Journal of Health Communication, 19:660–675, 2014 Copyright # Taylor & Francis Group, LLC ISSN: 1081-0730 print=1087-0415 online DOI: 10.1080/10810730.2013.837555
Social Support and Social Undermining as Explanatory Factors for Health-Related Quality of Life in People Living With HIV/AIDS JOHN OETZEL Department of Management Communication, University of Waikato, Hamilton, New Zealand
BRYAN WILCOX AND ASHLEY ARCHIOPOLI Department of Communication and Journalism, University of New Mexico, Albuquerque, New Mexico, USA
MAGDALENA AVILA Department of Health Exercise and Sport Science, University of New Mexico, Albuquerque, New Mexico, USA
CIA HELL, RICKY HILL, AND MICHAEL MUHAMMAD Department of Communication and Journalism, University of New Mexico, Albuquerque, New Mexico, USA This study aimed to examine the influence of social support (from personal networks and health care providers) and social undermining (from personal networks) on health-related quality of life (HRQOL; general health perceptions, physical functioning, and depression). Specifically, the authors aimed to identify the nature of the effects (direct, mediating, or moderating) of social support and social undermining on HRQOL. A total of 344 people living with HIV=AIDS and who were patients in a federally funded clinic in New Mexico completed a self-report survey questionnaire. The major findings of this study are the following: (a) social support and social undermining had direct and indirect effects on HRQOL—there was no evidence of a moderating effect of social support and social undermining; (b) for direct effects, social undermining was a stronger predictor of HRQOL than social support with social support variables having positive relations and social undermining variables having negative relations with HRQOL; and (c) for indirect effects, providers’ social support partially mediated the influence of unstable employment= unemployment and social undermining on HRQOL.
Health-related quality of life (HRQOL) is a key indicator for patients with people living with HIV=AIDS (PLWH) in the highly active antiretroviral therapy era (Jia, Uphold, Wu, Chen, & Duncan, 2005; Wu, 2000). As a result, researchers have Address correspondence to John Oetzel, Department of Management Communication, University of Waikato, Hamilton 3240, New Zealand. E-mail: [email protected]
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aimed to identify variables correlated with HRQOL (e.g., Nair & Muthurkrishna, 2010). Identification of these correlates can help researchers, providers, and patients identify the most effective points of intervention in the health delivery process. One such correlate is social support; social support has been found to be related positively to HRQOL for PLWH (Jia et al., 2007; Viswanathan, Anderson, & Thomas, 2005). Although social support has been found consistently to relate to HRQOL, there are some limitations in its study in this setting. First, not all studies have examined the effects of social support when controlling for other demographic, biomedical, and behavioral health variables. Second, most researchers assume that the presence of positive social support is the key factor for HRQOL when it may be the absence of negative social interaction (i.e., social undermining). Last, the association of social support and social undermining with HRQOL is undertheorized. The purpose of this study is to address these limitations. Specifically, this study aimed to determine whether social undermining and social support are associated with three types of HRQOL (general health perceptions, physical functioning, and depression) using three different theoretical models: (a) direct effects model controlling for demographic variables, medication adherence, and behavioral health; (b) a mediating model where social support mediates the effect of social undermining and other demographic stressors on HRQOL; and (c) a moderating model where the interaction of social support and social undermining affects HRQOL and alters the influence of social support on HRQOL.
Quality of Life and Social Support and Social Undermining HRQOL HRQOL refers to individual perceptions of well-being within physical, mental, and social spheres along with the perceived effect of health on day-to-day functions and includes multiple dimensions (Wu, 2000). For example, the MOS-HIV survey includes 10 dimensions: ‘‘general health perceptions, physical functioning, role functioning, pain, social functioning, mental health, energy, health distress, cognitive functioning, and quality of life’’ (Wu, 1999, p. 5). These dimensions can be summarized into a physical health summary and a mental health summary. For this study, we considered the general health perceptions and physical functioning of HRQOL and also depression specifically (i.e., not the general mental health summary). The limitation in selecting only three aspects of HRQOL is due primarily to the response burden of the participants in this present study and yet allows for measurement in a way consistent with the research on HRQOL (e.g., Jia et al., 2005; Wu, 2000). General health perceptions includes general health and illness, whereas physical functioning considers a range of physical limitations such as walking, lifting, and everyday functions such as eating and dressing (Wu, 1999). Depression is a mood disorder and includes several types including major depressive disorder (American Psychiatric Association, 2000). Prior research has found a number of correlates of HRQOL for PLWH. In terms of demographic characteristics, several factors have been identified: (a) older age results in lower HRQOL scores (Campsmith, Nakashima, & Davidson, 2003; Logie & Gadalla, 2009); (b) lower income negatively correlates with HRQOL (Campsmith et al., 2003; Logie & Gadalla, 2009); (c) ethnic minorities compared with non-Hispanic Whites have lower HRQOL (Campsmith et al., 2003); (d) formal education is positively related to HRQOL (Campsmith et al., 2003); and (e) people with unstable, temporary, or no employment have lower HRQOL scores compared with those with stable employment (Viswanathan et al., 2005). In terms of biomedical
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characteristics, the following variables have been identified: (a) CD4 is negatively associated with HRQOL (Campsmith et al., 2003; Jia et al., 2005), but also that the relation between CD4 and HRQOL varies over time (Jia et al., 2007); and (b) medication adherence is positively associated with HRQOL (Viswanathan et al., 2005). Linking Social Support and Social Undermining to HRQOL Social support refers to information or actions (actual or potential) that lead individuals to perceive that they are cared for or receive aid, assistance, and comfort from others when they need it (MacGeorge, Feng, & Burleson, 2011). In contrast, social undermining is a ‘‘negative form of social interaction characterized by active dislike and devaluing of an individual’’ (Gant & Nagda, 1993, p. 159). Social undermining is a distinct, yet negatively related factor to social support (Creed & Moore, 2006; Vinokur & van Ryn, 1993). This section describes the relations of social support and social undermining to HRQOL and then discusses three alternative theoretical frameworks tested in the present study. Social Support and Social Undermining The majority of studies find that perceived support is more strongly associated with well-being and mental health than actual support (Gruenewald & Seeman, 2010; MacGeorge et al, 2011). In addition, research has also illustrated that perceived social undermining has effects on mental health and well-being although direct comparison with actual undermining have not been undertaken (Cranford, 2004; Duffy, Ganster, Shaw, Johnson, & Pagon, 2006). This present study considers perceived support and undermining because of their prior relations with well-being and mental health. Specifically, we included two categories of social support in reference to friends and family—emotional and instrumental. The operationalization of emotional support in this study includes provision of love, affirmation, and caring, whereas instrumental support contains tangible aid. This choice is consistent with prior research examining health outcomes (e.g., Oetzel, Duran, Jiang, & Lucero, 2007). We also considered two types of social support from health care providers: information and emotional support (Galassi, Schanberg, & Ware, 1992). We included perceived quality of information from providers given the importance of health care providers as information sources given the complexity of the drug regimen among other factors for PLWH (Jia et al., 2007). In addition, health care providers offer affirmation and caring and this emotional support is particularly important in the context of social stigma for PLWH (Logie & Gadalla, 2009). In most studies, social undermining is conceptualized as a global construct (e.g., Duffy et al., 2006; Fleishman et al., 2000). However, Oetzel and colleagues (2007; see also Vinokur & van Ryn, 1993) considered two dimensions of social undermining, which we replicate for this present study. The first dimension is critical appraisal, which is the perception that the interaction provided is critical and hurtful (i.e., negative evaluation). The second dimension is isolation, which is the feeling of isolation that results from wanting to avoid family interactions (i.e., negative affect). Direct Effects The majority of research examining the relation between social support and undermining and HRQOL has examined positive direct effects for support and negative
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direct effects for undermining (e.g., Jia et al., 2005; Oetzel et al., 2007). For example, in a cross-sectional survey of 86 participants living with HIV, Viswanathan and colleagues (2005) found that social support was positively related to the physical component summary of the MOS-HIV in a model that considered demographics, CD4 cell count, and medication adherence. In a prospective cohort study of 197 men with HIV (from baseline to 12-month follow-up), Jia and colleagues (2005, 2007) found that family social support was positively related to change in physical and social functioning in a model including CD4 cell count, and active coping. These examples are consistent with the predominant pattern of studies including PLWH. In contrast, social undermining has not been well studied in PLWH. Fleishman and colleagues (2000) conducted a cross-sectional survey of 140 PLWH in San Diego and found that negative social interactions, compared with social support, were more strongly (and negatively) related to coping strategies and also with negative mood. In addition, further studies have found that social undermining is positively related to a variety of alcohol, drug, and mood disorders (i.e., negative indicators of well-being) in a variety of samples including anxiety disorders in undergraduate psychology students (Finch, 1998), and anxiety, mood, and substances use disorders in American Indian women (Oetzel et al., 2007). Scholars have explored the combined effects of social support and social undermining on a variety of health and job outcomes using the social negativity hypothesis. This hypothesis proffers that negative social relations will be more strongly associated than positive social interaction with health and job outcomes because negative interactions are generally unexpected and thus more salient (Cranford, 2004; Duffy, Ganster, & Pagon, 2002). These negative interactions also provoke more intense negative emotions than positive interactions (Duffy et al., 2002). Research has generally supported this hypothesis (Cranford, 2004; Duffy et al., 2002; Oetzel et al., 2007). For example, Duffy and colleagues (2002) surveyed 685 national police officers in Slovenia and found that social undermining was generally more strongly (and negatively) associated with organizational commitment, self-efficacy, and somatic behaviors than social support. Social undermining was also positively associated (and more strongly than social support) with counterproductive behaviors. Given this prior research and theorizing about direct effects, the following hypotheses were offered: Hypothesis 1: Social support is positively related with HRQOL. Hypothesis 2: Social undermining is negatively related to HRQOL. Hypothesis 3: Social undermining will be more strongly related to HRQOL than social support.
