Simulating the Impact of Sugar-Sweetened Beverage Warning Labels in Three Cities

Bruce Y. Lee, Marie Ferguson, Daniel L. Hertenstein, Atif Adam, Eli Zenkov, Peggy I. Wang, Michelle S. Wong, Joel Gittelsohn, Yeeli Mui, Shawn T. Brown

Research output: Contribution to journalArticle

Abstract

Introduction: A number of locations have been considering sugar-sweetened beverage point-of-purchase warning label policies to help address rising adolescent overweight and obesity prevalence. Methods: To explore the impact of such policies, in 2016 detailed agent-based models of Baltimore, Philadelphia, and San Francisco were developed, representing their populations, school locations, and food sources, using data from various sources collected between 2005 and 2014. The model simulated, over a 7-year period, the mean change in BMI and obesity prevalence in each of the cities from sugar-sweetened beverage warning label policies. Results: Data analysis conducted between 2016 and 2017 found that implementing sugar-sweetened beverage warning labels at all sugar-sweetened beverage retailers lowered obesity prevalence among adolescents in all three cities. Point-of-purchase labels with 8% efficacy (i.e., labels reducing probability of sugar-sweetened beverage consumption by 8%) resulted in the following percentage changes in obesity prevalence: Baltimore: -1.69% (95% CI= -2.75%, -0.97%, p<0.001); San Francisco: -4.08% (95% CI= -5.96%, -2.2%, p<0.001); Philadelphia: -2.17% (95% CI= -3.07%, -1.42%, p<0.001). Conclusions: Agent-based simulations showed how warning labels may decrease overweight and obesity prevalence in a variety of circumstances with label efficacy and literacy rate identified as potential drivers. Implementing a warning label policy may lead to a reduction in obesity prevalence. Focusing on warning label design and store compliance, especially at supermarkets, may further increase the health impact.

Original languageEnglish (US)
JournalAmerican Journal of Preventive Medicine
DOIs
StateAccepted/In press - Jan 1 2017

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Beverages
Obesity
Baltimore
San Francisco
Pediatric Obesity
Information Storage and Retrieval
Compliance
Food
Health
Population

ASJC Scopus subject areas

  • Epidemiology
  • Public Health, Environmental and Occupational Health

Cite this

Simulating the Impact of Sugar-Sweetened Beverage Warning Labels in Three Cities. / Lee, Bruce Y.; Ferguson, Marie; Hertenstein, Daniel L.; Adam, Atif; Zenkov, Eli; Wang, Peggy I.; Wong, Michelle S.; Gittelsohn, Joel; Mui, Yeeli; Brown, Shawn T.

In: American Journal of Preventive Medicine, 01.01.2017.

Research output: Contribution to journalArticle

Lee, Bruce Y. ; Ferguson, Marie ; Hertenstein, Daniel L. ; Adam, Atif ; Zenkov, Eli ; Wang, Peggy I. ; Wong, Michelle S. ; Gittelsohn, Joel ; Mui, Yeeli ; Brown, Shawn T. / Simulating the Impact of Sugar-Sweetened Beverage Warning Labels in Three Cities. In: American Journal of Preventive Medicine. 2017.
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abstract = "Introduction: A number of locations have been considering sugar-sweetened beverage point-of-purchase warning label policies to help address rising adolescent overweight and obesity prevalence. Methods: To explore the impact of such policies, in 2016 detailed agent-based models of Baltimore, Philadelphia, and San Francisco were developed, representing their populations, school locations, and food sources, using data from various sources collected between 2005 and 2014. The model simulated, over a 7-year period, the mean change in BMI and obesity prevalence in each of the cities from sugar-sweetened beverage warning label policies. Results: Data analysis conducted between 2016 and 2017 found that implementing sugar-sweetened beverage warning labels at all sugar-sweetened beverage retailers lowered obesity prevalence among adolescents in all three cities. Point-of-purchase labels with 8{\%} efficacy (i.e., labels reducing probability of sugar-sweetened beverage consumption by 8{\%}) resulted in the following percentage changes in obesity prevalence: Baltimore: -1.69{\%} (95{\%} CI= -2.75{\%}, -0.97{\%}, p<0.001); San Francisco: -4.08{\%} (95{\%} CI= -5.96{\%}, -2.2{\%}, p<0.001); Philadelphia: -2.17{\%} (95{\%} CI= -3.07{\%}, -1.42{\%}, p<0.001). Conclusions: Agent-based simulations showed how warning labels may decrease overweight and obesity prevalence in a variety of circumstances with label efficacy and literacy rate identified as potential drivers. Implementing a warning label policy may lead to a reduction in obesity prevalence. Focusing on warning label design and store compliance, especially at supermarkets, may further increase the health impact.",
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