Sarah D Mills1,2, Amanda Y Kong1,3,4, Alexandria E Reimold1, Chris D Baggett2,5, Christopher A Wiesen6, Shelley D Golden1,2. 1. Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA. 2. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA. 3. Department of Family and Preventive Medicine, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. 4. TSET Health Promotion Research Center, Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. 5. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA. 6. Odum Institute, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA.
Abstract
INTRODUCTION: Studies find differences in tobacco retailer density according to neighborhood sociodemographic characteristics, raising issues of social justice, but not all research is consistent. AIMS AND METHODS: This study examined associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints (2000, 2007, 2012, and 2017) and investigated if associations remained stable over time. Data on tobacco retailers came from the National Establishment Time-Series Database. Adjusted log-linear models examined the relationship between retailer density and census tract sociodemographic characteristics (% non-Hispanic Black [Black], % Hispanic, % vacant housing units, median household income), controlling for percentage of youth, urbanicity, and US region. To examine whether the relationship between density and sociodemographic characteristics changed over time, additional models were estimated with interaction terms between each sociodemographic characteristic and year. RESULTS: Tobacco retailer density ranged from 1.22 to 1.44 retailers/1000 persons from 2000 to 2017. There were significant, positive relationships between tobacco retailer density and the percentage of Black (standardized exp(b) = 1.05 [95% CI: 1.04% to 1.07%]) and Hispanic (standardized exp(b) = 1.06 [95% CI: 1.05% to 1.08%]) residents and the percentage of vacant housing units (standardized exp(b) =1.08 [95% CI: 1.07% to 1.10%]) in a census tract. Retailer density was negatively associated with income (standardized exp(b) = 0.84 [95% CI: 0.82% to 0.86%]). From 2000 to 2017, the relationship between retailer density and income and vacant housing units became weaker. CONCLUSIONS: Despite the weakening of some associations, there are sociodemographic disparities in tobacco retailer density from 2000 to 2017, which research has shown may contribute to inequities in smoking. IMPLICATIONS: This study examines associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints from 2000 to 2017. Although some associations weakened, there are sociodemographic disparities in tobacco retailer density over the study period. Research suggests that sociodemographic disparities in retailer density may contribute to inequities in smoking. Findings from this study may help identify which communities should be prioritized for policy intervention and regulation.
INTRODUCTION: Studies find differences in tobacco retailer density according to neighborhood sociodemographic characteristics, raising issues of social justice, but not all research is consistent. AIMS AND METHODS: This study examined associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints (2000, 2007, 2012, and 2017) and investigated if associations remained stable over time. Data on tobacco retailers came from the National Establishment Time-Series Database. Adjusted log-linear models examined the relationship between retailer density and census tract sociodemographic characteristics (% non-Hispanic Black [Black], % Hispanic, % vacant housing units, median household income), controlling for percentage of youth, urbanicity, and US region. To examine whether the relationship between density and sociodemographic characteristics changed over time, additional models were estimated with interaction terms between each sociodemographic characteristic and year. RESULTS: Tobacco retailer density ranged from 1.22 to 1.44 retailers/1000 persons from 2000 to 2017. There were significant, positive relationships between tobacco retailer density and the percentage of Black (standardized exp(b) = 1.05 [95% CI: 1.04% to 1.07%]) and Hispanic (standardized exp(b) = 1.06 [95% CI: 1.05% to 1.08%]) residents and the percentage of vacant housing units (standardized exp(b) =1.08 [95% CI: 1.07% to 1.10%]) in a census tract. Retailer density was negatively associated with income (standardized exp(b) = 0.84 [95% CI: 0.82% to 0.86%]). From 2000 to 2017, the relationship between retailer density and income and vacant housing units became weaker. CONCLUSIONS: Despite the weakening of some associations, there are sociodemographic disparities in tobacco retailer density from 2000 to 2017, which research has shown may contribute to inequities in smoking. IMPLICATIONS: This study examines associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints from 2000 to 2017. Although some associations weakened, there are sociodemographic disparities in tobacco retailer density over the study period. Research suggests that sociodemographic disparities in retailer density may contribute to inequities in smoking. Findings from this study may help identify which communities should be prioritized for policy intervention and regulation.
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