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Individual, social, and environmental factors associated with initiating methamphetamine injection :… Marshall, Brandon D.; Wood, Evan; Shoveller, Jean; Kerr, Thomas; Buxton, Jane; Montaner, Julio 2011

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Individual, Social, and Environmental Factors Associated withInitiating Methamphetamine Injection: Implications for Drug Useand HIV Prevention StrategiesBrandon DL. Marshall,British Columbia Centre for Excellence in HIV/AIDS, St. Paul's Hospital, 608-1081 Burrard Street,Vancouver, BC, Canada V6Z 1Y6School of Population and Public Health, University of British Columbia, 2206 East Mall,Vancouver, BC, Canada V6T 1Z3Evan Wood,British Columbia Centre for Excellence in HIV/AIDS, St. Paul's Hospital, 608-1081 Burrard Street,Vancouver, BC, Canada V6Z 1Y6Department of Medicine, University of British Columbia, St. Paul's Hospital, 608-1081 BurrardStreet, Vancouver, BC, Canada V6Z 1Y6Jean A. Shoveller,School of Population and Public Health, University of British Columbia, 2206 East Mall,Vancouver, BC, Canada V6T 1Z3Jane A. Buxton,School of Population and Public Health, University of British Columbia, 2206 East Mall,Vancouver, BC, Canada V6T 1Z3Division of Epidemiology, British Columbia Centre for Disease Control, 655 West 12th Ave,Vancouver, BC, Canada V5Z 4R4Julio SG. Montaner, andBritish Columbia Centre for Excellence in HIV/AIDS, St. Paul's Hospital, 608-1081 Burrard Street,Vancouver, BC, Canada V6Z 1Y6Department of Medicine, University of British Columbia, St. Paul's Hospital, 608-1081 BurrardStreet, Vancouver, BC, Canada V6Z 1Y6Thomas KerrBritish Columbia Centre for Excellence in HIV/AIDS, St. Paul's Hospital, 608-1081 Burrard Street,Vancouver, BC, Canada V6Z 1Y6Department of Medicine, University of British Columbia, St. Paul's Hospital, 608-1081 BurrardStreet, Vancouver, BC, Canada V6Z 1Y6AbstractThe purpose of this study was to determine the incidence and predictors of initiatingmethamphetamine injection among a cohort of injection drug users (IDU). We conducted alongitudinal analysis of IDU participating in a prospective study between June 2001 and May© Society for Prevention Research 2011Correspondence to: Thomas Kerr.uhri-tk@cfenet.ubc.ca .NIH Public AccessAuthor ManuscriptPrev Sci. Author manuscript; available in PMC 2012 June 1.Published in final edited form as:Prev Sci. 2011 June ; 12(2): 173–180. doi:10.1007/s11121-010-0197-y.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript2008 in Vancouver, Canada. IDU who had never reported injecting methamphetamine at thestudy's commencement were eligible. We used Cox proportional hazards models to identify thepredictors of initiating methamphetamine injection. The outcome was time to first report ofmethamphetamine injection. Time-updated independent variables of interest included sociodemo-graphic characteristics, drug use patterns, and social, economic and environmental factors. Of1317 eligible individuals, the median age was 39.9 and 522 (39.6%) were female. At the study'sconclusion, 200 (15.2%) participants had initiated injecting methamphetamine (incidence density:4.3 per 100 person-years). In multivariate analysis, age (adjusted hazard ratio [aHR]: 0.96 per yearolder, 95%CI: 0.95–0.98), female sex (aHR: 0.58, 95%CI: 0.41–0.82), sexual abuse (aHR: 1.63,95% CI: 1.18–2.23), using drugs in Vancouver's drug scene epicentre (aHR: 2.15 95%CI: 1.49–3.10), homelessness (aHR: 1.43, 95%CI: 1.01–2.04), non-injection crack cocaine use (aHR: 2.06,95%CI: 1.36–3.14), and non-injection methamphetamine use (aHR: 3.69, 95%CI: 2.03–6.70) wereassociated with initiating methamphetamine injection. We observed a high incidence ofmethamphetamine initiation, particularly among young IDU, stimulant users, homelessindividuals, and those involved in the city's open drug scene. These data should be useful for thedevelopment of a broad set of interventions aimed at reducing initiation into methamphetamineinjection among IDU.KeywordsMethamphetamine; Injection drug use; Risk behavior; Initiation; HIVIntroductionThe use of amphetamine-type stimulants (ATS) including methamphetamines (MA) is agrowing global health problem (United Nations Office on Drugs and Crime, 2009). ATSnow rank second only to cannabis as the most common illicit drugs used worldwide,representing approximately 34 million users (United Nations Office on Drugs and Crime,2008). In North America, household surveys indicate that past year prevalence of MA use isapproximately 0.3%–0.8% (Maxwell & Rutkowski, 