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Incarceration and drug use patterns among a cohort of injection drug users DeBeck, Kora; Kerr, Thomas; Li, Kathy; Milloy, M-J; Montaner, Julio; Wood, Evan Jan 31, 2009

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Incarceration and drug use patterns among a cohort of injectiondrug usersKora DeBeck1, Thomas Kerr1,2, Kathy Li1, M-J Milloy1, Julio Montaner1,2, and Evan Wood1,21 British Columbia Centre for Excellence in HIV/AIDS2 Department of Medicine, University of British Columbia, CanadaAbstractAims—Drug law enforcement remains the dominant response to drug-related harm. However, theimpact of incarceration on deterring drug use remains under-evaluated. We sought to explore therelationship between incarceration and patterns of drug use among people who inject drugs (IDU).Design—Using generalized estimating equations (GEE), we examined the prevalence andcorrelates of injection cessation among participants in the Vancouver Injection Drug User Studyfollowed over 9 years. In subanalyses, we used McNemar's tests and linear growth curve analysesto assess changes in drug use patterns before and after a period of incarceration among participantsreporting incarceration and those not incarcerated.Findings—Among 1603 IDU, 842 (53%) reported injection cessation for at least 6 months atsome point during follow-up. In multivariate GEE analyses, recent incarceration was associatednegatively with injection cessation [adjusted odds ratio (AOR) = 0.43, 95% confidence interval(CI) 0.37–0.50], whereas the use of methadone was associated positively with cessation (AOR =1.38, 95% CI 1.22–1.56). In subanalyses assessing longitudinal patterns of drug use amongincarcerated individuals and those not incarcerated over the study period, linear growth curveanalyses indicated that there were no statistically significant differences in patterns of drug usebetween the two groups (all P > 0.05).Conclusions—These observational data suggest that incarceration does not reduce drug useamong IDU. Incarceration may inhibit access to mechanisms that promote injection cessationamong IDU. In contrast, results indicate that methadone use is associated positively with injectioncessation, independent of previous frequency of drug use.KeywordsAddiction treatment; deterrence; drug law enforcement; drug policy; drug use patterns;incarceration; injection cessation; injection drug useCorresponding author: Evan Wood BC Centre for Excellence in HIV/AIDS 608 - 1081 Burrard Street Vancouver, BC V6Z 1Y6Canada uhri-ew@cfenet.ubc.ca.Declarations of interestDr. Julio Montaner has received grants from, served as an ad hoc adviser to or spoken at various events sponsored by: Abbott, ArgosTherapeutics, Bioject Inc., Boehringer Ingelheim, BMS, Gilead Sciences, GlaxoSmithKline, Hoffmann-La Roche, Janssen-Ortho,Merck Frosst, Pfizer, Schering, Serono Inc., TheraTechnologies, Tibotec and Trimeris. The authors declare no other competinginterests.NIH Public AccessAuthor ManuscriptAddiction. Author manuscript; available in PMC 2013 August 02.Published in final edited form as:Addiction. 2009 January ; 104(1): 69–76. doi:10.1111/j.1360-0443.2008.02387.x.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptINTRODUCTIONPublic policy makers continue to face complex challenges in attempting to reduce the health,social and economic costs associated with problematic substance use. While addiction isunderstood increasingly to be a health issue [1,2] the overarching global policy response toproblematic substance use continues to be dominated by drug law enforcement, which hasbeen shown to receive the overwhelming majority of drug policy funding [3,4].A central strategy of illicit drug law enforcement is to incarcerate drug users for drugpossession and other drug-related offences with the aim of deterring drug use and loweringthe supply and demand for drugs [2,3,5–7]. From 1999 to 2004 in Spain, France, Austria,Sweden and the United Kingdom more than 80% of drug law offences were for drug use orpossession for the purpose of use [8]. During this same period in the 29 countriescontributing data to the European Monitoring Centre for Drugs and Drug Addiction, all buttwo reported an increase in the number of drug offences [8]. In Canada, 30% of femaleprisoners and 14% of male prisoners in federal institutions are serving sentences for drug-related offences [9]. In the United States, 20% of inmates in state prisons and 55% ofinmates in federal prisons are incarcerated for drug offences [10,11].The fiscal costs associated with incarcerating individuals for drug-related offences aresubstantial; estimates suggest that more than $8 billion dollars in the United States and $573million dollars in Canada are spent annually to imprison those found guilty of drug-relatedoffences [6,12]. In Canada, the use of incarceration as a tool to manage substance use islikely to increase, as the federal government recently launched a new ‘National Anti-DrugStrategy’ which proposes to introduce new legislation for mandatory minimum prisonsentences for drug offences [13]. Despite the vast funding investments associated with thisapproach, the effectiveness of law enforcement and incarceration on deterring and reducingdrug use have not been well evaluated [14].In light of the continued emphasis on criminal justice approaches to address illicit drug use,we sought to test the policy assumption that incarceration deters drug use using longitudinaldata derived from a cohort study of people who inject drugs (IDU) in Vancouver, Canada.While IDU are a relatively small proportion of the overall drug-using population, themajority of problematic and harmful drug consumption takes place among this group [3].Therefore, we sought to explore the possible relationship between incarceration and changesin drug use patterns in this group to indicate whether current law enforcement approachesare producing their intended effects.METHODSThe Vancouver Injection Drug User Study (VIDUS) is a longitudinal cohort study thatbegan recruiting injection drug users (IDU) through self-referral and street outreach in May1996. The study has been described in detail previously [15]. Briefly, individuals wereeligible if they had injected drugs at least once in the previous month, resided in the greaterVancouver region and provided written informed consent. At baseline and every 6 months,subjects provide blood samples and complete an interviewer-administered questionnaire.The questionnaire elicits demographic data as well as information about recent drug usepatterns, human immunodeficiency virus (HIV) risk behaviour and experience with thecriminal justice system and addiction treatment programmes. All participants are given astipend ($20 CDN) at each study visit. The study has received ethical approval from theProvidence Health Care/University of British Columbia's Research Ethics Board.As a first analysis, we conducted a longitudinal study of factors associated with cessation ofinjection drug use to examine if periods of incarceration were associated with drugDeBeck et al. Page 2Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptcessation. In this analysis, we included all participants seen for baseline and follow-upinterviews from May 1996 to December 2005. Drug use cessation was defined as notreporting any injection drug use in the 6-month period prior to a follow-up interview.Incarceration was defined as ‘being in detention, prison, or jail overnight or longer’ in theprevious 6 months. Explanatory variables of interest included socio-demographicinformation such as: gender (female versus male), age (per year older) and Aboriginalethnicity (yes versus no). Drug use variables considered were measured at baseline and referto behaviours in the previous 6 months. They included: frequent heroin injection (= dailyversus < daily), frequent cocaine injection (= daily versus < daily) and frequent crackcocaine smoking (= daily versus < daily). Other characteristics considered included: currentparticipation in methadone treatment, residing in the Downtown Eastside in the last 6months (i.e. Vancouver's illegal drug use and HIV epicentre) and having regular paidemployment in the last 6 months. All variable definitions were identical to earlier reports[15].Because analyses of factors associated potentially with injection cessation included serialmeasures for each subject, we used generalized estimating equations (GEE) for binaryoutcomes with logit link for the analysis of correlated data to determine factors associatedwith injection cessation throughout the 9-year follow-up period. This approach has beenused successfully in previous analyses [16,17]. These methods provided standard errorsadjusted by multiple observations per person using an exchangeable correlation structure.Therefore, data from every participant follow-up visit was considered in this analysis. Forindividuals who missed follow-up appointments during the study period, missing data wereaddressed through the GEE estimating mechanism, which uses the all-available-pairsmethod to encompass the missing data [18]. As a first step, we conducted univariate GEEanalyses to determine factors associated with injection cessation. In order to adjust forpotential confounding, all variables of interest were entered into a fixed multivariate logisticGEE model.We were aware that any association