Social Support and Adherence: Differences Among Clients in an AIDS Day Health Care Program

Social Support and Adherence: Differences Among Clients in an AIDS Day Health Care Program Donald Gardenier, DNP, FNP-BC Claire M. Andrews, PhD, CNM, FAAN David C. Thomas, MD L. Jeannine Bookhardt-Murray, MD, AAHIVS Joyce J. Fitzpatrick, PhD, RN, FAAN

Challenges in care management threaten health outcomes in persons living with HIV (PLWH), who also have other medical and psychiatric diagnoses, substance use problems, or adjustment issues (comorbid PLWH). Integrated primary care programs have been developed to address multiple care needs in comorbid PLWH. The effectiveness of these models has not been shown empirically, in part because of multidisciplinary approaches to care. Adherence and its relationship to social support are key factors in favorable outcomes in HIV. The authors measured social support and adherence among clients in AIDS day health care, an integrated primary care program for comorbid PLWH. The level of social support among AIDS day health care clients who were adherent to their antiretroviral therapy was reported to be significantly higher than social support among those who were nonadherent. Implications of the differences in social support and adherence in the population are explored and discussed. Implications for nursing practice and future research are also addressed. (Journal of the Association of Nurses in AIDS Care, 21, 75-85) Copyright Ó 2010 Association of Nurses in AIDS Care Key words: adherence, AIDS day health care, comorbid, health outcomes, HIV, integrated care, social support

Since the transition of HIV infection to a chronic condition, managing comorbidities and extending treatments to new populations have become increasingly challenging. Effective viral suppressive therapy became widely available in 1996 (Yeni et al., 2004). Since then, life expectancies have increased for persons living with HIV infection (PLWH), so that other illnesses common in the population such as depression, substance use, and chronic hepatitis C need to be addressed concurrently with antiretroviral therapy (ART). As individuals living with chronic HIV infection age, many also acquire diabetes, hypertension, and other diseases associated with aging in the general population. Comorbidity related to HIV

Donald Gardenier, DNP, FNP-BC, is assistant professor, Division of General Internal Medicine, Mount Sinai School of Medicine, New York, and nurse practitioner, Harlem United Community AIDS Center, Inc, New York. Claire M. Andrews, PhD, CNM, FAAN, is professor, Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio. David C. Thomas, MD, is associate professor, Division of General Internal Medicine, Mount Sinai School of Medicine, New York. L. Jeannine Bookhardt-Murray, MD, AAHIVS, is medical director, Harlem United Community AIDS Center, Inc, New York. Joyce J. Fitzpatrick, PhD, RN, FAAN, is Elizabeth Brooks Ford Professor of Nursing, Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland.

JOURNAL OF THE ASSOCIATION OF NURSES IN AIDS CARE, Vol. 21, No. 1, January–February 2010, 75-85 doi:10.1016/j.jana.2009.06.007 Copyright Ó 2010 Association of Nurses in AIDS Care

76 JANAC Vol. 21, No. 1, January–February 2010

disease and ART, actual or potential medication interactions, and management of other chronic diseases combine to pose unprecedented challenges for PLWH and their care providers (Hubbard, 2006). As many as 40% of PLWH have comorbid medical conditions (Fultz et al., 2005). An even greater percentage of patients are depressed; as many as 45% in one study (Penzak, Reddy, & Grimsley, 2000) and greater than 50% in another (Yi et al., 2006). Taken together, behavioral health comorbidities such as depression, other psychiatric and substance use diagnoses, psychosocial stressors, and adjustment problems have a considerable negative impact on health outcomes in a significant proportion of PLWH. Comorbid PLWH are considered to be at increased risk for poor HIV-related health outcomes as a result of their cooccurring psychiatric diagnoses, past or current substance use, and/or adjustment issues (Israelski et al., 2007). Innovations in primary care delivery have taken place with the goal of improving outcomes in individuals with complex comorbid psychiatric and medical conditions. These innovations include intensive comanagement (Gardenier, Neushotz, & O’ConnorMoore, 2007), service colocation, coordinated case management, housing and legal assistance, nutrition programs, recreation, peer support, and medication management (Willenbring, 2005). Heterogeneity in program design allows a wide variety of needs in disparate patient populations to be addressed (Soto, Bell, & Pillen, 2004). One such approach is AIDS day health care (ADHC), a collaborative interdisciplinary health care model in which a comprehensive array of services to comorbid PLWH is offered (Murphy et al., 1999). The programs that evolved into today’s ADHC programs originated in the early days of the HIV pandemic. At a time when relatively little in the way of medical care was available for PLWH, programs offering nursing, pastoral care, housing, entitlement support, and activism served some of the needs of PLWH. As medical care for HIV took shape, it was added to the offerings of these community-based programs. ADHC programs eventually became integrated care programs offering a combination of full psychiatric, day treatment, medical care, case management, nutrition, substance use treatment, and referral services in addition to the core nursing,

