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. Author manuscript; available in PMC: 2022 Oct 1.
Published in final edited form as: Ethn Health. 2019 May 24;26(7):1012–1027. doi: 10.1080/13557858.2019.1620178

Black, White, or Green? The Effects of Racial Composition and Socioeconomic Status on Neighborhood-Level Tobacco Outlet Density

David O Fakunle 1,2,*, Frank C Curriero 3, Philip J Leaf 4, Debra M Furr-Holden 5, Roland J Thorpe Jr 6
PMCID: PMC6875694  NIHMSID: NIHMS1529876  PMID: 31124377

Abstract

Race has been shown to be a social construct, but its effects on health disparities and resource inequalities is substantial due to systems of oppression like segregation. Tobacco outlet density studies have reported a direct relationship between Black population percentage and tobacco outlet density, as well as inverse relationships between socioeconomic status and tobacco outlet density. It remains unclear whether socioeconomic status or race has a larger effect than the other. This study compared predominantly-Black and predominantly-White Maryland areas with similar socioeconomic status to examine the role of both race and socioeconomic status on tobacco outlet availability and tobacco outlet access. Influenced by past studies, the hypothesis was that there would be no difference in tobacco outlet availability and access in areas with similar socioeconomic status despite different majority racial concentrations. This study geocoded Maryland tobacco outlet addresses with 2011–2015 American Community Survey sociodemographic data. Two-sample t-tests were conducted comparing the mean values of sociodemographic variables and tobacco outlet density per Census Tract, and spatial lag based regression models were conducted to analyze the direct association between covariables and tobacco outlet density while accounting for spatial dependence between and within jurisdictions. Results showed that predominantly-White jurisdictions had lower tobacco outlet availability and access than predominantly-Black jurisdictions, despite similar socioeconomic status. Spatial lag model results showed that median household income and vacant houses had consistent associations with tobacco outlet density across most of the jurisdictions analyzed, and place-based spatial lag models showed direct associations between predominantly-Black jurisdictions and tobacco outlet availability and access.

Keywords: Tobacco outlets, Census Tracts, Race, Socioeconomic Status, Income

Introduction

Intersectionality of Race and Socioeconomic Status

It has been established that race is a social construct with no concrete biological or genetic basis by which to designate groups (Smedley & Smedley, 2005; Lopez, 1994; Williams, 1997). However, systems of oppression such as racism and discriminatory policies like slavery, segregation and redlining have made race a considerable determinant of social outcomes in the United States including income, wealth, housing, employment, criminal justice, and health. The historical implementation of systems of oppression has resulted in an intertwining of race and socioeconomic status, and while many of these policies are no longer explicitly used, their ramifications continue (Gaskin et. al, 2004; Williams et. al, 2010; Oliver & Shapiro, 2013; Williams & Collins, 1995; Thorpe et. al, 2017; Eisenhauer, 2001; Williams & Collins, 2001). The resultant inequalities in socioeconomic status by race are so pronounced that for many it is implied that Blacks are in a lower socioeconomic status (LaVeist, 2005; Williams, 1999).

The intertwining of race and socioeconomic status must be acknowledged and addressed in any tobacco outlet density research studies involving them. However, the field must first produce a strong epidemiological foundation of racial and socioeconomic determinants of tobacco outlet density before the moderation of race and socioeconomic status can be considered. Studies have reported relationships between racial composition and the availability of and access to tobacco retailers – specifically a direct (or positive) relationship between the percentage of Blacks and Latinos living in an area and tobacco outlet density, and an inverse (or negative) relationship between the percentage of Whites living in an area and tobacco outlet density (Peterson et. al, 2005; Hyland et. al, 2003; Fakunle et. al, 2010; Novak et. al, 2006; Lee et. al, 2017). Likewise, studies have also reported inverse relationships between an area’s socioeconomic composition and the availability of and access to tobacco retailers (Peterson et. al, 2005; Hyland et. al, 2003; Fakunle et. al, 2010; Schneider et. al, 2005; Fakunle et. al, 2016). Despite verification and re-verification from many studies, it is still not clear whether race or socioeconomic status is more salient in the association with tobacco outlet availability and access.

Evolutions in Tobacco Outlet Density Study Designs

In examining the relationship between race, socioeconomic status and tobacco outlet density, restriction, stratification and randomization are reliable methods for addressing confounding and aid in elucidating epidemiological relationships (Kestenbaum, 2009). Most tobacco outlet density studies have utilized regression analyses, a post-hoc methodology, to control for confounding and aid in explaining the relationship between sociodemographics and tobacco outlet density. As a result, researchers understand how race and socioeconomic status relate to tobacco outlet density (Fakunle et. al, 2010; Lee et. al, 2017; Fakunle et. al, 2016; Rodriguez et. al, 2013). However, tobacco outlet density research would benefit from utilizing other techniques to elaborate on the association with sociodemographics from different perspectives. Neighborhood components such as racial composition and socioeconomic status metrics can provide more detail as to how they relate to tobacco outlet density if they are thoroughly explored as essential elements of a socio-geographic environment. For example, the expansion of covariables provides a more thorough understanding of the relationship between socioeconomic status, race and tobacco outlet density beyond one measure – median household income (Fakunle et. al, 2018; Lee et. al, 2017; Fakunle et. al, 2016; Rodriguez et. al, 2013; Mayers et. al, 2012). Additionally, the expansion of socioeconomic covariables aims to address the lack of research on income inequality’s influence in health disparities such as tobacco outlet inequalities (Fakunle et. al, 2010; Subramanian & Kawachi, 2004; Ogneva-Himmelberger et. al, 2010; Fakunle et. al, 2018).

