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. Author manuscript; available in PMC: 2020 May 1.
Published in final edited form as: AIDS Care. 2019 Feb 3;31(5):563–571. doi: 10.1080/09540121.2018.1563281

Depression, social support, and stigma as predictors of quality of life over time: Results from an Asha-based HIV/AIDS intervention in India

Dana Rose Garfin 1, Sanghyuk Shin 2, Maria L Ekstrand 3, Kartik Yadav 4, Catherine L Carpenter 5, Sanjeev Sinha 6, Adeline M Nyamathi 7
PMCID: PMC6995752  NIHMSID: NIHMS1517771  PMID: 30714386

Abstract

Quality of life (QOL) is associated with better outcomes in HIV/AIDS populations, yet few studies have examined variables that predict improvements in QOL over time. We explored these issues in a sample (N=600) of Women Living with HIV/AIDS (WLH/A) in India, enrolled in a nurse-led Asha (Accredited Social Health Activist) intervention. Trained local interviewers ascertained self-report data using metrics previously validated in Indian populations at baseline and again 6 months later. Prior to enrollment, on average women were 34.31 (SD=6.96) years old and had received their diagnosis 48.16 months (SD=34.56) ago. Latent Class Analysis (LCA) was utilized to identify constellations of responses on key psychosocial indicators (depression, social support, internalized stigma and stigma fears) and their relationship with QOL over time. At baseline, mean depression, social support, internalized stigma, and stigma fears was M=9.18 (SD=3.08), M=1.08 (SD=0.22), M=2.30 (SD=0.35), and M=2.81 (SD=0.26), respectively. We identified three classes: Class 1) Highest Social Resources/ Lowest Depression; Class 2) Some Social Resources/ Highest Depression; and Class 3) Lowest Social Resources/ Higher Depression. At baseline, Class 3 reported the lowest QOL (M=0.25, SD=0.26); Class 1 reported the highest (M=0.37, SD=0.33). QOL in Class 2 did not differ from QOL in Class 3, likely due to the potent effects of high depression. After participation in the Asha intervention, at six-month follow-up, all groups reported improved QOL; class membership no longer predicted variability (contrast between Class 2 and 1=−0.05, 95% CI=−0.14, 0.04; contrast between Class 3 and 1=0.01, 95% CI=−0.03, 0.05; contrast between Class 3 and 2=0.07, 95% CI, −0.02, 0.15). Psychosocial indicators are important predictors of QOL; an Asha-supported approach to addressing HIV/AIDS-related factors may have broad applicability to improve QOL in WLH/A in India.

Keywords: AIDS, HIV, Quality of Life, Stigma, Depression, Social Support, Latent Class Analysis

Introduction

Quality of life (QOL) has been associated with better health and potentially longer survival times in HIV/AIDS populations (Bucciardini et al., 2014). QOL measures functioning and adaption on physical, social, emotional and cognitive domains, can be a proxy measure of HIV/AIDS-related disease progression and impairment (Erlandson, Allshouse, Jankowski, Mawhinney, Kohrt, & Campbell, 2014), and correlates with clinically-relevant HIV/AIDS-related physiological markers and symptomatology (Bucciardini et al., 2014; Mwesigire, Martin, Seeley, & Katamba, 2015; Nyamathi et al., 2017). Targeting QOL may help HIV-treatment adherence and improve effectiveness of HIV/AIDS-related interventions (Mwesigire et al., 2015).

Identifying profiles of Women Living with HIV/AIDS (WLH/A) in need of assistance and examining potential individual-level differences in response to treatment could inform targeted efforts to improve QOL. Latent class analysis (LCA) identifies subgroups, characterized by patterns of risk factors (i.e., tailoring variables) potentially related to differential responses to treatment (Lanza & Rhoades, 2013). Adaptive interventions can then implement varying decision rules regarding intervention type and dose according to key individual-level characteristics (Collins, Murphy, & Bierman, 2004).

