Educational attainment and response to HAART during initial therapy for HIV-1 infection

Journal of Psychosomatic Research 63 (2007) 207 – 216

Educational attainment and response to HAART during initial therapy for HIV-1 infectionB Linda G. Marca,b,c,*, Marcia A. Testac, Alexander M. Walkerc, Gregroy K. Robbinsd, Robert W. Shafere, Norman B. Andersonc,f, Lisa F. Berkmanc, for the ACTG Data Analysis Concept Sheet Study Team b

a Cornell HIV Clinical Trials Unit, New York, NY, USA Department of Psychiatry, Weill Medical College of Cornell, White Plains, NY, USA c Harvard School of Public Health, Boston, MA, USA d Massachusetts General Hospital, Boston, MA, USA e Stanford University Medical Center, Stanford, CA, USA f American Psychological Association, Washington, DC, USA

Received 24 September 2006

Abstract Objective: Previous research has demonstrated an association between educational attainment (EA) and negative physical and psychological outcomes. This study investigated whether EA is associated with regimen failure during initial therapy with highly active antiretroviral treatment (HAART) and whether adherence self-efficacy (ASE), a coping resource, moderates the relationship between EA and regimen failure. Methods: A secondary analysis of AIDS Clinical Trial Group Protocol 384, an international, multicenter, randomized, partially double-blinded trial, included 799 male and 181 female antiretroviral-naRve subjects (age, 37.0F9.5 years). Participants were recruited from 1998 to 1999 and followed for a median of 2.3 years across 81 centers. The dependent variable was btime to first regimen failure.Q Covariates include baseline HIV-1 log10RNA and CD4+ counts, self-reported adherence, study site, ASE, age, sex, race, treatment assignment, and baseline use of

nonantiretroviral medications. Results: ASE significantly moderated the relationship between EA and regimen failure. Results showed that for every 10-unit increase in ASE, individuals with bless than high schoolQ education had a 17% reduction in regimen failure (hazard ratio=0.83; 95% confidence interval=0.70–0.98) when compared to the reference group bcollege/graduate,Q even after adjusting for baseline factors known to contribute to regimen failure. The time to first regimen failure was shorter with decreasing EA, trending toward significance ( P=.08). Conclusions: There is a social gradient in HAART effectiveness, and ASE reduces the deleterious effects of lower EA on regimen failure. We recommend designing controlled interventions to evaluate the effectiveness of programs that increase ASE prior to initiation with HAART, particularly for those with lower EA. D 2007 Elsevier Inc. All rights reserved.

Keywords: Adherence self-efficacy; Antiretroviral; Educational attainment; HIV/AIDS; Socioeconomic status; Regimen failure

Introduction

B

During the study period, Dr. Marc received consultancy fees from Aventis, Behavioral Science International LLC, GlaxoSmithKline, ParkeDavis, and Phase V Technologies. 4 Corresponding author. Department of Psychiatry, Weill Medical College of Cornell, 21 Bloomingdale Road, Unit 2 South, White Plains, NY 10605, USA. Tel.: +1 646 541 6650. E-mail address: [email protected] (L.G. Marc). 0022-3999/07/$ – see front matter D 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.jpsychores.2007.04.009

The success or failure of antiretroviral therapy is not a purely biologic phenomenon [1–9] but rather the product of a complex interaction between biologic, behavioral, and societal factors [10–13]. While there is scarce evidence to support a social gradient in antiretroviral effectiveness, education has been known to be significantly related to both CD4 rate and change in HIV viral load [14]. There also have

