HIV-positive persons who use stimulants (eg, methamphetamine, cocaine, and crack cocaine) are at an elevated risk for more rapid HIV disease progression,1–4 although the underlying biologic or behavioral mechanisms for this disturbance have not been clearly elucidated.5–10 There is evidence that substance users are less likely to access highly active antiretroviral therapy (HAART),11,12 and stimulant users initiate HAART at lower T-helper (CD4+) cell counts than their peers who do not use substances.3,5 HIV-positive persons who engage in more regular stimulant use are also at greater risk for poorer adherence to HAART, which contributes to elevated viral load,6–8 greater risk of onward HIV transmission,13–15 and potentially faster HIV disease progression.5
Studies conducted to date have not consistently observed that stimulant users on HAART experience more negative health outcomes. One recent investigation with a cohort of substance users found that crack cocaine use predicted a greater rate of CD4+ cell count decline to less than 200 cells per microliter, an effect that was most pronounced among those who were not prescribed HAART at baseline.2 Kapadia et al3 also reported that stimulant-using women experienced a 2-fold faster rate of progression to AIDS and concurrently lower rates of HAART initiation. Consistent with these results, another study conducted with this cohort of women observed that persistent and intermittent crack cocaine users were more likely to develop an AIDS-defining illness (ADI), but only persistent crack cocaine users displayed a 3-fold greater AIDS-related mortality rate after controlling for adherence to HAART.4 In contrast, a study with a cohort of homeless and marginally housed persons with high rates of HAART utilization did not observe a significant association of current crack cocaine use with all-cause mortality.16 Because many studies have examined the effects of stimulant use irrespective of when or whether HAART was started, it is difficult to determine the extent to which negative health outcomes among stimulant users are attributable to delayed initiation of HAART or poorer adherence to HAART.
To inform evidence-based practice, this study examined whether time-varying stimulant use independently predicted increased risk of HIV disease progression outcomes after the initiation of HAART in the Multicenter AIDS Cohort Study (MACS). We hypothesized that stimulant use would be independently associated with increased all-cause mortality as well as progression to AIDS or all-cause mortality after accounting for HAART adherence.
Study Design and Procedures
Participants were enrolled in the MACS, an ongoing prospective study of HIV infection among gay and bisexual men as well as other men who have sex with men (MSM) in the United States.17,18 Enzyme-linked immunosorbent assays with confirmatory Western blot tests were performed on all participants at enrollment and every semiannual visit thereafter for initially HIV-negative participants. T-lymphocyte subsets were quantified using standardized flow cytometry, and HIV viral load was measured using standardized polymerase chain reaction methods.19,20 MACS protocols were approved by the institutional review boards of each of the participating centers. Informed consent was obtained from all participants.
This study included all MACS participants who initiated HAART, had at least 1 follow-up visit with assessment of stimulant use after initiation, and had data for covariates available within 2 years before stimulant use was assessed. This study used the data collected prospectively from MACS visits 26–57 (October 1996 to September 2012). Baseline was defined as the first visit after initiating HAART. The characterization of HAART regimens was guided by the DHHS/Kaiser Panel guidelines and defined as 3 or more antiretroviral drugs consisting of (1) one or more protease inhibitors, or (2) one nonnucleoside reverse transcriptase inhibitor, or (3) the nucleoside reverse transcriptase inhibitors—abacavir or tenofovir, or (4) an integrase or an entry inhibitor.21
Outcomes: AIDS and All-Cause Mortality
Using the 1993 Centers for Disease Control classification system,22 participants were assessed for an ADI during MACS visits. Participants met the criteria for AIDS if they were diagnosed with an ADI or had a CD4+ cell count of less than 200 cells per microliter or CD4+ cell percentage of <14. The date of AIDS diagnosis was confirmed through medical chart abstraction and interviews with medical providers. Using the National Death Index—Plus, final mortality information (including date and cause) was obtained for enrolled participants over the follow-up period.
Primary Predictor: Stimulant Use
Participants reported whether they had used methamphetamine, cocaine, crack cocaine, or ecstasy since their last MACS visit. Participants were categorized as reporting any stimulant use (1) or no stimulant use (0) at each visit. The time-varying cumulative proportion of MACS visits with any self-reported stimulant use was calculated. Compared with a reference group that reported no stimulant use (0%), patterns of intermittent (ie, 1%–49%, 50%–99%) and persistent (ie, 100%) stimulant use were characterized to investigate an expected dose–response association.
