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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Public Health. Author manuscript; available in PMC 2014 January 1.
Published in final edited form as:
PMCID: PMC3518356

Predictors of Active Injection Drug Use in a Cohort of Patients Infected With Hepatitis C Virus



We investigated potential risk factors for active injection drug use (IDU) in an inner-city cohort of patients infected with hepatitis C virus (HCV).


We used log-binomial regression to identify factors independently associated with active IDU during the first 3 years of follow-up for the 289 participants who reported ever having injected drugs at baseline.


Overall, 142 (49.1%) of the 289 participants reported active IDU at some point during the follow-up period. In a multivariate model, being unemployed (prevalence ratio [PR] = 1.93; 95% confidence interval [CI] = 1.24, 3.03) and hazardous alcohol drinking (PR = 1.67; 95% CI = 1.34, 2.08) were associated with active IDU. Smoking was associated with IDU but this association was not statistically significant. Patients with all 3 of those factors were 3 times as likely to report IDU during follow-up as those with 0 or 1 factor (PR = 3.3; 95% CI = 2.2, 4.9). Neither HIV coinfection nor history of psychiatric disease was independently associated with active IDU.


Optimal treatment of persons with HCV infection will require attention to unemployment, alcohol use, and smoking in conjunction with IDU treatment and prevention.

Hepatitis C virus (HCV) infection is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. About 130 million people are estimated to be infected worldwide with HCV,1 including 3.2 million in the United States,2 and mortality from HCV in the United States is increasing.3 Injection drug use (IDU) is the single most important risk factor for HCV infection in the United States 2,4 with an estimated 40% to 50% of infections attributable to IDU.5 Of increasing concern is the substantial proportion of HCV-infected patients who are coinfected with HIV.6 Because HIV and HCV are each transmitted by blood-contaminated needles and syringes, approximately 30% of all HIV-infected individuals are also infected with HCV1,7; in cohorts of intravenous drug users, the proportion of HCV-infected persons with HIV coinfection can be as high as 41%.8

Because IDU is a significant risk factor for HCV transmission, ongoing drug abuse is common in HCV-infected populations. Such ongoing drug use has been documented as a potential barrier in managing the infection.9,10 Moreover, former IDUs can be concerned about relapse with performing self-injection as part of interferon treatment.11 Thus, understanding factors associated with active IDU may inform pragmatic approaches to improving acceptability of HCV treatment and increasing patients’ chances of successfully treating their disease.

Other barriers to treatment of HCV infection have been described and are associated with IDU, such as alcohol use, psychiatric disease, and HIV coinfection.9,10,12-14 Concurrent alcohol abuse has, in some studies, distinguished persistence of IDU from cessation of IDU; however, in other reports, the association of heavy alcohol use did not remain after adjustment for known risk factors.15,16 Co-occurring mental disorders are frequently associated with poorer health and worse treatment outcomes among drug users and may lead to an increased level of drug use and riskier drug use behavior.17 HIV infection has been hypothesized to be associated with IDU in contrasting ways. Those who are HIV-infected may have more frequent contact with health services and thus referral to drug treatment; conversely, increased depression following diagnosis may lead to increased drug use.16

Individual patterns of drug use vary over time. Whereas some studies have indicated a trend toward decreased IDU over time in longer-term cohort studies, others have found that many injection drug users are unable to maintain sustained cessation of IDU.17-23 In addition to the direct morbidity and mortality associated with IDU, continued use may make it more difficult for patients to effectively manage their disease. Evaluating predictors of ongoing IDU in these populations may help identify avenues to facilitate long-term cessation of IDU. Our goals were to investigate risk factors for active IDU in a cohort of patients infected with hepatitis C, with specific focus on alcohol use, smoking, psychiatric disease, and HIV coinfection.


Data for this study came from the Hepatitis C, HIV, and Related Morbidity (CHARM) cohort at Boston University Medical Center (BUMC). The CHARM study was established in 2000 to prospectively evaluate the natural history of HCV infection and HIV–HCV coinfection in an inner-city, predominantly injection drug–using population. Study participants were HCV-infected (male or female, newly or chronically infected), were aged 18 years or older, and received their primary care at BUMC, its affiliated Neighborhood Health Centers, or the Boston Veterans Administration Medical Center. Patients were excluded from enrollment if they had a previous clinical liver event or if their HCV or HIV serostatus was unknown and they did not wish to be tested. The institutional review board at each participating institution reviewed and approved the study protocol, and all participants provided written informed consent. Between August 2000 and October 2003, 487 patients with HCV infection were fully enrolled, of whom 335 (69%) had at least 1 follow-up visit over the next 3 years. The 289 (86%) participants who reported having ever used injection drugs at the baseline interview were eligible to be included in the following analyses.