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been reports of a social gradient in the length of survival among persons infected with HIV. Some researchers have shown that individuals from lower socioeconomic backgrounds display a faster HIV progression to AIDS morbidity and AIDS-related mortality [15–19]; however, others have found no significant differences [20,21]. In most studies, socioeconomic status (SES) is often operationalized as educational attainment (EA), income, or occupation [22]. In this study, the measure of SES is education, which is a categorical four-level ordinal variable (i.e., less than high school, high school/general equivalency diploma, some college, and college/graduate degree). Adherence selfefficacy (ASE), or confidence in one’s ability to adhere to treatment regimens, is also essential for coping with HIV [23]. Thus, the primary objectives of this secondary data analysis were to: (a) investigate whether EA is associated with regimen failure during initial therapy with highly active antiretroviral treatment (HAART); and (b) investigate whether ASE, a coping resource, moderates the relationship between EA and regimen failure. SES and status-related stressors Dohrenwend [24] and Dohrenwend and Dohrenwend [25] suggested that individuals who occupy low social positions disproportionately experience bstatus-relatedQ stressors. In the field of HIV/AIDS, stressors are among the many psychosocial variables that are explored as possible predictors of disease progression beyond the biologic factors known to affect AIDS symptomatology [14,26]. Liem and Liem [27] also highlighted the chronicity of stressors experienced among individuals from lower social classes, which is also linked to negative physical and psychological outcomes. Persons holding low social positions are more strongly affected emotionally by undesirable life events than are their higher-status counterparts [28,29], and lower social status has an inverse relationship with behavioral, psychological, and chronic disease outcomes [22,30–36]. These phenomena are often explained by social stress theory, which posits that social inequality results in a wide range of negative health outcomes [37–39]. Stressors and immune functioning There is ample evidence to suggest that stress adversely influences the immune system [40–45]; for instance, lower SES and stress have been linked to asthma [44], lower secretory immunoglobin A [46], and respiratory illness [47]. In a critical review conducted by Cohen et al. [48], they reported that psychological stress impairs antibody response to immunizations. Evidence from other authors suggests that stress and emotional distress contribute to the progression of HIV to AIDS and to the length of survival among persons infected with HIV [49–57]. Specifically, stress has predicted a faster decline in CD4 cells [55,58,59] and reductions in natural killer cell populations and CD8

cell subsets [50,57], both of which are known predictors of HIV progression. Furthermore, empirical evidence supports that stress management is associated with immunological reconstitution in HIV+ gay men, resulting in higher CD4 counts [60]. HIV-related stress and coping self-efficacy Previous studies have documented the range of stressful challenges experienced by individuals infected with HIV [61,62]. Coping strategies (or styles) play an important role in an individual’s physical and psychological well-being when he or she is confronted with negative or stressful life events [63], and one’s response is influenced by a number of social and psychological factors [64]. Thus, coping encompasses cognitive and behavioral strategies that individuals use to manage both stressful situations and the negative reactions elicited by those events. This helps in lowering the probability of harm from a stressful encounter and/or in reducing negative emotional reactions. Within the context of HIV, several studies have shown that coping skills or efficacy is associated with decreases in negative affective states [65,66]. Coping is what a person does to manage one’s behavior and thus reduce the effects of stress; efficacy is the confidence that the person has to engage in a behavior that will benefit oneself [67]. Antiretroviral ASE is the belief that influences whether a person is likely to mobilize the skills needed to adhere to treatment in the face of challenges experienced by an individual with HIV disease [67]. Therefore, ASE has been shown to be a key factor in reducing the likelihood of failing to adhere to antiretroviral medication [68]. Present study The present study examined the association between EA and response to HAART during initial therapy for HIV-1 infection, and investigated whether ASE moderates this relationship. Features of our analysis allowed us to examine the relationship between EA and HAART failure, and provided a unique opportunity to examine the role of social status in response to antiretroviral therapy. The randomized clinical trial design included the recruitment of antiretroviral-naRve subjects, criteria for first regimen failure (clinical outcome), a follow-up period of N2 years, intent-to-treat analysis for regimen failure, and a number of covariates, including measures for education, sociodemographic factors, self-reported adherence, ASE, and immune functioning. In addition, we employed social theories of disease causation, which help to explain and predict the distribution of antiretroviral effectiveness across socially defined groups [11,12].