Predictors of subgroup membership: Depression, social support, and stigma

Responses to psychosocial indicators may help identify subgroup membership for WLH/A related to treatment-response and QOL over time. Potential QOL-relevant tailoring variables include depression, social support, and stigma (Rueda et al., 2016). Cross-sectional work has described social support as a mediator of depression (Hou et al., 2014) and vice versa (Jia et al., 2004; Simoni, Frick, Lockhart, & Liebovitz, 2002). Previous research has linked internalized stigma and depression in HIV populations in Southern India (Chan et al., 2017; Steward et al., 2011). Moreover, also in HIV-positive individuals in India, depression and stigma were highly correlated and depression predicted lower QOL (Charles et al., 2012).

Depression is common in HIV-infected individuals (Campos, Guimarães, & Remien, 2010) and is positively associated with lower QOL (Hou et al., 2014), ART non-adherence (Dworkin, Douglas, Rani, & Chakraborty, 2016) and lower gains during ART interventions (Cook et al., 2007). In a sample of highly-compliant-HIV-positive individuals, depression was negatively associated with QOL in all domains assessed (Jia et al., 2004). Improvements in depression have been linked with improved QOL (Bengtson et al., 2015).

Prior research indicated social support as an independent predictor of QOL in HIV/AIDS populations (Hou et al., 2014; Oetzel et al., 2014), with both direct and indirect effects (Oetzel et al., 2014). Multiple mechanisms may underlie these relationships. Social support may impact QOL and related outcomes through enhanced coping (Xiao et al., 2018), improved health behavior choices, increased emotional well-being, and reduction of physiological stress responses (Hostinar, Sullivan, & Gunnar, 2014). Social support has also been linked to higher ART adherence (Simoni et al., 2002) and CD4 cell count (Persson, Ostergren, Hanson, Lindgren, & Naucler, 2002).

Stigma is also negatively correlated with QOL (Nyamathi et al., 2017). It has been documented as a barrier to receiving care, both due to delays in health-care seeking (Steward et al., 2013) and denial of proper care from providers (Kiriazova et al., 2017). Internalized stigma is related to health care avoidance (Dos Santos et al., 2014) and delays in seeking treatment after testing HIV positive (Steward, Bharat, Ramakrishna, Heylen, & Ekstrand, 2013). Stigma fears are associated with impaired healthcare access, adherence, social interaction and social support (Carr & Gramling, 2004). Given increasing life expectancy for individuals on effective ART (Trickey et al., 2017), initiating and maintaining early ART treatment is critical to increasing survival and QOL.

Interventions targeting QOL for WLH/A

WLH/A in developing nations, including India, face critical challenges to receiving adequate care (Srivastava et al., 2017) that may be detrimental to QOL such as patriarchal cultural values leading to abuse and trauma (Panchanadeswaran & Koverola, 2005) and limited knowledge regarding HIV transmission and care (Pallikadavath, Garda, Apte, Freedman, & Stones, 2005). Multi-faceted-health-care provision that integrates task-shifting approaches using community health workers (CHW) to augment orthodox care may help address such challenges (Nyamathi et al., 2016). Task-shifting utilizes existing community structures and transfers tasks from highly-trained professionals to CHW with less or no prior healthcare training (Joshi et al., 2014). CHWs can increase intervention scalability and feasibility (Nyamathi et al., 2012) and benefit communities with shortages of highly-trained medical professionals (Joshi et al., 2014).

Our team’s Asha (Accredited Social Work Activist) WLH/A intervention in India implemented a CHW model that accounted for WLH/A’s unique challenges. Asha are community members and offer culturally-sensitive and relatable, cost-effective care; they provide logistical, medical, and social support (Nyamathi et al., 2016), and work to reduce the impact of stigma. They help medical professionals transfer and reinforce health information, providing an important bridge between WLH/A and the HIV/AIDS-treatment-community (Nyamathi et al., 2016).

The present analyses examined whether psychosocial variables measured at baseline could help classify subgroups of WLH/A enrolled in an Asha-supported intervention and whether subgroup membership predicted QOL at baseline, change over time, and at 6-month follow-up.

Method

Baseline and 6-month follow-up data was collected from 600 WLH/A in Southern India enrolled in a larger nurse-led-Asha-support randomized control trial (RCT). The RCT implemented a factorial design: 1) Standard education (Asha-support-attention-control); 2) Enhanced-nutrition education; 3) Enhanced-nutrition supplements; and 4) Combined nutrition education and supplements. (All groups integrated Asha support). Ethics approval was obtained in US and India.

