4.1
Employment Unemployed 238 5.61 4.4 0.005
Unable to work/disabled 408 5.75 4.5
Employed 215 4.39 3.9
Hazard drink Not hazardous drinking 297 5.09 4.4 0.009
Hazard drinking 301 6.02 4.3
No drinks in the past year 238 5.10 4.5
Marijuana Unreported 96 5.28 4.1 0.006
None 494 5.01 4.4
Occasionally use 173 5.98 4.4
Regular use 119 6.24 4.7
Other drug Yes 292 6.35 4.3 < 0.001
No 532 4.83 4.4
Depression 0–4, None-minimal 296 2.60 2.5 < 0.001
5–9, Mild 268 5.07 3.8
10–14, Moderate 131 6.73 3.8
> 15, Moderately severe or severe 155 9.99 4.5
Anxiety 0–4, None-minimal 366 2.96 3.0 < 0.001
5–9, Mild 219 5.42 3.8
10–14, Moderate 133 7.26 3.6
≥15, Severe 129 10.2 4.7
PTSDc No 609 4.33 3.9 < 0.001
Yes 245 8.07 4.6
CD4 0–199 112 5.26 4.8 0.6783
200–499 286 5.46 4.3
≥ 500 420 5.29 4.2
ARTd adherence ≥ 95% 516 5.09 4.5 < 0.001
< 95% 235 6.19 4.3

SD standard deviation

a

SSC total score from 0 to 20.

b

P value reflects Kruskal–Wallis.

c

Post-traumatic stress disorder.

d

Anti-retro-viral therapy

Depression, anxiety, and PTSD symptoms were all highly significantly associated with higher SCC scores. A stepwise increase can be observed between ordered categories of depression, anxiety, and PTSD and mean SCC score. Those with severe depression had a SCC score almost four times higher than those with either no or minimal depression; similar to the trend for anxiety and SCC score. Those with PTSD symptoms above the cut-off scored almost twice as high in their SCC score than those below the cut-off.

Multivariable Analysis

In multivariable analyses, age, substance use, and mental health were all significant predictors of continuous SCC score (Table 3). Specifically, participants age 45–54 and 55 or older saw a SCC score increase of about one point when compared to those ages 18–34 (β = 1.21, p < 0.001 and β = 1.6, p < 0.0001, respectively). Other drug use was found to be significantly associated with approximately a half-point increase in SCC score (β = 0.64, p < 0.05), though hazardous drinking and marijuana use was not. Individuals with any level of depression (mild to severe) indicated higher SCC score compared to those with no or minimal (0 to 4; maximum recorded = 24) depressive symptoms; beta coefficients ranged from 2.51 (mild) to 7.31 (severe), p < 0.001. No differences in SCC score were detected in gender, race, and education strata.

Table 3.

Factors associated with increased subjective cognitive complaints among persons with HIV infection: multivariate analysis

Predictor β SE Wald 95% CI Chi-Sq
P-value
Age
  18–34 Referent
  35–44 0.254 0.3464 0.424 0.933 0.463
  45–54 1.206 0.3405 0.538 1.873 0.0004
  ≥ 55 1.557 0.3756 0.821 2.293 <.0001
Other drug
  No Referent
  Yes 0.641 0.2899 0.073 1.209 0.027
Depression
  0–4, None-minimal Referent
  5–9, Mild 2.512 0.2909 1.942 3.082 < .0001
  10–14, Moderate 4.255 0.4273 3.418 5.093 < .0001
  ≥ 15, Moderately severe or severe 7.308 0.4700 6.386 8.229 < .0001

Table reflects all significant relationships with subjective cognitive complaints

Factors Associated with Poor HIV Outcomes

Adherence

We used binary logistic regression to assess the impact SCC on HIV related outcomes (Table 4). SCCs were significantly associated with ART adherence (≥ 95%), when adjusting for covariates including race, hazardous drinking, and marijuana use. Such that, one unit increase in SCC symptoms resulted in 5% lower odds of adherence to ART (OR 0.95, p < 0.01, CI 0.91–0.98). However, SCCs were no longer statistically significant after the inclusion of depressive symptoms. Those with no to minimal symptoms of depression had more than 2 times greater odds of ART adherence (OR2.1, p < 0.01, CI 1.12–4.29), compared to those with greater depression scores. In the final model, race/ethnicity, hazardous drinking, marijuana use, and depression were significantly associated with ART adherence. Specifically, being Hispanic, drinking hazardously, using marijuana regularly, and having moderately severe or severe depression were associated with decreased ART adherence.