Methods Study population This was a secondary data analysis of AIDS Clinical Trial Group Protocol 384 (ACTG 384), an international,

L.G. Marc et al. / Journal of Psychosomatic Research 63 (2007) 207 – 216

multicenter, randomized, partially double-blinded, controlled trial with 980 evaluable antiretroviral-naRve subjects at 81 centers in the United States and Italy [69,70]. The original study was designed to evaluate different strategies of initiating antiretroviral treatment in HIV-1-infected individuals with b7 days of previous antiretroviral therapy (design and selection criteria fully described previously [71]). Study subjects had no clinically significant abnormalities or serious acute illnesses within the 14 days before study entry. From 1998 to 1999, HIV-1-infected antiretroviral-naRve individuals were randomized to three-drug and four-drug regimens, and followed for a median of 2.3 years. Study participants had a median age of 37 years (S.D.F9.5), with median plasma HIV-1 RNA at 4.98 log10 and a median CD4+ count of 274.5. Subjects were predominately male (81.5%), with 46.5% of this population being non-Hispanic White, 35.0% being non-Hispanic Black, and 16.6% being Hispanic (regardless of race) (Table 1). Race/ ethnicity was self-reported by each participant. Information on gender and risk factor category for mode of transmission was also collected, including a detailed history on intravenous drug use (never, previous, or current user) and current use of baseline nonantiretroviral medications (Table 1). ACTG 384 subjects were asked for permission to be included in future analyses as part of informed consent. This analysis was approved by the institutional review board of the primary author at the time of the study.

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Measures Outcome measure (dependent variable) The outcome of interest for this analysis of ACTG 384 was time to first regimen failure, which was described as virologic failure or toxicity causing discontinuation in the parent study [69–71]. These end points were defined as (a) time to reaching salvage regimen—failure of two three-drug regimens or study dropout (primary end point); and (b) time to virologic failure— defined as N200 copies/ml RNA viral load. Immunological functioning, including CD4+ count and HIV-1 log10RNA, was measured at baseline and at regular intervals. Self-reported adherence The ACTG Adherence Questionnaire was used to capture information on actual pill counts taken over the study period (based on the number of doses taken over the last 4 days) [72]. Subjects reported the number of pills missed at each patient visit for each medication byesterday,Q ba day before yesterday (2 days ago),Q b3 days ago,Q and b4 days agoQ on Week 4, on Week 16, and every 16 weeks thereafter. Since the internal consistency between these four responses and between medications was relatively high (aN.85), an overall regimen adherence score was calculated for each visit as the average of the four responses across all medications. The

Table 1 Baseline ACTG 384 population characteristics by EA

Demographics Subjects [n (%)] Age in years MeanFS.D. Range Gender [n (%)] Male Female Race/ethnicity (%) Non-Hispanic White Non-Hispanic Black Hispanic Asian/Pacific Islander American Indian/Alaskan/unknown Determinants of immune functioning CD4 count (medianFS.D.) Log10RNA (medianFS.D.) Intravenous drug users (current and previous) Self-reported adherence (median)a ASE (mean)b Current nonantiretroviral medicationsc

All

Less than high school

High school/general College/ equivalency diploma Some college graduate

Missing

980

143 (14.6)

299 (30.5)

282 (28.8)

228 (23.3)

28 (2.9)

37.0F9.5 17–72

38.0F12.3 17–72

36.1F9.1 17–66

36.6F8.8 19–72

37.8F9.0 21–67

38.0F9.3 26–57

0.264 0.094

0.026 0.039

799 (81.5) 181 (18.5) 456 339 163 17 5

(46.5) (34.6) (16.6) (1.7) (0.5)

0.129 0.221 35 (24.5) 60 (42.0) 48 (33.6) – –

0.283 0.403 137 (45.8) 123 (41.1) 34 (11.4) – –

0.298 0.243 129 98 45 8 –

(45.7) (34.8) (16) (2.8)

139 49 33 6 –

(61) (21.5) (14.5) (2.6)

16 (57.1) 9 (32.1) 3 (10.7)

278.5F229.6 272F306.8 309.5F317.8 5.06F0.90 5.02F0.88 5.01F1.0 19 6 4

99.6 73.1 325

99.6 75.0 99

99.5 70.0 110

99.0 77.0 57

Frequency distribution includes Asians (n=17), American Indians/Alaskan/Eskimos/other (n=5). ns=Not significant. a Eight hundred fifty-eight participants provided information on self-reported adherence behavior. b Nine hundred fifty-four participants reported ASE. c Three hundred twenty-five participants reported a history of current baseline nonantiretroviral medication use.

ns

b.001 (male/female) b.001 (White/non-White)