Study Data

Participants completed a detailed baseline questionnaire and returned for follow-up interviews every 12 months. Baseline and follow-up questionnaires included questions on demographic and socioeconomic factors, cigarette smoking, homelessness, and incarceration over the previous year. Participants were asked their race according to the following classifications: White (non-Hispanic), Black (non-Hispanic), Hispanic, Asian/Pacific Islander, American Indian/Alaska Native, and other; in the analysis, the latter 3 categories were combined into “other.” Substance use questions included the Addiction Severity Index and Alcohol Use Disorders Identification Test (AUDIT) questionnaires24,25 and also included questions about frequency of substance use during the previous year.

Physical examinations and chart reviews were obtained at baseline and every 12 months, to collect available information on HIV/AIDS medical history (including CD4 counts and antiretroviral therapy) and psychiatric history. As all CHARM cohort participants received their primary care at BUMC and affiliated centers, few participants were seen at other sites. Patients were asked annually about any hospitalizations outside BUMC, for which records were obtained; however, such hospitalizations were uncommon.

To equalize follow-up between study participants (and thus opportunity for active IDU), we included the first 3 years of follow-up data, through December 2006, for all study participants in the present analysis. Persons were eligible for follow-up until they died, moved out of the study area, or withdrew from the study. Persons who were incarcerated during the study were not able to be followed while incarcerated, but were eligible for continued follow-up after release from incarceration.

Because some injection drug users transition into and out of active IDU over relatively short periods of time, a point prevalence of active IDU at any given time may underestimate the actual amount of active IDU that occurs in a population. Drawing on the longitudinal data of the CHARM cohort, we assessed active IDU in this study over the 3-year period of follow-up to distinguish persons with active IDU from those who did not inject drugs during this time period. We included only those CHARM participants with a history of IDU in the present analysis. We defined study participants as active injection drug users if they reported active IDU at any point during their follow-up (or at the baseline interview). We defined participants as former injection drug users if they reported no active IDU at any point during the follow-up period. We calculated the prevalence of any active IDU over follow-up as the proportion of eligible participants who reported any active IDU during the 3-year follow-up period.

To control for stage of HIV disease at enrollment in the study, we collected the nadir CD4 value for each study participant at baseline. We drew questions on current alcohol use directly from the AUDIT questionnaire. This instrument has been used extensively and is well-characterized in substance use research as a means of determining whether alcohol consumption has become hazardous to a person’s health (i.e., AUDIT score of ≥ 8).25 Participants also were asked at baseline if they were currently smoking any cigarettes. History of psychiatric disease was noted during the baseline chart review, with a clinical determination of whether the condition was an active problem at study entry. Situational factors, including housing, employment, incarceration, and marital status, were obtained by self-report at baseline. Definitions of homelessness vary widely between studies.26 For this study, patients were asked where they usually live and were considered homeless if they selected “homeless or other shelter” or “on the street.”


Using the classifications of IDU described earlier, we evaluated risk factors for active IDU. We then compared the prevalence of any active IDU during follow-up across subgroups of various risk factors. To identify factors that distinguished HCV-infected persons with continued IDU from those who had previously used injection drugs but maintained cessation of use, we calculated univariate statistical associations between baseline risk factors and active IDU as prevalence ratios (PRs) and tested for statistical significance with the χ2 test. We calculated 95% confidence intervals (CIs) by using a normal approximation of the binomial distribution. To identify a set of independent predictors of active IDU, we used multivariate models to simultaneously control for the effects of the various risk factors examined. We considered variables with a PR of at least 1.2 (or PR = 0.83) in univariate analysis in the multivariate models, using log-binomial regression to calculate adjusted PRs and associated 95% CIs.27 We incorporated variables 1 at a time into the multivariate model, starting with those that had the strongest univariate associations, and these were kept in the model if they retained an association of at least 1.2 (or PR = 0.83) and a P < .2. We performed all statistical analyses with SAS version 9.2 (SAS Institute, Cary, NC).


