Methamphetamine (meth) is a highly addictive drug with serious medical, societal, and economic consequences [1–12]. Nationally 0.5% of the civilian noninstitutionalized US population report using meth use in the prior 12 months . Among men who have sex with men (MSM), meth use is up to 20 times more prevalent [14–16], compared to the general population, and is considered a major factor in driving the US MSM HIV epidemic: meth use is associated with high-risk sexual behavior [12,17–26], HIV seroconversion [27,28], sexually transmitted disease (STD) incidence [29,30], and drug-resistant HIV .
Pharmacologic therapies have proven successful in treating heroin, nicotine, and alcohol dependence. However, there are no pharmacologic therapies approved for meth dependence, limiting treatment options [32,33]. Behavioral interventions for meth users have reduced meth use and lowered drug-associated HIV risk behaviors [33–36], however, these interventions are time-intensive, and treatment trials report high rates of drop-out , loss to follow-up [38,39], and relapse . Furthermore, few meth-using MSM access substance use treatment services [40,41]. A pharmacologic option for meth-dependent persons, either as a stand-alone treatment or as an adjunct to behavioral therapy, could provide an important and much-needed addition to current treatments for meth dependence.
Pharmacologic agents with the most promise to treat meth dependence include those that work on neurobiologic pathways dysregulated by meth use. Meth and other stimulants increase synaptic levels of dopamine by inhibiting the activity of dopamine reuptake transporters and by increasing release of vesicular dopamine stores . The level of euphoria produced by meth is positively associated with dopamine levels and occupancy of the dopamine receptor [43–46]. Brain imaging research shows that amphetamines increase dopamine levels especially within the nucleus accumbens, the part of the brain that transacts reward and reinforcement in addictive disorders [47–52]. Whereas acute meth use disturbs dopamine levels, prolonged meth use results in chronically depressed CNS dopaminergic activity in the absence of meth [42,43,53–56].
Meth withdrawal symptoms vary in severity, but include anxiety, anhedonia, and depression [36,57–59] and are thought to be due to rapidly decreasing levels of dopamine in the nucleus accumbens following drug cessation [57,60,61]. Relapse is postulated to be driven by the desire to alleviate withdrawal symptoms by restoring CNS dopamine levels to the level found in the presence of drug [62–64].
Bupropion, a norepinephrine and dopamine reuptake inhibitor , is FDA-approved for the treatment of depression, smoking cessation, and seasonal affective disorder. Bupropion binds to the dopamine transporter and has been shown to increase dopamine transmission in both the nucleus accumbens and prefrontal cortex . We hypothesized that bupropion would stabilize central dopamine tone in the absence of meth and alleviate meth withdrawal symptoms, thereby reducing relapse. Phase I, double-blind studies among meth users show that bupropion significantly attenuates subjective effects of meth: meth-dependent individuals who received escalating doses of meth after 6 days of bupropion treatment reported decreased feelings of being ‘high’ and had decreased cravings compared with those who received placebo .
Two randomized, placebo-controlled trials [68,69] evaluating bupropion for the treatment of meth dependence found that bupropion did not reduce meth use overall; however, there were reductions in meth-positive urines among lighter users. Limits to these trials include low retention rates (52%  and 34% ) and adherence measurement techniques that may overestimate adherence . These trials enrolled mainly treatment-seeking heterosexuals and did not measure adherence to study medication using electronic medication event monitoring systems (MEMS).
We are not aware of any pharmacologic trials for meth dependence that have focused exclusively on MSM populations at extremely high risk for transmitting or acquiring HIV in the context of their meth-related sexual behavior. Such populations would be an important target for any efficacious pharmacologic intervention, which would have the potential not only to reduce meth use but also HIV risk behavior. This is especially important as only a minority of meth-dependent MSM access substance use treatment services, even when behavioral treatment is available [40,41].
We sought to enroll actively using meth-dependent MSM who were not in meth treatment programs, in an effort to engage and retain a population not often represented in pharmacologic trials. Given the chaotic nature of meth-users' lives, it was important to determine whether this population can engage in a pharmacologic trial and adhere to study schedules and procedures. We also evaluated whether meth users could adhere to the study drug using self-report and MEMS.
Thirty meth-dependent, sexually active MSM were randomized to receive bupropion (n = 20) or placebo (n = 10) for 12 weeks. Eligibility criteria included meth dependence by Structured Clinical Interview for DSM disorders (SCID), interest in reducing or stopping meth use, age 18–60 years, anal sex with men in past 3 months while using meth, meth-metabolite positive urine at screening, no acute medical or psychiatric illness, and baseline safety labs without clinically significant abnormalities. We excluded individuals with a history of seizure or high risk for seizure, evidence of current major depression by SCID or history of antidepressant use within the past 4 weeks, current use of pseudoephedrine-containing products (which may cause false-positive urines for meth use), and HIV-infected individuals with CD4 cell count below 200 cells/μl.