2008). MA use and dependence aregenerally more common among young people (Iritani et al., 2007; Springer et al., 2007),homeless and marginally housed persons (Das-Douglas et al., 2008), and men who have sexwith men (MSM) (Reback et al., 2008; Shoptaw & Reback, 2007). Less is known about theuse of MA among people who inject drugs (IDU), although its use is particularly commonamong subpopulations of young IDU (Inglez-Dias et al., 2008) and MSM-IDU (Ibañez etal., 2005; Kral et al., 2005).Chronic MA use has been associated with various physical and psychological harms(Buxton & Dove, 2008; Darke et al., 2008). The literature demonstrating a link between MAuse and high-risk sexual behavior among MSM is substantial (Halkitis et al., 2001; Prestageet al., 2007; Semple et al., 2002), with several studies showing associations between MA useand HIV seroconversion (Buchacz et al., 2005; Plankey et al., 2007). A growing literaturehas demonstrated how injecting MA (versus non-injection modes of consumption) isassociated with more severe symptoms of dependence and a greater number of health andsocial problems (McKetin et al., 2008; Semple et al., 2004). Evidence suggests that amongIDU, transitioning to MA use increases HIV risk and has other important negative healthimplications. For example, compared to persons who inject other drugs, MA injectors aremore likely to report sexual risk behaviors including sex work and unprotected vaginal andanal intercourse (Lorvick et al., 2006; Molitor et al., 1999). Furthermore, IDU who injectMA are more likely to engage in injection-related risk behavior including syringe sharing(Fairbairn et al., 2007), experience non-fatal overdose (Fairbairn et al., 2008), and in somesettings, test positive for HIV (Buavirat et al., 2003). A recent systematic review alsoMarshall et al. Page 2Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptconcluded that MA injectors experience an increased risk of mortality compared to otherIDU (Singleton et al., 2009).Given the adverse health outcomes noted above, interventions to prevent transitions toinjecting MA should be a public health priority. However, few studies have been conductedto examine MA initiation among IDU and thus little evidence base exists to inform thedevelopment of prevention strategies. Limited evidence indicates that the majority of MAusers consume other drugs prior to the initiation of use (Brecht et al., 2007). Qualitativework suggests that social factors play an important role in MA initiation; for example,several studies have found that sex partners and friends often offer MA to new users andprepare the drug for administration (Sheridan et al., 2009; Sherman et al., 2008). Very littleresearch has examined transitions to MA injection, although coping style and sensationseeking are often given as primary motivations for initiation among younger MA injectors,while substitution for other drugs is more commonly reported among older IDU (Brecht etal., 2007; Nakamura et al., 2009). In response to the lack of evidence to inform effectiveprevention interventions, we conducted this study to determine the incidence of initiatingMA injection and to examine the individual, social, environmental, and economic predictorsof initiation among a prospective cohort of adult IDU.MethodsThe Vancouver Injection Drug Users Study (VIDUS) is an ongoing open prospective cohortof adult IDU in Vancouver, Canada. Recruitment occurred through self-referral, word ofmouth, and street outreach. Persons were eligible to participate in the study if she/he hadinjected drugs at least once in the previous 6 months, were greater than 14 years of age,resided in the greater Vancouver region, and provided informed consent. At baseline andsemi-annually, participants completed an interviewer-administered questionnaire elicitingsociodemographic data as well as information pertaining to drug use patterns, risk behaviors,and health care utilization. Nurses collected blood samples for HIV and hepatitis C serologyand also provided basic medical care and referrals to appropriate health care services.Participants received $20 for each study visit. Other recruitment and follow-up methodshave been published elsewhere (Tyndall et al., 2003). The study has been approved by theUniversity of British Columbia/Providence Health Care Research Ethics Board.All participants who completed a baseline survey and at least one interview during the studyperiod (June 2001 to May 2008) were eligible for inclusion. We constructed a study sampleof MA injection-naïve individuals by