Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jun 1.
Published in final edited form as: AIDS Care. 2019 Jun 5;32(6):681–688. doi: 10.1080/09540121.2019.1622635

HIV-related stigma, depression, and social support are associated with health-related quality of life among patients newly entering HIV care

C Chapman Lambert 1,*, A Westfall 2, R Modi 2, RK Amico 3, C Golin 4, J Keruly 5, EB Quinlivan 4, HM Crane 5, A Zinski 2, B Turan 6, JM Turan 7, MJ Mugavero 2
PMCID: PMC6893118  NIHMSID: NIHMS1534491  PMID: 31167537

Abstract

Entering HIV care is a vulnerable time for newly diagnosed individuals that necessitates frequent medical visits and initiation of life-long antiretroviral therapy (ART). At this time, there is potential to exacerbate psychosocial difficulties, such as depression and stigma. These psychosocial difficulties contribute to poor health-related quality of life (HRQOL) that in turn, may influence health behaviors including ART adherence, the driver of viral suppression. Understanding HRQOL in people newly entering HIV care is critical and has the potential to guide practice and research. This exploratory cross-sectional study examined demographic, clinical, and psychosocial factors associated with limitations in four specific domains of HRQOL (mobility, usual activities, pain, and depression/anxiety or mood) among persons initially entering outpatient HIV care at four sites in the United States (n = 335). In the unadjusted analysis, female gender was significantly associated with sub-optimal HRQOL with women having increased odds of reporting HRQOL challenges with pain, mood, mobility, and usual activity when compared to men. The adjusted models demonstrated attenuation of parameter estimates and loss of statistical significance for the associations with impaired HRQOL observed among women in unadjusted analyses, suggesting psychosocial factors related to HRQOL are complex and interrelated. Findings are consistent with a robust literature documenting gender-related health disparities. Programs aimed at improving HRQOL for persons initially entering HIV care are warranted generally, and specifically for women, and must address modifiable psychosocial factors such as stigma, via mechanisms including coping and social support.

Keywords: HIV/AIDS, Quality of life, EuroQOL, Engagement, stigma

Engagement in care is critical for persons living with HIV (PLWH) newly entering medical care with evidence supporting sustained retention and high-level adherence to antiretroviral therapy (ART) as strategies for decreasing morbidity and mortality rates (1–3). PLWH newly entering HIV care often have new or exacerbated psychological distress (e.g., depression and anxiety) and other psychosocial sequelae such as stigma and sub-optimal social support, which influence health-related quality of life (HRQOL) (4). HRQOL is defined as an individual’s perception of their overall physical and mental health (5). Inadequate social support, high levels of pain, high levels of perceived stigma, increased psychological distress, more recent HIV diagnosis, and poor physical functioning have been associated with worse HRQOL among individuals previously established in care (9–12). Additional factors associated with worse HRQOL include low CD4 T-lymphocyte count and high HIV viral load (VL), as well as increased age and female gender (8; 13; 14).

In the era of contemporary HIV management, assessment of HRQOL continues to be critical, particularly early in the trajectory of HIV medical treatment, a vulnerable time that portends long-term outcomes (6). Recent research examining the relationship between HIV and HRQOL has focused on individuals previous established in HIV care (9–12; 14–16), but little is known about newly diagnosed PLWH initiating HIV care. Understanding factors associated with HRQOL in PLWH initiating HIV care can assist providers in predicting care engagement and adherence, and aid researchers with developing targeted interventions. The primary goal of this study was to assess clinical and psychosocial factors associated with HRQOL among PLWH initiating outpatient HIV medical care.

Methods

Sample and setting

This analysis utilized baseline data from the integrating ENGagement and Adherence Goals upon Entry (iENGAGE) study (NCT1900236, 1R01AI103661) (17). iENGAGE was a randomized behavioral trial aimed to enhance timely and sustained VL suppression among PLWH initiating HIV care at four Center for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) clinical sites in the US (Alabama, Maryland, North Carolina, and Washington) (18; 19). For the purpose of this sub-study, we used baseline clinical and survey data from 335 of 372 study participants who met the following criteria: 1) identified as male or female; 2) identified as Black or White race. These criteria allowed for race and gender comparisons. These analyses did not include patients who self-identified as transgender due to small numbers (n=6).

Measures

Health-related Quality of life.

The outcome of interest was HRQOL measured by the EuroQOL, a validated instrument for measuring health status (20; 21). The EuroQOL instrument consists of 5-domains (EQ-5D), Mobility, Self-Care, Usual Activity, Pain/Discomfort, Depression/Anxiety, each assessed by a single question and analyzed separately (22; 23). For this study, we will refer to Depression/Anxiety as Mood and Pain/Discomfort as Pain. Response options (none, moderate, and severe) were collapsed into a binary variable with categories of “none” and “moderate to severe”, a common way to analyze EuroQOL (9).

Depression.

Measured using the 8-item Patient Health Questionnaire (PHQ-8), characterizes depressive symptoms experienced by participants over the past 2 weeks on a 4-point Likert-like scale (“not at all” = 0 to “nearly every day” = 3) (24–26). Scores were summed with a range of 0-24. A score of 10 or greater is consistent with major depression, a commonly used cutoff point (24). In our sample, the PHQ-8 had a Cronbach’s alpha of .86.

Social Support.

Measured using the 4-item abbreviated Medical Outcomes Study Social Support Survey (MOS-4) (27; 28). Four types of social support are measured, each by a single item: informational support, tangible support, positive social interaction, and affectionate support. Items were rated on a 5-point scale ranging from “none of the time” (1) to “all of the time” (5). A composite score was obtained by summing responses to all items, with higher scores reflecting greater availability of support. Cronbach’s alpha in this study sample was 0.92.

HIV-related Stigma.

We assessed multiple dimensions of HIV-related stigma using two scales: anticipated stigma and the revised HIV Stigma Scale. The Anticipated Stigma scale consisted of 27-items assessing participants’ perceived likelihood of being treated differently because of their HIV status by family, friends, or healthcare workers (29). Each subscale consisted of 9-items with responses ranging from 1 (very unlikely) to 5 (very likely). The Cronbach’s alpha in this study sample was 0.91. The revised HIV Stigma Scale consisted of four subscales: enacted stigma, disclosure concerns, negative self-image, and concern with public attitudes about PLWH (30). Items for each subscale were rated using a 4-point Likert-like scale ranging from “strongly agree” (1) to “strongly disagree” (4). Composite scores for each scale were generated by summing responses to all items associated with the subscale, with higher scores reflecting higher stigma. The Cronbach’s alpha in this study sample was 0.94.

Demographic.

Demographic data included site, and self-reported gender, race, health insurance status, and HIV transmission risk factors.

Clinical data.

The most recent CD4 T-lymphocyte (cell/cubic ml) and HIV VL (copies/ml) values were extracted from electronic medical records.

Data Analysis

All data were analyzed using SAS version 9.4. Statistical significance was set at a 2-sided 95% confidence level (α = 0.05). Participants were clustered within 4-sites; therefore, we controlled for site in all models. Unadjusted univariate logistic regression analyses were used to assess the association between each demographic, clinical, and psychosocial variable and poor HRQOL for each specific EQ-5D domain. Variables found to be significantly associated with each HRQOL domain were entered into the multiple logistic regression analyses to examine which variables were independent predictors of each HRQOL domain. Sequential multivariable models were used to assess the relationship between non-modifiable and modifiable variables and HRQOL. In model 1, race, gender, age, and type of insurance were included as non-modifiable socio-demographic factors. In model 2, we added the 4 social support variables, each stigma variable, and depression (PHQ8), which are considered modifiable psychosocial factors and potential intervention targets.

Results

Sample Characteristics

Participant demographic and clinical characteristics are presented in Table 1. Descriptive statistics on the five HRQOL domains and the modifiable psychosocial predictor variables (i.e., social support, depression, disclosure, and stigma) are presented in Table 2.

Table 1.

Demographic and Clinical Characteristics of the Participants.

Variable Categories N or Mean (SD) %
Age (years) 37.6 (12.3)
Race Black 227 67.8
White 108 32.2
Gender Male 267 79.7
Female 68 20.3
Race x Gender Black Male 174 51.9
White Male 93 27.8
Black Female 53 15.8
White Female 15 4.5
HIV Risk Factor Heterosexual 114 35.1
IVDU 19 5.9
MSM 192 59
Insurance Type Private 142 42.9
Public 113 34.2
Uninsured 76 22.9
Baseline CD4 T-Lymphocyte count (cells/ml) Overall 380.6 (267.2)
<200 88 27.3
200-350 79 24.5
>350 155 48.1
Missing 13
Baseline Viral Load (copies/mL) <10,000 85 28.3
>10,000 215 71.7
Missing 35
Open in a new tab

Table 2.