Mediating Effects Within the social support literature, one theoretical position is that that social support is a buffer to trauma or stress (Goldsmith, 2004; MacGeorge et al., 2011). Support helps to alleviate some of the negative effect of stress on health outcomes (MacGeorge et al., 2011). MacGeorge and colleagues (2011) reviewed the extant literature and identified ‘‘impressive evidence that supportive communication can affect physical health by buffering physiological reactions to stress’’ (p. 326). In the context of the present study, social support may buffer the effects of negative stressors such as stress and negative demographic conditions associated with living with HIV. Research also shows that social support may buffer social undermining. For example, Duffy and colleagues (2002) found some evidence that social support from
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one source attenuated the effect of social undermining from another source on passive counterproductive behaviors and somatic complaints. However, very few studies have examined the buffering effects of social support for the effect of social undermining on HRQOL and other outcomes. Despite this limitation, the strong body of evidence demonstrating the buffering effect of social support (MacGeorge et al., 2011) leads to the positing of the fourth hypothesis: Hypothesis 4: Social support mediates the effect of social undermining and demographic variables on HRQOL.
Moderating Effects A final theoretical model proposes that social undermining and social support have an interaction effect on health and other outcomes. Specifically, the extant literature suggests a stress-exacerbation model. The stress-exacerbation model suggests that social undermining exacerbates the effect of stress on health outcomes (Cranford, 2004; Vinokur, Price, & Caplan, 1996). Thus, negative social interaction creates further stress on people than the original stressful event=condition. For example, Cranford surveyed 181 married couples at two time points and found that time one social undermining by one’s spouse moderated the association between time one stress and time two depressive symptoms. Specifically, social undermining made the stress=depression relation stronger. In contrast, social support did not buffer the stress=depression relation. In addition, and more directly related to the present study, other scholars have examined the degree to which social undermining moderates the relation between social support and health outcomes. The findings have been somewhat mixed. Vinokur and colleagues (1996) examined 815 married couples in which one partner recently became unemployed. They found that people with low support and high undermining have the lowest relational satisfaction and highest depression. This finding may indicate that there is an exacerbating effect for undermining when there is a low level of support. Duffy and colleagues (2002) found that high levels of social undermining along with high levels of social support had a positive effect on counterproductive behaviors and negative effects on organizational commitment, selfefficacy, and well-being. Last, Creed and Moore’s (2006) study of unemployed and underemployed individuals found that there was no moderating effect of social support and social undermining on distress or coping. The literature appears to support the moderating effect of social undermining on social support and health outcomes although it is unclear the nature of the moderation. Thus, the fifth hypothesis is posited: Hypothesis 5: Social undermining moderates the relationship between social support and HRQOL. None of the studies examining mediation or moderation have been conducted with samples involving PLWH and HRQOL. This study attempts to identify the potential mechanisms explaining the relation between social support and social undermining and HRQOL for PLWH and considers the following research question: RQ: Which of the three theoretical models appears to provide the best explanation of the effect of social support and social undermining on HRQOL?
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Method Sampling Frame and Participants The sampling frame included 1,362 clients who used HIV services at federally funded clinics in New Mexico. Participants were recruited to respond to the survey questionnaire in three ways: (a) before a focus group set up at each clinic (n ¼ 46); (b) via mailed invitation from the state agency in charge of the program (n ¼ 281); and (c) through a web-based survey (n ¼ 17). Of the original 1,362 mailings, 150 were returned; thus, the sample of 344 clients yielded an effective response rate of 28%. The response rate is likely conservative as the agency estimates that as many as 40% of clients’ addresses are not accurate. The mean age of the sample was 49.28 (SD ¼ 10.2). The median monthly income of the sample was $1,485.83 (SD ¼ $1221.33). Table 1 displays the demographics characteristics of the sample.
Table 1. Demographic characteristics of the sample Characteristic Sex Male Female Ethnicity White, non-Hispanic Hispanic American Indian Multi-ethnic Other (African American, Asian-Pacific Islander, Other) Sexual Identity Gay Heterosexual Other (Bisexual, Transgender, Queer, Questioning) Education Not HS graduate HS graduate Some college College graduate Graduate school Language Spoken at Home English Spanish Other Type of Relationship Married=civil union Partner Divorced=separated=widowed Single Work Status Unemployed, unable to work, or temporary employment Employed full- or part-time Other (retired, homemaker, school) Insurance Private Insurance
Percentage (n) 89.6 (303) 10.4 (35) 52.4 32.6 3.8 5.3 5.9
(179) (111) (13) (18) (20)
68.6 (225) 20.1 (66) 11.2 (37) 9.4 17.6 42.4 20.3 10.3
(32) (60) (144) (69) (35)
96.2 (331) 27.9 (96) 5.2 (18) 11.0 35.8 16.2 37.0
(36) (117) (53) (121)
49.4 (159) 29.2 (94) 21.4 (69) 29.5 (102)
Note: Language spoken at home percentages are greater than 100 as participants could select multiple categories.
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Measures and Questionnaire The dependent variables were general health perceptions, physical health, and depression. General health perceptions was measured with four items from the MOS-HIV survey and the single item measuring quality of life (Wu, 1999; Wu et al., 1991). Physical functioning was measured with six items from the MOS-HIV. These subscales were selected because there is abundant evidence of reliability and validity of the measures (Wu, Revicki, Jacobson, & Malitz, 1997). Depression was measured with the nine-item subscale from the Patient Health Questionnaire. This subscale was chosen because it is an easy to use, self-administered screener that has ample evidence of reliability and validity from prior studies (e.g., Lowe, Kroenke, Herzog, & Grafe, 2004; Spitzer et al., 2000). For the present study, Cronbach’s alpha was .91 for the general health perceptions, .91 for physical functioning, and .92 for depression. General health perceptions was measured on a 5-point scale ranging from 1 (definitely false) to 5 (definitely true), physical functioning was measured on a 3-point scale ranging from 1 (yes, limited a lot) to 3 (no), and depression was measured on a 4-point scale ranging from 1 (nearly everyday) to 4 (not at all). For these and all scales, missing values were replaced with series mean if the participant had fewer than 10% of the items missing. The independent variables included demographic characteristics, medication adherence, alcohol use, and social support and undermining. Demographic characteristics were measured with self-report categories. Medication adherence was measured with four items following the recommendations of Pearson, Simoni, Hoff, Kurth, and Martin (2007). Specifically, we asked clients how often they missed a dose of any HIV medicine over past 7 and 30 days and also how often they missed the time schedule of their medicine over the same two time periods. Cronbach’s alpha was .83 for the four items in the present study and the measurement was a 4-point scale ranging from 1 (not once=less than once a week) to 4 (every day). Alcohol abuse was measured with the five-item subscale from the Patient Health Questionnaire, which like the depression subscale has ample evidence of validity (e.g., Lowe et al., 2004). Social support and social undermining were measured with 20 total items (Oetzel et al., 2007). The items were validated and demonstrated to be internally consistent with a sample of American Indian women (Oetzel et al., 2007). For the present study, the scales were slightly revised in the following manner: (a) written to be appropriate for a general audience with HIV=AIDS; (b) the scaling was changed to be a five-item Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree); and (c) one critical appraisal item was removed and one isolation item was added. The final subscales consisted of emotional support (six items; a ¼ .81), instrumental social support (five items; a ¼ .83), critical appraisal (five items; a ¼ .79) and isolation (four items; a ¼ .78). Confirmatory factor analysis demonstrated that the four factor model was a good fit to the data, v2(164, N ¼ 344) ¼ 377.76, p < .01, RMR ¼ .077, IFI ¼ .929, CFI ¼ .929. Health care provider support was measured with 10 items from the patients’ reactions assessment (Galassi et al., 1992). Five items measured the perceived quality of information and five items measured emotional support (originally called affective). These subscales have demonstrated validity and reliability in prior research (Galassi et al., 1992). For the present study, the two-factor model provided a reasonable fit to the data, v2(164, N ¼ 344) ¼ 278.06, p < .01, RMR ¼ .069, IFI ¼ .907, CFI ¼ .907. Information had a Cronbach’s alpha of .92 and emotional support had an alpha of .88; both were measured on 5-point Likert scales. Before administering the survey, we took two steps to increase the accessibility of the survey questionnaire for clients. First, we had the survey (and all related
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materials) translated and back-translated into Spanish; 13 completed the survey questionnaire in Spanish. Second, we asked a health literacy expert to review the items and suggest wording changes so the survey (particularly the instructions) was accessible at a seventh-grade reading level and the format was clear and easy to follow.