excluding all participants who reported ever injectingMA at first study visit. The outcome of interest was ascertained by examining responses tothe question, “In the last 6 months, which of the following drugs did you inject? We definedan event as the first instance of answering “amphetamine (e.g., speed),”,“methamphetamine,”,or “crystal meth.”Rhodes’ risk environment framework (2002) was used to inform the selection of potentialpredictors of MA injection initiation. In accordance with this framework, we hypothesizedthat a broad set of individual, social, environmental, and economic factors act to increase thelikelihood of transitions in drug use and subsequent risk behavior. We also included aspotential confounders sociodemographic and other individual characteristics that have beenfound in previous literature to be associated with MA initiation and use (Brecht et al., 2004;Hayatbakhsh et al., 2009; Inglez-Dias et al., 2008). We included variables such as age (peryear older), sex (female vs. male), sexual orientation (lesbian, gay, bisexual, transgender/transsexual [LGBT] vs. heterosexual), age at first injection (per year older), and childhoodsexual abuse (CSA). Due to the small number of individuals representing ethnic minoritiesin the sample, we dichotomized ethnicity as Caucasian (white) vs. other. We also includedMarshall et al. Page 3Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptdrug use variables, including non-injection crack cocaine use, injection heroin use, injectioncocaine use, and non-injection methamphetamine use. Social, economic, and environmentalvariables considered included: relationship status (married or common law vs. single orcasually dating); syringe sharing; injecting with a sex partner or friends, respectively;current enrolment in a methadone maintenance therapy program; homelessness; buying orusing drugs in the downtown eastside (DTES) area of Vancouver (i.e., the city's open drugscene epicentre), respectively; currently having an area restriction or outstanding warrant;and injecting drugs while incarcerated (e.g., detention, prison, or jail). Unless otherwiseindicated, all variables refer to the 6-month period preceding the date of the interview.We compared the sociodemographic characteristics of those who initiated MA injectionversus those who did not using the Pearson chi-square test and the Wilcoxon rank sum test.We then used the Kaplan-Meier method to generate the survival function and cumulativeincidence of MA injection initiation over the study period. Based on previous research fromour setting demonstrating increased rates of MA use among street-involved youth (Wood etal., 2008), we stratified the survival function by age at baseline (i.e., <24 versus ≥24). Thetime to initiating MA injection was estimated by taking the midpoint between the date of thefirst interview during which MA injection was reported and the preceding interview inwhich the participant was MA-injection naïve. To examine changes in the values of theexplanatory variables over time, Cox proportional hazards models were used to calculate theunadjusted hazard ratio for each variable. We used a lagged method to estimate theassociation between each independent variable and the outcome of interest. Specifically, toavoid associations attributable to reverse causation, the information recorded at the lastfollow-up prior to the estimated date of MA injection initiation was used for these analyses.Since the primary objective of this study was to determine the set of individual, social,environmental, and economic factors which best predicted MA injection initiation, we choseto construct an explanatory multivariate model. A modified backward stepwise regressionwas used to select covariates based on two criteria: the Akaike information criterion (AIC)and p-values (Harrell, 2001). Lower AIC values indicate a better overall fit and lower p-values indicate higher variable significance. Starting with a full model containing allcandidate variables, covariates were removed sequentially in order of decreasing p-values.At each step, the p-values of each variable and the overall AIC were recorded, with the finalmodel having the lowest AIC. This model building procedure has been justified elsewhere(Lima et al., 2008). Statistical analysis was conducted using SAS version 9.1.3 (SASInstitute Inc., Cary, North Carolina) and all p-values are two-sided.ResultsBetween June 2001 and May 2008, 1,878 participants completed a baseline and at least onefollow-up interview and were eligible for this analysis. We