Introduction

Urban drug scenes, which are typically characterized by a high prevalence of illicit drug dealing and use, violence and poverty, can be found in many urban cities around the world [1–3]. While the visibility and size may vary across settings, such drug scenes are conceptualized as distinct risk environments that can increase the vulnerability to poor health among the inhabitants [4–6]. Repressive law enforcement is a commonly employed measure to address complex problems within drug scenes, although this approach has been shown to increase gun violence and homicide rates [7], and they typically fail to produce sustained changes in public order within drug scenes [8]. Conversely, it is also common that drug scenes are sites for a variety of assertive health and social services due to the concentration of marginalized populations, including people who use illicit drugs (PWUD), and the range of health challenges many contend with [1,3,9]. While some European cities have been successful in closing down their drug scenes via coordinated law enforcement and public health efforts, globally, many urban drug scenes remain active. This raises concerns that these settings may attract young PWUD and facilitate ongoing cycles of problematic substance use [1].

PWUD are known to have high residential mobility (hereafter ‘mobility’), which has been shown to be associated with poor health outcomes such as HIV infection [10,11]. Previous research has suggested that there are likely many competing “push-pull” factors that influence PWUD’s decision to move, including access to drugs, poverty, employment, safety, previous involvement in the criminal justice system, and enrolment in addiction treatment programs [10,12,13]. While these diverse factors have been explored primarily through qualitative and ethnographic research, few quantitative studies have attempted to identify and characterize common patterns of moving into urban drug scenes among PWUD. This knowledge gap makes it challenging to address potential vulnerabilities associated with mobility among PWUD when planning a health service system within and outside of the urban drug scenes.

The Downtown Eastside (DTES) neighborhood in the city of Vancouver, Canada is known as one of the largest urban open drug scenes in North America, with widespread illicit drug use, as well as a high prevalence of poverty, violence, mental health and infectious diseases [14–16]. Also prevalent in the DTES are single room occupancy hotels (SROs), which are typically marked by sub-optimal and unhygienic conditions and yet represent the primary housing option for low-income populations, especially those dependent on government assistance [17,18]. Further, like other urban drug scenes elsewhere, the DTES is also home to a concentration of a range of low-threshold health and social services, including homeless shelters, supportive housing, food banks, on-demand addiction treatment, and innovative harm reduction services [16]. In this context, using data from three long-running, large prospective cohort studies of PWUD in Vancouver, we sought to characterize longitudinal patterns of moving into the DTES over the past decade, and identify socio-demographic, behavioural and social-structural factors associated with mobility patterns among PWUD.

It is important to note that the present study does not assume that moving into the DTES automatically represents changes in health or socioeconomic status of individuals. However, given the public health challenges associated with this neighborhood as described above, the present study aimed to better understand PWUD’s mobility patterns into the urban drug scene in a large cohort of community-recruited PWUD and thereby inform the planning of health and social services provision within and outside of the neighborhood to improve the health of PWUD. This applied policy perspective focusing on the health is important considering that punitive law enforcement has historically been a dominant approach to address illicit drug use [19] and has typically aimed to remove PWUD from certain neighborhoods or public spaces [20]. This approach has largely neglected PWUD’s experiences of social and economic sufferings that may have driven their problematic drug use, and has likely served to compound high mobility among this population [8]. While some qualitative and ethnographic studies have explored how social, structural and geographical factors interact with individual drug use and shape PWUD’s mobility [12,13], these research findings have not been corroborated by larger quantitative studies, likely due to the difficulty obtaining longitudinal mobility data from PWUD. In this regard, our study is uniquely positioned to contribute rare quantitative evidence to advance theories regarding factors associated with mobility patterns among PWUD [10,12,13]. Also, given that many of the health services targeted for PWUD requires continuum of care (e.g., opioid agonist maintenance therapy), an empirical examination of mobility patterns among PWUD such as this is important to inform more effective provision of health and social services among this population, which has long taken a back seat behind the law enforcement-based policy approach to illicit drug use [19].

