Web-scraped data included the outlet address and offerings . We geocoded the addresses to 2019 block groups using the ArcGIS World Geocoding Service . are the most granular geographic level at which the Census Bureau reports demographic and socioeconomic characteristics, and are considered reasonable proxies for neighborhoods.32Covariates included in the adjustment set were factors hypothesized to confound the policy–outlets relationship.We conceptualized race–ethnicity as socially-defined categories that reflect the distribution of risk, opportunities, and discrimination.Racial–ethnic groups were not mutually exclusive: Asian, Black, and White racial groups were defined irrespective of Hispanic identity, and the Hispanic group included people of any. Primary analyses adjusted for the proportions of Asian, Black, and Hispanic residents. Analyses considering effect measure modification by the racial–ethnic composition also utilized percent White. To quantify the association of local policies with outlet densities, we used a hierarchical Bayesian spatiotemporal Poisson regression. This approach uses conditional autoregressive random effects to account for spatial autocorrelation in outlet densities across neighboring block groups that otherwise gives incorrect statistical inferences . The model specification is presented in Box 1. We modeled outlet counts relative to the expected count assuming a distribution directly proportional to land area to reflect physical access.The primary associations of interest were the areal relative risks of outlets associated with each policy. We included block group-level spatially structured random intercepts to account for dependence of neighboring units, block group-level random intercepts and slopes assuming independence-of-units to allow the level and linear trend in outlets to vary independently for each block group, and jurisdiction level random intercepts to account for time-constant characteristics of jurisdictions.First,rolling grow table to characterize places with outlets, we fit spatiotemporal models with each covariate in turn as the only fixed predictor.
Then we estimated the associations of the policies with outlets, adjusting for all co-variates. We considered two sets of policy effects: First, associations for outlet bans among all study areas, and second, for the jurisdictions permitting outlets , associations for the 6 policies regulating outlet density/location. For both sets, we estimated the overall association of the policies with outlets and used interaction terms to test whether the associations varied by block group median income or racial–ethnic composition. To report interaction results, we computed associations for block groups at the 25th and 75th percentile of each moderating variable. For all estimates, we report the marginal posterior means and 95% credible intervals. Following recommended practice and prior empirical work, we implemented estimation using Integrated Nested Laplace Approximation with the INLA package within R version 4.0.4.37–We used the “BYM2” spatiotemporal model instead of the typical BYM or Leroux specification because this method better handles non-contiguous county “islands” and generates clearly interpretable parameters.Based on reference guides and prior empirical work, we used the INLA default priors.We considered a five-unit change in the Watanabe-Akaike information criterion to indicate improved model fit.Statistical code is provided in eAppendix 3. Because policies regulating the density or location of outlets are particularly relevant to urban areas, we considered models restricted to cities, excluding unincorporated county areas. Second, we tested models with expected counts of outlets proportional to population instead of land area . Third, because we did not observe all possible combinations six density- or location-related policies relevant to jurisdictions that permitted outlets , we summarized the combined effects of the six policies by estimating models replacing the individual policy variables with a summed policy count score . Fourth, we tested whether removing random effects led to better model fit.Table 2 presents characteristics of the study block groups.
The study covered 24 million people with varied demographics, socioeconomic positions, commercial environments, and political orientations. Of the 238 jurisdictions with residential populations, 182 banned outlets. Outlet bans were more common in jurisdictions with more White residents, higher median income, and less poverty. Among the six policies applicable to jurisdictions allowing outlets, the most common were buffers around sensitive locations , location restrictions , and density limits.Nearly half of jurisdictions allowing outlets required buffers between one cannabis outlet and another. Across the study jurisdictions, the total number of outlets increased from 170 in 2018 to 390 in 2020. Five percent of outlets were in jurisdictions that banned them, reflecting gaps in implementation, enforcement, and grandfathering. Table 3 presents the associations of each block group characteristic with observed outlet counts relative to expected. Throughout the study period, most block groups had 0 outlets— fewer than the number expected assuming a distribution directly proportional to land area. Across models, the proportion of the marginal variance in the block group random intercepts explained by the BYM2 spatially structured block group random intercepts ranged from 0.02 to 0.53. The Figure presents the adjusted associations of outlet bans with cannabis outlet counts, overall and by neighborhood median income and racial–ethnic composition. As hypothesized, bans were associated with substantially lower outlet counts . These associations were more pronounced for block groups at the 75th percentiles of median income , percent White residents , and percent Asian residents , and at the 25th percentile of percent Hispanic residents . We improved model fit by incorporating interaction terms between outlet bans and median income, percent Asian, percent Hispanic, or percent White , but not percent Black. Associations between each policy and outlet counts varied, and were generally imprecise. Outlet counts were lower in jurisdictions adopting location restrictions and buffers between outlets . In contrast, outlet counts were higher in jurisdictions that placed buffer zones around sensitive location such as schools and limits on over concentration in vulnerable areas . For density limits and buffers around alcohol outlets , estimates were uninformative. There was some heterogeneity in policy associations by block group median income and racial–ethnic composition, but not in a consistent direction. For these models, incorporating interaction terms between the policies and percent Hispanic improved model fit , but not incorporating interaction terms between policies and median income or other racial–ethnic composition variables did not.
