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. Author manuscript; available in PMC: 2026 Mar 1.
Published in final edited form as: AIDS Care. 2025 Jan 23;37(3):396–409. doi: 10.1080/09540121.2025.2454340

A longitudinal analysis of neurocognitive profiles in South African women with HIV

Alberta Van der Watt 1,*, Georgina Spies 2, Soraya Seedat 3
PMCID: PMC11922652  NIHMSID: NIHMS2053165  PMID: 39847687

Abstract

HIV and the consequences of HIV-associated neurocognitive disorders (HAND) including limited activities of daily living disproportionally affect South African women. Longitudinal neurocognitive data on women with HIV with high trauma exposure are limited. We tracked longitudinal neurocognitive profiles of South African women with HIV (baseline n=140) compared to women without HIV (baseline n=156). We determined if lifetime and childhood trauma exposure and posttraumatic stress symptoms (PTSS) were significantly correlated with global deficit scores (GDS). We assessed neurocognitive performance at baseline, 1-year, and 5-years. We used mixed linear models to determine between-group differences and neurocognitive profiles over time. We used Pearson’s correlations to determine correlations with GDS. There were no significant between-group differences in GDS. Both groups showed a significant decline in GDS (i.e., improved cognition) between baseline and 1-year follow-up (p<0.01), but no significant change between 1-year and 5-year follow-up. There were significant group differences in learning (p=0.02) and attention/working memory (p=0.01) at baseline, with HIV+ status associated with higher deficit scores. Childhood neglect was correlated with baseline GDS among women with HIV. GDS was not significantly correlated with lifetime trauma exposure or PTSS. Our results support the use of antiretroviral treatment to improve and/or maintain neurocognition in women with HIV. Future research should focus on the specific types of trauma exposure, specifically neglect, and its association with HAND.

Keywords: AIDS, Good health and well-being, HAND, HNRC Battery, LMIC

Introduction

Sub-Saharan Africa, including South Africa, is the region most affected by the Human Immunodeficiency Virus (HIV) pandemic (UNAIDS, 2019). In South Africa, approximately 7.4 million people (15 years and over) have HIV with most (approximately 4.8 million, 64.86%) being women (UNAIDS, 2022).

HIV enters the central nervous system (CNS) by crossing the blood-brain barrier (Daneman & Prat, 2015), causing neurological injury and a progressive decline in neurocognitive function (Cysique & Brew, 2019). HIV-associated neurocognitive disorders (HAND) are prevalent among people with HIV (PWH) (see Wei et al., 2020 for review). Indeed, among South African PWH the prevalence of HAND ranges from 23.5% (Joska et al., 2010) to 80% (Robbins et al., 2011). HAND mostly affects neurocognitive domains subserved by sub-cortical brain regions (Gawron et al., 2019; Guha et al., 2016; Nichols et al., 2019) including executive functions (Heaton et al., 2011), attention (Maki et al., 2015; Su et al., 2015), concentration and memory (Smail & Brew, 2018), information processing and psychomotor speed (Kanmogne et al., 2020), language (Hoare et al., 2016), verbal learning (Maki et al., 2015), and sensory perception. Given the cognitive domains affected by HAND, the negative impact on quality of life and functioning in PWH is unsurprising (see Alford & Vera, 2018 for review). Even milder cases of neurocognitive impairment (e.g., asymptomatic neurocognitive impairment [ANI] and HIV-associated mild neurocognitive disorder [MND)]), can still negatively impact people’s daily lives including health-related decisions (e.g., treatment adherence (Albert et al., 1999; Casaletto et al., 2017)), finding and retaining employment (Casaletto et al., 2017), and maintaining healthy interpersonal relationships (Terpstra et al., 2018; Tozzi et al., 2015).

In South Africa, HIV and its negative sequelae disproportionately affect women. Spies and colleagues (2017) found a significant combined effect of HIV and childhood trauma exposure on neurocognitive decline in executive and verbal fluency. A systematic review of 15 studies (of which five were conducted in South Africa) also highlighted that potential traumatic events, stressful life events, and posttraumatic stress disorder (PTSD) are significant risk factors for neurocognitive impairment among PWH (Spies et al., 2020).

Despite the high prevalence of HIV, HAND, and trauma exposure among South African women (UNAIDS, 2022) and the associated negative impact on daily life and functioning (Alford & Vera, 2018; Kordovski et al., 2019), longitudinal studies are few (Nyamayaro et al., 2019). The lack of normative data in low- and middle-income countries (LMICs) (Nyamayaro et al., 2019) including South Africa (Deist et al., 2023) is an impediment (Deist et al., 2023).

We determined longitudinal neurocognitive profiles of women with HIV compared to women without HIV using recently developed South African norms (Deist et al., 2023) of the HIV Neurobehavioral Centre’s (HNRC) (HIV Neurobehavioral Research Centre, 2022) International Neurobehavioral Battery (HNRC Battery). We determined the correlation between neurocognitive function (using global deficit scores (GDS)) with PTSS and lifetime and childhood trauma exposure. Data were collected through an ongoing study investigating biological endophenotypes of HIV and childhood trauma in South African women with HIV (project ID: 2333) which was approved by the Health Research Ethics Committee at Stellenbosch University (N07/07/153).

Method

Sample and procedure

We purposively recruited a convenience sample of women with HIV and without HIV directly from the community or through community health care centres. Participants were eligible if they (i) had the ability to read and write in English, Afrikaans, or isiXhosa at 5th grade level; (ii) had no current/past history of schizophrenia, bipolar disorder, other psychotic disorders, or current seizure disorder of any cause; (iii) did not indicate current substance or alcohol abuse/dependence and/or use of psychotropic medication; (iv) had no history of prior significant head injury or CNS infections of neoplasms; (v) and had a negative hepatitis B/C status. ELISA confirmed Participants’ HIV status.

Following initial screening, participants underwent neuromedical, neurocognitive, and neuroimaging assessment on-site at Stellenbosch University (Tygerberg campus). We collected data at baseline (HIV−: n = 156; HIV+: n = 140), 1-year (HIV−: n = 77; HIV+: n = 73), and 5-year follow-up (HIV−: n = 55; HIV+: n = 30).

Participants received refreshments during data collection and were reimbursed for transport costs to and from the data collection site. Participants provided informed consent prior to participation.

Measures

At baseline, socio-demographic data and a battery of measures was completed, including the HNRC Battery, the Life Events Checklist (LEC) (Gray et al., 2004), the Childhood Trauma Questionnaire short form (CTQ-SF) (Bernstein & Fink, 1998), and the Davidson Trauma Scale (DTS) (Davidson et al., 1997). Measures were translated into isiXhosa and Afrikaans using forward- and back-translation techniques. Measures were completed in the participants’ language of choice.

HNRC Battery

At baseline, 1-year, and 5-year follow-up a trained research psychologist and research nurse administered the HNRC Battery. It was developed to asses HAND and distinguish between three classifications: (i) asymptomatic neurocognitive impairment (ANI); (ii) HIV-associated mild neurocognitive disorder (MND); or (iii) HIV-associated dementia (HAD) (HIV Neurobehavioral Research Centre, 2022). The HNRC Battery assesses learning, delayed recall, processing speed, attention/working memory, executive function, verbal fluency, and motor ability (Heaton et al., 2010). Test instructions and stimuli were adapted, where necessary, to fit the South African cultural context (see Spies et al., 2017 for details).

