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Background: Although tobacco control efforts continue to try to prevent minors from purchasing tobacco, it remains a leading cause of premature death. One such effort is Food and Drug Administration (FDA) compliance checks. Studies have found that tobacco density is high in areas of lower socioeconomic status (SES). I hypothesized that as tobacco retailer density is higher in lower SES neighborhoods, these neighborhoods were more likely to be included in FDA compliance checks. To examine equity of inspection coverage, I created profiles to analyze if the checks were conducted equitably across retailers based on neighborhood characteristics and if retailers had an equal chance of being checked. I hypothesized that the checks were not being conducted equitably, along with a second and separate hypothesis, that different store types would be cited more than others. I also examined the placement of stores within New Jersey to understand the equity behind tobacco prevention programs.
Method: I used three sources of public data from New Jersey. Once these data were matched together and cleaned, I conducted a latent profile analysis (LPA), creating profiles based on tobacco density from census tracts. No study has used LPA to examine tobacco density or FDA compliance checks. Regression analyses estimated the likelihood that checks occurred more in profiles with high tobacco density and areas of lower SES. Regression analysis also examined the prediction that FDA is more likely to cite nonchain convenience stores Geospatial analysis examined store placement to determine equity relating to an Assurances of Voluntary Compliance (AVC) agreement.
Results: Among the 10, 810 licensed retailers in this data set, nonchain convenience stores (28.6%) are the largest category, followed by “other tobacco outlets” (16.2%), including bars, Asian food stores, and restaurants. Other categories (by order of number within the data) include liquor stores (15.3%), gas stations (14.5%), chain convenience stores (9.9%), pharmacies (5.5%), supermarkets (4%), dollar stores (3%), and smoke/vape shops (3%). The LPA results showed five profiles that predicted the likelihood of being checked. When examining the likelihood of a violation by store type, gas stations and nonchain convenience stores were more likely to be cited for a violation than chain convenience stores were. The study also found that AVC stores were not placed equitably across the state.
Conclusions: The results of this study provide some policy implications: a) provide better smoking cession programs and interventions in tobacco-retailer-dense areas, b) increase the reach of AVCs to chains that are over-represented in neighborhoods with greater retail density, and c) provide culturally competent and diverse retailer education on tobacco sales. Many small business owners are faced with language barriers that hinder them in understanding the relevant provisions. In addition, stronger enforcement of the “no tobacco sale” citation may be a solution to issues highlighted by theories used in this study and tobacco-related results.

http://dissertations.umi.com/gsnb.rutgers:12446

Ph.D.

Includes bibliographical references

doi:10.7282/t3-0bds-xk16

The author owns the copyright to this work.