Women lived in rural Andhra Pradesh near one of four high-HIV-prevalence sites. Each site had one Community Health Center and 1–2 Primary Care Centers (PHCs) women were recruited from. Geographic randomization (four regions randomized into one of four arms) was implemented to minimize group cross-over effects. Inclusion criteria included WLH/A who were: (a) aged 18–50; (b) receiving ART for ≥ 3 months, validated by an ART card given by the district hospital to all ART patients; (c) demonstrating CD4 levels ≥ 100 cells/mm3; and (d) living with a child aged 3–8.

Flyers were posted in the PHCs; interested women privately met with research staff, received study information, provided written-informed consent (signature/thumbprint) and complete a 2-minute-eligibility screener. Eligible participants completed a second written-informed (signature/thumbprint) consent.

Baseline data collection was staggered over two years (May 2014-November 2016), enrolling 100 women at a time (25 per condition) in six rounds. Follow-up data were collected six months after baseline. Data was collected via face-to-face interviews in a private location, recorded via secure-tablet application hosted by UCLA on Google and Microsoft cloud servers. Individual files were kept onsite in locked cabinets; responses were deidentified to ensure confidentiality. Additional variables were collected; only those pertinent to present analyses are presented herein.

Asha Selection Training

Asha were lay village women, selected and trained by the Principal Investigators (PIs) and Project Director (PD). Asha eligibility included: age 20–50; education ≥ 8th grade; interest in caring for WLH/A; history of community service; and residence in similar villages (and similar caste membership) as the WLH/A.

Asha training included education about study protocol, needs of WLH/A, basic HIV/AIDS disease progression, importance of ART adherence, strategies to assist WLH/A in coping with their illness, and maintaining participant confidentiality. Four Asha, employed full- time, were assigned to each program and trained per program design. Quarterly quality-assurance assessments occurred.

Asha-support Program

The Asha-support program was derived from focus-group sessions with community members prior to commencement of study procedures for cultural relevance and to address specific challenges of WLH/A in India. A minimum of six group sessions (three modules with two sessions each; see Table I) were delivered by experts in a large room at the research site. (Intervention groups that received nutrition education participated in two additional models.) Asha-support augmented and reinforced group-session content and provided additional support regarding ART adherence and self-care. Ashas attended all group sessions with participants, visited women 1:1 weekly, monitored barriers to and recording of ART adherence, accompanied women to medical visits, and provided additional assistance.

Table I.

Outline of group-based modules

Keeping Healthy
Sessions 1 & 2 (Module 1A and 1B):

  1. Getting support; talking to my Asha and my doctor/nurse; learning about HIV/AIDS.

  2. Learning about and taking ART; recognizing an OI; healthy eating

  3. Keeping with healthy routines.

  4. Deciding with whom to share health problems.
Caregiving
Module 2A and 2B:

  1. Breastfeeding safely or learning to sterilize the formula.

  2. Promoting care-giving for family while caring for my own mental and physical well-being.

  3. Engaging in life skill education.

  4. Staying in sync with my children; identifying their needs.
Staying upbeat
Module 3A and 3B:

  1. Finding the joys in life; creating a social network for myself

  2. Dealing well with problems; Keeping positive relations with family

  3. Taking active steps to improve my life every day

  4. Getting psychological assistance
Healthy eating for Self and Family
Module 4A and 4B: (Program 2 & 4 only)

  1. Learning about low-cost foods that are nutritious

  2. Where to purchase foods

  3. Learning how to monitor my food intake

  4. Considering ways to eat healthy

  5. Participating in cooking classes
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Measures

Depression.

A modified Center for Epidemiologic Depression Scale (CES-D), short version (Radloff, 1977) was used. The CES-D has been established as a reliable and valid measure in India and elsewhere (Nyamathi et al., 2011). It was dichotomized at ≥ 10 according to standard guidelines, given α=.52 (Zhang et al., 2012).

Social support.