A randomized, double-blind, placebo-controlled, two-arm pilot study with 2: 1 randomization to bupropion vs. placebo.
Participants were actively recruited at the municipal STD and HIV clinics, by street outreach in gay neighborhoods, at bars and events such as circuit parties, at community-based organizations serving MSM, and at needle-exchange programs. Recruitment flyers were posted at locations of active recruitment, in local newspapers and gay print media, and on social networking websites. Participants were also given recruitment materials to pass on to others. Potential participants completed a brief telephone screen to assess initial eligibility and, if eligible, were scheduled for an in-person screening visit.
All participants gave informed consent using IRB-approved consent forms. A 10-item ‘trial concept quiz,’ containing true/false questions was used to verify participants' basic understanding of the trial. Participants were required to answer 100% of the questions correctly in two attempts before being enrolled.
After informed consent, all participants received the following during the two screening visits: a complete history and physical, complete blood count, metabolic panel liver function tests, and urine meth testing. Rapid qualitative urine meth testing was conducted onsite using immunochromatographic meth-metabolite detection tests provided by Medtox Diagnostics, Burlington, NC. Participants with unknown HIV status received HIV rapid testing and counseling; HIV-positive participants received CD4 and HIV viral load tests.
Treatment assignment occurred through double-blinded block (blocks of four) randomization, ensuring that 10 participants received placebo and 20 bupropion. The study statistician provided the randomization code to the Drug Product Services Laboratory at University of California, San Francisco (UCSF).
All participants were seen weekly for urine specimen collection and substance-use counseling. Symptom-directed physical exams, safety labs, and behavioral assessments were performed at baseline and at the 4, 8, and 12-week visits. HIV risk-reduction counseling and testing was repeated for HIV-negative participants at the final visit. Participants were paid $10 for weekly visits and $35 for screening and at weeks 0, 4, 8, and 12.
Substance use risk reduction counseling
All participants received weekly 30-min substance use counseling. The counseling was modified from a standardized, manual-driven psychosocial treatment program using cognitive behavioral therapy  and motivational interviewing techniques [72,73], and incorporated the Stages of Change Model  that has been used in brief behavioral interventions to treat substance use [75–77]. Counseling was provided by trained study staff closely supervised by a clinical psychologist in weekly quality assurance sessions.
Bupropion 150 mg XL and matching placebo were supplied by the Drug Product Services Laboratory at UCSF and dispensed in bottles with a MEMS cap. Participants were instructed to take one pill every morning for 1 week and then two pills every morning for the remainder of the study. After week 12, at the conclusion of the intervention part of the study, participants were instructed to taper by taking one pill daily for 14 days to avoid any untoward effects of bupropion discontinuation. All study staff and participants were blinded to treatment assignment. At study completion, participants were asked to guess treatment assignment.
Medication adherence counseling and evaluation
The study clinician provided medication adherence counseling including information about the importance of taking medication daily and how to handle missed doses. We used two common methods of assessing adherence in this study: adherence as measured by MEMS caps and self-reported adherence using the 4-day Structured Self-Report, the validated AIDS Clinical Trial Group measure [78,79], which inquires about the number of doses missed over each of the preceding 4 days to determine percent adherence. MEMS cap adherence was defined as the number of distinct days on which the MEMS cap bottle was opened during the study, divided by 84, the number of doses expected in 12 weeks.
All participants were asked weekly about potential adverse events; symptom-driven physical exams and safety laboratory monitoring were done at weeks 4, 8, and 12. Adverse events were classified using the ‘Division of AIDS (DAIDS) Table for Grading Severity of Adult Adverse Experiences for HIV Prevention Trials Network.’ 
Audio-computer assisted self-interview measures
The following measures were assessed using audio-computer assisted self-interview (ACASI) to minimize underreporting of risk activities [81–83] and to enhance standardization of data collection.
Drug use included the frequency of meth and other drug use, including route of administration, and the sharing of drug paraphernalia.
Substance use treatment included receipt of any substance use treatment services, self-help group participation, or drug-related hospitalizations.
Center for Epidemiologic Studies Depression Rating Scale (CES-D) was used to assess the degree of clinically significant depressive symptoms (scores >16)  in trial participants.
Severity of Dependence Scale was used to measure the severity of meth dependence. Each of the five scale items was scored on a 4-point scale (0–3). A higher summary score indicates a higher level of dependence.
Sexual risk behavior included the number of male anal sex partners; the number of unprotected anal sex episodes; and the number of HIV-positive, negative, and unknown serostatus anal sex partners and unprotected anal sex acts with these partners in the past 4 weeks.
Reasons for non-adherence were assessed by asking participants to choose from a list of common reasons for medication nonadherence including ‘being high on meth,’ ‘being away from home,’ ‘busy with other things,’ or ‘simply forgot.’