Descriptive Statistics on HRQOL, Depression, Social Support, and Stigma.

Variable Categories N or Mean (SD) % or range
EuroQOL
Depression/anxiety Moderate/extreme 182 54.7
Pain Moderate/extreme 147 44.4
Mobility Some problems/unable 47 14.1
Self-care Some problems/unable 10 3
Usual Activities Some problems/unable 63 18.9
Social Support Information Support 3.22 (1.38) 1.00 – 5.00
Tangible Support 2.90 (4.52) 1.00 – 5.00
Affectionate Support 3.61 (1.44) 1.00 – 5.00
Positive Social Interaction 3.43 (1.32) 1.00 – 5.00
Depression None 214 68.1
Moderate/Severe 100 31.9
Disclosure of HIV Status Yes 256 76.6
No 78 23.4
Anticipated Stigma Family 2.67 (1.37) 1.00 – 5.00
Friends 2.75 (1.28) 1.00 – 5.00
Healthcare provider 1.80 (0.94) 1.00 – 5.00
Earnshaw HIV Stigma Enacted 2.17 (0.71) 1.00 – 4.00
Disclosure 3.05 (0.60) 1.00 – 4.00
Negative Self Image 2.28 (0.74) 1.00 – 4.00
Public 2.70 (0.68) 1.00 – 4.00
Open in a new tab

Bivariate and Multivariate Analyses

Detailed results of bivariate analyses for each EuroQOL domain are presented in Table 3, with sequential multivariate analyses presented in Tables 4 and 5.

Table 3.

Unadjusted Models for Pain, Depression, Mobility & Usual Activity

Variable Pain OR (95% CI) Depression OR (95% CI) Mobility OR (95% CI) Usual Activity OR (95% CI)
Female 2.28 (1.30 – 4.01) 2.44 (1.35 – 4.40) 2.98 (1.52 – 5.84) 2.36 (1.26 – 4.41)
Male 1.0 1.0 1.0 1.0
Black 1.17 (0.71 – 1.93) 0.61 (0.37 – 1.00) 1.55 (0.71 – 3.40) 1.03 (0.54 – 1.95)
White 1.0 1.0 1.0 1.0
Black Female 2.41 (1.15 – 5.04) 1.44 (0.68 – 3.06) 5.05 (1.62 – 15.76) 2.29 (0.94 – 5.55)
Black Male 1.22 (0.69 – 2.14) 0.56 (0.33 – 0.98) 2.27 (0.79 – 6.50) 1.16 (0.54 – 2.47)
White Female 3.39 (1.05 – 10.88) 2.72 (0.71 – 10.43) 7.67 (1.86 – 31.55) 3.94 (1.18 – 13.16)
White Male 1.0 1.0 1.0 1.0
Age (per 10 years) 1.22 (1.01 – 1.46) 0.79 (0.66 – 0.95) 1.68 (1.32 – 2.15) 1.35 (1.09 – 1.67)
HIV risk factor
 Heterosexual 2.22 (1.36 – 3.62) 1.38 (0.85 – 2.23) 3.60 (1.81 – 7.15) 2.38 (1.31 – 4.32)
 IVDU 1.24 (0.48 – 3.25) 3.39 (1.08 – 10.62) 1.36 (0.29 – 6.46) 1.51 (0.46 – 4.90)
 MSM 1.0 1.0 1.0 1.0
Insurance Type
 Public 3.08 (1.67 – 5.66) 1.05 (0.58 – 1.88) 4.33 (1.82 – 10.30) 3.43 (1.63 – 7.20)
 Uninsured 1.86 (1.03 – 3.36) 1.55 (0.86 – 2.78) 3.00 (1.23 – 7.32) 1.56 (0.70 – 3.52)
 Private 1.0 1.0 1.0 1.0
CD4+ T cell count
 <200 1.57 (0.91 – 2.71) 0.52 (0.30 – 0.91) 1.07 (0.51 – 2.22) 1.78 (0.91 – 3.46)
 200- 350 0.67 (0.37 – 1.19) 0.57 (0.32 – 1.00) 0.59 (0.24 – 1.41) 1.05 (0.50 – 2.17)
 >350 1.0 1.0 1.0 1.0
Social support
 Emotional 0.79 (0.67 – 0.93) 0.81 (0.69 – 0.95) 0.83 (0.67 – 1.04) 0.88 (0.72 – 1.07)
 Tangible 0.95 (0.82 – 1.10) 0.84 (0.72 – 0.97) 0.95 (0.77 – 1.17) 1.04 (0.87 – 1.25)
 Affectionate 0.82 (0.70 – 0.96) 0.68 (0.57 – 0.80) 0.90 (0.72 – 1.12) 0.84 (0.69 – 1.01)
  Positive social interaction 0.72 (0.60 – 0.85) 0.65 (0.54 – 0.78) 0.71 (0.55 – 0.90) 0.76 (0.62 – 0.94)
 Total (per 10 units) 0.90 (0.83 – 0.97) 0.85 (0.78 – 0.92) 0.92 (0.82– 1.03) 0.92 (0.84 – 1.02)
Anticipated Stigma
Family 1.22 (1.03 – 1.45) 1.42 (1.20 – 1.69) 1.41 (1.09 – 1.81) 1.23 (0.99 – 1.51)
Friend 1.23 (1.03 – 1.47) 1.53 (1.27 – 1.84) 1.60 (1.21 – 2.11) 1.33 (1.06 – 1.67)
Healthcare provider 1.14 (0.90 – 1.44) 1.09 (0.86 – 1.38) 1.59 (1.17 – 2.17) 1.07 (8.80 – 1.44)
HIV Stigma
 Enacted stigma 1.27 (0.92 – 1.76) 1.90 (1.35 – 2.67) 1.86 (1.14 – 3.03) 1.39 (0.92 – 2.11)
 Disclosure concern 0.94 (0.64 – 1.39) 1.98 (1.33 – 2.96) 1.05 (0.60 – 1.84) 1.21 (0.73 – 1.99)
  Negative self image 1.34 (0.98 – 1.84) 2.67 (1.86 – 3.75) 1.74 (1.12 – 2.72) 1.52 (1.02 – 2.26)
 Public Stigma 1.25 (0.89 – 1.76) 1.45 (1.03 – 2.05) 1.66 (0.98 – 2.79) 1.49 (0.94 – 2.37)
Depression
Moderate- severe 2.89 (1.73 – 4.81) --- 2.68 (1.36 – 5.27) 4.84 (2.62 – 8.95)
None 1.0 --- 1.0 1.0
Disclosure
Yes 0.92 (0.55 – 1.55) 0.89 (0.53 – 1.50) 0.74 (0.36 – 1.50) 0.88 (0.46 – 1.67)
No 1.0 1.0
Open in a new tab