Medicine Nursing Dentistry Psychiatry Mental Health Case Management Recreation Expressive Therapies Nutrition

Health Care

Transitional Housing

Housing Women and Children

Scatter Site Housing Vocational Education

Prevention HIV testing Community outreach Education Training Harm Reduction

Figure 1. AIDS Day Health Care Model.

spiritual care, community outreach, and housing missions (see Figure 1). ADHC programs typically serve those who are at highest risk of poor health outcomes as a result of their HIV infection and psychiatric and substance use diagnoses. A plurality are coinfected with hepatitis C, and most also have at least one chronic medical diagnosis that complicates care. Most have experienced some kind of psychological trauma, and ameliorating disrupted social systems is a central therapeutic principle of ADHC care (Murphy et al., 1999). Although ADHC programs and other integrated care models have shown promise in improving care delivery, integrated care lacks a consistent definition and approach and has not been adequately studied. Its effectiveness has therefore been difficult to demonstrate. As primary care providers strive to extend care to more challenging patient populations, care integration is likely to increase in importance, which in turn increases the need to evaluate and improve integrated models (Cunningham et al., 2007). Nonadherence to ART is a widespread clinical challenge (Chesney, 2003; Reynolds et al., 2004). In reviews of adherence, collaborative patientprovider relationships, targeted patient education, fitting regimens to patient lifestyles, aggressive side-effect management, and practice medication regimens have been identified as proactive interventions that may improve adherence (Chesney, 2003; Molassiotis, Morris, & Trueman, 2007; Waters & Nelson, 2007). Psychosocial factors affecting adherence are complex (Godin, Cote, Naccache, Lambert, & Trottier,

Gardenier et al. / Social Support and Adherence in ADHC

2005). Simoni, Frick, and Huang (2006) reported that social support predicted lower levels of negative affect and greater spirituality. Spirituality predicted selfefficacy, which in turn predicted self-reported adherence and favorable plasma HIV/RNA concentrations (viral loads) at 6 months. A four-tiered model with spirituality, negative affect, and self-efficacy all correlating with both social support and adherence was proposed. DiIorio et al. (2009) tested a social cognitive theory-based adherence framework. The researchers used structural equation modeling to determine the relationships among a number of psychosocial variables and reported that decreased social support was related to increased depression, which in turn decreased adherence. The strongest relationships were between social support and depression and between efficacy and adherence. Although a theoretical definition of adherence has not yet been proposed, these and other models have been tested. Researchers have identified and advocated the use of intervention points based on psychosocial variables appropriate to specific populations and care delivery frameworks (Simoni, Frick, Pantalone, & Turner, 2003). The frameworks that have been tested thus far have been focused on defining adherence and therefore have not been readily usable as frameworks for clinical practice. A framework with clinical utility as well as research applications would be of interest to nurses. Such a framework could be used to design interventions, coordinate interdisciplinary care, evaluate that care in terms of health outcomes, and then to revise and/ or redesign interventions. Social support and adherence have both been firmly associated in the literature with health outcomes in HIV as well as with other chronic diseases and have the potential to form the basis of a conceptual framework in the ADHC clinical setting. Whereas social support and adherence to care have been linked to each other and to positive health outcomes in many populations including PLWH, testing to see if they are related in the ADHC population, which has not been studied previously, will determine if the same link exists in this at-risk patient population. A link between social support and adherence among clients in the ADHC population could serve as a basis for clinical intervention including

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coordination of care and for future research. The purpose of this study was to describe perceived social support in comorbid PLWH who were enrolled in an ADHC program and to explore differences in social support between ADHC clients who were adherent to ART and those who were not.

Methods Participants All participants in this study had HIV infection and were at risk for poor health outcomes as a result of a comorbid psychiatric diagnosis, past or current substance use, and/or significant adjustment issues. All participants were enrolled in the ADHC program and were covered by Medicaid. Participants were approached during nonstructured program hours. Those who agreed to participate signed informed consent authorizing a chart review. They were approached a second time to review the information extracted from their medical records and to complete the surveys. Participants were reimbursed $4 for their travel expenses and received a rubber wristband for their participation. The study was approved by the Case Western Reserve University Institutional Review Board. Measures Participants reviewed and corrected chartextracted background data and completed the Social Provision Scale (SPS) (Cutrona & Russell, 1987) and the AIDS Clinical Trial Group (ACTG) adherence follow-up instrument (Chesney et al., 2000). Age, gender, ethnicity, year of HIV diagnosis, year of admission into the ADHC, usual ADHC attendance, most recent CD41 T cell count, and most recent viral load made up the background data collected from all study participants. Participants who were taking ART were asked if they did so in the ADHC’s voluntary directly observed therapy (DOT) program and, if so, for how long. The SPS measures social support and yields six component subscale scores: reliable alliance, guidance, attachment, social integration, reassurance of worth, and opportunity to provide nurturance. The six subscales are categorized as either

