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Perceived Social Support Among Adults Struggling With Adherence to HIV Care and Treatment

Enriquez, Maithe, PhD, APRN, FAAN*; Mercier, Deborah A., PhD, RN, AOCNS; Cheng, An-Lin, PhD; Banderas, Julie W., PharmD, FCCP, BCPS

Journal of the Association of Nurses in AIDS Care: May-June 2019 - Volume 30 - Issue 3 - p 362–371
doi: 10.1097/JNC.0000000000000059
Feature Article

Few studies have examined perceived social support among the subgroup of the HIV population that struggles with adherence to HIV care and treatment. A secondary analysis from 2 HIV medication adherence intervention studies was conducted using a mixed method design. Participants were not taking HIV medications as prescribed or had fallen out of HIV care. Two major themes emerged from the qualitative data analysis: extreme isolation and constant turmoil. Overall social support scores were low, as measured by the Medical Outcomes Study Social Support Survey. Convergent qualitative data excerpt corresponded with low scores on every dimension of the Medical Outcomes Study Social Support Survey. Findings from our study indicate that adults living with HIV who struggled with taking HIV medications lacked social support in many areas of their lives. Interventions that focus on perceptions of available social support may be helpful for individuals with ongoing HIV medication adherence challenges.

Maithe Enriquez, PhD, APRN, FAAN, is Associate Professor and Director, Participatory Health Research Graduate Certificate Program, University of Missouri, Columbia, Missouri, USA. Deborah A. Mercier, PhD, RN, AOCNS, is Director of Medical Affairs, NewLink Genetics, Ames, Iowa, USA. An-Lin Cheng, PhD, is Associate Professor and Director of Research and Statistical Consult Service Department, Biomedical and Health Informatics, University of Missouri, Kansas City, School of Medicine, Kansas City, Missouri, USA. Julie W. Banderas, PharmD, FCCP, BCPS, is Assistant Dean, Graduate Health Professions; Professor, Department of Internal Medicine, School of Medicine, University of Missouri, Kansas City, Kansas City, Missouri, USA, and is Professor, Department of Biomedical and Health Informatics, School of Medicine, University of Missouri, Kansas City, Kansas City, Missouri, USA.

Corresponding author: Maithe Enriquez, e-mail:

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Suboptimal adherence to lifelong medications in people living with chronic health conditions, including HIV disease, has been well documented (Conn, Ruppar, Enriquez & Cooper, 2016). Social support has been identified as a facilitator of medication adherence, and numerous HIV medication adherence studies that include a social support component have been conducted (Takada et al., 2014; Woodward & Pantalone, 2012). Increased social support can be a protective factor in coping with challenges imposed by living with HIV disease (Benoit, 2015; Garcia et al., 2015).

Social support has been described as a concept that includes the structural component of individual social relationships and the functional components the relationships provide (Uchino, 2004). Structural social support has been defined in terms of social integration, social isolation, and social network characteristics (Garcia et al., 2015). For example, the size or strength of a social network would be in the realm of structural support. On the other hand, functional support refers to the perception of the adequacy of the social support that is available. In terms of the role of structural versus functional support on life dealings, structural support has a direct effect, whereas functional support is believed to be more relevant with regard to the ability to aid in coping and in buffering the impact of stress or events (Murrell, Norris, & Chipley, 1992).

The Medical Outcomes Study Social Support Survey (MOS-SSS) measures functional support. A person's perceptions of available support (i.e., functional support) have been reported as most important (Cohen & Syme, 1985; Cohen & Wills, 1985; House & Kahn, 1985). Despite extensive use of the MOS-SSS in the HIV literature (Huynh, Kinsler, Cunningham, & Sayles, 2013; Turan et al., 2016), only one published study was located that examined the particular dimensions of the MOS-SSS, and the impact of those dimensions on HIV medication adherence (Kelly, Hartman, Graham, Kallen, & Giordano, 2014). The sample for that study consisted of treatment-naive individuals with newly diagnosed HIV with social support as a predictor of success through the cascade of HIV care. Findings from the study in treatment-naive individuals found that increased social support and tangible social support, in particular, predicted high adherence to HIV medications (Kelly et al., 2014). However, there has been a dearth of published literature about the role of functional social support for the subgroup of people living with HIV (PLWH) who have experienced ongoing challenges with treatment adherence.