HAND was computed using newly generated South African norms (Deist et al., 2023). Neurocognitive tests’ raw scores were converted into scaled scores which were used to generate prediction equations using a multiple fractional polynomial (MFP) procedure. This procedure (Royston & Altman, 1994) includes entering demographic variables known to account for significant variance in raw scores (including age, education, ethnicity, and neurocognitive test language) into the regression equation. We used the adjusted MFP model to generate predicted test scores and calculated residual scores for each participant. The residual scores were converted to demographically corrected T scores, which were then converted to deficit scores as an estimate of the severity of neurocognitive impairment. The deficit scores were categorised as 0 (T score ≥ 40) = normal cognition; 1 (T score 35–39) = mild neurocognitive impairment; 2 (T score 30–34) = mild-to-moderate neurocognitive impairment; 3 (T score 25–29) = moderate neurocognitive impairment; 4 (T score 20–24) = moderate-to-severe NC impairment; and 5 (T score < 20) = severe neurocognitive impairment. A global deficit score (GDS) was determined by averaging the deficit scores across all tests. Participants with a GDS ≥ 0.50 designates having neurocognitive impairment.

Life Events Checklist

The LEC (Gray et al., 2004) is a reliable measure of exposure to potentially traumatic events to facilitate the diagnosis of posttraumatic stress disorder. Using a nominal scale (1 = happened to me, 2 = witnessed it, 3 = learned about it happening to someone close, 4 = part of my job, 5 = not sure/not applicable), participants indicated their experience of potentially traumatic events. Direct exposure to traumatic events (either experienced or witnessed) were scored and totalled, with higher total scores indicating more traumatic life experiences (Gray et al., 2004). The LEC has proven reliability in the South African setting (e.g., Van der Watt et al., 2023; Ventimiglia et al., 2020).

Childhood Trauma Questionnaire Short Form

The CTQ-SF (Bernstein & Fink, 1998) is a standardised, retrospective 28-item self-report inventory measuring the severity of different types of childhood trauma (emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect). Items were rated on a Likert-type scale ranging from 1=never true, to 5=very often true. Total scores for the subscales and full measure were summed, respectively. The CTQ-SF has proven reliability (α = 0.879) in the same sample population (Spies et al., 2017). In the present sample, the Cronbach’s alpha for the CTQ-SF was 0.91.

Davidson Trauma Scale

The DTS (Davidson et al., 1997) is a 17-items self-report measure of PTSS frequency and severity. Each item was rated on a 5-point Likert scale (0 to 4) to indicate the frequency (number of times experienced) and severity (perceived upsetting nature) of the symptom over the past week, respectively. Items were summed to indicate PTSS severity (range = 0 to 136). A cut-off score of 40 indicates significant PTSS. The DTS has proven internal consistency with α=0.99 (frequency) and α=0.98 (severity) (Davidson et al., 1997). In the present sample, the Cronbach’s alphas for the DTS were 0.98 (frequency), 0.98 (severity), and 0.99 (total).

Data analysis

Data were analysed using SPSS and Statistica. Descriptive statistics are provided. HAND was computed using recently generated South African norms (Deist et al., 2023). We considered potential covariates (LEC, CTQ-SF, and DTS scores) in all analyses. Statistical significance was set at p=0.05.

We compared the groups’ socio-demographic characteristics using chi-square tests (for categorical variables) and Analysis of Variance (ANOVA) for continuous variables. Significant between-group differences were included as potential covariates in further analyses.

We used mixed linear models (MLM) to determine participants’ neurocognitive profiles over time (using GDS and the domain-specific deficit scores), while controlling for age, education, marital status, total CTQ-SF, total LEC-5, and total DTS scores. MLM allowed for the use of all data points at each timepoint. Thus, we could include incomplete cases, increasing the usable sample size. We used LSD post-hoc analyses to determine between-group and within-group differences at varying time points. We corrected for multiple comparisons using Fisher LSD. Eta-square represented effect size.

Lastly, we used Pearson’s correlations to determine the correlation between LEC, CTQ-SF, and DTS and participants’ GDS scores at each time-point. This was informed by previously shown associations between trauma exposure and neurocognition (e.g., Kavanaugh et al., 2017; Spies et al., 2017), and the association between PTSS and neurocognition (e.g., Jacob et al., 2019; Jak et al., 2016).

Results

At baseline, women without HIV (HIV− group) were aged 18 to 50 years (mean=28.53, SD=8.41), while women with HIV (HIV+ group) were aged 21 to 46 years (mean=33.07, SD=6.79). There was a significant between-group difference in age (F(1)=25.80, p<0.01, ƞ2=0.08), and highest education obtained (p=0.03), with a strong trend towards a difference in home language (p=0.08), and marital status (p=0.06). While lifetime trauma exposure was similar between the groups (see Table 1), women with HIV had significantly higher CTQ-SF (p<0.01) and DTS (p=0.02) scores.

Table 1.

Baseline, 1-year follow-up, and 5-year follow-up sample characteristics and between-group differences

Variable BASELINE 1-YEAR FOLLOW-UP 5-YEAR FOLLOW-UP
HIV−
(n=156)		 HIV+
(n=140)		 p
(Cramer’s V)		 HIV−
(n=77)		 HIV+
(n=73)		 p
(Cramer’s V)		 HIV−
(n=55)		 HIV+
(n=30)		 p
(Cramer’s
V)
Socio-demographic data n(%) n(%) n(%) n(%) n(%) n(%)
Highest education obtained 0.03 (0.12) 0.03 (0.17) 0.89 (0.02)
    Grade 8 or less 13(8.3) 23(16.4) 5(6.5) 13(17.8) 5(9.1) 3(10.0)
    Grade 9 and above 143(91.7) 117(83.6) 72(93.5) 60(82.2) 50(90.9) 27(90.0)
Marital status 0.06 (0.11) 0.96 (0.004) 0.09 (0.19)
    Single 109(69.9) 111(79.3) 53(68.8) 50(68.5) 32(58.2) 23(76.7)
    Non-single 47(30.1) 29(20.7) 24(31.2) 23(31.5) 23(41.8) 7(23.3)
Home language 0.08 (0.15) 0.97 (0.003) 0.46 (0.08)
    English - 4(2.9) - - - -
    Afrikaans 2(1.3) 4(2.9) - - 1(1.8) -
    Xhosa 150(96.2) 131(93.6) 76(98.7) 72(98.6) 54(98.2) 30(100.0)
    Other 4(2.6) 1(0.7) 1(1.3) 1(1.4) - -
Household income 0.41 (0.05) 0.03 (0.18) 0.55 (0.07)
    Less than ZAR40 000 152(97.4) 134(95.7) 72(93.5) 73(100.0) 47(85.5) 27(90.0)
    More than ZAR40 000 4(2.6) 6(4.3) 5(6.5) - 8(14.5) 3(10.0)
Employment status 0.06 (0.11) 0.52 (0.05) 0.62 (0.05)
    Unemployed 119(76.3) 93(66.4) 50(64.9) 51(69.9) 36(65.5) 18(60.0)
    Employed 37(23.7) 47(33.6) 27(35.1) 22(30.1) 19(34.5) 12(40.0)
Handedness 0.42 (0.05) 0.01 (0.21) 0.06 (0.20)
    Left 10(6.4) 6(4.3) 9(11.7) 1(1.4) 6(10.9) -
    Right 146(93.6) 134(95.7) 68(88.3) 72(98.6) 49(89.1) 30(100.0)
Trauma exposure and PTSS
data		 M(SD) M(SD) p(ƞ2) M(SD) M(SD) p(ƞ2) M(SD) M(SD) p(ƞ2)
LEC Total 4.96(3.43) 5.29(3.21) 0.40(0.002) 4.22(3.05) 4.71(2.56) 0.29(0.01) 4.41(2.89) 5.31(2.99) 0.18(0.02)
CTQ-SF Total 38.87(14.59) 53.21(18.43) <0.01(0.16) - - - - - -
    Physical abuse 6.77(4.06) 9.91(5.93) <0.01(0.09)
    Sexual abuse 5.96(3.15) 8.00(5.26) <0.01(0.05)
    Emotional abuse 9.37(5.12) 12.12(6.30) <0.01(0.06)
    Emotional neglect 8.72(4.77) 12.46(5.09) <0.01(0.13)
    Physical neglect 8.06(3.33) 10.72(4.15) <0.01(0.11)
Davidson Trauma Scale Total 13.37(27.09) 21.59(33.29) 0.02(0.02) 13.33(20.25) 11.58(22.56) 0.62(0.002) 29.44(33.67) 44.50(40.07) 0.07(0.04)
    Frequency Total 6.71(13.51) 10.99(16.88) 0.02(0.02) 6.56(10.18) 6.00(12.02) 0.76(0.001) 14.69(16.81) 22.23(20.05) 0.07(0.03)
    Severity Total 6.65(13.66) 10.60(16.57) 0.03(0.02) 6.68(10.07) 5.58(11.06) 0.52(0.003) 14.75(16.87) 22.27(20.03) 0.07(0.04)
HIV clinical data M(SD) Range M(SD) Range M(SD) Range
    CD4 count NA 425.04(244.86) 25-1529 NA 490.37(277.09) 74-1264 NA 634.92(384.80) 60-1591
    Viral load 88130.13(338795.32) 0-320000 27228.99(66291.23) 0-370852 10454.33(33075.35) 1-156733
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Note. Cramer’s V ≤ 0.2 = weak, 0.2 < Cramer’s V ≥ 0.6 = moderate, Cramer’s V > 0.6 = strong; ƞ2 > 0.01 = small, ƞ2> 0.06 = medium, ƞ2> 0.14 = large; CTQ-SF = Childhood Trauma Questionnaire short form; LEC = Life Events Checklist; PTSS = Posttraumatic stress symptoms