Another reality is that most tobacco outlet density studies have focused on one geographic area or assemblage of areas (Peterson et. al, 2005; Hyland et. al, 2003; Schneider et. al, 2005; Mayers et. al, 2012). A common recommendation of tobacco outlet density researchers is the establishment of policy reform to reduce racial and socioeconomic inequalities in tobacco access and availability (Peterson et. al, 2005; Fakunle et. al, 2010; Lee et. al, 2017). While there are many factors that affect the success of a policy such as enforcement and political relationships, one explanation of the lack of proactive tobacco outlet policy may be that the interpretations and patterns reported in tobacco outlet density studies have not been generalized beyond the study locale, particularly if the area exhibits sociodemographic homogeneity (Ribisl et. al, 2017). Therefore, the results reported are interpreted as a phenomenon reflective of the study area. To address this limitation, one of the next steps that have been taken is the comparison of sociodemographic relationships across multiple areas that are similar or different based on one or more discernable characteristics. The comparisons of areas and their respective tobacco outlet densities provide a different perspective of how area compositions and population dynamics affect relationships with tobacco outlet density – specifically, a perspective of relativity (Fakunle et. al, 2010; Fakunle et. al, 2016; Fakunle et al, 2018). To that end, residential Census Tracts have been the prevailing spatial unit of measurement in tobacco outlet density research, yet other spatial units have been utilized in tobacco outlet density studies such as census block groups, which are smaller and more refined than Residential Census Tracts (Ogneva-Himmelberger et. al, 2010; Gorman et. al, 2001). Similar studies in alcohol outlet density have also used census block groups as the spatial unit of measurement Morrison et. al, 2016; Grubesic et. al, 2016; Yu et. al, 2010). While there is no consensus unit of measurement, Residential Census Tracts are the most frequently used. Census block groups, while more refined than Residential Census Tracts, have more variation which can lead to analytical instability. Likewise, analyses of broad jurisdictions like cities, counties or states may lead to results that do not allow for inference (Auchincloss et. al, 2012). Therefore, Residential Census Tracts are currently the best spatial units that both exude distinct neighborhood characteristics yet provide manageable data and potentially generalizable analysis results.

Measures of Tobacco Outlet Density

In support of the goal of disentangling the complex relationship between race and socioeconomic status and better understanding how upstream factors like race and socioeconomic status influence tobacco outlet availability and access within and across locales, this study utilized two measures of tobacco outlet density to acknowledge potential urban-rural differences in tobacco outlet availability and access. This resulted from preliminary analyses of Maryland jurisdictions that concluded that comparisons of predominantly-Black jurisdictions and predominantly-White jurisdictions with similar median household income would involve comparisons of predominantly-urban areas to predominantly-rural areas. Therefore, for availability, tobacco outlet density was measured as the number of tobacco outlets per 1,000 persons per Residential Census Tract, consistent with recent studies (Fakunle et. al, 2016; Fakunle et. al, 2018). For access, tobacco outlet density was measured as the number of tobacco outlets per 10km of roadway, consistent with several past tobacco outlet density studies (Peterson et. al, 2005; Fakunle et. al, 2010; Schneider et. al, 2005).

Race vs. Socioeconomic Status: Which Matters More?

Fakunle and colleagues previously conducted two studies aimed at examining the nuances of race, socioeconomic status and tobacco outlet density, first by building off previous literature and second by utilizing the socio-ecological heterogeneity of Maryland. The first study examined two predominantly-Black regions in Maryland, Baltimore City and Prince George’s County, and concluded that the area with higher socioeconomic status, Prince George’s County, had lower tobacco outlet density than Baltimore City, the area with lower socioeconomic status, despite similar racial concentration (Fakunle et. al, 2016). The second study examined several predominantly-White regions – Baltimore County, Montgomery County, Howard County, Lower Eastern Shore and Western Maryland – and like the first study concluded that areas with higher socioeconomic status had lower tobacco outlet density, despite similar racial concentrations (Fakunle et. al, 2018). As a result, the two studies were able to elucidate a socioeconomic gradient, within racial similarity (both Black and White), in relation to tobacco outlet density. As a continuation of that work, this study’s goal of this was to parse out the effects of racial concentration on tobacco outlet density among areas with similar magnitudes of socioeconomic status. Utilizing jurisdictions in Maryland, this study compared areas with high-Black and high-White population percentages to determine if either or both racial concentrations correlated with tobacco outlet density despite the jurisdictions being socioeconomically comparable. Influenced by these prior studies, the primary hypothesis was that there would be no differences in tobacco outlet availability and access among jurisdictions with similar socioeconomic status despite different majority racial concentrations. The secondary hypothesis, again based on prior studies, was that median household income would be the most consistent predictor of tobacco outlet density and access.

Methods

Study Areas

The following jurisdictions were chosen for inclusion in this study based on preliminary examination of Black population percentage, White population percentage and median household income (see Figure 1). Baltimore City is in northeast Maryland had 199 Census Tracts, was predominantly Black (~65%), and had an average median household income totaling $44,264. Baltimore County is in northeast Maryland, had 211 Census Tracts was predominantly White (~66%), and had an average median household income totaling $73,114. Lower Eastern Shore (Dorchester County, Somerset County, Wicomico County and Worcester County) is in southern Maryland, had 50 Census Tracts, was predominantly White (~72%), and had an average median household income totaling $49,470. Prince George’s County is in south-central Maryland, had 218 Census Tracts, was predominantly Black (~67%), and had an average median household income totaling $77,378. Western Maryland (Allegany County, Garrett County and Washington County) is in western Maryland, had 62 Census Tracts, was predominantly White (~87%), and had an average median household income totaling $48,164. For reference, the Black population percentage for the state of Maryland was 29.5%, the White population percentage for the state of Maryland was 57.6% and the median household income totaled $74,551.

Figure 1:

Figure 1:

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Geographical Map of Study Areas

I – Baltimore City, Maryland

II – Baltimore County, Maryland

III – Lower Eastern Shore, Maryland (Dorchester County, Somerset County, Wicomico County and Worcester County)

IV – Prince George’s County, Maryland

V – Western Maryland (Allegany County, Garrett County and Washington County)

Data

Census Tract demographic data were obtained from the 2011–2015 American Community Survey (ACS), made available via the United States Census website. The American Community Survey, inaugurated in 2005, is a perennial survey administered by the U.S. Census Bureau that acquires data on the sociodemographic dynamics of people living in the United States (U.S. Census Bureau, 2017). The five-year pooled estimate of sociodemographic data was selected over the one-year and three-year pooled estimates because of the larger dataset that included data for all areas, thus allowing for examination of small Residential Census Tracts and assuring greater reliability. Of the 1,406 Census Tracts in Maryland, 18 had a total population of less than 600 persons and consistent with past methodology were excluded from analyses (Fakunle et. al, 2016).