The 18-item MOS Social Support Scale (Sherbourne & Stewart, 1991), previously used in India (Suhadev et al., 2009), assesses support (e.g., emotional/informational, affectionate, tangible) availability (1=none of the time, 5=all of the time). Social support was low: (M=1.08, SD=0.22, α=.93); 60.67% (n=364) reported social support an average of “none of the time.” Responses were dichotomized (0=no social support; 1=any social support).

Stigma fears.

Developed for persons living with HIV/AIDS (Ekstrand, 2017), with versions previously used in India (Heylen, Panicker, Chandy, Steward, & Ekstrand, 2015) this measure consists of five subscales (family, friends, healthcare workers, community, work) pertaining to fear of being stigmatized. Fear of divorce was not included as over half (60%) of the sample was not married. Several work-related items were eliminated: factor analyses revealed lack of relevance for our sample (e.g., losing promotion, work relocation). Alpha=.80 for remaining items. Responses were dichotomized into high/low at the median (0=low stigma fear; n=279, 46.50%; 1=high stigma fear, n=321, 53.50%).

Internalized stigma.

This 10-item measure previously used in India assessed the extent respondents believed that, as HIV-infected people they deserved stigmatization (Ekstrand, Bharat, Ramakrishna, & Heylen, 2012; Steward et al., 2011); α=.76. Responses were dichotomized high/low at the median (0=low internalized stigma; n=298, 49.67%; 1= high internalized stigma; n=302, 50.33%).

Quality of life (QOL).

The 10-item short form Quality of Life Enjoyment and Satisfaction Questionnaire (Endicott, Nee, Harrison, & Blumenthal, 1993), has been internationally validated and previously implemented in India (Heylen et al., 2015; Ritsner, Kurs, Kostizky, Ponizovsky, & Modai, 2002). Items were reverse coded for improved interpretation (higher values=greater QOL). Reliability was good at baseline (α=.80) and follow-up (α=.86).

Covariates.

Demographics (education, marital status, and income); % ART adherence (Giordano, Guzman, Clark, Charlebois, & Bangsberg 2004); opportunistic infections (8 common included TB, febrile illness, candida, diarrhea); and food insecurity (The Household Food Insecurity Access Scale; Coates, Swindale, & Bilinsky, 2007) were assessed at baseline. Intervention group membership was included as a potential predictor of baseline QOL.

Analytic strategy

A two-step approach (Roy & Raver, 2014; Savage & Birch, 2016) was implemented. First, guided by theory and exploratory analyses, latent classes were identified using standard model fit guidelines (i.e., Akaike information criteria [AIC] and Bayesian information criteria [BIC]). LCA identifies subgroups of individuals sharing common patterns among a set of variables and helps identify optimal number of classes to describe a given population (Lanza & Cooper, 2016). Individuals are assigned to a class based on their probability of belonging to that class given their data on the observed variables that help define the latent classes. Social support, depression, internalized stigma, and stigma fears were evaluated as predictors of latent class membership.

Since the four indicator variables were initially continuous measures, a series of latent profile analyses (LPA) were conducted using a Gaussian distribution, then compared to results of the LCA, which utilized dichotomized versions of the variables in a logit distribution. (LCA and LPA are conceptually identical). Potential iterations were set at 100.

Each observation has a predicted class that can be identified based on the calculation of their predicted posterior class probability. These are predicted probabilities and responses may vary within classes. These probabilities define the latent classes and, after selection, are stored to be used as an independent variable for subsequent analyses.

Variables that predicted QOL at baseline were examined in bivariate regressions; those significant were included in longitudinal multivariate models. Next, a repeated measures approach was implemented using STATA’s mixed command to examine whether membership in latent class was associated with QOL over time. Independent variables included LCA class membership, time, LCA class × time interaction, and covariates that were statistically significant (as tested by OLS regressions) at baseline.

Results

Description of the sample

Average age was 34.3 (range 19–50, SD=6.97). The majority were widowed (n=308; 51.33%); 39.67% (n=238) were married, and 9% (n=54) were divorced. On average they had 1.86 children (SD=0.80, range 1–6).