Attitudes about trial participation assessed participants' level of satisfaction with the trial and whether they would participate in a similar trial in the future.
To assess feasibility of enrolling and retaining meth-dependent MSM, we computed the proportions of participants eligible and enrolled among those recruited and screened, the proportion of scheduled visits completed, scheduled urines collected, and the proportion of participants retained to the end of the study. We compared proportions across arms using Wilcoxon and Fisher's exact tests as appropriate to assess the comparability of participants by treatment assignment at baseline. Attendance at weekly visits and provision of urine samples were compared using binomial models with robust standard errors to accommodate potential overdispersion arising from within-person correlation.
To assess acceptability of bupropion and placebo, we examined adherence to study drug by the two measures described above. Percent adherence was compared by study arm using the Wilcoxon test. We compared time to the first study drug interruption of at least 1 week using the log-rank test.
To explore safety and tolerability, we computed the proportions of those experiencing adverse events and compared adverse event rates by treatment assignment using Fisher's exact test.
We used normal-logistic models fitted with random intercepts to assess between-group differences in meth-metabolite positive urine samples and self-reports of the number of serodiscordant anal sex partners. Analogous random effects negative binomial models, which are suitable for count outcomes with larger variance than under the Poisson model, were used to compare numbers of male partners as well as numbers of male partners with whom meth was used in the past 4 weeks. In these analyses, the baseline value was included, and the treatment effect was estimated by the interaction between treatment and follow-up. To be eligible for the study, participants had to provide a meth-metabolite-positive urine at a screening visit; however, not all urine results were positive at the randomization visit, at which the baseline values were ascertained. We omitted the week 1 urine result from the analysis of urine positivity because it takes approximately 1 week to achieve appropriate blood levels of bupropion. Sensitivity analyses were conducted that compared the inclusion and exclusion of the week one urine results.
Screening, recruitment, and randomization
Figure 1 shows results for screening, recruitment, assignment and retention for the study period from October 2006 to August 2007. Three hundred and twenty-five people were assessed for initial eligibility by a telephone prescreen (Fig. 1). Of those eligible by telephone prescreen, 54 (17%) signed informed consent and were assessed further for eligibility. Of the 54, 17 were ineligible, seven declined further participation, and 30 were randomized. Thus 9% of the total of 325 prescreened were randomized (Fig. 2).
We recruited a diverse sample (50% White, 20% Hispanic, and 10% Black), of whom 43% were HIV-positive (Table 1). Baseline demographic characteristics were similarly distributed in both arms with the exception of income (P = 0.01). Fifty-three percent reported using meth between 3 and 7 days/week and 53% of participants reported using meth with sex at least 50% of the time. The most common route of meth administration was smoking (87%), followed by injecting (50%) and snorting (47%). A minority (40%) of participants had previously sought substance use treatment or self-help programs for meth use. Mean severity of dependence scale score was high (5.9 +/−3.5) SCID score. Symptoms of depression were elevated (mean CESD score 20.1 +/−11.5) and 20 participants (67%) had CES-D scores at least 16 at baseline. At study completion, mean changes in CESD were +2.9 +/− 10.1 and −1.9 +/− 13.3 in the placebo and bupropion arms, respectively. These differences were not statistically significant by Wilcoxon rank-sum test (P value = 0.21). Forty-seven percent reported having health insurance and 60% reported having a regular healthcare provider. The most commonly used other substances were marijuana (63% of participants), poppers (43% of participants), and club drugs including GHB, ketamine and ecstasy/MDMA (40% of participants) (data not shown).
Twenty-seven participants (90%) completed the trial with no significant differences by treatment assignment. Overall, 89% of monthly follow-up ACASI risk assessments were completed (bupropion 87%, placebo 93%; P = 0.38), 81% of weekly follow-up study visits were attended (bupropion 80%, placebo 81%; P = 0.96), and 81% of all scheduled weekly urine samples were collected (bupropion 80%, placebo 81%; P = 0.96).
Adherence to study drug as measured by MEMS caps was 60% (59% bupropion, 62% placebo; P = 0.98), whereas adherence by self-report was 81% (85% bupropion, 75% placebo; P = 0.21). The correlation between adherence as measured by MEMS and by self-report was 54%. The most common reasons given for nonadherence included ‘simply forgot’ (18%), ‘busy with other things’ (18%), ‘away from home’ (16%), ‘change in daily routine’ (16%), ‘slept through dose’ (10%) and ‘high on meth’ (9%). Three participants in each group, including the three who did not complete the trial, had at least a week-long medication discontinuation prior to study completion (P = 0.37). Time to the first week-long medication discontinuation did not differ by treatment assignment (P = 0.48 by log-rank test).