Maryland tobacco outlet data – including retailer names, contact information and retail/mailing locations – were obtained from the Maryland State Licensing Bureau, which provided the addresses for retailers with an active Cigarette, Special Cigarette, Other Tobacco Product (OTP) or Tobacconist licenses as of April 30, 2017. Tobacco outlet retailer addresses were geocoded via MD iMap – the State of Maryland’s Mapping and GIS Data Portal – the most current publicly available geocoding service for the state. Of the addresses provided (n = 2,851), only five needed to be modified: one determined to be a duplicate (deleted), one determined to be out-of-state (Florida) with no alternative address given (deleted), one determined to have two adjacent addresses (second address added), one determined to be closed (deleted), and one geocoded with the mailing address due to the outlet being a food truck. Most of the licensed tobacco outlets were successfully geocoded after the first iteration. Of the revised total addresses (n = 2,849), all but 144 were successfully geocoded via the Batch Address Look-Up service. The 144 entries that did not return a geocode were cross-referenced with Google Maps and other internet-based resources (e.g., retailer websites) to verify the correct address. After verification, the addresses were re-run via the Single Address Look-Up service of which all but 19 were successfully geocoded. In total, 2,830 of the 2,849 addresses (99.3%) were successfully geocoded. The addresses were then merged with Maryland sociodemographic data via the Spatial Join tool in ArcGIS. It was then determined that a total of 3 tobacco outlets were located among the 18 Census Tracts excluded from analyses.

Eight variables measuring racial composition, socioeconomic status and built environment were selected from the ACS dataset to provide the greatest available amount of information to explain associations of interest. The measures included in the study were the total population, the total number of individuals who identify as Black or African American (converted to a percentage), the total number of individuals who identify as White (converted to a percentage), the percentage of individuals 25 years and over who have obtained at least a Bachelor’s degree, the total number of vacant housing units (utilized in spatial analysis models as per 100 units), the total number of individuals 16 years and older who are actively in the labor force (converted to a percentage), median house hold income (expressed in 2015 inflation-adjusted dollars), and the Gini index of income inequality (presented as a coefficient). The Gini index of income inequality is a statistical measure that is utilized to measure the distribution of wealth and income among a country’s residents (Gini, 1921). The range of a Gini coefficient is usually between 0, representing total equality (everyone has the same income and/or wealth) and 1, representing total inequality (one person has all the income and/or wealth).

Analyses

Two-sample t-tests were conducted to compare the mean values per Census Tract of the study areas and provide a baseline measure of differences in tobacco outlet density and sociodemographic characteristics across pairs of areas. Consistent with prior research the decision was made to focus on the ecological profile of each area via the mean characteristics, acknowledging that each area had variation among the individual Census Tract sociodemographics yet acknowledging that many tobacco policies are made on the local level and higher (Fakunle et. al, 2016). Rather than utilizing an analysis that compared all the study areas with each other, such as the Kruskal-Wallis Test or ANOVA (used in earlier tobacco outlet density studies), the area pairs for each t-test were specifically selected based on similarities and differences in median household income, the prevailing proxy of socioeconomic status in tobacco outlet density research. This is a replication of methodology which aimed to analyze similarities and differences in sociodemographics and tobacco outlet density from an ecological perspective reflective of the natural composition of the areas. The two-sample t-tests were conducted via the SPSS statistical package (IBM Corp., 2017).

Spatial lag Poisson regression models were built to examine the direct individual and collective effects of sociodemographics on tobacco outlet density both within and across jurisdictions. Consistent with previous studies, the outcome was the mean number of tobacco outlets per 1,000 persons per Census Tract (Fakunle et. al, 2016; Fakunle et. al, 2018). The models were conducted to determine if there were differences in the magnitude of relationship between sociodemographics and tobacco outlet density based on the location. The covariates were spatially lagged, meaning the models included not only a tract-level specific covariate effect (focal effect) but also an effect for the averaged covariate in the adjacent proximal Census Tracts (spatial lag effect). To conduct spatial analyses of social factors and tobacco outlet density, geospatial structures containing the sociodemographic and tobacco outlet density data of each residential Census Tract were created and spatial smoothing on the model covariables was conducted to assure more consistent outcomes tobacco outlet density measures across the established Census Tracts, particularly in rural and suburban areas (Auchincloss et. al, 2012). The spatial smoothing was based on population, so areas with a higher population were weighted more heavily than area with a lower population. Additionally, an offset variable based on population was included in the models.

Four models were conducted for each study area: a univariable model for each covariable, a multivariable model for focal effect covariables, a multivariable model for focal effect and spatially lagged covariables (which shows the relationship between covariables and tobacco outlet density in both the immediate and adjacent neighborhoods), and a multivariable model for focal effect and spatially lagged covariables, and interaction terms between the focal effect and spatially lagged covariables (which shows the relationship between covariables and tobacco outlet density in both the immediate and adjacent neighborhoods, as well as how the proximity affects the individual relationships). The final multivariable model was built via the stepwise process with each of the focal and spatial lag covariables, and exponentiated coefficients were reported and magnified for easier interpretation: per 10% change in population percentage, per $10,000 change in median household income, per 1% change in income inequality, and per 100 vacant houses. Due to the high number of significant coefficients in the models, the results presented focal effect and/or spatially lagged covariables that exhibited a consistent relationship (direct or inverse) across all four models. Chi-square statistics were conducted to determine the extent of Poisson model overdispersion in the final model compared to the null model.