–

274.5F228.4 255.5F208.7 275F228.0 4.98F0.87 4.99F0.88 4.90F0.82 90 22 39 99.5 70.4 59

P

100 –

ns ns ns .002 b.001 b.001

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adherence summary score was calculated as the percentage of the prescribed regimen taken: [1 (proportion pills missed)100]. ASE Self-efficacy was conceptualized as bthe belief in one’s ability to perform a behaviorQ [73]. The ACTG Baseline Adherence Questionnaire [72] operationalized the construct ASE by measuring self-reports of personal and situational variables, including: (a) beliefs about the effectiveness of antiretroviral medication, and (b) confidence in one’s ability to adhere to antiretroviral medications as directed (Antiretroviral Adherence Self-Efficacy). This was measured using a three-item four-point scale (not at all sure to very sure). Patients were asked whether they would be able to take all or most of the medications as directed; whether they thought the medications would have a positive effect on their health; and, if they did not take the medications exactly as instructed, whether the HIV in their bodies would become resistant to HIV medications. For analytical purposes, scores were rescaled to a 100-point scale (0=worst health to 100=best health), and detailed psychometric and correlational analyses have been previously reported elsewhere [68]. Interaction term: ASE The interaction term was the product of ASEEA. This formally tested whether ASE moderates the relationship between EA and regimen failure. Indicator variables (0, 1) represented each of the four categories of EA, multiplying them by ASE. These interactions were divided by 10 to interpret the results for every 10-unit increase in ASE. Statistical analysis Cox proportional hazard models were used for the timeto-event end point of first regimen failure. A test for trend was performed on EA, an ordinal categorical variable. An adjusted Cox model controlled for baseline HIV-1 log10RNA and CD4+ counts, self-reported adherence, study site, ASE, age, sex, race, treatment assignment, and use of nonantiretroviral medications. To test the coping selfefficacy hypothesis, an interaction indicator term for ASE by education was entered into an adjusted Cox model, including indicators for education, ASE, and all other previously described covariates that are known to be associated with regimen failure. A one-way analysis of variance was used to test for differences in the means for log10RNA, CD4 counts, selfreported adherence, ASE, and age across the categories of EA. Chi-square analysis was used to test for differences in the proportions by race and sex. All statistical tests were two tailed ( Pb.05), with confidence intervals calculated at 95%. Data analysis was performed using Stata Version 9 [74] and SPSS Version 10 [75].

Results Baseline characteristics Across the four educational groups, 143 (14.6%) did not complete high school, 299 (30.5%) completed high school, and 282 (28.8%) completed some college, which ranged from post high school but less than completion of a 4-year college degree; there were 228 (23.3%) subjects who earned at least a 4-year college or graduate degree (Table 1). Several demographic characteristics varied across EA (Table 1). Female subjects reportedly had less education than male subjects ( Pb.001), and the distribution of EA for non-Hispanic Whites was significantly higher than those of all other racial/ethnic groups ( Pb.001). Baseline CD4 counts and HIV log10RNA did not significantly differ across EA (Table 1); however, the mean ASE increased with increasing education ( Pb.001), and as EA increased, the proportion of individuals reporting the use of nonantiretroviral medications decreased ( Pb.001). Overall median scores for self-reported adherence (median= 99.6%; P=.002) and ASE (median=77.8; Pb.001) were relatively high and were found to be positively associated with EA, respectively. Regimen failure Our study results showed that the time to first regimen failure trended towards being shorter for individuals with lower EA ( P=.08) (Table 2). Hazard ratios and 95% confidence intervals (95% CIs) for the lowest categories of EA compared to the highest category are 1.33 (95% CI=0.93– 1.91), 1.25 (95% CI=0.93–1.67), and 1.15 (95% CI=0.87– 1.57), respectively, for individuals who attained less than high school, high school, and some college education. In an adjusted Cox model, the magnitude of the hazard ratio became attenuated, yet the inverse gradient in regimen failure persisted (Table 2), even after controlling for covariates known to be associated with HIV-1 viral suppression, such as baseline HIV-1 log10RNA, CD4+ count, self-reported adherence, ASE, age, sex, race, and treatment assignment, in addition to nonantiretroviral medication use and study site. Results also showed that individuals with higher baseline measures of log10RNA (HR=1.35; 95% CI=1.14–1.59), current intravenous drug use (HR=3.56; 95% CI=1.44– 8.77), and Black subjects (HR=1.42; 95% CI=1.12–1.80) were significantly more likely to experience first regimen failure than their respective counterparts. Post hoc analyses revealed that non-Hispanic Black subjects had significantly lower baseline CD4 counts ( P=.007), lower self-reported adherence ( P=.036), and lower ASE ( P=.01) when compared to non-Hispanic White subjects. There were no significant baseline differences observed for current intravenous drug users (N=8) compared to previous and never users (N=82) across similar parameters. However, the