We included the 289 members of the CHARM cohort who had ever used injection drugs and had at least 1 follow-up visit over the next 3 years after enrollment in these analyses. Of these, 179 (62%) were coinfected with HIV at baseline. The population was 65% male and was racially and ethnically diverse, with 48% being of Black race and 21% of Hispanic ethnicity. Characteristics of the study participants are provided in Table 1.

Baseline Characteristics of Hepatitis C Virus–Infected Participants Who Have Ever Used Injection Drugs (N = 289): CHARM Cohort; Boston, MA; 2000–2006

The median age at initiation of IDU in this cohort was 20 years, representing a median of 24.3 years since initiation at baseline study enrollment. Heroin was the most commonly injected drug, with 92% of the cohort having ever injected heroin and 79% reporting ever having injected heroin daily. Cocaine was also widely used, with 72% of the cohort reporting ever injecting cocaine and 49% having ever injected the drug daily.

Of a possible 837 study visits, 628 (75%) were successfully completed. Of the 209 missed visits, 9 were missed because of incarceration and 4 were missed because the participants had moved. No patients withdrew from the study. Persons without an entire 3 years of follow-up were more likely than those who had complete follow-up to be male (71.5% vs 56.5%, respectively), but were less likely to be coinfected with HIV (56% vs 69.5%). However, the participants who missed at least 1 study visit had a similar frequency of a history of daily IDU (87%) as those who attended all 3 follow-up examinations (88%). Data from chart reviews, on the other hand, were available for more than 95% of study participants.

Univariate Analysis

Over the course of study follow-up, the prevalence of any active IDU in the cohort was 49.1% (n = 142), whereas the other 50.9% reported no active IDU throughout the study period. In univariate analysis, men were slightly less likely than women to report any active IDU over follow-up (Table 2); those younger than 40 years were significantly more likely to use injection drugs (PR = 1.54; 95% CI = 1.23, 1.92). Baseline social factors such as incarceration (PR = 1.38; 95% CI = 0.98, 1.96), marital status (PR = 0.66; 95% CI = 0.38, 1.14), unemployment (PR = 2.16; 95% CI = 1.43, 3.27), and homelessness (PR = 1.72; 95% CI = 1.37, 2.15) were found to be associated with active IDU. Those who reported active hazardous alcohol use at baseline were more likely to report active IDU compared with those who were not using alcohol at baseline (PR = 1.96; 95% CI = 1.55, 2.47); we also observed a similar effect of active cigarette smoking on active IDU (PR = 1.80; 95% CI = 1.20, 2.70).

Univariate Associations of Study Factors With Any Active Intravenous Drug Use During the 3-Year Follow-Up Among Hepatitis C Virus–Infected Patients Who Have Ever Used Injection Drugs: CHARM Cohort; Boston, MA; 2000–2006

Those with a history of daily heroin use through any route were more likely to report active IDU during follow-up than those who did not (PR = 1.66; 95% CI = 1.13, 2.45), although the same effect was not seen for a history of daily cocaine use through any route (PR = 1.08). In addition, a history of having received methadone treatment was related to active IDU in the univariate analysis (PR = 1.30; 95% CI = 1.01, 1.67). Although a history of psychiatric disease was associated with reported IDU (PR = 1.28; 95% CI = 0.97, 1.68), this association was not statistically significant. No association was observed between HIV coinfection and any active IDU during the study (PR = 0.97).

Multivariate Analysis

To identify a set of independent predictors of active IDU, we considered several factors in multivariate models, including HIV coinfection, history of psychiatric disease, and active alcohol use, as well as gender, age, race, a history of incarceration, marital status, unemployment, homelessness, active smoking, a history of daily heroin use, and previous methadone treatment. After we controlled for multiple factors, 4 were found to be independently associated with any active IDU over follow-up (Table 3): unemployment, active hazardous drinking, active cigarette smoking, and a history of daily heroin use. Even after we controlled for other factors, HIV coinfection showed no association with active IDU over the course of follow-up. We also considered the effect of nadir CD4 count at study entry among those with HIV infection and found no association; thus, we did not include CD4 count in further models. Other factors that were found to have a moderate association with active IDU in univariate analysis, including history of psychiatric disease, were no longer associated with IDU in the multivariate models.