The importance of recruiting intervention study participants who have had medication adherence problems has been discussed by investigators as a strategy to better understand how interventions may help increase adherence behaviors and improve health outcomes (Conn et al., 2016). However, recruiting people into research studies who are struggling with HIV medication adherence can be challenging, due to issues such as mistrust (Alvarez, Vasquez, Mayorga, Feaster, & Mitrania, 2006). We found only one published study that addressed social support and medication adherence in a sample of adult PLWH who had challenges to medication adherence, and no change in adherence was found when providing treatment-specific support to PLWH (Taylor, Neilands, Dilworth, & Johnson, 2010). However, it is important to note that the study examined structural (i.e., received) social support as a predictor of medication adherence rather than functional (i.e., perceived) social support (Taylor et al., 2010). Structural and functional social supports are felt to contribute differently to the ability to buffer challenging events (Murrell et al., 1992). The differences in social support may be important to PLWH.

Our mixed methods study was a secondary data analysis that examined functional (i.e., perceived) social support in a group of adults who had experienced repeated challenges with HIV medication adherence. Social support dimensions in the MOS-SSS, together with theoretical underpinnings from the Stress-Buffering Theory and House's Conceptualization of Social Support, were used as the study's theoretical framework (Cohen & Syme, 1985; Cohen & Wills, 1985; House & Kahn, 1985). We proposed that in the presence of ongoing socioeconomic and psychological stressors, functional social support would buffer the stress of health-related behavioral issues, such as the challenges of strict lifelong adherence to HIV medications.

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A mixed methods convergent design (Creswell, 2013) merges two sets of existing data results into an overall interpretation (Creswell & Clark, 2018). This design is a well-known approach to mixing research methods (Creswell & Clark, 2018). This convergent design uses different, but complementary, data on the same topic to address a research question (Morse & Niehaus, 2009). Convergent design allows qualitative and quantitative data to be analyzed during the same phase of research, which was required for our secondary data analysis (Dunn, Arslanian-Engoren, DeKoekkoek, Jadack, & Scott, 2015).

Qualitative and quantitative data from two studies that examined the same HIV medication adherence intervention (Enriquez et al., 2015, 2018) were used. The parent studies used the same eligibility and inclusion criteria for recruitment. Participants were community-dwelling adults, 18 years and older, living with HIV who were not taking HIV medications as prescribed, and/or were not engaged in HIV care and did not have a suppressed HIV viral load.

The qualitative data from both parent studies were merged, followed by a separate merger of quantitative data from both studies. Each new merged data set was analyzed separately, and only brought together during the interpretation phase of data analysis. The qualitative and quantitative data were synthesized and compared in the final analysis (Bryman, 2006). The analysis was a particular form of secondary analysis, namely, an amplified analysis, whereby data from several data sets are combined for new analytical purposes or to explore new research questions.

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Data for our secondary analysis came from two HIV medication adherence intervention studies conducted in the Midwest United States. Both parent studies recruited samples of adult PLWH who were not fully engaged in HIV treatment (i.e., not attending HIV care appointments and/or not adhering to HIV medications), had experienced repeated challenges with medication adherence (i.e., one or more HIV medication regimens stopped due to nonadherence), and did not have a suppressed HIV viral load (i.e., viral load > 1,000 copies/mL). The parent studies examined an intervention that was facilitated by a peer (i.e., PLWH) who worked closely with a nurse liaison. All participants had access to care and to HIV medications.

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Data Collection and Analysis

Qualitative data, collected in the form of field notes, were analyzed using content analysis (Weber, 1990). Intervention facilitators collected field notes, by hand, during each intervention session with study participants in both parent studies. Intervention facilitators received training about how to take field notes and used a field note–taking guide. The field notes were transcribed verbatim and uploaded into the qualitative software program dedoose (SocioCultural Research Consultants, LLC, Los Angeles, CA, 2014). Transcripts were uploaded and organized so that the qualitative data could be examined, explored, coded, and later integrated with quantitative data (Talanquer, 2014).

Quantitative data included in our analysis were responses to the MOS-SSS at baseline (i.e., study enrollment time point) and self-reported demographic and health data. Demographic data included age, gender, race/ethnicity, and education status. Health-related variables included the number of previous HIV medication regimens and years living with HIV. The MOS-SSS measured perceived (i.e., functional) social support (Sherbourne & Stewart, 1991).