between injection cessation and incarceration could beobserved because those who were incarcerated were inherently more or less likely to ceasedrug use, or because those who had continued or stopped injecting drugs may be inherentlymore or less likely to be incarcerated. To assess more closely the relationship betweenincarceration and drug use patterns, we conducted secondary analyses on drug use patternsbefore and after a period of incarceration and compared these with drug use patterns amonga group of non-incarcerated participants interviewed during the same time-periods. Theapproach of comparing before and after patterns of drug use among incarcerated versus non-incarcerated groups was used to account for the cohort effect, in which drug use behaviourschange over time [19]. Specifically, with cohorts of adult injection drug users, decliningtrends in many drug use behaviours are observed commonly as cohort participants age andprogress through their drug use careers [20,21]. Hence, we expected most patterns of druguse to decline from the ‘before’ to ‘after’ periods in both groups as a function of time. Tocontrol for these expected changes, and isolate more clearly the independent relationshipbetween incarceration and drug use patterns, our primary interest was not to considerchanges in drug use patterns over time, but rather to assess whether longitudinal trends weredifferent between the two groups.As a first step in subanalyses, we identified all VIDUS participants with no previous historyof incarceration who reported being incarcerated at some point during follow-up. Amongthese individuals, only participants who had at least one study visit before incarceration andat least one study visit after incarceration were eligible for inclusion. Eligibility for being acontrol included having no previous history of incarceration, no report of incarceration atany point during the entire 9-year study period and at least three study visits. Because casesDeBeck et al. Page 3Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptwere identified throughout the 9-year study period, observations for controls were selectedat a frequency that matched the proportion of cases identified at each study follow-up. Forexample, if 8% of cases were identified at the fifth study follow-up, 8% of all controls wereselected to use that same study follow-up. We elected to match our controls to cases basedon time rather than on subject characteristics, as we have observed that drug use patternshave changed over study follow-up [22,23]. This matching approach has been employedsuccessfully in other longitudinal analyses using IDU cohort data spanning an extendedperiod of time [24]. To examine if there were significant differences between the cases andcontrol groups with regard to demographic characteristics, simple descriptive comparisonsusing χ2 tests and Wilcoxon rank sum tests were undertaken.As a second step, we examined the proportion of cases reporting selected drug usebehaviours in the study visits before and after the reported incarceration period for both theincarcerated and non-incarcerated groups. Characteristics measured at the study follow-upthat included the report of incarceration were not included in analyses. Differences in thebefore and after drug use behaviours were assessed for each group using McNemar's test. Asnoted for the first analysis, drug use patterns of interest all refer to behaviours in the past 6months and include: any heroin use (yes versus no), any cocaine use (yes versus no), anycrack use (yes versus no), frequent heroin injection (= daily versus < daily), frequent cocaineinjection (= daily versus < daily), frequent crack cocaine smoking (= daily versus < daily)and injection cessation (yes versus no). Because declines in most drug use behaviour wereexpected for both groups, we were interested primarily in identifying instances wheredifferent trends emerged over time between the two groups.To test formally for differences over time and between groups, linear growth curve modelswere constructed. These models combine logistic regression and growth curve analyses.This statistical approach has been employed in illicit drug use research, as the methodenables the identification of changes over time and the incorporation of interaction terms todetermine if the changes over time between two groups are statistically significant [25,26].We performed logistic growth curve analyses using Proc GENMOD in SAS version 9.1 forselected drug use behaviours as outcome variables in each model with group (incarceratedversus non-incarcerated) and period (before versus after) as the explanatory variables. Toadjust for differences in participants’ baseline risk profiles, models were modified byincluding the propensity scores calculated through logistic regression from the followingfactors measured at baseline: age, gender, ethnicity, Downtown Eastside residence, frequentheroin injection, frequent crack use, frequent cocaine injection, methadone treatment andregular paid employment [27]. Propensity scores for injection cessation could not becalculated because, by definition, all study participants were injection drug users at baseline.As such, the linear growth curves for injection cessation were adjusted for age, gender andethnicity. For all analyses all P-values were two-sided, with statistical significance set at P <0.05. All statistical analyses were performed using SAS software version 9.1 (SAS Institute,Cary, NC, USA).RESULTSA total of 1603 participants were recruited during the study period, including 584 (36%)women and 435 (27%) people reporting Aboriginal ancestry. The median age of participantsat baseline was 33 years [interquartile range (IQR) = 26–40]. This sample contributed to 15748 observations over the study period. A total of 1218 (76%) participants completed atleast three study follow-up visits and the median number of follow-up visits was 10 (IQR =4–16) over a median of 60 (IQR 24–96) months’ follow-up per participant. Among thissample, a total of 842 (53%) reported injection cessation at some point during follow-up. OfDeBeck et al. Page 4Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptthe 15 748 observations included in the primary analysis, 3731 (24%) involved a report ofinjection cessation over the previous 6 months.The univariate GEE analyses of behavioural and socio-demographic variables are presentedin Table 1. Factors found to be associated significantly with injection cessation in univariateanalyses included: older age (per year older) [odds ratio (OR) = 1.10, 95% confidenceinterval (CI) 1.09–1.12]; Downtown Eastside residence (OR = 0.40, 95% CI 0.34–0.46);frequent heroin injection (OR = 0.74, 95% CI 0.63–0.87); recent incarceration (OR = 0.34,95% CI 0.30–0.40); participation in methadone treatment (OR = 1.50, 95% CI 1.33–1.69);and regular paid employment (OR = 2.36, 95% CI 2.02–2.76).In the multivariate GEE analysis, also shown in Table 1, factors that remained associatedindependently with injection cessation in our logistic model included: older age [adjustedodds ratio (AOR) = 1.01, 95% CI 1.00–1.02]; Aboriginal ethnicity (AOR 1.22, 95% CI1.00–1.47); Downtown Eastside residence (AOR = 0.43, 95% CI 0.37–0.50); recentincarceration (AOR = 0.43, 95% CI 0.37–0.50); participation in methadone treatment (AOR= 1.38, 95% CI 1.22–1.56); and regular paid employment (AOR = 2.00, 95% CI 1.71–2.35).In the subanalyses which examined behaviours before and after incarceration, 889participants fitted the criteria for inclusion. Compared to participants excluded from theanalysis because of limited follow-up (i.e. less than three visits), included participants weremore likely to be older (median age 35.1 years versus 30.0 years, P = < 0.001); to be female(P = 0.009); and to identify as Aboriginal (P = 0.045). Among those included in the analysis,147 (17%) met the criteria of having a period of incarceration at some point during follow-up and the remaining 742 (83%) were included in the non-incarcerated group. Participantsreporting a period of incarceration were significantly younger than the non-incarceratedcontrol group [median age 33.4 (IQR: 26.3–39.0) versus 35.5 (IQR: 28.8–41.3), P = 0.004].No significant differences between the two groups were observed with respect to gender andethnicity.The proportion of each group reporting selected drug use behaviours before and after aperiod of incarceration, as well as the results of McNemar's test assessing whether thesechanges were statistically significant at P < 0.05, are reported in Table 2. Overall, theprevalence of drug use was higher in the incarcerated group versus the non-incarceratedgroup. However, as expected, a reduction in drug use was observed in each group with theexception of crack cocaine use, which increased in both groups when the pre- and post-incarceration time-periods were compared. Patterns of change in the variables ‘any cocaineuse’, ‘frequent heroin injection’ and ‘injection cessation’ were statistically similar for boththe incarcerated and non-incarcerated control groups. Differences in drug use trendsobserved for both incarcerated and non-incarcerated groups included: any heroin use (P =0.071 versus P < 0.001, respectively); any crack use (P = 0.572 versus P = 0.095); frequentcocaine injection (P = 0.170 versus P < 0.001); and frequent crack use (P = 0.706 versus P <0.001). However, linear growth curve analyses (see Table 3), modified by propensity scores,showed that none of the trends among the incarcerated and non-incarcerated groups werestatistically significant.DISCUSSIONThe present longitudinal study demonstrated that recent incarceration was associatednegatively with drug use cessation, whereas use of methadone was associated positivelywith drug use cessation. Conversely, in subanalyses, injection cessation increased afterperiods of incarceration. However, this trend was not statistically significantly different fromthe increase in injection cessation observed among the non-incarcerated group, suggestingDeBeck et al. Page 5Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptthat this change was probably a result of a cohort effect rather than being attributable to theexperience of incarceration. Furthermore, comparisons of all other examined drug usepatterns before and after a period of incarceration among incarcerated and non-incarceratedgroups were not significantly different, suggesting that incarceration is not associatedindependently with significant reductions in drug consumption.Although we are unaware of a similar long-term longitudinal study, our findings areconsistent with earlier cross-sectional studies that have failed to establish a positiveassociation between drug use cessation and cumulative time spent in prison [28,29]. Thisobservation may, in part, be attributable to the previously described destabilizing effect ofincarceration on IDU [30]. Our findings are also consistent with previous researchsuggesting that injection cessation is associated negatively with residing in a neighbourhoodwith a high prevalence of drug market activity (such as Vancouver's Downtown Eastside)[29]. In addition, although previous cross-sectional investigations have found the intensity ofdrug use to have an independent negative impact on cessation [28], our analysis supportsother investigations which found that drug use profiles do not predict injection drug usecessation reliably [31]. Our results also support previous investigations [32] indicating thataddiction treatment is associated positively with injection cessation.Identifying factors which appear to promote and support injection cessation is important forpolicy makers aiming to reduce the prevalence of high-risk drug use. Our finding regardingthe positive association between methadone treatment and injection cessation isencouraging, and reinforces the importance of investing in methadone programs for heroin-using IDU [33]. It is also encouraging that frequent drug use did not appear to be asignificant barrier to subsequent injection cessation, indicating that transitions out ofinjection are possible even for individuals engaging in high-intensity drug use. Conversely,from a policy perspective, it is a concern that our findings do not support the current policyassumption that imprisoning high-risk drug users deters and reduces their drug use. Rather,our analyses found no statistically significant differences in drug use patterns amongincarcerated and non-incarcerated injection drug users over time. Although further study isnecessary, our findings may be explained by previous studies suggesting that incarcerationmay reduce access to mechanisms (i.e. addiction treatment, social support, employment) thatpromote injection cessation among IDU [30]. This finding is of great concern, given thenumber of individuals incarcerated for drug use and the fiscal costs associated with thispolicy approach.When assessing the appropriateness of using incarceration as a tool to manage problematicsubstance use, it is also important to consider briefly the growing body of evidencedemonstrating that IDU face elevated health risks in prison settings [34]. For example, inmany areas IDU report injecting drugs in prisons [35–37] and qualitative investigations havedocumented and described how the nature of the prison environment perpetuates theadoption of risky injection practices among these individuals [38,39]. Epidemiologicalanalyses have further established an independent relationship between HIV infection andrecent incarceration [40], and among IDU in Vancouver it has been estimated that at least20% of HIV infections may have been acquired in prison [41]. This evidence suggests thatto offset the health risks posed by incarceration, the benefits of this approach ought to bewell established and substantive.It appears that an assessment of the effectiveness and benefits of incarcerating individualsfor drug use is warranted to ensure that resources are not being invested in policyapproaches that are either harmful and/or ineffective. This appears especially relevant