Results Preliminary Analysis Before conducting the multivariate models, descriptive and bivariate statistics were calculated. Table 2 displays the descriptive information for the demographic characteristics by the dependent variables, whereas Table 3 displays the descriptive information (means, standard deviations, and correlations) for the social support and undermining variables along with the dependent variables. Some of the demographic categories were collapsed to accommodate the multivariate modeling. The categorizations were done for conceptual reasons and=or statistical reasons (i.e., to have sufficient numbers within the categories). Specifically, the variables were Table 2. Descriptive statistics of demographics with dependent variables Depression Independent variables Sex Male Female Ethnicity White, non-Hispanic Ethnic minority Sexual Identity Heterosexual Gay or Other Education HS graduate or less More than HS graduate Type of Relationship Partner No partner Work Status Unemployed, unable to work, or temporary employment Employed full- or part-time or other Insurance Private insurance No private insurance Alcohol Abuse Yes No Age Monthly Income
General health perceptions
Physical functioning
M
SD
M
SD
M
3.10 3.03
.80 .88
3.42 3.41
1.06 .97
2.45 2.40
.57 .62
3.18 3.01
.78 .82
3.54 3.27
1.05 1.02
2.48 2.40
.55 .59
2.98 3.13
.82 .80
3.23 3.45
1.09 1.03
2.39 2.46
.59 .57
2.99 3.14
.84 .79
3.12 3.49
1.06 1.03
2.29 2.49
.60 .56
3.20 3.02
.72 .85
3.39 3.40
1.06 1.05
2.46 2.41
.59 .57
2.88
.84
3.04
1.03
2.21
.61
3.34
.69
3.77
.93
2.67
.42
3.25 3.04
.75 .82
3.66 3.31
1.02 1.05
2.60 2.38
.53 .59
2.71 3.17
.89 .76 .09 .21
3.17 3.46
1.02 1.05 .04 .20
2.37 2.46
.65 .65 .12 .23
Note: Age and monthly income are correlations. Higher numbers on depression indicate lower levels of depression. Significant difference between the groups at p < .05.
SD
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3.39 2.44 3.10 4.11 4.30 3.77 3.71 2.24 2.61 4.48
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
1.05 .58 .80 .92 .86 .86 1.01 .85 1.03 .88
SD
Note: Numbers on the diagonals are Cronbach’s alphas. p < .05, p < 01.
General health perceptions Physical functioning Depression Information Emotional support (HC provider) Emotional support Instrumental support Critical appraisal Isolation Medication adherence
M
Variable .91 .64 .57 .31 .29 .35 .32 .38 .44 .13
1 .91 .48 .25 .28 .31 .33 .27 .29 .11
2
Table 3. Descriptive statistics of independent and dependent variables
.92 .30 .23 .41 .37 .45 .55 .19
3
.92 .63 .32 .35 .27 .26 .17
4
.88 .31 .35 .27 .29 .18
5
.81 .71 .49 .60 .14
6
.83 .47 .60 .12
7
.79 .66 .06
8
.78 .16
9
.83
10
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categorized in the following manner: ethnicity (ethnic minority or not), sexual identity (heterosexual or not), education (more than high school graduate or not), type of relationship (partner or not), insurance (private or not), and work status (unemployed=unstable or stable). Before testing the direct, mediating, and moderating effects, several steps were undertaken to create parsimonious models and also provide interpretable coefficients. First, confirmatory factor analysis demonstrated that the four latent constructs of social support, health care provider support, social undermining, and HRQOL were a good fit to the nine subscales, v2(21, N ¼ 344) ¼ 64.07, p < .01, RMR ¼ .030, IFI ¼ .968, CFI ¼ .968, and had the following loadings: (a) social support—emotional (.84) and instrumental (.84); (b) health care provider support—information (.82) and emotional (.79); (c) social undermining—critical appraisal (.74) and isolation (.91); and (d) HRQOL—general health perceptions (.83), physical functioning (.71), and depression (.72). Thus, the four latent constructs rather than the individual subscales were used to directly test the hypotheses. Second, given that there were multiple demographic variables significantly related to the dependent variables, a multiple regression model was used to identify the key demographic and medical=behavioral variables for the combined HRQOL variable. The analysis revealed that the only significant variable besides social support and social undermining was work status. Thus, only work status was included in the models to test the hypotheses. Last, the social support and social undermining variables were mean-centered in order to make the findings interpretable in case there was a moderating effect (Hayes, Glynn, & Huge, 2011). Multivariate Modeling To address the hypotheses and research question, latent variable structural equation models were examined using AMOS 20.0 with bootstrapped standard errors as suggested by Hayes (2009). Figures 1–3 display the three models and final coefficients. The direct effects model for HRQOL was not a good fit to the data, v2(32, N ¼ 344) ¼ 347.14, p < .01, RMR ¼ .188, IFI ¼ .779, CFI ¼ .770. The model explains 41% of the variance in the latent HRQOL variable with work status (stable employment being greater than unstable=unemployed), health care provider support (þ),
Figure 1. Direct effect model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01.
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Figure 2. Mediating model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01, p < .05.
and social undermining (–) as significant predictors. A test of the equality of regression coefficients (Rencher, 1995) found that social undermining was more strongly related to HRQOL than health care provider support, Z ¼ 2.87, p < .01 or social support, Z ¼ 3.19, p < .01. These findings support the first three hypotheses although there is still the possibility of a better explanatory model. The mediating effects model demonstrated a good fit to the data, v2(28, N ¼ 344) ¼ 102.32, p < .01, RMR ¼ .050, IFI ¼ .948, CFI ¼ .947. The model explains 49% of the variance in the latent HRQOL variable with work status (stable greater than unstable=unemployed), health care provider support (þ), and social undermining (–) as significant predictors. Social undermining (but not work status) was related to social support, but social support did not significantly relate to HRQOL. Work status and social support were related directly with health care provider support,
Figure 3. Moderating model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01.
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which then related with HRQOL. About 10% of the total effects for social undermining and 7% of the total effects for work status were attributed to the indirect effects. Thus, the fourth hypothesis was supported as there is some evidence of a mediating effect of social undermining and work status through health care provider support particularly. The moderating effects model for HRQOL was not a good fit to the data, v2(51, N ¼ 344) ¼ 456.43, p < .01, RMR ¼ .167, IFI ¼ .733, CFI ¼ .730. The model explains 41% of the variance in the latent HRQOL variable and yet the interaction terms were not significant predictors. Thus, the fifth hypothesis was not supported as there is no evidence in this sample that social undermining moderates the effect of social support or health care provider support on HRQOL.
