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. 2018 Feb;54(2):197-204.
doi: 10.1016/j.amepre.2017.11.003. Epub 2017 Dec 14.

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

Bruce Y Lee  1 , Marie C Ferguson  2 , Daniel L Hertenstein  2 , Atif Adam  2 , Eli Zenkov  3 , Peggy I Wang  2 , Michelle S Wong  4 , Joel Gittelsohn  2 , Yeeli Mui  5 , Shawn T Brown  3
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Simulating the Impact of Sugar-Sweetened Beverage Warning Labels in Three Cities

Bruce Y Lee et al. Am J Prev Med. 2018 Feb.

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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Figures

Figure 1
Figure 1
Model diagram. Notes: Illustrative representation of food store availability to agents in their environment. Pr(eating) = probability that agent eats at location type; Pr(SSB) = probability that agent consumes an SSB from location type. Letter following agent characteristics, Pr(eating) and Pr(SSB) corresponds to references for these values in Table 1.
Figure 2
Figure 2
Decrease in obesity prevalence across cities.
Figure 3
Figure 3
Comparing the impact of key model variables on change in obesity prevalence in Baltimore City. Notes: This figure summarizes the sensitivity analyses, i.e., investigating how varying different unknown factors affects the change in obesity prevalence for Baltimore City. Each factor analyzed is represented by a horizontal bar, where the width of each bar represents the range of impact on obesity prevlance across a range of values for that factor (listed above the bar). Additional lines on the bar represent mean obesity prevalence from intermediate factor values. The dotted line marks 0% change in obesity prevalence, or the change in obesity prevalence at the beginning of each scenario.
See this image and copyright information in PMC

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