Demographics and HIV disease markers were included to adjust for possible confounding. Age at each MACS study visit was calculated using self-reported date of birth and treated as a time-varying continuous covariate (centered at 50 years). Self-reported race/ethnicity was categorized as white (reference group), African American/black, and Hispanic/Latino or other ethnic minority. Self-reported highest level of education completed at enrollment was categorized as high school or less (reference group), some college (grades 13–15), and college graduate or greater (grade 16 or more). CD4+ cell count before HAART initiation was measured using peripheral venous blood samples for the MACS visit before starting HAART. For those who began HAART before enrolling in the MACS, pre-HAART CD4+ cell count was measured using medical chart abstraction. Participants with a pre-HAART CD4+ cell count of greater than or equal to 500 cells per microliter (reference group) were compared to those with 499–350, 349–200, and <200 cells per microliter. Time-varying CD4+ cell count, log10HIV viral load, and self-reported HAART adherence were lagged (approximately 6 months) to adjust for time-dependent confounding with stimulant use. Self-reported HAART adherence was measured using a single item where participants indicated how often they took HAART medications as prescribed by selecting one of the following options: 100%, 95%–99%, 75%–94%, and <75%.
Health status indicators and behavioral factors were measured as possible confounders. Hepatitis C virus (HCV) coinfection was defined as antibody or RNA positive at baseline. Other lagged time-varying health status indicators measured at each visit included body mass index (weight in kilograms/height in square meters), high blood pressure (ie, systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or diagnosed with hypertension and use of antihypertensive medications), and dyslipidemia (ie, fasting total cholesterol ≥200 mg/dL or low-density lipoprotein ≥130 mg/dL or high-density lipoprotein <40 mg/dL or triglycerides ≥150 mg/dL or use of lipid-lowering medications with self-report of clinical diagnosis in the past). Lagged, time-varying self-reported physical health and mental health were assessed using the SF-36 Physical Component Summary and Mental Component Summary scores.23 Participants with scores of 16 or greater on Centers for the Epidemiologic Study of Depression scale were categorized as reporting clinically significant distress,24,25 which was examined as a lagged time-varying covariate. Finally, binge drinking (ie, ≥5 alcoholic drinks per day for at least once a month) and cigarette smoking in the last 6 months were included as time-varying covariates.
Marginal Structural Model Analyses
Because we were concerned that declining health might lead to a subsequent reduction in the use of stimulants (ie, a “sick quitter” effect), marginal structural modeling was used to address time-dependent confounding.26,27 This requires an initial, longitudinal unordered multinomial logistic model with time-varying stimulant use [4 categories: 0% (reference), 1%–49%, 50%–99%, and 100%] as the outcome to obtain stabilized weights for the final weighed models. The logistic model for determining the numerator of the weights for stimulant use included all time-fixed covariates (ie, site, race/ethnicity, education, baseline HCV status, and pre-HAART CD4+ cell count), number of visits from baseline, and cumulative percent of the previous 3 visits where any stimulant use was reported. To obtain the denominator of the weights for stimulant use, all time-varying covariates were also included (ie, age, CD4+ cell count, HIV viral load, self-reported HAART adherence, body mass index, high blood pressure, dyslipidemia, self-reported physical health, self-reported mental health, Centers for the Epidemiologic Study of Depression scores ≥16, binge drinking, and cigarette smoking). Similarly, a second logistic model determined the weights of remaining uncensored to control for informative dropout. The final stabilized weights were calculated by multiplying the weights of stimulant use and weights of remaining uncensored. If the weights were >4, they were set to 4.
The primary analyses consisted of separate weighted pooled logistic regression models for time to all-cause mortality and time to AIDS or all-cause mortality. Pooled logistic regression is a standard method for the analysis of discrete-time survival data, involving expansion of the binary outcome data to reflect a time-to-event outcome.28 A weighted competing risk analysis was also performed with a pooled multinomial logistic model (ie, alive, AIDS-related mortality, and non-AIDS mortality) to examine the association of stimulant use with AIDS-related and non-AIDS mortality separately. This was a discrete version of a competing risk analysis based on cumulative incidence functions.28
The sample included 1313 HIV-positive MSM contributing 19,270 person-visits. The median number of observations per participant was 15 (interquartile range, 7–20). There were 190 deaths (50% AIDS related; 38% non-AIDS related, and 12% indeterminate) during the 8.5-year median follow-up period (interquartile range, 4.2–11.5 years). The crude mortality rate was 14.5% [95% confidence interval (CI): 12.6 to 16.5]. Among the 648 participants (8657 person-visits) without AIDS at the initiation of HAART, 15% (N = 97; 95% CI: 12 to 18) developed AIDS over the course of follow-up. Table 1 provides detailed information regarding the demographic and clinical characteristics of participants.
