We used data from a longitudinal cohort of 1,875 IDUs in Baltimore City to investigate the association between living in a poorer neighborhood and the probability of injection cessation. Inverse probability weighted models showed that IDUs living in more impoverished neighborhoods were less likely to stop injecting drugs, independent of individual-level covariates.
We examined the sensitivity of results to different modeling strategies for handling selection into neighborhoods. After finding an inverse dose-response association between levels of poverty and the probability of injection cessation in both crude unadjusted models and models adjusted for time-fixed baseline covariates, we used multivariable regression to adjust for an array of measured individual-level covariates hypothesized to influence our exposure and outcome; this approach is commonly utilized by neighborhood effects studies (refer, for example, to Nandi et al. (39
), Williams and Latkin (40
), Wu et al. (41
), and Maas et al. (42
)). We then compared these results with those from a stabilized inverse probability weighted regression model with random effects.
Overall, there was a significant inverse association between neighborhood poverty and the probability of cessation in models that used IPW to handle confounding but not when the traditional regression approach was used. These divergent results suggest that use of traditional regression to handle confounding in neighborhood effects studies may induce bias because the individual-level characteristics frequently adjusted for may be time-varying covariates affected by prior exposure. For example, as illustrated in , time-varying covariates such as employment in the formal economy may predict whether a drug user lives in a relatively wealthier neighborhood. In turn, living in a wealthier neighborhood may influence future employment prospects and the probability of drug injection cessation. Similarly, drug use covariates related to a drug user's proclivity for injecting with others may predict whether a drug user lives in a poorer neighborhood with more shooting galleries versus a relatively richer neighborhood with fewer opportunities to share injection equipment, which may then influence injection behaviors during a subsequent time period.
Figure 1. Causal diagram of the effect of neighborhood poverty on cessation of injection drug use (IDU). “Covariates” denote time-varying variables (i.e., age, employment, formal income, jail, homelessness, human immunodeficiency virus status, any (more ...)
Analyses of our data showed that sociodemographic characteristics, particularly employment status, were time-varying covariates affected by prior levels of exposure. By adjusting for individual-level factors related to socioeconomic status, studies on drug use may overcontrol for covariates on the pathway between a neighborhood exposure and outcome or induce selection bias due to collider stratification (24
). Further neighborhood effects research should investigate the use of IPWs for handling confounding due to determinants of neighborhood selection.
Although work on the influence of the neighborhood environment on injection drug use cessation is sparse, our main finding of an inverse association between neighborhood poverty and the probability of cessation is consistent with prior observational work on illicit drug use (40
). For example, a study of 1,305 adults recruited from high-drug-risk areas in Baltimore City found that neighborhood poverty was significantly associated with current heroin and cocaine use even after accounting for network attributes (40
); another analysis conducted among 835 adults from the same study population showed that IDUs in more disordered Baltimore neighborhoods had higher levels of depression and that depression was associated with greater injection frequency (47
The level of socioeconomic deprivation in a neighborhood may influence the probability of cessation through a number of distinct pathways. For example, less impoverished neighborhoods may engender a social context more conducive to cessation through the enforcement of informal social control mechanisms and subsequent inhibition of dense drug use networks, as well as through the promotion of resources facilitating integration into mainstream society and the formal economy. The potential importance of the social environment in less socioeconomically deprived neighborhoods is supported by work showing associations between levels of disorder and the extent of drug use networks and between drug use networks and individual cessation (10
An alternative mechanism relates to the material environment of neighborhoods. For example, less impoverished neighborhoods may inhibit opportunities for purchasing drugs while promoting access to treatment resources. Clearly, a combination of these potential mechanisms, as well as others not discussed here, may explain the association we observed. Future work should focus on elucidating the pathways between socioeconomic deprivation and cessation of injection drug use.
There were a number of limitations to this study. First, the use of IPWs does not address unmeasured confounding. We attempted to minimize unmeasured confounding by including measures of as many recognized potential confounders as possible. Second, our weights were not multiplied across time and therefore do not account for cumulative effects of neighborhood poverty on cessation. However, longitudinal patterns of injection in the ALIVE study show that patterns of relapse are substantially more frequent than patterns of consistent abstinence of injection drug use (37
), suggesting that the immediate 6-month interval preceding each study visit may be more important than long-term cumulative effects with respect to injection patterns. Third, we used only those variables measured in the prior 6 months as predictors in our weight models. To assess the sensitivity of our results to this decision, we conducted an analysis that incorporated the prior 2 visits in our weight models and found that our effect estimates did not change appreciably. Fourth, we excluded a number of visits, including those when a participant did not provide identifiable address information and those for which information on covariates of interest was missing. This exclusion may have introduced selection bias. However, the ALIVE study has standardized procedures for data collection and quality control and has minimized occurrence of missing data, particularly given its transient drug-using study population. Additionally, the distributions of key demographic factors were not significantly different in the study sample before and after missing visits were excluded. Fifth, we excluded residences outside of Baltimore City because they represented a limited number of contributions as well as visits during which the participant reported no address/homelessness. However, this occurrence was relatively uncommon, and we geocoded visits to shelter addresses if provided. Sixth, respondents were geocoded to their census tract of residence. It is plausible that respondents are influenced by multiple contexts outside of their own neighborhoods, and further work should attempt to quantify the impact of multiple environments on drug injection behaviors. Seventh, we relied on census tracts as proxies for neighborhoods. Although census tracts are a more practical unit of analysis because secondary data are more commonly aggregated to these units, they may be less sociologically valid to the extent that they represent the underlying processes theorized to be significant to the health of their residents. In this case, the availability of census tract data outweighed the potential benefits of using other boundaries, such as neighborhood statistical areas, which may have been more sociologically valid. Finally, we used 1990 US Census data to define our measure of neighborhood poverty. Although levels of poverty changed among Baltimore census tracts between 1990 and 2000, a sensitivity analysis using linearly interpolated poverty values produced qualitatively similar findings.
Our findings suggest that the neighborhood environment may be an important determinant of drug injection behaviors independent of individual-level characteristics. In contrast to adjusted models, models fit with inverse probability of exposure weights showed a robust association between neighborhood poverty and the probability of injection cessation. Future neighborhood effects research should consider the use of IPW as a method to address confounding by determinants of neighborhood selection, specifically for handling time-varying confounders affected by prior exposure. Some of the most important factors influencing persistent drug use among IDUs may be the environments in which risk is produced rather than individual-level factors that are frequently the emphasis of drug use research.
Our work has important implications for treatment delivery. Pairing drug treatment with job training or other programs that facilitate mobility into more economically stable neighborhood environments may increase the probability of injection drug cessation. Further work elucidating the pathways linking the neighborhood environment to drug use behaviors is warranted.