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Table 2 EA as a predictor of time to regimen failure

Less than high school High school Some college College/graduate P=.08 for trend test ASEb

Cox

Adjusted Cox

Adjusted with interaction

Model 1

Model 2a

Model 3a

HR

95% CI

HR

95% CI

HR

95% CI

1.33 1.25 1.15 1

0.93–1.91 0.93–1.67 0.86–1.57 –

1.13 1.11 0.97 1

0.76–1.67 0.80–1.55 0.69–1.35 –

4.24 1.97 3.24 1

1.18–15.27 0.57–6.76 0.94–11.17 –

1.09

0.96–1.24

0.834 0.93 0.854 1.00

0.70–0.98 0.80–1.09 0.73–0.99 –

Interaction terms ASELess Than High Schoolb ASEHigh Schoolb ASESome Collegeb ASECollege/Graduateb Determinants of immune functioning Log10RNA CD4 Nonantiretroviral use Current intravenous drug user Self-reported adherence Sociodemographics Italian site Age Female gender Non-Hispanic Black

1.3544 0.99 1.04 3.564 0.8844

1.14–1.59 0.99–1.00 0.81–1.33 1.44–8.77 0.85–0.90

1.354 1.00 1.06 3.354 0.8844

1.14–1.60 0.99–1.00 0.83–1.37 1.34–8.30 0.85–0.90

0.73 0.99 0.99 1.4244

0.25–2.17 0.98–1.01 0.73–1.36 1.12–1.80

0.74 0.99 0.94 1.424

0.25–2.19 0.98–1.00 0.68–1.29 1.12–1.80

a

Controlling for treatment assignment. ASE and ASE-education indicator categories are divided by 10. The respective HRs reflect a percent change [(1 HR)100)] for every 10-unit increase in ASE. 4 .01NPN.001. 44 Pb.001. b

sample size of current users is small; thus, the results lack power to detect meaningful and reliable differences.

self-reported adherence compared to those with low ASE ( P=.05). Using stepwise linear regression results showed that self-reported adherence increased significantly as

Self-efficacy as a modifier of the education–regimen failure relationship Results showed that for every 10-unit increase in ASE, individuals with bless than high schoolQ education had a 17% reduction in regimen failure (HR=0.83; 95% CI=0.70–0.98; P=.03) when compared to the reference group (college/ graduate). Similarly, individuals with bsome collegeQ had a 15% reduction in regimen failure (HR=0.85; 95% CI=0.73– 0.99; P=.04) compared to the reference group. Associations among self-reported adherence, ASE, and EA A comparison of mean self-reported adherence was stratified by levels of EA across low versus high ASE (Fig. 1). Dichotomized ASE was defined by a median split [(lowb78) vs. (highz78)]. There was a significant difference in self-reported adherence and self-efficacy across levels of education. Among individuals with high school education, those with high ASE reported higher mean selfreported adherence compared to those with low ASE ( P=.02). Similarly, among those with college/graduate education, those with high ASE reported higher mean

Fig. 1. Comparison of mean self-reported adherence stratified by levels of EA, across low versus high ASE, defined by a median split [(lowb78) vs. (highz78). Among individuals with high school education, those with high ASE reported higher mean self-reported adherence compared to those with low ASE ( P=.02). Similarly, among individuals with college/graduate education, those with high ASE reported higher mean self-reported adherence compared to those with low ASE ( P=.05). Using stepwise linear regression, self-reported adherence increased significantly as education increased ( P=.05) and as ASE increased ( Pb.001) (ProbNF for modelV.001; adjusted R 2=.05).