Materials and methods

Results

Participant characteristics

In total, 906 participants were eligible for the present study and provided 9,317 observations. The median number of semi-annual study follow-up per participant was 10 (quartile [Q] 1–3: 5–14). As shown in Table 1, 280 (30.9%) were female, 247 (27.3%) self-identified as Indigenous, and the median age was 30 (Q 1–3: 22–44) years. In total, 697 participants were excluded from the analysis because they did not complete at least four study visits. Compared to those included (n = 906), those excluded were younger (median: 23 [Q1-3: 20–26]; p<0.001) and less likely to self-identify as Indigenous (n = 134, 19.2%, p<0.001). There was no significant difference with regard to sex between the two samples (female, n = 219, 31.4%, p = 0.825).

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Table 1. Sample characteristics and bivariable multinomial logistic regression analyses of baseline factors associated with mobility trajectory groups among people who use drugs in Vancouver, Canada (n = 906).

https://doi.org/10.1371/journal.pone.0224993.t001

Latent class growth analysis

The LCGA resulted in a final cubic model with four trajectories (gradual move to DTES vs. move in then out vs. early move to DTES vs. consistently living outside DTES) (BIC = 3637.40; average posterior probability of ≥0.73). These four-group trajectories of mobility are visualized in Fig 1. As shown, the largest group (Group 1, 52.8%) represented those who had consistently lived outside of DTES. The second largest group (Group 2, 19.5%) included those who had gradually moved into DTES over time, with the slope of this trajectory increasing around 2.5 years after the baseline. Group 3 (15.8%) included individuals who had first moved into DTES but then moved out of DTES at a later time point. The smallest group (Group 4, 11.9%) included those who had made early move into DTES as represented by an early spike in the slope at around 6 months after the baseline.

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Fig 1. Four-group trajectories of moving into DTES, adjusting for baseline demographic characteristics (age, sex, and ethnicity).

Red: Consistently living outside of DTES (Group 1). Green: Gradual move into DTES (Group 2). Blue: Move in then out (Group 3). Black: Early move into DTES (Group 4).

https://doi.org/10.1371/journal.pone.0224993.g001

In terms of the three baseline demographic covariates (age, sex and ethnicity) included in the LCGA model, age was significantly and negatively associated with membership in the “gradual move to DTES” group (odds ratio [OR]: 0.65; 95% confidence interval [CI]: 0.51–0.81) and the “move in then out” group (OR: 0.61; 95% CI: 0.48–0.79) compared to the “consistently living outside DTES” group. Indigenous ethnicity was significantly and positively associated with belonging to the “early move to DTES” group (OR: 2.38; 95% CI: 1.41–4.00) or the “move in then out” group (OR: 3.12; 95% CI: 1.75–5.57) compared to the “consistently living outside DTES” group.

Multinomial logistic regression analysis

The results of bivariable and multivariable multinomial logistic regression analyses of baseline factors associated with mobility trajectory patterns (the reference category = the “consistently living outside DTES” group) are shown in Table 2. In multivariable analyses, compared to the “consistently living outside of DTES” group, factors independently associated with the “gradual move to DTES” group included: incarceration (adjusted odds ratio [AOR]: 2.20; 95% CI: 1.29–3.74) and HIV seropositivity (AOR: 0.49; 95% CI: 0.27–0.88). Factors independently associated with the “move in then out” group included: living in an SRO (AOR: 4.01; 95% CI: 2.19–7.37) or being homeless (AOR: 1.88; 95% CI: 1.17–3.03) as opposed to being stably housed, as well as daily injection drug use (AOR: 2.64; 95% CI: 1.45–4.81). Factors independently associated with the “early move to DTES” included: living in an SRO (AOR: 3.40; 95% CI: 1.73–6.68) as opposed to being stably housed, daily injection drug use (AOR: 3.80; 95% CI: 1.84–7.82), incarceration (AOR: 3.73; 95% CI: 2.08–6.70), and HCV seropositivity (AOR: 2.30; 95% CI: 1.24–4.27).

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Table 2. Multivariable multinomial logistic regression analyses of baseline factors associated with mobility trajectory groups among people who use drugs in Vancouver, Canada (n = 906).

https://doi.org/10.1371/journal.pone.0224993.t002

Discussion

In our community-recruited sample of PWUD in Vancouver, nearly half of the participants who had resided outside the DTES at baseline moved into the DTES at some point over the 11-year study period. Among those who moved into the DTES, approximately one-third moved out thereafter. Both younger PWUD and Indigenous participants were more likely to have high mobility as they were more likely to move in and then out of the DTES. Overall, baseline factors that were independently and positively associated with moving into DTES included: unstable housing, recent incarceration, daily injection drug use and HCV seropositivity. In contrast, those living with HIV at baseline were less likely to move into the DTES.