After each individual jurisdiction was analyzed via the four models, jurisdictions that were compared to each other in the two-sample t-tests were then compared to each other via place-based interaction Poisson models. The model was based on the multivariable model for focal effect and spatially lagged covariables and the interaction terms between the focal effect and spatially lagged covariables. Place-based interaction Poisson models were conducted to determine if there were differences in the magnitude of the relationship between sociodemographic covariates and tobacco outlet density based on location. Consistent with the hypothesis of socioeconomic status relating to tobacco outlet despite similar racial concentration, it was proposed that the strength of relationship between covariables and tobacco outlet density would be greater in the jurisdiction with lower socioeconomic status (signified by an exponentiated beta different than 1). While the direction of the relationship was noteworthy, the salience was in showing that the degree to which covariables related to tobacco outlet density varied between two jurisdictions. To assure consistency the jurisdiction with the lower tobacco outlet density was set as the reference variable, and due to the high number of significant coefficients in the model, this section highlighted covariates that exhibited a consistent relationship (direct or inverse) among both focal effects and spatial lag effects. All spatial analyses were conducted via the R software package (The R Project for Statistical Computing, 2018).

Results

Two-Sample T-Tests

The two-sample t-tests’ results were contrary to the primary hypothesis. Specifically, predominantly-Black Prince George’s County and predominantly-White Baltimore County had no significant differences in median household income, population, and vacant houses. Prince George’s County had a statistically significantly higher labor force participation rate and lower income inequality coefficient, while Baltimore County had a significantly higher percentage of individuals aged 25 years and older with at least a Bachelor’s degree. However, Prince George’s County had significantly higher tobacco outlet access and tobacco outlet availability than Baltimore County (see Table 1). It was also observed that predominantly-Black Baltimore City and predominantly-White Western Maryland and Lower Eastern Shore had no significant differences in median household income and labor force participation rate. Baltimore City had a statistically significantly higher percentage of individuals aged 25 years and older with at least a Bachelor’s degree than Western Maryland, Western Maryland had a statistically significantly larger population and significantly lower income inequality than Baltimore City, and Lower Eastern Shore had a statistically significantly larger population, larger number of vacant houses and lower income inequality than Baltimore City. However, Baltimore City had statistically significantly higher tobacco outlet access and tobacco outlet availability than both Western Maryland and Lower Eastern Shore (see Tables 2 & 3).

Table 1:

Descriptives of Sociodemographics and Tobacco Outlet Density of Prince George’s County and Baltimore County, Maryland

Mean Characteristic Per Census Tract Prince George’s County (# Tracts = 218) Baltimore County (# Tracts = 211) t-statistic df
Population (SD) 4,095.49 (1,546.78) 3,900.28 (1,638.74) 1.27 427
Black Population Percentage (SD) 64.88 (24.82) 24.92 (26.55) 16.11 427
White Population Percentage (SD) 19.93 (17.80) 66.21 (26.96) -21.05 427
Median Household Income (SD) $77,378 ($26,329) $73,114 ($26,299) 1.68 427
Gini Coefficient (SD) 0.36 (0.05) 0.40 (0.05) -8.28 427
Percentage of Individuals Aged 25+ with at Least a Bachelor’s Degree (SD) 29.98 (14.57) 35.52 (19.86) -3.30 427
Labor Force Participation Rate (SD) 72.65 (6.52) 66.34 (8.97) 8.35 427
Number of Vacant Houses 111.41 (85.62) 112.71 (95.46) 0.15 427
Tobacco Outlets per 1000 (SD) 0.56 (0.64) 0.35 (0.49) 3.81 427
Tobacco Outlet per 10km of Roadway (SD) 0.92 (1.22) 0.43 (0.53) 5.36 427
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Table 2:

Descriptives of Sociodemographics and Tobacco Outlet Density of Baltimore City and Western Maryland

Mean Characteristic Per Census Tract Baltimore City (# Tracts = 199) Western Maryland (# Tracts = 62) t-statistic df
Population (SD) 3,127.91 (1,387.09) 4,074.71 (1,808.71) −4.35 259
Black Population Percentage (SD) 63.38 (34.14) 7.96 (11.78) 12.54 259
White Population Percentage (SD) 30.13 (30.58) 87.38 (13.78) −14.28 259
Median Household Income (SD) $44,264 ($23,899) $48,164 ($18,290) −1.18 259
Gini Coefficient (SD) 0.46 (0.06) 0.41 (0.07) 5.50 259
Percentage of Individuals Aged 25+ with at Least a Bachelor’s Degree (SD) 27.36 (22.60) 18.40 (9.22) 3.04 259
Labor Force Participation Rate (SD) 62.26 (11.85) 59.57 (11.47) 1.57 259
Number of Vacant Houses 273.66 (172.24) 276.47 (555.03) 0.06 259
Tobacco Outlets per 1000 (SD) 2.57 (2.72) 0.22 (0.29) 6.78 259
Tobacco Outlets per 10km of Roadway (SD) 3.45 (3.36) 0.13 (0.21) 7.77 259
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Table 3:

Descriptives of Sociodemographics and Tobacco Outlet Density of Baltimore City and Lower Eastern Shore, Maryland

Mean Characteristic Per Census Tract Baltimore City (# Tracts = 199) Lower Eastern Shore (# Tracts = 50) t-statistic df
Population (SD) 3,127.91 (1,387.09) 4,244.30 (1,882.36) −4.71 247
Black Population Percentage (SD) 63.38 (34.14) 23.12 (21.36) 7.95 247
White Population Percentage (SD) 30.13 (30.58) 71.70 (22.59) −9.01 247
Median Household Income (SD) $44,264 ($23,899) $49,470 ($20,400) −1.42 247
Gini Coefficient (SD) 0.46 (0.06) 0.44 (0.05) 2.17 247
Percentage of Individuals Aged 25+ with at Least a Bachelor’s Degree (SD) 27.36 (22.60) 25.13 (11.18) 0.68 247
Labor Force Participation Rate (SD) 62.26 (11.85) 60.38 (11.29) 1.01 247
Number of Vacant Houses 273.66 (172.24) 917.82 (2,170.31) −4.16 247
Tobacco Outlets per 1000 (SD) 2.57 (2.72) 0.64 (1.46) 4.84 247
Tobacco Outlets per 10km of Roadway (SD) 3.45 (3.36) 0.31 (0.64) 6.57 247
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Spatial Lag Models