excluded 541 (28.8%)individuals who reported injecting MA prior to the beginning of the study period, as well as20 (1.5%) for whom MA use data were not available; therefore, 1317 MA-injection naïveparticipants were included in the final study sample. Participants who had already initiatedand were thus excluded did not differ with respect to age but were more likely to be maleand of Caucasian ethnicity (both p<0.001). The median age at first interview during thestudy period was 39.9 (IQR: 32.2–46.1), 522 (39.6%) were female, and the majority (n =716, 54.5%) were of Caucasian ethnicity. Detailed sociodemographic information of thestudy sample is provided in Table 1. To investigate potential loss to follow up bias, wecompared the sociodemographic characteristics of the 177 (13.4%) participants who neverreturned for follow-up with those who remained in the study. Participants lost to follow updid not vary with respect to age (p=0.809), sex (p=0.493), ethnicity (p=0.807), sexual abuse(p=0.993), or baseline crack use (p=0.396) and non-injection MA use (p=0.253). However,Marshall et al. Page 4Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscriptthose lost to follow-up were more likely be homeless at baseline (26.7% vs. 19.3%,p=0.023).During the 7-year study period, eligible participants contributed 4,638 person-years offollow-up over 8,955 interviews. Thus, the average amount of time between follow-upinterviews was 6.2 months. In total, 200 individuals reported initiating MA injection,resulting in an overall incidence density of 4.3 per 100 person-years (95%CI: 3.7–4.9 per100 person-years). The Kaplan-Meier curve and cumulative incidence of MA injectioninitiation stratified by age at study entry are shown in Fig. 1. Among young injectors (i.e.,less than 24 years of age), the cumulative incidence of MA injection reached almost 40%over the 7-year study period.The results of the Cox proportional hazards analyses are shown in Table 2. The results of thebivariate analyses are shown in the first two columns, and all variables retained in the finalmultivariate model are displayed in the last two columns of Table 2. Factors that remainedsignificant in multivariate analysis and were positively associated with an increased hazardof MA injection initiation included: CSA (adjusted hazard ratio [aHR]=1.63, 95%CI: 1.18–2.23, p=0.004), using drugs in the DTES (aHR=2.15, 95% CI: 1.49–3.10, p<0.001),homelessness (aHR=1.43, 95% CI: 1.01–2.04, p=0.047), non-injection crack use(aHR=2.06, 95%CI: 1.36–3.14, p=0.001) and non-injection MA use (aHR=3.69, 95%CI:2.03–6.70, p<0.001). Older age (aHR=0.96 per year, 95%CI: 0.95–0.98, p<0.001) andfemale sex (AOR=0.58, 95%CI: 0.41–0.82, p=0.002) were protective for MA injectioninitiation. We note that while gender was not associated with initiation in bivariate analysis,the adjusted estimate was highly significant. Further investigation revealed that theprotective effect of female gender not seen in bivariate analysis was due to the higherprevalence of CSA among women.As a sub-analysis, we sought to determine whether a different model-building protocol otherthan an AIC-based approach significantly altered the interpretation of our results. To do so,we fit a multivariate model consisting of all variables significant at p<0.05 in bivariateanalyses. The two modeling strategies produced the same set of predictors (data not shown),thus suggesting that the results displayed in Table 2 are robust and not an artifact ofpredictor selection procedure.DiscussionThe present study revealed a high incidence of MA injection initiation, particularly amongyoung IDU, stimulant users, the homeless, and among those involved in the city's open drugscene. These results indicate that a variety of individual, social, and environmental factorsincrease the likelihood of initiating MA use among established injectors, and suggest that abroad set of interventions based on a risk environment framework are required to preventMA injection initiation and resultant harms.This analysis demonstrates that several individual-level factors were independentlyassociated with MA injection initiation among a cohort of adult IDU. For example, ourresults support previous research showing that young people are at high risk of MA injectioninitiation (Wood et al., 2008); therefore, young IDU should be a major focus of interventionsthat seek to prevent MA injection initiation. However, given that many participants initiatedMA injection relatively late in their drug use careers, we argue that preventive interventionsshould