78 JANAC Vol. 21, No. 1, January–February 2010 Table 1. Instrumental Provisions • Reliable Alliance • Guidance Emotional Provisions • Attachment •

Social Integration

• •

Reassurance of Worth Opportunity for Nurturance

Knowledge that one can count on tangible aid when it is needed Advice and information from a trusted source Closeness and intimacy that fosters a sense of security A sense of belonging to a group with similar interests and concerns Recognition of one’s abilities and competence The feeling that one is needed by others

Figure 2. The Social Provisions.

instrumental or emotional provisions. The instrumental provisions (reliable alliance and guidance) are most often derived from professional sources such as health care providers, counselors, or social service providers. Emotional provisions (attachment, social integration, reassurance of worth, and opportunity to provide nurturance) are derived from personal resources such as family, significant others, peers, or friends (See Figure 2). Cronbach’s alpha for the SPS was .93, indicating good reliability in this study (Cutrona & Russell, 1987). The ACTG adherence follow-up instrument measures self-reported short-term ART adherence and self-reported reasons for missing medications. Participants who reported taking at least 95% of their medications were classified as adherent; those taking less than 95% were considered nonadherent. Cronbach’s alpha for the ACTG follow-up adherence instrument was .93, indicating good reliability (Reynolds et al., 2007).

Results Background data characteristics, levels of social support and social provisions, and adherence to ART were described. Mean social support among nonadherent and adherent participants was compared. Levels of social support and adherence were compared according to background data characteristics, and frequencies of reasons for missing medications questions were calculated.

Sample Characteristics (N 5 56)

Characteristic Age (yrs) Gender Male Female Transgender Race/ethnicity African American Hispanic/Latino Mixed race White/Caucasian Years since HIV diagnosis Years in ADHC Usual ADHC Attendance . 3 days/week 3 days per week , 3 days/week Most recent CD4 Most recent viral load Undetectable (, 75) Detectable ($ 75) DOT participatione No Yes Time in DOTf , 6 months $ 6 months

M (± SD) or f (%) 50.5 (6 8.5) 36 (64%) 19 (34%) 1 (2%) 43 (77%) 10 (18%) 2 (4%) 1 (2%) 16.3 (6 5.3)a 5.5 (6 4.2)b 41 (73%) 12 (21%) 3 (5%) 457 (6 372)c 33 (59%) 23 (41%)d 30 (59%) 21 (41%) 6 (29%) 16 (71%)

NOTE: ADHC 5 AIDS day health care, DOT 5 directly observed therapy. a. Mdn 5 17, range 5 2–27. b. Mdn 5 5, range 5 , 1-16. c. Mdn 5 385, range 5 27-1,950. d. M 5 43,947 (6 74,371), Mdn 5 10,000, range 5 435229,457. e. n 5 51. f. n 5 21.

approached refused to sign the informed consent document. Of those who signed informed consent, 56 were available to complete the surveys. According to the study protocol, not returning to complete the questionnaire was interpreted as a withdrawal, and any data extracted from those participants’ records was excluded from the analysis. Of the 56 study participants, 51 (91%) were prescribed ART. Sample characteristics are shown in Table 1.

Sample Characteristics

Social Support

A total of 66 eligible individuals signed informed consent. No prospective participant who was

Mean SPS scores for the sample and for the adherent and nonadherent groups are shown in

Gardenier et al. / Social Support and Adherence in ADHC Table 2.

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Mean Social Support Scores Scale

All Subjects N 5 56

Adherent n 5 28

Nonadherent n 5 23

p value

Total SPS Emotional Instrumental Reliable alliance Guidance Attachment Social integration Reassurance of worth Opportunity for nurturance

72.66 (6 9.45) 25.71 (6 2.97) 46.96 (6 7.24) 12.89 (6 1.73) 12.82 (6 1.84) 12.00 (6 2.19) 12.02 (6 1.73) 11.77 (61.68) 11.39 (6 1.89)

75.07 (6 9.06) 26.36 (6 3.06) 48.71 (6 6.62) 13.07 (6 1.72) 13.29 (6 1.68) 12.43 (62.13) 12.46 (6 1.97) 12.29 (6 1.61) 11.71 (6 1.90)

68.70 (6 9.40) 24.61 (6 2.68) 44.09 (6 7.65) 12.61 (6 1.53) 12.00 (6 1.81) 11.22 (6 2.19) 11.35 (6 1.37) 11.04 (6 1.67) 11.00 (6 2.05)

.02 .03 .03 .32 .01 .05 .02 .01 .21

NOTE: SPS 5 Social Provision Scale.

Table 2. Spearman rank order correlation showed a positive relationship between social support and most recent CD41 T cell count (rs 5 .28; p 5 .04). Mean social support among participants who participated in DOT (n 5 21) was 67.60 (6 9.6) compared with 75.50 (6 8.3) among those who did not (n 5 30). The difference was significant (p 5 .04). Social support differences based on all other background data characteristics were not statistically significant. Adherence Of the 51 participants who had been prescribed ART, 28 (55%) were adherent, and 23 (45%) were nonadherent. Mean CD41 T cell count among the adherent participants was 546 (6 338, Mdn 5 449, range 5 68-1,483). Among the nonadherent participants, mean CD41 T cell count was 349 (6 388, Mdn 5 287, range 5 27-1,950). The difference was statistically significant (p 5 .004). Among those not participating in DOT, 22 were adherent, and 8 were not. Among those participating in DOT, 6 were adherent, and 15 were not. The difference was statistically significant (p 5 .002). The remaining background data characteristics were not significantly different between adherent and nonadherent participants. Results of the reasons for missing medication questions are shown in Table 3.

Discussion The majority of the 56 ADHC clients who participated in this study were African American, and all

but one was either African American, Hispanic/ Latino, or of mixed race. The length of time they had been living with HIV, the length of time they had been participating in the ADHC program, and level of ADHC participation varied. Their age range, 34 to 69, reflected a general trend toward longer life among PLWH (Battaglioli-DeNero, 2006). The percentage of participants who were prescribed ART (91%) was greater than anticipated, reflecting current guidelines for earlier treatment initiation. Current regimens are also less complicated. The simplest is one pill taken once daily. Lower pill burdens have made ART accessible to patients who could not have managed earlier, more complicated regimens. Adherence among the 5 participants in this study who were prescribed one pill once a day was 100%. Conclusions cannot be drawn based on this small subgroup, but evidence that lower pill burdens could benefit comorbid PLWH warrants further study. Higher treatment rates also reflect a key component of ADHC care delivery: DOT. Accepting assistance in medication management has become an increasingly common care intervention among nonadherent ADHC participants and has become a means of access to ART for a group of PLWH who had effectively not had access to treatment. Whereas adherence rates in this subgroup were lower, most of these individuals had never been able to adhere to an antiretroviral regimen for any length of time. Thus, the observed adherence rate, although low, was likely higher than could be expected without DOT among this high-risk population. All of the ADHC clients who were approached agreed to sign a consent to participate in this study. The ADHC population has not been studied

80 JANAC Vol. 21, No. 1, January–February 2010 Table 3.

Reasons for Missing Medications (n 5 40)

Reason

No Never f

Yes Rarely f

Sometimes f

Often f

Total No (%)

Total Yes (%)

Away Busy Forgot Too many Side effects Others notice Harmful Slept Sick Depressed Times Ran out Felt good

13 15 15 32 24 37 28 18 23 25 23 21 26

9 5 3 3 2 2 4 5 4 5 4 10 2

17 17 15 2 11 1 6 16 11 10 11 8 12

1 3 7 3 3 0 2 1 2 0 2 1 0

32.5 37.5 37.5 80 60 92.5 70 45 57.5 62.5 57.5 52.5 65

67.5 62.5 62.5 20 40 7.5 30 55 42.5 38.5 42.5 47.5 35

previously, but in the literature, similar populations have been characterized as difficult to study (Boyer & Indyk, 2006; Cunningham et al., 2007). No refusals to participate in this study could indicate that difficulty in studying a comorbid or at-risk population may depend as much on study design and the approach of the researchers as on fixed characteristics of the population. Use of the SPS to measure social support had not previously taken place in the comorbid HIV-infected or any similar population. It was hypothesized that the mean social support score in this study would be less than the 82.45 (6 9.89) observed in a dissimilar comparison group (Cutrona & Russell, 1987). The observed overall mean score was 72.66 (6 9.45) in this study, indicating that this hypothesis was correct. The reasoning behind the hypothesis included the higher prevalence of psychiatric and medical illness, disrupted social systems, and other psychosocial stressors in the ADHC population. Scores were higher in the instrumental provisions than in the emotional provisions, likely reflecting the professional support and assistance that constitute core ADHC services. Scores for the emotional provisions, for which study participants would be more reliant on their personal social systems, were all lower. A high rate of disrupted family systems resulting from psychiatric comorbidity and substance use are possible reasons for the observed difference in categorical scores. The lowest mean subscale score was in the opportunity for nurturance subscale, the social provision that

