Setting: Communities & Government

Making CHOICES in a Health Department: Case 2 (Advanced)

People drawing on a whiteboard

In this advanced teaching case, which builds on Case 1, a fictional health department continues to work with the CHOICES Project’s Learning Collaborative Partnership to determine how to implement an evidence-based strategy that requires substantial investment, but they face a variety of additional challenges such as state politics and the complexities of health policy.

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Making CHOICES in a Health Department: Case 1 (Introductory)

In this introductory teaching case, a fictional health department engages with the CHOICES Project’s Learning Collaborative Partnership to help them narrow down a list of potential strategies to reduce childhood obesity in their county through a cost-effectiveness lens.

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Brief: Safe Routes to School in Wisconsin

Young girl riding bike at the Safe Routes to School Family Fun Night Event in Neenah, Wisconsin in May 2018; orange cone in the background

The information in this brief is intended only to provide educational information.

This brief summarizes a CHOICES Learning Collaborative Partnership model examining the expansion of a regional Safe Routes to School program in K-8 public and private schools in Wisconsin. Safe Routes to School Programs help children safely walk and bike to school by incorporating principles of the six E’s: engagement, encouragement, equity, engineering, education, and evaluation.

The Issue

In Wisconsin, just three out of every 10 children achieve the 60 minutes of physical activity recommended daily for health.1 Over recent decades, the number of students walking and bicycling to school has declined,2 eliminating an important physical activity opportunity. Adopting programs that make it safer and easier to walk or bike to school can increase the number of students using these physically active travel modes and can also allow students to incorporate physical activity into a daily routine.3 Every child should have the opportunity to be healthy, and all kids need opportunities to be physically active, no matter where they live or where they go to school. This study estimates the cost-effectiveness of increasing funding and diversifying funding sources to expand a regional model for Safe Routes to School programs for those schools that have not yet implemented comprehensive Safe Routes to School programs in Wisconsin.

About Safe Routes to School

Safe Routes to School programs that adopt the six E’s, including improvements to local sidewalks and roads around schools, providing pedestrian and bike safety education, and offering encouragement and promotion activities, can increase the number of students walking and bicycling to school.2 This study looked at the scaled expansion of East Central Wisconsin Regional Planning Commission’s Safe Routes to School program to other regional planning commissions across Wisconsin. A state-wide Safe Routes to School Program Coordinator would work with regional SRTS Coordinators and advisory committees, providing oversight and administration of the allocated funding to support projects in their region. Each regional planning commission would coordinate education, encouragement, and promotion activities across funded schools in their region. Local municipalities would lead projects to improve the safety of sidewalks and road infrastructure.

Comparing Costs and Outcomes

A CHOICES cost-effectiveness analysis compared the costs and outcomes of expanding Safe Routes to School in Wisconsin with the costs and outcomes associated with not implementing the program over 10 years (2020-2030).

Implementing Safe Routes to School in Wisconsin is an investment in child health. By the end of 2030:

If Safe Routes to School was expanded in Wisconsin, then by the end of 2030, 151,000 children would attend schools with safer transportation environments, and children who start walking or biking to school would get 48 more active minutes per week. This program would cost $58 per child annually to implement, for those children attending schools that adopt Safe Routes to School programs.

Conclusions and Implications

Every student should be able to walk or bike to school safely. Expanding East Central’s regional Safe Routes to School model in Wisconsin could support safer walking and biking environments and provide programmatic education and encouragement initiatives for 151,000 elementary and middle school students over 10 years. We estimate that the Safe Routes to School program, which includes education and promotion activities, improvements to sidewalks and road infrastructure, and coordination support, would cost $58 per student per year. Over 10 years, these activities to expand the regional SRTS model in Wisconsin would cost about $215,000 per school. At the same time, more than 8,000 students would start walking and biking to school, and they would get 48 more minutes of physical activity per week. This translates to better health outcomes and more kids at a healthy weight in Wisconsin, with 16 fewer cases of obesity in the year 2030 alone.

In addition to getting students more active,4,5 SRTS initiatives may also reduce the risk of pedestrian and bicycle injury, exposure to unsafe traffic, and air pollution.3,6,7 Greater safety, improved health from increased physical activity, and lesser environmental impact from decreased automobile use provide economic benefits to the community.8 In Wisconsin, the costs of implementing SRTS projects could be offset by savings associated with reduced vehicle travel, potentially amounting to $2.19 million in environment-related cost savings over 10 years. Further, families whose students start walking or bicycling would also drive less and could save an average of $1,120 by not driving their students to school. Walking and biking are great ways for kids to be active, and this program invests in ways to ensure that more students can do so safely while developing healthy lifestyle habits that would continue into adulthood.