Table 4.

Adjusted Models for Pain and Depression

Variable Pain (n = 327) Depression (n = 329)
Model 1 Model 2 Model 1 Model 2
Black 0.97 (0.57 – 1.65) 0.90 (0.46 – 1.81) 0.50 (0.29 – 0.86) 0.75 (0.35 – 1.61)
White 1.0 1.0 1.0 1.0
Female 1.78 (0.98 – 3.26) 1.24 (0.53 – 2.90) 3.77 (1.93 – 7.37) 4.02 (1.42 – 11.42)
Male 1.0 1.0 1.0 1.0
Age (per 10 years) 1.13 (0.93 – 1.37) 1.17 (0.90 – 1.52) 0.69 (0.56 – 0.84) 0.65 (0.46 – 0.91)
Insurance Type
 Public 2.62 (1.39 – 4.93) 2.45 (1.06 – 5.65) 1.02 (0.54 – 1.94) 0.97 (0.39 – 2.43)
 Uninsured 1.90 (1.04 – 3.47) 1.31 (0.61 – 2.85) 1.57 (0.85 – 2.90) 1.75 (0.75 – 4.12)
 Private 1.0 1.0 1.0 1.0
Social support
 Emotional -- 0.91 (0.66 – 1.26) -- 1.09 (0.74 – 1.59)
 Tangible -- 1.31 (0.99 – 1.73) -- 1.23 (0.91 – 1.66)
 Affectionate -- 0.95 (0.70 – 1.28) -- 0.63 (0.44 – 0.93)
 Positive social interaction -- 0.76 (0.52 – 1.11) -- 1.11 (0.72 – 1.70)
Anticipated Stigma
 Family -- 1.20 (0.88 – 1.64) -- 1.03 (0.73 – 1.45)
 Friend -- 1.15 (0.76 – 1.74) -- 1.42 (0.89 – 2.27)
 Health care provider -- 1.07 (0.74 – 1.56) -- 0.91 (0.57 –1.46)
HIV Stigma
 Enacted stigma -- 0.61 (0.33 – 1.13) -- 1.19 (0.60 – 2.37)
 Disclosure concerns -- 0.98 (0.50 – 1.94) -- 1.31 (0.61 – 2.81)
 Negative self-image -- 0.89 (0.53 – 1.49) -- 1.54 (0.85 – 2.81)
 Public -- 1.02 (0.56 – 1.88) -- 0.47 (0.23 – 0.96)
Depression
 Moderate- severe -- 2.68 (1.26 – 5.70) -- 11.45 (4.04 – 32.50)
 None -- 1.0 -- 1.0
Open in a new tab

Table 5.