corresponds with the perception that others seek and rely on social support that was provided, in this case, by the study participant. It is not unusual for the families, friends, and significant others of individuals with substance use problems to develop alternate sources of social support as attempts to derive social support from the substance-using individual fail. Such disrupted relationships can result, for example, in divorce or the loss of children to foster care. In less extreme examples, the system may remain intact but support is simply sought from an alternate source when reliance on the substance-using individual fails. In situations in which the individual recovers from substance use, disrupted family and social systems may not return to baseline. Most ADHC participants have past or current substance use problems and likely struggle to maintain and/or reestablish relationships in which others rely on them for social support. Such relationships would be more challenging, for example, than relationships with peers with shared experiences or social integration (Cutrona & Russell, 1987; Eckenrode, 1983). Social integration scores were higher than opportunity for nurturance scores in this study, likely reflecting these dynamics. The highest mean subscale score was in reliable alliance. The ADHC creates a safe environment in which participants can seek support and assistance. Staff are knowledgeable in a wide variety of areas, and access is open and informal. The high scores in reliable alliance could be interpreted as having the feeling of a safety net, reflecting the goals of ADHC care (Murphy et al., 1999).

Gardenier et al. / Social Support and Adherence in ADHC

The difference in the social support subscale between the adherence groups was statistically significant (p 5 .02), suggesting an association between adherence and social support as has been observed in other groups of PLWH (Bader et al., 2006; Bodenlos et al., 2007; Simoni et al., 2006). Mean subscale scores were higher for instrumental provisions and lower for emotional provisions in both the adherent and nonadherent groups. The lowest mean subscale scores in both adherence groups were in opportunity for nurturance. The difference between adherence groups was not statistically significant, possibly indicating the extent to which all ADHC participants struggle with this provision. In the nonadherent group, however, the two lowest mean subscale scores, in opportunity for nurturance and reassurance of worth, were virtually identical, with a statistically significant betweengroup difference in reassurance of worth. This key difference between adherence groups could be important in understanding motivators of adherence in the ADHC population and warrants further study. The difference between the two instrumental provisions is perceptual: Guidance is the provision of assistance, whereas reliable alliance is the knowledge that aid will be available when it is needed. Mean guidance scores were highest in the adherent group, whereas mean reliable alliance scores were highest in the nonadherent group. The difference in mean guidance scores was statistically significant, but the difference in mean reliable alliance scores was not. That the two groups would believe that aid would be available when needed at equal rates but experience the provision of aid differently is another potential key to understanding adherence among ADHC participants. Adherent participants had significantly higher CD41 T cell counts than nonadherent participants. Higher CD41 T cell counts are an expected clinical outcome of adherence to treatment for chronic infection. Undetectable viral load is another expected clinical outcome of adherence, but in this sample the difference between adherent and nonadherent participants was not statistically significant. The lack of a statistically significant difference in viral load between adherent and nonadherent groups probably reflected the sensitivity of the measurement and the fact that viral load was measured in this study only once. ADHC clients also fail and therefore change regimens more frequently than other PLWH, likely

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causing a higher occurrence of detectable viral loads among adherent individuals than might be observed in other groups of PLWH. Examination of social support based on sample characteristics showed significant differences in the same two variables as comparisons of adherence: CD41 T cell counts and DOT participation. CD41 T cell counts were significantly higher among the adherent participants as well as in those reporting more social support. Social support and adherence were also higher among participants who chose not to use DOT services. The concurrence of results between these two study variables supported the assertion that social support and adherence were related in the ADHC population. The lower adherence rate among DOT participants was likely explained by the voluntary nature of the program and by characteristics of those participating in DOT. The majority of the ADHC participants who chose DOT were individuals who had essentially never been able to adhere to ART for any length of time. Thus the observed adherence rate, although seemingly low, was likely higher than could be expected among this high-risk population if DOTwere not available to them. The most frequently reported reasons for missing medications included being away from home, being busy with other things, and forgetting. These results were consistent with other studies of PLWH (Bader et al., 2006; Chesney, 2003). The least frequently reported reason for missing medications was not wanting others to notice. Not wanting others to notice was more closely related to emotional rather than instrumental social support provisions. The highest means among emotional provisions were in attachment and social integration. Although significant statistical correlations cannot be made solely on the basis of this study’s results, attachment and social integration were the two emotional provisions that would seem to be most closely linked to ADHC goals and practice framework (Murphy et al., 1999). ADHC interventions, therefore, may be a reason for the high rate at which participants reported never missing medications based on being seen by others. Limitations of the Study This study showed an association between social support and adherence among ADHC clients, as

82 JANAC Vol. 21, No. 1, January–February 2010

seen in other studies of PLWH (Bader et al., 2006; Bodenlos et al., 2007; Simoni et al., 2006). This study was not designed to establish causality, and the authors therefore cannot say that ADHC participation leads to increased social support or to increased adherence, or that increased social support leads to increased adherence in the ADHC setting. This sample represented the population from which it was drawn: comorbid PLWH enrolled in an ADHC program operating in two New York City locations. The results, therefore, may not be generalizable to other ADHC programs or to non-ADHC populations. Multidisciplinary care models are widely diverse, as are the individuals they serve, and application of these results to other populations may therefore be challenging. As a means of protecting participants in this initial study of ADHC, participants who did not return to complete the questionnaire after signing an informed consent form were considered to have withdrawn from the study. Many of the ADHC participants had active psychiatric illnesses and adjustment problems, and this was taken into account during the planning phases of the study. The authors knew that some of the participants might not have the wherewithal to purposely withdraw from the study and took nonreturn to imply withdrawal; data from these study participants were therefore excluded from the analyses. Given the actual experience of no adverse events in the study and the potential to glean important information on the characteristics of those individuals who did not return for study completion, these data should be included in future studies of the population. Randomization would have strengthened the study. Attendance patterns of ADHC participants are somewhat random, however, which potentially mitigated some of the bias that may have been introduced by convenience sampling. Random sampling would require a larger population, which would in turn require either the inclusion of participants from multiple ADHC programs or recruitment over a longer time period.

Strengths of the Study This study was uncomplicated and inexpensive to conduct. It did not disrupt program operation or result in any adverse events. The study showed that research could be integrated into existing care models without

disruption and that valuable information is neither complicated nor costly to generate. No potential participant refused to take part in this study. Whereas the underlying reasons for this occurrence are not known, some aspects of the study are worth noting. The study was conducted by a researcher who was familiar with and known to the study population. ADHC staff and clients were involved in the process: The researcher answered questions about the study and client input was incorporated into the design, recruitment, and data collection. These aspects of the study contributed to the ease with which participants were recruited. The successes realized in the operationalization of this study suggest that appropriate design and a sensitive approach can lead to successful research in at-risk and comorbid populations. Although small sample size is traditionally considered a limitation, there is an advantage to limiting the sample to unique patient populations such as this one. Whereas limitations based on sample characteristics make studies less generalizable to outside populations, the more uniform characteristics derived from a sample such as the one in this study make results more applicable to the particular population in which the study was conducted. Unique populations such as this one frequently do not benefit from research conducted elsewhere because their features limit the generalizability of the results. By contrast, directly applicable results increase the potential for translation of study results into care interventions that have the potential to benefit the study population. This study was small and focused, and although these aspects constitute limitations as previously discussed, they could also be seen as advantages. Implications for Nursing Practice Social support is one of the most widely studied psychosocial variables as well as one of the most consistently linked to favorable health outcomes in multiple populations (Cobb, 1979; Southwick, Vythilingam, & Charney, 2005). The link has been shown consistently in PLWH (Gonzalez et al., 2004; Green, 1993). Multiple definitions and valid and reliable methods of measuring social support can be found in the literature (House & Khan, 1985; Hupcey, 1998; Hutchinson, 1991). The

Gardenier et al. / Social Support and Adherence in ADHC

numerous definitions and instruments seem in review not to weaken or confound the linkages; rather, researchers have cited the consistency of the link between social support and positive health outcomes and recommend tailoring methods of measuring social support to specific populations to optimize the goals of the research (Norbeck, 1981). Social support is often mediated by other variables, and intermediate variables have been shown to affect the relationship between social support and adherence. The mediation of variables has been tested and described in multiple models, but a specific relationship between multiple variables has not yet been proposed. Given the urgent care needs of comorbid PLWH and the unlikelihood that a theoretical definition that includes all the variables that affect adherence is imminent, the selection of an intervention point appropriate to the population under study and testing an intervention with the goal of improving health outcomes in that population has been cited as a reasonable approach to creating and testing health outcome interventions (DiIorio et al., 2009; Simoni et al., 2003). The ADHC care framework places an emphasis on socialization and peer support. Care delivery is coordinated, but the multidisciplinary model could be optimized if there were a practice framework in which interventions could be designed around a particular measurable variable or variables. Such a framework would need to be sufficiently broad that a diverse team of caregivers could design, implement, and evaluate multidisciplinary care interventions with the goal of improving clients’ skills in particular areas. The SPS and its component definition of social support is unique among social support measures. The SPS does not presume an understanding either of social support itself or of its customary sources. In other patient populations it might be sufficient to ask if a patient receives social support and whether he or she is satisfied with the support received, but ADHC clients’ sources of social support are sometimes nontraditional. The SPS lends itself to measuring social support and its components without regard to the source of the support, thus avoiding a potential cultural bias. The SPS is also focused on particular skills and supportive actions rather than on a preconceived notion of what social support is. Distinguishing social