Urine drug screen results
At randomization, 73% of participants had meth-metabolite-positive urines (bupropion 65%, placebo 90%; P = 0.21). The proportion of meth-metabolite positive urines at follow-up visits decreased in both groups (Table 2). After accounting for the initial difference in meth-metabolite-positive urines, using a normal-logistic model, the reductions were similar in the two groups (P = 0.63). Results were similar if week 1 meth-metabolite urine results were included and in an analysis imputing positive results for missing urine meth-metabolite results.
Sexual risk behavior
At baseline, the 20 participants in the bupropion group had a median of 3.5 male sexual partners in the past 4 weeks, whereas the 10 participants in the placebo group had a median of 13.5 partners (P = 0.11 by Wilcoxon test; Table 3). After adjusting for the baseline difference, the declines were similar (P = 0.46). The number of male partners with whom meth was used also declined during the trial. After adjusting for the baseline difference, the declines in male partners with whom meth was used were again similar (P = 0.71). Comparable declines across both groups were also seen in unprotected insertive (P = 0.90) and receptive (P = 0.62) anal sex with serodiscordant partners. The declines in prevalence of unprotected serodiscordant anal sex were similar in both groups (P = 0.09).
Safety and tolerability
There were no serious adverse events in the study. The most common adverse events were unrelated to study drug and were mild to moderate (grade 1 or 2) liver function test elevations, dermatologic conditions (grade 1 or 2), or electrolyte abnormalities (grade 1). There was one grade 3 ALT elevation. There were no significant differences in adverse events by treatment assignment (P = 0.11). One participant in the bupropion arm was diagnosed with HIV and rectal gonorrhea. Another participant in the bupropion arm reported agitation which resolved after study drug dose reduction.
Attitudes about trial participation and assessment of blinding
At study completion, 96% of volunteers were highly satisfied or satisfied with study participation, whereas 11% (3/2-7) found study participation very or somewhat difficult. Ninety-three percent (25/27) of participants completing the study reported that they would be likely to participate in a similar study in the future. Participants at study completion were asked to guess their treatment assignment. In the placebo group, five of nine or 56% guessed they were on placebo. In the bupropion group, nine of 18 (50%) guessed correctly. Treatment guessing accuracy between two groups did not differ significantly by Fisher's exact test (P = 1.00).
In this randomized, double-blind, placebo-controlled trial, we demonstrated that it was feasible to enroll and retain actively using, high-risk, meth-dependent MSM in a pharmacologic intervention trial, outside of a drug treatment program, with excellent rates of participation in study visits, procedures, and follow-up evaluations. Given the high rates of meth use among MSM, the associations among meth use, sexual risk behavior and HIV, and the fact that most meth-dependent MSM have not accessed current treatment options, it was important to demonstrate that high-risk MSM are willing to participate in pharmacologic studies for meth treatment. We found a high level of enthusiasm for our study as indicated by high participation rates and interest in joining similar studies in the future. Retention rates were substantially higher than in the two previously reported studies of bupropion for meth dependence [68,69].
Stimulant use is associated with lower medication adherence, including to HIV medications among HIV-positive meth users [17,85–88]. Our adherence rates were comparable to adherence rates among drug users in other studies [86,88]; however, our rates were lower than reported in the previous bupropion studies [68,69]. We used the more rigorous MEMS measure, as is common in antiretroviral medication adherence trials [70,87]. Our findings that self-reported adherence (81%) was higher than MEMS-measured adherence (60%) is consistent with the evidence that self-report tends to overestimate antiretroviral adherence .
Consistent with behavioral studies of MSM [33,69], multiple measures of sexual risk behavior including the number of male partners, the number of male partners with whom meth was used, and unprotected serodiscordant anal intercourse, decreased during trial participation in both study arms. These findings indicate that meth-dependent MSM engaged in pharmacologic interventions with concomitant substance use counseling can reduce their sexual risk behaviors. Most adverse events were mild to moderate in nature, expected due to the known side effect profile of bupropion, and there were no differences by treatment assignment.
The study has limitations. We had a relatively low phone prescreen to randomization ratio (9%) which should be considered when interpreting conclusions regarding feasibility. Among those assessed in person for eligibility, the randomization ratio was much higher (56%). This was a phase II pilot study and was not powered to assess the efficacy of bupropion and thus our comparisons by treatment assignment should be interpreted with this limitation in mind. Also, compared with other drug treatment studies, often done in drug-treatment centers, our once-weekly study visit schedule may be considered ‘low intensity.’ More intensive study visit requirements would have likely reduced our relatively high participation rates.
Despite the above limitations, our study demonstrates that it is feasible to enroll actively using, meth-dependent MSM in a pharmacologic trial with excellent attendance, compliance with study activities, and retention. Results of participants' assessment of whether they took bupropion or placebo show no compelling evidence of unblinding. Our modest adherence rates suggest that upcoming studies of pharmacologic interventions for meth dependence should be accompanied by adherence support measures that address the complex needs of this population. Sexual risk behavior declined during participation in this pharmacologic and substance use counseling intervention, suggesting potential of drug use reduction as a key part of HIV prevention interventions that target groups at highest risk. We must intensify our efforts to identify potential pharmacologic therapies for meth dependence, and to enroll high-risk populations, both to reduce meth-related morbidity and to further prevent HIV acquisition and transmission.