Adjusted Models for Mobility and Usual Activities

Variable Mobility (n = 328) Usual Activities (n = 330)
Model 1 Model 2 Model 1 Model 2
Black 1.21 (0.53 – 2.79) 01.20 (0.34 – 4.25) 0.85 (0.43 – 3.27) 0.55 (0.22 – 1.40)
White 1.0 1.0 1.0 1.0
Female 1.79 (0.86 – 3.75) 0.76 (0.18 – 3.23) 1.67 (0.85 – 3.27) 1.21 (0.43 – 3.42)
Male 1.0 1.0 1.0 1.0
Age (per 10 years) 1.62 (1.24 – 2.12) 1.68 (1.07 – 2.65) 1.24 (0.99 – 1.57) 1.42 (1.01 – 2.00)
Insurance Type
 Public 3.23 (1.30 – 8.05) 6.24 (1.51 – 25.84) 2.95 (1.36 – 6.40) 3.61 (1.22 – 10.72)
 Uninsured 3.55 (1.37 – 9.17) 2.80 (0.63 – 12.42) 1.66 (0.72 – 3.79) 1.30 (0.40 – 4.23)
 Private 1.0 1.0 1.0 1.0
Social support
 Emotional -- 1.18 (0.69 – 2.04) -- 0.95 (0.62 – 1.48)
 Tangible -- 1.39 (0.86 – 2.25) -- 1.49 (0.99 – 2.25)
 Affectionate -- 1.05 (0.62 – 1.78) -- 0.82 (0.54 – 1.22)
 Positive social interaction -- 0.57 (0.29 – 1.10) -- 0.80 (0.49 – 1.30)
Anticipated Stigma
 Family -- 1.20 (0.68 – 2.13) -- 1.12 (0.74 – 1.68)
 Friend -- 2.04 (0.88 – 4.76) -- 1.54 (0.89 – 2.68)
 Health care provider 2.40 (1.36 – 4.24) 0.79 (0.47 – 1.31)
HIV Stigma
 Enacted stigma -- 0.99 (0.33 – 3.03) -- 0.42 (0.18 – 0.98)
 Disclosure concerns 1.05 (0.27 – 4.13) 1.77 (0.67 – 4.71)
 Negative self image -- 1.48 (0.61 −3.60) -- 0.76 (0.38 – 1.52)
 Public 0.33 (0.10 – 1.15) 0.95 (0.40 – 2.25)
Depression
 Moderate- severe -- 0.63 (0.18 – 2.18) -- 4.00 (1.53 – 10.44)
 None -- 1.0 -- 1.0
Open in a new tab

Discussion

Among 335 participants newly entering HIV medical care enrolled in the iENGAGE behavioral clinical trial, we observed important sub-group differences in HRQOL. Women had increased odds of reporting worse HRQOL than their male counterparts across most quality of life domains. Sequential multivariable models demonstrated attenuation of parameter estimates and loss of statistical significance for many of the associations with impaired HRQOL observed in unadjusted analyses, particularly those seen among women. We postulate that complex relationships exist between the independent variables we measured and HRQOL that explain the observed findings in sequential multivariable modeling. For example, the attenuation of the relationship with poor HRQOL among women when sequentially adding other socio-demographic and psychosocial factors to models suggests that these additional, staged variables contribute to lower HRQOL reported by women across multiple domains. These results suggested a potential role for insurance type, perhaps as a proxy for socio-economic status, and the psychosocial domains of social support, stigma, and depression, as contributors to the poor HRQOL reported by women, and potentially modifiable intervention targets.