For the HIV+ group, the mean CD4 lymphocyte count was 425.04 cells/mm3 (SD=244.86, range=25 – 1529 cells/mm3) and the mean viral load was 88 130.13 copies/mL (SD=338 795.32, range=0-320 000 copies/mL), with 20.6% having a viral load lower than the detectable level of 40 copies/mL at baseline. Most participants (77.6%) had Clade C subtype HIV. Fifty-eight participants (41.4%) reported antiretroviral (ARV) use at baseline, 56 (76.7%) at 1-year, and 28 (93.3%) at 5-year follow-up.

Neurocognition

There was no significant between-group difference in GDS at baseline (p=0.24, see Table 2). Post-hoc analysis indicated the greatest between-group difference in GDS at 5-year follow-up (p=0.06), with HIV+ status associated with a higher GDS (i.e., poorer performance) compared to HIV-negative status.

Table 2.

Longitudinal neurocognitive profiles and between-group differences

Neurocognitive deficit
score		 BASELINE 1-YEAR FOLLOW-UP 5-YEAR FOLLOW-UP
HIV−
(n=156)		 HIV+
(n=140)		 LSD post-
hoc p 		HIV−
(n=77)		 HIV+
(n=73)		 LSD post-
hoc p 		HIV−
(n=55)		 HIV+
(n=30)		 LSD post-
hoc p
LS Mean
(SE)		 LS Mean
(SE)		 LS Mean
(SE)		 LS Mean
(SE)		 LS Mean
(SE)		 LS Mean
(SE)
Global 0.30(0.03) 0.34(0.03) 0.24 0.20(0.03) 0.25(0.03) 0.24 0.19(0.04) 0.29(0.04) 0.06
Verbal fluency 0.37(0.05) 0.43(0.05) 0.38 0.20(0.06) 0.38(0.06) 0.02 0.10(0.07) 0.29(0.08) 0.06
Learning 0.16(0.04) 0.28(0.04) 0.02 0.14(0.05) 0.14(0.05) 0.99 0.16(0.05) 0.22(0.07) 0.42
Delayed recall 0.26(0.05) 0.32(0.05) 0.39 0.12(0.06) 0.15(0.06) 0.70 0.20(0.07) 0.33(0.09) 0.27
Executive function 0.27(0.04) 0.35(0.05) 0.16 0.23(0.06) 0.22(0.06) 0.83 0.20(0.07) 0.33(0.09) 0.20
Processing speed 0.36(0.05) 0.38(0.05) 0.75 0.23(0.06) 0.24(0.06) 0.90 0.21(0.06) 0.29(0.08) 0.42
Motor ability 0.29(0.06) 0.28(0.06) 0.91 0.21(0.07) 0.29(0.07) 0.39 0.22(0.08) 0.27(0.10) 0.63
Attention/working memory 0.20(0.05) 0.36(0.05) 0.01 0.12(0.06) 0.33(0.06) 0.01 0.15(0.07) 0.30(0.09) 0.20
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In terms of specific neurocognitive domains, there were significant between-group differences in learning (p=0.02) and attention/working memory (p=0.01) domains at baseline, with HIV+ status associated with higher deficit scores (i.e., poorer performance) compared to HIV-negative status. There were significant between-group differences at 1-year follow-up for verbal fluency (p=0.02) and attention/working memory (p=0.01). For both domains, HIV+ status was associated with higher deficit scores (i.e., poorer performance). We found no other significant between-group differences.

Longitudinal neurocognitive profiles

Both groups showed a significant decline in GDS (i.e., improvement in performance) between baseline and 1-year follow-up (p<0.01), but no significant difference between 1-year (p=0.66) and 5-year (p=0.37) follow-up (see Table 2). No group*time interaction effect was observed in GDS (F(2,248)=0.72, p=0.49, ƞ2<0.01) (see Figure 1).

Figure 1.

Figure 1.

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Between-group differences in GDS scores over time

There was a trend towards a group*time interaction effect for verbal fluency (F(2,263)=2.04, p=0.13, ƞ2=0.02) and learning (F(2,284)=1.94, p=0.15, ƞ2<0.01). Both groups showed a decline in verbal fluency deficit scores (i.e., improvement) over time (see Figures 2 and 3). Women with HIV showed a sharp decline in learning deficit scores at 1 year (i.e., cognitive improvement, LSD post-hoc p<0.01), followed by a slight increase at 5 years (LSD post-hoc p=0.26).

Figure 2.

Figure 2.

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Between-group differences in verbal fluency deficit scores over time

Figure 3.

Figure 3.

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Between-group differences in learning deficit scores over time

Factors correlated with neurocognitive function

Among women without HIV, GDS was significantly and positively correlated with childhood physical neglect (p=0.015, r=0.271) and total CTQ-SF scores (p=0.040, r=0.231) at 1-year follow-up (see Table 3).

Table 3.

Factors correlated with global deficit scores

BASELINE 1-YEAR FOLLOW-UP 5-YEAR FOLLOW-UP
HIV−
(n=156)		 HIV+
(n=140)		 HIV−
(n=77)		 HIV+
(n=73)		 HIV−
(n=55)		 HIV+
(n=30)
r r r r r r
CTQ_PA −0.011 0.077 0.165 0.094 0.058 −0.144
CTQ_SA −0.053 −0.030 −0.095 0.075 0.012 0.130
CTQ_EA 0.059 0.148 0.183 0.199 0.130 −0.160
CTQ_EN −0.007 0.246** 0.208 0.113 0.084 −0.009
CTQ_PN 0.024 0.269** 0.271* 0.250* 0.085 −0.015
CTQ_Total 0.009 0.196* 0.231* 0.206 0.120 −0.075
LEC_Total −0.083 0.076 0.178 0.151 −0.212 0.274
DTS_F 0.072 −0.027 0.063 0.136 −0.082 0.288
DTS_S 0.078 −0.033 0.063 0.060 −0.083 0.286
DTS_Total 0.075 −0.030 0.061 0.102 −0.083 0.287
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Note. *p<0.05, **p<0.01. CTQ=Childhood Trauma Questionnaire; DTS=Davidson Trauma Scale, EA=emotional abuse, EN=emotional neglect, GDS=global deficit score, LEC=Life Events Checklist, M=mean, PA=physical assault, PN=physical neglect, r=Pearson’s correlation; S=Severity, SA=sexual assault, SD=standard deviation.