Discussion Hypotheses The first hypothesis proposed that social support variables are positively related with HRQOL. The bivariate findings demonstrated support for these hypotheses. In addition, health care provider support was significantly related to HRQOL in the multivariate models. These findings are consistent with the larger literature that has found that social support is positively related to HRQOL even when controlling for other demographic and biomedical variables (e.g., Jia et al., 2007; Viswanathan et al., 2005). These findings speak to the importance of positive social interactions in general, but prior studies did not specifically consider social undermining and its influence. The second hypothesis predicted that social undermining variables are negatively related to HRQOL and the third hypothesis proposed that social undermining would be more strongly related to HRQOL than social support. The bivariate and multivariate findings demonstrated support for these hypotheses. Furthermore, the multivariate models found that social undermining was more strongly associated with HRQOL than social support. These findings are consistent with prior research that has found social undermining to be a more important predictor of mental health=well-being than social support (Fleishman et al., 2000; Oetzel et al., 2007). The fourth hypothesis predicted a mediating effect for social support. The latent variable structural model found that health care provider support partially mediated the influence of social undermining and work status on HRQOL. Provider support appears to be able to buffer some of the negative effects of social undermining and unemployment=unstable employment on HRQOL. The findings are consistent with the literature on social support in general (MacGeorge et al., 2011) and on social undermining in particular (Duffy et al., 2002). Specifically, Duffy and colleagues (2002) found that support from one source can moderately attenuate the effects of social undermining from another source. The fifth hypothesis posited a moderating effect for social undermining on the social support=HRQOL relation. However, there was no evidence of a moderating effect. Prior research has found some evidence of social undermining moderating the influence of social support on health outcomes (e.g., Cranford, 2004) although prior research has not directly studied PLWH and HRQOL. One possible explanation for the lack of findings is the fact that the support and undermining scales in the present study provided an overall assessment of the participants’ networks. Given the constraints of data collection, it was not possible to isolate the sources of support and undermining in participants’ social networks. It is possible that undermining from family members exacerbates a lack of support from others in a social network (Vinokur et al., 1996).
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Overall, to answer the research question, the evidence available in the present study suggests that a combined direct and indirect effect model appears to be the best explanatory model. Thus, the findings in the present study are consistent with a buffering model of support (MacGeorge et al., 2011) and the social negativity hypothesis (Cranford, 2004). PLWH face many stressors and ideally expect to find support, kindness, and respect when they interact with family and friends. Unfortunately, some PLWH perceive isolation from family members and friends and stigma from society in general. The isolation can include feelings of isolation, the desire to avoid interacting with family, and simply feeling let down. These types of interactions are not what are needed, desired, or expected and thus they likely become salient to PLWH. However, health care providers who offer support can attenuate some of these negative effects. Implications The present study has a number of research and practical implications. First, this study demonstrates the importance of considering both social support and undermining. Most social support and HRQOL researchers do not consider social undermining (e.g., Jia et al., 2007; Viswanathan et al., 2005). The findings of this study indicate that it is more important to avoid negative interactions than to have positive interactions. In addition, the present study helps to identify important variables for researchers and practitioners. The multivariate models included work status as well as the support and undermining variables. Socioeconomic status has been found to be associated positively with HRQOL using such factors as income, education, work status, and insurance (Campsmith et al., 2003; Logie & Gadalla, 2009). The present study included all of these variables and found that work status is the best indicator of socioeconomic status in terms of HRQOL. Overall, the unique contribution of the present study is that a multitude of variables were considered simultaneously and yet a limited number of variables were significantly correlated with HRQOL. These correlates of HRQOL are good targets for interventions (Mrus et al., 2006) and have implications for program administrators, funders, health care providers, and case managers. Specifically, these findings suggest interventions may want to consider ways to enhance work status, reduce social isolation, and enhance support from providers (in addition to addressing the basic medical needs of PLWH). In this manner, the findings are consistent with research suggesting the importance of ancillary services for PLWH (Chin, Botsko, Behar, & Finkelstein, 2009). Ancillary services (such as social and behavioral health services) are critical intermediate means to ensuring that PLWH remain in treatment and enhance HRQOL (Chin et al., 2009). In the context of reduced funding for HIV programs, it is important that funders and program administrators not to remove critical ancillary services for PLWH because they are integral to treatment and HRQOL. One type of ancillary service that could be provided to address social isolation is online support groups (Mo & Coulson, 2010). Online support groups have proliferated in recent years and are one means to address some of the social isolation. However, being a part of an online group is not simply enough; certain behaviors will help empower PLWH. Mo and Coulson (2010) surveyed 340 PLWH in online groups and found that posting (as opposed to lurking) resulted in higher receipt of social support, receiving useful information, and higher satisfaction with other group members. Perhaps such online groups can also discuss effective strategies for avoiding negative interactions and that case managers can also reinforce these strategies. Strategies for avoiding negative interactions have not been well studied in online support groups.
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Limitations and Conclusion There are some limitations of the present study. First, the unique cultural context of New Mexico may limit generalizability of the findings. New Mexico is a rural, culturally diverse, and relatively poor state. Socioeconomic challenges are many and the rural nature of the state might make social isolation more of an issue for PLWH than in other urban settings. In addition, the survey questionnaire also had a fairly low response rate that may limit generalizability as well. The response rate was reasonable given that there was no follow-up because of privacy concerns and the possibility that we did not have accurate addresses for many of the clients. Given the concern in the study was focused on building multivariate models (i.e., internal validity rather than external validity), the low response rate is not a major factor. Last, the cross-sectional nature of the survey questionnaire limits the ability to identify causal relations between social support and undermining and HRQOL. Correlates have been clearly identified, even controlling for other factors, but we do not know whether the correlates create change in HRQOL. Future research can better identify these factors over time for PLWH. In summary, this study sought to identify the role of social support and social undermining for HRQOL when controlling for demographics, medication adherence, and behavioral health factors and also to also test the theoretical explanation of the nature of the effects of support and undermining. The findings demonstrate the importance of avoiding social undermining and receiving support from health care providers in order to enhance HRQOL. In theory, the findings illustrate backing for the social negativity hypothesis and the buffering effect of social support. Furthermore, the findings emphasize the importance of avoiding negative interactions more than establishing positive ones. In addition, the findings illustrate that work status is an important correlate for HRQOL for PLWH. In the context of improving the HRQOL of PLWH, the findings suggest that certain ancillary services are important to maintain even though funding is limited from states and the federal government.
Funding This project was funded by a contract from the New Mexico Infectious Disease Bureau (FY11UNM021041).
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Duffy, M. K., Ganster, D. C., Shaw, J. D., Johnson, J. L., & Pagon, M. (2006). The social context of undermining behavior at work. Organizational Behavior and Human Decision Processes, 101, 105–126. Finch, J. F. (1998). Social undermining, support satisfaction, and affect: A domain-specific lagged effects model. Journal of Personality, 66, 315–334. Fleishman, J. A., Sherbourne, C., Crystal, S., Collins, R. L., Marshall, G. N., Kelly, M., . . . Hays, R. D. (2000). Coping, conflictual social interactions, social support, and mood among HIV-infected persons. American Journal of Community Psychology, 28, 421–453. Galassi, J. P., Schanberg, R., & Ware, W. B. (1992). The patient reactions assessment: A brief measure of the quality of the patient–provider medical relationship. Psychological Assessment, 4, 346–351. Gant, L. M., & Nagda, B. A. (1993). Effects of social support and undermining on African American workers’ perceptions of coworker and supervisor relationships and psychological well-being. Social Work, 38, 158–164. Goldsmith, D. J. (2004). Communicating social support. New York, NY: Cambridge University Press. Gruenewald, T. L., & Seeman, T. E. (2010). Social support and physical health: Links and mechanisms. In A. Steptoe (Ed.), Handbook of behavioral medicine (pp. 224–236). New York, NY: Springer Science. Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408–420. Hayes, A. F., Glynn, C. J., & Huge, M. E. (2011). Cautions regarding the interpretation of regression coefficients and hypothesis tests in linear models with interactions. Communication Methods and Measures, 6, 1–11. Jia, H., Uphold, C., Wu, S., Chen, G., & Duncan, P. (2005). Predictors of changes in health-related quality of life among men with HIV infection in the HAART era. AIDS Patient Cars & STDs, 19, 395–405. Jia, H., Uphold, C. R., Yu, Z., Wu, S., Chen, G., Findley, K., & Duncan, P. W. (2007). A further investigation of health-related quality of life over time among men with HIV infection in the HAART era. Quality of Life Research, 16, 961–968. Logie, C., & Gadalla, T. (2009). Meta-analysis of health and demographic correlates of stigma towards people living with HIV. AIDS Care, 21, 742–753. Lowe, B., Kroenke, K., Herzog, W., & Grafe, K. (2004). Measuring depression outcome with a brief self-report instrument: Sensitivity to change of the Patient Health Questionnaire (PHQ-9). Journal of Affective Disorders, 81, 61–66. MacGeorge, E. L., Feng, B., & Burleson, B. R. (2011). Supportive communication. In M. L. Knapp & J. A. Daly (Eds.), The Sage handbook of interpersonal communication (pp. 317–354). Thousand Oaks, CA: Sage. Mo, P. H., & Coulson, N. S. (2010). Empowering processes in online support groups among people living with HIV=AIDS: A comparative analysis of ‘lurkers’ and ‘posters.’ Computers in Human Behavior, 26, 1183–1193. Mrus, J. M., Leonard, A. C., Yi, M. S., Sherman, S. N., Fultz, S. L., Justice, A. C., & Tsevat, J. (2006). Health related quality of life in veterans and non-veterans with HIV=AIDS. Journal of General Internal Medicine, 21, S39–S47. Nair, K., & Muthurkrishna, N. (2009). Psychological well-being and health related quality of life among a group of low-income women living with HIV=AIDS in South Africa. Journal of Psychology in Africa, 19, 517–529. Oetzel, J., Duran, B., Jiang, Y., & Lucero, J. (2007). Social support and social undermining as correlates for alcohol, drug, and mental disorders in American Indian women presenting for primary care at an Indian Health Service hospital. Journal of Health Communication, 12, 187–206.