M.D. drafted the manuscript, assisted with the analysis, and primarily interpreted the data.
D.M.S. assisted in the conception and design of the study, acquisition, analysis and interpretation of data, and revising the manuscript for important intellectual content.
T.M. assisted in the conception and design of the study, acquisition of the data, and revising the manuscript for important intellectual content.
G.-M.S. assisted in the analysis and interpretation of data, and revising the manuscript for important intellectual content.
P.L.C. assisted in the analysis and interpretation of data, and revising the manuscript for important intellectual content.
E.V. assisted in the design of the study, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, and the statistical analysis.
S.S. assisted in the conception and design of the study, interpretation of data, and critical revision of the manuscript for important intellectual content.
G.N.C. conceived and designed the current study, assisted with analysis, interpreted the data, and in the critical revision of the manuscript for important intellectual content. He also obtained funding and material support and provided supervision.
Support: R21 DA021090
The funder did not have any role in the design and conduct of the study; collection, management, analysis or interpretation of the data; or preparation, review or approval of the manuscript.
All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
1. Increasing morbidity and mortality associated with abuse of methamphetamine: United States, 1991–1994. MMWR Morb Mortal Wkly Rep 1995; 44:882–886.
2. Treatment Episode Data Set (TEDS). Highlights: 2003. National admissions to substance abuse treatment services. DASIS Series: S-27. 2005 (DHHS Publication No. (SMA) 05–4043). http://www.oas.samhsa.gov/2k5/meth/meth.htm
3. Roberts DL, Ball J. Amphetamine and methamphetamine emergency department visits, 1995–2002 The drug abuse warning network. The DAWN report. July 2004. http://www.oas.samhsa.gov/2k4amphetamines.pdf
. [Accessed 22 February 2007]
4. Brecht ML, Greenwell L, Anglin MD. Methamphetamine treatment: trends and predictors of retention and completion in a large state treatment system (1992–2002). J Subst Abuse Treat 2005; 29:295–306.
5. Meredith CW, Jaffe C, Ang-Lee K, Saxon AJ. Implications of chronic methamphetamine use: a literature review. Harv Rev Psychiatry 2005; 13:141–154.
6. Maxwell JC. Emerging research on methamphetamine. Curr Opin Psychiatry 2005; 18:235–242.
7. Curtis EK. Meth mouth: a review of methamphetamine abuse and its oral manifestations. Gen Dent 2006; 54:125–129, quiz 130.
8. Saini T, Edwards PC, Kimmes NS, Carroll LR, Shaner JW, Dowd FJ. Etiology of xerostomia and dental caries among methamphetamine abusers. Oral Health Prev Dent 2005; 3:189–195.
9. Williams N, Covington JS 3rd. Methamphetamine and meth mouth: an overview. J Tenn Dent Assoc 2006; 86:32–35.
10. Tominaga GT, Garcia G, Dzierba A, Wong J. Toll of methamphetamine on the trauma system. Arch Surg 2004; 139:844–847.
12. Shoptaw S, Peck J, Reback CJ, Rotheram-Fuller E. Psychiatric and substance dependence comorbidities, sexually transmitted diseases, and risk behaviors among methamphetamine-dependent gay and bisexual men seeking outpatient drug abuse treatment. J Psychoactive Drugs 2003; 35(Suppl 1):161–168.
13. SAMHSA. The NSDUH report: methamphetamine use. Rockville, MD: Substance Use and Mental Health Services Administration; 26 January 2007.
14. SAMHSA Office of Applied Studies. National survey on drug use and health; 2004.
15. Stall R, Paul JP, Greenwood G, Pollack LM, Bein E, Crosby GM, et al. Alcohol use, drug use and alcohol-related problems among men who have sex with men: the Urban Men's Health Study. Addiction 2001; 96:1589–1601.
16. Thiede H, Valleroy LA, MacKellar DA, Celentano DD, Ford WL, Hagan H, et al. Regional patterns and correlates of substance use among young men who have sex with men in 7 US urban areas. Am J Public Health 2003; 93:1915–1921.
17. Marquez C, Mitchell SJ, Hare CB, John M, Klausner JD. Methamphetamine use, sexual activity, patient-provider communication, and medication adherence among HIV-infected patients in care, San Francisco. AIDS Care 2009; 21:575–582.
18. Mansergh G, Shouse RL, Marks G, Guzman R, Rader M, Buchbinder S, Colfax GN. Methamphetamine and sildenafil (Viagra) use are linked to unprotected receptive and insertive anal sex, respectively, in a sample of men who have sex with men. Sex Transm Infect 2006; 82:131–134.