Among women with HIV, GDS was significantly and positively correlated with childhood emotional neglect (baseline p=0.003, r=0.246), childhood psychical neglect (baseline p=0.001, r=0.269; 1-year follow-up p=0.043, r=0.250), and total CTQ-SF scores (baseline p=0.021, r=0.196).

Discussion

In our sample, there was no significant difference in GDS between women with and without HIV over time. We found significant between-group differences in verbal fluency (, learning, and attention/working memory, with HIV+ status being associated with higher deficit scores (i.e., poorer performance). Childhood neglect was correlated with baseline GDS among women with HIV.

Neurocognition

Considering current evidence on the high prevalence of HAND in South Africa (Joska et al., 2010; Robbins et al., 2011) and the link between HIV and poorer neurocognitive performance (Spudich et al., 2019; Vance et al., 2022), we hypothesised higher GDS (i.e., poorer performance) among women with HIV over time. However, there was no between-group difference in GDS. This finding could be explained by the high self-reported use of ARVs in this cohort of women (93.3% at 5-year follow-up). Research suggests that ARV treatment may buffer against the negative effect of HIV on neurocognition (Spies et al., 2017; Spudich, 2013; Yakasai et al., 2015). This links to the increase in CD4 count and decrease in viral load evident in our sample.

Another plausible explanation for this finding is the inclusion of recently developed South African norms in the computation of GDS. While prior analyses in a smaller female sample (HIV+ n=67; HIV− n=50) followed up to 1 year indicated a significant between-group difference in neurocognitive function (Spies et al., 2017), these findings were based on raw scores unadjusted for demographically corrected norms. Literature highlights the importance of having country and/or population-specific norms for neurocognitive assessment (Kiselica et al., 2024; Raudeberg et al., 2021; Villalobos et al., 2023), including in South Africa (Anderson, 2001). Furthermore, estimated HAND prevalence in Sub-Saharan Africa varies greatly (between 18.8% and 88.3%) and may be overestimated (see Nweke et al., 2021 for review). In our sample of women with HIV, GDS never reached the ≥ 0.50 cut-off indicative of neurocognitive impairment, supporting the theory of Nweke and colleagues (2021). This supports the notion that neuropsychological test norms facilitate accurately determining whether participants’ test results fall within the range of what could normally be expected of healthy individuals with similar backgrounds and demographic characteristics (Heaton & Marcotte, 2000). Further, our findings highlight the importance of considering the sensitivity and discriminating ability of all the tests combined (i.e., GDS) versus separately considering individual tests or domains.

We found a significant between-group difference in the domains of verbal fluency and attention and working memory, for which women with HIV had higher deficit scores (i.e., poorer performance) than their uninfected counterparts. Research indicates a correlation between categorical fluency deficits and working and semantic memory (Iudicello et al., 2008), possibly explained by a difficulty in switching between semantic memory networks in PWH (Ivanova et al., 2023). This goal directed process of searching and retrieval (necessary for naming animals as part of a test of verbal fluency in the HNRC battery, for example) forms part of executive functions (Stuss & Alexander, 2009) which is typically affected by HAND (Heaton et al., 2011). Research suggests that impaired verbal fluency in PWH may be related to switching dysfunction which is secondary to neurotoxicity (Ivanova et al., 2023; Woods et al., 2004), often associated with ART (Lanman et al., 2021). Additionally, executive and verbal fluency impairments are associated with the neurodegeneration of the frontostriatal system, including atrophy of the caudate nucleus (Hestad et al., 1993; Thames et al., 2012), with putamen volume being predictive of switching difficulties specifically (Thames et al., 2012). Language deficits in PWH have also been linked to the development of opportunistic infections (such as progressive multifocal leukoencephalopathy) which may result in perisylvian lesions (Heaton et al., 2011), although aphasia is rarely formally diagnosed in PWH (Ivanova et al., 2023).

Longitudinal neurocognitive profiles

Women with HIV showed a significant decrease in GDS (i.e., improved cognition) between baseline and 1-year follow-up, with a slight non-significant increase from 1-year to 5-year follow-up. This is contrary to prior research indicating a progression from asymptomatic NI to symptomatic NI over time in women with HIV (Rourke et al., 2021). While practice effects of serial neuropsychological testing (Jutten et al., 2020) should also be considered, the significant decrease in GDS among women with HIV may be explained by the greater increase in self-reported ARV treatment between baseline and 1-year follow-up (mean difference=35.3%), compared to a lesser increase between 1-year and 5-year follow-up (mean difference=16.6%). Prior studies indicated a significant improvement in cognitive function at three months (Nakasujja et al., 2010), six months (Sacktor et al., 2006), and one year (Cross et al., 2013) following initiation of ARV treatment. The latter, conducted among South African HIV clade C infected patients found that this improvement remained irrespective of central nervous system penetration effectiveness (CPE). Yet, it is important to note that meta-analytic evidence found that ARV treatment was mainly effective in improving cognitive function of PWH with poor physical conditions and immune status (i.e. with advanced HIV infection), but does not considerably improve cognition in the entire HIV population (Gao et al., 2020).

Factors correlated with neurocognitive function

Contrary to prior research (e.g., Jacob et al., 2019; Jak et al., 2016), we found no significant correlations between PTSS and GDS. The relatively low levels of self-reported PTSS, with the mean DTS score only approaching the threshold for significance (cut-off score of 40) (Davidson et al., 1997) at 5-year follow-up in women with HIV (M=44.50, SD=40.07) may explain our findings. Similarly, unlike prior studies (Deiss et al., 2019; Rubin et al., 2023), we found no significant correlation between GDS and lifetime trauma (LEC scores). However, consistent with prior research (e.g., Kavanaugh et al., 2017; Spies et al., 2017), we found a significant positive correlation between total CTQ-SF and GDS at baseline (women with HIV) and 1-year follow-up (women without HIV). The correlations found for GDS and total CTQ-SF scores may have been driven by the neglect trauma sub-type (especially in women with HIV, where correlations of emotional and physical neglect with GDS were stronger than the correlation with CTQ-SF total scores at baseline). The LEC does not include emotional and physical neglect as potentially traumatic events. These findings warrant further investigation.

Implications

Despite the initial decrease in GDS (i.e., neurocognitive improvement), women with HIV showed continued mild-moderate impairment, which may negatively impact their daily lives (Casaletto et al., 2017; Terpstra et al., 2018; Tozzi et al., 2015). Research on ARV intensification for treating existing neurocognitive impairment in PWH is mixed. For example, one study found that adding maraviroc was effective in improving cognition (Gates et al., 2016), while a large randomized, double-blind, placebo-controlled trail indicated that it was ineffective (Letendre et al., 2023). Thus, clinicians should consider alternatives such as the adjunctive use of medication generally used in neuroinflammatory, neuropsychiatric, and metabolic disorders to enhance neuroprotection and treating HAND (Kolson, 2022). For example, citalopram which does not have the neurotoxic drawback often associated with highly active antiretroviral therapy often used in sub-Saharan Africa showed some efficacy in treating HAND (Ojagbemi, 2021). Additionally, research on natural compounds (Kurapati et al., 2015), specifically curcumin, in treating HAND via improving mitochondrial functioning and synaptic growth in rats (Gong et al., 2012; Tang et al., 2013) have promising findings (Omeragic et al., 2020) – even though further research is required.

Both restorative and compensatory cognitive neurorehabilitation activities (Weber et al., 2013) could be considered for the women with HIV who show mild-moderate neurocognitive impairment. Whilst limited, research showed promising results (Becker et al., 2012; Boivin et al., 2010; Vance et al., 2012), especially when implemented at a young age when it is possible to harness the potential for neuroplasticity (Boivin et al., 2010). This could further be augmented with cognitive therapy (Vance et al., 2021), cognitive exercise such as computerised cognitive training (Vance et al., 2019), and aerobic exercise (Kolson, 2022). These alternatives are important to consider for improving the quality of life of women with HIV.

