Tobacco control · 2025
Supply and demand effects between tobacco retailer density and smoking prevalence.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Golden Shelley D, Kuo Tzy-Mey, Combs Todd, Kong Amanda Y, Ribisl Kurt M, Baggett Chris D
Аннотация
OBJECTIVE: Places with more tobacco retailers have higher smoking prevalence levels, but whether this is because retailers locate where people who smoke live or whether tobacco availability prompts tobacco use is unknown. In this study, we compare the role of consumer demand with that of tobacco supply in longitudinal, area-based associations of tobacco retailer density with smoking prevalence.
METHODS: We merged annual adult smoking prevalence estimates derived from the USA Behavioural Risk Factor Surveillance System data with annual county estimates of tobacco retailer density calculated from the National Establishment Time Series data for 3080 counties between 2000 and 2010. We analysed relationships between retailer density and smoking in 3080 counties, using random intercept cross-lagged panel models and employing two measures of tobacco retailer density capturing the number of likely tobacco retailers in a county divided by either the population or land area.
RESULTS: Both density models provided evidence of significant demand and supply effects; in the population-based model, the association of smoking prevalence in 1 year with tobacco retailer density in the next year (standardised coefficient=0.038, p<0.01) was about double the association between tobacco retailer density with subsequent smoking prevalence (0.017, p<0.01). The reverse was true in the land area-based model, where the supply effect (0.042, p<0.01) was more than 10 times stronger than the demand effect (0.003, p<0.01).
CONCLUSIONS: Policies that restrict access to retail tobacco have the potential to reduce smoking prevalence, but pairing such policies with interventions to reduce consumer demand remains important.
METHODS: We merged annual adult smoking prevalence estimates derived from the USA Behavioural Risk Factor Surveillance System data with annual county estimates of tobacco retailer density calculated from the National Establishment Time Series data for 3080 counties between 2000 and 2010. We analysed relationships between retailer density and smoking in 3080 counties, using random intercept cross-lagged panel models and employing two measures of tobacco retailer density capturing the number of likely tobacco retailers in a county divided by either the population or land area.
RESULTS: Both density models provided evidence of significant demand and supply effects; in the population-based model, the association of smoking prevalence in 1 year with tobacco retailer density in the next year (standardised coefficient=0.038, p<0.01) was about double the association between tobacco retailer density with subsequent smoking prevalence (0.017, p<0.01). The reverse was true in the land area-based model, where the supply effect (0.042, p<0.01) was more than 10 times stronger than the demand effect (0.003, p<0.01).
CONCLUSIONS: Policies that restrict access to retail tobacco have the potential to reduce smoking prevalence, but pairing such policies with interventions to reduce consumer demand remains important.