Latent class identification

A three-class solution using dichotomized predictors best characterized the sample at baseline. Models had difficulty converging using continuous predictors in the LPA and had poorer model fit compared to LCA models. Moreover, the various profile groups in the Gaussian distribution had extreme proportions across classes (e.g., 98% in certain classes compared to 1% or 2 % in others), likely due to relative homogeneity between individuals for key observed variables (e.g., extremely low social support at baseline).

The best model fit suggested a three class solution using an LCA. BIC and AIC were lower with the three class solution (BIC=3171; AIC=3109) compared to the four (BIC=3200; AIC=3177) or five (BIC=3205; AIC=3177) class solutions. Thus three latent classes were identified: 1) Lower depression/ higher support / lower stigma fears (Highest Social Resources/ Lowest Depression); 2) Highest depression/ low support / lower internalized stigma/ lower stigma fears (Some Social Resources/ Highest Depression) 3) High depression/ lower social support/ higher stigma (Lowest Social Resources/High Depression). See Table II for percentages and probabilities. At follow-up, the sample as a whole exhibited lower depression (M=0.05, SD=0.32), internalized stigma, (M=0.004, SD=0.03), stigma fears (M=0.01, SD=0.13) and improved social support (M=4.96, SD=0.09) compared to baseline (for paired t-tests comparing baseline to follow-up scores all p-values < .001).

Table II.

Percent and probabilities of women of endorsing depression, internalized stigma, social support, and stigma fears at baseline

Entire sample Class 1:

Lower depression/ higher support / lower internalized/ lower stigma fears		 Class 2:

High depression/ low support / lower internalized stigma/ lower stigma fears		 Class 3:

High depression / lower social support/ high internalized stigma/ high stigma fears /
Latent group membershipa N=600 n=249
41.40%		 n=30
5%		 n=321
53.50%
Dichotomized Score Continuous Score Dichotomized Score Continuous Score Dichotomized Score Continuous Score Dichotomized Score Continuous Score
N % M SD n % M SD N % M SD n % M SD
Depression 147 24.50 9.18 3.08 36 14.46 8.51 2.88 30 100 14.37 2.36 81 25.23 9.21 2.82
Social support 236 39.33 1.08 0.22 127 51.00 1.13 0.33 0 0 1 0 109 33.96 1.04 0.07
Internalized stigma 302 50.33 2.30 0.35 103 41.37 2.26 0.34 0 0 2.12 0.21 199 61.99 2.34 0.14
Stigma fears 321 53.50 2.81 0.26 0 0 2.63 0.29 0 0 2.61 0.28 321 100 2.97 0.04
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a

Based on maximum posterior probabilities. Dichotomized scores represent % high.

Quality of life over time

Table III presents mean QOL by class membership at baseline and follow-up. Table IV presents results of repeated-measures analyses of QOL from baseline to 6-month follow up. The following covariates predicted QOL at baseline and were included in repeated-measures analyses: monthly household income in Rupees (b=−0.0002; 95% CI, −0.0002, −0.0001; p<.001), % ART adherence (b=0.002; 95% CI, 0.00004, 0.004; p=.045), food insecurity (b=−0.13; 95% CI, −0.19, −0.07; p<.001), and intervention group membership. Compared to Group 1 (attention control), Group 2 (b=0.07, 95% CI, 0.01, 0.14, p=.029) and Group 4 (b=0.07, 95% CI, 0.01, 0.14, p=.029) reported higher QOL. The interaction between class membership and time was significant: change in QOL over time was highest for lowest resource group. Table V presents contrasts tests of change in QOL for each class from baseline to follow-up: all groups exhibited statistically significant gains in QOL from baseline to 6-month follow-up. Table VI presents test of marginal contrasts (i.e., tests of simple effects): QOL scores between groups were statistically different at baseline although not at six-month follow-up.

Table III.

Means, standard deviations, and differences between groups of Quality of Life at baseline and six months

Quality of Life
Baseline
M(SD)		 Quality of Life
Six-months
M(SD)		 Change in
Quality of Life
M(SD)
Class 1: Highest Social Resources/ Lowest Depression 0.37 (0.33) 2.93(0.16) 2.56(0.40)
Class 2: Some Social Resource / Highest Depression 0.27(0.20) 2.89(0.25) 2.62(0.35)
Class 3: Lower Social Resources / High Depression 0.25(0.26) 2.93(0.16) 2.68(0.35)
ANOVA test of between group differences F(2, 597)=12.81, p <.001 F(2, 597)=1.04, p=0.355 F(2, 597)= 8.34 p<.001
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Table IV.