Table 4.

Factors associated with ART adherence (95% or greater) among 889 persons living with HIV, multivariate logistic regression

Characteristic ORa point
estimate
Wald 95% CI

SCC score (continuous) 0.99 0.94 1.04
18–34 vs. ≥ 55 1.05 0.57 1.94
35–44 vs. ≥ 55 0.75 0.42 1.31
45–54 vs. ≥ 55 1.19 0.73 1.95
Female vs. male 0.86 0.57 1.31
Hispanic vs. not Hispanic, Whiteb 0.55 0.31 0.98
Not Hispanic, Black vs. not Hispanic, White 0.66 0.40 1.06
Not Hispanic, other vs. not Hispanic, White 0.90 0.31 2.62
< High school vs. > high school 0.76 0.48 1.21
High school diploma or equivalent vs. > high school 0.91 0.57 1.44
Not hazardous drinking vs. no drinks in the past year 0.65 0.39 1.08
Hazardous drinking vs. no drinks in the past yearb 0.50 0.30 0.83
Occasionally marijuana use vs. none 0.85 0.53 1.35
Regular use marijuana use vs. noneb 0.58 0.34 0.96
No other drug use vs. other drug use 1.29 0.87 1.92
Depression 1–4, none-minimal vs. ≥ 15, moderatelyc severe or severe 2.2 1.12 4.29
Depression 5–9, mild vs. ≥ 15, moderately severe or severe 0.94 0.52 1.68
Depression 10–14, moderate vs. ≥ 15, moderately severe or severe 1.2 0.63 2.26
a

Odds ratio.

b

p < .05

c

p < .01

Retention in HIV Care

Female sex was significantly associated with increased retention in HIV care, nearly 2.5 greater odds (OR 2.3, p < 0.01, CI 1.5–3.7), and ages 35—44 were associated with decreased retention. Otherwise, no factors were associated with retention in HIV care.

Discussion

The primary aims of this manuscript were to determine the predictors of SCC in PLWH and to determine the utility of subjective cognition in determining risk for poor HIV-associated health outcomes, which included ART adherence and HIV care retention. SCC were significantly associated with a number of predictors including: age, drug and alcohol use, and psychiatric symptoms (depression, anxiety, PTSD). The finding related to alcohol use is consistent with a previous study examining problematic alcohol use and subjective memory complaints in PLWH, which suggests that higher levels of hazardous alcohol use is associated with poorer self-reported measures of every day memory function, with a particular emphasis on increased forgetting of events, and poorer prospective [37]. The personality trait of neuroticism, a risk factor for anxiety, has often been shown to impact reports of SCC. The concern is that the catastrophizing associated with neuroticism and symptoms of anxiety bias SCC scores, thus minimizing their utility as a screener for objective cognitive impairment. For instance, while controlling for age, gender, objective cognition [38], depression and anxiety accounted for 38% of the variance in SCC in a sample of community-dwelling older adults.

The association with age is consistent with the HAND model of cognitive-aging in PLWH, which can be viewed as the product of two important factors: a growing population of PLWH that are subject to age-associated neuropathology present in the general population, but also the unique deleterious impact that HIV can have on the central nervous system during early stages of infection and the persistent inflammation that accompanies HIV infection [39]. The success of antiretroviral therapy has allowed many PLWH to have nearly normal life expectancies, thus expanding the number of individuals with age-associated neuropathology.

With regard to psychiatric symptoms, while the prevalence of SCC seems to parallel cognitive aging literature, the prevalence of depressive symptoms appears to exceed estimates of late-life depression. While the association of depressive symptoms with SCC (as well as personality factors) in cognitive aging is well-established, the presentation in PLWH appears noteworthy in terms of the severity and prevalence of depressive symptoms. Lifetime prevalence of depression in PLWH is an estimated 42% [40]. In our sample, the prevalence of moderate depression was 15% and moderately severe or severe was 18%. Due to this high prevalence rate in PLWH, future research may be well served to further examine the interaction of depressive symptoms and cognition. For instance, brief SCC measures may be used as practical monitoring tools, but also employed to provide exploratory evidence as to which cognitive domains are most impacted by depressive symptoms. Blair et al. [41], assessed a subsample of subjects (memory clinic) with depressive symptoms, half of which did not meet criteria for a neurodegenerative disease. Analysis of cognitive subdomains (performance of neuropsychological tests) revealed a pattern of executive/attentional dysfunction as the most distinctive cognitive profile in those with depressive symptoms.