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Pearson, C. R., Simoni, J. M., Hoff, P., Kurth, A. E., & Martin, D. P. (2007). Assessing antiretroviral adherence via electronic drug monitoring and self-report: An examination of key methodological issues. AIDS & Behavior, 11, 161–173. Rencher, A. (1995). Methods of multivariate analysis. New York, NY: Wiley. Spitzer, R. L., Williams, J. B. W., Kroenke, K., Hornyak, R., McMurray, J., & the PHQ Obstetrics-Gynecology Study Group. (2000). Validity and utility of the PRIME-MD Patient Health Questionnaire in assessment of 3000 obstetric-gynecologic patients: The PRIME-MD Patient Health Questionnaire obstetrics-gynecology study. American Journal of Obstetrics and Gynecology, 183, 759–769. Vinokur, A. D., Price, R. H., & Caplan, R. D. (1996). Hard times and hurtful partners: How financial strain affects depression and relationship satisfaction of unemployed persons and their spouses. Journal of Personality and Social Psychology, 71, 166–179. Vinokur, A. D., & van Ryn, M. (1993). Social support and undermining in close relationships: Their independent effects on the mental health of unemployed persons. Journal of Personality and Social Psychology, 65, 350–359. Viswanathan, H., Anderson, R., & Thomas, I. (2005). Nature and correlates of SF-12 physical and mental quality of life components among low-income HIV adults using an HIV service center. Quality of Life Research, 14, 935–944. Wu, A. W. (1999). MOS-HIV health survey user’s manual. Retrieved from http://chipts.cch. ucla.edu/assessment/pdf/assessments/MOS-HIV%20Users%20Manual%20%20Draft.pdf Wu, A. W. (2000). Quality of life assessment comes of age in the era of highly active antiretroviral therapy. AIDS, 14, 1449–1451. Wu, A. W., Revicki, D. A., Jacobson, D., & Malitz, F. E. (1997). Evidence for reliability, validity and usefulness of the Medical Outcomes Study HIV health survey (MOS-HIV). Quality of Life Research, 6, 481–493. Wu, A. W., Rubin, H. R., Matthews, W. C., Ware, J. E., Brysk, L. T., Hardy, W. D., . . . Richman, D. D. (1991). A health status questionnaire using 30 items from the medical outcomes study. Medical Care, 29, 786–798.
Social Support and Social Undermining as Explanatory Factors for Health-Related Quality of Life in People Living With HIV/AIDS JOHN OETZEL Department of Management Communication, University of Waikato, Hamilton, New Zealand
BRYAN WILCOX AND ASHLEY ARCHIOPOLI Department of Communication and Journalism, University of New Mexico, Albuquerque, New Mexico, USA
MAGDALENA AVILA Department of Health Exercise and Sport Science, University of New Mexico, Albuquerque, New Mexico, USA
CIA HELL, RICKY HILL, AND MICHAEL MUHAMMAD Department of Communication and Journalism, University of New Mexico, Albuquerque, New Mexico, USA This study aimed to examine the influence of social support (from personal networks and health care providers) and social undermining (from personal networks) on health-related quality of life (HRQOL; general health perceptions, physical functioning, and depression). Specifically, the authors aimed to identify the nature of the effects (direct, mediating, or moderating) of social support and social undermining on HRQOL. A total of 344 people living with HIV=AIDS and who were patients in a federally funded clinic in New Mexico completed a self-report survey questionnaire. The major findings of this study are the following: (a) social support and social undermining had direct and indirect effects on HRQOL—there was no evidence of a moderating effect of social support and social undermining; (b) for direct effects, social undermining was a stronger predictor of HRQOL than social support with social support variables having positive relations and social undermining variables having negative relations with HRQOL; and (c) for indirect effects, providers’ social support partially mediated the influence of unstable employment= unemployment and social undermining on HRQOL.
Health-related quality of life (HRQOL) is a key indicator for patients with people living with HIV=AIDS (PLWH) in the highly active antiretroviral therapy era (Jia, Uphold, Wu, Chen, & Duncan, 2005; Wu, 2000). As a result, researchers have Address correspondence to John Oetzel, Department of Management Communication, University of Waikato, Hamilton 3240, New Zealand. E-mail: [email protected]
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aimed to identify variables correlated with HRQOL (e.g., Nair & Muthurkrishna, 2010). Identification of these correlates can help researchers, providers, and patients identify the most effective points of intervention in the health delivery process. One such correlate is social support; social support has been found to be related positively to HRQOL for PLWH (Jia et al., 2007; Viswanathan, Anderson, & Thomas, 2005). Although social support has been found consistently to relate to HRQOL, there are some limitations in its study in this setting. First, not all studies have examined the effects of social support when controlling for other demographic, biomedical, and behavioral health variables. Second, most researchers assume that the presence of positive social support is the key factor for HRQOL when it may be the absence of negative social interaction (i.e., social undermining). Last, the association of social support and social undermining with HRQOL is undertheorized. The purpose of this study is to address these limitations. Specifically, this study aimed to determine whether social undermining and social support are associated with three types of HRQOL (general health perceptions, physical functioning, and depression) using three different theoretical models: (a) direct effects model controlling for demographic variables, medication adherence, and behavioral health; (b) a mediating model where social support mediates the effect of social undermining and other demographic stressors on HRQOL; and (c) a moderating model where the interaction of social support and social undermining affects HRQOL and alters the influence of social support on HRQOL.