References

  1. Child and Adolescent Health Measurement Initiative. 2018-2019 National Survey of Children’s Health (NSCH) data query. https://www.nschdata.org/browse/survey/results?q=7700&r=51. Accessed October 29, 2020.

  2. McDonald, NC. Active transportation to school: trends among US schoolchildren, 1969–2001. American Journal of Preventive Medicine. 2007; 32(6), 509-516.

  3. Stewart O, Vernez Moudon A, Claybrooke C. Multistate Evaluation of Safe Routes to School Programs. American Journal of Health Promotion. 2014;28(3);S89-S96.

  4. Cooper, Jago, Southard, Page. Active Travel and Physical Activity across the School Transition: The PEACH Project. Medicine & Science in Sports & Exercise. 2012; 44(13); 1890–1897.

  5. Huang WY, Wong SH, He G. Is change to active travel to school an important source of physical activity for Chinese children? Pediatric Exercise Science. 2017; 29(1):161-168.

  6. DiMaggio, C, & Li, G. Effectiveness of a safe routes to school program in preventing school-aged pedestrian injury. Pediatrics. 2013;131(2);290-296.

  7. DiMaggio C, Chen Q, Muennig PA, Li G. Timing and effect of a safe routes to school program on child pedestrian injury risk during school travel hours: Bayesian changepoint and difference-in-difference analysis. Injury Epidemiol. 2014;1:17.

  8. Jacob V, Chattopadhyay SK, Reynolds JA, et al. Economics of Interventions to Increase Active Travel to School: A Community Guide Systematic Review. American Journal of Preventive Medicine. 2021;60(1):e27-e40.

Suggested Citation:

McCulloch SM, Barrett JL, Reiner JF, Cradock AL. Wisconsin: Safe Routes to School {Issue Brief}. Wisconsin Department of Health Services, Division of Public Health, Madison, WI, & East Central Wisconsin Regional Planning Commission, Menasha, WI and the CHOICES Learning Collaborative Partnership at the Harvard T.H. Chan School of Public Health, Boston, MA; May 2021. For more information, please visit www.choicesproject.org

Funding for the Survey of the Health of Wisconsin (SHOW) was provided by the Wisconsin Partnership Program PERC Award (233 AAG9971). The authors would also like to thank the University of Wisconsin Survey Center, SHOW administrative, field, and scientific staff, as well as all the SHOW participants for their contributions to this study.

The design for this brief and its graphics were developed by Molly Garrone, MA and partners at Burness.

This issue brief was developed at the Harvard T.H. Chan School of Public Health in collaboration with the Wisconsin Department of Health Services through participation in the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) Learning Collaborative Partnership. This brief is intended for educational use only. This work is supported by The JPB Foundation and the Centers for Disease Control and Prevention (U48DP006376). The findings and conclusions are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention or other funders.

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Report: Hawaii: Sugary Drink Fee

Kids in Hawaii holding water bottles

The information in this report is intended to provide educational information on the cost-effectiveness of sugary drink excise fees.

Executive Summary

Continually rising rates of obesity represent one of the greatest public health threats facing the United States. Obesity has been linked to excess consumption of sugary drinks. Federal, state, and local governments have considered implementing taxes on sugary drinks to reduce consumption, reduce obesity, and provide a new source of government revenue.1-3 A fee on sugary drinks is a similar strategy to increase the price of sugary drinks, improve health, and provide revenue.

We modeled implementation of a state fee on sugary drinks at fees of $0.03/ounce, $0.02/ounce, and $0.01/ounce. This report summarizes the results of the $0.01/ounce fee. Results for additional fee rates can be found in Appendices 1 and 2 (please see pages 15 and 16 in the PDF of the report).

The $0.01/ounce fee is projected to be cost-saving and result in lower levels of sugary drink consumption, thousands of cases of obesity prevented, and tens of millions of dollars in health care cost savings. The fee is projected to save $3.05 in health care costs per dollar invested.

Background 

Although consumption of sugary drinks (defined as all drinks with added caloric sweeteners) has declined in recent years, adolescents and young adults in the United States consume more sugar than the Dietary Guidelines for Americans 2020-2025 recommend, with persistent racial/ethnic disparities.4-6 According to recent estimates, 56% of adults and 63% of youth in Hawaii report drinking soda.7-8 The percentage of adults in Hawaii who report drinking soda varies by racial and ethnic group, with more consumption reported by Native Hawaiian, Other Pacific Islander, and Black residents and less consumption reported by Chinese, Japanese, Filipino, and White residents.7 On average, teenagers in Hawaii consume more sugary drinks than other age groups, and nearly half of Hawaii teens (46%) drink one or more sugary drinks per day.9 Public health researchers have suggested that excess intake of sugary drinks may be one of the single largest drivers of the obesity epidemic in the U.S.10 An estimated 57% of adults and 28% of youth in Hawaii have overweight or obesity.8,10

Targeted marketing contributes to differences in consumption levels by income level. People who live in low-income areas are more exposed to advertisements for sugary drinks than those who live in high-income areas.11 Exposure to advertisements may influence sugary drink consumption levels in these communities, as low-income consumers on average consume more sugary drinks than higher income consumers.12

Consumption of sugary drinks increases the risk of chronic diseases through changes in body mass index (BMI), insulin regulation, and other metabolic processes.13-14 Randomized intervention trials and longitudinal studies have linked increases in sugary drink consumption to excess weight gain, diabetes, cardiovascular disease, and other health risks.13-14 There are persistent racial and ethnic disparities across both sugary drink consumption levels and rates of obesity and chronic disease.4-6 In light of this evidence, the Dietary Guidelines for Americans 2020-202515 recommend that individuals limit their sugary drink intake in order to manage body weight and reduce risk of chronic disease.

Targeted pricing strategies have emerged as one recommended strategy to reduce consumption of sugary drinks.16 This strategy has been studied by public health experts, who have drawn on the success of tobacco taxation and decades of economic research to model the estimated financial and health impact of a sugary drink tax.17-20 In Hawaii, rising costs of health care and insurance premiums are impacting businesses under Hawaii’s Prepaid Health Care Act.21 Passage of a sugary drink fee has been discussed as an effective strategy to reduce Hawaii’s health care costs.21-22 Public poll results in Hawaii show that there is support of a sugary drink fee as an approach to reduce obesity.22

This report aims to model the projected effect of a Hawaii sugary drink fee on health and disease outcomes over a decade. All drinks with added caloric sweeteners were considered subject to the fee, while 100% juice and milk products were considered exempt. The fee would be reflected in the posted price of sugary drinks, similar to an excise tax.

Key Terms
  • Fee: a consumption fee collected from retailers or distributors and is reflected in the posted price, like an excise tax; a sales tax in contrast is applied after purchase of the item

  • Pass-through rate: how much of the fee on distributors is passed on to consumers as an increase in shelf price; a percent ranging from 0% (none of the fee) to 100% (all of the fee)

  • Price elasticity of demand: how much consumer purchasing behavior changes following a change in price of an item

How would a fee work?

How would a fee work? A fee on distributors is passed through and leads to an increase in price. Price elasticity of demand leads to a decrease in demand. *Why is the fee structured like an excise tax rather than sales tax? Since an excise tax is mostly or entirely included in the price consumers see, it is more likely to affect consumer purchase behavior than a sales tax, which is added at the register.

MODELING FRAMEWORK: How fees on sugary drinks can lead to better health

A state fee is linked to change in BMI through change in sugary drink price and consumption   

Logic model: Sugary drink fee - Distributors are affected. Leads to an increase in sugary drink price, and consumers are affected. This leads to a decrease in sugary drink consumption, and consumers are affected. This finally leads to a decrease in BMI, and consumers are affected.

How does a fee on distributors affect the price paid by consumers?

Sugary drink fee affects distributors. This raises the price of sugary drinks, affecting the consumer.Since the cost of a sugary drink fee is incorporated directly into the beverage’s sticker price, a fee structured like an excise tax will likely influence consumer purchasing decisions more than a comparable sales tax that is added onto the item at the register. We assume 100% pass-through of the fee over 10 years and assume the fee would be adjusted annually for inflation. Our pass-through rate estimate is supported by empirical studies of excise taxes in Mexico and France that demonstrate near-complete pass-through rates to consumers.23 Short-term studies for the local tax in Berkeley indicate imperfect, or less than 100%, pass-through.2,24-25 The Hawaii Department of Health used an existing tool26 to collect beverage price data from 257 venues in Hawaii, including 74 supermarkets, 74 grocery stores, 90 limited-service venues, 18 fast food restaurants, and one gas station/convenience store. Retail prices were weighted by national share of purchase and consumption.26 The weighted price of sugary drinks in Hawaii was $0.07 per ounce. For example, a $0.01/ounce fee would raise the price of a 12-ounce can of soda from $0.84/can to $0.96/can.

