title: Retail tobacco exposure: using geographic analysis to identify areas with excessively high retail density.
abstract: BACKGROUND
  There is great disparity in tobacco outlet density (TOD), with density highest in low-income areas and areas with greater proportions of minority residents, and this disparity may affect cancer incidence. We sought to better understand the nature of this disparity by assessing how these socio-demographic factors relate to TOD at the national level.
  
  
  METHODS
  Using mixture regression analysis and all of the nearly 65,000 census tracts in the contiguous United States, we aimed to determine the number of latent disparity classes by modeling the relations of proportions of Blacks, Hispanics, and families living in poverty with TOD, controlling for urban/rural status.
  
  
  RESULTS
  We identified six disparity classes. There was considerable heterogeneity in relation to TOD for Hispanics in rural settings. For Blacks, there was no relation to TOD in an urban moderate disparity class, and for rural census tracts, the relation was highest in a moderate disparity class.
  
  
  CONCLUSIONS
  We demonstrated the utility of classifying census tracts on heterogeneity of tobacco risk exposure. This approach provides a better understanding of the complexity of socio-demographic influences of tobacco retailing and creates opportunities for policy makers to more efficiently target areas in greatest need.
authors.affiliation: Centers for Behavioral and Preventive Medicine, Miriam Hospital, Providence, RI;
authors.forename: Daniel
authors.initials: D
authors.lastname: Rodriguez
authors.forename: Heather A
authors.initials: HA
authors.lastname: Carlos
authors.forename: Anna M
authors.initials: AM
authors.lastname: Adachi-Mejia
authors.forename: Ethan M
authors.initials: EM
authors.lastname: Berke
authors.forename: James
authors.initials: J
authors.lastname: Sargent
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data; D003132:Commerce / Q000706:statistics & numerical data*; D062305:Geographic Mapping; D054624:Health Status Disparities; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D011203:Poverty / Q000706:statistics & numerical data; D011204:Poverty Areas; D012044:Regression Analysis; D012111:Residence Characteristics; D012424:Rural Population / Q000706:statistics & numerical data; D012959:Socioeconomic Factors; D019460:Tobacco Industry / Q000706:statistics & numerical data*; D062789:Tobacco Products / Q000191:economics* / Q000706:statistics & numerical data; D014481:United States; D014505:Urban Population / Q000706:statistics & numerical data