Quality of Life and Social Support and Social Undermining HRQOL HRQOL refers to individual perceptions of well-being within physical, mental, and social spheres along with the perceived effect of health on day-to-day functions and includes multiple dimensions (Wu, 2000). For example, the MOS-HIV survey includes 10 dimensions: ‘‘general health perceptions, physical functioning, role functioning, pain, social functioning, mental health, energy, health distress, cognitive functioning, and quality of life’’ (Wu, 1999, p. 5). These dimensions can be summarized into a physical health summary and a mental health summary. For this study, we considered the general health perceptions and physical functioning of HRQOL and also depression specifically (i.e., not the general mental health summary). The limitation in selecting only three aspects of HRQOL is due primarily to the response burden of the participants in this present study and yet allows for measurement in a way consistent with the research on HRQOL (e.g., Jia et al., 2005; Wu, 2000). General health perceptions includes general health and illness, whereas physical functioning considers a range of physical limitations such as walking, lifting, and everyday functions such as eating and dressing (Wu, 1999). Depression is a mood disorder and includes several types including major depressive disorder (American Psychiatric Association, 2000). Prior research has found a number of correlates of HRQOL for PLWH. In terms of demographic characteristics, several factors have been identified: (a) older age results in lower HRQOL scores (Campsmith, Nakashima, & Davidson, 2003; Logie & Gadalla, 2009); (b) lower income negatively correlates with HRQOL (Campsmith et al., 2003; Logie & Gadalla, 2009); (c) ethnic minorities compared with non-Hispanic Whites have lower HRQOL (Campsmith et al., 2003); (d) formal education is positively related to HRQOL (Campsmith et al., 2003); and (e) people with unstable, temporary, or no employment have lower HRQOL scores compared with those with stable employment (Viswanathan et al., 2005). In terms of biomedical
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characteristics, the following variables have been identified: (a) CD4 is negatively associated with HRQOL (Campsmith et al., 2003; Jia et al., 2005), but also that the relation between CD4 and HRQOL varies over time (Jia et al., 2007); and (b) medication adherence is positively associated with HRQOL (Viswanathan et al., 2005). Linking Social Support and Social Undermining to HRQOL Social support refers to information or actions (actual or potential) that lead individuals to perceive that they are cared for or receive aid, assistance, and comfort from others when they need it (MacGeorge, Feng, & Burleson, 2011). In contrast, social undermining is a ‘‘negative form of social interaction characterized by active dislike and devaluing of an individual’’ (Gant & Nagda, 1993, p. 159). Social undermining is a distinct, yet negatively related factor to social support (Creed & Moore, 2006; Vinokur & van Ryn, 1993). This section describes the relations of social support and social undermining to HRQOL and then discusses three alternative theoretical frameworks tested in the present study. Social Support and Social Undermining The majority of studies find that perceived support is more strongly associated with well-being and mental health than actual support (Gruenewald & Seeman, 2010; MacGeorge et al, 2011). In addition, research has also illustrated that perceived social undermining has effects on mental health and well-being although direct comparison with actual undermining have not been undertaken (Cranford, 2004; Duffy, Ganster, Shaw, Johnson, & Pagon, 2006). This present study considers perceived support and undermining because of their prior relations with well-being and mental health. Specifically, we included two categories of social support in reference to friends and family—emotional and instrumental. The operationalization of emotional support in this study includes provision of love, affirmation, and caring, whereas instrumental support contains tangible aid. This choice is consistent with prior research examining health outcomes (e.g., Oetzel, Duran, Jiang, & Lucero, 2007). We also considered two types of social support from health care providers: information and emotional support (Galassi, Schanberg, & Ware, 1992). We included perceived quality of information from providers given the importance of health care providers as information sources given the complexity of the drug regimen among other factors for PLWH (Jia et al., 2007). In addition, health care providers offer affirmation and caring and this emotional support is particularly important in the context of social stigma for PLWH (Logie & Gadalla, 2009). In most studies, social undermining is conceptualized as a global construct (e.g., Duffy et al., 2006; Fleishman et al., 2000). However, Oetzel and colleagues (2007; see also Vinokur & van Ryn, 1993) considered two dimensions of social undermining, which we replicate for this present study. The first dimension is critical appraisal, which is the perception that the interaction provided is critical and hurtful (i.e., negative evaluation). The second dimension is isolation, which is the feeling of isolation that results from wanting to avoid family interactions (i.e., negative affect). Direct Effects The majority of research examining the relation between social support and undermining and HRQOL has examined positive direct effects for support and negative
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direct effects for undermining (e.g., Jia et al., 2005; Oetzel et al., 2007). For example, in a cross-sectional survey of 86 participants living with HIV, Viswanathan and colleagues (2005) found that social support was positively related to the physical component summary of the MOS-HIV in a model that considered demographics, CD4 cell count, and medication adherence. In a prospective cohort study of 197 men with HIV (from baseline to 12-month follow-up), Jia and colleagues (2005, 2007) found that family social support was positively related to change in physical and social functioning in a model including CD4 cell count, and active coping. These examples are consistent with the predominant pattern of studies including PLWH. In contrast, social undermining has not been well studied in PLWH. Fleishman and colleagues (2000) conducted a cross-sectional survey of 140 PLWH in San Diego and found that negative social interactions, compared with social support, were more strongly (and negatively) related to coping strategies and also with negative mood. In addition, further studies have found that social undermining is positively related to a variety of alcohol, drug, and mood disorders (i.e., negative indicators of well-being) in a variety of samples including anxiety disorders in undergraduate psychology students (Finch, 1998), and anxiety, mood, and substances use disorders in American Indian women (Oetzel et al., 2007). Scholars have explored the combined effects of social support and social undermining on a variety of health and job outcomes using the social negativity hypothesis. This hypothesis proffers that negative social relations will be more strongly associated than positive social interaction with health and job outcomes because negative interactions are generally unexpected and thus more salient (Cranford, 2004; Duffy, Ganster, & Pagon, 2002). These negative interactions also provoke more intense negative emotions than positive interactions (Duffy et al., 2002). Research has generally supported this hypothesis (Cranford, 2004; Duffy et al., 2002; Oetzel et al., 2007). For example, Duffy and colleagues (2002) surveyed 685 national police officers in Slovenia and found that social undermining was generally more strongly (and negatively) associated with organizational commitment, self-efficacy, and somatic behaviors than social support. Social undermining was also positively associated (and more strongly than social support) with counterproductive behaviors. Given this prior research and theorizing about direct effects, the following hypotheses were offered: Hypothesis 1: Social support is positively related with HRQOL. Hypothesis 2: Social undermining is negatively related to HRQOL. Hypothesis 3: Social undermining will be more strongly related to HRQOL than social support.
Mediating Effects Within the social support literature, one theoretical position is that that social support is a buffer to trauma or stress (Goldsmith, 2004; MacGeorge et al., 2011). Support helps to alleviate some of the negative effect of stress on health outcomes (MacGeorge et al., 2011). MacGeorge and colleagues (2011) reviewed the extant literature and identified ‘‘impressive evidence that supportive communication can affect physical health by buffering physiological reactions to stress’’ (p. 326). In the context of the present study, social support may buffer the effects of negative stressors such as stress and negative demographic conditions associated with living with HIV. Research also shows that social support may buffer social undermining. For example, Duffy and colleagues (2002) found some evidence that social support from
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one source attenuated the effect of social undermining from another source on passive counterproductive behaviors and somatic complaints. However, very few studies have examined the buffering effects of social support for the effect of social undermining on HRQOL and other outcomes. Despite this limitation, the strong body of evidence demonstrating the buffering effect of social support (MacGeorge et al., 2011) leads to the positing of the fourth hypothesis: Hypothesis 4: Social support mediates the effect of social undermining and demographic variables on HRQOL.
Moderating Effects A final theoretical model proposes that social undermining and social support have an interaction effect on health and other outcomes. Specifically, the extant literature suggests a stress-exacerbation model. The stress-exacerbation model suggests that social undermining exacerbates the effect of stress on health outcomes (Cranford, 2004; Vinokur, Price, & Caplan, 1996). Thus, negative social interaction creates further stress on people than the original stressful event=condition. For example, Cranford surveyed 181 married couples at two time points and found that time one social undermining by one’s spouse moderated the association between time one stress and time two depressive symptoms. Specifically, social undermining made the stress=depression relation stronger. In contrast, social support did not buffer the stress=depression relation. In addition, and more directly related to the present study, other scholars have examined the degree to which social undermining moderates the relation between social support and health outcomes. The findings have been somewhat mixed. Vinokur and colleagues (1996) examined 815 married couples in which one partner recently became unemployed. They found that people with low support and high undermining have the lowest relational satisfaction and highest depression. This finding may indicate that there is an exacerbating effect for undermining when there is a low level of support. Duffy and colleagues (2002) found that high levels of social undermining along with high levels of social support had a positive effect on counterproductive behaviors and negative effects on organizational commitment, selfefficacy, and well-being. Last, Creed and Moore’s (2006) study of unemployed and underemployed individuals found that there was no moderating effect of social support and social undermining on distress or coping. The literature appears to support the moderating effect of social undermining on social support and health outcomes although it is unclear the nature of the moderation. Thus, the fifth hypothesis is posited: Hypothesis 5: Social undermining moderates the relationship between social support and HRQOL. None of the studies examining mediation or moderation have been conducted with samples involving PLWH and HRQOL. This study attempts to identify the potential mechanisms explaining the relation between social support and social undermining and HRQOL for PLWH and considers the following research question: RQ: Which of the three theoretical models appears to provide the best explanation of the effect of social support and social undermining on HRQOL?
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Method Sampling Frame and Participants The sampling frame included 1,362 clients who used HIV services at federally funded clinics in New Mexico. Participants were recruited to respond to the survey questionnaire in three ways: (a) before a focus group set up at each clinic (n ¼ 46); (b) via mailed invitation from the state agency in charge of the program (n ¼ 281); and (c) through a web-based survey (n ¼ 17). Of the original 1,362 mailings, 150 were returned; thus, the sample of 344 clients yielded an effective response rate of 28%. The response rate is likely conservative as the agency estimates that as many as 40% of clients’ addresses are not accurate. The mean age of the sample was 49.28 (SD ¼ 10.2). The median monthly income of the sample was $1,485.83 (SD ¼ $1221.33). Table 1 displays the demographics characteristics of the sample.
Table 1. Demographic characteristics of the sample Characteristic Sex Male Female Ethnicity White, non-Hispanic Hispanic American Indian Multi-ethnic Other (African American, Asian-Pacific Islander, Other) Sexual Identity Gay Heterosexual Other (Bisexual, Transgender, Queer, Questioning) Education Not HS graduate HS graduate Some college College graduate Graduate school Language Spoken at Home English Spanish Other Type of Relationship Married=civil union Partner Divorced=separated=widowed Single Work Status Unemployed, unable to work, or temporary employment Employed full- or part-time Other (retired, homemaker, school) Insurance Private Insurance
Percentage (n) 89.6 (303) 10.4 (35) 52.4 32.6 3.8 5.3 5.9
(179) (111) (13) (18) (20)
68.6 (225) 20.1 (66) 11.2 (37) 9.4 17.6 42.4 20.3 10.3
(32) (60) (144) (69) (35)
96.2 (331) 27.9 (96) 5.2 (18) 11.0 35.8 16.2 37.0
(36) (117) (53) (121)
49.4 (159) 29.2 (94) 21.4 (69) 29.5 (102)
Note: Language spoken at home percentages are greater than 100 as participants could select multiple categories.