The results of the spatial lag models, among other findings, showed that median household income exhibited an inverse relationship with tobacco outlet availability and access in all five study areas and in the focal and spatial lag effects in 13 of the 27 total spatial models. Conversely, median household income exhibited a direct relationship with availability and access in the focal and spatial lag effects in 8 of the 27 models. Additionally, the models showed that there were significant differences in the magnitude of relationships between covariables and tobacco outlet density – both availability and access – for all the comparisons in the study. The differences in the magnitude of relationships were exhibited by areas when regressed on tobacco outlet density, with another area as a reference variable. Differences were also exhibited within areas among the focal and spatial lag effects, meaning that the proximity of neighborhoods affected how the sociodemographic variables in their individual areas related to tobacco outlet density. Additionally, there was a consistent direct relationship between vacant houses and tobacco outlet availability and access in most of the study areas, which supports findings reported by Lee and colleagues (2017). It was not possible to statistically analyze relationships between models utilizing differing outcome measures, but it was observed that apart from some outliers (e.g., labor force participation rate in Prince George’s County), most relationships with covariables were of similar direction and magnitude regardless if the outcome was tobacco outlet availability or tobacco outlet access. (see Tables 4–6).

Table 4:

Models #1 & #2 – Spatial Lag Regression Model Coefficients for Sociodemographic Covariables on Tobacco Outlet Access in Western Maryland – 2011–2015 (n = 62)

Variable Univariable Model Multivariable Model
Focal Effects Focal & Spatial Lag
Exponentiated Beta p-value Exponentiated Beta p-value Exponentiated Beta p-value
Focal Effects
White Population Percentage (per 10%) 0.72 <0.001 0.84 <0.001 1.16 <0.001
Median Household Income (per $10000) 0.84 <0.001 0.86 <0.001 0.89 <0.001
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 1.04 <0.001 1.13 <0.001 1.08 <0.01
Labor Force Participation Rate
(per 10%)		 0.89 <0.001 0.98 <0.001 0.83 <0.001
Gini Income Inequality Coefficient (per 1%) 1.04 <0.001 1.07 <0.001 1.07 <0.001
Vacant Houses (x100) 0.88 <0.001 0.87 <0.001 0.92 <0.001
Spatial Lag
White Population Percentage (per 10%) 0.69 <0.001 0.72 <0.001
Median Household Income (per $10000) 0.90 <0.001 1.07 <0.001
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 1.00 0.99 0.84 <0.001
Labor Force Participation Rate (per 10%) 1.10 <0.001 1.65 <0.001
Gini Income Inequality Coefficient (per 1%) 1.03 <0.001 1.14 <0.001
Vacant Houses (x100) 0.91 <0.001 0.91 <0.001
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Table 6:

Model # 4 – Sociodemographic Variable Interaction Coefficients of Spatial Lag Regression on Tobacco Outlet Access in Prince George’s County, Maryland Relative to Baltimore County, Maryland – 2011–2015 (n = 218)

Variable Multivariable Model
Focal & Spatial Lag Focal & Spatial Lag Interaction
Exponentiated Beta p-value Exponentiated Beta p-value
Focal Effects
Median Household Income (per $10000) 0.95 <0.001 1.00 0.01
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 0.95 <0.001 1.00 <0.001
Labor Force Participation Rate (per 10%) 0.83 <0.001 1.00 <0.001
Gini Income Inequality Coefficient (per 1%) 0.86 <0.001 1.66 <0.001
Vacant Houses (x100) 0.93 <0.001 1.00 <0.001
Spatial Lag
Median Household Income (per $10000) 0.73 <0.001
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 1.00 0.35
Labor Force Participation Rate (per 10%) 0.76 <0.001
Gini Income Inequality Coefficient (per 1%) 0.82 <0.001
Vacant Houses (x100) 1.00 0.001
County 2.12 <0.001
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Discussion

The aim of this study was to compare tobacco outlet availability and tobacco outlet access in predominantly-Black areas and predominantly-White areas with similar socioeconomic status to determine if either or both racial concentrations correlated with tobacco outlet density despite the jurisdictions being socioeconomically similar. It was intended to provide a more comprehensive examination of the relationship between race, socioeconomic status and tobacco outlet density, with the aim of informing and guiding both future tobacco outlet density research and more proactive tobacco outlet control policies. The utilization of matching and comparisons have allowed studies to show the patterns associated between socioeconomic status and tobacco outlet density within areas of similar racial concentrations, and this study utilized similar methods (Fakunle et. al, 2016; Fakunle et. al, 2018). The first key finding was that the two-sample t-tests conducted in this study revealed findings that added a dynamic to the relationship between racial concentration, socioeconomic status and tobacco outlet density, but did not support the hypothesis. Contrary to the hypothesis, predominantly-White jurisdictions consistently had lower tobacco outlet availability and tobacco outlet access than predominantly-Black jurisdictions, despite similar socioeconomic status. Results of the place-based interaction Poisson models further supported a rejection of the hypothesis by showing that location affected the relationship between sociodemographics and tobacco outlet availability and access while accounting for socioeconomic status. For example: Prince George’s County, a predominantly-Black jurisdiction, had a statistically significant direct relationship, with a magnitude different than 1, with both tobacco outlet availability and tobacco outlet access when Baltimore County, a predominantly-White jurisdiction, was set as the reference variable. The collective findings suggest that socioeconomic status, even when similar, does not remove racial differences in tobacco outlet availability and access. The second key finding was that, in agreement with the hypothesis, median household income exhibited a consistent relationship with tobacco outlet availability and access in the focal and spatial lag effects of most of the study models. Of the 21 models for which median household income was consistent in focal and spatial lag effects most exhibited inverse relationships, which was also in agreement with previous research (Fakunle et. al, 2018).