The MOS-SSS is a 19-item, 5-point Likert scale instrument that collects data to report a total score for perceived functional social support and a score for 4 subdomains (Sherbourne & Stewart, 1991). The MOS-SSS was developed for use with patients with chronic conditions and is available for use in the public domain. The subdomain/subscale emotional support has eight items (e.g., someone to turn to for suggestions about how to deal with personal problems). The subdomain/subscale tangible support has four items (e.g., someone to prepare my meals if unable to do it myself). The subdomain/subscale affectionate support has three items (e.g., someone who hugs me). The subdomain/subscale support through positive social interaction has three items (e.g., someone to do something enjoyable with). Finally, the 19th item is an additional question, someone to do things with to help get my mind off things, which is only used in the calculation of the overall social support score. Scores can range from 0 to 100, and, in a general U.S. population, the mean (M) overall score was 70.1 (SD = 24.2), with subscale scores ranging from 69.6 to 73.7 (Sherbourne & Stewart, 1991). Higher scores indicate more overall social support. The MOS-SSS has high internal reliability (Cronbach's alpha, 0.91–0.97) and stability (0.72–0.78). The total social support score and the subdomain/subscale scores were transformed, as recommended by the authors of the scale, and reported as interval data for analysis (Sherbourne & Stewart, 1991).

Descriptive statistics, frequencies, and percentages were used to describe the sample's characteristics. MOS-SSS scores were transferred to a 0–100 scale to make comparisons to published studies. Data analysis triangulation was used to examine relationships between self-reported total social support scores, social support subscale scores within the specific dimensions of the MOS-SSS, and the perceptions of social support (i.e., qualitative data).

The qualitative data analysis was conducted first. Two researchers worked independently to code qualitative data and identify categories and themes. To ensure consistency of coding, an electronic version of a coding manual was created in dedoose. To test qualitative analysis procedures, a pilot involving the coding of three participants' field notes was conducted. After all coding was completed, the two researchers then came together to discuss their independent analyses to reach 100% agreement on final categories/themes. Three researchers, including a biostatistician, conducted the quantitative analysis after completion of the qualitative analysis. Four researchers contributed to convergence/triangulation of the qualitative and quantitative data and interpretation of findings.

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The study sample (N = 50; Table 1) consisted primarily of African American adults (68%) ranging in age from 18 to 54 years, who self-identified as male (n = 32), female (n = 16), or transgender (n = 2), and had a high school education (69%). Participants had been living with HIV for an average of 12 years (range = 1–32 years) and had been prescribed an average of 3 HIV medication regimens. Qualitative data showed predominantly a sample of adults who were living in poverty; many had experienced abuse/violence, had emotional distress, and distrusted others.

Table 1

Table 1

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Quantitative Results

An exploratory analysis was conducted of baseline (study enrollment time point) MOS-SSS data. Raw scores of 49 individual participants on each of the 19 MOS-SSS items were available for analysis. Baseline MOS-SSS data were missing for 1 participant, and only 49 participants were included in the analysis. To obtain each subscale score, the means of scores for each question contained in the subscale were calculated. To obtain an overall social support score, the means of the scores for all 19 questions were calculated. The emotional/informational support subscale consisted of eight items (α = 0.948), the tangible support subscale consisted of four items (α = 0.904), the affectionate support subscale consisted of three items (α = 0.901), and the positive social interaction subscale consisted of three items (α = 0.940). Finally, to compare the calculated means to published means in the literature, individual subscale scores were transformed to a 0–100 scale using the following formula, based on recommendations from development and testing of the scale (Table 2):

Table 2

Table 2

When compared with other published studies that have used the MOS-SSS (Evon, Esserman, Ramcharran, Bonner, & Fried, 2011; Li, Chen, Chang, Chou, & Chen, 2015), our study participants appeared to have low perceptions of functional social support. Our results were considerably lower (M = 62) when compared with published studies focusing on individuals living with other chronic health conditions (e.g., individuals with hepatitis C: M = 83; Evon et al., 2011; cervical cancer survivors: M = 81; Li et al., 2015). A comparison of MOS-SSS scores from the literature that have been reported for various populations is shown in Table 3.

Table 3

Table 3

Pearson's product-moment correlation and Spearman's rank correlation coefficients or analysis of variance examined relationships between MOS-SSS scores and health and demographic characteristics, depending on the level of measurement. No significant relationships were found between any social support scale scores on health (i.e., number of years living with HIV, number of previous HIV medication regimens) or demographic characteristics (i.e., age or gender).

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Qualitative Results

During the parent studies, participants who received an individual-level behavioral intervention were asked about their past and current struggles with HIV medication adherence. The initial intervention study visit focused on identifying present and past barriers to engagement in HIV care and adherence to treatment. A checklist was used that included potential barriers that had been identified through an in-depth literature review (Enriquez et al., 2018). Field notes included the setting, people in the room during the visit, and the conversation with the participant. These field notes included study personnel observations and quotes from participants. Details were added to the field notes regarding phone contacts with participants because these contacts also reflected participant social support needs. Participants were aware that study personnel were generating detailed field notes during the visits and were given the opportunity to read their field notes if desired. Hand-written field notes were transcribed verbatim.

