Starting in July 2016, San Francisco, Calif., will require prominent warning labels on most sugar-sweetened beverage (SSB) advertisements (i.e., “WARNING: Drinking beverages with added sugar(s) contributes to obesity, diabetes, and tooth decay”). The purpose of this project is to collect baseline data on the presence and types of SSB print advertising visible in a sample of commercial blocks, sports venues, and transport locations in San Francisco and San Jose (control community). This will be part of a larger four-year study that will compare the presence of the warning label on covered advertising in San Francisco pre- to post-policy, compare the prevalence of advertising classes subject to regulation visible in both cities pre- and post-policy, and assess the characteristics of compliant and non-compliant advertising over time in relation to type and racial/ethnic targeting. Study findings will document the feasibility, effectiveness, and impact of warning labels as an obesity prevention tool.
Start Date: July 2016
ID #: CAS041
Organization: Public Health Institute
Project Lead: Lynn Silver, MD, MPH, FAAP
Effects of a front-of-package disclosure on accuracy in assessing children’s drink ingredients: two randomised controlled experiments with US caregivers of young childrenThis study aimed to test the effects of a standardized front-of-package (FOP) disclosure statement (indicating added sugar, non-nutritive sweetener (NNS) and juice content) on accuracy in assessing ingredients and perceived healthfulness of children’s drinks. In two randomized controlled experiments, the same participants (six hundred and forty-eight U.S. caregivers of young children ages 1-5 years) viewed More
Association Between Child Sugary Drink Consumption and Serum Lipid Levels in Electronic Health RecordsSugar-sweetened beverage (SSB) and fruit juice (FJ) consumption may promote lipid abnormalities in childhood. We examined the association between SSB/FJ intake and lipid levels using electronic health record data for 2816 adolescents. Multivariable logistic regression models treated clinical cutpoints for abnormal lipid levels (triglycerides [TG], high-density lipoprotein (HDL), low-density lipoprotein [LDL], and total cholesterol) as More