Given our findings that GDS were significantly and positively correlated with childhood neglect specifically, as opposed to lifetime trauma exposure, we recommend screening patients with HIV for childhood neglect. Women with HIV who have experienced childhood neglect may be at a particular risk for HAND – albeit mild-moderate – and early detection and intervention is important (Ian et al., 2016).

Limitations and future recommendations

The smaller sample, especially at 5-year follow-up may limit the statistical power of our findings. While this is a limitation to the study, the attrition rate is unsurprising given research indicating 1-year attrition rates among HIV studies ranging between 24.62 % (Krishnan et al., 2011), 26% (Dareng et al., 2018), and 33.4% (Fleishman et al., 2012), with similar attrition rates reported in other longitudinal studies conducted among clinical samples in South Africa with 54.1% (van der Watt et al., 2018) and 33.6% (Ventimiglia et al., 2020). Nonetheless, the longitudinal design is a strength. Using MLMs also allowed all cases with at least one GDS to be included in the analysis instead of only including cases with full data sets for all three time points. Thus, we were able to include a larger sample. For both groups, a greater proportion of women earned above ZAR 40 000 at the 5-year follow-up (14.5% of women without HIV and 10.0% of women with HIV) as compared to baseline (2.6% of women without HIV and 4.3% of women with HIV), with p<0.001 for both groups (also see Supplementary Table A). This suggests that in future studies additional effort should be made to retain women with a lower level of income.

While we hypothesised that the high rate of self-reported ARV treatment contributed to the lack of a significant difference in GDS between women with HIV and women without HIV, we did not objectively measure medication adherence. Future research should consider incorporating objective measures of medication adherence to lend support to our hypothesis.

We did not consider the type of ARV regime to conclusively comment on whether CPE impacted our findings. Nonetheless, given that most data were collected after 2010, when the recommended ARV regime changed to tenofovir with a lower CPE ranking, our hypothesis has some merit.

We acknowledge that using self-report demographic and trauma exposure measures may have resulted in social desirability bias. Future research should include objective measures such as medical and social services records.

Lastly, our sample was predominantly Xhosa speaking, limiting the generalisability of our findings to women with a different home language. While we did control for home language in all analyses, future research should include women with different home languages to reflect the South African population (Statista, 2024).

Conclusion

We assessed neurocognitive profiles of South African women with and without HIV. Contrary to prior research we found no significant between-group difference in GDS. This may be explained by the high self-reported ARV use. We found a significant between-group difference for learning and attention/working memory, with HIV+ status being associated with higher deficit scores (i.e., poorer performance). Within the HIV+ group, we found a significant decline in GDS (i.e., neurocognitive improvement) between baseline and 1-year follow-up, with a slight increase in GDS between 1-year follow-up and 5-year follow-up. The initial decline in GDS may be explained by increased ARV uptake during this period. Lastly emotional and physical childhood neglect was significantly and positively correlated with baseline GDS among women with HIV, while no significant correlation was found for lifetime trauma exposure or PTSS. More research is needed on the specific types of trauma exposure, specifically neglect, and its association with neurocognition.

Supplementary Material

Supp 1

Acknowledgements

We thank the women who participated in this study. We thank Professor Martin Kidd for assisting with the statistical analyses and norming of the neurocognitive data.

Sources of financial support

AvdW received funding from the National Institutes of Health Pittsburgh & Stellenbosch Universities HIV-Associated Cardio-Metabolic, Chronic Kidney and Neuropsychiatry Diseases Research Training Fellowship Programme to write this manuscript. Additional research support was provided by a CFAR grant awarded to S Seedat [P30-AI036214] and the HIV Neurobehavioral Research Center (HNRC; National Institutes of Health [grant number P30-MH62512]). This work is supported by the South African PTSD Research Programme of Excellence and the South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit.

Footnotes

Declarations of interest

The authors declare no conflicts of interest.

Data availability

Data are available upon reasonable request.