Multivariate Analysis of Baseline Predictors of Any Active Intravenous Drug Use During the 3-Year Follow-Up Among Hepatitis C Virus–Infected Patients Who Have Ever Used Injection Drugs: CHARM Cohort; Boston, MA; 2000–2006

The observation that participants with HIV coinfection did not have a higher prevalence of active IDU than those without HIV infection could potentially be affected by the time since diagnosis of HIV infection. Participants with HIV coinfection were diagnosed with HIV infection a median of 8.6 years before being enrolled in the CHARM study (range = 0.1–20.2 years). When we stratified the analysis by time since diagnosis of HIV infection, we found that persons who had been diagnosed with HIV within 5 years before enrollment in the cohort had a lower prevalence of active IDU than those who had been diagnosed more than 5 years before enrollment (37.8% vs 50.5%, respectively), although this difference did not reach statistical significance (P = .15).

Three of the risk factors included in the multivariate analysis were considered to be potentially modifiable–unemployment, active hazardous drinking, and active cigarette smoking. If such modifiable factors were found to be associated with active IDU over time, they may represent opportunities to focus interventions to support changes in IDU behavior. Thus, in a secondary analysis, we created a summary variable to represent the number of modifiable risk factors present for each study participant, to evaluate the effect of multiple risk factors on active IDU. A majority of the cohort (n = 182; 63%) reported at least 2 of these risk factors at baseline. In univariate analysis, participants with any 2 of the modifiable factors were 2.2 times as likely to report active IDU (95% CI = 1.5, 3.3) as those with no or 1 modifiable factor, whereas those with all 3 modifiable barriers were 3.3 times as likely to report any active IDU over follow-up (95% CI = 2.2, 4.9). These associations did not change when we controlled for the other factors included in the previous multivariate analysis (data not shown).


In this cohort of injection drug users who were in clinical care for chronic HCV infection, approximately half reported active IDU at some point during 3 years of follow-up, whereas the other half had ceased IDU and maintained that cessation throughout the study period. Among persons with any active IDU over follow-up, most reported some periods of nonuse as well. Reduced IDU over time has been observed among injection drug users in some studies. However, relapse is common over long periods of time.17-23 Analysis of the AIDS Link to Intravenous Experience (ALIVE) study cohort, for example, which involves a group of more than 1000 street-recruited injection drug users in Baltimore, Maryland, found that although most people did report some period of cessation, only a minority succeeded in ceasing IDU completely over follow-up.15

In our study, we found that unemployment was among the factors most strongly associated with active IDU. Persons who were unemployed at baseline were almost twice as likely to report any active IDU over the course of study follow-up. Whereas active IDU can lead to unemployment,21 being currently employed was an independent predictor of cessation of IDU for 1 year or more in a cohort of young street-based injection drug users in Canada.28 In the ALIVE study, injection drug users who were unemployed or homeless at baseline had a longer time to first cessation of IDU.23 Although homelessness was associated with reporting any active IDU over follow-up in the univariate analysis of the CHARM cohort, the association did not remain after we controlled for other factors in the multivariate model. Lack of employment contributes to social instability, which, like homelessness, does not support cessation of IDU.23,26,29 Because the relative timing of employment status with respect to IDU behavior could not be determined in the present study, it also is possible that ongoing IDU itself increased the likelihood of not being employed at baseline.

The use of other substances, including hazardous alcohol use, was also associated with reporting active IDU over follow-up in multi-variate analysis in our study population. In some studies of drug users, concurrent heavy alcohol use has been associated with IDU in univariate analysis, but this association did not remain after we controlled for other factors.15,16 However, other studies have found alcohol use to be independently associated with a longer time to IDU cessation,23,30 whereas 1 study reported increased alcohol use in association with cessation of IDU.31 In the present study, the effect of active cigarette smoking was weaker than that of hazardous alcohol use. Although the adjusted PR was not statistically significant, a similar association was observed in the ALIVE cohort study for smoking cigarettes and a shorter median time to relapse in a multivariate model, which also controlled for alcohol use.23 It has been hypothesized that the ability to abstain from smoking and from opiate and cocaine use shares some common behavioral and biochemical mechanisms of addiction.32

HIV infection has been hypothesized to predict both decreases in IDU–those who are HIV-infected frequently have more contact with counseling and health services–as well as increases in IDU–those who are HIV-infected may experience more depression following diagnosis, which may lead in some to increased drug use.17,33,34 Participants in our study who were coinfected with HIV were not more likely to use injection drugs over the course of follow-up compared with those who were infected with HCV alone. The hypothesized effects of HIV on active IDU may not apply in this setting where all participants were dealing with major chronic infections. In addition, all participants in this study were actively in contact with clinical care and the opportunity for referral to risk-reduction and drug treatment programs.