Study data indicate that gender-specific strategies to address HRQOL are needed among women initiating HIV care. This is vital as depression and suboptimal ART adherence have been associated with poor HRQOL (12; 31). Women living with HIV have lower rates of ART adherence, higher rates of depressive symptoms, and worse HRQOL when compared to their male counterparts (8; 32–34). The intersection of depression, worse HRQOL, and suboptimal adherence appear to be more challenging for women highlighting the need for interventions addressing these domains to enhance wellbeing and HIV outcomes, including HRQOL.

Social support is a modifiable factor associated with HRQOL. In this study, persons who reported higher perceived social support were less likely to have impaired pain and mood-related HRQOL (see Table 3). The importance of this relationship has been documented previously (11; 14). Access to social support has the potential to improve outcomes such as HRQOL (11). Therefore, future research should consider social support interventions and programming as a potential method of enhancing HRQOL among persons new to HIV care.

Consistent with previous studies, higher perceived HIV-related stigma was associated with poor HRQOL (8; 10; 35). Fear of perceived HIV-related stigma or discrimination often leads PLWH to isolate themselves from others (36). In addition, fear of stigma could result in disengagement from HIV care (i.e., missed medical visits, poor medication adherence, and medication discontinuation) and subsequent poor health outcomes (37). Therefore, stigma-reduction interventions are critical at care entry as they have potential to improve engagement in care and medication adherence, but also to enhance HRQOL in PLWH.

This study is not without limitations. First, we reported on cross-sectional data, which limits our ability to infer causality. At the conclusion of the iENGAGE study, we will assess changes in HRQOL at 48-weeks follow-up relative to the baseline assessment in both the intervention and control groups. Second, the enrolled sample is not representative of all PLWH in the US, but results may be generalized to PLWH in similar clinical settings. In addition, this study was not powered to detect difference in HRQOL between White women and other race-by-gender variables; therefore, stratified results should be interpreted with caution. Last, the measures were largely self-reported increasing the potential of information bias and social desirability bias. Yet, self-administered electronic questionnaires, as were used in this study, have been shown to reduce such bias (38).

Conclusion

Our findings suggest that social support, depression, and HIV-related stigma were significantly related to HRQOL across multiple domains, with women experiencing more problems than men. Researchers should use qualitative and mixed methods approaches to gain a better understand of the complex relationships between non-modifiable and modifiable factors and HRQOL, in order to inform targeted interventions aimed at improving HRQOL among PLWH initiating medical care. Our findings of group differences in HRQOL among women who are new to HIV care can be used to inform multifaceted, gender-specific interventions aimed at optimizing HRQOL and reducing health disparities, centering on social support, and addressing stigma and depression.

Acknowledgements

We would like to thank the iENGAGE study team and the participants who enrolled in the study. Research reported in this publication was supported by the National Institute of Allergy and Infectious Disease of the National Institute of Health under award number R01AI103661 and Diversity Supplement to MJM parent iENGAGE study under number R01AI103661-S1.

Footnotes

Declaration of interest statement

The authors report no real or perceived vested interests that relate to this article that could be construed as a conflict of interest.