in theUnited States, which has the highest incarceration rate in the world [42], as well as inCanada, where the federal government has introduced a new National Anti-Drug StrategyDeBeck et al. Page 6Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptwhich relies heavily upon incarceration as a means to address problematic substance use[13].There are several limitations to be noted in this study. First, as with most other cohortstudies of IDU, VIDUS is not a random sample and therefore these findings may notgeneralize to other IDU populations. In addition, as with all long-term cohort studies ofIDU, loss of participants over follow-up is an issue; however, more than 75% of participantscompleted three or more study visits. Secondly, this study relied upon self-reportedinformation concerning patterns of drug use over the previous 6 months and is susceptible torecall bias as well as socially desirable reporting. In the present study this may have led toan under-reporting of drug consumption resulting in the level of drug use beingunderestimated. However, it is notable that individuals reporting cessation of drug use in thisstudy also reported injection drug use previously. Thirdly, despite extensive multivariateadjustment, the association between incarceration and injection cessation observed in theprimary analysis could be influenced by confounders not measured by the study instrument.Similarly, despite the use of propensity score calculations, it is not possible to control for allpossible differences between study groups. Fourthly, reports of incarceration and methadoneuse relied on self-report and may be subject to recall bias. However, we have no reason tosuspect that this bias would be differential between cases and controls. In addition, detailsregarding the length of each incarceration event were not available. As a result, the measurefor incarceration used in these analyses did not incorporate the duration of prison sentences,precluding the detection of potential dose effects of incarceration on drug use patterns.Further study of the possible impact of the duration of periods of incarceration on drug usepatterns is needed. Finally, there are limitations involved in relying on statistical criteria toassess whether drug use trends between two groups are meaningfully different [43].Nevertheless, a priori criteria (e.g. P < 0.05) are an established means of assessing whetherobserved values are significantly different.In sum, the current investigation did not observe statistically significant differences in druguse patterns measured longitudinally among IDU experiencing a period of incarceration incomparison to IDU who did not experience incarceration. Although further study isnecessary, our findings imply that incarceration does not have long-term positive effects onIDU drug use patterns. In addition, the current investigation indicates that methadone has thepotential to support injection cessation among IDU, independent of previous drug usefrequency. Given the elevated risks to health faced by IDU in prison settings and themonetary costs associated with this component of drug law enforcement, it appears thatfurther investigation to identify and establish the benefits of incarcerating IDU is required.In addition, the cost-effectiveness and impact of community diversion programmes for non-violent drug offenders requires further evaluation.AcknowledgmentsWe would particularly like to thank the VIDUS participants for their willingness to participate in the study. We alsothank Drs Steffanie Strathdee, Richard Harrigan, David Patrick, Mark Tyndall, Bonnie Devlin, John Charette,Caitlin Johnston, Vanessa Volkommer, Steve Kain, Sidney Crosby, Christy Power, Cody Callon, Nancy Laliberte,Sue Currie, Deborah Graham, Carley Taylor, Tricia Collingham and Peter Vann for their research andadministrative assistance. The study was supported by the US National Institutes of Health (R01 DA011591) andCIHR grant (MOP-67262). K.D. is supported by a Michael Smith Foundation for Health Research Senior GraduateTrainee Award and a Canadian Institutes of Health Research Doctoral Research Award. T.K. is supported by theMichael Smith Foundation for Health Research and the Canadian Institutes for Health Research.References1. Macdonald, Z.; Tinsley, L.; Collingwood, J.; Jamieson, P.; Pudney, S. Measuring the Harm fromIllegal Drugs Using the Drug Harm Index. Home Office; London: 2005.DeBeck et al. Page 7Addiction. 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Intensive injection cocaineuse as the primary risk factor in the Vancouver HIV-1 epidemic. AIDS. 2003; 17:887–93.