Populations of injection drug users often have a high prevalence of comorbid psychiatric disease,13,14 which we have found to be true of the CHARM cohort as well.35 Half of the study population had a history of depression, and one third had a history of some other form of psychiatric disease. Although a history of psychiatric disease was associated with active IDU over follow-up in the univariate analysis, this variable did not remain an independent predictor of reported IDU in the final multivariable model. Detailed information on treatment and severity of psychiatric disease was not available for these analyses; thus, we were unable to evaluate the association between management of psychiatric disease over time and active IDU.

Three of the risk factors that we identified as strong predictors of ongoing IDU were potentially modifiable–unemployment, hazardous alcohol use, and active cigarette smoking. When we considered the number of modifiable factors reported by each participant, we found that most of the cohort had at least 2 of these factors and that approximately 15% had all 3. Participants were more likely to report any active IDU over follow-up as their number of potentially modifiable risk factors increased.


Our study had several limitations. Overall, nearly one quarter of scheduled study visits were missed. Injection drug users are known to be a difficult population to follow.36 As a result, most prospective cohort studies of drug-using populations achieve follow-up rates that would be considered inadequate in studies of other populations. In previous studies of injection drug users in the United States, follow-up rates have typically varied between 70% and 79%.37-39 Thus, our completion of 75% of study visits is consistent with reports from similar populations. Although data from chart reviews were more than 95% complete, data for time-dependent variables such as current drug use, which were assessed at study visits, were less complete. If missed visits were more likely during periods of active IDU, the occurrence of IDU may be underestimated in our study population.

Another potential limitation of this study is that data on IDU were gathered via self-report at baseline and follow-up. Substance abuse and related behaviors are sensitive and highly stigmatized, and research suggests that self-reported drug use may not always be accurate and may be slightly underreported.40 Underreporting of IDU, however, would be expected to lead to a diminution in the observed associations with IDU status because of nondifferential misclassification.


Ongoing IDU can result in significant morbidity and mortality as a direct result of IDU (e.g., bacterial infections, overdose) and represents a potential barrier in the effective management of HCV and HIV.9 Persons who are actively injecting may be less likely to maintain adherence to antiretroviral treatment of HIV infection41 and generally are not candidates for interferon treatment of HCV infection, although some persons with active IDU have been successfully treated.10 We identified several key risk factors for ongoing IDU in a cohort of patients in clinical care for their chronic HCV and HIV infections. Because of the potential for continued transmission of HCV and HIV, ongoing IDU among patients in clinical care for these infections is of substantial concern.

Our findings highlight the need to recognize social instability and other substance use as important cofactors of continued IDU over time. A few of these factors are potentially modifiable, and targeted strategies might provide an approach that, combined with effective IDU treatment programs,42 could enhance management of HCV infection. Combining such programs has been demonstrated to be effective; smoking cessation strategies have been incorporated into methadone treatment programs with some success.32,43 Efforts to optimize HCV treatment over the long term will need to consider the full spectrum of IDU risk factors to successfully maintain long-term changes. Without an overall strategy to address these factors, substantial numbers of persons may remain unable to achieve the most effective management of their chronic HCV infection.


The project described was supported by the National Institute of Drug Abuse (award DA19841).

Note. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Drug Abuse or the National Institutes of Health.


Contributors C. Reed led the analyses and the writing. C. Bliss and J. H. Samet assisted with the analyses. S. O. Stuver and D. J. Cotton assisted with the study implementation and management and the analyses. T. Heeren assisted with the study design and the analyses. S. Tumilty assisted with the study implementation and management. C. R. Horsburgh Jr assisted with the study design, implementation, management, and analyses.

Reprints can be ordered at by clicking the “Reprints” link.

Human Participant Protection The institutional review board at Boston University Medical Center and at Boston Veterans Administration Medical Center reviewed and approved the study protocol, and all participants provided written informed consent.


1. Global Burden of Hepatitis C Working Group Global burden of disease (GBD) for hepatitis C. J Clin Pharmacol. 2004;44(1):20–29. [PubMed]
2. Armstrong GL, Wasley A, Simard EP, et al. The prevalence of hepatitis C virus infection in the United States, 1999 through 2002. Ann Intern Med. 2006;144(10):705–714. [PubMed]
3. Ly KN, Xing J, Klevens RM, Jiles RB, Ward JW, Holmberg SD. The increasing burden of mortality from viral hepatitis in the United States between 1999 and 2007. Ann Intern Med. 2012;156(4):271–278. [PubMed]
4. Alter MJ. Epidemiology of hepatitis C virus infection. World J Gastroenterol. 2007;13(17):2436–2441. [PubMed]
5. Daniels D, Grytdal S, Wasley A. Surveillance for acute viral hepatitis–United States, 2007. MMWR Surveill Summ. 2009;58(3):1–27. [PubMed]
6. Bini EJ, Currie SL, Shen H, et al. National multicenter study of HIV testing and HIV seropositivity in patients with chronic hepatitis C virus infection. J Clin Gastroenterol. 2006;40(8):732–739. [PubMed]
7. Sherman KE, Rouster SD, Chung RT, Rajicic N. Hepatitis C virus prevalence among patients infected with human immunodeficiency virus: a cross-sectional analysis of the US adult AIDS Clinical Trials Group. Clin Infect Dis. 2002;34(6):831–837. [PubMed]
8. Murrill CS, Weeks H, Castrucci BC, et al. Age-specific seroprevalence of HIV, hepatitis B virus, and hepatitis C virus infection among injection drug users admitted to drug treatment in 6 US cities. Am J Public Health. 2002;92(3):385–387. [PubMed]
9. Broers B, Helbling B, Francois A, et al. Barriers to interferon-alpha therapy are higher in intravenous drug users than in other patients with acute hepatitis C. J Hepatol. 2005;42(3):323–328. [PubMed]
10. Edlin BR, Kresina TF, Raymond DB, et al. Overcoming barriers to prevention, care, and treatment of hepatitis C in illicit drug users. Clin Infect Dis. 2005;40(Suppl 5):S276–S285. [PMC free article] [PubMed]
11. Fleming CA, Tumilty S, Murray JE, Nunes D. Challenges in the treatment of patients coinfected with HIV and hepatitis C virus: need for team care. Clin Infect Dis. 2005;40(suppl 5):S349–S354. [PubMed]
12. Dalgard O, Bjoro K, Hellum K, et al. Treatment of chronic hepatitis C in injecting drug users: 5 years’ follow-up. Eur Addict Res. 2002;8(1):45–49. [PubMed]
13. Darke S, Ross J. Polydrug dependence and psychiatric comorbidity among heroin injectors. Drug Alcohol Depend. 1997;48(2):135–141. [PubMed]
14. Rodríguez-Llera MC, Domingo-Salvany A, Brugal MT, Silva TC, Sanchez-Niubo A, Torrens M. Psychiatric comorbidity in young heroin users. Drug Alcohol Depend. 2006;84(1):48–55. [PubMed]
15. Galai N, Safaeian M, Vlahov D, Bolotin A, Celentano DD. Longitudinal patterns of drug injection behavior in the ALIVE Study cohort, 1988–2000: description and determinants. Am J Epidemiol. 2003;158(7):695–704. [PubMed]
16. van Ameijden EJ, van den Hoek JA, Hartgers C, Coutinho RA. Risk factors for the transition from non-injection to injection drug use and accompanying AIDS risk behavior in a cohort of drug users. Am J Epidemiol. 1994;139(12):1153–1163. [PubMed]
17. Stein MD, Solomon DA, Herman DS, Anderson BJ, Miller I. Depression severity and drug injection HIV risk behaviors. Am J Psychiatry. 2003;160(9):1659–1662. [PubMed]
18. Gossop M, Green L, Phillips G, Bradley B. Lapse, relapse and survival among opiate addicts after treatment. A prospective follow-up study. Br J Psychiatry. 1989;154:348–353. [PubMed]
19. Hser YI, Hoffman V, Grella CE, Anglin MD. A 33-year follow-up of narcotics addicts. Arch Gen Psychiatry. 2001;58(5):503–508. [PubMed]
20. Hser YI, Huang D, Chou CP, Anglin MD. Trajectories of heroin addiction: growth mixture modeling results based on a 33-year follow-up study. Eval Rev. 2007;31(6):548–563. [PubMed]
21. Nurco DN, Cisin IH, Balter MB. Addict careers. II. The first ten years. Int J Addict. 1981;16(8):1327–1356. [PubMed]