In sensitivity analyses restricted to cities and towns, density limits, location restrictions, sensitive location buffers, alcohol outlet buffers, and buffers between outlets showed no discernible association with outlet counts, but over concentration limits remained associated with more outlets . Results for sensitivity analyses with expected outlet counts proportional to population were similar to the main results . In models utilizing the sum of the six policies as the primary exposure, greater policy stringency was associated with a moderate but imprecise reduction in overall outlets , with more pronounced but imprecise associations for block groups with high proportions of Hispanic residents and Asian residents . Models removing block group random slopes and spatially structured and unstructured block group random intercepts fit the data better than models including these components, but there were no substantive differences in the estimated associations . The one exception was for density limits, for which removing the block group random effects changed the RR from 0.99 to 1.54 .In this spatiotemporal analysis of city and county cannabis control policies, we found that local policies banning outlets were strongly associated with lower geographic densities of recreational cannabis outlets. In jurisdictions that did not ban outlets, we evaluated the potential for specific local policies to limit densities and promote equitable distribution of outlets. Here, our findings were mixed: some policies were associated with fewer outlets and others with more, but estimates were imprecise. Outlets disproportionately opened in block groups with more Hispanic residents and less socioeconomic advantage, yet local policies restricting outlets did not appear to counteract this pattern. Instead, in jurisdictions adopting outlet bans, the lower outlet counts were most pronounced for block groups with higher incomes, and more White and Asian residents. For jurisdictions permitting outlets, the six policy associations followed no consistent pattern in terms of the most-affected block groups. These findings are important for public health and health equity because if city and county policies can effectively limit outlet densities, they may encourage safer population levels of consumption.To promote health equity, such policies would need to encourage greater reductions in outlets in vulnerable neighborhoods. Our finding that outlets disproportionately opened in block groups with more Hispanic residents and less socioeconomic advantage is likely driven by the disproportionate absence of outlet bans in these places. These findings are consistent with prior research reporting similar patterns for California, Colorado, Washington, Oregon, and Canada.Economic theory suggests that outlets are likely to open in low-income areas but adjacent to high-income areas because this placement maximizes sales opportunities while minimizing operating costs.
Although we are not aware of any evidence that the economic benefits of outlets accrue to the neighborhoods where outlets are located, outlets may offer economic opportunities for community members. This idea has motivated explicit efforts by some localities to prioritize retail licenses for individuals and communities negatively impacted by the past criminalization of cannabis.Yet, to the extent that outlets are harmful to health—this is still an open question—regulators should be concerned about the potential implications of the uneven distribution of outlets for health equity.Although most localities in our study banned cannabis outlets, some outlets persisted in banned areas. Policies are rarely universally effective, indoor plant table or perfectly and equally enforced. Outlets may be present in places with local bans for several reasons, including enforcement gaps and overriding laws that grandfathered licenses to outlets in banned areas. Still, outlet bans appear to be a highly effective tool for communities seeking to control the proliferation of outlets. Although outlet bans apply to all block groups within the jurisdiction, outlet bans appeared more effective in areas with more social advantage . The frequency and consequences of differential enforcement across neighborhoods should be investigated. For the six local policies limiting outlet densities and locations, the magnitudes of most associations were meaningful, but there was insufficient statistical support to make firm conclusions. Imprecision arose because most jurisdictions banned outlets, outlets were rare, and spatial auto correlation was high. If results are truly null, this would be unsurprising, as many well-meaning policies are ineffective. If the estimates are real differences, any interpretations are conditional on meeting the assumptions necessary for causal inference . The negative associations we observed for location limits and buffers between outlets may reflect effective policies. The positive associations we observed for sensitive location buffers and over concentration limits may reflect reverse causation whereby policies are adopted in response to high concentrations of outlets and are either ineffective or have not yet had time to work.
A central challenge here is disentangling the causal effects of policies from confounding—whereby advantaged communities adopt restrictive policies. If results for the six policies are real causal effects, they did not appear to systematically benefit socially advantaged block groups. This might be expected because high socioeconomic status, White, and other advantaged groups may use their disproportionate political power to exclude cannabis outlets from opening in their neighborhoods.Residential segregation along racial–ethnic and socioeconomic lines set the stage for “not-in-my-backyard” activism.NIMBY initiatives have thwarted public health equity on issues ranging from homelessness to AIDS, alcohol control, substance use treatment, and air pollution.Cannabis legalization has raised concern that NIMBYism and other mechanisms of structural racism would lead to regulations that protected White, advantaged communities from outlets while increasing density in non-White or disadvantaged communities. If estimated associations for the six policies reflect causation, the findings suggest that these policies are unlikely to counteract inequitable distributions of outlets , but also unlikely to exacerbate inequalities. Local policymakers seeking to address the inequitable distribution of outlets may need to test alternative strategies. Our findings are also important in light of research showing that recreational outlets are co-located with alcohol outlets.High densities of alcohol outlets are associated with binge drinking, crime, and injuries; are disproportionately located in marginalized communities; and can be regulated by local policies.New cannabis outlets generate potential for dual-burden harms associated with the spatial co-location of cannabis and alcohol outlets, particularly in communities with less power to deter this activity. Siloed policy approaches—rather than integrated approaches that consider co-location—may further exacerbate problems, yet we found only one locality that regulated the locations of cannabis outlets in relation to alcohol outlets. To be cautious, localities should consider policies regulating co-location of alcohol and cannabis outlets, and the health implications of alcohol–cannabis outlet co-location should be assessed.