References

  1. Albert SM, Weber CM, Todak G, Polanco C, Clouse R, McElhiney M, Rabkin J, Stern Y, & Marder K (1999). An observed performance test of medication management ability in HIV: Relation to neuropsychological status and medication adherence outcomes. AIDS and Behavior, 3(2), 121–128. 10.1023/A:1025483806464 [DOI] [Google Scholar]
  2. Alford K, & Vera JH (2018). Cognitive Impairment in people living with HIV in the ART era: A Review. British Medical Bulletin, 127(1), 55–68. 10.1093/bmb/ldy019 [DOI] [PubMed] [Google Scholar]
  3. Anderson SJ (2001). On the Importance of Collecting Local Neuropsychological Normative Data. South African Journal of Psychology, 31(3), 29–34. 10.1177/008124630103100304 [DOI] [Google Scholar]
  4. Becker JT, Dew MA, Aizenstein HJ, Lopez OL, Morrow L, Saxton J, & Tárraga L (2012). A pilot study of the effects of internet-based cognitive stimulation on neuropsychological function in HIV disease. Disability and Rehabilitation, 34(21), 1848–1852. 10.3109/09638288.2012.667188 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bernstein D, & Fink L (1998). Childhood Trauma Questionnaire: A retrospective self-report manual. The Psychological Corporation. [Google Scholar]
  6. Boivin MJ, Busman RA, Parikh SM, Bangirana P, Page CF, Opoka RO, & Giordani B (2010). A pilot study of the neuropsychological benefits of computerized cognitive rehabilitation in Ugandan children with HIV. Neuropsychology, 24(5), 667–673. 10.1037/a0019312 [DOI] [PubMed] [Google Scholar]
  7. Casaletto KB, Weber E, Iudicello JE, & Woods SP (2017). Real-World Impact of HIV-Associated Neurocognitive Impairment. In Chiaravalloti ND & Goverover Y (Eds.), Changes in the Brain (pp. 211–245). Springer New York. 10.1007/978-0-387-98188-8_10 [DOI] [Google Scholar]
  8. Cross HM, Combrinck MI, & Joska JA (2013). HIV-associated neurocognitive disorders: Antiretroviral regimen, central nervous system penetration effectiveness, and cognitive outcomes. South African Medical Journal, 103(10), 758. 10.7196/SAMJ.6677 [DOI] [PubMed] [Google Scholar]
  9. Cysique LA, & Brew BJ (2019). Vascular cognitive impairment and HIV-associated neurocognitive disorder: A new paradigm. Journal of NeuroVirology, 25(5), 710–721. 10.1007/s13365-018-0706-5 [DOI] [PubMed] [Google Scholar]
  10. Dareng EO, Olaniyan Y, Adebamowo SN, Eseyin OR, Odutola MK, Obiefuna EM, Offiong RA, Pharoah PP, & Adebamowo CA (2018). Age, HIV status, and research context determined attrition in a longitudinal cohort in Nigeria. Journal of Clinical Epidemiology, 100, 32–43. 10.1016/j.jclinepi.2018.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Davidson JRT, Book SW, Colket JT, Tupler LA, Roth S, David D, Hertzberg M, Mellman T, Beckham JC, Smith RD, Davidson RM, Katz R, & Feldman ME (1997). Assessment of a new self-rating scale for post-traumatic stress disorder. Psychological Medicine, 27(1), 153–160. 10.1017/S0033291796004229 [DOI] [PubMed] [Google Scholar]
  12. Deiss R, Campbell Cdr. J., Watson CW-M, Moore RC, Crum-Cianflone NF, Wang X, Ganesan A, Okulicz Lt. Col. J., Letendre S, Maves RC, Moore DJ, & Agan BK (2019). Posttraumatic Stress Disorder and Neurocognitive Impairment in a U.S. Military Cohort of Persons Living with HIV. Psychiatry, 1–12. 10.1080/00332747.2019.1586503 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Deist M, Suliman S, Kidd M, Franklin D, Cherner M, Heaton RK, Spies G, & Seedat S (2023). Neuropsychological Test Norms for the Assessment of HIV-Associated Neurocognitive Impairment Among South African Adults. AIDS and Behavior. 10.1007/s10461-023-04029-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Fleishman JA, Yehia BR, Moore RD, Korthuis PT, & Gebo KA (2012). Establishment, Retention, and Loss to Follow-Up in Outpatient HIV Care. JAIDS Journal of Acquired Immune Deficiency Syndromes, 60(3), 249–259. 10.1097/QAI.0b013e318258c696 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gao C, Meng J, Xiao X, Wang M, Williams AB, & Wang H (2020). Antiretroviral therapy improves neurocognitive impairment in people living with HIV? A meta-analysis. International Journal of Nursing Sciences, 7(2), 238–247. 10.1016/j.ijnss.2020.03.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gates TM, Cysique LA, Siefried KJ, Chaganti J, Moffat KJ, & Brew BJ (2016). Maraviroc-intensified combined antiretroviral therapy improves cognition in virally suppressed HIV-associated neurocognitive disorder. AIDS, 30(4), 591–600. 10.1097/QAD.0000000000000951 [DOI] [PubMed] [Google Scholar]
  17. Gawron N, Choiński M, Szymańska-Kotwica B, Pluta A, Sobańska M, Egbert AR, Desowska A, Wolak T, Horban A, Firląg-Burkacka E, Bieńkowski P, Sienkiewicz-Jarosz H, Scińska-Bieńkowska A, Biswal B, Rao SM, Bornstein R, & Łojek E (2019). Effects of age, HIV, and HIV-associated clinical factors on neuropsychological functioning and brain regional volume in HIV+ patients on effective treatment. Journal of NeuroVirology, 25(1), 9–21. 10.1007/s13365-018-0679-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gong Z, Yang L, Tang H, Pan R, Xie S, Guo L, Wang J, Deng Q, Xiong G, Xing Y, & Dong J (2012). Protective effects of curcumin against human immunodeficiency virus 1 gp120 V3 loop-induced neuronal injury in rats. Neural Regeneration Research, 7(3), 171–175. 10.3969/j.issn.1673-5374.2012.03.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gray MJ, Litz BT, Hsu JL, & Lombardo TW (2004). Psychometric Properties of the Life Events Checklist. Assessment, 11(4), 330–341. 10.1177/1073191104269954 [DOI] [PubMed] [Google Scholar]
  20. Guha A, Brier MR, Ortega M, Westerhaus E, Nelson B, & Ances BM (2016). Topographies of Cortical and Subcortical Volume Loss in HIV and Aging in the cART Era. JAIDS Journal of Acquired Immune Deficiency Syndromes, 73(4), 374–383. 10.1097/QAI.0000000000001111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Heaton RK, Cysique LA, Jin H, Shi C, Yu X, Letendre S, Franklin D, Ake C, Vigil O, Atkinson J, Marcotte T, Grant I, Wu Z, & HNRC group. (2010). Neurobehavioral effects of human immunodeficiency virus infection among former plasma donors in rural China. Journal of Neurovirology, 16(2), 185–188. 10.3109/13550284.2010.481820 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, LeBlanc S, Corkran SH, Duarte NA, Clifford DB, Woods SP, Collier AC, Marra CM, Morgello S, Mindt MR, Taylor MJ, Marcotte TD, Atkinson JH, Wolfson T, Gelman BB, … Grant I (2011). HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: Differences in rates, nature, and predictors. Journal of NeuroVirology, 17(1), 3–16. 10.1007/s13365-010-0006-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Heaton RK, & Marcotte T (2000). Clinical neuropsychological tests and assessment techniques. In Handbook of Neuropsychology (2nd ed., Vol. 1, pp. 27–52). Elsevier. [Google Scholar]
  24. Hestad K, McArthur JH, Dal Pan GJ, Selnes OA, Nance-Sproson TE, Aylward E, Mathews VP, & McArthur JC (1993). Regional brain atrophy in HIV-1 infection: Association with specific neuropsychological test performance. Acta Neurologica Scandinavica, 88(2), 112–118. 10.1111/j.1600-0404.1993.tb04201.x [DOI] [PubMed] [Google Scholar]
  25. HIV Neurobehavioral Research Center. (2022). HIV Neurobehavioral Research Center International Neurobehavioral Battery [Test]. HIV Neurobehavioral Research Center. https://hnrc.hivresearch.ucsd.edu/index.php [Google Scholar]
  26. Hoare J, Phillips N, Joska JA, Paul R, Donald KA, Stein DJ, & Thomas KGF (2016). Applying the HIV-associated neurocognitive disorder diagnostic criteria to HIV-infected youth. Neurology, 87(1), 86–93. 10.1212/WNL.0000000000002669 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ian E, Gwen CL, Soo CT, Melissa C, Chun-Kai H, Eosu K, Hyo-Youl K, Asad K, Scott L, Chung-Ki LP, Anekthananon T, Jordan TG, Han-Ting W, & Wing-Wai W (2016). The burden of HIV-associated neurocognitive disorder (HAND) in the Asia-Pacific region and recommendations for screening. Asian Journal of Psychiatry, 22, 182–189. 10.1016/j.ajp.2015.10.009 [DOI] [PubMed] [Google Scholar]
  28. Iudicello JE, Woods SP, Weber E, Dawson MS, Scott JC, Carey CL, Grant I, & The Hiv Neurobehavioral Research Ce. (2008). Cognitive mechanisms of switching in HIV-associated category fluency deficits. Journal of Clinical and Experimental Neuropsychology, 30(7), 797–804. 10.1080/13803390701779578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ivanova G, Alhrahsheh R, & Kukov K (2023). Cognitive Deficits: Verbal and Semantic Fluency in People Living withHIV and AIDS. Current HIV Research, 21(3), 202–212. 10.2174/1570162X21666230613124240 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Jacob SN, Dodge CP, & Vasterling JJ (2019). Posttraumatic stress disorder and neurocognition: A bidirectional relationship? Clinical Psychology Review, 72, 101747. 10.1016/j.cpr.2019.101747 [DOI] [PubMed] [Google Scholar]
  31. Jak AJ, Crocker LD, Aupperle RL, Clausen A, & Bomyea J (2016). Neurocognition in PTSD: Treatment Insights and Implications. In Vermetten E, Baker DG, & Risbrough VB (Eds.), Behavioral Neurobiology of PTSD (Vol. 38, pp. 93–116). Springer International Publishing. 10.1007/7854_2016_62 [DOI] [PubMed] [Google Scholar]
  32. Joska JA, Fincham DS, Stein DJ, Paul RH, & Seedat S (2010). Clinical Correlates of HIV-Associated Neurocognitive Disorders in South Africa. AIDS and Behavior, 14(2), 371–378. 10.1007/s10461-009-9538-x [DOI] [PubMed] [Google Scholar]
  33. Jutten RJ, Grandoit E, Foldi NS, Sikkes SAM, Jones RN, Choi S, Lamar ML, Louden DKN, Rich J, Tommet D, Crane PK, & Rabin LA (2020). Lower practice effects as a marker of cognitive performance and dementia risk: A literature review. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 12(1). 10.1002/dad2.12055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kanmogne GD, Fonsah JY, Umlauf A, Moul J, Doh RF, Kengne AM, Tang B, Tagny CT, Nchindap E, Kenmogne L, Franklin D, Njamnshi DM, Kuate CT, Mbanya D, Njamnshi AK, & Heaton RK (2020). Effects of HIV infection, antiretroviral therapy, and immune status on the speed of information processing and complex motor functions in adult Cameroonians. Scientific Reports, 10(1), 14016. 10.1038/s41598-020-70981-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kavanaugh BC, Dupont-Frechette JA, Jerskey BA, & Holler KA (2017). Neurocognitive deficits in children and adolescents following maltreatment: Neurodevelopmental consequences and neuropsychological implications of traumatic stress. Applied Neuropsychology: Child, 6(1), 64–78. 10.1080/21622965.2015.1079712 [DOI] [PubMed] [Google Scholar]
  36. Kiselica AM, Karr JE, Mikula CM, Ranum RM, Benge JF, Medina LD, & Woods SP (2024). Recent Advances in Neuropsychological Test Interpretation for Clinical Practice. Neuropsychology Review, 34(2), 637–667. 10.1007/s11065-023-09596-1 [DOI] [PubMed] [Google Scholar]
  37. Kolson DL (2022). Developments in Neuroprotection for HIV-Associated Neurocognitive Disorders (HAND). Current HIV/AIDS Reports, 19(5), 344–357. 10.1007/s11904-022-00612-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kordovski VM, Tierney SM, & Woods SP (2019). Conceptualizing and Assessing Everyday Functioning in the Context of HIV-Associated Neurocognitive Disorders. In Cysique LA & Rourke SB (Eds.), Neurocognitive Complications of HIV-Infection (Vol. 50, pp. 329–346). Springer International Publishing. 10.1007/7854_2018_78 [DOI] [PubMed] [Google Scholar]
  39. Krishnan S, Wu K, Smurzynski M, Bosch RJ, Benson CA, Collier AC, Klebert MK, Feinberg J, Koletar SL, & ALLRT/A5001 Team. (2011). Incidence Rate of and Factors Associated with Loss to Follow-up in a Longitudinal Cohort of Antiretroviral-Treated HIV-Infected Persons: An AIDS Clinical Trials Group (ACTG) Longitudinal Linked Randomized Trials (ALLRT) Analysis. HIV Clinical Trials, 12(4), 190–200. 10.1310/hct1204-190 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kurapati KRV, Atluri VS, Samikkannu T, Garcia G, & Nair MPN (2015). Natural Products as Anti-HIV Agents and Role in HIV-Associated Neurocognitive Disorders (HAND): A Brief Overview. Frontiers in Microbiology, 6, 1444. 10.3389/fmicb.2015.01444 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lanman T, Letendre S, Ma Q, Bang A, & Ellis R (2021). CNS Neurotoxicity of Antiretrovirals. Journal of Neuroimmune Pharmacology, 16(1), 130–143. 10.1007/s11481-019-09886-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Letendre SL, Chen H, McKhann A, Roa J, Vecchio A, Daar ES, Berzins B, Hunt PW, Marra CM, Campbell TB, Coombs RW, Ma Q, Swaminathan S, Macatangay BJC, Morse GD, Miller T, Rusin D, Greninger AL, Ha B, … the A5324 Study Team. (2023). Antiretroviral Therapy Intensification for Neurocognitive Impairment in Human Immunodeficiency Virus. Clinical Infectious Diseases, 77(6), 866–874. 10.1093/cid/ciad265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Maki PM, Rubin LH, Valcour V, Martin E, Crystal H, Young M, Weber KM, Manly J, Richardson J, Alden C, & Anastos K (2015). Cognitive function in women with HIV: Findings from the Women’s Interagency HIV Study. Neurology, 84(3), 231–240. 10.1212/WNL.0000000000001151 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nakasujja N, L Skolasky R, Musisi S, Allebeck P, Robertson K, Ronald A, Katabira E, Clifford DB, & Sacktor N (2010). Depression symptoms and cognitive function among individuals with advanced HIV infection initiating HAART in Uganda. BMC Psychiatry, 10(1), 44. 10.1186/1471-244X-10-44 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Nichols MJ, Gates TM, Soares JR, Moffat KJ, Rae CD, Brew BJ, & Cysique LA (2019). Atrophic brain signatures of mild forms of neurocognitive impairment in virally suppressed HIV infection. AIDS, 33(1), 55–66. 10.1097/QAD.0000000000002042 [DOI] [PubMed] [Google Scholar]
  46. Nweke MC, Okemuo AJ, Uduonu EM, Ugwu PI, Nwachukwu C, & Mshunqane N (2021). Meta-analysis of factors affecting prevalence estimates of HIV-associated neurocognitive disorder in sub-Saharan Africa. South African Journal of Science, 117(9/10). 10.17159/sajs.2021/8575 [DOI] [Google Scholar]
  47. Nyamayaro P, Chibanda D, Robbins RN, Hakim J, & Gouse H (2019). Assessment of neurocognitive deficits in people living with HIV in Sub Saharan Africa: A systematic review. The Clinical Neuropsychologist, 33(sup1), 1–26. 10.1080/13854046.2019.1606284 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ojagbemi A. (2021). HIV Associated Neurocognitive Disorders Subsidence Through Citalopram Addition in Anti-retroviral Therapy (HANDS-CARE): A Concept Note. Frontiers in Neurology, 12, 658705. 10.3389/fneur.2021.658705 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Omeragic A, Kayode O, Hoque MT, & Bendayan R (2020). Potential pharmacological approaches for the treatment of HIV-1 associated neurocognitive disorders. Fluids and Barriers of the CNS, 17(1), 42. 10.1186/s12987-020-00204-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Raudeberg R, Iverson GL, & Hammar Å (2021). The importance of clinical normative data for conceptualizing neuropsychological deficits in people with schizophrenia spectrum disorders. Applied Neuropsychology: Adult, 28(6), 752–760. 10.1080/23279095.2019.1699098 [DOI] [PubMed] [Google Scholar]
  51. Robbins RN, Remien RH, Mellins CA, Joska JA, & Stein DJ (2011). Screening for HIV-Associated Dementia in South Africa: Potentials and Pitfalls of Task-Shifting. AIDS Patient Care and STDs, 25(10), 587–593. 10.1089/apc.2011.0154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rourke SB, Bekele T, Rachlis A, Kovacs C, Brunetta J, Gill MJ, Carvalhal A, Cysique LA, Marcotte T, & Power C (2021). Asymptomatic neurocognitive impairment is a risk for symptomatic decline over a 3-year study period. AIDS, 35(1), 63–72. 10.1097/QAD.0000000000002709 [DOI] [PubMed] [Google Scholar]
  53. Royston P, & Altman DG (1994). Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling. Applied Statistics, 43(3), 429. 10.2307/2986270 [DOI] [Google Scholar]
  54. Rubin LH, Maki PM, Dastgheyb RM, Steigman PJ, Burke-Miller J, Xu Y, Jin W, Sosanya O, Gustafson D, Merenstein D, Milam J, Weber KM, Springer G, & Cook JA (2023). Trauma Across the Life Span and Multisystem Morbidity in Women With HIV. Psychosomatic Medicine, 85(4), 341–350. 10.1097/PSY.0000000000001192 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Sacktor N, Nakasujja N, Skolasky R, Robertson K, Wong M, Musisi S, Ronald A, & Katabira E (2006). Antiretroviral therapy improves cognitive impairment in HIV+ individuals in sub-Saharan Africa. Neurology, 67(2), 311–314. 10.1212/01.wnl.0000225183.74521.72 [DOI] [PubMed] [Google Scholar]
  56. Smail RC, & Brew BJ (2018). HIV-associated neurocognitive disorder. In Handbook of Clinical Neurology (Vol. 152, pp. 75–97). Elsevier. 10.1016/B978-0-444-63849-6.00007-4 [DOI] [PubMed] [Google Scholar]
  57. Spies G, Fennema-Notestine C, Cherner M, & Seedat S (2017). Changes in cognitive function in women with HIV infection and early life stress. AIDS Care, 29(1), 14–23. 10.1080/09540121.2016.1204417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Spies G, Mall S, Wieler H, Masilela L, Castelon Konkiewitz E, & Seedat S (2020). The relationship between potentially traumatic or stressful events, HIV infection and neurocognitive impairment (NCI): A systematic review of observational epidemiological studies. European Journal of Psychotraumatology, 11(1), 1781432. 10.1080/20008198.2020.1781432 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Spudich S. (2013). HIV and Neurocognitive Dysfunction. Current HIV/AIDS Reports, 10(3), 235–243. 10.1007/s11904-013-0171-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Spudich S, Robertson KR, Bosch RJ, Gandhi RT, Cyktor JC, Mar H, Macatangay BJ, Lalama CM, Rinaldo C, Collier AC, Godfrey C, Eron JJ, McMahon D, Jacobs JL, Koontz D, Hogg E, Vecchio A, & Mellors JW (2019). Persistent HIV-infected cells in cerebrospinal fluid are associated with poorer neurocognitive performance. Journal of Clinical Investigation, 129(8), 3339–3346. 10.1172/JCI127413 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Statista. (2024). Distribution of languages spoken by individuals inside and outside of households in South Africa 2018. https://www.statista.com/statistics/1114302/distribution-of-languages-spoken-inside-and-outside-of-households-in-south-africa/
  62. Stuss DT, & Alexander MP (2009). Frontal Lobe Syndrome. In Encyclopedia of Neuroscience (pp. 375–381). Elsevier. 10.1016/B978-008045046-9.00417-4 [DOI] [Google Scholar]
  63. Su T, Schouten J, Geurtsen GJ, Wit FW, Stolte IG, Prins M, Portegies P, Caan MWA, Reiss P, Majoie CB, & Schmand BA (2015). Multivariate normative comparison, a novel method for more reliably detecting cognitive impairment in HIV infection. AIDS, 29(5), 547–557. 10.1097/QAD.0000000000000573 [DOI] [PubMed] [Google Scholar]
  64. Tang H, Pan R, Fang W, Xing Y, Chen D, Chen X, Yu Y, Wang J, Gong Z, Xiong G, & Dong J (2013). Curcumin ameliorates hippocampal neuron damage induced by human immunodeficiency virus-1. Neural Regeneration Research, 8(15), 1368–1375. 10.3969/j.issn.1673-5374.2013.15.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Terpstra AR, Worthington C, Ibáñez-Carrasco F, O’Brien KK, Yamamoto A, Chan Carusone S, Baltzer Turje R, McDougall P, Granger W, Thompson V, DeSousa M, Creal L, Rae A, Medina C, Morley E, & Rourke SB (2018). “I’m Just Forgetting and I Don’t Know Why”: Exploring How People Living With HIV-Associated Neurocognitive Disorder View, Manage, and Obtain Support for Their Cognitive Difficulties. Qualitative Health Research, 28(6), 859–872. 10.1177/1049732318761364 [DOI] [PubMed] [Google Scholar]
  66. Thames AD, Foley JM, Wright MJ, Panos SE, Ettenhofer M, Ramezani A, Streiff V, El-Saden S, Goodwin S, Bookheimer SY, & Hinkin CH (2012). Basal ganglia structures differentially contribute to verbal fluency: Evidence from Human Immunodeficiency Virus (HIV)-infected adults. Neuropsychologia, 50(3), 390–395. 10.1016/j.neuropsychologia.2011.12.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Tozzi AE, Carloni E, Gesualdo F, Russo L, & Raponi M (2015). Attitude of Families of Patients with Genetic Diseases to Use m-Health Technologies. Telemedicine and E-Health, 21(2), 86–89. 10.1089/tmj.2014.0080 [DOI] [PubMed] [Google Scholar]
  68. UNAIDS. (2019). UNAIDS Data 2019. UNAIDS. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.unaids.org/sites/default/files/media_asset/2019-UNAIDS-data_en.pdf [Google Scholar]
  69. UNAIDS. (2022). South Africa 2022: HIV and AIDS estimates. UNAIDS. https://www.unaids.org/en/regionscountries/countries/southafrica [Google Scholar]
  70. Van der Watt A, Kidd M, Roos A, Lesch E, & Seedat S (2023). Romantic relationship dissolutions are significantly associated with posttraumatic stress symptoms as compared to a DSM-5 Criterion A event: A case-case–control comparison. European Journal of Psychotraumatology, 14(2), 2238585. 10.1080/20008066.2023.2238585 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. van der Watt A, Suryapranata APSP, & Seedat S (2018). Prospective interepisodal mood monitoring in patients with affective disorders: A feasibility study. Neuropsychiatric Disease and Treatment, 14, 517–524. 10.2147/NDT.S152620 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Vance DE, Del Bene VA, Frank JS, Billings R, Triebel K, Buchholz A, Rubin LH, Woods SP, Li W, & Fazeli PL (2022). Cognitive Intra-individual Variability in HIV: An Integrative Review. Neuropsychology Review, 32(4), 855–876. 10.1007/s11065-021-09528-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Vance DE, Fazeli PL, Cheatwood J, Nicholson C, Morrison S, & Moneyham LD (2021). Targeting HIV-Related Neurocognitive Impairments with Cognitive Training Strategies: Insights from the Cognitive Aging Literature. Current Topics in Behavioral Neurosciences, 50, 503–515. 10.1007/7854_2018_80 [DOI] [PubMed] [Google Scholar]
  74. Vance DE, Fazeli PL, Cheatwood J, Nicholson WC, Morrison SA, & Moneyham LD (2019). Computerized Cognitive Training for the Neurocognitive Complications of HIV Infection: A Systematic Review. The Journal of the Association of Nurses in AIDS Care: JANAC, 30(1), 51–72. 10.1097/JNC.0000000000000030 [DOI] [PubMed] [Google Scholar]
  75. Vance DE, Fazeli PL, Ross LA, Wadley VG, & Ball KK (2012). Speed of processing training with middle-age and older adults with HIV: A pilot study. The Journal of the Association of Nurses in AIDS Care: JANAC, 23(6), 500–510. 10.1016/j.jana.2012.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Ventimiglia I, Van der Watt A, Kidd M, & Seedat S (2020). Association between trauma exposure and mood trajectories in patients with mood disorders. Journal of Affective Disorders, 262, 237–246. 10.1016/j.jad.2019.10.057 [DOI] [PubMed] [Google Scholar]
  77. Villalobos D, Torres-Simón L, Pacios J, Paúl N, & Del Río D (2023). A Systematic Review of Normative Data for Verbal Fluency Test in Different Languages. Neuropsychology Review, 33(4), 733–764. 10.1007/s11065-022-09549-0 [DOI] [PubMed] [Google Scholar]
  78. Weber E, Blackstone K, & Woods SP (2013). Cognitive neurorehabilitation of HIV-associated neurocognitive disorders: A qualitative review and call to action. Neuropsychology Review, 23(1), 81–98. 10.1007/s11065-013-9225-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Wei J, Hou J, Su B, Jiang T, Guo C, Wang W, Zhang Y, Chang B, Wu H, & Zhang T (2020). The Prevalence of Frascati-Criteria-Based HIV-Associated Neurocognitive Disorder (HAND) in HIV-Infected Adults: A Systematic Review and Meta-Analysis. Frontiers in Neurology, 11, 581346. 10.3389/fneur.2020.581346 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Woods SP, Conover E, Rippeth JD, Carey CL, Gonzalez R, Marcotte TD, Heaton RK, Grant I, & HIV Neurobehavioral Research Center (HNRC) Group. (2004). Qualitative aspects of verbal fluency in HIV-associated dementia: A deficit in rule-guided lexical-semantic search processes? Neuropsychologia, 42(6), 801–809. 10.1016/j.neuropsychologia.2003.11.010 [DOI] [PubMed] [Google Scholar]
  81. Yakasai AM, Gudaji MI, Muhammad H, Ibrahim A, Owolabi LF, Ibrahim DA, Babashani M, Mijinyawa MS, Borodo MM, Ogun AS, & Habib AG (2015). Prevalence and Correlates of HIV-Associated Neurocognitive Disorders (HAND) in Northwestern Nigeria. Neurology Research International, 2015, 1–9. 10.1155/2015/486960 [DOI] [PMC free article] [PubMed] [Google Scholar]

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