Content analysis of the transcribed field notes was reviewed for a priori social support themes through an excerpting process using inductive reasoning in which phrases and categories emerged from the data through careful examination and constant comparison of the data (Elo & Kyngas, 2008; Weber, 1990). Two researchers coded the qualitative data in two rounds, separately and then together. Themes began to emerge from the data following axial coding (Yin, 2015). Excerpts from the field notes described participant isolation, lack of friends, loneliness, poor support from family/friends/partner, stigma, and rejection/hate from others. Descriptions of unstable life circumstances, abuse/violence, emotional distress, vulnerability, and safety were also common themes during the second round of coding. Upon completion of axial coding, through a process of theming the data (Saldaña, 2015), two major themes and three subthemes emerged (Table 4). The number (%) of participants whose field notes contained excerpts within certain themes or subthemes constituted evidence because this was an indication about whether the point being made (theme or subtheme) was heard frequently or was a rare exception in the data.

Table 4

Table 4

The major theme of extreme isolation corresponded to the MOS-SSS dimension of Positive Social Interaction. Participants who felt isolated frequently described a lack of people in their lives. Nearly 80% of the participants whose field notes were analyzed described some form of extreme isolation: poor relationships with family and friends, low levels of participation in social activities, and/or the absence of someone to talk with about having HIV.

The second major theme, constant turmoil, described situations of financial stress, housing, food, and transportation needs, the need for help with medication access, immigration issues, instability, and feelings of being overwhelmed with life. Almost all field notes analyzed in our study showed evidence of constant turmoil in the participants' lives. Field notes were replete with examples and discussions of commotion, confusion, disturbance, and agitation with life circumstances. These situations corresponded with the MOS-SSS dimension of tangible support.

Subthemes of loneliness, structural vulnerability, and emotional distress were prevalent in participants' lives because they struggled to focus on dramatic life circumstances with what appeared to be very little social support. Loneliness resonated throughout the field notes. Many discussed a lack of friends. One participated stated, “People are only nice to me to get my (food) stamps.” Another stated, “I now have one good friend. Will sometimes let me bathe and eat there. I plan on going over there. All my other friends are shaky and will stab you in the back, sadly.” Moreover, several participants discussed their pets as a source of social interaction, for example, one participant stated, “I like to go home after work to my cat (keeps) me company.”

Structural vulnerability is an individual's position in a social hierarchy that imposes physical-emotional suffering in patterned ways (Bourgois, Holmes, Sue, & Quesada, 2017) and was described by many participants. One participant described his situation as follows: “(I) want to work … don't like the idea of being kept by (my) partner … feel like a prisoner being taken care of …” In addition, emotional distress was prevalent throughout the qualitative data. This distress included discussions of dealing with physical/emotional abuse, molestation, rape, sex work, being used for sex by “friends” for food or a place to live, substance abuse, and deaths of friends/family members/partners. Many participants had experienced emotional disturbances in the past and the repercussions endured into the present.

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Convergence of Qualitative and Quantitative Data

By mixing research methods in a convergent design, relationships between social support scores on the MOS-SSS and perceptions of social support in adult PLWH who were not taking HIV medications and had experienced medication adherence challenges were examined. A multiple triangulation approach, data triangulation, and method triangulation, was used in an attempt to gain greater insight (Creswell, 2013).

The dimensions of the MOS-SSS were used for triangulation of data based on the following definitions (Sherbourne & Stewart, 1991). Low emotional/informational support involved a lack of expressions of positive affect, empathic understanding/stigma, encouragement of expressions of feelings, and/or little or no offering of advice, information, feedback, or guidance. Low tangible support involved a lack of material aid or behavioral assistance. Low positive social interaction involved a lack of availability of persons to do fun things with, and low affectionate support involved a lack of expressions of love and affection. Examples of triangulation that are convergent data combining quantitative data from the MOS-SSS dimensions of social support with qualitative data excerpts in each theme are shown in Table 5. Convergent qualitative data excerpts corresponded to low quantitative social support scores in every dimension of the MOS-SSS, further confirming that adults in our study lacked social support in many areas of their lives.

Table 5

Table 5

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Participants enrolled in our study were adults struggling with engagement in HIV care and adherence to HIV treatment. None of the participants had a suppressed HIV viral load at enrollment into the parent studies. PLWH who do not engage in treatment and hence do not sustain viral load suppression are at risk for negative health outcomes. Furthermore, this subgroup is also at higher risk of transmitting HIV to other individuals. Maintaining an undetectable (i.e., suppressed) viral load results in effectively no risk of sexually transmitting HIV to another individual (Prevention Access Campaign, 2018). Hence, there is a need to focus on the subgroup of individuals who do not have a suppressed HIV viral load. Findings from our mixed methods study may provide insight for strategies to help re-engage this priority population in HIV treatment.

