A geospatial analysis of age disparities in resolute localities of tobacco and vaping-specific storefronts in California

Longitudinal analysis of tobacco and vape retail density in California

INTRODUCTION

Tobacco retailer density may be associated with greater youth initiation and reduced success during quit attempts; however, the extent to which tobacco retailer density has changed overtime across multiple categories of retailers has not been reported.

METHODS

Data on licensed tobacco retailers within California from 2015–2019 were obtained from the California Department of Tax and Fee Administration. Store type was categorized by automated cross-referencing with Yelp. Geolocations were aggregated at county level for analyzing longitudinal trends in changes in tobacco retail density including demographic characteristics.

RESULTS

The number of active CA tobacco retailer licenses increased from 19825 in 2015 to 25635 in 2019. The highest percent increase in tobacco retailer licenses (9.1%) was observed in 2017. The number of specialized tobacco stores was highest in Los Angeles, San Diego, and Riverside counties. We observed a significant increase in the number of active licenses for non-specialized and specialized tobacco stores, both overall and after controlling for the size of populations within each region. Time was a statistically significant predictor for the number of active licenses for only non-specialized stores, after adjusting for covariates. Regional volume of retailers was positively associated with higher proportion of women, lower median household income, and higher proportion of Hispanic residents.

CONCLUSIONS

Monitoring the changes in tobacco retail density and associated sociodemographic factors over time can help to identify communities at higher risk for tobacco and nicotine product exposure and access, and its associated health disparities.

Association of tobacco retailer count with smoking population versus vaping population in California (2019)

BACKGROUND

Access to tobacco products, including vape products, from local brick-and-mortar stores influences the exposure, uptake, and use of these products in local communities.

METHODS

Licensed tobacco retailers in California were classified as specialized tobacco/vape stores or non-specialized stores by obtaining categories published on Yelp. California smoking and vaping prevalence data were obtained from the 500 cities project and ESRI community analyst tool respectively. A series of simple linear regression tests were performed, at the zip code level, between the retailer count in each store category and smoking/vaping population. The Getis-Ord Gi* and Anselin Local Moran's I statistics were used for characterization of tobacco retail density hotspots and cold spots.

RESULTS

The association between CA smoking/vaping population and number of tobacco retailers was statistically significant for all store categories. Variability in smoking population was best explained by variability in non-specialized storefronts(R2=0.84). Spatial variability in tobacco-only storefronts explained the least proportion of variability in the overall smoking population. Similar results were obtained specific to vaping population, although the proportion of population explained by variability in the number of non-specialized storefronts was comparatively lower(R2=0.80).

CONCLUSIONS

Localities with greater numbers of non-specialized tobacco retailers had higher rates of smoking/vaping populations, and this association was much stronger for localities with greater numbers of specialized retailers. Non-specialized storefronts may represent convenient access points for nicotine products, while specialized storefronts may represent critical access points for initiation. Hence, regulations that address the entirety of the tobacco/vaping retail environment by limiting widespread access from non-specialized stores and reducing appeal generated by specialized retailers should be incorporated in future tobacco regulatory science and policymaking.

Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study

Background

The number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement.

Objective

The goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies.

Methods

Geolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status.

Results

The percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7% vs 66.4%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one’s own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities.

Conclusions

Our study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one’s own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment.