Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988–2000

Drug and Alcohol Dependence 83 (2006) 147–156

Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988–2000 Nina G. Shah a,∗ , Noya Galai b , David D. Celentano b , David Vlahov c , Steffanie A. Strathdee d a b

Epidemiology and Response Division, New Mexico Department of Health, 1190 St. Francis Drive, P.O. Box 26110, Santa Fe, NM 87502-6110, USA Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA c Center for Urban Epidemiologic Studies, New York Academy of Medicine, 1216 Fifth Avenue, New York, NY 10029, USA d University of California San Diego School of Medicine, Division of International Health and Cross Cultural Medicine, 9500 Gilman Drive, Mailstop 0622, San Diego, CA 92093, USA Received 25 May 2005; received in revised form 3 November 2005; accepted 7 November 2005

Abstract Objective: To determine predictors of injection drug use cessation and subsequent relapse among a cohort of injection drug users (IDUs). Methods: IDUs in Baltimore, MD were recruited through community outreach in 1988–1989. Among IDUs with at least three follow-up visits, parametric survival models for time to injection cessation (≥6 months) and subsequent relapse were constructed. Results: Of 1327 IDUs, 94.8% were African American, 77.2% were male, median age was 34 years, and 37.7% were HIV-infected. Among 936 (70.5%) subjects who ceased injection, median time from baseline to cessation was 4.0 years. Three-quarters subsequently resumed injection drug use, among whom median time to relapse was 1.0 year. Factors independently associated with a shorter time to cessation were: age <30 years, stable housing, HIV seropositivity, methadone maintenance treatment, detoxification, abstinence from cigarettes and alcohol, injecting less than daily, not injecting heroin and cocaine together, and not having an IDU sex partner. Factors independently associated with shorter time to injection relapse were male gender, homelessness, HIV seropositivity, use of alcohol, cigarettes, non-injection cocaine, sexual abstinence and having a longer time to the first cessation. Conclusions: This study provides strong support for targeting cessation efforts among young IDUs and severely dependent, unstably housed, and HIV-infected individuals. © 2005 Elsevier Ireland Ltd. All rights reserved. Keywords: Injection drug use; Cessation; Relapse; Methadone maintenance; HIV infection; Homelessness

1. Introduction Injection drug use is associated with considerable social, economic and medical consequences. Drug-related societal and economic costs are attributable to losses in productivity due to drug-related morbidity, premature mortality and crime (Harwood et al., 1999). Injection drug users (IDUs) experience increased risk of developing endocarditis, cellulitis and abscesses, and acquiring blood borne infections such as human immunodeficiency virus (HIV) and hepatitis C (HCV) through needle sharing. In Baltimore, MD, a prospective study of IDUs found that within 1 year of initiating injection the prevalence

∗

Corresponding author. Tel.: +1 505 476 3607; fax: +1 505 827 0013. E-mail address: [email protected] (N.G. Shah).

0376-8716/$ – see front matter © 2005 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.drugalcdep.2005.11.007

of HIV, HBV and HCV was 14%, 50% and 65%, respectively (Garfein et al., 1996). Although several studies have shown that IDUs are capable of reducing their frequency of needle sharing, in settings where HIV infection is prevalent, low levels of risk behaviors can give rise to HIV incidence rates as high as 3–4% per year (van Ameijden and Coutinho, 1998). These observations underscore the need to consider strategies to encourage cessation of injection as the primary goal of harm reduction a major public health priority (Langendam et al., 2000; Stimson et al., 1996). For opioid-dependent persons, opiate agonist therapies such as methadone maintenance (National Consensus Development Panel on Effective Medical Treatment of Opiate Addiction, 1998; van Ameijden and Coutinho, 2001; Sullivan et al., 2005), buprenorphine (Raisch et al., 2002) and heroin-assisted treat-

148

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

ment (Rehm et al., 2001) have been found to significantly reduce drug use. However, in countries such as the US, drug users experience considerable barriers accessing drug treatment, including the lack of third party reimbursement and lengthy waiting times for limited existing drug treatment slots (Shah et al., 2000; Lewis, 1999). Among populations of IDUs in North America, only 18–36% are estimated to be receiving some form of drug treatment at any given time (Joseph et al., 2000). Relapse is a major characteristic of drug dependence and remains a primary problem in treating drug abuse. Individuals need to be engaged in drug treatment for an adequate length of time in order to reap the greatest benefits (Goldstein et al., 2000; Hartel and Schoenbaum, 1998). However, half of those admitted to methadone maintenance treatment (MMT) programs in the US leave within a year for a variety of reasons (Simpson et al., 1997). Generally, drug users who are retained in MMT for only a short time experience higher relapse rates than those who spend a longer time engaged in treatment (Simpson, 1981; Toumbourou et al., 1998). Many IDUs require multiple attempts at drug treatment before becoming drug-free (Raisch et al., 2002; Bammer et al., 2000). The relapsing and remitting nature of drug dependence is a major issue in the study of the natural history of drug use (Galai et al., 2003). Few empirical investigations have described predictors of time to injection cessation and relapse to injection among well-characterized samples of out-of-treatment IDUs. In the present study, we sought to examine predictors of time to first injection cessation and, given cessation, to identify predictors associated with time to subsequent relapse to injection. These findings may help identify appropriate interventions and support systems to reduce the negative health and social consequences of injection drug use. 2. Methods 2.1. Population The rationale, design and methods of the AIDS link to intravenous experiences (ALIVE) cohort study have been described previously (Vlahov et al., 1991; Anthony et al., 1991). Briefly, subjects were eligible to participate if they were at least 18 years old, were AIDS-free at enrollment and had injected illicit drugs within the past 10 years. Over 80% of subjects reported having been recruited by another study participant or friend, rather than referral by a drug treatment program, public health program or trained street outreach worker. A total of 2946 IDUs were enrolled into the ALIVE Study, though the present study was restricted to 1327 participants who were active injectors at baseline and had at least three follow-up visits, to permit study of factors associated with injection cessation and subsequent relapse to injection drug use. That is, a minimum of three visits after enrollment was needed for the participant to be “at risk” for one cessation and one relapse. Recruitment began in 1988–1989 and follow-up for this analysis continued through December 2000. Study visits were scheduled every 6 months with ongoing effort to minimize loss to follow-up using mail and phone reminders and street tracking.

2.2. Data collection At study entry and semi-annually thereafter, participants underwent venipuncture for HIV antibody testing and completed an interviewer-administered questionnaire on sociodemographic characteristics, risk behaviors for HIV infection, health status indicators, and health service utilization. Sociodemographic variables of interest included gender, age, ethnicity, employment, education, recent homelessness and history of incarceration. Drug use and sexual risk behaviors referred to the previous 6 months and included frequency of injection, needle sharing, overdose, shooting gallery attendance, non-injection drug use, number and type of sexual partners, trading sex for money or drugs, and unprotected sex. Data on hospital admissions, drug treatment and HIV-related symptoms were also collected at each assessment. The baseline questionnaire had a number of unique questions regarding the initiation of injection drug use, including age of first injection and how long it took to reach the highest frequency (peak) of use. Based on this information, the variable “time to peak drug use” was created. The outcome of injection cessation was defined as the first self-report of not having injected drugs within the prior 6 months. Relapse was defined as the first selfreport of injection following cessation. Note that the exact time of relapse within the prior 6-month interval was not known and thus the event time for analysis was the date of the first report of relapse to injection. Thus, the variability for injection cessation was within a window of 6–12 months, and the time window for relapse was 1 day to less than 6 months. 2.3. Statistical analyses Characteristics of the study sample were described using medians and inter-quartile range for continuous variables and frequencies for categorical variables. The population included in the present analysis was compared to those excluded by simple χ2 -tests for categorical variables and t-tests or non-parametric tests for continuous variables. Two separate survival analyses were performed: time from enrollment (where all participants reported injecting) to first cessation of drug injection was assessed; then, time from cessation to first relapse to injection was assessed among those who reported cessation and had at least one visit following cessation. Factors affecting time to outcome (cessation or relapse) were evaluated using parametric survival models, assuming a log-normal distribution (accelerated failure time) (Hosmer and Lemeshow, 1999). In the accelerated failure time model, the effect of a covariate is expressed as either accelerating (shortening) or decelerating (lengthening) time to the event of interest. The log-normal survival models assume that the hazard function has a log-normal shape with two parameters (mean and dispersion) which are estimated from the data. The use of parametric survival models allows a convenient calculation of the predicted survival curve for any specific combination of explanatory variables as well as the underlying hazard. Further, covariate effects are shown as time ratios (TR), rather than hazard ratios, and hence have an interpretation on the time scale. For example,

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

a time ratio of 0.5 for a binary factor x, implies that the predicted survival time for the “high-risk” group (x = 1) is shorter by 50% compared to the survival time for the “low-risk” group (x = 0). The effects on survival are seen in all the percentiles of survival time including quartiles and median. Similarly, values of TR > 1 indicate a protective effect since time to event in the x = 1 group is longer than the time for the group with x = 0. Note that factors usually termed “risky” or harmful will be associated with TR > 1 for the injection cessation analysis but TR < 1 for the relapse to injection outcome. The overall model fit, assuming the log-normal shape of the hazard function, was assessed using Cox–Snell residuals and found acceptable (Cox and Snell, 1968). To ensure that predictors preceded the reported periods of injection cessation and relapse to injection, the analyses used data lagged one semi-annual visit. Covariates with statistical significance at levels α ≤ 0.10 in univariate models were entered into exploratory multivariate models for the respective outcomes. The final multivariate models were developed using a manual stepwise backward procedure. 3. Results Of the 2946 participants enrolled in the ALIVE Study, 2232 had at least one follow-up visit. Of these, a total of 905 participants were determined ineligible for this study, where 425 were not actively injecting at baseline, 401 were actively injecting at baseline but had less than three follow-up visits, and 79 were missing baseline data for injection. Ineligible participants differed significantly from eligible participants as they were older (median age of 35.0 versus 34.0), more likely to be male (83.1% versus 77.2%), non-African American (14.6% versus 5.2%), HIV-uninfected (72.3% versus 62.3%), employed (25.1% versus 20.8%), used drugs less often (21.7% injected daily versus 51.4%), and a smaller proportion engaged in risky sexual (54.8% versus 75.3% reported unprotected sex; 35.4% versus 47.5% reported sex with an IDU partner) and drug-using behaviors (29.1% versus 85.2% reported sharing works/needles; 10.8% versus 23.7% reported shooting gallery attendance), though no differences were found by history of incarceration and recent sexual activity. Overall, ineligible participants had a more stable lifestyle and were not as severely addicted as the eligible participants at baseline. Of the 1327 IDUs eligible for analysis at baseline, 94.8% were African American, 77.2% were male, 42.2% were high school graduates, 79.2% were unemployed and 37.7% were HIV-infected. The median age was 34 (1st, 3rd inter-quartile range (IQR): 30, 38 years). The types of drugs injected during the 6 months prior to enrollment were heroin alone (71.6%), cocaine alone (81.4%), and heroin and cocaine together, referred to as speedball (69.2%). Nearly one-fifth (18.3%) gave a recent history (prior 6 months) of drug detoxification at baseline, and 9.7% had recently been receiving methadone treatment. The median number of visits per year for participants over the follow-up period was 1.8 (mean = 1.7 visits). Fig. 1 shows the crude time-to-event graphs for first injection cessation and subsequent relapse to injection drug use. Of the

149

Fig. 1. Kaplan–Meier of observed time from baseline to first cessation of injection and given cessation, time to first relapse to injection, in the ALIVE Study, Baltimore, MD, 1988–2000.

1327 IDUs contributing 5553 person-years of observation, 936 (70.5%) reported at least one episode of cessation of injection drug use corresponding to a cumulative incidence rate of 16.8 per 100 person years. Note that although the total follow-up period was 12 years, the person-time used in the analysis was censored at the first occurrence of the event. The crude estimated median time from baseline to first cessation was 4.0 years (1st, 3rd IQR: 1.7, 8.5 years). Of the initial group, 40 (3.0%) IDUs died before the median time to cessation (mid-1992) and did not report a single cessation episode, and 106 (8%) were lost to follow-up (did not return after 1 January 1999 but were not deceased according to the National Death Index records). Among the 936 IDUs reporting cessation of injection drug use, 898 (95.9%) had at least one follow-up visit following cessation and were thus eligible for the analysis of predictors of relapse to injection. Of the 898 persons contributing 1727 person-years over the follow-up period, 678 (75.5%) reported at least one subsequent episode of relapse to injection, corresponding to a cumulative relapse incidence rate of 39.2 per 100 person years. The crude estimated median time from injection cessation to first relapse to injection was 1.0 year (1st, 3rd IQR: 0.5, 3.2 years). Of the IDUs reporting cessation, 83 (9.2%) died thereafter with no report of subsequent relapse. 3.1. Univariate associations 3.1.1. Cessation of injection. Factors found to be associated with shorter time to cessation included age younger than 30 years (time-ratio (TR) = 0.69), being employed (TR = 0.84), being HIV-infected (TR = 0.78), being admitted to a hospital (TR = 0.78), reporting MMT (TR = 0.58) and detoxification (TR = 0.70) (Tables 1 and 2). In contrast, median time to cessation was longer for African Americans compared to non-African Americans by 34% (TR = 1.34), by 44% for those reporting recent homelessness (TR = 1.44), by 21% for those with a lengthy duration of injection drug use (>10 years, TR = 1.21), by 50% for smoking cigarettes (TR = 1.50), 59% for alcohol use (TR = 1.59), 31% for sharing needles (TR = 1.31), 48% for drug overdose (TR = 1.48), 73% for injecting speedball

150

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

Table 1 Time from baseline to first cessation of injection among 1327 active injection drug users in the ALIVE Study, Baltimore, MD, 1988–2000 (univariate associations for baseline characteristics and time-dependent data based on log-normal survival models: time ratios and median years to first cessation) Person-years

Time to cessation of injection (years) Time ratioa (95% confidence interval)

Median

5301 251

1.34 (1.00, 1.78) 1.0

3.8 2.9

Age groupings <30 years 30–34 years 35–39 years ≥40 years

515 1115 1519 2404

0.69 (0.57, 0.85) 0.87 (0.73, 1.03) 0.98 (0.83, 1.16) 1.0

2.9 3.6 4.1 4.1

Employed at baseline Yes No

1094 4452

0.84 (0.72, 0.99) 1.0

3.3 3.9

Duration of injection at baseline >10 years ≤10 years

3539 2014

1.21 (1.06, 1.39) 1.0

4.1 3.4

2300 3253

0.78 (0.68, 0.89) 1.0

3.3 4.2

4170 807

1.44 (1.18, 1.74) 1.0

5.4 3.8

Baseline characteristics Race African American Non-African American

Time-dependent datab HIV status, past 6 months HIV-infected HIV-uninfected Homelessness, past 6 months Yes No a b

Note that TR > 1 represents longer time to cessation of injection and hence “higher risk”. Time-dependent variables concurrent with the outcome.

compared to heroin or cocaine alone (TR = 0.1.73) and 79% for daily injection (TR = 1.79). For sexual behaviors, the estimated median time to injection cessation was prolonged for those reporting anonymous sex partners by 58% (compared to sexual abstinence, TR = 1.58), IDU sex partners by 53% (compared to sexual abstinence, TR = 1.53), trading sex by 34% (TR = 1.34) and for those reporting unprotected sex by 18% (TR = 1.18). However, men having sex with men had a shorter time to cessation (TR = 0.65). There was no effect on time to cessation of injection drug use for gender, education, history of incarceration, age of first injection, time to peak drug use, use of a needle exchange program, shooting gallery attendance, illicit non-injection drug use or HIV-related symptoms (data not shown). 3.1.2. Relapse to injection. The estimated median time from cessation of injection to first relapse to injection was significantly shorter for persons who were HIV-infected (TR = 0.80), who reported recent homelessness (TR = 0.54) and a history of incarceration (TR = 0.70) (Table 3). Considering substance use, smoking cigarettes shortened median time to relapse by 36% (TR = 0.64), 29% for alcohol use (TR = 0.71), 31% and 38% for non-injection cocaine and heroin use, respectively (TR = 0.69 and 0.62). Regarding initiation of drug injection, older age at first injection was associated with longer time to relapse (TR = 1.07 per 5 years). On the other hand, the estimated median time from cessation to relapse was extended for females by

22% (TR = 1.22), 40% for African Americans (TR = 1.40) and 31% for those who experienced less than 1.5 years to first cessation (TR = 1.31). For sexual behaviors, those reporting sex, but not with an IDU or anonymous partner, was estimated to lengthen time to relapse by 34% and 30%, respectively (compared to sexual abstinence, TR = 1.34 and 1.30). There were no statistical differences in estimated time to relapse to injection for age, education, health care utilization, duration of injection drug use at baseline, engaging in sex trade, unprotected sex, using marijuana, crack or HIV-related symptoms (data not shown). As an example, Fig. 2 panel shows the crude observed timeto-event graphs for homelessness and time to injection cessation and subsequent relapse to injection drug use. The time to cessation for IDUs reporting homelessness was 44% longer (TR = 1.44) than for those who reported stable housing. The time to relapse to injection was nearly 50% faster (TR = 0.54) for those reporting homelessness relative to those who did not. This example shows the “harmful” effects of being homeless for both outcomes, where homelessness lengthened the time to injection cessation (TR > 1) and accelerated the time to relapse to injection (TR < 1). 3.2. Multivariate models Table 4 presents results from the multivariate models. Independent predictors of longer time to first cessation of

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

151

Table 2 Time from baseline to first cessation of injection among 1327 active injection drug users in the ALIVE Study, Baltimore, MD, 1988–2000 (univariate associations for lagged time-dependent data based on log-normal survival models: time ratios and median years to first cessation) Person-years

Time to cessation of injection (years) Time ratioa (95% confidence interval)

Median

1195 4353

0.78 (0.67, 0.90) 1.0

3.1 4.0

Methadone maintenance treatment No methadone maintenance treatment

601 4946

0.58 (0.47, 0.71) 1.0

2.3 4.1

Detoxification program No detoxification program

759 4783

0.70 (0.59, 0.83) 1.0

2.8 4.0

Injection behavior Needle sharing No needle sharing

3834 1319

1.31 (1.11, 1.55) 1.0

4.4 3.4

Drug overdose No drug overdose

312 3434

1.48 (1.12, 1.96) 1.0

5.6 3.8

Daily injection frequency Less often than daily injection

2928 2625

1.79 (1.57, 2.04) 1.0

5.2 2.9

Injected speedball Injected cocaine or heroin alone

4187 1363

1.73 (1.52, 1.99) 1.0

4.5 2.6

5151 400

1.50 (1.19, 1.88) 1.0

3.9 2.6

4541 1007

1.59 (1.35, 1.87) 1.0

4.2 2.6

4347 1202

1.27 (1.08, 1.51) 1.0

4.0 3.1

No sex partners One sex partner Two or more sex partners

1478 2226 1848

1.0 1.15 (0.96, 1.37) 1.32 (1.10, 1.59)

3.2 3.7 4.3

Sex with anonymous partner Sex but not with anonymous partner No sex

662 3686 1202

1.58 (1.24, 2.00) 1.23 (1.03, 1.45) 1.0

4.9 3.8 3.1

Sex with IDU partner Sex with non-IDU partner No sex

2264 2083 1202

1.53 (1.28, 1.84) 1.07 (0.89, 1.28) 1.0

4.8 3.3 3.1

Trading sex No trading sex

1124 4429

1.34 (1.14, 1.59) 1.0

4.8 3.6

Any unprotected sex Always protected sex

3061 2491

1.18 (1.02, 1.35) 1.0

4.0 3.4

MSM sex No MSM sex

283 5270

0.65 (0.47, 0.91) 1.0

2.5 3.8

Lagged time-dependent datab Health care utilization Hospital admission No hospital admission

Non-injection drug use Cigarette use No cigarette use Alcohol use No alcohol use Sexual behavior Any sex No sex

a b

Note that TR > 1 represents longer time to cessation of injection and hence “higher risk”. To ensure that predictors preceded the reported period of injection cessation, the analyses used data lagged one semi-annual visit.

injection drug use included recent homelessness (adjusted (adj) TR = 1.36), daily injection (adj TR = 1.55), cigarette smoking (adj TR = 1.49), alcohol use (adj TR = 1.33), having an IDU sexual partner (compared to sexual abstinence, adj TR = 1.37) and injecting speedball (adj TR = 1.39). Conversely, factors predictive of a shorter time to cessation of

injection included age less than 30 years (adj TR = 0.79), being HIV-infected (adj TR = 0.83), and enrollment in MMT (adj TR = 0.70) and detoxification programs (adj TR = 0.61). Given cessation of injection, independent predictors of a longer duration from cessation to subsequent relapse to

152

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

Table 3 Time from first cessation of injection to relapse to injection among 898 injection drug users who reported a cessation in the ALIVE Study, Baltimore, MD, 1988–2000 (univariate associations based on log-normal survival models: time ratios and median years from first cessation to relapse) Person-years

Time ratioa (95% confidence interval)

Median

455 1272

1.22 (1.01, 1.46) 1.0

1.60 1.31

1660 67

1.40 (1.00, 1.94) 1.0

1.40 1.00

990 736

0.70 (0.60, 0.83) 1.0

1.21 1.73

–

1.07 (1.00, 1.15)

392 833 502

1.17 (0.94, 1.44) 1.19 (1.00, 1.43) 1.0

1.43 1.46 1.23

712 1015

1.31 (1.11, 1.54) 1.0

1.64 1.26

736 991

0.80 (0.68, 0.93) 1.0

1.22 1.53

104 1255

0.54 (0.43, 0.68) 1.0

0.76 1.42

1413 313

0.64 (0.50, 0.82) 1.0

1.30 2.04

Alcohol use No alcohol use

780 947

0.71 (0.61, 0.83) 1.0

1.17 1.64

Heroin use No heroin use

97 1629

0.69 (0.51, 0.93) 1.0

0.98 1.41

Cocaine use No cocaine use

54 1671

0.62 (0.46, 0.85) 1.0

0.88 1.41

1256 471

1.30 (1.10, 1.53) 1.0

1.49 1.15

Sex with anonymous partner Sex but not with anonymous partner No sex

111 1145 471

1.31 (0.95, 1.82) 1.30 (1.09, 1.54) 1.0

1.51 1.49 1.15

Sex with IDU partner Sex with non-IDU partner No sex

248 1008 471

1.17 (0.92, 1.49) 1.34 (1.12, 1.52) 1.0

1.35 1.53 1.15

Baseline characteristics Gender Female Male Race African American Non-African American History of incarceration at baseline Yes No Injection initiation Age of first injection (per 5 year increase) Time to peak use Quick peak, within 1 year 1–5 years ≥6 years <1.5 years to first cessation ≥1.5 years to first cessation Time-dependent datab HIV status, past 6 months HIV-infected HIV-uninfected Homelessness, past 6 months Yes No Lagged time-dependent datac Non-injection drug use Cigarette use No cigarette use

Sexual behavior Any sex No sex

a b c

Time to relapse to injection (years)

Note that TR > 1 represents longer time to relapse to injection and hence “lower risk”. Time-dependent variables concurrent with the outcome. To ensure that predictors preceded the reported period of relapse to injection, the analyses used data lagged one semi-annual visit.

injection were female gender (adj TR = 1.22), non-IDU sexual partner (compared to sexual abstinence, adj TR = 1.26) and less than 1.5 years from study enrollment to cessation (adj TR = 1.43). Independent predictors of a shorter median time

from injection cessation to relapse to injection were being HIVinfected (adj TR = 0.83), recently homeless (adj TR = 0.59), use of cigarettes (adj TR = 0.73), alcohol (adj TR = 0.76) and noninjection cocaine (adj TR = 0.46).

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

153

Table 4 Multivariate associations based on log-normal survival models for time from baseline to first cessation of injection and given cessation, time to first relapse to injection, in the ALIVE Study, Baltimore, MD, 1988–2000: adjusted time ratios with 95% confidence intervals (95% C.I.)

Female Age <30 years HIV-infected Homelessness Methadone maintenance treatment Detoxification program Daily injection Injected speedball Cigarette use Alcohol use Non-injection cocaine use Sex with IDU partnerb Sex with non-IDU partnerb <1.5 years to first cessation a b

Cessation of injection adjusted time ratio (95% C.I.)

Relapse to injection adjusted time ratio (95% C.I.)

NSa 0.79 (0.65, 0.94) 0.83 (0.73, 0.96) 1.36 (1.12, 1.65) 0.70 (0.56, 0.86) 0.61 (0.51, 0.72) 1.55 (1.35, 1.79) 1.39 (1.20, 1.62) 1.49 (1.17, 1.88) 1.33 (1.12, 1.57) NSa 1.37 (1.13, 1.65) 1.07 (0.89, 1.29) N/A

1.22 (1.02, 1.46) NSa 0.83 (0.72, 0.97) 0.59 (0.47, 0.73) N/A N/A N/A N/A 0.73 (0.58, 0.92) 0.76 (0.66, 0.89) 0.62 (0.46, 0.84) 1.18 (0.93, 1.49) 1.26 (1.06, 1.49) 1.43 (1.23, 1.68)

NS indicates not statistically significant. Reference group are those reporting sexual abstinence in past 6 months.

Fig. 2. Effect of being homeless on time to cessation and subsequent time to relapse: crude observed time-to-event from baseline to first cessation of injection and given cessation, time to first relapse to injection, in the ALIVE Study, Baltimore, MD, 1988–2000.

4. Discussion An encouraging finding of the study was that 71% of active IDUs in the cohort were successful in abstaining from drug injection for at least 6 months at some point during follow-up; however, of these, 76% relapsed, half within just 1 year. IDUs participating in this study routinely received drug and HIV risk

reduction counseling at the end of each visit (i.e., after questionnaire was completed), assisted referrals for treatment and learned about harm reduction strategies at each study visit. Despite these efforts, only half of the cohort reported a first 6-month interval of non-injection drug use within 4 years of enrollment. This is a considerably long time frame during which participants continued to engage in risky behaviors with major health and social consequences. Similar difficulties have been observed in Amsterdam where long-standing harm reduction strategies exist, but risky injection behaviors such as borrowing/sharing needles have proven to be difficult to eliminate (van Ameijden et al., 1998). An important finding is that IDUs younger than 30 years had a shorter time to cessation compared to older IDUs. At the study baseline, older IDUs had been injecting for an average of 17 years, compared to 7 years for IDUs younger than 30 years old. A hypothesis is that younger IDUs were less heavily entrenched in the drug-using lifestyle, and less likely to have established stable injector partnerships and consistent drug sources, compared to older IDUs. Despite challenges inherent in working with more recent initiates (Doherty et al., 2000), younger drug users are an important group to reach. Not surprisingly, IDUs with a more severe dependence, indicated by injecting at least daily or using speedball, had an almost 50% longer time to injection cessation than those with less severe dependence. These results are consistent with other studies (Col´on et al., 2001; Hemby et al., 1999). Participation in drug treatment was associated with shorter time to injection cessation, consistent with numerous other studies. The use of opioid agonist therapies reduce heroin withdrawal symptoms and craving, thus reducing injection frequency (Booth et al., 1996; Kwiatkowski and Booth, 2001; Shore et al., 1996; Gossop et al., 2002; Metzger and Navaline, 2003) or result in complete abstinence (Langendam et al., 2000). Unfortunately, information on drug treatment was not consistently collected from this community sample during periods of non-injection, so we were not able to assess the overall effect of drug treatment

154

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

on time to relapse to injection. Although it is generally recognized that detoxification alone is inadequate as a treatment for opiate addiction, IDUs attending a detoxification program had a faster time to cessation than those who did not, after adjusting for enrollment in MMT. This could be a reflection of these IDUs’ stronger motivation for quitting drug use, known to be an important predictor of cessation (Simpson and Joe, 1993; Simpson et al., 1997). It is important to note that although IDUs may stop drug use by injection, they may actively continue non-injection drug use. In fact, 34% (n = 302) of IDUs who reported injection cessation subsequently used non-injection heroin and/or cocaine, and 13% (n = 117) did so within the first year following cessation. Notably, non-injection cocaine use predicted a more rapid time to relapse to injection. It is possible that among out-of-treatment IDUs, efforts to sustain abstinence from injection are weakened by non-injection cocaine use. IDUs reporting unstable housing, alcohol and cigarette use had a longer time to cessation and a shorter time to subsequent relapse. Unstable housing can impede opportunities for achieving injection cessation and create an unfavorable setting to remain drug-free (Song et al., 2000; Celentano et al., 1991). Similarly, the effects of alcohol and cigarette use showed that addiction is a multi-faceted disease and clinicians should address nicotine and alcohol use in the opiate-dependence recovery process. Interestingly, HIV-infected persons had a shorter time to first injection cessation but also a shorter time to subsequent relapse. This suggests that IDUs may be particularly motivated to stop injecting after learning of their HIV-positive status (Celentano et al., 1994), indicating the need to target referrals to drug treatment among this vulnerable population. A previous study in this cohort and other research has found that HIV-infected IDUs transition between periods of non-injection and injection more often than HIV-uninfected IDUs (Galai et al., 2003; Rehm et al., 2001). Studies show that HIV-infected IDUs lack adequate social support and coping abilities (Avants et al., 2001; Belding et al., 1996), indicating the need for wide-spectrum interventions, especially considering the risk of HIV transmission. A noteworthy finding in this study was that while sociodemographic and drug-using behaviors were predictive of cessation and relapse, sexual partnerships were also important. Compared to IDUs reporting no sex in the previous 6 months, IDUs who reported having an IDU sexual partner had a longer time to cessation, while those who reported non-IDU sex partners during periods of injection abstinence had a longer time to subsequent relapse. Social networks have shown to influence injection cessation. IDUs who report fewer personal network members with whom they used drugs were more likely to stop injecting than those with larger, drug-related networks (Latkin et al., 1999; Schroeder et al., 2001). IDUs tend to select sexual partners and form emotional bonds with persons from within their network (Johnson et al., 2002) and who may interact in similar social scenes (Friedman et al., 1999). Sexual activity with a non-IDU partner lengthened time to relapse; intimate relationships outside the drug-using network may have tacitly supported abstinence (Sibthorpe and Lear, 1994). Companionship outside the drug-

using network also provides a supportive environment to prevent relapsing to injection. A longer time to relapse was associated with being female, possibly reflecting stronger social support, presence of familial obligations (Lundy et al., 1995) and sexual partnership with nondrug users (Warner et al., 2004). Social psychological research finds that social networks among women are particularly salient and interventions such as participation in peer support groups during periods of abstinence may be more beneficial to women than men (DiNitto et al., 2002). The gender difference for relapse should be further investigated and replicated in other contexts. Several study limitations need to be addressed. First, injection frequency was self-reported; report of any injection versus none is less likely to be subject to recall bias compared to injection frequency. However, we and others have found selfreported drug use to be both accurate and reliable (Latkin et al., 1993; Safaeian et al., 2001; McElrath et al., 1994). As mentioned, joining this cohort study also reflected entry into risk reduction. The median duration of injection for IDUs at study baseline was 14 years and considering the long history of drug use prior to study enrollment, the first reported cessation during the follow-up period might not have been the first time that participants were able to stop using drugs by injection. Third, results from this study may be most applicable to inner-city, predominantly African American IDUs. Lastly, participants who were excluded initially or later lost to follow-up were more likely to be HIV-uninfected and less severely addicted at baseline than those included and not lost to follow-up. It is possible that IDUs who were HIV-uninfected and not as severely drug-dependent were somewhat underrepresented in this sample; the proportion lost to follow-up was small (8%) and likely did not unduly affect our findings. In conclusion, this analysis showed that there are a number of behavioral and social factors affecting the time it takes IDUs to stop injection and relapse. Even among severely dependent IDUs, the majority was successful in achieving injection abstinence for at least 6 months. Based on the results, we can better understand the circumstances affecting the time to cessation and resuming injection: (1) Severely dependent IDUs, users of other substances (alcohol, cigarettes, cocaine) and homeless persons should be targeted since they take longer, on average, to stop injection and are faster to relapse; (2) Younger IDUs and being HIV-infected may warrant special attention in cessation efforts; (3) Drug network peers and sexual partners of IDUs should be encouraged to jointly attend treatment in an effort to expand programs to meet the needs of participants, promote injection cessation and improve the sustainability of recovery.