Before performing the marginal structural modeling analyses, the distribution of the final stabilized weights was examined. Before trimming, the averages in the 4 stimulant groups were very close to 1. The minimum values in each group were >0.15 and the number exceeding 4 was very small, <1%, and only in 1 group. This provided support for the validity of the marginal structural pooled logistic regression model.
As shown in Table 2, results of marginal structural model analyses demonstrated that HCV coinfection at baseline [adjusted odds ratio (AOR) = 2.11; 95% CI: 1.40 to 3.17] and pre-HAART CD4+ cell counts from 200 to 349 cells per microliter (AOR = 1.84; 95% CI: 1.07 to 3.16) and <200 cells per microliter (AOR = 4.37; 95% CI: 2.68 to 7.12) were associated with increased odds of all-cause mortality over follow-up. No level of stimulant use was significantly associated with increased odds of all-cause mortality. Results of a competing risk analysis also indicated that stimulant use was not significantly associated with increased odds of AIDS-related or non-AIDS mortality separately.
HCV-coinfected participants (AOR = 2.28; 95% CI: 1.29 to 4.02) had increased odds of progression to AIDS or all-cause mortality over follow-up. However, no level of stimulant use was significantly associated with increased odds of progression to AIDS or all-cause mortality. Where participants reported stimulant use at 50%–99% and 100% of visits, there were comparable associations with progression to AIDS or all-cause mortality. Consequently, a secondary analysis was conducted to combine these categories of time-varying stimulant use. Findings suggested that where participants reported using stimulants at 50% or more of study visits, there was a 1.5-fold increase in the odds of progression to AIDS or all-cause mortality over follow-up (AOR = 1.54; 95% CI: 1.02 to 2.33; P < 0.05) compared with those who reported no stimulant use.
This study of HIV-positive MSM observed that stimulant use over time was not significantly associated with greater odds of all-cause mortality. This absence of a statistically significant association of stimulant use with mortality was unchanged in competing risk analysis that examined AIDS-related and non-AIDS mortality separately. In secondary analyses with a subset of participants without AIDS at HAART initiation, any self-reported stimulant use at 50% or more of study visits was associated with a 1.5-fold increase in the odds of progression to AIDS or all-cause mortality. Although stimulant use was not linked to overall mortality, more frequent stimulant use was modestly associated with HIV disease progression (ie, AIDS or all-cause mortality) in men without AIDS at HAART initiation.
This study indicated that men with a pre-HAART CD4+ cell count of less than 350 cells per microliter had increased odds of all-cause mortality. This underscores the expected benefits of early HAART initiation to optimize health outcomes.13,29 Although HIV-positive stimulant users are more likely to experience difficulties with adherence,6,7 ensuring that these patients have access to HAART at higher CD4+ cell counts could optimize health outcomes and potentially decrease onward HIV transmission rates similar to non–stimulant-using individuals.13 In the context of HIV care, implementing evidence-based interventions to enhance adherence as well as promoting linkages to formal substance abuse treatment would enhance the quality of care that stimulant-using MSM receive and could maximize the benefits that this population derives from HAART.30–32
Findings from this study must be interpreted in context of some important limitations. First and foremost, the overall mortality rate in the MACS was relatively low after the initiation of HAART, and this may have limited statistical power to detect an association of stimulant use with mortality outcomes. It is also noteworthy that stimulant use was assessed using self-report measures that did not adequately characterize patterns of use, route(s) of administration, or screen for the presence of a stimulant use disorder. Different stimulants or modes of stimulant administration could increase risk for specific illnesses, including those that are indicative of clinical AIDS. For example, there is some evidence that smoking stimulants such as crack cocaine increases risk for pulmonary illnesses like tuberculosis and bacterial pneumonia.33,34 Finally, only 26% of cohort members reported any stimulant use at baseline; cohort studies that systematically enroll larger samples of active and former stimulant users would measure with greater precision any associations between stimulant use and specific negative health outcomes. Future cohort studies should also include biomarkers of recent stimulant use, diagnostic interviews for stimulant use disorders, and multimethod assessment of HAART adherence.