[PubMed: 12660536]41. Hagan H. The relevance of attributable risk measures to HIV prevention planning. AIDS. 2003;17:911–3. [PubMed: 12660539]42. Walmsley, R. World Prison Population List. Home Office; London, UK: 2003.43. Gelman A, Stern H. The difference between ‘significant’ and ‘not significant’ is not itselfstatistically significant. Am Stat. 2006; 60:328–31.DeBeck et al. Page 9Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptDeBeck et al. Page 10Table 1Factors associated with cessation of injection drug use (n = 1603).Univariate GEE of factors associated withcessation of injection drug useMultivariate logistic GEE of factors associatedwith cessation of injection drug useCharacteristic OR(95% CI) P-value AOR (95% CI) P-valueRecent incarcerationa    yes versus no 0.34 (0.30–0.40) <0.001 0.43 (0.37–0.50) <0.001Older age    per year older 1.10 (1.09–1.12) <0.001 1.01 (1.00–1.02) 0.047Gender    female versus male 1.07 (0.91–1.26) 0.402 1.08 (0.90–1.30) 0.405Aboriginal ethnicity    yes versus no 1.10 (0.92–1.30) 0.300 1.22 (1.00–1.47) 0.046Downtown Eastside residencya    yes versus no 0.40 (0.34–0.46) <0.001 0.43 (0.37–0.50) <0.001Frequent heroin injection    yes versus no 0.74 (0.63–0.87) 0.004 0.91 (0.75–1.10) 0.319Frequent crack usea    yes versus no 0.98 (0.74–1.31) 0.902 1.21 (0.90–1.64) 0.208Frequent cocaine injection    yes versus no 0.89 (0.76–1.05) 0.169 1.17 (0.98–1.40) 0.083Methadone treatment    yes versus no 1.50 (1.33–1.69) <0.001 1.38 (1.22–1.56) <0.001Regular paid employment    yes versus no 2.36 (2.02–2.76) <0.001 2.00 (1.71–2.35) <0.001GEE: generalized estimating equation; OR: odds ratio; AOR: adjusted odds ratio; CI: confidence intervalaactivities or situations referring to previous 6 months.Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptDeBeck et al. Page 11Table 2Drug use patterns among incarcerated group (n = 147) and non-incarcerated control group (n = 742) for beforeand after a period of incarceration.Drug use behaviour Before n (%) After n (%) P-value*Any heroin use    Incarcerated 93 (63.3) 82 (55.8) 0.071    Non-incarcerated 438 (59.3) 388 (52.3) <0.001Any cocaine use    Incarcerated 108 (73.5) 85 (57.8) <0.001    Non-incarcerated 480 (64.7) 403 (54.3) <0.001Any crack use    Incarcerated 71 (48.3) 75 (51.0) 0.572    Non-incarcerated 317 (42.7) 343 (46.2) 0.095Frequent heroin injection    Incarcerated 50(34.0) 41 (27.9) 0.095    Non-incarcerated 235 (31.7) 214 (28.8) 0.092Frequent cocaine injection    Incarcerated 46 (31.3) 37 (25.2) 0.170    Non-incarcerated 205 (27.6) 150 (20.2) <0.001Frequent crack use    Incarcerated 28 (19.1) 30 (20.4) 0.706    Non-incarcerated 99 (13.3) 142 (19.1) <0.001Injection cessation    Incarcerated 9 (6.1) 24 (16.3) 0.003    Non-incarcerated 96 (12.9) 169 (22.8) <0.001*P-value denotes McNemar's test score. All drug use variables refer to behaviours in the past 6 months.Addiction. Author manuscript; available in PMC 2013 August 02.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptDeBeck et al. Page 12Table 3Results of the linear growth curve analyses with each drug use behaviour modelled as the outcome—adjustedby propensity scoresa.Drug use behaviours Slope (95% confidence interval) P-valueAny heroin use    Incarcerated −0.261 (−0.604 – 0.082)    Non-incarcerated −0.282 (−0.426 – −0.138) 0.917Any cocaine use    Incarcerated −0.816 (−1.214 – −0.417)    Non-incarcerated −0.439 (−0.600 – −0.279) 0.097Any crack use    Incarcerated 0.163 (−0.214 – 0.540)    Non-incarcerated 0.146 (−0.025 – 0.316) 0.926Frequent heroin injection    Incarcerated −0.270 (−0.631 – 0.092)    Non-incarcerated −0.138 (−0.299 – 0.022) 0.525Frequent cocaine injection    Incarcerated −0.337 (−0.774 – 0.100)    Non-incarcerated −0.416 (−0.609 – −0.223) 0.751Frequent crack use    Incarcerated 0.133 (−0.317 – 0.582)    Non-incarcerated 0.450 (0.206 – 0.694) 0.239Injection cessationb    Incarcerated 1.077 (0.331 – 1.823)    Non-incarcerated 0.684 (0.474 – 0.894) 0.353Slope represents differences in drug use behaviours among the incarcerated and the non-incarcerated control group over time; P-value representsinteraction term.aPropensity scores calculated from the following characteristics measured at baseline: age, gender, ethnicity, Downtown Eastside residence,frequent heroin injection, frequent crack use, frequent cocaine injection, methadone treatment, and regular paid employment.bPropensity scores for injection cessation could not be calculated as all participants were injection drug users at baseline; hence the linear growthcurves for injection cessation did not incorporate propensity scores but they were adjusted for age, gender and ethnicity.Addiction. Author manuscript; available in PMC 2013 August 02.

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