Acknowledgments The authors are indebted to the ALIVE participants for imparting their experiences. We also thank the ALIVE Study staff for their generous time and support in conducting the study. This research was funded by grants DA04334 and DA12568 from the National Institute on Drug Abuse.

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

References Anthony, J.C., Vlahov, D., Nelson, K.E., 1991. New evidence on intravenous cocaine use and the risk of infection with human immunodeficiency virus type 1. Am. J. Epidemiol. 134, 1175–1189. Avants, S.K., Warburton, L.A., Margolin, A., 2001. How injection drug users coped with testing HIV-seropositive: implications for subsequent healthrelated behaviors. AIDS Educ. Prev. 13, 207–218. Bammer, G., Battison, L., Ward, J., Wilson, S., 2000. The impact of retention of expansion of an Australian public methadone program. Drug Alcohol Depend. 58, 173–180. Belding, M.A., Iguchi, M.Y., Lamb, R.J., Lakin, M., Terry, R., 1996. Coping strategies and continued drug sue among methadone maintenance patients. Addict. Behav. 21, 389–401. Booth, R.E., Crowley, T.J., Zhang, Y., 1996. Substance abuse treatment entry, retention and effectiveness: out-of-treatment opiate injection drug users. Drug Alcohol Depend. 2, 11–20. Celentano, D.D., Vlahov, D., Cohn, S., Anthony, J.C., Solomon, L., Nelson, K.E., 1991. Risk factors for shooting gallery use and cessation among intravenous drug users. Am. J. Publ. Health 81, 1291–1295. Celentano, D.D., Munox, A., Cohn, S., Nelson, K.E., Vlahov, D., 1994. Drugrelated behavior change for HIV transmission among American injection drug users. Addiction 89, 1309–1317. Col´on, H.M., Robles, R.R., Deren, S., Sahai, H., Finlinson, H.A., And´ıa, J., Cruz, M.A., Kang, S.Y., Oliver-V´elez, D., 2001. Between-city variation in frequency of injection among Puerto Rican injection drug users: East Harlem, NY and Bayamon, PR. J. Acquir. Immune Defic. Syndr. 27, 405–413. Cox, D.R., Snell, E.J., 1968. Analysis of Survival Data. Chapman & Hall, London. DiNitto, D.M., Webb, D.K., Rubin, A., 2002. Gender differences in duallydiagnosed clients receiving chemical dependency treatment. J. Psychoactive Drugs 34, 105–117. Doherty, M.C., Garfein, R.S., Monterroso, E., Brown, D., Vlahov, D., 2000. Correlates of HIV infection among young adult short-term injection drug users. AIDS 14, 717–726. Friedman, S., Curtis, R., Neaigus, A., Jose, B., Des Jarlais, D., 1999. Social Networks, Drug Injectors’ Lives and HIV/AIDS. Kluwer Academic, New York. Galai, N., Safaeian, M., Vlahov, D., Bolotin, A., Celentano, D.D., ALIVE Study, 2003. Longitudinal patterns of drug injection behavior in the ALIVE Study cohort, 1988–2000: description and determinants. Am. J. Epidemiol. 158, 695–704. Garfein, R.S., Vlahov, D., Galai, N., Doherty, M.C., Nelson, K.E., 1996. Viral infections in short-term injection drug users: the prevalence of the hepatitis C, hepatitis B, human immunodeficiency, and human T-lymphotropic viruses. Am. J. Publ. Health 86, 655–661. Goldstein, M.F., Deren, S., Magura, S., Kayman, D.J., Beardsley, M., Tortu, S., 2000. Cessation of drug use: impact of time in treatment. J. Psychoactive Drugs 32, 305–310. Gossop, M., Marsden, J., Stewart, D., Treacy, S., 2002. Change and stability of change after treatment of drug misuse: 2-year outcomes from the National Treatment Outcome Research Study (UK). Addict. Behav. 27, 155–166. Hartel, D.M., Schoenbaum, E.E., 1998. Methadone treatment protects against HIV infection: two decades of experience in the Bronx, New York City. Publ. Health Rep. 113, 107–113. Harwood, H.J., Fountain, D., Fountain, G., 1992. Economic cost of alcohol and drug abuse in the United States, 1992. Addiction 94, 631–635. Hemby, S.E., Co, C., Dworkin, S.I., Smith, J.E., 1999. Synergistic elevations in nucleus accumbens extracellular dopamine concentrations during self-administration of heroin/cocaine combinations (speedballs) in rats. J. Pharmacol. Exp. Ther. 288, 274–280. Hosmer Jr., D.W., Lemeshow, S., 1999. Applied Survival Analysis: Regression Modeling of Time to Event Data. John Wiley and Sons, New York. Johnson, R.A., Gerstein, D.R., Pach III, A., Cerbone, F.G., Brown, J., 2002. HIV risk behaviors in African-American drug injector networks: implica-