19. Colfax G, Coates TJ, Husnik MJ, Huang Y, Buchbinder S, Koblin B, et al. Longitudinal patterns of methamphetamine, popper (Amyl nitrite), and cocaine use and high-risk sexual behavior among a cohort of San Francisco men who have sex with men. J Urban Health Bull N Y Acad Med 2005; 82:i62–i70.
20. Mansergh G, Colfax GN, Marks G, Rader M, Guzman R, Buchbinder S. The Circuit Party Men's Health Survey: findings and implications for gay and bisexual men. Am J Public Health 2001; 91:953–958.
21. Schwarcz S, Scheer S, McFarland W, Katz M, Valleroy L, Chen S, Catania J. Prevalence of HIV infection and predictors of high-transmission sexual risk behaviors among men who have sex with men. Am J Public Health 2007; 97:1067–1075.
22. Colfax G, Vittinghoff E, Husnik MJ, McKirnan D, Buchbinder S, Koblin B, et al. Substance use and sexual risk: a participant- and episode-level analysis among a cohort of men who have sex with men. Am J Epidemiol 2004; 159:1002–1012.
23. Wong W, Chaw JK, Kent CK, Klausner JD. Risk factors for early syphilis among gay and bisexual men seen in an STD clinic: San Francisco, 2002–2003. Sex Transm Dis 2005; 32:458–463.
24. Hirshfield S, Remien RH, Walavalkar I, Chiasson MA. Crystal methamphetamine use predicts incident STD infection among men who have sex with men recruited online: a nested case-control study. J Med Internet Res 2004; 6:e41.
25. Semple SJ, Zians J, Grant I, Patterson TL. Impulsivity and methamphetamine use. J Subst Abuse Treat 2005; 29:85–93.
26. Semple SJ, Zians J, Grant I, Patterson TL. Methamphetamine use, impulsivity, and sexual risk behavior among HIV-positive men who have sex with men. J Addict Dis 2006; 25:105–114.
27. Thiede H, Jenkins RA, Carey JW, Hutcheson R, Thomas KK, Stall RD, et al. Determinants of recent HIV infection among Seattle-area men who have sex with men. Am J Public Health 2009; 99(Suppl 1):S157–S164.
28. Plankey MW, Ostrow DG, Stall R, Cox C, Li X, Peck JA, Jacobson LP. The relationship between methamphetamine and popper use and risk of HIV seroconversion in the multicenter AIDS cohort study. J Acquir Immune Defic Syndr 2007; 45:85–92.
29. Hirshfield S, Remien RH, Humberstone M, Walavalkar I, Chiasson MA. Substance use and high-risk sex among men who have sex with men: a national online study in the USA. AIDS Care 2004; 16:1036–1047.
30. Chesney MA, Koblin BA, Barresi PJ, Husnik MJ, Celum CL, Colfax G, et al. An individually tailored intervention for HIV prevention: baseline data from the EXPLORE study. Am J Public Health 2003; 93:933–938.
31. Colfax GN, Vittinghoff E, Grant R, Lum P, Spotts G, Hecht FM. Frequent methamphetamine use is associated with primary nonnucleoside reverse transcriptase inhibitor resistance. AIDS 2007; 21:239–241.
32. Kampman KM. The search for medications to treat stimulant dependence. Addict Sci Clin Pract 2008; 4:28–35.
33. Shoptaw S, Reback CJ, Peck JA, Yang X, Rotheram-Fuller E, Larkin S, et al. Behavioral treatment approaches for methamphetamine dependence and HIV-related sexual risk behaviors among urban gay and bisexual men. Drug Alcohol Depend 2005; 78:125–134.
34. Reback CJ, Larkins S, Shoptaw S. Changes in the meaning of sexual risk behaviors among gay and bisexual male methamphetamine abusers before and after drug treatment. AIDS Behav 2004; 8:87–98.
35. Stall RD, Paul JP, Barrett DC, Crosby GM, Bein E. An outcome evaluation to measure changes in sexual risk-taking among gay men undergoing substance use disorder treatment. J Stud Alcohol 1999; 60:837–845.
36. Rawson RA, Gonzales R, Brethen P. Treatment of methamphetamine use disorders: an update. J Subst Abuse Treat 2002; 23:145–150.
37. Maglione M, Chao B, Anglin MD. Correlates of outpatient drug treatment drop-out among methamphetamine users. J Psychoactive Drugs 2000; 32:221–228.
38. Rawson RA, Huber A, Brethen P, Obert J, Gulati V, Shoptaw S, Ling W. Status of methamphetamine users 2–5 years after outpatient treatment. J Addict Dis 2002; 21:107–119.
39. Rawson RA, Marinelli-Casey P, Anglin MD, Dickow A, Frazier Y, Gallagher C, et al. A multi-site comparison of psychosocial approaches for the treatment of methamphetamine dependence. Addiction 2004; 99:708–717.