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Measures and Questionnaire The dependent variables were general health perceptions, physical health, and depression. General health perceptions was measured with four items from the MOS-HIV survey and the single item measuring quality of life (Wu, 1999; Wu et al., 1991). Physical functioning was measured with six items from the MOS-HIV. These subscales were selected because there is abundant evidence of reliability and validity of the measures (Wu, Revicki, Jacobson, & Malitz, 1997). Depression was measured with the nine-item subscale from the Patient Health Questionnaire. This subscale was chosen because it is an easy to use, self-administered screener that has ample evidence of reliability and validity from prior studies (e.g., Lowe, Kroenke, Herzog, & Grafe, 2004; Spitzer et al., 2000). For the present study, Cronbach’s alpha was .91 for the general health perceptions, .91 for physical functioning, and .92 for depression. General health perceptions was measured on a 5-point scale ranging from 1 (definitely false) to 5 (definitely true), physical functioning was measured on a 3-point scale ranging from 1 (yes, limited a lot) to 3 (no), and depression was measured on a 4-point scale ranging from 1 (nearly everyday) to 4 (not at all). For these and all scales, missing values were replaced with series mean if the participant had fewer than 10% of the items missing. The independent variables included demographic characteristics, medication adherence, alcohol use, and social support and undermining. Demographic characteristics were measured with self-report categories. Medication adherence was measured with four items following the recommendations of Pearson, Simoni, Hoff, Kurth, and Martin (2007). Specifically, we asked clients how often they missed a dose of any HIV medicine over past 7 and 30 days and also how often they missed the time schedule of their medicine over the same two time periods. Cronbach’s alpha was .83 for the four items in the present study and the measurement was a 4-point scale ranging from 1 (not once=less than once a week) to 4 (every day). Alcohol abuse was measured with the five-item subscale from the Patient Health Questionnaire, which like the depression subscale has ample evidence of validity (e.g., Lowe et al., 2004). Social support and social undermining were measured with 20 total items (Oetzel et al., 2007). The items were validated and demonstrated to be internally consistent with a sample of American Indian women (Oetzel et al., 2007). For the present study, the scales were slightly revised in the following manner: (a) written to be appropriate for a general audience with HIV=AIDS; (b) the scaling was changed to be a five-item Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree); and (c) one critical appraisal item was removed and one isolation item was added. The final subscales consisted of emotional support (six items; a ¼ .81), instrumental social support (five items; a ¼ .83), critical appraisal (five items; a ¼ .79) and isolation (four items; a ¼ .78). Confirmatory factor analysis demonstrated that the four factor model was a good fit to the data, v2(164, N ¼ 344) ¼ 377.76, p < .01, RMR ¼ .077, IFI ¼ .929, CFI ¼ .929. Health care provider support was measured with 10 items from the patients’ reactions assessment (Galassi et al., 1992). Five items measured the perceived quality of information and five items measured emotional support (originally called affective). These subscales have demonstrated validity and reliability in prior research (Galassi et al., 1992). For the present study, the two-factor model provided a reasonable fit to the data, v2(164, N ¼ 344) ¼ 278.06, p < .01, RMR ¼ .069, IFI ¼ .907, CFI ¼ .907. Information had a Cronbach’s alpha of .92 and emotional support had an alpha of .88; both were measured on 5-point Likert scales. Before administering the survey, we took two steps to increase the accessibility of the survey questionnaire for clients. First, we had the survey (and all related
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materials) translated and back-translated into Spanish; 13 completed the survey questionnaire in Spanish. Second, we asked a health literacy expert to review the items and suggest wording changes so the survey (particularly the instructions) was accessible at a seventh-grade reading level and the format was clear and easy to follow.
Results Preliminary Analysis Before conducting the multivariate models, descriptive and bivariate statistics were calculated. Table 2 displays the descriptive information for the demographic characteristics by the dependent variables, whereas Table 3 displays the descriptive information (means, standard deviations, and correlations) for the social support and undermining variables along with the dependent variables. Some of the demographic categories were collapsed to accommodate the multivariate modeling. The categorizations were done for conceptual reasons and=or statistical reasons (i.e., to have sufficient numbers within the categories). Specifically, the variables were Table 2. Descriptive statistics of demographics with dependent variables Depression Independent variables Sex Male Female Ethnicity White, non-Hispanic Ethnic minority Sexual Identity Heterosexual Gay or Other Education HS graduate or less More than HS graduate Type of Relationship Partner No partner Work Status Unemployed, unable to work, or temporary employment Employed full- or part-time or other Insurance Private insurance No private insurance Alcohol Abuse Yes No Age Monthly Income
General health perceptions
Physical functioning
M
SD
M
SD
M
3.10 3.03
.80 .88
3.42 3.41
1.06 .97
2.45 2.40
.57 .62
3.18 3.01
.78 .82
3.54 3.27
1.05 1.02
2.48 2.40
.55 .59
2.98 3.13
.82 .80
3.23 3.45
1.09 1.03
2.39 2.46
.59 .57
2.99 3.14
.84 .79
3.12 3.49
1.06 1.03
2.29 2.49
.60 .56
3.20 3.02
.72 .85
3.39 3.40
1.06 1.05
2.46 2.41
.59 .57
2.88
.84
3.04
1.03
2.21
.61
3.34
.69
3.77
.93
2.67
.42
3.25 3.04
.75 .82
3.66 3.31
1.02 1.05
2.60 2.38
.53 .59
2.71 3.17
.89 .76 .09 .21
3.17 3.46
1.02 1.05 .04 .20
2.37 2.46
.65 .65 .12 .23
Note: Age and monthly income are correlations. Higher numbers on depression indicate lower levels of depression. Significant difference between the groups at p < .05.
SD
668
3.39 2.44 3.10 4.11 4.30 3.77 3.71 2.24 2.61 4.48
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
1.05 .58 .80 .92 .86 .86 1.01 .85 1.03 .88
SD
Note: Numbers on the diagonals are Cronbach’s alphas. p < .05, p < 01.
General health perceptions Physical functioning Depression Information Emotional support (HC provider) Emotional support Instrumental support Critical appraisal Isolation Medication adherence
M
Variable .91 .64 .57 .31 .29 .35 .32 .38 .44 .13
1 .91 .48 .25 .28 .31 .33 .27 .29 .11
2
Table 3. Descriptive statistics of independent and dependent variables
.92 .30 .23 .41 .37 .45 .55 .19
3
.92 .63 .32 .35 .27 .26 .17
4
.88 .31 .35 .27 .29 .18
5
.81 .71 .49 .60 .14
6
.83 .47 .60 .12
7
.79 .66 .06
8
.78 .16
9
.83
10
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categorized in the following manner: ethnicity (ethnic minority or not), sexual identity (heterosexual or not), education (more than high school graduate or not), type of relationship (partner or not), insurance (private or not), and work status (unemployed=unstable or stable). Before testing the direct, mediating, and moderating effects, several steps were undertaken to create parsimonious models and also provide interpretable coefficients. First, confirmatory factor analysis demonstrated that the four latent constructs of social support, health care provider support, social undermining, and HRQOL were a good fit to the nine subscales, v2(21, N ¼ 344) ¼ 64.07, p < .01, RMR ¼ .030, IFI ¼ .968, CFI ¼ .968, and had the following loadings: (a) social support—emotional (.84) and instrumental (.84); (b) health care provider support—information (.82) and emotional (.79); (c) social undermining—critical appraisal (.74) and isolation (.91); and (d) HRQOL—general health perceptions (.83), physical functioning (.71), and depression (.72). Thus, the four latent constructs rather than the individual subscales were used to directly test the hypotheses. Second, given that there were multiple demographic variables significantly related to the dependent variables, a multiple regression model was used to identify the key demographic and medical=behavioral variables for the combined HRQOL variable. The analysis revealed that the only significant variable besides social support and social undermining was work status. Thus, only work status was included in the models to test the hypotheses. Last, the social support and social undermining variables were mean-centered in order to make the findings interpretable in case there was a moderating effect (Hayes, Glynn, & Huge, 2011). Multivariate Modeling To address the hypotheses and research question, latent variable structural equation models were examined using AMOS 20.0 with bootstrapped standard errors as suggested by Hayes (2009). Figures 1–3 display the three models and final coefficients. The direct effects model for HRQOL was not a good fit to the data, v2(32, N ¼ 344) ¼ 347.14, p < .01, RMR ¼ .188, IFI ¼ .779, CFI ¼ .770. The model explains 41% of the variance in the latent HRQOL variable with work status (stable employment being greater than unstable=unemployed), health care provider support (þ),
Figure 1. Direct effect model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01.