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education increased ( P=.05) and as ASE increased ( Pb.001) (ProbNF for modelV.001; adjusted R 2=.05). The regression model also controlled for several covariates, such as age ( P=.002) and sex ( P=.012). Racial categories and mode of HIV transmission were dropped from the final model because of nonsignificance. Furthermore, data in Fig. 1 also illustrate that individuals with high ASE did not significantly differ in mean self-reported adherence across educational levels, suggesting that having high ASE matters more than education in this group. By contrast, among those with low ASE, education appears to be an important factor given that, as education increased, so did self-reported adherence.

Discussion Based on data from a randomized controlled trial, our study provides evidence of a social gradient response to HAART. The magnitude of time gained before regimen failure by having higher EA is illustrated in Fig. 2. We found that ASE moderates the relationship between EA and regimen failure, and time to first regimen failure was shorter with decreasing EA, trending towards significance ( P=.08). These results may have implications for antiretroviral resistance. As the goal of initial therapy is to reduce viral load to an undetectable level, the first regimen is the most critical one for maximizing a lasting response [76]. Failure of the initial therapy may compromise the success of future regimens, as cross-resistance within a class is common [70] and contributes to an increase in HIV-related morbidity and mortality [77–81]. It is also important to recognize that our findings are indicative primarily of male study participants and that female stress levels, especially among those with lower education, may be higher. Consequently, the negative impact of EA on regime failure may be even more marked

Fig. 2. Time to regimen failure, stratified by EA. The model adjusts for treatment assignment, ASE, ASEEA interaction, log10RNA, CD4, nonantiretroviral medication use, current intravenous drug use, self-reported adherence, study site, age, gender, and race.

for them. Moreover, results herein might be particularly relevant to HIV care in developing countries, where the majority of persons are considerably less educated than people in industrialized nations. Since little is known about the impact of stressors on immune functioning in resourcepoor settings, there is a need to promote biobehavioral research in these parts of the world. The aim is to improve and develop innovative methods of measurement and data collection techniques that integrate behavioral and social science research with biologic outcomes. Our second finding suggests that ASE, a coping resource, moderates the relationship between EA and regimen failure. Results showed that for every 10-unit increase in ASE, regimen failure significantly decreased among study subjects by 17% for those who attained bless than high school educationQ and by 15% for those who attained bless than a 4-year college degree,Q when compared to the reference group (college/graduate) (Table 2). One interpretation of this finding is that individuals who did not complete higher educational milestones may experience a chronicity of stressful life challenges contributing to negative health outcomes when compared to those who completed educational milestones. Interestingly, among these two groups of noncompleters, those with higher ASE appeared to gain more from the protective benefits of the coping resource, resulting in a better response to HAART. Furthermore, since results were derived from data on developed nations, it is unknown whether they will extrapolate to resource-poor settings. However, practitioners have shown that coping resources (e.g., directly observed therapy) that support adherence (and daily monitoring) in developing nations result in very high adherence among the very poor and undereducated populations [82–84]. In our analysis, the main study findings suggest that education is a determinant of HAART effectiveness, and individuals of lower EA with higher ASE respond better to treatment during initial therapy with HAART. The clinical meaningfulness of the 15–17% decrease in regimen failure associated with the ASE effect should be considered in light of other treatment comparisons. For example, examining the results from the parent trial [69], the largest treatment difference among the six pharmaceutical regimen arms yielded an HR of 0.71, reflecting a 29% lower risk of regimen failure. Considering that EA is only one of many social variables, the 15–17% decrease (equating to nearly 60% of the largest difference among treatment arms) seems quite sizable. Thus, we must consider what proportion of regimen failure is modifiable through social variables (in contrast to biologic determinants). Hence, we recommend that future studies be designed to evaluate the effectiveness of programs that increase ASE prior to initiation with HAART in an effort to optimize durable viral suppression and minimize resistance, particularly for those with lower EA. Once effective interventions are identified, these programs could influence behavioral and cognitive responses required for the specific demands of