40. Brecht ML, von Mayrhauser C, Anglin MD. Predictors of relapse after treatment for methamphetamine use. J Psychoactive Drugs 2000; 32:211–220.
41. Shallow S. Persons attending substance abuse treatment programs, San Francisco, 2004. San Francisco: Department of Public Health Community Behavioral Health Agency; 2004.
42. Nordahl TE, Salo R, Leamon M. Neuropsychological effects of chronic methamphetamine use on neurotransmitters and cognition: a review. J Neuropsychiatry Clin Neurosci 2003; 15:317–325.
43. Volkow ND, Wang GJ, Fowler JS, Logan J, Gatley SJ, Wong C, et al. Reinforcing effects of psychostimulants in humans are associated with increases in brain dopamine and occupancy of D(2) receptors. J Pharmacol Exp Ther 1999; 291:409–415.
44. Drevets WC, Gautier C, Price JC, Kupfer DJ, Kinahan PE, Grace AA, et al. Amphetamine-induced dopamine release in human ventral striatum correlates with euphoria. Biol Psychiatry 2001; 49:81–96.
45. Berke JD, Hyman SE. Addiction, dopamine, and the molecular mechanisms of memory. Neuron 2000; 25:515–532.
46. Wilson JM, Kalasinsky KS, Levey AI, Bergeron C, Reiber G, Anthony RM, et al. Striatal dopamine nerve terminal markers in human, chronic methamphetamine users. Nat Med 1996; 2:699–703.
47. Everitt BJ, Wolf ME. Psychomotor stimulant addiction: a neural systems perspective. J Neurosci 2002; 22:3312–3320.
48. Lingford-Hughes A, Nutt D. Neurobiology of addiction and implications for treatment. Br J Psychiatry 2003; 182:97–100.
49. Cami J, Farre M. Drug addiction. N Engl J Med 2003; 349:975–986.
50. Berman S, O'Neill J, Fears S, Bartzokis G, London ED. Abuse of amphetamines and structural abnormalities in the brain. Ann N Y Acad Sci 2008; 1141:195–220.
51. Salo R, Ursu S, Buonocore MH, Leamon MH, Carter C. Impaired prefrontal cortical function and disrupted adaptive cognitive control in methamphetamine abusers: a functional magnetic resonance imaging study. Biol Psychiatry 2009; 65:706–709.
52. Tokunaga M, Seneca N, Shin RM, Maeda J, Obayashi S, Okauchi T, et al. Neuroimaging and physiological evidence for involvement of glutamatergic transmission in regulation of the striatal dopaminergic system. J Neurosci 2009; 29:1887–1896.
53. McCann UD, Wong DF, Yokoi F, Villemagne V, Dannals RF, Ricaurte GA. Reduced striatal dopamine transporter density in abstinent methamphetamine and methcathinone users: evidence from positron emission tomography studies with [11C]WIN-35,428. J Neurosci 1998; 18:8417–8422.
54. Sekine Y, Iyo M, Ouchi Y, Matsunaga T, Tsukada H, Okada H, et al. Methamphetamine-related psychiatric symptoms and reduced brain dopamine transporters studied with PET. Am J Psychiatry 2001; 158:1206–1214.
55. Di Chiara G, Imperato A. Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats. Proc Natl Acad Sci U S A 1988; 85:5274–5278.
56. O'Dell SJ, Weihmuller FB, Marshall JF. Methamphetamine-induced dopamine overflow and injury to striatal dopamine terminals: attenuation by dopamine D1 or D2 antagonists. J Neurochem 1993; 60:1792–1799.
57. Ellinwood EH, King G, Lee TH. Chronic amphetamine use and abuse. In: Watson SJ, editor. Psychopharmacology: the fourth generation of progress CD-ROM. Philadelphia, Pennsylvania: Lippincott, Williams & Wilkins; 1998.
58. Jaffe J. Amphetamine (or amphetamine-like)-related disorders. In: Kaplan HI, Sadock BJ, editors. Comprehensive textbook of psychiatry. Baltimore: Williams & Wilkins; 1995. pp. 791–799.
59. Simon SL, Domier C, Carnell J, Brethen P, Rawson R, Ling W. Cognitive impairment in individuals currently using methamphetamine. Am J Addict 2000; 9:222–231.
60. Nestler EJ. Molecular basis of long-term plasticity underlying addiction. Nat Rev Neurosci 2001; 2:119–128.
61. Gawin FH, Ellinwood EH Jr. Cocaine and other stimulants. Actions, abuse, and treatment. N Engl J Med 1988; 318:1173–1182.
62. Koob GF, Caine SB, Parsons L, Markou A, Weiss F. Opponent process model and psychostimulant addiction. Pharmacol Biochem Behav 1997; 57:513–521.