also include strategies for older IDU in addition to programs targeted to youngerpopulations and new injectors. Our finding that childhood sexual abuse was independentlyassociated with MA injection initiation is not surprising given previous researchdemonstrating a high prevalence of CSA among MA treatment samples (Messina et al.,Marshall et al. Page 5Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript2008) and the existence of a dose-response relationship between frequency of CSA andlikelihood of MA initiation in young adulthood (Hayatbakhsh et al., 2009). Although moreresearch is required to establish the causal relationship between CSA and MA use, onepossible explanation is that individuals with psychopathology arising from traumaticchildhood experiences gravitate towards MA use as a coping strategy and form of self-medication (Halkitis & Shrem, 2006; Jaffe et al., 2005). CSA has also been shown to predictengagement in other adverse health behaviors including injection drug use initiation and sexwork (Ompad et al., 2005; Stoltz et al., 2007); therefore, tailored and targeted programs thatprovide support and services to drug users who have experienced CSA are recommended.Transitions from non-injection to injection heroin use have been relatively well-described(Des Jarlais et al., 2007; Neaigus et al., 2001, 2006); furthermore, extensive poly-drug use(including the concurrent use of amphetamine-type substances) and transitions to MAinjection have also been observed among heroin users (Darke et al., 1999). We found thatthe non-injection use of MA was a strong and independent predictor of initiating MAinjection, which supports previous studies demonstrating that transitions from non-injectionto injection modes of MA consumption are common (Darke et al., 1994; Wood et al., 2008).Crack cocaine use was also found to predict MA injection initiation, which complementsprevious research demonstrating that crack smoking is a major predictor of initiation intoinjection drug use among youth (Fuller et al., 2001; Roy et al., 2003). Preliminary work alsosuggests that MA use is less persistent and has shorter periods of regular use over the lifecourse as compared to heroin and cocaine (Hser et al., 2008). Further research is required tofully elucidate the typologies and trajectories of MA use in this setting.Macro-level factors including drug market conditions are also believed to play an importantrole in drug use transitions (Des Jarlais et al., 2007). For example, although precursorregulations in the United States have resulted in substantial but transient reductions in MApurity and MA-related hospital admissions (Cunningham & Liu, 2003), a recently publishedstudy examining the effect of Canadian MA precursor regulations suggested that thesepolicies were associated with increases in MA-related hospital admissions (Callaghan et al.,2009). Clearly, conventional supply reduction strategies, particularly those operating in theabsence of other “demand reduction” interventions, have failed to reduce MA supply anduse in Canada. It is for these reasons that a comprehensive approach, including programsthat seek to reduce the demand for MA, have been strongly endorsed by organizationsincluding the United Nations Office on Drugs and Crime (2009).Consistent with the risk environment framework, social and environmental factors thatfacilitate exposure to broader drug use scenes are also found to predict MA injectioninitiation. For example, we observed a strong relationship between involvement in the city'sopen drug scene and an increased incidence of MA injection. Further research is required toinvestigate the impact of these environments on drug use initiation and transitions; however,a recent network analysis of IDU living in Winnipeg, Canada identified a strong relationshipbetween a higher connectedness to communal injection drug use settings and HIV riskbehavior and polydrug use (Wylie et al., 2007). It may be that an open drug scene representsone such setting in which individuals are more likely to be introduced to novel drugs andmodes of use. Future studies should investigate how interventions that alter or preventexposure to open drug scenes mitigate the risk of initiating MA injection. For example,supervised injecting facilities have been shown to be effective micro-environmentalinterventions that modify the drug using environment and thus reduce risk behavior andother drug-related harms (Kerr et al., 2007). Finally, our finding that homelessness wasindependently associated with MA injection initiation