There is emerging evidence that even HIV-positive persons who achieve sustained viral suppression are at an elevated risk for developing HIV-associated non-AIDS conditions.35 However, this study did not examine relationships between stimulant use and specific illnesses that are not AIDS defining. Stimulants such as cocaine also have well-characterized deleterious effects on cardiovascular functioning,36,37 and future research should examine whether stimulant use increases risk of cardiovascular events or cardiovascular-related death among HIV-positive persons. Bearing in mind that HCV coinfection was independently associated with more than a 2-fold increase in the odds of progression to AIDS or all-cause mortality, the effects of stimulant use on hepatoxicity could accelerate the onset or course of hepatic diseases.38 Although further research is needed to examine stimulant-induced end-organ damage in HIV-positive persons, findings from this study have important clinical implications. Stimulant-using MSM should have access to HAART because they derive life-saving benefits that are comparable with nonusers, and comprehensive approaches to HIV care could optimize the effectiveness of HAART with this population.32
Data in this article were collected by the Multicenter AIDS Cohort Study (MACS) with centers (Principal Investigators) at Johns Hopkins University Bloomberg School of Public Health (Joseph Margolick), U01-AI35042; Northwestern University (Steven Wolinsky), U01-AI35039; University of California, Los Angeles (Roger Detels), U01-AI35040; University of Pittsburgh (Charles Rinaldo), U01-AI35041; and the Center for Analysis and Management of MACS, Johns Hopkins University Bloomberg School of Public Health (Lisa Jacobson), UM1-AI35043. Web site located at http://www.statepi.jhsph.edu/macs/macs.html.
1. Carrico AW. Substance use and HIV disease progression in the HAART era: implications for the primary prevention of HIV. Life Sci. 2011;88:940–947.
2. Baum MK, Rafie C, Lai S, et al.. Crack-cocaine
use accelerates HIV disease progression in a cohort of HIV-positive drug users. J Acquir Immune Defic Syndr. 2009;50:93–99.
3. Kapadia F, Cook JA, Cohen MH, et al.. The relationship between non-injection drug use behaviors on progression to AIDS and death in a cohort of HIV seropositive women in the era of highly active antiretroviral therapy
use. Addiction. 2005;100:990–1002.
4. Cook JA, Burke-Miller JK, Cohen MH, et al.. Crack cocaine
, disease progression, and mortality
in a multicenter cohort of HIV-1 positive women. AIDS. 2008;22:1355–1363.
5. Carrico AW, Bangsberg DR, Weiser SD, et al.. Psychiatric correlates of HAART utilization and viral load among HIV-positive impoverished persons. AIDS. 2011;25:1113–1118.
6. Carrico AW, Johnson MO, Moskowitz JT, et al.. Affect regulation, stimulant use, and viral load among HIV-positive persons on anti-retroviral therapy. Psychosom Med. 2007;69:785–792.
7. Carrico AW, Riley ED, Johnson MO, et al.. Psychiatric risk factors for HIV disease progression: the role of inconsistent patterns of antiretroviral therapy utilization. J Acquir Immune Defic Syndr. 2011;56:146–150.
8. Ellis RJ, Childers ME, Cherner M, et al.. Increased human immunodeficiency virus loads in active methamphetamine
users are explained by reduced effectiveness of antiretroviral therapy. J Infect Dis. 2003;188:1820–1826.
9. Shoptaw S, Stall R, Bordon J, et al.. Cumulative exposure to stimulants and immune function outcomes among HIV-positive and HIV-negative men in the Multicenter AIDS Cohort Study. Int J STD AIDS. 2012;23:576–580.
10. Carrico AW, Johnson MO, Morin SF, et al.. Stimulant use is associated with immune activation and depleted tryptophan among HIV-positive persons on anti-retroviral therapy. Brain Behav Immun. 2008;22:1257–1262.
11. McGowan CC, Weinstein DD, Samenow CP, et al.. Drug use and receipt of highly active antiretroviral therapy
among HIV-infected persons in two U.S. clinic cohorts. PLoS One. 2011;6:e18462.
12. Westergaard RP, Ambrose BK, Mehta SH, et al.. Provider and clinic-level correlates of deferring antiretroviral therapy for people who inject drugs: a survey of North American HIV providers. J Int AIDS Soc. 2012;15:10.
13. Cohen MS, Chen YQ, McCauley M, et al.. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365:493–505.
14. Montaner JS, Lima VD, Barrios R, et al.. Association of highly active antiretroviral therapy
coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. Lancet. 2010;376:532–539.