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provisions also provides a potentially useful framework for designing multidisciplinary interventions to address the particular aspects of social support in which care recipients show need. Health outcomes such as adherence could then be measured in the population to see if an intervention has had an effect. Future Research Social support and adherence to therapeutic regimens have both been firmly linked to favorable health outcomes in various patient populations, including substantial evidence in diverse groups of PLWH. This evidence has been shown despite multiple definitions and measurement strategies for both adherence and social support. This study was designed to ascertain whether the link between social support and adherence existed in this heretofore unstudied population. Evidence from this study suggested that it does and, as such, provided a basis for future study of this population and similar groups for whom innovative multidisciplinary care strategies are needed. In addition to measuring and evaluating care interventions, social support and adherence could be further studied with the goal of establishing a link between these concepts and ADHC participation. If ADHC participation could be shown to lead to enhanced social support and/or increased adherence, the evidence basis for the ADHC care model would become more firmly established. Future research could also incorporate other psychosocial variables that are of likely importance among the individuals who receive care in ADHC programs and other integrated primary care practices. Variables such as resilience, spirituality, self-efficacy, and empowerment, among others, are all likely to play a significant role in the health of individuals who receive care in integrated models and deserve to be studied.

Conclusion This study showed an association between social support and adherence among ADHC clients. It established a basis for further study of ADHC programs and the individuals who receive care in them. Linking social support and adherence, two variables associated with positive health outcomes, will help ADHC programs establish an evidence

84 JANAC Vol. 21, No. 1, January–February 2010

base for practice. Such a base could help improve the delivery of care in programs designed to reach at-risk comorbid populations.

Clinical Considerations

 The psychosocial basis of adherence is complex and poorly understood.  When selecting an intervention point for comorbid populations for whom adherence is a challenge, nurses should consider the appropriateness of the variable to the population as well as the ability to translate research results into one or more practice interventions.  Nurses have specific skills and knowledge well-suited to coordinating multidisciplinary efforts aimed at improving adherence according to a theoretical framework.  Nurses can play a crucial role in reaching populations that have been characterized as difficult to study.

Acknowledgment This study was supported by a grant from the National Organization of Nurse Practitioner Faculties.

References Bader, A., Kremer, H., Erlich-Trungenberger, I., Rojas, R., Lohman, M., Deobald, O., et al. (2006). An adherence typology: Coping, quality of life, and physical symptoms of people living with HIV/AIDS and their adherence to antiretroviral treatment. Medical Science Monitor, 12, 493-500. Battaglioli-DeNero, A. (2006). Strategies for improving patient adherence to therapy and long-term patient outcomes. Journal of the Association of Nurses in AIDS Care, 18(Suppl. 1), S17-S22. Bodenlos, J., Grothe, K., Whitehead, D., Konkle-Parker, D., Jones, G., & Brantley, P. (2007). Attitudes toward health care providers and appointment attendance in HIV/AIDS patients. Journal of the Association of Nurses in AIDS Care, 18, 65-73. Boyer, A., & Indyk, D. (2006). Shaping the garments of care: Tools for maximizing adherence potential. In D. Indyk (Ed.), The geometry of care: Linking resources, research,

and community to reduce degrees of separation between HIV treatment and prevention (pp. 151-165). Binghamton, NY: The Haworth Press. Chesney, M. (2003). Adherence to HAART regimens. AIDS Patient Care and STDs, 17(4), 169-177. Chesney, M., Ickovics, J., Chambers, D., Gifford, A., Neidig, J., Zwickl, B., et al. (2000). Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: The AACTG adherence instruments. AIDS Care, 12, 255-266. Cobb, S. (1979). Social support and health through the life course. In M. Riley (Ed.), Aging from birth to death: Interdisciplinary perspectives (pp. 93-106). Boulder, CO: Westview Press. Cunningham, C., Sohler, N., Wong, M., Relf, M., Cunningham, W., Drainoni, M., et al. (2007). Utilization of health care services in hard-to-reach marginalized HIV-infected individuals. AIDS Patient Care and STDs, 21, 177-186. Cutrona, C., & Russell, D. (1987). In: W. Jones & D. Periman (Eds.), Advances in personal relationships (pp. 37-67). Greenwich, CT: JAI Press. DiIorio, C., McCarty, F., DePadilla, L., Resnicow, K., Holstad, M., Yeager, K., et al. (2009). Adherence to antiretroviral medication regimens: A test of a psychosocial model. AIDS Behavior, 10, 10-22. Eckenrode, J. (1983). The mobilization of social supports: Some individual constraints. American Journal of Community Psychology, 11, 509-528. Fultz, S., Goulet, J., Weissman, S., Rimland, D., Leaf, D., Gilbert, C., et al. (2005). Differences between infectious disease-certified physicians and general medicine-certified physicians in the level of comfort with providing primary care to patients. Clinical Infectious Diseases, 41, 738-743. Gardenier, D., Neushotz, L., & O’Connor-Moore, N. (2007). Medical/psychiatric comanagement by nurse practitioners in chronic hepatitis C treatment: A case study. Archives of Psychiatric Nursing, 21(2), 87-90. Godin, G., Cote, J., Naccache, H., Lambert, L., & Trottier, S. (2005). Prediction of adherence to antiretroviral therapy: A one-year longitudinal study. AIDS Care, 17, 493-504. Gonzalez, J., Penedo, F., Antoni, M., Duran, R., Fernandez, M., McPherson-Baker, S., et al. (2004). Social support, positive states of mind, and HIV treatment adherence in men and women living with HIV/AIDS. Health Psychology, 23, 413-418. Green, G. (1993). Social support and HIV. AIDS Care, 5, 87-104. House, J., & Khan, R. (1985). Measures and concepts of social support. In S. Cohen & S. Syme (Eds.), Social Support and Health (pp. 83-108). San Diego: Academic Press. Hubbard, M. (2006). Dealing with the obstacles in adhering to highly active antiretroviral therapy. Journal of the Association of Nurses in AIDS Care, 17, 18-25. Hupcey, J. (1998). Clarifying the social support theory-research linkage. Journal of Advanced Nursing, 27, 1231-1241.