References

  1. Panel on Antiretroviral Guidelines for Adults and Adolescents. (2018). Guidelines for the use of Antiretroviral Agents in HIV-1-infected Adults and Adolecents. Retrieved from https://aidsinfo.nih.gov/contentfiles/lvguidelines/adultandadolescentgl.pdf.
  2. Samji H, Cescon A, Hogg RS, et al. ; Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada; PloS One; (2013): 8(12); e81355. doi: 10.1371/journal.pone.0081355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Gunthard HF, Saag MS, Benson CA, et al. ; Antiretroviral Drugs for Treatment and Prevention of HIV Infection in Adults: 2016 Recommendations of the International Antiviral Society-USA Panel; JAMA; (2016): 316(2); 191–210. doi: 10.1001/jama.2016.8900 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Degroote S, Vogelaers D, & Vandijck DM; What determines health-related quality of life among people living with HIV: an updated review of the literature; Arch Public Health; (2014): 72(1); 40. doi: 10.1186/2049-3258-72-40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Centers for Disease Control and Prevention. (2016). Health-Related Qualiy of Life (HRQOL). Retrieved from https://www.cdc.gov/hrqol/
  6. Mugavero MJ, Lin HY, Willig JH, et al. ; Missed visits and mortality among patients establishing initial outpatient HIV treatment; Clinical Infectious Diseases; (2009): 48(2); 248–256. doi: 10.1086/595705 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Hays RD, Cunningham WE, Sherbourne CD, et al. ; Health-related quality of life in patients with human immunodeficiency virus infection in the United States: results from the HIV Cost and Services Utilization Study; American Journal of Medicine; (2000): 108(9); 714–722. [DOI] [PubMed] [Google Scholar]
  8. Vyavaharkar M, Moneyham L, Murdaugh C, et al. ; Factors associated with quality of life among rural women with HIV disease; AIDS and Behavior; (2012): 16(2); 295–303. doi: 10.1007/s10461-011-9917-y [DOI] [PubMed] [Google Scholar]
  9. Merlin JS, Westfall AO, Chamot E, et al. ; Pain is independently associated with impaired physical function in HIV-infected patients; Pain Medicine; (2013): 14(12); 1985–1993. doi: 10.1111/pme.12255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Holzemer WL, Human S, Arudo J, et al. ; Exploring HIV stigma and quality of life for persons living with HIV infection; Journal of the Association of Nurses in AIDS Care; (2009): 20(3); 161–168. doi: 10.1016/j.jana.2009.02.002 [DOI] [PubMed] [Google Scholar]
  11. Li XM, Yuan XQ, Wang JJ, et al. ; Evaluation of impact of social support and care on HIV-positive and AIDS individuals’ quality of life: a nonrandomised community trial; Journal of Clinical Nursing; (2017): 26(3-4); 369–378. doi: 10.1111/jocn.13377 [DOI] [PubMed] [Google Scholar]
  12. Gakhar H, Kamali A, & Holodniy M; Health-related quality of life assessment after antiretroviral therapy: a review of the literature; Drugs; (2013): 73(7); 651–672. doi: 10.1007/s40265-013-0040-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Tomita A, Garrett N, Werner L, et al. ; Health-related quality of life dynamics of HIV-positive South African women up to ART initiation: evidence from the CAPRISA 002 acute infection cohort study; AIDS and Behavior; (2014): 18(6); 1114–1123. doi: 10.1007/s10461-013-0682-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Pereira M, & Canavarro MC; Gender and age differences in quality of life and the impact of psychopathological symptoms among HIV-infected patients; AIDS and Behavior; (2011): 15(8); 1857–1869. doi: 10.1007/s10461-011-9928-8 [DOI] [PubMed] [Google Scholar]
  15. Mbada CE, Onayemi O, Ogunmoyole Y, et al. ; Health-related quality of life and physical functioning in people living with HIV/AIDS: a case-control design; Health Qual Life Outcomes; (2013): 11; 106. doi: 10.1186/1477-7525-11-106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Honiden S, Sundaram V, Nease RF, et al. ; The effect of diagnosis with HIV infection on health-related quality of Life; Quality of Life Research; (2006): 15(1); 69–82. doi: 10.1007/s11136-005-8485-x [DOI] [PubMed] [Google Scholar]
  17. Modi R, Amico KR, Knudson A, et al. ; Assessing effects of behavioral intervention on treatment outcomes among patients initiating HIV care: Rationale and design of iENGAGE intervention trial; Contemporary Clinical Trials; (2018): 69; 48–54. doi: 10.1016/j.cct.2018.03.003 [DOI] [PubMed] [Google Scholar]
  18. Kitahata MM, Rodriguez B, Haubrich R, et al. ; Cohort profile: the Centers for AIDS Research Network of Integrated Clinical Systems; International Journal of Epidemiology; (2008): 37(5); 948–955. doi: 10.1093/ije/dym231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. ClinicalTrials.gov. (2013). Integrating Engagement and Adherence Goals Upon Entry iENGAGE to Control HIV. Bethesda, MD: National Library of Medicine Retrieved from clinicaltrials; gov/ct2/show/NCT01900236. [Google Scholar]
