Health & place · 2024
Exploring the association between neighborhood disadvantage and cannabis retail density: A multi-measure analysis.
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Kephart Lindsay, Rees Vaughan W, Subramanian S V, Giovenco Daniel P
Аннотация
INTRODUCTION: There is growing interest in the relationship between neighborhood disadvantage and increased cannabis retail density, driven by evidence suggesting higher density is associated with increased cannabis use. Yet little is known on how this relationship varies across different measures of cannabis retail density. This study explores how measures of neighborhood advantage and disadvantage relate to four cannabis retail density measures in the US.
METHODS: Data on licensed recreational cannabis retailers (n = 5586) were obtained from 18 state agency websites, geocoded, and spatially joined to 3369 census tracts to calculate four retail density measures: count per tract, cannabis retailers per 1000 population, per square mile, and per 10 miles of roadway. Multilevel regression models assessed the association between three Index of Concentration at the Extremes (ICE) measures-capturing tract concentration of racial and economic advantage/disadvantage-and the four cannabis retail density measures.
RESULTS: Census tracts with the highest concentrations of economic and racialized/economic disadvantage exhibited greater odds of increased cannabis retail density across all measures, compared to tracts with the highest concentration of advantage. Tracts with the greatest concentration of racialized populations did not show a higher count or density per population but did exhibit higher density per square mile and per roadway.
CONCLUSION: On average, cannabis retail density is higher in neighborhoods with the greatest structural disadvantage. Researchers, public health agencies, and policymakers should use multiple measures of cannabis retailer density in surveillance and evaluation efforts to identify policy strategies that would most effectively reduce the clustering of cannabis retailers in areas primarily occupied by low-income or racialized populations.
METHODS: Data on licensed recreational cannabis retailers (n = 5586) were obtained from 18 state agency websites, geocoded, and spatially joined to 3369 census tracts to calculate four retail density measures: count per tract, cannabis retailers per 1000 population, per square mile, and per 10 miles of roadway. Multilevel regression models assessed the association between three Index of Concentration at the Extremes (ICE) measures-capturing tract concentration of racial and economic advantage/disadvantage-and the four cannabis retail density measures.
RESULTS: Census tracts with the highest concentrations of economic and racialized/economic disadvantage exhibited greater odds of increased cannabis retail density across all measures, compared to tracts with the highest concentration of advantage. Tracts with the greatest concentration of racialized populations did not show a higher count or density per population but did exhibit higher density per square mile and per roadway.
CONCLUSION: On average, cannabis retail density is higher in neighborhoods with the greatest structural disadvantage. Researchers, public health agencies, and policymakers should use multiple measures of cannabis retailer density in surveillance and evaluation efforts to identify policy strategies that would most effectively reduce the clustering of cannabis retailers in areas primarily occupied by low-income or racialized populations.