To our knowledge, this is the first study that characterizes longitudinal patterns of mobility into an urban drug scene among a large sample of PWUD over an extended period of time. In particular, the four distinct mobility patterns that were identified by the LCGA are novel findings that we would have never been able to identify without using this analytical technique. The differences and similarities in the predictors of these mobility patterns provide some nuanced insight into how broader social-structural factors might influence mobility in this socially marginalized population and have some important implications for the planning of health and social services targeting this population.

While reasons for moving into DTES need to be further examined, the finding that the availability of drugs or alcohol, having family or friends in the neighborhood, as well as health and social services were among the most commonly cited reasons for visiting the DTES (though they were not the reasons for moving into the DTES) reflects the concentration of both illicit drug markets and the availability of low-threshold public services in this neighborhood [33,34]. Our findings suggest that these factors may have acted as pull factors in some PWUD’s decision to move into the DTES. For example, we found that markers of high intensity drug use, including daily injection drug use and HCV infection, were positively associated with early move to and the subsequent settlement in the DTES. For these PWUD, the proximity to illicit drug markets might have been a reason to relocate into the DTES, while the availability of some health and/or social services might have kept them in the neighborhood. Consistent with this interpretation, the escalating use of social services associated with moving into the DTES has also been found among individuals who were homeless and mentally ill [34]. However, it should be noted that only approximately 20% of participants reported coming to the DTES to access health and social services (10% for health services, and 10% for other services), with a greater number reporting that they came to the neighborhood for drugs/alcohol (30%) or to see family and friends (15.9%). In terms of healthcare, there is substantial evidence indicating that many PWUD experience discrimination in conventional healthcare settings [35,36]. It may be that the service providers in the DTES, given their extensive experience with the population, are providing more culturally appropriate and less discriminatory care, and by consequence many PWUD are actively seeking care there rather than relying on healthcare settings elsewhere.

Younger PWUD were more likely to gradually move into the DTES or move in then out of DTES. Our findings are in line with previous ethnographic studies describing that young PWUD in Vancouver symbolically associate the DTES neighborhood with danger and try to avoid visiting or seek to exit the neighborhood once having moved in [12,13]. This is in contrast to a commonly held claim that urban drug scenes attract youth [2]. In this regard, Sandberg and Pedersen criticized such claims as being based upon the almost hegemonic public representations of youth and PWUD as passive and irrational, and might serve to misinform public policies [20]. Our findings also indicate more nuanced mobility trajectories of younger PWUD, resisting the overly simplistic assumption that young PWUD are drawn to urban drug scenes. The findings call for more research into and policy action to address some potential social-structural drivers of young PWUD’s mobility into urban drug scenes, including homelessness associated with aging out of foster care [37].

Consistent with previous research [12,38], we observed large effects of baseline housing status on mobility trajectories. Specifically, PWUD who were homeless at baseline were more likely to move into the DTES but relocate out of the DTES later. This finding is congruent with a previous study showing persistently high residential mobility among homeless populations even after implementing housing interventions [39]. In our study setting, those who were homeless at baseline might have had to come to the DTES for shelters or other affordable housing options, such as SROs. Likewise, some of those who resided in SROs at baseline were also more likely to move in then out of the DTES. These findings may speak to the vulnerability of SRO tenants, particularly PWUD, to evictions and the subsequent residential instability [17,40]. The type of housing an individual is able to find in the DTES may influence settlement status in the DTES after the move. With an ongoing major housing crisis impacting our study setting, SROs are likely among the most common (or arguably the only) housing options available to our study population [18,41], which might have led to recurring housing instability. Even if they did not, poor conditions of many SROs have been shown to be associated with a range of negative health outcomes [17]. While future research should investigate the types and quality of housing that those participants typically resided in after moving within and outside of the DTES (and if they became or continued to be homeless after the move), it is likely that the housing quality was poor in the context of the ongoing housing crisis in our study setting. Therefore, our findings point to the need to establish affordable housing options that meet a minimum housing and health standards both within and outside of the DTES and make sure that a variety of housing types be offered to accommodate various needs (e.g., housing for families vs. singles, supportive housing with on-site supports and connections to health care services, etc.).