  20. Lubetkin EI, Jia H, Franks P, et al. ; Relationship among sociodemographic factors, clinical conditions, and health-related quality of life: examining the EQ-5D in the U.S. general population; Quality of Life Research; (2005): 14(10); 2187–2196. doi: 10.1007/s11136-005-8028-5 [DOI] [PubMed] [Google Scholar]
  21. Johnson JA, & Coons SJ; Comparison of the EQ-5D and SF-12 in an adult US sample; Quality of Life Research; (1998): 7(2); 155–166. [DOI] [PubMed] [Google Scholar]
  22. Rabin R, & de Charro F; EQ-5D: a measure of health status from the EuroQol Group; Annals of Medicine; (2001): 33(5); 337–343. [DOI] [PubMed] [Google Scholar]
  23. EuroQol G; EuroQol--a new facility for the measurement of health-related quality of life; Health Policy; (1990): 16(3); 199–208. [DOI] [PubMed] [Google Scholar]
  24. Kroenke K, Strine TW, Spitzer RL, et al. ; The PHQ-8 as a measure of current depression in the general population; Journal of Affective Disorders; (2009): 114(1-3); 163–173. doi: 10.1016/j.jad.2008.06.026 [DOI] [PubMed] [Google Scholar]
  25. Razykov I, Ziegelstein RC, Whooley MA, et al. ; The PHQ-9 versus the PHQ-8--is item 9 useful for assessing suicide risk in coronary artery disease patients? Data from the Heart and Soul Study; Journal of Psychosomatic Research; (2012): 73(3); 163–168. doi: 10.1016/j.jpsychores.2012.06.001 [DOI] [PubMed] [Google Scholar]
  26. Monahan PO, Shacham E, Reece M, et al. ; Validity/reliability of PHQ-9 and PHQ-2 depression scales among adults living with HIV/AIDS in western Kenya; Journal of General Internal Medicine; (2009): 24(2); 189–197. doi: 10.1007/s11606-008-0846-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gjesfjeld CD, Greeno CG, & Kim KH; A Confirmatory Factor Analysis of an Abbreviated Social Support Instrument: The MOS-SSS; Research on Social Work Practice; (2008): 18(3); 231–237. [Google Scholar]
  28. Dour HJ, Wiley JF, Roy-Byrne P, et al. ; Perceived social support mediates anxiety and depressive symptom changes following primary care intervention; Depression and Anxiety; (2014): 31(5); 436–442. doi: 10.1002/da.22216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Earnshaw VA, Smith LR, Chaudoir SR, et al. ; HIV stigma mechanisms and well-being among PLWH: a test of the HIV stigma framework; AIDS and Behavior; (2013): 17(5); 1785–1795. doi: 10.1007/s10461-013-0437-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Bunn JY, Solomon SE, Miller C, et al. ; Measurement of stigma in people with HIV: a reexamination of the HIV Stigma Scale; AIDS Education and Prevention; (2007): 19(3); 198–208. doi: 10.1521/aeap.2007.19.3.198 [DOI] [PubMed] [Google Scholar]
  31. Monteiro F, Canavarro MC, & Pereira M; Factors associated with quality of life in middle-aged and older patients living with HIV; AIDS Care; (2016): 28 Suppl 1; 92–98. doi: 10.1080/09540121.2016.1146209 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Vyavaharkar M, Moneyham L, Corwin S, et al. ; Relationships between stigma, social support, and depression in HIV-infected African American women living in the rural Southeastern United States; Journal of the Association of Nurses in AIDS Care; (2010): 21(2); 144–152. doi: 10.1016/j.jana.2009.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ike N, Hernandez AL, An Q, et al. (2015). Care and viral suppression among women, 18 US jurisdictions. Paper presented at the CROI 2015.
  34. Robertson K, Bayon C, Molina JM, et al. ; Screening for neurocognitive impairment, depression, and anxiety in HIV-infected patients in Western Europe and Canada; AIDS Care; (2014): 26(12); 1555–1561. doi: 10.1080/09540121.2014.936813 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Herrmann S, McKinnon E, Hyland NB, et al. ; HIV-related stigma and physical symptoms have a persistent influence on health-related quality of life in Australians with HIV infection; Health Qual Life Outcomes; (2013): 11; 56. doi: 10.1186/1477-7525-11-56 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Turan B, Smith W, Cohen MH, et al. ; Mechanisms for the Negative Effects of Internalized HIV-Related Stigma on Antiretroviral Therapy Adherence in Women: The Mediating Roles of Social Isolation and Depression; Journal of Acquired Immune Deficiency Syndromes; (2016): 72(2); 198–205. doi: 10.1097/QAI.0000000000000948 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Turan B, Budhwani H, Fazeli PL, et al. ; How Does Stigma Affect People Living with HIV? The Mediating Roles of Internalized and Anticipated HIV Stigma in the Effects of Perceived Community Stigma on Health and Psychosocial Outcomes; AIDS and Behavior; (2016). doi: 10.1007/s10461-016-1451-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Stirratt MJ, Dunbar-Jacob J, Crane HM, et al. ; Self-report measures of medication adherence behavior: recommendations on optimal use; Translational Behavioral Medicine; (2015): 5(4); 470–482. doi: 10.1007/s13142-015-0315-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES