The Health and Economic Impact of Using a Sugar Sweetened Beverage Tax to Fund Fruit and Vegetable Subsidies in New York City: A Modeling Study.
Zhouyang LouStella S YiJennifer PomeranzRachel SussRienna RussoPasquale E RummoHeesun EomJunxiu LiuYiyi ZhangAndrew E MoranBrandon K BellowsNan KongYan LiPublished in: Journal of urban health : bulletin of the New York Academy of Medicine (2022)
Low fruit and vegetable (FV) intake and high sugar-sweetened beverage (SSB) consumption are independently associated with an increased risk of developing cardiovascular disease (CVD). Many people in New York City (NYC) have low FV intake and high SSB consumption, partly due to high cost of fresh FVs and low cost of and easy access to SSBs. A potential implementation of an SSB tax and an FV subsidy program could result in substantial public health and economic benefits. We used a validated microsimulation model for predicting CVD events to estimate the health impact and cost-effectiveness of SSB taxes, FV subsidies, and funding FV subsidies with an SSB tax in NYC. Population demographics and health profiles were estimated using data from the NYC Health and Nutrition Examination Survey. Policy effects and price elasticity were derived from recent meta-analyses. We found that funding FV subsidies with an SSB tax was projected to be the most cost-effective policy from the healthcare sector perspective. From the societal perspective, the most cost-effective policy was SSB taxes. All policy scenarios could prevent more CVD events and save more healthcare costs among men compared to women, and among Black vs. White adults. Public health practitioners and policymakers may want to consider adopting this combination of policy actions, while weighing feasibility considerations and other unintended consequences.
Keyphrases
- public health
- healthcare
- cardiovascular disease
- global health
- mental health
- primary care
- low cost
- randomized controlled trial
- systematic review
- health information
- type diabetes
- physical activity
- quality improvement
- machine learning
- meta analyses
- body mass index
- polycystic ovary syndrome
- big data
- cross sectional
- general practice
- deep learning
- electronic health record
- weight gain
- weight loss
- adipose tissue
- cardiovascular events
- middle aged
- breast cancer risk