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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: J Health Psychol. 2020 Jan 8;26(11):2010–2019. doi: 10.1177/1359105319897783

Sociodemographic and psychosocial correlates of resilience among older adults living with HIV in the Deep South

Monique J Brown 1, J Stewart Trask 1, Jiajia Zhang 1, Mohammad Rifat Haider 1, Xiaoming Li 1
PMCID: PMC7340561  NIHMSID: NIHMS1594227  PMID: 31912745

Abstract

This cross-sectional study assessed the psychosocial and sociodemographic correlates of resilience among older adults living with HIV. Data were obtained from 103 men and 53 women aged 50 years and older in South Carolina. Multivariable linear regression models showed that employment (any) (B: 3.52; 95% confidence interval : 1.04, 5.99), education (B: −3.56; 95% confidence interval: −6.15, −0.98), time since diagnosis (B: 0.18; 95% confidence interval: 0.04, 0.31), and social support (B: 0.27; 95% confidence interval: 0.20, 0.34) were associated with resilience. Interventions tailored for older adults living with HIV to support resilience could facilitate social support, particularly for those who are newly diagnosed, unemployed, and have lower educational attainment.

Keywords: coping, HIV, older person, protective factors, social support

Introduction

Older adults living with HIV

Due to the advent of and improvements in antiretroviral therapy (ART), individuals who have been diagnosed with HIV can now live much longer, resulting in a growing proportion of people living with HIV (PLHV) who are aged 50 years and older (AIDSinfo, 2019). In 2016, in the United States, people older than 50 years accounted for 17 percent of new HIV diagnoses, and in 2015, 47 percent of PLHV were 50 years and older (Centers for Disease Control and Prevention, 2018). Within the population of older adults living with HIV (OALH), bisexual and gay men accounted for 49 percent of new diagnoses, heterosexual men for 15 percent, and heterosexual women for 24 percent (Centers for Disease Control and Prevention, 2018). Women older than 55 years are also the only age group in which HIV diagnoses did not decline from 2010 to 2016 (Centers for Disease Control and Prevention, 2019). To provide the best treatment for this group, it is vital that the amount of research conducted on OALH reflects the growing number of this population and studies consider the differences and similarities in psychosocial characteristics that may exist among OALH.

Resilience

Resilience theory uses a strengths-based perspective (rather than a deficit-focused approach) to examine how individuals overcome harmful effects of exposure to adversity (Fergus and Zimmerman, 2005). This perspective of resilience focuses on the process—how an individual can increase coping and adaptation strategies through their interaction with others and their environment (Wang et al., 2015). This theory implies that resilience can be formed through adverse life experiences and, therefore, can be taught to help individuals adapt to challenging situations (Wang et al., 2015). Examining resilience allows for a focus on internal and external positive factors such as self-efficacy and social support, or aspects that help to reduce HIV risk behaviors and promote positive behaviors in individuals (Zimmerman, 2013). This focus can be vital when forming a treatment plan for PLHV as it may help to reduce the levels of stress and anxiety and increase overall mental well-being and adherence to treatment (Fang et al., 2015).

Among Chinese populations, interventions that focus on building personal strength, coping skills, and enhancing social support have been linked with greater resilience (Yang et al., 2018; Yu et al., 2014). Therefore, it is important to ensure that men and women aging with HIV receive resources that may improve their coping and adaptation strategies and, hence, their resilience. As a result, it is crucial to determine the psychosocial and sociodemographic correlates of resilience among OALH to determine specific groups that may benefit from resilience interventions using a strengths-based approach.