Limitations

Contrasting results from previous literature relating to our SCC measure was somewhat challenging, as the selected content reflects a subset of items from the Cognitive Difficulties Scale [27]. However, some more generalized comparisons may still be informative. One item from the Zlatar Subjective Cognitive Decline scale [17], ‘misplacing belongings’ was similar to an item from the current study, namely, TROUBLE FINDING THINGS. The Zlatar et al. sample [42] was comprised of a clinic-based sample of older adults with cognitive impairment. In this study, 55% of subjects endorsed at least some difficulty with this memory task, and for the current study, 76% off subjects some endorsed difficulty.

Other limitations include the inability to determine causality of the relationship between depression, age, and drug use as predictors of memory complaints. Our subset of items taken from Cognitive Difficulties Scale, and the general question, THINKING & MEMORY, resulted in a rather novel but brief SCC questionnaire. This made prevalence estimates across previous investigations difficult. We did not collect data on objective cognitive tests and therefore cannot make assertions about the relationship between these memory complaints and objective cognitive complaints. Despite these limitations, our sample captured statewide HIV surveillance data from varying gender, race, and geographic locations. Additionally, disentangling the associations between depression, SCC, and the relationship to HIV-related health outcomes, is made more difficult by the fact that depression questionnaires often incorporate items with subjective cognition. For example, ‘Concentration difficulties’ (MADRS scale), ‘Keeping my mind on what I was doing’ (CES-D scale) and the GDS question, ‘Do you feel you have more problems with memory than most’ [43]. The PHQ-8 depression scale used in this study is no different, with one item related to psychomotor speed and a second related to concentration—‘Trouble concentrating on things, such as reading the newspaper or watching television.’ Finally, despite being sufficiently unidimensional, a minority of the variance in the SCC scale is likely related to cognitive constructs beyond memory (e.g., orientation and attention); any error related to these competing domains are likely amplified by the brevity of our SCC, thus reducing power in multivariant analysis. However, the addition of a few well-targeted items may improve the performance of our particular SCC measure.

Conclusion and Implications

Although SCC impact people living with HIV, these data provide limited evidence that SCC are associated with adverse HIV outcomes. The high frequency of SCC in PLWH suggests that the topic warrants continued investigation. Our measure only included five items, and it is possible that some domains may be more effective than others in predicting health outcomes (e.g., attention related). Examining the longitudinal association between subjective cognitive complaint types and mood on HIV-related health outcomes should improve clarity. Future investigations should particularly surveil the impact of SCC on additional HIV outcomes. These results provide relevant socio-demographic characteristics, substance use factors, and mental health systems that can help in monitoring SCC in broad populations of PLWH. Finally, in selecting brief easy to administer tools for monitoring cognitive and depressive symptoms in PLWH, researchers and clinicians should take care to avoid over-lapping content in SCC and depression metrics, as well as exploring subjective cognitive complaint measures that tap separable cognitive domains. Additionally, clinicians may tailor interventions accordingly depending upon the factors causing SCC. For example, motivational interviewing and cognitive behavioral therapy have demonstrated efficacy for reducing substance use and mental health symptoms [44].

Funding

National Institute on Alcohol Abuse and Alcoholism (NIAAA; U24AA022002; Recipient-Cook), National Institute on Alcohol Abuse and Alcoholism (NIAAA; T32AA025877; Recipient – Cook), National Institute on Alcohol Abuse and Alcoholism (NIAAA; F31AA024060; Recipient – Bryant), National Institute on Alcohol Abuse and Alcoholism (NIAAA; K01AA025306; Recipient – Porges).

Footnotes

Conflict of interest The authors declare that they have no conflicts of interest.

Ethics Approval All procedures performed in studies were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent Informed consent was obtained from all individual participants in the study.

Consent for Publication The authors affirm that human research participants provided informed consent for publication of their deidentified data.

Data Availability

The authors confirm that the data supporting the findings of this study are available within the article.

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