155

tions of injection-partner mixing and partnership characteristics. Addiction 97, 1011–1024. Joseph, H., Stancliff, S., Landgrod, J., 2000. Methadone maintenance treatment (MTT): a review of historical and clinical issues. Mt. Sinai J. Med. 67, 347–364. Kwiatkowski, C.F., Booth, R.E., 2001. Methadone maintenance as HIV risk reduction with street-recruited injecting drug users. J. Acquir. Immune Defic. Syndr. 26, 483–489. Langendam, M.W., Van Brussel, G.H.A., Coutinho, R.A., van Ameijden, E.J.C., 2000. Methadone maintenance and cessation of injection drug use: results from the Amsterdam cohort study. Addiction 95, 591–600. Latkin, C.A., Vlahov, D., Anthony, J.C., 1993. Socially desirable responding and self-reported HIV infection risk behaviors among intravenous drug users. Addiction 88, 517–526. Latkin, C.A., Knowlton, A.R., Hoover, D., Mandell, W., 1999. Drug network characteristics as a predictor of cessation of drug use among adult injection drug users: a prospective study. Am. J. Drug Alcohol Abuse 25, 463–473. Lewis, D.C., 1999. Access to narcotic addiction treatment and medical care: prospects for the expansion of methadone maintenance treatment. J. Addict. Dis. 18, 5–21. Lundy, A., Gottheil, E., Serota, R.D., Weinstein, S.P., Sterling, R.C., 1995. Gender differences and similarities in African American crack cocaine abusers. J. Nerv. Ment. Dis. 183, 260–266. McElrath, K., Chitwood, D.D., Griffin, D.K., Comerford, M., 1994. The consistency of self-reported HIV risk behavior among injection drug users. Am. J. Publ. Health 84, 1965–1970. Metzger, D.S., Navaline, H., 2003. HIV prevention among injection drug users: the need for integrated models. J. Urban Health 80 (4 Suppl. 3), iii.59–iii.66. National Consensus Development Panel on Effective Medical Treatment of Opiate Addiction, 1998. Effective medical treatment of opiate addiction. JAMA 280, 1936–1943. Raisch, D.W., Fye, C.L., Boardman, K.D., Sather, M.R., 2002. Opioid dependence treatment, including buprenorphine/naloxone. Ann. Pharmacother. 36, 312–321. Rehm, J., Gschwend, P., Steffen, T., Gutzwiller, F., Dobler-Mikola, A., Uchtenhagen, A., 2001. Feasibility, safety, and efficacy of injectable heroin prescription for refractory opioid addicts: a follow-up study. Lancet 358, 1417–1420. Safaeian, M., Brookmeyer, R., Vlahov, D., Latkin, C., Marx, M., Strathdee, S.A., 2001. Validity of self-reported needle exchange attendance among injection drug users: implications for program evaluation. Am. J. Epidemiol. 155, 169–175. Schroeder, J.R., Latkin, C.A., Hoover, D.R., Curry, A.D., Knowlton, A.R., Celentano, D.D., 2001. Illicit drug use in one’s social network and in one’s neighborhood predicts individual heroin and cocaine use. Ann. Epidemiol. 11, 389–394. Shah, N.G., Celentano, D.D., Vlahov, D., Stambolis, V., Johnson, L., Nelson, K.E., Strathdee, S.A., 2000. Correlates of enrolment in methadone maintenance treatment programs differ by HIV-serostatus. AIDS 14, 2035– 2043. Shore, R.E., Marmor, M., Titus, S., Des Jarlais, D.C., 1996. Methadone maintenance and other factors associated with intraindividual temporal trends in injection-drug use. J. Subst. Abuse Treat. 13, 241–248. Sibthorpe, B., Lear, B., 1994. Circumstances surrounding needle use transitions among injection drug users: implications for HIV intervention. Int. J. Addict. 29, 1245–1257. Simpson, D.D., 1981. Treatment for drug abuse. Follow-up outcome and length of time spent. Arch. Gen. Psychiatr. 38, 875–880. Simpson, D.D., Joe, G.W., 1993. Motivation as a predictor of early dropout from drug abuse treatment. Psychotherapy 30, 357–367. Simpson, D.D., Joe, G.W., Rowan-Szal, G.A., 1997. Drug abuse treatment and process effects on follow-up outcomes. Drug Alcohol Depend. 47, 227–235. Song, J.Y., Safaeian, M., Strathdee, S.A., Vlahov, D., Celentano, D.D., 2000. The prevalence of homelessness among injection drug users with and without HIV infection. J. Urban Health 77, 678–687.

156

N.G. Shah et al. / Drug and Alcohol Dependence 83 (2006) 147–156

Stimson, G.V., Adelekan, M., Rhodes, T., 1996. The diffusion of drug injecting in developing countries. Int. J. Drug Policy 7, 245–255. Sullivan, L.E., Metzger, D.S., Fudala, P.J., Fiellin, D.A., 2005. Decreasing international HIV transmission: the role of expanding access to opioid agonist therapies for injection drug users. Addiction 100, 150–158. Toumbourou, J.W., Hamilton, B., Fallon, B., 1998. Treatment level progress and time spent in treatment in the prediction of outcomes following drugfree therapeutic community treatment. Addiction 93, 1054–1064. van Ameijden, E.J.C., Coutinho, R.A., 1998. Maximum impact of HIV prevention measures targeted at injection drug user. AIDS 12, 625–633.

van Ameijden, E.J.C., Coutinho, R.A., 2001. Large decline in injecting drug use in Amsterdam, 1986–1998: explanatory mechanisms and determinants of injecting transitions. J. Epidemiol. Community Health 55, 356–363. Vlahov, D., Anthony, J.C., Munoz, A., Margolick, J., Nelson, K.E., Celentano, D.D., Solomon, L., Polk, B.F., 1991. The ALIVE Study, a longitudinal study of HIV-1 infection in intravenous drug users: description of methods and characteristics of participants. NIDA Res. Monogr. 109, 75–100. Warner, L.A., Alegria, M., Canino, G., 2004. Remission from drug dependence symptoms and drug use cessation among women drug users in Puerto Rico. Arch. Gen. Psychiatr. 61, 1034–1041.