Sociodemographic Disparities in Tobacco Retailer Density in the United States, 2000–2017

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. This study examined associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States (US) at four timepoints (2000, 2007, 2012, 2017) and investigated if associations remained stable over time. Methods 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 1.22-1.44 retailers/1,000 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, 1.07) and Hispanic (standardized exp(b)=1.06 (95% CI: 1.05, 1.08) residents and the percentage of vacant housing units (standardized exp(b)=1.08 (95% CI: 1.07, 1.10) in a census tract. Retailer density was negatively associated with income (standardized exp(b)=0.84 (95% CI: 0.82, 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 time points 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.

Area-level sociodemographic differences in tobacco availability examined with nationwide tobacco product retail licence data in Finland

“…The results on income and tobacco retailer density are consistent with previous research. Higher median household income has been associated with lower tobacco retailer density in the USA10 11 33 and Scotland 16. Further, areas with a higher percentage of residents living in poverty have higher density of tobacco retailers in the USA 11 32.…”

“…A number of investigations have shown that areas with lower sociodemographic composition have higher densities of tobacco retailers 10–15. The current evidence is predominantly from the USA, Australia and New Zealand.…”

“…7 9 A number of investigations have shown that areas with lower sociodemographic composition have higher densities of tobacco retailers. [10][11][12][13][14][15] The current evidence is predominantly from the USA, Australia and New Zealand. From Europe, Scottish 16 and German 17 investigations suggest that, according to income, the most deprived areas have the highest densities of tobacco retailers.…”

Impacts of Tax and Flavor Tobacco Policies on San Francisco Bay Area Tobacco Prices

“…Socio-Demographic Characteristics. Studies have shown associations between greater tobacco retail density (Kong et al, 2021;Mills et al, 2022) and lower tobacco prices (Lee et al, 2015), particularly in neighborhoods with larger proportions of Black, Hispanic/Latino, and lower-income residents. Thus, to control for the socio-demographic composition of cities, we linked retail data to 2015-2019 American Community Survey (U.S. Census Bureau, 2021a) estimates at the place level to obtain 5-year period estimates by city for median income (in U.S. dollars) and proportions (multiplied by 100%) of non-Hispanic Black and Latino populations.…”

Tobacco use characteristics, treatment preferences, and motivation to quit among adults accessing a day shelter in Oklahoma City

“…Participant characteristics were compared by MTQS (low, moderate, high) using analysis of variance (ANOVA) and chi-square tests. Following each ANOVA or chi-square test, Tukey's and Bonferroni adjustments were applied (respectively) to post-hoc comparisons ( Mills et al., 2022 ; Rothman, 1990 ) in order to identify differences across high-to-low, moderate-to-low, and moderate-to-high motivation groups. A multivariate ordinal logistic regression (OLR) analysis with stepwise selection was conducted to evaluate all variables that were significantly associated with MTQS ( p ’s<0.05) together in a single model.…”