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stressful challenges experienced by individuals infected with HIV. Our results also show that individuals with higher baseline measures of log10RNA, current intravenous drug users, and Black subjects were significantly more likely to experience first regimen failure than their respective counterparts. However, the analysis including a subgroup of intravenous drug users lacks power because the sample size is too small. Furthermore, at the time of study entry, Black study participants, compared to their White counterparts, had significantly lower CD4 counts, lower selfreported adherence, and lower ASE. Our findings also raise important considerations for future antiretroviral trials—that detailed socioeconomic information be captured in an effort to evaluate the impact of economic and human social hierarchies on HAART effectiveness. Unfortunately, within the biomedical paradigm, the relationship between SES and physiological outcomes has been inadequately explored. Most large datasets that contain good measures of SES do not have detailed physiological data; conversely, most studies with laboratory and sophisticated physiological data fail to assess SES thoroughly [22,85]. While ACTG 384 study data did not capture the required biomarkers to test a third research question (Is SES associated with the progression of HIV via the hypothalamic–pituitary–adrenal axis and the alteration of the autonomic, neuroendocrine, and immune systems?), we consider this a plausible hypothesis because several literature reports link SES to psychobiologic factors, which are known to influence the progression of HIV [26,86–90]. For example, there is evidence linking SES with allostasis [91,92]. The literature in this area suggests that the accumulation of allostatic load due to repeated stressor exposure alters the autonomic nervous system (ANS), stimulating the release of norepinephrine, and is also associated with hypersecretion of glucocorticoids, impairing immune functioning in both human [40] and nonhuman primates [93–96]. Cole et al. [97] provide supportive evidence that norepinephrine enhances the cellular expression of HIV-1 in vitro and suggest that ANS activity may significantly contribute to residual HIV replication in vivo, impairing response to HAART. Similarly, data from Markham et al. [98] suggest a potent effect of glucocorticoids on HIV replication in vitro, yet little work has been done to examine this in vivo. Therefore, HAART effectiveness may be influenced by the complex interaction of behavioral, environmental, and physiological factors that are contextualized by economic and prestige-related indicators (e.g., income, education, occupation, and/or neighborhood poverty level) [99]. As supporting evidence, gross national product, a macrolevel indicator, has been shown to be a strong predictor of cardiovascular outcomes in a multinational trial [100]. Thus, further research on the social gradient of therapeutic effectiveness is essential, employing an interdisciplinary model that evaluates multiple levels of

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influence (i.e., social, environmental, behavioral, psychological, organ, cellular, and molecular) [101–103]. In summary, our study findings show a social gradient in HAART effectiveness that is partially explained by the relationship between educational level and ASE. Our recommendation is to design studies to evaluate the effectiveness of programs that increase ASE prior to initiation with HAART, particularly for those with lower educational attainment. Acknowledgments Dr. Marc completed this work in partial fulfillment of her doctoral dissertation at the Harvard School of Public Health. She was supported by unrestricted educational grants from Novartis and GlaxoSmithKline, and received a Pfizer independent research grant for Data Analysis Concept Sheet 222. At the time this paper was submitted for publication, she was a T32 NIMH Fellow (MH19132) at the Weill Medical College of Cornell University and a Co-Investigator in the Cornell Clinical Trails Unit, New York, NY, USA (AI-69419). Special thanks to Dr. Margaret Chesney for contributing significant comments and for reviewing the final draft of this manuscript. Dr. Chesney is a member of the ACTG 384 Outcomes Committee and currently the Deputy Director for the National Center for Complementary and Alternative Medicine, National Institutes of Health (Bethesda, MD, USA). She is the developer of the Adherence Self-Efficacy Scale at the Center for AIDS Prevention Studies at University of California San Francisco. We thank the participating ACTU personnel and study volunteers for their contributions to this project. We also acknowledge the contributions of the ACTG 384 team, the ACTG 384 adherence substudy (ACTG 731 and 5031s) team investigators, and from the Harvard School of Public Health, Dr. Max Su for his statistical support, as well as Dr. Emma Sanchez for her comments on the social gradient of health in the final drafts of this manuscript. The ACTG 384 Protocol Team was supported by grants from the US National Institutes of Health, National Institute of Allergy and Infectious Diseases, Adult AIDS Clinical Trials Group (AI38858), The Ohio State University (AI25924), Harvard University (AI27659), and University of North Carolina (AI25868) AIDS Clinical Trial Units. Pharmaceutical support was provided by Agouron Pharmaceuticals, Inc.; Bristol-Myers Squibb Co.; DuPont Pharmaceutical Co.; GlaxoSmithKline; Merck and Co., Inc.; and Pfizer.

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