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Abstract
Background Evidence of racial/ethnic inequalities in tobacco outlet density is limited by: (1) reliance on studies from single counties or states, (2) limited attention to spatial dependence, and (3) an unclear theory-based relationship between neighbourhood composition and tobacco outlet density.
Methods In 97 counties from the contiguous USA, we calculated the 2012 density of likely tobacco outlets (N=90 407), defined as tobacco outlets per 1000 population in census tracts (n=17 667). We used 2 spatial regression techniques, (1) a spatial errors approach in GeoDa software and (2) fitting a covariance function to the errors using a distance matrix of all tract centroids. We examined density as a function of race, ethnicity, income and 2 indicators identified from city planning literature to indicate neighbourhood stability (vacant housing, renter-occupied housing).
Results The average density was 1.3 tobacco outlets per 1000 persons. Both spatial regression approaches yielded similar results. In unadjusted models, tobacco outlet density was positively associated with the proportion of black residents and negatively associated with the proportion of Asian residents, white residents and median household income. There was no association with the proportion of Hispanic residents. Indicators of neighbourhood stability explained the disproportionate density associated with black residential composition, but inequalities by income persisted in multivariable models.
Conclusions Data from a large sample of US counties and results from 2 techniques to address spatial dependence strengthen evidence of inequalities in tobacco outlet density by race and income. Further research is needed to understand the underlying mechanisms in order to strengthen interventions.
- Tobacco
- PUBLIC HEALTH POLICY
- SMOKING
- Health inequalities
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Introduction
Cigarettes, the only consumer product that kills almost half of its users when used as directed, are sold in ∼375 000 stores in the USA.1 Tobacco outlets contribute to the toll of tobacco use through several mechanisms. Widespread availability reduces search costs to obtain tobacco products, and convenient access encourages use and undermines quit attempts.2–4 US stores contain an average of 30 tobacco advertisements,1 and exposure to retail tobacco marketing is a risk factor for smoking initiation5 and promotes impulse purchases.6 In addition, the retail availability and marketing of tobacco products normalise their use.7 ,8
Smoking prevalence has a steep socioeconomic gradient,9 and morbidity and mortality from smoking are not equally distributed by race.10 Inequalities in the retail availability of tobacco products may contribute to disparities in smoking and tobacco-related disease. The distribution of retailers varies widely depending on community characteristics with more retailers in neighbourhoods with lower incomes and greater proportions of African-American residents.11–15 The issue of outlet density is characterised as a problem of social justice.16 Unfortunately, our understanding of racial/ethnic inequalities in tobacco outlet density has been limited by three factors: (1) the geographical area of prior studies, (2) lack of corrections for spatial autocorrelation and (3) atheoretical explanations for evidence of disparities.
Geography: Most studies about racial/ethnic disparities examine tobacco outlet density in a single county or state, which limits generalisability. The one exception uses a national data set from a single source of likely tobacco outlets and a limited set of outlet types (ie, tobacco stores, grocery stores, gas stations and convenience stores).13 Other studies use national samples but do not directly address inequalities in tobacco outlet density.17 We used a national sample of tobacco outlets sourced from two business listings and included additional store types such as alcohol retailers, discount department stores such as Walmart, and pharmacies.
Spatial autocorrelation: Limited attention has been paid to spatial autocorrelation (things closer together are more similar than things farther apart), which can lead to violations of regression assumptions about independence and result in SEs that are underestimated.18 Thus, the type 1 error rate for existing studies of tobacco outlet density may be inflated if spatial autocorrelation is not taken into account.14 While studies have used spatial autocorrelation in the analyses of tobacco outlet inequalities in other cities and states,14 ,19–21 the one national study of tobacco outlet density inequalities identified significant inequalities by race, ethnicity and income but did not address spatial autocorrelation.13 This is of concern because when spatial regression approaches were used in a Boston, Massachusetts study, they found no significant neighbourhood demographic correlates of tobacco outlet density.21
Theoretical explanations: Prior research has not developed theoretical explanations for associations between racial/ethnic neighbourhood composition and tobacco outlet density. Associations between neighbourhood composition and tobacco outlet density are usually partially but not fully (with rare exception20) accounted for by measures of socioeconomic status in the neighbourhood, such as neighbourhood income or poverty status.13 ,14 ,19 Following theories of neighbourhood inequalities,22 resources are important in the production of health inequalities; however, resources contributing to these inequalities go well beyond measures of income to include other social and economic neighbourhood characteristics. The stability of neighbourhoods and economic, social and political resources can influence capital for retailer investment, accessibility to potential customers based on neighbourhood safety and transportation infrastructure, and owner decisions to expand.22 ,23 Using a model from city planning on neighbourhood stability and change,24 we identified two proxies for neighbourhood stability: the proportion of vacant housing units and the proportion of rental housing units. Similar measures have been used in studies of neighbourhood deprivation and smoking25 and in studies assessing perceptions of neighbourhood well-being.26 City planning research has noted the important role of home ownership both in perceptions of neighbourhood and in promoting social connectedness in neighbourhoods.27 ,28 These measures of neighbourhood resources have not been addressed in the tobacco outlet density literature.
Given the three limitations to the existing literature, the goals of this paper are to (1) assess inequalities in tobacco outlet density at the census tract level in a national study of 97 US counties by race, ethnicity and income; (2) conduct analyses addressing spatial autocorrelation; and (3) assess the association of tobacco outlet density with indicators of two types of neighbourhood resources that were derived from the city planning literature.
Methods
This study is part of the National Cancer Institute and State and Community Tobacco Control Research Initiative-funded Advancing Science and Policy in the Retail Environment (ASPiRE) Study conducted by the Stanford Prevention Research Center, UNC Gillings School of Global Public Health, and Washington University in St Louis. The ASPiRE Study included 97 counties that were selected with probability proportional to population size from all counties in the lower 48 US states. In 2010, 79 million people (26% of the US population) lived in these 97 counties.29
Tobacco outlet list creation
There is no national list of tobacco outlets. For the 97 counties, we obtained business lists of likely tobacco outlets in 2012 from North American Industry Classification System (NAICS) Association and Reference USA. Detailed methods about the sample were published previously.30 Briefly, we restricted likely tobacco outlets to stores with primary codes for supermarkets and grocery stores, convenience stores, tobacco shops, gasoline stations with convenience stores, warehouse clubs and supercenters, news dealers and newsstands, alcohol stores (except state-owned liquor stores), pharmacies (top 50 chains only), discount department stores (Walmart only), and other gasoline stations. We removed duplicate addresses and chains that had no-tobacco-sales policies. The approach has been validated in a field study in a state (North Carolina) without licensing31 and was the recommended approach in a methods review.32 There were few e-cigarette or vape shops in 2012, so our approach did not explicitly include or exclude these stores.
Measures
Dependent variable and areal unit
There were 90 407 likely tobacco outlets in the 97 counties in 2012. Following previous research13 ,30 we calculated tobacco outlet density as the number of tobacco outlets per 1000 population in a census tract. We defined neighbourhoods as census tracts. In the 97 counties, there were 17 941 census tracts. Since small populations can render estimates unstable, we removed the 266 tracts with fewer than 250 households and excluded 8 tracts that were missing economic data from all analyses. These 274 tracts contained 603 tobacco outlets.
Neighbourhood demographics
Using 2010 Census data, we calculated the proportion of each census tract's population identifying as black/African-American and Asian/Pacific Islander (alone or in combination with other races), identifying as Hispanic or Latino ethnicity, and identifying as white race alone. We scaled these in 10s (eg, 12%=1.2). We used American Community Survey 5-year estimates (2008–2012) for median household income in 2012 dollars.33 Since income is relative (eg, $34 000 a year may be above average in one county and well below average in another), we standardised income within each county using z-scores. We scaled these in 10s (eg, −0.12=−1.2) so that a one-unit change represents a change of 0.1 SDs.
Neighbourhood characteristics
We used two variables to capture characteristics of neighbourhood stability and neighbourhood revitalisation: the percentage of housing units that are renter occupied and the percentage of housing units that are vacant.27 ,34 Both variables came from American Community Survey estimates33 and were scaled in 10s. The average correlation among all predictor variables was rs=−0.08.
Analysis
Tobacco outlet density was our dependent variable and census tract characteristics were our predictor variables. Standard statistical approaches such as linear regression assume each census tract in the analysis provides independent information that is not correlated with its neighbours. Yet, things closer together share more characteristics than things further apart, violating the assumption of independence in regression models.18 We assessed our dependent variable for spatial dependence and found that there was a significant spatial clustering of tract tobacco outlet density, Moran's I=0.10, ppsuedo=0.001. There was also significant clustering in the ordinary least squares regression residuals, Moran's I=0.06, ppsuedo=0.001. To address this non-independence, we used spatial regression.
To address spatial autocorrelation we used two approaches. We first used a spatial error approach in GeoDa software (V.1.6.7). We created a second-order queen weights matrix and implemented the spatial regression approach with the error as a function of the error at nearby locations. In a second approach we fitted a covariance function to the errors in R software (V.3.2.2) using a weights matrix of all distances between tracts (17 667×17 667). Besides providing more information about the autocorrelation structure, this approach also is less sensitive to edge effects, which can be a problem with spatial error approaches.18 Code to fit this approach was written by one of the authors (DLS) in the R programming language. We ran analyses using each spatial regression approach and compared the pattern of results between the two approaches.
Our modelling approach consisted of seven separate unadjusted models and one full model. We modelled the relationship of tobacco outlet density with (separately) (1) household income, (2) Asian/Pacific Islander race, (3) black/African-American race, (4) Hispanic ethnicity, (5) white race, (6) vacant housing, (7) renter occupancy and (8) an adjusted model including all variables except white race (to avoid multicollinearity as proportion of black/African-American race and proportion of white race were negatively correlated). Neither GeoDa's multicollinearity diagnostics nor our examination of the correlation matrix of the independent variables suggested problems with multicollinearity.
We interpret inequalities from the unadjusted models; unadjusted models allow us to directly assess ‘on the ground’ neighbourhood inequalities (ie, Are there more tobacco outlets in neighbourhoods with a greater proportion of residents who identify as a given racial or ethnic group?) as opposed to hypothetical neighbourhoods (ie, Are there more tobacco outlets in neighbourhoods with a greater proportion of residents who identify as a given racial or ethnic group when other neighbourhood characteristics are statistically held constant?). Since no human participants were involved in this study, Institutional Review Board (IRB) approval was not sought.
Results
The average density was 1.3 retailers per 1000 persons. Table 1 shows descriptive statistics about the 17 667 census tracts included in this study.
Census tract characteristics, n=17 667
Spatial autocorrelation was present in these data. In addition, the two approaches to spatial regression produced the same pattern of results to one decimal place. We report the results of the first approach using GeoDa software. Ordinary least squares regression is reported in online supplementary table S1.
supplementary table
Census tract tobacco outlet density ordinary least squares regression models, n=17,667
We first report unadjusted relationships between tract racial/ethnic composition and income in relation to tobacco outlet density. In these models, we identified a significant negative association between tract median household income and tobacco outlet density (table 2). We found a significant, albeit small, positive association between tobacco outlet density and the proportion of residents identifying as black/African-American. The opposite association was found for the proportions of Asian/Pacific Islander and white residents. Our results, however, suggest no evidence of a tobacco outlet density inequality by neighbourhood proportion of Latino or Hispanic residents. These relationships are plotted by deciles in figure 1.
Census tract tobacco outlet density models, n=17 667
Tobacco retailer density by tract characteristics in deciles, n=17 667, 2012.
We next report an adjusted model that controlled for tract income, racial/ethnic composition and the two variables serving as proxies for neighbourhood resources. In this model, income continued to show a significant negative association with outlet density as did the proportion of Asian residents. However, the proportion of black/African-American and Hispanic residents had a negative association with tobacco outlet density. The proxies for neighbourhood resources limitations (vacant housing and renter-occupied units) both were positively correlated with greater outlet density after controlling for neighbourhood racial/ethnic composition and income.
Discussion
We assessed inequalities in tobacco outlet density at the census tract level in a national study of 97 US counties by race, ethnicity and income. This study confirmed smaller county-level and state-level studies as well as the one prior national study that found inequalities in tobacco outlet density by black/African-American (positive association) and white (negative association) neighbourhood racial composition as well as with neighbourhood income (negative association).11–14 ,19 ,20 ,35 This study is the first we are aware of to examine Asian/Pacific Islander neighbourhood racial composition in relation to tobacco outlet density, and the association was negative. We did not identify an association between neighbourhood Hispanic ethnicity composition and tobacco outlet density that has been found in previous research.13
One explanation of the higher tobacco outlet density in neighbourhoods with a larger proportion of black residents and higher income is suggested by studies showing that retailers in neighbourhoods with lower income and higher proportions of black residents tend to be smaller.36 This could be due to historical differences in resource investment (eg, redlining), racially biased retailer decisions to (not) expand and invest resources in larger stores, and the impact of neighbourhood segregation.37 Previous research suggests that Asian enclaves may be healthier neighbourhoods,38 and further research is needed to determine whether the negative association observed here is replicable and to explore variation by racial/ethnic subgroup.
In a previous study of tobacco outlet density in the USA, Rodriguez et al13 found a large positive relationship between the logged proportion of Hispanic residents and logged tobacco outlet density in a multivariable model controlling for a variety of socioeconomic variables, urbanicity and neighbourhood composition measures. The different results regarding Hispanic ethnicity in the current study may stem from a number of reasons. First, our study area is more urban than the continental USA, given the sampling strategy that selected counties with probability based on population size. Second, the current study reported unadjusted models and, in the adjusted model, used different control variables.
In our second aim, we sought to implement analysis addressing spatial autocorrelation and we used two spatial regression model approaches. These results were robust to the choice of analysis method, suggesting that the basic spatial regression lag and error models available in GeoDa, Stata and R may be sufficient and that custom models may not be necessary. Spatial regression approaches, while indicated here given the positive spatial autocorrelation, may be even more critical when there are higher levels of autocorrelation than were present in our data.
In our third aim, we sought to assess the association of two indicators of neighbourhood stability from the city planning literature with tobacco outlet density, proportions of vacant housing and rental housing. Control for these variables in multivariable models resulted in a negative association between black/African-American racial composition and tobacco outlet density. Similar results were found for Hispanic ethnicity. While our models show inequalities in tobacco outlet density exist on the ground, they suggest that in a counterfactual world where these neighbourhood stability measures were held constant across neighbourhood racial/ethnic composition tobacco outlet density inequalities would be reversed. That is, black and Hispanic racial/ethnic composition would be protective against tobacco outlet density similar to what is seen in the food retailer inequalities literature (ie, fewer sources of healthy foods in neighbourhoods with a greater proportion of black residents).36
Of course, neighbourhood characteristics are not constant across racial composition in the USA,37 but this finding suggests that neighbourhood stability measures may be an important piece of understanding tobacco outlet density. Indeed, the field of organisational ecology, which conceptualises populations of organisations (ie, tobacco outlets) as influenced by how they adapt to local competition and resources,23 would suggest that changes to the well-being of neighbourhoods might ameliorate inequalities in tobacco outlet density. Theoretical frameworks of neighbourhood health inequalities22 used in combination with organisational ecology23 may suggest future ways of understanding inequalities in tobacco outlet density.