LaVeist and colleagues showed in their Exploring Health Disparities in Integrated Communities (EHDIC) study that racial differences in health outcomes exist between Black and White populations that live within the same socioeconomic circumstances (LaVeist et. al, 2011). The implication is that connotation of racial health disparities must be interpreted through the lens of institutional racism – in the case of the EHDIC study, segregation. This indication is encouraged by other health disparities studies (Williams et. al, 2010; Thorpe et. al, 2017; Williams & Collins, 2001; LaVeist, 2005). Likewise, similar results were seen after analyses of tobacco outlet availability and tobacco outlet access in predominantly-White and predominantly-Black locales with similar socioeconomic status (Fakunle et.al, 2016; Fakunle et. al, 2018). The collective inference from those previous findings with the findings of this study suggest that tobacco control policies should be sensitive to racial and socioeconomic gradients. Specifically, predominantly-Black neighborhoods experience higher concentrations of tobacco outlets than predominantly-White neighborhoods even if the socioeconomic status of both neighborhoods is similar and therefore, may not be afforded systemic protective factors against tobacco outlet availability and access despite the lack of economic barriers.

The results of this study contribute to the knowledge of place-based disparities and how disparities in tobacco outlet availability and access are related to race, particularly the relationship between higher Black populations and greater availability and access to tobacco outlets. One explanation of these relationships is the history of Blacks and social mobility in the United States (Trotter, 199l; Tolnay, 2003; Lemann, 1991). Many jurisdictions, in response to the growing number of new Black residents after the end of slavery and Reconstruction, enacted racist laws to severely restrict what employment they could secure and where they could live among other limitations. Federal policies such as the National Interstate and Defense Highways Act in 1956 led to mass exodus of Whites and their economic bases into the suburbs, leaving many Blacks and other non-White populations to deal with crumbling urban infrastructures and a weakened economic base. Despite advances in civil rights there has yet to be a reversal of the lingering residuals of systemic disadvantage that continue to plague many urban metropolitan locales, and in many urban areas it has worsened. It is beyond the reach of this study to make a definitive statement as to whether predominantly-Black areas are specifically targeted with greater availability of tobacco outlets or if Blacks are more likely to live in areas with higher tobacco outlet density. However, research has provided evidence of the tobacco industry’s target marketing of Black communities using point-of-sale advertising and advertising in various publications (Moore et. al, 1996; King et. al, 2001; Laws et. al, 2002; Landrine et. al, 2005; Alpert et. al, 2008). Therefore, it is plausible that the tobacco industry may encourage more businesses to sell tobacco products in Black communities than they encourage to sell in White communities, regardless of the socioeconomic status of either community, given the additional stressors suffered by Blacks that may promote unhealthy coping mechanisms such as tobacco use (Clark et. al, 1999; Harrell, 2000; Brondolo et. al, 2009).

Context is important in understanding the complex relationship between race and socioeconomic status. While multiple measures of SES were analyzed and did explain some of the spatial variation in tobacco outlet availability and access across the study areas as shown in the Chi-squared values, spatial variation remained. Therefore, there are more factors that must be considered to fully explain relationships with tobacco outlet access and availability. One of those factors, as previously mentioned, is urbanicity. This study’s omission of urbanicity as a covariable was based on previous tobacco outlet density research that concluded no significant influence. However, it is acknowledged that there would have been a benefit to including urbanicity, as a valid measure of built environment, within the methodology. Future studies should make such a consideration.

Nevertheless, this study’s strengths include observation that the majority racial percentage and various SES measure did account for some spatial variation in all the study areas, exemplifying the importance of establishing a solid epidemiological foundation of determinants. In that same vein, another strength is the study design and analytical approach. The utilization of Maryland’s ecological heterogeneity, coupled with the utilization of both simple (two-sample t-tests) and complex (spatial lag Poisson regression) statistical techniques, produced basic and nuanced inferences essential for understanding the dynamics of race, socioeconomic status and tobacco outlet density. It was intended for this study to present an efficacious approach for exploring and crystalizing both fundamental and consequential relationships between sociodemographics and tobacco outlet density, with the aim of future research improving study design and analytical efficiency. As with other studies on place-based disparities, the contextual interaction of race and socioeconomic status should not be disregarded but rather utilized as the basis for progressive tobacco outlet control and strict enforcement of existing tobacco control policies. Additionally, it should be an integral part of all health disparities research.

This study concludes that predominantly-White areas have lower tobacco outlet availability and access than predominantly-Black areas, despite both areas having similar socioeconomic status. It is suggested that socioeconomic status across racial composition, while seemingly comparable, should be contextualized with an acknowledgment of chronic inequalities created and perpetuated by systems of oppression such as racism, and that place-based disparities of tobacco outlet availability and access have a racial gradient and a socioeconomic gradient. Therefore, tobacco control policies should be attuned to racial differences in addition to socioeconomic differences in neighborhoods and communities.

Table 5:

Model #3 – Spatial Lag Covariable Interaction Regression Model Coefficients on Tobacco Outlet Access in Lower Eastern Shore, Maryland – 2011–2015 (n = 50)

Variable Multivariable Model
Focal & Spatial Lag Focal & Spatial Lag Interaction
Exponentiated Beta p-value Exponentiated Beta p-value
Focal Effects
White Population Percentage (per 10%) 1.03 <0.01 0.99 <0.001
Median Household Income (per $10000) 0.19 <0.001 1.00 <0.001
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 0.75 <0.001 1.02 <0.001
Labor Force Participation Rate (per 10%) 5.50 <0.001 1.03 <0.001
Gini Income Inequality Coefficient (per 1%) 0.14 <0.001 125.59 <0.001
Vacant Houses (x100) 0.99 <0.01 1.00 <0.001
Spatial Lag
White Population Percentage (per 10%) 1.21 <0.001
Median Household Income (per $10000) 0.27 <0.001
Percentage of Individuals 25+ with at least a Bachelor’s Degree (per 10%) 0.73 <0.001
Labor Force Participation Rate(per 10%) 7.05 <0.001
Gini Income Inequality Coefficient (per 1%) 0.14 <0.001
Vacant Houses (x100) 0.98 <0.001
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Acknowledgments

Funding: This study was supported by the National Institute on Drug Abuse (T32DA007292) and the National Institute on Minority Health and Health Disparities (U54MD000214, R01AG054363 and K02AG059140).

This study was supported by the National Institute on Drug Abuse (T32DA007292) and the National Institute on Minority Health and Health Disparities (U54MD000214, R01AG054363 and K02AG059140).

Footnotes

Conflict of Interest: Dr. Fakunle declares that he has no financial or nonfinancial conflict of interest. Dr. Curriero declares that he has no financial or nonfinancial conflict of interest. Dr. Leaf declares that he has no financial or nonfinancial conflict of interest. Dr. Furr-Holden declares that she has no financial or nonfinancial conflict of interest. Dr. Thorpe declares that he has no financial or nonfinancial conflict of interest.

Compliance with Ethical Standards

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Contributor Information

David O. Fakunle, Kaiser Research Fellow, School of Community Health & Policy – Morgan State University, Baltimore, Maryland; Department of Mental Health – Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Frank C. Curriero, Department of Epidemiology – Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Philip J. Leaf, Department of Mental Health – Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Debra M. Furr-Holden, Division of Public Health – Michigan State University College of Human Medicine, Flint, Michigan.

Roland J. Thorpe, Jr., Department of Health, Behavior & Society – Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

References

  1. Alpert HR, Koh HK, & Connolly GN (2008). After the Master Settlement Agreement: targeting and exposure of youth to magazine tobacco advertising. Health Affairs, 27(6), w503–w512. [DOI] [PubMed] [Google Scholar]
  2. Auchincloss AH, Gebreab SY, Mair C, & Diez Roux AV (2012). A review of spatial methods in epidemiology, 2000–2010. Annual review of public health, 33, 107–122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barnett E, & Halverson J (2000). Disparities in premature coronary heart disease mortality by region and urbanicity among black and white adults ages 35-64, 1985–1995. Public health reports, 115(1), 52. [PMC free article] [PubMed] [Google Scholar]
  4. Bower KM, Thorpe RJ Jr, Rohde C, & Gaskin DJ (2014). The intersection of neighborhood racial segregation, poverty, and urbanicity and its impact on food store availability in the United States. Preventive medicine, 58, 33–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brondolo E, Gallo LC, & Myers HF (2009). Race, racism and health: disparities, mechanisms, and interventions. Journal of behavioral medicine, 32(1), 1. [DOI] [PubMed] [Google Scholar]
  6. Clark R, Anderson NB, Clark VR, & Williams DR (1999). Racism as a stressor for African Americans: A biopsychosocial model. American psychologist, 54(10), 805. [DOI] [PubMed] [Google Scholar]
  7. Duncan CM (2000). Worlds apart: Why poverty persists in rural America. Yale University Press. [Google Scholar]
  8. Eisenhauer E (2001). In poor health: Supermarket redlining and urban nutrition. GeoJournal, 53(2), 125–133. [Google Scholar]
  9. Fakunle D, Morton CM, & Peterson NA (2010). The importance of income in the link between tobacco outlet density and demographics at the tract level of analysis in New Jersey. Journal of ethnicity in substance abuse, 9(4), 249–259. [DOI] [PubMed] [Google Scholar]
  10. Fakunle DO, Milam AJ, Furr-Holden CD, Butler J III, Thorpe RJ Jr, & LaVeist TA (2016). The inequitable distribution of tobacco outlet density: the role of income in two Black Mid-Atlantic geopolitical areas. Public health, 136, 35–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fakunle DO, Thorpe RJ Jr, Furr-Holden CDM, Curriero FC, & Leaf PJ (2018). Does tobacco outlet inequality extend to high-White Mid-Atlantic jurisdictions? A study of socioeconomic status and density. J Racial Ethn Health Disparities. 2019 April;6(2):409–418. doi: 10.1007/s40615-018-00538-9. Epub 2018 Nov 16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gaskin DJ, Headen AE Jr, & White-Means SI (2004). Racial disparities in health and wealth: The effects of slavery and past discrimination. The review of Black political economy, 32(3–4), 95–110. [Google Scholar]
  13. Gini C (1921). Measurement of inequality of incomes. The economic journal, 31(121), 124–126. [Google Scholar]
  14. Gorman DM, Speer PW, Gruenewald PJ, & Labouvie EW (2001). Spatial dynamics of alcohol availability, neighborhood structure and violent crime. Journal of studies on alcohol, 62(5), 628–636. [DOI] [PubMed] [Google Scholar]
  15. Grubesic TH, Wei R, Murray AT, & Pridemore WA (2016). Comparative approaches for assessing access to alcohol outlets: exploring the utility of a gravity potential approach. Population health metrics, 14(1), 25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Harrell SP (2000). A multidimensional conceptualization of racism‐related stress: Implications for the well‐being of people of color. American journal of Orthopsychiatry, 70(1), 42–57. [DOI] [PubMed] [Google Scholar]
  17. Hartley D (2004). Rural health disparities, population health, and rural culture. American Journal of Public Health, 94(10), 1675–1678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hyland A, Travers MJ, Cummings KM, Bauer J, Alford T, & Wieczorek WF (2003). Tobacco outlet density and demographics in Erie County, New York. American Journal of Public Health, 93(7), 1075–1076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. IBM Corp. (2017). SPSS Statistics for Windows, Version 24.0 [computer software].
  20. Kawachi I, & Kennedy BP (1999). Income inequality and health: pathways and mechanisms. Health services research, 34(1 Pt 2), 215. [PMC free article] [PubMed] [Google Scholar]
  21. Kestenbaum B (2009). Epidemiology and biostatistics: an introduction to clinical research. Springer Science & Business Media. [Google Scholar]
  22. King C III, & Siegel M (2001). The Master Settlement Agreement with the tobacco industry and cigarette advertising in magazines. New England Journal of Medicine, 345(7), 504–511. [DOI] [PubMed] [Google Scholar]
  23. Landrine H, Klonoff EA, Fernandez S, Hickman N, Kashima K, Parekh B, … & Weslowski Z (2005). Cigarette advertising in Black, Latino, and White magazines, 1998–2002: an exploratory investigation. Ethnicity & disease, 15(1), 63–67. [PubMed] [Google Scholar]