Results of repeated measures analyses of predicted class and quality of life between baseline and 6-month follow-upa

B SE Z p-value 95% CI
 Predicted classb
  1: Highest Social Resources/ Lowest Depression
  2: Some Social Resources/Highest Depression −0.12 0.05 −2.55 .011 −0.21 −0.03
  3: Lowest Social Resources/ High Depression −0.11 0.02 −5.52 <.001 −0.15 −0.07
 Time 2.56 0.02 121.38 <.001 2.51 2.60
 Predicted classb × timec
  1: Highest Social Resources/ Lowest Depression × baseline (reference group)
  2: Some Social Resources/ Highest Depression × 6-month 0.06 0.06 1.00 .318 −0.06 0.19
  3: Lowest Social Resources/ High Depression × 6-month follow-up 0.13 0.03 4.56 <.001 0.07 0.18
Constant 0.49 0.05 9.42 <.001 0.39 0.60
Random effects parameters Estimate SE 95% CI
 Subject: constant 4.27e-20 3.67e-20 7.93e-21, 2.30e-19
 Residual 0.06 0.002 0.05, 0.06
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a

Analyses control for monthly household income in Rupees, % ART adherence, and food insecurity at baseline and intervention group; test of independence between predicted class and intervention group were significant, χ2(6)=34.41, p<.001.

b
  1. Low depression/ high social support / low stigma fears
  2. High depression/ low social support / low internalized stigma/ low stigma fears
  3. High depression / low social support/ high internalized stigma / high stigma fears
b

Baseline=reference group for time

Table V.

Test of contrasts between baseline and 6 month follow-up by latent class on quality of life

Contrast SE z p 95% CI
Time@ predicted classa
(6m vs baseline) 1 (Highest Social Resources/ Lowest Depression) 2.56 0.02 121.38 <.001 2.51 2.60
(6m vs baseline) 2 (Some Social Resources/ Highest Depression) 2.62 0.06 43.19 <.001 2.50 2.74
(6m vs baseline) 3 (Lowest Social Recourses/ High Depression 2.68 0.02 144.71 <.001 2.65 2.72
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a

1=Low depression/ high social support / low stigma fears; 2=High depression/ low social support / low internalized stigma / low stigma fears; 3=High depression/ low social support/ high internalized stigma/ high stigma fears

Table VI.

Tests of contrasts between latent classes on quality of life at baseline and 6 months

Contrast SE Z p-value 95% CI
Predicted classa@ time
(2 vs 1) baseline −0.12 0.05 −2.55 .011 −0.21 −0.03
(2 vs 1) 6mo −0.05 0.05 −1.15 .252 −0.14 0.04
(3 vs 1) baseline −0.11 0.02 −5.52 <.001 −0.15 −0.07
(3 vs 1) 6 mo 0.01 0.02 0.70 .483 −0.03 0.05
(3 vs 2) baseline 0.003 0.05 0.06 .949 −0.09 0.09
(3 vs 2) 6 mo 0.07 0.05 1.47 .141 −0.02 0.16
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a
  1. Highest Social Resources/ Lowest Depression (low depression/ high social support / low stigma fears)
  2. Some Social Resources/ Highest Depression (high depression/ low social support / low internalized stigma / low stigma fears)
  3. Lowest Social Resources/ Higher Depression (high depression/ low social support/ high internalized sigma / high stigma fears)

Discussion

All participants received Asha-supported intervention and experienced significant improvements in QOL across three latent classes that differentiated QOL at baseline. At baseline, members of Class 1, who had the highest psychosocial resources (lower stigma, higher social support, lowest depression) reported higher baseline QOL relative to other groups, in line with prior research (Charles et al., 2012; Xiao et al., 2017); members of Class 2 and 3 (both with relatively higher depression) were not significantly different from each other. At six month follow-up, all groups improved QOL. Latent class membership no longer explained variability in QOL although Class 3 improved the most, likely due to lowest baseline scores. This suggests a compressive intervention that concurrently targets psychological, social, behavioral, and physiological needs may help WLH/A overcome baseline vulnerabilities, improving QOL and overall health for many.