22. Nurco DN, Cisin IH, Balter MB. Addict careers. III. Trends across time. Int J Addict. 1981;16(8):1357–1372. [PubMed]
23. Shah NG, Galai N, Celentano DD, Vlahov D, Strathdee SA. Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988-2000. Drug Alcohol Depend. 2006;83(2):147–156. [PubMed]
24. Mäkelä K. Studies of the reliability and validity of the Addiction Severity Index. Addiction. 2004;99(4):398–410. discussion 1–8. [PubMed]
25. Reinert DF, Allen JP. The Alcohol Use Disorders Identification Test (AUDIT): a review of recent research. Alcohol Clin Exp Res. 2002;26(2):272–279. [PubMed]
26. Song JY, Safaeian M, Strathdee SA, Vlahov D, Celentano DD. The prevalence of homelessness among injection drug users with and without HIV infection. J Urban Health. 2000;77(4):678–687. [PMC free article] [PubMed]
27. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162(3):199–200. [PubMed]
28. Steensma C, Boivin JF, Blais L, Roy E. Cessation of injecting drug use among street-based youth. J Urban Health. 2005;82(4):622–637. [PMC free article] [PubMed]
29. Corsi KF, Kwiatkowski CF, Booth RE. Treatment entry and predictors among opiate-using injection drug users. Am J Drug Alcohol Abuse. 2007;33(1):121–127. [PubMed]
30. Evans JL, Hahn JA, Lum PJ, Stein ES, Page K. Predictors of injection drug use cessation and relapse in a prospective cohort of young injection drug users in San Francisco, CA (UFO Study) Drug Alcohol Depend. 2009;101(3):152–157. [PMC free article] [PubMed]
31. Anglin MD, Almog IJ, Fisher DG, Peters KR. Alcohol use by heroin addicts: evidence for an inverse relationship. A study of methadone maintenance and drug-free treatment samples. Am J Drug Alcohol Abuse. 1989;15(2):191–207. [PubMed]
32. Shoptaw S, Rotheram-Fuller E, Yang X, et al. Smoking cessation in methadone maintenance. Addiction (Abingdon, England) 2002;97(10):1317–1328. discussion 25. [PubMed]
33. Deren S, Beardsley M, Coyle S, Singer M. HIV serostatus and risk behaviors in a multisite sample of drug users. J Psychoactive Drugs. 1998;30(3):239–245. [PubMed]
34. Macalino GE, Ko H, Celentano DD, et al. Drug use patterns over time among HIV-seropositive and HIV-seronegative women: the HER study experience. J Acquir Immune Defic Syndr. 2003;33(4):500–505. [PubMed]
35. Fleming CA, Christiansen D, Nunes D, et al. Health-related quality of life of patients with HIV disease: impact of hepatitis C coinfection. Clin Infect Dis. 2004;38(4):572–578. [PubMed]
36. Scott CK. A replicable model for achieving over 90% follow-up rates in longitudinal studies of substance abusers. Drug Alcohol Depend. 2004;74(1):21–36. [PubMed]
37. Friedmann PD, Lemon SC, Anderson BJ, Stein MD. Predictors of follow-up health status in the Drug Abuse Treatment Outcome Study (DATOS) Drug Alcohol Depend. 2003;69(3):243–251. [PubMed]
38. Selwyn PA, Hartel D, Lewis VA, et al. A prospective study of the risk of tuberculosis among intravenous drug users with human immunodeficiency virus infection. N Engl J Med. 1989;320(9):545–550. [PubMed]
39. Thomas DL, Astemborski J, Rai RM, et al. The natural history of hepatitis C virus infection: host, viral, and environmental factors. JAMA. 2000;284(4):450–456. [PubMed]
40. Darke S. Self-report among injecting drug users: a review. Drug Alcohol Depend. 1998;51(3):253–263. discussion 67–68. [PubMed]
41. Bouhnik AD, Chesney M, Carrieri P, et al. Non-adherence among HIV-infected injecting drug users: the impact of social instability. J Acquir Immune Defic Syndr. 2002;31(suppl 3):S149–S153. [PubMed]
42. Edlin BR. Prevention and treatment of hepatitis C in injection drug users. Hepatology. 2002;36(5 suppl 1):S210–S219. [PMC free article] [PubMed]
43. Stein MD, Anderson BJ. Nicotine and drug interaction expectancies among methadone maintained cigarette smokers. J Subst Abuse Treat. 2003;24(4):357–361. [PubMed]