Gardenier et al. / Social Support and Adherence in ADHC Hutchinson, C. (1991). Social support: Factors to consider when designing studies that measure social support. Journal of Advanced Nursing, 29, 1521-1526. Israelski, D., Prentiss, D., Lubega, S., Balmas, G., Garcia, P., Muhammad, M., et al. (2007). Psychiatric co-morbidity in vulnerable populations receiving primary care for HIV/ AIDS. AIDS Care, 19, 220-225. Molassiotis, A., Morris, K., & Trueman, I. (2007). The importance of patient-clinician relationship in adherence to antiretroviral medication. International Journal of Nursing Practice, 13, 370-376. Murphy, N., Messina, W., Getter, E., Gutterman, L., Martin, T., Rincon, P., et al. (1999). The Village AIDS Day Treatment Program: A model of interdisciplinary and interdependent care. The American Journal of Occupational Therapy, 52, 561-565. Norbeck, J. (1981). Social support: A model for clinical research and application. Advances in Nursing Science, 3, 43-59. Penzak, S., Reddy, Y., & Grimsley, S. (2000). Depression in patients with HIV infection. American Journal of Health System Pharmacy, 57, 376-389. Reynolds, N., Sun, J., Nagaraja, H., Gifford, A., Wu, A., & Chesney, M. (2007). Optimizing measurement of self-reported adherence with AIDS clinical trials group adherence group questionnaire: A cross-protocol analysis. Journal of Acquired Immune Deficiency Syndrome, 46, 402-409. Reynolds, N., Testa, M., Marc, L., Chesney, M., Neidig, J., Smith, S., et al. (2004). Factors influencing medication adherence beliefs and self-efficacy in persons naive to antiretroviral therapy: A multicenter, cross-sectional study. AIDS and Behavior, 8, 141-150. Simoni, J., Frick, P., & Huang, B. (2006). A longitudinal evaluation of medication adherence among HIV-positive men and women on antiretroviral therapy. Health Psychology, 25, 74-81. Simoni, J., Frick, P., Pantalone, D., & Turner, B. (2003). Antiretroviral adherence interventions: A review of current literature and ongoing studies. Topics in HIV Medicine, 11, 185-198. Soto, T., Bell, J., & Pillen, M. (2004). Literature on integrated HIV care: A review. AIDS Care, 16(Suppl 1), S43-S55. Southwick, S., Vythilingam, M., & Charney, D. (2005). The psychobiology of depression and resilience to stress: Implications for prevention and treatment. Annual Review of Clinical Psychology, 1, 255-291. Waters, L., & Nelson, M. (2007). Why do patients fail HIV therapy? International Journal of Clinical Practice, 61, 983-990. Willenbring, M. (2005). Integrating care for patients with infectious, psychiatric, and substance use disorders: Concepts and approaches. AIDS, 19(Suppl 3), S227-S237. Yeni, P., Hammer, S., Hirsch, M., Saag, M., Schechter, M., Carpenter, C., et al. (2004). Treatment for adult HIV infection. Journal of the American Medical Association, 292, 251-265.

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Yi, M., Mrus, J., Wade, T., Ho, M., Hornung, R., Cotton, S., et al. (2006). Religion, spirituality, and depressive symptoms in patients with HIV/AIDS. Journal of General Internal Medicine, 21, S21-S27.