Similarly, we found that PWUD who had recently been incarcerated at baseline were more likely to move to and stay in the DTES, and the effect of incarceration was independent of housing status at baseline. Previous research has demonstrated that incarceration is strongly associated with persistent housing instability among people who were homeless or vulnerably housed at baseline [42]. Incarceration has also been shown to be associated with profound socioeconomic marginalization more broadly resulting from the criminal justice system involvement [43]. It is likely that PWUD who had been incarcerated had to resort to the DTES not only for transitional housing options but also for other social services for individuals released from custody, which are concentrated in the neighborhood. They might stay in the DTES due to the continued socioeconomic instability including difficulty obtaining employment [43].

We also found that Indigenous PWUD were more likely to move into the DTES early or move in then out of DTES. Previous research has suggested that Indigenous peoples in Canada generally experience higher rates of relocation within urban cities compared to their non-Indigenous counterparts, and their residential mobility is often involuntary, reflective of poor living conditions on reserves, as well as intergenerational in nature and influenced by colonial practices [26–28]. For example, Belanger, Awosoga and Head estimated the prevalence of homelessness among urban Indigenous peoples in Canada at 6.97%, while a national average was only 0.78% [28]. The overrepresentation of Indigenous peoples among the homeless has been attributed to the history of colonial policies, which have marginalized, displaced and dispossessed Indigenous peoples of their lands and thereby dismantled their family and cultural structures [28]. While further study is warranted to examine the extent to which socio-historic structures influence trajectories of Indigenous PWUD in our study population, it is noteworthy that previous studies associate frequent mobility of Indigenous peoples with poverty, racism, housing evictions, violence and criminal justice system involvement, as well as poor health outcomes [27,44]. Past research has also shown that Indigenous peoples often move back and forth between their home communities and inner-city centres [45,46]. Clearly, more research, both quantitative and qualitative, is needed to examine the unique contexts of moving into DTES and the consequences among Indigenous PWUD, accounting for broader socio-historic and structural factors. Also, given that the negative effects of colonial legacies extend across multiple generations, it is important to ensure that a reconciliation process, which has been called for by the Truth and Reconciliation Commission of Canada [47], be implemented.

Finally, those living with HIV at baseline were less likely to move into the DTES. Over the past decade, extensive multidisciplinary HIV care programs have been implemented in this setting following the emergence of a major epidemic of HIV infection with an aim to improve linkage, engagement and retention in evidence-based healthcare among those living with HIV [48]. Multidisciplinary teams provide outreach, peer support, clinical care, facilitation for housing and other social services, and addiction treatment services for PWUD [48]. Past research has shown high rates of engagement in HIV care among PWUD in this setting during a period of these community-wide HIV care initiatives [49]. Such integrated care models may have established social support networks outside of the DTES and prevented PWUD living with HIV from moving into the urban drug scene.

This study has some limitations. First, self-reported data may be influenced by some reporting bias, although such data have been shown to be mostly valid in studies involving PWUD [50]. Second, a non-random sample used in the present study limits the generalizability of our findings, although we note that it is virtually impossible to conduct random sampling of PWUD in the absence of registries of PWUD. In addition, our sample restrictions (i.e., completing at least four study visits) may have resulted in causing some selection bias. Given that the excluded were younger but less likely to self-identify as Indigenous, our findings may reflect the mobility patterns of older and/or Indigenous PWUD rather than younger and/or non-Indigenous participants, although it is difficult to estimate the direction of bias. Third, as with all observational research, the relationships between the explanatory variables and outcome assessed may be under the influence of unmeasured confounding, although we sought to address this bias with multivariable adjustment involving key predictors of mobility patterns. For example, we did not have data on histories of mobility prior to study enrolment, which may have influenced the observed associations. Also, while we used the baseline characteristics for the purpose of predicting distinct mobility trajectories, future research may examine time-varying effects of these characteristics. Further, because we did not have data on moving into areas other than the DTES, we were unable to identify the factors that were uniquely associated with moving into the DTES vs. other locations.

In sum, nearly half of PWUD in our sample moved into the DTES over the 11-year study period. Younger PWUD and Indigenous participants appeared to have particularly high rates of mobility, as did those with markers of social-structural vulnerability and high intensity drug use. These findings indicate a need to tailor the existing social and health services within the DTES for those moving in there and expand affordable housing options outside the DTES.

Supporting information

Acknowledgments

The authors thank the study participants for their contribution to the research, as well as current and past researchers and staff.