  24. LaVeist TA (2005). Disentangling race and socioeconomic status: a key to understanding health inequalities. Journal of Urban Health, 82(3), iii26–iii34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. LaVeist T, Pollack K, Thorpe R Jr, Fesahazion R, & Gaskin D (2011). Place, not race: disparities dissipate in southwest Baltimore when blacks and whites live under similar conditions. Health affairs, 30(10), 1880–1887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Laws MB, Whitman J, Bowser DM, & Krech L (2002). Tobacco availability and point of sale marketing in demographically contrasting districts of Massachusetts. Tobacco Control, 11(suppl 2), ii71–ii73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lee JG, Sun DL, Schleicher NM, Ribisl KM, Luke DA, & Henriksen L (2017). Inequalities in tobacco outlet density by race, ethnicity and socioeconomic status, 2012, USA: results from the ASPiRE Study. J Epidemiol Community Health, jech-2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lemann N (2011). The promised land: The great black migration and how it changed America. Vintage. [Google Scholar]
  29. Lopez IFH (1994). The social construction of race: Some observations on illusion, fabrication, and choice. Harv CR-CLL Rev, 29, 1. [Google Scholar]
  30. Mayers RS, Wiggins LL, Fulghum FH, & Peterson NA (2012). Tobacco outlet density and demographics: a geographically weighted regression analysis. Prevention Science, 13(5), 462–471. [DOI] [PubMed] [Google Scholar]
  31. Moore DJ, Williams JD, & Qualls WJ (1996). Target marketing of tobacco and alcohol-related products to ethnic minority groups in the United States. Ethnicity and Disease, 6, 83–98. [PubMed] [Google Scholar]
  32. Morrison C, Gruenewald PJ, & Ponicki WR (2016). Race, ethnicity, and exposure to alcohol outlets. Journal of studies on alcohol and drugs, 77(1), 68–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Novak SP, Reardon SF, Raudenbush SW, & Buka SL (2006). Retail tobacco outlet density and youth cigarette smoking: a propensity-modeling approach. American Journal of Public Health, 96(4), 670–676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Ogneva-Himmelberger Y, Ross L, Burdick W, & Simpson SA (2010). Using geographic information systems to compare the density of stores selling tobacco and alcohol: youth making an argument for increased regulation of the tobacco permitting process in Worcester, Massachusetts, USA. Tobacco control, 19(6), 475–480. [DOI] [PubMed] [Google Scholar]
  35. Oliver M, & Shapiro T (2013). Black wealth/white wealth: A new perspective on racial inequality. Routledge. [Google Scholar]
  36. Painter N (1991). The Great Migration in historical perspective: New dimensions of race, class, and gender (Vol. 669). Indiana University Press. [Google Scholar]
  37. Peterson NA, Lowe JB, & Reid RJ (2005). Tobacco outlet density, cigarette smoking prevalence, and demographics at the county level of analysis. Substance use & misuse, 40(11), 1627–1635. [DOI] [PubMed] [Google Scholar]
  38. Reid RJ, Peterson NA, Lowe JB, & Hughey J (2005). Tobacco outlet density and smoking prevalence: Does racial concentration matter? Drugs: education, prevention and policy, 12(3), 233–238. [Google Scholar]
  39. Ribisl KM, Luke DA, Bohannon DL, Sorg AA, & Moreland-Russell S (2016). Reducing disparities in tobacco retailer density by banning tobacco product sales near schools. Nicotine & Tobacco Research, 19(2), 239–244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Rodriguez D, Carlos HA, Adachi-Mejia AM, Berke EM, & Sargent JD (2013). Predictors of tobacco outlet density nationwide: a geographic analysis. Tobacco Control, 22(5), 349–355. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Schneider JE, Reid RJ, Peterson NA, Lowe JB, & Hughey J (2005). Tobacco outlet density and demographics at the tract level of analysis in Iowa: implications for environmentally based prevention initiatives. Prevention Science, 6(4), 319–325. [DOI] [PubMed] [Google Scholar]
  42. Smedley A, & Smedley BD (2005). Race as biology is fiction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race. American Psychologist, 60(1), 16. [DOI] [PubMed] [Google Scholar]
  43. Subramanian SV, & Kawachi I (2004). Income inequality and health: what have we learned so far? Epidemiologic reviews, 26(1), 78–91. [DOI] [PubMed] [Google Scholar]
  44. The R Project for Statistical Computing (2018). R Version 3.5.1. [computer program].
  45. Thorpe RJ Jr, Griffith DM, & Bruce MA (2017). Racism as a fundamental determinant of health for Black boys. In Linking Health and Education for African American Students’ Success (pp. 39–52). Routledge. [Google Scholar]
  46. Tolnay SE (2003). The African American “great migration” and beyond. Annual Review of Sociology, 29(1), 209–232. [Google Scholar]
  47. U.S. Census Bureau (2017, January 22). American Factfinder. Retrieved from http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml
  48. Williams DR (1997). Race and health: basic questions, emerging directions. Annals of epidemiology, 7(5), 322–333. [DOI] [PubMed] [Google Scholar]
  49. Williams DR (1999). Race, socioeconomic status, and health the added effects of racism and discrimination. Annals of the New York Academy of Sciences, 896(1), 173–188. [DOI] [PubMed] [Google Scholar]
  50. Williams DR, & Collins C (1995). US socioeconomic and racial differences in health: patterns and explanations. Annual review of sociology, 21(1), 349–386. [Google Scholar]
  51. Williams DR, & Collins C (2001). Racial residential segregation: a fundamental cause of racial disparities in health. Public health reports, 116(5), 404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Williams DR, Mohammed SA, Leavell J, & Collins C (2010). Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186(1), 69–101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yu D, Peterson NA, Sheffer MA, Reid RJ, & Schnieder JE (2010). Tobacco outlet density and demographics: analysing the relationships with a spatial regression approach. Public health, 124(7), 412–416. [DOI] [PubMed] [Google Scholar]

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