Psychosocial and sociodemographic characteristics and resilience

Psychosocial characteristics that may influence resilience among PLHV include depression, HIV-related stigma, and social support (Earnshaw et al., 2015; Garrido-Hernansaiz et al., 2017; Rzeszutek et al., 2017; Spies and Seedat, 2014; Yu et al., 2017). Depression has been shown to be negatively associated with resilience among predominantly Black women aged 21–50 years living with HIV in the Western Cape, South Africa, and of mean age 41 years (standard deviation (SD) = 9) living with HIV in the US Mid-South (Spies and Seedat, 2014; Thurston et al., 2018). This association has also been seen among young (age range: 19–36 years) newly diagnosed homosexual Chinese men (Yu et al., 2017). In addition, research has shown that HIV-related stigma is also negatively associated with resilience among PLHV of mean age 33 years (SD = 8) receiving care at a sexually transmitted infections healthcare center in Madrid, Spain (Garrido-Hernansaiz et al., 2017). Majority of this population were men (98%), homosexual (87%), and from Spain (57%), while 39 percent were from Latin America and 4 percent were from other countries. Social support has also been shown to be positively associated with resilience among men and women living with HIV aged 19–76 years living in Warsaw, Poland (Rzeszutek et al., 2017). Social support has also been considered a resilience resource among a majority Black (56%), male (59%), and heterosexual (82%) population living with HIV in Connecticut aged 21–68 years (M (SD) = 50 (8.6)) and among African American women aged 18 years and older living with HIV in Chicago, Illinois, and Birmingham, Alabama (Earnshaw et al., 2015; Lipira et al., 2019).

Sociodemographic characteristics may also impact resilience (Dale et al., 2014; Woodward et al., 2017). Research has shown that employment is positively associated with resilience among high-risk HIV-negative sexual minority men (Woodward et al., 2017) and among majority Black women (91%) living with HIV aged 24–65 years in the Women’s Interagency HIV Study in Chicago (Dale et al., 2014). Greater resilience and strength-based resources, which may improve a person’s ability to strive through adversity, have also been associated with higher income among high-risk HIV-negative sexual minority men (Woodward et al., 2017) and among African American women aged 18 years and older living with HIV in Chicago, Illinois, and Birmingham, Alabama (Lipira et al., 2019), and with education among men who have sex with men aged 18–60 years in Boston, Massachusetts (Mimiaga et al., 2009). Research has also shown that among PLHV in Ontario, Canada, older adults who were diagnosed in the pre–highly active antiretroviral therapy (HAART) period had more strategies geared toward resilience compared to those who were diagnosed post-HAART (Harris et al., 2018). Studies on resilience and race among PLHV tend to focus on Black populations (Barry et al., 2018; McNair et al., 2018; Subramaniam et al., 2017; Wilson et al., 2016). Some of these studies have highlighted the resilience of young Black gay and bisexual men aged 18–30 years via interventions (Barry et al., 2018) and its association with HIV risk behaviors such as lower condomless anal sex among Black men who have sex with men in the US Deep South of mean (SD) age 30 (11) years (McNair et al., 2018). Other research has assessed varying profiles of resilience among young Black gay and bisexual men in New York City (Wilson et al., 2016). In addition, despite adverse experiences, such as trauma, discrimination, and violence, African American women living with HIV aged 30–67 years from a community-based agency in a metropolitan statistical area of the Midwestern United States showed resilience (Subramaniam et al., 2017). Nevertheless, studies examining potential sociodemographic disparities in resilience are lacking, especially among OALH in the Southern United States.

Study aims

Therefore, the primary aim of this study was to examine selected sociodemographic and psychosocial characteristics associated with resilience among older adults age 50 years and older living with HIV. These findings may provide new information that may help in the design of intervention programs geared toward improving resilience among OALH and specific groups, especially in the US South.

Design and methods

Data source and study population

Data for this cross-sectional study were collected from May 2018 to September 2018 from 402 PLHV who were at least 18 years of age, living with HIV, and were willing to participate in a 35- to 40-minute survey conducted using a pencil-and-paper questionnaire. The participants were all receiving care at a large immunology clinic in South Carolina in the United States that provides comprehensive HIV services (Brown et al., 2019).

The staff at the immunology clinic coordinated with the study team for data collection and introduced patients who were living with HIV to the study. Any interested patient was directed to a research team member, who then asked the patient if they were willing to participate in the anonymous survey. There was a high participation rate with more than 80 percent of invited patients choosing to participate in the study. Participants who agreed to participate gave informed consent, and the paper-and-pencil survey was conducted in private, designated areas in the clinic. All patients who chose to participate received a US$20 gift card for their participation. As the current study focused on OALH, we included participants who were 50 years and older (N = 156). The study protocol was approved by the University of South Carolina Institutional Review Board.

Measures

Resilience was operationalized by the 10-item Connor–Davidson Resilience Scale (CD-RISC 10), which was developed using factor analysis from the 25-item scale (Campbell-Sills and Stein, 2007). Participants were asked to report how they handled stressful events. Examples of items included “adapt to change” and “deal with whatever comes my way.” Each item was scored using a 5-point response option ranging from 0 “Not at all” to 4 “Extremely.” Some items were recoded so that higher scores reflected greater resilience. Sum scores ranged from 0 to 40. The standardized Cronbach’s alpha value for the CD-RISC 10 was 0.92 overall.

Depressive symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001). Items obtained data on depressive symptoms over the past 2 weeks such as “little interest or pleasure in doing things” and “feeling down, depressed or hopeless.” The nine items were summed to obtain a total score with a range from 0 to 27. Higher scores represented greater depressive symptoms. Cronbach’s alpha value for the PHQ-9 in the current study population was 0.94 overall.

Internalized HIV-related stigma was measured by the 12-item short version (Reinius et al., 2017) of the HIV Stigma Scale (Berger et al., 2001). The 12 items were summed to obtain a sum score with a range of 2–48. Higher scores reflected higher levels of HIV-related stigma. Cronbach’s alpha value for the 12-item stigma scale in the current study population was 0.87 overall.

Social support was measured by 19 questions adapted from the Medical Outcomes Study (MOS) Social Support Survey (Sherbourne and Stewart, 1991). Responses were based on a Likert-type scale ranging from 0 “None of the time” to 4 “All of the time.” Participants were asked how often various types of support were available when needed, for example, “Someone you can count on to listen to you when you need to talk.” Social support was operationalized as a continuous variable. Cronbach’s alpha value for the 19-item social support scale in the current study population was 0.98 overall.

Sociodemographic characteristics considered in the study included gender (men vs women), age in years (50–64 vs. 65 and older), and race (Black and other vs White); ethnicity (Hispanic vs non-Hispanic); education (less than high school, high school, some college vs bachelor’s/postgraduate); employment (yes vs no); yearly income (<US$10,000, US$10,000–US$49,000 vs US$50,000); and time since diagnosis (⩽5 years, >5 to ⩽10 years, >10 to ⩽20 years vs >20 years). The question on gender asked “What is your current gender?” All respondents older than 50 years identified as a man or woman.

Analytic approach

Descriptive statistics were used to examine the distribution of sociodemographic characteristics among the study population. The values of p based on Welch’s statistics were used to assess statistically significant differences in mean resilience scores by sociodemographic characteristics. Correlation statistics between resilience and depressive symptoms, and HIV-related stigma were also obtained. Bivariate and multivariable linear regression models were used to determine the association between each sociodemographic and psychosocial characteristic (gender, employment, income, age, race, education, time since diagnosis, depression, HIV-related stigma, and social support) and resilience, and adjusting for all psychosocial and sociodemographic characteristics. We included psychosocial and sociodemographic characteristics based on prior research showing their associations with resilience (De Araújo et al., 2017; Garrido-Hernansaiz et al., 2017; Harris et al., 2018; Mimiaga et al., 2009; Nikolova et al., 2015; Rzeszutek et al., 2017; Spies and Seedat, 2014; Woodward et al., 2017). Previous research has also suggested not excluding variables based on statistical significance (Sun et al., 1996). We obtained parameter estimates (B) and 95-percent confidence intervals (CIs) for the associations based on the assumptions of normality for the residual errors, which is a representation of the variation of the outcome (resilience) that is not explained by the independent variables. Due to small cell sizes and the distribution of characteristics, in the adjusted analyses, other race (n = 4) and Hispanic ethnicity (n = 7, non-Hispanic participants) were removed from analyses. Due to its distribution, education was recoded to ⩽high school and ⩾some college. Income was recoded to <US$50,000 and ⩾US$50,000. All analyses were conducted in SAS version 9.4 (SAS Institute, Cary, NC).

Results

The overall mean (SD) of resilience was 27.0 (8.5). Table 1 shows the sample characteristics overall and resilience scores according to sample characteristics among a sample of 156 older adults living in South Carolina. There were statistically significant differences in resilience scores by education, employment, income, and time since diagnosis. As education and income increased, resilience increased. Participants who were employed had higher scores of resilience, and resilience scores varied with time since diagnosis. Depressive symptoms were also negatively correlated with resilience (r = −0.189, p = 0.022), while social support was positively correlated with resilience (r = 0.508, p < 0.001).

Table 1.

Sample characteristics overall and resilience scores according to sample characteristics among a sample of 156 older adults living in South Carolina.

Characteristics Overall, N (%) Resilience, mean (SD) p valuea
Gender
 Women 53 (34.0) 25.5 (9.4) 0.068
 Men 103 (66.0) 27.7 (8.0)
Age in years (mean, SD) 58.4 (7.8) 0.031b 0.709
 50–64 132 (84.6) 27.0 (8.5) 0.970
 65+ 24 (15.4) 26.9 (8.7)
Race 0.975
 Black 112 (72.7) 27.1 (8.9)
 White 38 (24.7) 26.7 (7.4)
 Other 4 (2.6) 26.8 (10.2)
Ethnicity 0.289
 Hispanic 118 (94.4) 27.3 (8.5)
 Non-Hispanic 7 (5.6) 22.0 (11.8)
Education 0.031
 Less than high school 22 (17.3) 22.3 (10.3)
 High school 37 (29.1) 26.3 (7.8)
 Some college 42 (33.1) 27.5 (9.3)
 Bachelor’s/postgraduate 26 (20.5) 30.5 (7.7)
Employed 0.011
 Yes 78 (62.4) 29.4 (8.1)
 No 47 (37.6) 25.3 (9.3)
Income 0.003
 < US$10,000 40 (28.2) 23.8 (9.2)
 US$ 10,000-US$49,000 79 (55.6) 28.6 (6.7)
 US$50,000+ 23 (l6.2) 31.3 (7.l)
Time since diagnosis in years (mean, SD) 17.6 (9.5) 0.091b 0.279
 ⩽5 18 (12.3) 22.4 (9.l) 0.009
 >5 to ⩽10 24 (16.4) 29.9 (6.9)
 >10 to ⩽20 49 (33.6) 25.3 (7.8)
 >20 55 (37.7) 28.8 (8.4)
Depressive symptoms (mean, SD) 4.9 (6.2) −0.l89b 0.022
HIV-related stigma (mean, SD) 27.0 (8.5) −0.065b 0.436
Social support (mean, SD) 50.3 (20.8) 0.508b <0.001
Open in a new tab

SD: standard deviation.

Bold values are statistically significant at p < 0.05.

a

p values are based on Welch’s statistics.

b

p values are based on the Pearson correlation coefficient, r.

Table 2 shows the association between sociodemographic and psychosocial characteristics (depression, HIV-related stigma, and social support) and higher resilience. The bivariate analysis showed that education and depression were negatively associated while employment, income, and social support were positively associated with resilience. The multivariable analysis showed that as time since diagnosis increased and social support increased, resilience also increased (B = 0.18; 95% CI: 0.04, 0.31; B = 0.27; 95% CI: 0.18, 0.33, respectively). OALH who were employed reported higher resilience (B = 3.52; 95% CI: 1.04, 5.99) and those with lower educational attainment (high school graduate or less) reported lower resilience (B = −3.56; 95% CI: −6.15, −0.98)

Table 2.

Bivariate and multivariable linear regression examining the association between sociodemographic and psychosocial correlates and higher resilience among older adults living with HIV in South Carolina.

Correlates Bivariate models Multivariable model
B 95% CI B 95% CI
Gender (men vs women) 2.25 −0.62, 5.11 0.80 −1.97, 3.56
Employment (employed vs unemployed) 4.12 0.86, 7.38 3.52 1.04, 5.99
Income (<US$50,000 vs ⩾US$50,000) 3.85 1.93, 5.78 −0.81 −4.26, 2.64
Age (continuous) 0.03 −0.15, 0.21 −0.11 −0.29, 0.08
Race (Black vs White) 0.34 −2.85, 3.53 1.59 −1.03, 4.21
Education (⩽HS vs bachelor’s/Grad) −3.75 −6.92, −0.57 −3.56 −6.15, −0.98
Time since diagnosis (continuous) 0.080 −0.07, 0.23 0.18 0.04, 0.31
Depression −0.26 −0.48, −0.04 −0.07 −0.30, 0.16
HIV-related stigma −0.07 −0.24, 0.11 0.05 −0.14, 0.23
Social support 0.21 0.15, 0.27 0.27 0.20, 0.34
Open in a new tab

HS: high school; CI: confidence interval.

Bold B (parameter estimates) and 95-percent confidence intervals are statistically significant at p < 0.05.

Bivariate models: Each correlate or independent variable was regressed separately on the dependent variable (resilience).

Multivariable model: Gender, employment, income, age, race, education, time since diagnosis, depression, HIV-related stigma, and social support.

Discussion

To our knowledge, this is the first study to examine sociodemographic and psychosocial correlates of resilience among OALH living in the Southern United States. The main findings in the current study were that time since diagnosis, social support, and employment were positively associated with higher resilience scores on the CD-RISC 10, while lower educational attainment was associated with lower scores.

The strengths-based perspective suggests that resilience is formed through adverse experiences and, hence, can be taught to help individuals adapt to challenging situations (Wang et al., 2015). Resilience has been found to reduce stress and improve physical, emotional, and functional well-being, and overall quality of life among majority Black adults (58%) aged 50 years and older living with HIV from New York City, Columbus, and Cincinnati, Ohio (Fang et al., 2015). In a community population of respondents to the Mid-South Social Survey from the metropolitan statistical area of Memphis, Tennessee, the mean score of resilience obtained was 31.8 (5.4) (Campbell-Sills and Stein, 2007), which is higher than the mean scores of resilience in the overall study population in the current study at 27.0 (8.5). These findings suggest that programs geared toward improving resilience may benefit OALH.

Time since diagnosis was associated with higher resilience. This finding suggests that as time since diagnosis increased, resilience also increased. It is interesting that this finding persists even among adults aged 50 years and older. One study found that as men and women aged 50 years and older in Southern Ontario, Canada age, they continue to accept limitations, maintain social support networks, engage in activities, and stay positive (Solomon et al., 2018). Our finding supports Harris et al., who found that adults aged 50 years and older living in Ontario, Canada, who were diagnosed before the implementation of HAART, and had lived with HIV for more than 30 years, had developed more ways to be resilient through self-care and were more engaged in their HIV care compared to those who were diagnosed after the implementation of HAART (Harris et al., 2018). Our findings also suggest that programs enhancing resilience for older adults who are newly diagnosed, despite other sociodemographic and psychosocial characteristics, are warranted.