For the majority of our sample, baseline-risk markers hung together in an expected manner (i.e., lower psychosocial resources with higher depression) (Hou et al., 2014). Indeed, Class 1 and Class 3 comprised 95% of our sample. Recent structural equation modeling similarly demonstrated relationships between lower social support, higher stigma, poor mental health, and lower QOL in a vulnerable HIV/AIDS sample (Mitchell et al., 2017), with lower QOL negatively correlated with viral suppression (Mitchell et al., 2017), emphasizing QOL as an important target for comprehensive HIV/AIDS care. Yet findings simultaneously emphasize the important relationship between depression and QOL. Class 2 members reported lower stigma compared to other groups but were more similar to Class 3 in terms of QOL scores than to Class 1. This emphasizes the critical link between depression, social support and QOL for WLH/A in India (Deshmukh, Borkar, & Deshmukh, 2017); a potent relationship was evident despite availability of some social resources. Although most HIV/AIDS-related research finds positive linear relationships between high depression and both low social support (Charles et al., 2012; Kalomo, 2017) and high stigma (Kalomo, 2017), for a minority of WLH/A in India, these interrelationships may be more complex, with depression and low social support detrimental to QOL despite availability of some psychosocial resources. Notwithstanding efforts of their community to intervene (evidenced by lower stigma), for highly depressed individuals, more structured support-based intervention may be necessary.

Results demonstrate the broad benefit of using lay CHWs to augment standard medical care: all classes improved QOL from baseline to follow-up. Previous work has demonstrated the effectiveness of the nurse-led Asha-based approach in WLH/A on improving outcomes including stigma (Nyamathi et al., 2013), adherence (Nyamathi et al., 2012), and depression (Nyamathi et al., 2012), which likely improve via reciprocal, interrelated mechanisms. Recent work in Southern India using a CHW-administered psychological intervention for depression found it superior to treatment as usual for depression and secondary outcomes (Patel et al., 2017). The approach benefited the community long-term in terms of cost savings and improved resident health and functioning (Patel et al., 2017).

Limitations

Our study has several limitations. Groups reported extremely low social support and high stigma at baseline, creating potential floor effects. Classes were not balanced: Class 2 (Highest Depression/Some Resources) comprised only 5% of the sample. Measures were self-reported and thus subject to bias. An attention-control was implemented for ethical reasons. Lastly, the unique characteristics of our sample may limit generalizability: future studies could explore these questions in other populations.

Conclusion

Constellations of scores on psychosocial indicators may elucidate variability in QOL in WLH/A in India. Depression, with or without additional psychosocial resources, may be a potent predictor of low QOL. Interventions combining focused education with Asha-support may expand existing structures within the community (Nyamathi et al., 2012), be beneficial for WLH/A with a range of psychosocial characteristics, help WLH/A overcome baseline vulnerabilities, and be appropriate for broad dissemination.

Contributor Information

Dana Rose Garfin, Sue & Bill Gross School of Nursing; Mail Code: 3959; University of California, Irvine, Irvine, CA 92697.

Sanghyuk Shin, Sue & Bill Gross School of Nursing; Mail Code: 3959; University of California, Irvine, Irvine, CA 92697.

Maria L. Ekstrand, UCSF School of Medicine, 550 16th. Street, San Francisco CA 94158.

Kartik Yadav, Sue & Bill Gross School of Nursing; Mail Code: 3959; University of California, Irvine, Irvine, CA 92697.

Catherine L. Carpenter, UCLA Center for Human Nutrition, David Geffen School of Medicine at UCLA, 14-193 Warren Hall, 900 Veteran Ave, Box 951742, Los Angeles, CA 90095.

Sanjeev Sinha, All India Institute of Medical Sciences, Department of Medicine, E-26, Ansari Nagar (west), AIIMS campus, New Delhi-110029, India.

Adeline M. Nyamathi, Sue & Bill Gross School of Nursing; Mail Code: 3959; University of California, Irvine, Irvine, CA 92697.

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