15. Mayer KH, Skeer MR, O'Cleirigh C, et al.. Factors associated with amplified HIV transmission behavior among American men who have sex with men engaged in care: implications for clinical providers. Ann Behav Med. 2014;47:165–171.
16. Riley ED, Bangsberg DR, Guzman D, et al.. Antiretroviral therapy, hepatitis C virus, and AIDS mortality
among San Francisco's homeless and marginally housed. J Acquir Immune Defic Syndr. 2005;38:191–195.
17. Kaslow RA, Ostrow DG, Detels R, et al.. The Multicenter AIDS Cohort Study: rationale, organization, and selected characteristics of the participants. Am J Epidemiol. 1987;126:310–318.
18. Dudley J, Jin S, Hoover D, et al.. The Multicenter AIDS Cohort Study: retention after 9 1/2 years. Am J Epidemiol. 1995;142:323–330.
19. Giorgi JV, Cheng HL, Margolick JB, et al.. Quality control in the flow cytometric measurement of T-lymphocyte subsets: the multicenter AIDS Cohort Study experience. The Multicenter AIDS Cohort Study Group. Clin Immunol Immunopathol. 1990;55:173–186.
20. Fahey JL, Taylor JM, Manna B, et al.. Prognostic significance of plasma markers of immune activation, HIV viral load and CD4 T-cell measurements. AIDS. 1998;12:1581–1590.
21. DHHS/Henry J. Kaiser Family Foundation Panel on Clinical Practices for the Treatment of HIV Infection. Guidelines for the use of antiretroviral agents in HIV-infected adults and adolescents. 2008. Available at: http://aidsinfo.nih.gov/contentfiles/AdultandAdolescentGL.pdf
. Accessed November 3, 2008 Revision.
22. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep. 1992;41:1–19.
23. Ware JE, Kosinski M. Interpreting SF-36 summary health measures: a response. Qual Life Res. 2001;10:405–413; discussion 415–420.
24. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
25. Mills TC, Paul J, Stall R, et al.. Distress and depression in men who have sex with men: the Urban Men's Health Study. Am J Psychiatry. 2004;161:278–285.
26. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656–664.
27. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–560.
28. Rabe-Hesketh S, Skrondal A. Multilevel and Longitudinal Modeling Using Stata: Categorical Responses, Counts, and Survival, 3rd Edition, Volume II. College Station, TX: Stata Press; 2012.
29. Thompson MA, Aberg JA, Hoy JF, et al.. Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel. JAMA. 2012;308:387–402.
30. Chaiyachati KH, Ogbuoji O, Price M, et al.. Interventions to improve adherence to antiretroviral therapy: a rapid systematic review. AIDS. 2014;28(suppl 2):S187–S204.
31. Shoptaw S, Montgomery B, Williams CT, et al.. Not just the needle: the state of HIV-prevention science among substance users and future directions. J Acquir Immune Defic Syndr. 2013;63(suppl 2):S174–S178.
32. Committee to Review Data Systems for Monitoring HIV Care. In: Volberding PA, Aidala A, Celentano D, et al.. Monitoring HIV Care in the United States: A Strategy for Generating National estimates of HIV Care and Coverage. Washington, DC: Institute of Medicine National Academy of Sciences; 2012.
33. Webber MP, Schoenbaum EE, Gourevitch MN, et al.. A prospective study of HIV disease progression in female and male drug users. AIDS. 1999;13:257–262.
34. Howard AA, Klein RS, Schoenbaum EE, et al.. Crack cocaine
use and other risk factors for tuberculin positivity in drug users. Clin Infect Dis. 2002;35:1183–1190.
35. Deeks SG. Immune dysfunction, inflammation, and accelerated aging in patients on antiretroviral therapy. Top HIV Med. 2009;17:118–123.
36. Newlin DB, Wong CJ, Stapleton JM, et al.. Intravenous cocaine
decreases cardiac vagal tone, vagal index (derived in lorenz space), and heart period complexity (approximate entropy) in cocaine
abusers. Neuropsychopharmacology. 2000;23:560–568.
37. Phillips K, Luk A, Soor GS, et al.. Cocaine
cardiotoxicity: a review of the pathophysiology, pathology, and treatment options. Am J Cardiovasc Drugs. 2009;9:177–196.
38. Carvalho M, Carmo H, Costa VM, et al.. Toxicity of amphetamines: an update. Arch Toxicol. 2012;86:1167–1231.