Social support was also positively associated with higher resilience among OALH. This finding supports studies, which suggest that social support is associated with resilience among young gay and bisexual men aged 16–24 years living with HIV from 14 geographically diverse Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) sites in the United States and among men and women aged 19–76 years living with HIV in Warsaw, Poland (Hussen et al., 2017; Rzeszutek et al., 2017). In addition, social support is considered a resilience resource among African American women aged 18 years and older living with HIV in Chicago, Illinois, and Birmingham, Alabama (Lipira et al., 2019); high-risk, HIV-negative sexual minority men (Woodward et al., 2017); and a majority Black (56%) male (59%) heterosexual (82%) population living with HIV in Connecticut aged 21–68 years (Earnshaw et al., 2015). Social support from the family and community is described to be a major resilience resource among African American women aged 22–67 years living with HIV in South Carolina in the United States (Qiao et al., 2019). This finding indicates that even among OALH living in the South, specifically South Carolina, interventions geared toward improving social support may also improve resilience among this population. However, accentuating resilience may also improve social support as individuals who are resilient may be more likely to seek social support systems from peers and/or community groups. Therefore, interventions geared toward improving resilience may also increase social support resources for OALH.

Employment and education were positively associated with resilience where OALH who were employed had higher resilience, and OALH with lower educational attainment had lower resilience. Previous research has shown that socioeconomic status including employment and education (Woodward et al., 2017) may act as a resilience resource in HIV prevention among high-risk, HIV-negative sexual minority men and among majority Black women (91%) living with HIV in Chicago aged 24–65 years (Dale et al., 2014). The findings in the current study suggest that programs providing employment or educational activities for OALH may help to improve their resilience. Indeed, previous studies have suggested that interventions and prevention efforts increasing employment for women living with HIV in Chicago aged 24–65 years may also improve their resilience (Dale et al., 2014).

Limitations and strengths

There are some limitations to consider in interpreting the results of the current study. The study population consisted of OALH who were receiving care from an immunology clinic in South Carolina. Therefore, the results might not be generalizable to other populations of OALH, including those who may not be receiving care. Other variables, which may have impacted resilience, especially among older adults, but were not measured in the current study, include instrumental activities of daily living (IADLs) and neurocognitive functioning (Fazeli et al., 2019). We also included all variables in the multivariable model irrespective of statistical significance in the bivariate model. In addition, we were unable to examine these associations among transgender and other gender minority populations due to a lack of gender minority populations aged 50 years in the current sample. Nevertheless, the study had some strengths. Adjusted estimates controlled for behavioral and socioeconomic characteristics, which may alter the true association between sex and resilience, and its components. Cronbach’s alpha estimates showed high internal reliability (Tavakol and Dennick, 2011) of the CD-RISC 10, PHQ-9, HIV Stigma Scale and adapted MOS Social Support Scale for the study population.

Conclusion

Intervention programs focusing on cultivating resilience may be beneficial for OALH who have been recently diagnosed. Specialized programs that are geared toward improving specific components of resilience such as optimism and fostering social support may be especially beneficial for older adults who have been recently diagnosed with HIV. These programs should help these older adults to cope with life stressors, to practice self-care, and to be engaged with their medical care. Interventions that improve social support, employment opportunities, and educational activities among OALH may also improve resilience among this population. Larger quantitative studies should examine stratified mediation pathways to identify modifiable psychosocial factors that may improve resilience among older men and women living with HIV. Future qualitative studies among OALH identifying as part of gender and sexual minority communities are also warranted to identify potential focal points for intervention programs and the relevant populations.

Acknowledgements

We would like to thank the following individuals who coordinated and participated in survey instrument development and data collection: Joi Anderson, Amir Bhochhibhoya, Michelle Deming, Akeen Hamilton, LaDrea Ingram, Crystal Stafford, as well as the administrative staff of the immunology clinic where this study was conducted. Finally, we are deeply grateful for the willingness of study participants to share their time and experiences with us. We take seriously our commitment to use study findings to improve outcomes for people living with HIV in South Carolina and recognize that this work could not be done without their participation and contributions.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the South Carolina SmartState Program®. M.J.B. is supported by grant K01MH115794 from the National Institute of Mental Health. The sponsors had no role in the design, analysis, or decision to publish these findings. The content is solely the responsibility of the authors and does not necessarily represent the official views of the South Carolina SmartState Program or the National Institutes of Health.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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