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Figure 2. Mediating model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01, p < .05.
and social undermining (–) as significant predictors. A test of the equality of regression coefficients (Rencher, 1995) found that social undermining was more strongly related to HRQOL than health care provider support, Z ¼ 2.87, p < .01 or social support, Z ¼ 3.19, p < .01. These findings support the first three hypotheses although there is still the possibility of a better explanatory model. The mediating effects model demonstrated a good fit to the data, v2(28, N ¼ 344) ¼ 102.32, p < .01, RMR ¼ .050, IFI ¼ .948, CFI ¼ .947. The model explains 49% of the variance in the latent HRQOL variable with work status (stable greater than unstable=unemployed), health care provider support (þ), and social undermining (–) as significant predictors. Social undermining (but not work status) was related to social support, but social support did not significantly relate to HRQOL. Work status and social support were related directly with health care provider support,
Figure 3. Moderating model. Measurement variables and error terms omitted for clarity of presentation. Numbers are unstandardized coefficients (standard error). p < .01.
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which then related with HRQOL. About 10% of the total effects for social undermining and 7% of the total effects for work status were attributed to the indirect effects. Thus, the fourth hypothesis was supported as there is some evidence of a mediating effect of social undermining and work status through health care provider support particularly. The moderating effects model for HRQOL was not a good fit to the data, v2(51, N ¼ 344) ¼ 456.43, p < .01, RMR ¼ .167, IFI ¼ .733, CFI ¼ .730. The model explains 41% of the variance in the latent HRQOL variable and yet the interaction terms were not significant predictors. Thus, the fifth hypothesis was not supported as there is no evidence in this sample that social undermining moderates the effect of social support or health care provider support on HRQOL.
Discussion Hypotheses The first hypothesis proposed that social support variables are positively related with HRQOL. The bivariate findings demonstrated support for these hypotheses. In addition, health care provider support was significantly related to HRQOL in the multivariate models. These findings are consistent with the larger literature that has found that social support is positively related to HRQOL even when controlling for other demographic and biomedical variables (e.g., Jia et al., 2007; Viswanathan et al., 2005). These findings speak to the importance of positive social interactions in general, but prior studies did not specifically consider social undermining and its influence. The second hypothesis predicted that social undermining variables are negatively related to HRQOL and the third hypothesis proposed that social undermining would be more strongly related to HRQOL than social support. The bivariate and multivariate findings demonstrated support for these hypotheses. Furthermore, the multivariate models found that social undermining was more strongly associated with HRQOL than social support. These findings are consistent with prior research that has found social undermining to be a more important predictor of mental health=well-being than social support (Fleishman et al., 2000; Oetzel et al., 2007). The fourth hypothesis predicted a mediating effect for social support. The latent variable structural model found that health care provider support partially mediated the influence of social undermining and work status on HRQOL. Provider support appears to be able to buffer some of the negative effects of social undermining and unemployment=unstable employment on HRQOL. The findings are consistent with the literature on social support in general (MacGeorge et al., 2011) and on social undermining in particular (Duffy et al., 2002). Specifically, Duffy and colleagues (2002) found that support from one source can moderately attenuate the effects of social undermining from another source. The fifth hypothesis posited a moderating effect for social undermining on the social support=HRQOL relation. However, there was no evidence of a moderating effect. Prior research has found some evidence of social undermining moderating the influence of social support on health outcomes (e.g., Cranford, 2004) although prior research has not directly studied PLWH and HRQOL. One possible explanation for the lack of findings is the fact that the support and undermining scales in the present study provided an overall assessment of the participants’ networks. Given the constraints of data collection, it was not possible to isolate the sources of support and undermining in participants’ social networks. It is possible that undermining from family members exacerbates a lack of support from others in a social network (Vinokur et al., 1996).
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Overall, to answer the research question, the evidence available in the present study suggests that a combined direct and indirect effect model appears to be the best explanatory model. Thus, the findings in the present study are consistent with a buffering model of support (MacGeorge et al., 2011) and the social negativity hypothesis (Cranford, 2004). PLWH face many stressors and ideally expect to find support, kindness, and respect when they interact with family and friends. Unfortunately, some PLWH perceive isolation from family members and friends and stigma from society in general. The isolation can include feelings of isolation, the desire to avoid interacting with family, and simply feeling let down. These types of interactions are not what are needed, desired, or expected and thus they likely become salient to PLWH. However, health care providers who offer support can attenuate some of these negative effects. Implications The present study has a number of research and practical implications. First, this study demonstrates the importance of considering both social support and undermining. Most social support and HRQOL researchers do not consider social undermining (e.g., Jia et al., 2007; Viswanathan et al., 2005). The findings of this study indicate that it is more important to avoid negative interactions than to have positive interactions. In addition, the present study helps to identify important variables for researchers and practitioners. The multivariate models included work status as well as the support and undermining variables. Socioeconomic status has been found to be associated positively with HRQOL using such factors as income, education, work status, and insurance (Campsmith et al., 2003; Logie & Gadalla, 2009). The present study included all of these variables and found that work status is the best indicator of socioeconomic status in terms of HRQOL. Overall, the unique contribution of the present study is that a multitude of variables were considered simultaneously and yet a limited number of variables were significantly correlated with HRQOL. These correlates of HRQOL are good targets for interventions (Mrus et al., 2006) and have implications for program administrators, funders, health care providers, and case managers. Specifically, these findings suggest interventions may want to consider ways to enhance work status, reduce social isolation, and enhance support from providers (in addition to addressing the basic medical needs of PLWH). In this manner, the findings are consistent with research suggesting the importance of ancillary services for PLWH (Chin, Botsko, Behar, & Finkelstein, 2009). Ancillary services (such as social and behavioral health services) are critical intermediate means to ensuring that PLWH remain in treatment and enhance HRQOL (Chin et al., 2009). In the context of reduced funding for HIV programs, it is important that funders and program administrators not to remove critical ancillary services for PLWH because they are integral to treatment and HRQOL. One type of ancillary service that could be provided to address social isolation is online support groups (Mo & Coulson, 2010). Online support groups have proliferated in recent years and are one means to address some of the social isolation. However, being a part of an online group is not simply enough; certain behaviors will help empower PLWH. Mo and Coulson (2010) surveyed 340 PLWH in online groups and found that posting (as opposed to lurking) resulted in higher receipt of social support, receiving useful information, and higher satisfaction with other group members. Perhaps such online groups can also discuss effective strategies for avoiding negative interactions and that case managers can also reinforce these strategies. Strategies for avoiding negative interactions have not been well studied in online support groups.
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Limitations and Conclusion There are some limitations of the present study. First, the unique cultural context of New Mexico may limit generalizability of the findings. New Mexico is a rural, culturally diverse, and relatively poor state. Socioeconomic challenges are many and the rural nature of the state might make social isolation more of an issue for PLWH than in other urban settings. In addition, the survey questionnaire also had a fairly low response rate that may limit generalizability as well. The response rate was reasonable given that there was no follow-up because of privacy concerns and the possibility that we did not have accurate addresses for many of the clients. Given the concern in the study was focused on building multivariate models (i.e., internal validity rather than external validity), the low response rate is not a major factor. Last, the cross-sectional nature of the survey questionnaire limits the ability to identify causal relations between social support and undermining and HRQOL. Correlates have been clearly identified, even controlling for other factors, but we do not know whether the correlates create change in HRQOL. Future research can better identify these factors over time for PLWH. In summary, this study sought to identify the role of social support and social undermining for HRQOL when controlling for demographics, medication adherence, and behavioral health factors and also to also test the theoretical explanation of the nature of the effects of support and undermining. The findings demonstrate the importance of avoiding social undermining and receiving support from health care providers in order to enhance HRQOL. In theory, the findings illustrate backing for the social negativity hypothesis and the buffering effect of social support. Furthermore, the findings emphasize the importance of avoiding negative interactions more than establishing positive ones. In addition, the findings illustrate that work status is an important correlate for HRQOL for PLWH. In the context of improving the HRQOL of PLWH, the findings suggest that certain ancillary services are important to maintain even though funding is limited from states and the federal government.
Funding This project was funded by a contract from the New Mexico Infectious Disease Bureau (FY11UNM021041).
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