supports other studies demonstrating astrong link between unstable housing status and engagement in HIV risk behaviour amongIDU (Coady et al., 2007;Corneil et al., 2006).Marshall et al. Page 6Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptAlthough increased resources are required to reduce the risks associated with injection druguse broadly, the results of this study have important implications for interventions whichaim to prevent transitions to MA injection and avert MA-specific risks and harms. Giventhat factors both endogenous (e.g., age) and exogenous (e.g., involvement in open drugscenes) to the individual were independently associated with initiating MA injection, weargue that comprehensive programs that address a broad set of individual, social, structural,and environmental factors are required to prevent MA initiation among IDU. Since limitedevidence exists to suggest the long-term effectiveness of supply reduction strategies(Borders et al., 2008; Callaghan et al., 2009) alternative interventions that address economicand social inequities are recommended. A growing literature has demonstrated thatstructural interventions based on a risk environment approach effectively reduce HIV riskamong marginalized populations (Blankenship et al., 2006; Des Jarlais, 2000). We arguethat a similar framework may be equally appropriate for implementing programs that aim toprevent MA injection initiation. For example, the expansion of stable and low-thresholdhousing programs for active drug users has been posited as a highly effective structural HIVprevention strategy (Shubert & Bernstine, 2007). Our results suggest that low-thresholdhousing may also prevent transitions to other modes and types of drug use by way ofreducing exposure to chronic homelessness and open drug scenes among substance-usingpopulations. We also point to research demonstrating that efficacious treatment modalitiesare available for patients with MA dependence (Hser et al., 2005; Rawson et al., 2004).Although psychosocial approaches are the mainstay of MA treatment, some substitutiontherapies are promising (Rose & Grant, 2008). While further research in this area is needed,the immediate expansion of evidence-based treatment for MA dependence among IDUpopulations as a means of preventing the transition to MA injection should be a publichealth priority.There are several limitations of this study that should be noted. We were unable to obtain arandom sample of injectors; therefore, the findings cannot necessarily be generalized to theentire IDU community or to other populations. However, we note that the sociodemographiccharacteristics of our sample are similar to those of other studies conducted in BritishColumbia (Public Health Agency of Canada, 2006). Furthermore, the geographic patterns ofMA production and availability vary across North America (Maxwell & Rutkowski, 2008).In this manner, the observed incidence and predictors of MA initiation in this study may notbe representative of other urban centers in North America or elsewhere. The study is alsosusceptible to recall bias and socially desirable reporting, although we have no reason tobelieve that the magnitude of these biases would differ between MA initiates and non-initiates. Since a question ascertaining lifetime history of MA injecting was not added untilthe second round of baseline interviews, we were not able to obtain this information for 268(14.3%) participants. However, since methamphetamines were uncommon in Vancouverprior to 2001 (Buxton, 2005), few of these individuals would have initiated MA injectingbefore enrolment; thus, we expect the magnitude of this bias to be acceptably small. Finally,as in other survival analyses of observational data, noninformative censoring may havebiased the results. However, we did not observe any sociodemographic differences betweenthose lost to follow-up and those who remained in the study.In summary, we observed a high incidence of methamphetamine injection initiation among acohort of established injectors. An important limitation of many previous studiesinvestigating the relationship between MA use and HIV risk behavior among IDU is thecross-sectional nature of the analyses, precluding conclusions regarding temporalrelationships. We report here a longitudinal analysis demonstrating that several factorsamenable to public health intervention preceded the initiation of MA injection. Given therisks and harms associated with MA use among IDU populations, the development,implementation and evaluation of these programs should be