All participants in our study reported low levels of perceived social support, as measured by the MOS-SSS. Two distinct, but interconnected, themes emerged from the qualitative data: extreme isolation and constant turmoil. These themes were associated with a lack of social support in a group of adult PLWH who were disengaged from care and treatment. Subthemes, including loneliness, structural vulnerability, and emotional distress, were also commonly described by participants. Results were congruent with other research reports (Houston, Osborn, Lyons, Masvawure, & Raja, 2015) stating that for many PLWH, social support of any type was perceived to be scarce or nonexistent.

The majority of study participants described experiences of extreme isolation, which has been linked to the subjective concept of loneliness (Turan et al., 2016). Although aging with HIV did not emerge as a theme in our study, it may have contributed to feelings of loneliness expressed by study participants. More than half of the study sample (53%) ranged in age from 45 to 54 years. Because PLWH live longer, they may experience loneliness as a component of aging. A few participants discussed situations of loneliness in their lives that were associated with aging (e.g., empty nest syndrome). In addition, older PLWH have been reported to experience social isolation related to aging and stigma (Shen et al., 2018). Hence, aging could have played a role in the prominent feelings of loneliness in our study sample. Researchers should address aging as a component of interventions that aim to enhance social support for PLWH.

The fact that a person does not receive support during a given period does not mean that the person is unsupported. Received support is confounded with need and may not accurately reflect the amount of support that is available to a person. Results warrant exploring the combination of social support and behavioral health services to enhance adherence because emotional health issues were prevalent in our participants' lives. For example, the constant turmoil experienced by participants in our study may have contributed to impaired emotional health and/or poor quality of life. Other research has suggested that although social support correlated with increased adherence, future adherence interventions should consider adding a mental health treatment component to social support dimensions for best effect (Huynh et al., 2013). Findings from our study support the recommendation for more of a focus on mental health.

Despite interesting results, there were limitations to our study, which must be considered when interpreting the findings. Our study was a secondary analysis of existing data from two small, non–publicly available data sets from medication adherence intervention studies. Qualitative data for our secondary analysis were from field notes written by study personnel who served as intervention facilitators in the parent studies. Despite the fact that interventionists had extensive training on field note–taking procedures, such data may not be as reliable or valid as audio- or video-taped qualitative data. The parent studies were conducted in one metropolitan area and included only adults who had experienced challenges with adherence to HIV care and treatment. As such, the generalizability of the results of this study is limited.

Nevertheless, given that little research has been conducted to examine perceived social support in individuals who are disengaged from HIV care and treatment, our study makes an important contribution to the literature. It is clear that the participants, all of whom were struggling with HIV medication adherence or had fallen out of HIV care, perceived very low levels of social support. Low levels of functional social support may have contributed to the inability to engage in HIV care and take life-saving HIV medications as prescribed.

HIV medication adherence studies have included social support variables in an effort to enhance medication-taking behaviors and reach HIV viral load suppression (Simoni et al., 2007; Taylor et al., 2010). However, findings from our study suggest that more emphasis needs to be placed on functional support, the perceived adequacy of available social support (Friedman et al., 2017). More research is warranted to gain a better understanding of support need (perceived) versus support provision (received) for the priority population of individuals who do not have a suppressed HIV viral load because they are disengaged from HIV care or are struggling with adherence to HIV treatment.

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Findings from our study indicate that adults living with HIV who struggle with taking HIV medications may perceive a lack of social support. Interventions that focus on perceptions of available social support could be helpful for individuals who have dropped out of care or who experience ongoing HIV medication adherence challenges.

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Key Considerations

  • Social support is a facilitator of HIV medication adherence.
  • Individuals with suboptimal adherence to HIV care and treatment may perceive low levels of social support.
  • More emphasis on functional support (i.e., the perceived adequacy of available social support) may be helpful to individuals who have fallen out of HIV care or who are not taking HIV medications as prescribed.
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The authors report no real or perceived vested interests related to this article that could be construed as a conflict of interest.

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This study was funded, in part, by the National Institute of Nursing Research (5 K01 NR014409-03, PI: M. Enriquez) and the American Nurses Foundation. The authors are grateful to the individuals who voluntarily participated in the parent intervention studies and to colleagues Gerry Ortego, Deana Hayes, Michael Reese, LaTrischa Miles, Rose Farnan BSN, Alicia Downes LMSW, and Amanda Enriquez LMSW for their support of this endeavor.

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          HIV; medication adherence; mixed methods; social support

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