This research expanded on a previous national study13 by using a broader definition of tobacco outlets (including Walmart, a major distributor of tobacco products, retailers from the top 50 pharmacy chains, and non-state owned alcohol retailers) sourced from two business listing services. In addition, the current study addressed spatial dependence and incorporated theory-informed measures of neighbourhood stability.
Limitations
First, we did not address the role of store type, and store type may be patterned by neighbourhood characteristics; thus, we cannot examine the role of store type in explaining the identified inequalities. Second, there are a number of challenges with using spatial data. While we have addressed spatial autocorrelation and used a theoretically appropriate areal unit, previous research using these data identified possible edge effects (ie, where areal units outside of the study area might have influenced results if included).30 However, the fact that the GeoDa model yielded the same results as the explicit covariance model, which is less sensitive to edge effects, suggests that edge effects in this data are negligible. Third, the sampling design was intended to yield a representative sample of US tobacco outlets, not US counties. Therefore, the results may not generalise to tobacco outlets in other counties. Fourth, our indicators of neighbourhood resources may themselves be proxies for unmeasured historical and ongoing forms of institutional discrimination by race that influence the availability of resources in a given neighbourhood and are ultimately associated with tobacco outlet density. Fifth, our racial/ethnic groups are aggregated and do not distinguish intragroup differences (eg, Japanese vs Hmong ancestry). Sixth, this cross-sectional study cannot assess causality. Finally, although source data for likely tobacco outlets were derived using a validated13 ,31 and recommended strategy,32 in the absence of a national licensing list, the computation of density may be subject to measurement error.
Policy implications
Given evidence of racial/ethnic and income disparities in tobacco outlet density, policy interventions39 should be assessed for their contribution to reducing these inequalities. Retailer reduction can be achieved using strategies that limit the number of retailers (eg, licensing cap), the types of retailers that can sell tobacco (eg, tobacco-free pharmacies), and the locations of tobacco outlets (eg, banning sales near schools).15 ,40 Evidence suggests that the latter could eliminate tobacco outlet inequalities by race and income in Missouri and New York State.15
Strengthening the well-being of neighbourhoods24 may have the potential to change the retailer mix in ways that reduce tobacco outlet density overall as well as reduce inequalities.
Conclusions
There are inequalities in the density of tobacco outlets by neighbourhood income, racial/ethnic composition and neighbourhood stability. Such inequalities likely compound other existing inequalities in neighbourhood resources, tobacco retailer marketing and the effects of residential segregation. Policy efforts to reduce outlet density and to revitalise neighbourhoods should be examined for their potential to reduce inequitable exposure to tobacco outlets.
What is already known on this subject
Tobacco outlet density is associated with racial/ethnic neighbourhood composition and neighbourhood income in state-level and county-level studies and in one national study. Few studies address issues of spatial autocorrelation or use theory-informed approaches to investigate the possible reasons for these inequalities.
What this study adds
This is the first national study to address tobacco outlet density inequalities while also addressing spatial autocorrelation and examining theory-informed neighbourhood characteristics in explaining identified inequalities.
Acknowledgments
An earlier version of this work was presented at the 2016 Society for Research on Nicotine and Tobacco Annual Meeting, Chicago, Illinois, USA.
References
Footnotes
Twitter Follow Joseph Lee @Joseph_GL_Lee
Contributors JGLL and LH originated the study. JGLL and DLS conducted the data analysis. NMS advised on data analysis. All authors interpreted the data. JGLL wrote the first draft of the manuscript. All authors provided critical revisions and edits to the manuscript. All authors approved the final version of the manuscript.
Funding Research reported in this publication was supported by the National Cancer Institute of the US National Institutes of Health under award number U01CA154281 as part of the State & Community Tobacco Control Research Initiative.
Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Competing interests JGLL and KMR receive licensing royalties from a store audit/compliance and mapping system, Counter Tools (http://countertools.org), owned by the University of North Carolina at Chapel Hill. KMR has served as an expert consultant in litigation against cigarette manufacturers and internet tobacco vendors.
Provenance and peer review Not commissioned; externally peer reviewed.