a public health priority.Marshall et al. Page 7Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptAcknowledgmentsWe would particularly like to thank the VIDUS participants for their willingness to be included in the study as wellas current and past VIDUS investigators and staff. We would specifically like to thank Dr. Thomas Patterson,Deborah Graham, Peter Vann, Caitlin Johnston, Steve Kain, and Calvin Lai for their research and administrativeassistance. This work was supported by the US National Institutes of Health (NIH) [R01 DA011591] and theCanadian Institutes of Health Research (CIHR) [RAA-79918]. Thomas Kerr is supported by the Michael SmithFoundation for Health Research (MSFHR) and the CIHR. Brandon Marshall is supported by senior graduate traineeawards from MSFHR and CIHR.ReferencesBlankenship KM, Friedman SR, Dworkin S, Mantell JE. Structural interventions: Concepts, challengesand opportunities for research. Journal of Urban Health. 2006; 83:59–72. [PubMed: 16736355]Borders TF, Booth BM, Han X, Wright P, Leukefeld C, Falck RS, et al. Longitudinal changes inmethamphetamine and cocaine use in untreated rural stimulant users: Racial differences and theimpact of methamphetamine legislation. Addiction. 2008; 103:800–808. 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[PubMed:17074527]Marshall et al. Page 11Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptFig. 1.Younger age is associated with methamphetamine injection initiation among a cohort ofinjection drug users, 2001–2008 (n=1317)Marshall et al. Page 12Prev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptMarshall et al. Page 13Table 1Sociodemographic characteristics of injection drug users who did and who did not initiate methamphetamineinjection, 2001–2008 (n = 1317)Characteristic Initiated MA Injection n=200 Did Not Initiate MA Injection n=1117 p-valueAge† (median, IQR) 36 (28–43) 40 (33–46) <0.001Age of First Injection (median, IQR) 18 (15–23) 19 (16–25) 0.002Sex (n, %)    Female 75 (37.5) 447 (40.0) 0.503    Male 125 (62.5) 670 (60.0)Ethnicity (n, %)    Caucasian 114 (57.0) 602 (53.9) 0.308    Aboriginal* 74 (37.0) 394 (35.3)    Asian 5 (2.5) 52 (4.7)    Black 5 (2.5) 35 (3.1)    Other 2 (1.0) 34 (3.0)Sexual Orientation (n, %)    LGBTa 16 (9.2) 81 (10.2) 0.678    Heterosexual 158 (90.8) 710 (89.8)†age at first interview during study period*Aboriginal includes self-identified First Nation, Inuit, or Métis ancestryaLGBT=lesbian, gay, bisexual, transgender/transsexualPrev Sci. Author manuscript; available in PMC 2012 June 1.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptMarshall et al. Page 14Table 2Cox proportional hazards model of time to initiating methamphetamine injection among a cohort of injectiondrug users (n = 1317)Characteristic Unadjusted HR* (95% CI) p-value Adjusted HR* (95% CI) p-valueAge (per year older) 0.96 (0.95–0.98) <0.001 0.96 (0.95–0.98) <0.001Sex (female vs. male) 0.86 (0.64–1.14) 0.291 0.58 (0.41–0.82) 0.002Ethnicity (Caucasian vs. other) 1.22 (0.92–1.61) 0.173Relationship Status (married vs. single/dating) 0.63 (0.42–0.93) 0.019Sexual Orientation (LGBTa vs. heterosexual) 0.86 (0.52–1.44) 0.576Sexual Abuse‡ (yes vs. no) 1.44 (1.08–1.90) 0.012 1.63 (1.18–2.23) 0.004Age of First Injection (per year older) 0.98 (0.96–0.99) 0.016Buy Drugs in DTESc† (yes vs. no) 2.40 (1.71–3.36) <0.001Use Drugs in DTESc† (yes vs. no) 2.78 (1.97–3.92) <0.001 2.15 (1.49–3.10) <0.001Homeless† (yes vs. no) 2.34 (1.68–3.25) <0.001 1.43 (1.01–2.04) 0.047Non-injection Crack Use† (yes vs. no) 3.14 (2.11–4.67) <0.001 2.06 (1.36–3.14) 0.001Non-injection MAb Use† (yes vs. no) 4.54 (2.52–8.16) <0.001 3.69 (2.03–6.70) <0.001Injection Heroin Use† (yes vs. no) 2.15 (1.59–2.89) <0.001Injection Cocaine Use† (yes vs. no) 1.71 (1.24–2.35) 0.001Inject with a Sex Partner† (yes vs. no) 1.17 (0.77–1.76) 0.463Inject with a Friend† (yes vs. no) 1.82 (1.35–2.44) <0.001Syringe Sharing† (yes vs. no) 1.75 (1.07–2.85) 0.025Warrant or Area Restriction¶ (yes vs. no) 2.02 (1.35–3.00) 0.001Methadone Maintenance Therapy¶ (yes vs. no) 0.89 (0.66–1.21) 0.463Inject while Incarcerated† (yes vs. no) 3.93 (0.97–15.91) 0.055Note: Variable selection based on AIC and type III p-values as described in (Lima et al., 2008).*HR=Hazard RatioaLGBT=lesbian, gay, bisexual, transgender/transsexualbMA=methamphetaminecDTES=Downtown Eastside†refers to activities in the past 6 months‡refers to lifetime experiences¶refers to current experiences.Prev Sci. Author manuscript; available in PMC 2012 June 1.

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