63. Koob GF. Drug addiction: the yin and yang of hedonic homeostasis. Neuron 1996; 16:893–896.
64. Koob GF, Le Moal M. Drug abuse: hedonic homeostatic dysregulation. Science 1997; 278:52–58.
65. Stahl SM. Essential psychopharmacology. 2nd ed New York: Cambridge University Press; 2000.
66. Rau KS, Birdsall E, Hanson JE, Johnson-Davis KL, Carroll FI, Wilkins DG, et al. Bupropion increases striatal vesicular monoamine transport. Neuropharmacology 2005; 49:820–830.
67. Corbit L, Newton T, Roache J, et al. Bupropion treatment attenuates methamphetamine effects in methamphetamine-experienced volunteers. Paper presented at: Society for Neuroscience Annual Meeting; 23–27 October 2004; San Diego.
68. Elkashef AM, Rawson RA, Anderson AL, Li SH, Holmes T, Smith EV, et al. Bupropion for the treatment of methamphetamine dependence. Neuropsychopharmacology 2008; 33:1162–1170.
69. Shoptaw S, Heinzerling KG, Rotheram-Fuller E, Steward T, Wang J, Swanson AN, et al. Randomized, placebo-controlled trial of bupropion for the treatment of methamphetamine dependence. Drug Alcohol Depend 2008; 96:222–232.
70. Liu H, Golin CE, Miller LG, Hays RD, Beck CK, Sanandaji S, et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med 2001; 134:968–977.
71. Brief D, Bollinger A, Horton G, LoCastro JS. Relapse prevention treatment for cocaine addiction: the RPT-C manual. Bethesda, MD: Medication Development Division, National Institute on Drug Abuse; 1998.
72. Miller W, Rollnick S. Motivational interviewing. 2nd ed. New York: Guilford Press; 2002.
73. Miller WR. Motivational interviewing with problem drinkers. Behaviour Psychother 1983; 11:147–172.
74. DiClemente CC, Prochaska JO, Gilbertini M. Self-efficacy and the stages of self change in smoking. Cognit Ther Res 1985; 9:181–200.
75. Dunn C, Deroo L, Rivara FP. The use of brief interventions adapted from motivational interviewing across behavioral domains: a systematic review. Addiction 2001; 96:1725–1742.
76. Samet JH, Rollnick S, Barnes H, Beyond CAGE. A brief clinical approach after detection of substance abuse. Arch Intern Med 1996; 156:2287–2293.
77. Rollnick S, Miller W. What is motivational interviewing? Cognit Behav Psychother 1995; 25:325–334.
78. Bangsberg D, Hecht F, Charlebois E, Chesney M, Moss A. Comparing objectives measure of adherence to HIV antiretroviral therapy: Electronic medication monitors and unannounced pill counts. AIDS Behav 2001; 5:275–281.
79. Bangsberg DR, Hecht FM, Charlebois ED, Zolopa AR, Holodniy M, Sheiner L, et al. Adherence to protease inhibitors, HIV-1 viral load, and development of drug resistance in an indigent population. AIDS 2000; 14:357–366.
81. Metzger DS, Koblin B, Turner C, Navaline H, Valenti F, Holte S, et al. Randomized controlled trial of audio computer-assisted self-interviewing: utility and acceptability in longitudinal studies. HIVNET Vaccine Preparedness Study Protocol Team. Am J Epidemiol 2000; 152:99–106.
82. Grund JP, Friedman SR, Stern LS, Jose B, Neaigus A, Curtis R, Des Jarlais DC. Syringe-mediated drug sharing among injecting drug users: patterns, social context and implications for transmission of blood-borne pathogens. Soc Sci Med 1996; 42:691–703.
83. Aral SO, Hughes JP, Stoner B, Whittington W, Handsfield HH, Anderson RM, Holmes KK. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am J Public Health 1999; 89:825–833.
84. Radloff L. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Measur 1977; 1:385–411.
85. Reback CJ, Larkins S, Shoptaw S. Methamphetamine abuse as a barrier to HIV medication adherence among gay and bisexual men. AIDS Care 2003; 15:775–785.
86. Hinkin CH, Barclay TR, Castellon SA, Levine AJ, Durvasula RS, Marion SD, et al. Drug use and medication adherence among HIV-1 infected individuals. AIDS Behav 2007; 11:185–194.
87. Levine AJ, Hinkin CH, Marion S, Keuning A, Castellon SA, Lam MM, et al. Adherence to antiretroviral medications in HIV: differences in data collected via self-report and electronic monitoring. Health Psychol 2006; 25:329–335.
88. Hinkin CH, Hardy DJ, Mason KI, Castellon SA, Durvasula RS, Lam MN, Stefaniak M. Medication adherence in HIV-infected adults: effect of patient age, cognitive status, and substance abuse. AIDS 2004; 18(Suppl 1):S19–S25.
© 2010 Lippincott Williams & Wilkins, Inc.