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, 106 (10), 1865-71

Impact of the Berkeley Excise Tax on Sugar-Sweetened Beverage Consumption

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Impact of the Berkeley Excise Tax on Sugar-Sweetened Beverage Consumption

Jennifer Falbe et al. Am J Public Health.

Abstract

Objectives: To evaluate the impact of the excise tax on sugar-sweetened beverage (SSB) consumption in Berkeley, California, which became the first US jurisdiction to implement such a tax ($0.01/oz) in March 2015.

Methods: We used a repeated cross-sectional design to examine changes in pre- to posttax beverage consumption in low-income neighborhoods in Berkeley versus in the comparison cities of Oakland and San Francisco, California. A beverage frequency questionnaire was interviewer administered to 990 participants before the tax and 1689 after the tax (approximately 8 months after the vote and 4 months after implementation) to examine relative changes in consumption.

Results: Consumption of SSBs decreased 21% in Berkeley and increased 4% in comparison cities (P = .046). Water consumption increased more in Berkeley (+63%) than in comparison cities (+19%; P < .01).

Conclusions: Berkeley's excise tax reduced SSB consumption in low-income neighborhoods. Evaluating SSB taxes in other cities will improve understanding of their public health benefit and their generalizability.

Figures

FIGURE 1—
FIGURE 1—
Participant Flow During Pre- (2014) and Posttax (2015) Periods in Berkeley, CA, and Comparison Cities (Oakland and San Francisco, CA) Note. Complete-case analysis of water consumption excluded those additionally missing data on water consumption and included 285 before the tax and 501 after the tax in Berkeley and 606 before the tax and 1045 after the tax in comparison cities.
FIGURE 2—
FIGURE 2—
Adjusted Mean Consumption of Sugar-Sweetened Beverages (SSBs) and Water Before and After the Tax in Berkeley, CA, and Comparison Cities (Oakland and San Francisco, CA) Note. Adjusted means and 95% confidence intervals were obtained by using the margins command in Stata/IC version 13.1 (StataCorp LP, College Station, TX) after running generalized linear models adjusting for neighborhood, gender, age, education, race/ethnicity, and language. P values shown are for the difference between Berkeley and comparison cities in change in consumption and come from the generalized linear models.

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