An increase in sugary drink price affects consumers, and leads to a decrease in sugary drink consumption among consumers.

How does increasing the price of sugary drinks change individual sugary drink consumption?

To estimate current sugary drink consumption levels in Hawaii, we adjusted national estimates from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 by race and ethnicity group using reported adult sugary drink consumption from the 2014 Hawaii Behavioral Risk Factor Surveillance System27 and youth sugary drink consumption from the 2012, 2013, and 2017 Hawaii Rethink Your Drink Study.28 How much consumers will change their purchases in response to price changes is called price elasticity for demand. We assume for every 10% increase in the price of sugary drinks, there will be a 12% reduction in purchases (a mean own-price elasticity of demand of -1.21).29 Recent research on the Berkeley, CA $0.01/ounce tax found a 21% reduction in sugary drink intake among low-income populations consistent with this estimate.24,30-33

A decrease in sugary drink consumption among consumers leads to a decrease in BMI among consumers.

What are the individual health effects of decreasing sugary drink consumption?

Research has shown that decreasing sugary drink consumption can have positive effects on health in adults and youth. We estimated the impact of a change in sugary drink intake on body mass index (BMI), accounting for dietary compensation, based on rigorous studies identified in evidence reviews.20 The relationship among adults was modeled based on the range of estimated effects from four large, multi-year longitudinal studies, which indicated that a one-serving reduction in sugary drinks was associated with a BMI decrease of 0.21 kg/m2 to 0.57 kg/m2 in adults over a 3-year period.14,34-36 Among youth, we used evidence from a double-blind randomized controlled trial conducted over 18 months, which found that an additional 8-ounce serving of sugary drinks led to a 2.2 pound greater weight gain.37

Reach

This strategy applies to all youth and adults in Hawaii. However, the model only looks at the effects on those 2 years of age and older.*

*BMI z-scores were used in our analyses, which are not defined for children under 2 years of age.

Cost

We assume the fee will incur start up and ongoing labor costs for fee administrators in the Hawaii Department of Health. To implement the strategy, the Hawaii Department of Health would need to process fee statements and conduct audits. Businesses would also need to prepare fee statements and participate in audits, which would require labor from private fee accountants. Cost information was drawn from the Hawaii state bottle recycling program and localities with planned or implemented fees on soft drinks.20,38 The cost and benefit estimates do not include expected revenue from the fee.

CHOICES Microsimulation Model

The CHOICES microsimulation model for Hawaii was used to calculate the costs and effectiveness over 10 years (2017–27). Cases of obesity prevented were calculated at the end of the model period in 2027. The model was based on prior CHOICES work,20,39 and created a virtual population of Hawaii residents using data from: the U.S. Census, American Community Survey, Behavioral Risk Factor Surveillance System,40 NHANES, National Survey of Children’s Health,41 the Medical Expenditure Panel Survey, multiple national longitudinal studies, and obesity prevalence data provided by the Hawaii Department of Health. Using peer-reviewed methodology, we forecasted what would happen to this virtual population with and without a sugary drink fee to model changes in disease and mortality rates and health care costs due to the fee.

CHOICES microsimulation model: start in 2027 and simulate to 2027. Start with a virtual population using data from the 2010 U.S. census. Then take into account population factors, such as population growth and BMI trends. Then take into account individual factors, such as body growth, personal characteristics (e.g. dietary intake), and smoking. Then, input the intervention (dietary intake/physical activity). Then, look at health status (obesity) and outcomes (obesity, health care costs, and mortality).

 

Results: $0.01/ounce State Fee on Sugary Drinks

Overall, the model shows that a sugary drink fee is cost-saving. Compared to the simulated natural history without a fee, the fee is projected to result in lower levels of sugary drink consumption, fewer cases of obesity, fewer deaths, and health care cost savings greater than $30 million over the 10-year period under consideration.

The estimated reduction in obesity attributable to the fee leads to lower projected health care costs, offsetting fee implementation costs and resulting in net cost savings. The difference between total health care costs with no strategy and lower health care costs with a strategy represent health care costs saved; these savings can be compared to the cost of implementing the fee to arrive at the metric of health care costs saved per $1 invested.

Results for additional fee rates can be found in Appendix 1 ($0.02/ounce fee) and Appendix 2 ($0.03/ounce fee). Please see pages 15 and 16 in the PDF of the report.

An infographic of results after the 1 cent state fee on sugary drinks.

 

Outcome $0.01/ounce fee
Mean
(95% uncertainty interval)
10 Year Reach* 1,690,000
(1,690,000; 1,690,000)
First Year Reach* 1,440,000
(1,440,000; 1,440,000)
Decrease in 12-oz Servings of Sugary Drinks per Person in the First Year* 55
(32; 117)
Mean Reduction in BMI Units per Person* -0.155
(-0.288; -0.040)
10 Year Intervention Implementation Cost per Person $5.86
($5.85; $5.87)
Total Intervention Implementation Cost Over 10 Years $9,900,000
($9,900,000; $9,900,000)
Annual Intervention Implementation Cost $990,000
($990,000; $990,000)
Health Care Costs Saved Over 10 Years $30,200,000
($10,500,000; $74,300,000)
Net Costs Difference Over 10 Years -$20,300,000
(-$64,400,000; -$581,000)
Quality Adjusted Life Years (QALYs) Gained Over 10 Years 1,790
(608; 4,530)
Years of Life Gained Over 10 Years 505
(157; 1,300)
Deaths Prevented Over 10 Years* 142
(38; 313)
Years with Obesity Prevented Over 10 Years 43,300
(14,900; 109,000)
Health Care Costs Saved per $1 Invested Over 10 Years $3.05
($1.06; $7.51)
Cases of Obesity Prevented in 2027* 6,040
(2,160; 15,100)
Cases of Childhood Obesity Prevented in 2027* 877
(321; 2,280)
Cost per Year with Obesity Prevented Over 10 Years Cost-saving
Cost per QALY Gained Over 10 Years Cost-saving
Cost per YL Gained Over 10 Years Cost-saving
Cost per Death Averted Over 10 Years Cost-saving

Uncertainty intervals are estimated by running the model 1,000 times, taking into account both uncertainty from data sources and virtual population projections, and calculating a central range in which 95 percent of the model results fell.
All metrics reported for the population over a 10-year period and discounted at 3% per year, unless otherwise noted.
*Not discounted.

 

There are differences in sugary drink consumption and obesity prevalence by race and ethnicity in Hawaii. The CHOICES model used Hawaii data to build a virtual Hawaii population. Without any strategy:

Sugary drink consumption is highest in the Native Hawaiian and Other Pacific Islander populations

White residents drink 271 12 ounce servings of sugary drinks per year per person; Native Hawaiian residents drink 356 12 ounce servings of sugary drinks per year per person; Filipino residents drink 337 12 ounce servings of sugary drinks per year per person; Japanese residents drink 262 12 ounce servings of sugary drinks per year per person; Other Asian residents drink 319 12 ounce servings of sugary drinks per year per person; Other Pacific Islander residents drink 524 12 ounce servings of sugary drinks per year per person; Residents of Other races drink 325 12 ounce servings of sugary drinks per year per person; The average resident drinks 319 12 ounce servings of sugary drinks per year per person

Obesity prevalence is highest in the Native Hawaiian and Other Pacific Islander populations

14% of White residents have obesity; 30% of Native Hawaiian residents have obesity; 20% of Filipino residents have obesity; 13% of Japanese residents have obesity; 9% of Other Asian residents have obesity; 49% of Pacific Islander residents have obesity; 21% of residents of Other races have obesity; On average, 20% of Hawaii residents have obesity

Results: $0.01/ounce State Fee on Sugary Drinks By Race and Ethnicity Groups

Outcome White
Mean
(95% uncertainty interval)
Native Hawaiian
Mean
(95% uncertainty interval)
Filipino
Mean
(95% uncertainty interval)
Japanese
Mean
(95% uncertainty interval)
Other Asian
Mean
(95% uncertainty interval)
Other Pacific Islander 
Mean
(95% uncertainty interval)
Other
Mean
(95% uncertainty interval)
Decrease in 12-oz Serving of Sugary Drinks per Person in the First Year* 47
(27; 99)
62
(36; 132)
58
(34; 123)
45
(26; 97)
55
(32; 118)
91
(53; 192)
56
(33; 120)
QALYS Gained Over 10 Years 318
(91; 976)
491
(144; 1,350)
336
(98; 953)
197
(55; 548)
104
(28; 296)
153
(40; 440)
189
(54; 535)
Years of Life Gained Over 10 Years 88
(13; 276)
127
(27; 356)
111
(22; 327)
64
(8; 192)
33
(2; 99)
40
(3; 133)
41
(7; 127)
Years with Obesity Prevented Over 10 Years 6,720
(2,010; 20,800)
13,160
(3,930; 37,100)
6,900
(2,110; 19,200)
3,580
(1,080; 9,950)
1,970
(611; 5,470)
5,260
(1,670; 14,400)
5,730
(1,750; 16,050)
Cases of Obesity Prevented in 2027* 977
(293; 2,950)
1,790
(536; 4,980)
969
(296; 2,710)
542
(161; 1,530)
290
(87; 799)
671
(202; 1,880)
803
(248; 2,260)
Cases of Childhood Obesity Prevented in 2027* 136
(38; 450)
396
(121; 1,100)
158
(47; 443)
52
(14; 150)
28
(7; 80)
22
(70; 608)
197
(58; 551)

Uncertainty intervals are estimated by running the model 1,000 times, taking into account both uncertainty from data sources and virtual population projections, and calculating a central range in which 95 percent of the model results fell.
All metrics reported for the population over a 10-year period and discounted at 3% per year, unless otherwise noted.
*Not discounted.

SNAPSHOT IN 2027

Cases of obesity prevented in Hawaii from a $0.01/ounce state fee on sugary drinks by race and ethnicity group*

In 2027, 977 cases of obesity would be prevented among White residents; 1,790 cases of obesity would be prevented among Native Hawaiian residents; 969 cases of obesity would be prevented among Filipino residents; 542 cases of obesity would be prevented among Japanese residents; 290 cases of obesity would be prevented among Other Asians residents; 671 cases of obesity would be prevented among Other Pacific Islander residents; and 803 cases of obesity would be prevented among residents of other races

Some groups are projected to experience larger declines in consumption of servings* of sugary drinks per person in Hawaii in the first year of fee implementation

In the first year of fee implementation, White residents would drink 47 fewer 12 ounce servings of sugary drinks per person; Native Hawaiian residents would drink 62 fewer 12 ounce servings of sugary drinks per person; Filipino residents would drink 58 fewer 12 ounce servings of sugary drinks per person; Japanese residents would drink 45 fewer 12 ounce servings of sugary drinks per person; Other Asian residents would drink 55 fewer 12 ounce servings of sugary drinks per person; Other Pacific Islander residents would drink 91 fewer 12 ounce servings of sugary drinks per person; and Residents of Other races would drink 56 fewer 12 ounce servings of sugary drinks per person

Impact on Diabetes

We estimated the impact of the fee-induced reduction in sugary drink intake on diabetes incidence for adults ages 18-79 years using a published meta-analysis of the relative risk of developing diabetes due to a one-serving change in sugary drink consumption42 as well as state-level estimates of diabetes incidence43 and prevalence.44 On average, each 8.5 ounce serving of sugary drinks per day increases the risk of diabetes by 18%.42$0.01 per ounce state fee on sugary drinks would lead to a 6% reduction in diabetes incidence and 390 cases of diabetes would be prevented; in addition, there would be $1.15 million in dental decay treatment total cost savings over 10 years (societal), and $173,000 in dental decay treatment total cost savings over 10 years (Medicaid)

In Hawaii, we estimated that the proposed sugary drink fee would lead to a 6% reduction in diabetes incidence in the sugary drink fee models. Impact on diabetes incidence was calculated over a one-year period once the fee reaches its full effect. Impact on diabetes was calculated based on summary results from the model, not directly via microsimulation.

Impact on Tooth Decay

We estimated the impact of a sugary drink fee on tooth decay cost using a longitudinal analysis of the relationship between intake of sugars and tooth decay in adults. On average, for every 10 grams higher intake of sugar per day, there is an increase in decayed, missing, and filled teeth (i.e. tooth decay) of approximately 0.10 over 10 years.45 There are many studies showing a similar relationship between higher intake of sugars and tooth decay in children and youth46 and thus we assume the same relationship as found in adults.

We used 2018 Hawaii Department of Human Services Med-Quest procedure code47 data to estimate a Medicaid cost of treating tooth decay as: $234 for a permanent crown on Oahu or $562 on neighboring islands, and $39 for a filling on Oahu or $55 on neighboring islands. These codes reflect treatment for one surface and do not reflect higher reimbursement rates for multi-surface treatment, temporary crowns, or potential flat fee schedules. Based on analysis of data on tooth decay, fillings, and crowns for the U.S. population from NHANES 1988-1994 (the last year crowns and fillings were separately reported),48 we estimate that 78.9% of tooth decay in children and 43.5% of tooth decay in adults are treated. Using this same data set, we estimate that 97% of treatment for children is fillings and 82.5% of treatment for adults is fillings.

To estimate Medicaid-specific savings in costs of dental treatment, we used state estimates of the numbers of people enrolled in Medicaid and CHIP and the proportion receiving Medicaid dental services. Because of limited Medicaid dental coverage for adults in Hawaii, only children are included in the Medicaid-specific calculations. In Hawaii, we estimated that a $0.01/ounce fee would lead to a total of $173,000 in Medicaid savings over a period of 10 years due to a reduction in treatment of tooth decay. The Medicaid reimbursement fee estimates may underestimate the total cost savings of tooth decay treatment projected here as dental providers may charge higher amounts to patients.

Expected Yearly Sugary Drink Fee Revenue

The annual revenue from a state fee on sugary drinks is likely to be substantial. According to the Rudd Center Revenue Calculator for Sugary Drink Taxes49 a $0.01/ounce fee in Hawaii could raise as much as $42.9 million in 2020.

Actual fee revenue may be lower than these projected estimates due to several factors. The Rudd Center Revenue estimates are based on regional sales data adjusted for state or city specific demographics;50 sales data for specific states and/or cities within those regions may vary from the regional average. Retailers may have inventories of sugary drinks obtained before the fee was implemented. There may also be some distributors/manufacturers that are non-compliant with the fee. The Rudd Center notes that their revenue projections “should be adjusted downward by 10% – 30%.”49

Rudd Center Revenue Projections (2020)

$0.01/oz fee on sugary drinks

$0.02/oz fee on sugary drinks

$0.03/oz fee on sugary drinks

Assuming 100% of Rudd Center projections

$42.9 million

$65.8 million

$68.8 million

Assuming 90% of Rudd Center projections

$38.6 million

$59.2 million

$62.0 million

Assuming 70% of Rudd Center projections

$30.0 million

$46.1 million

$48.2 million

Considerations for Health Equity

Concerns have been raised regarding the impact of the fee on households with low-income, because lower-income populations tend to consume more sugary drinks.12 Economic studies indicate that with a sugary drink tax, consumers will buy less of these products.29 This change in purchasing is substantial, so that consumers can be expected to spend less on sugary drinks after a fee is implemented. Using sales data from the Rudd Center Revenue Calculator for Sugary Drink Taxes,49 along with local price data,26,28 we project that individuals and households in Hawaii will spend less money on sugary drinks after a $0.02 per oz fee: about $104 less per year per person, and $314 per year less for an average household. This would free up disposable income for other consumer purchases. A typical consumer in Hawaii who continues to consume these beverages after the fee is in place would be expected to pay fees of about $2.50 per week, or $129/year.

In addition to these changes in spending, health benefits are projected to be greatest among low-income individuals. We project that more health benefits from this policy will accrue to low-income consumers; the same is true for a number of racial and ethnic groups. As noted above, in Hawaii, the percentage of adults who drink one or more soda per day varies by racial and ethnic group.7 Under the proposed fee, we project that Native Hawaiian, Other Pacific Islander, and Filipino Hawaii residents would see more cases of obesity prevented than expected for the average Hawaii resident. On that basis, the proposed fee should decrease disparities in obesity outcomes and improve health equity.

These expected changes in consumption and health outcomes have led economists to conclude that low-income populations benefit substantially from sugary drink taxes.51

Savings Per Year

$0.01/oz fee on sugary drinks

$0.02/oz fee on sugary drinks

$0.03/oz fee on sugary drinks

Individual savings on sugary drinks

$36

$104

$203

Household savings on sugary drinks

$109

$314

$614

Total Hawaii savings on sugary drinks

$20.6 million

$59.4 million

$116 million

Implementation Considerations

Revenue raised from a sugary drink fee can be reinvested in low-income communities. For instance, in Berkeley, CA, sugary drink fee revenue has been allocated for spending on school and community programs, many serving low-income populations or communities of color, to promote healthy eating and diabetes and obesity preventions.52 Public support for such fees generally increases with earmarking for preventive health activities.53

There is opposition from the food and beverage industry, which spends billions of dollars promoting their products.54 Relatively small beverage excise taxes are currently applied across many states. The proposed fee is likely to be sustainable if implemented based on the successful history of tobacco cigarette stamp excise taxes. There is potential for a shift in social norms of sugary drink consumption based on evidence from tobacco control tax and regulatory efforts.55 This shift in norms can be facilitated by assessing sugary drinks, which reduces the attractiveness of non-caloric beverages and discourages consumers from selecting any sugary drink options when making beverage decisions.

Conclusion

We project that the proposed fee policy will prevent thousands of cases of childhood and adult obesity, prevent new cases of diabetes, increase healthy life years, and save more in future health care costs than it costs to implement. Savings in future health care costs would lead to a slowing of rising health care premium costs for employers and individuals across Hawaii. Revenue from the fee can be used for education and health promotion efforts. Implementing the fee could also serve as a powerful social signal to reduce sugar consumption.

Citation

Irvin L, Inoue K, Bowie A, Ching L, Starr R, Ryan J, White BS, La Chica T, Gortmaker SL, Long MW, Ward  ZJ, Giles CM, Barrett JL, Resch SC, Greatsinger A, Garrone ME, Tao H, Flax CN, Cradock AL. Hawaii: Sugary Drink Fee [Report]. Hawaii Department of Health, Hawaii Public Health Institute, Honolulu, HI, and the CHOICES Learning Collaborative Partnership at the Harvard T.H. Chan School of Public Health, Boston, MA; March 2021. For more information, please visit www.choicesproject.org

The design for this report and its graphics were developed by Molly Garrone, MA and partners at Burness.

Funding

Funded by The JPB Foundation. Results are those of the authors and not the funders.

For further information, contact choicesproject@hsph.harvard.edu

Appendices

Results for additional fee rates can be found in Appendix 1 ($0.02/ounce fee) and Appendix 2 ($0.03/ounce fee). Please see pages 15 and 16 in the PDF of the report.

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Report: California: A Sugary Drink Excise Tax

The information in this report is intended to provide educational information on the cost-effectiveness of sugary drink excise taxes.

Executive Summary

Health impact of a $0.02 per ounce state excise tax on sugary drinks; 69 fewer 12 ounce sugary drinks servings per person in the first year; 198,000 cases of obesity prevent in 2030; 13,900 cases of diabetes prevented; 502,000 reduction in decayed, missing, or filled teeth over 10 years (Medi-Cal); Cost impact of a $0.02 per ounce state excise tax on sugary drinks; $46.89 health care costs saved per $1 invested; $1.79 billion saved in net costs; decrease in spending on sugary drinks per household in the first year is $142; $395.million saved in dental decay treatment costs over 10 years (Medi-Cal)In California, health disparities and inequities persist for specific subsets of the population – the reasons for this are deep-seated and multi-faceted. Sugary drink consumption is one health behavior for which disparities exist, and it has been linked to excess weight gain, obesity, and the incidence of type 2 diabetes, heart disease, and cancer. Federal, state, and local governments have considered excise taxes on sugary drinks to reduce consumption, reduce obesity and associated chronic disease, and provide a new source of government revenue.1,2,3 In California, statewide legislative measures to introduce sugary drink excise taxes have been proposed for a number of years in efforts to improve the health of Californians and reduce inequities, but none have passed.4

We modeled the implementation of a state excise tax on sugary drinks in California at a tax rate of $0.02/ounce. CHOICES cost-effectiveness analysis compared the costs and outcomes over a 10-year time horizon (2020-2030) of implementing a tax with the costs and outcomes associated with not implementing a tax.

The tax modeled is projected to be cost-saving (that is, the tax saves more in future health care costs than it costs to implement even absent the potential revenues). The tax is projected to decrease sugary drink consumption among California residents, prevent nearly 200,000 cases of obesity, and save more than 1.8 billion dollars in health care costs. People who consume sugary drinks are expected to spend less on these drinks with the tax in place. We also project that non-Latino Black/African American and Latino California residents will experience even greater health benefits than the average resident after the tax is implemented. These results are summarized below and in the complete report.

 

Background 

In California, health disparities and inequities persist for specific subsets of the population – the reasons for this are deep-seated and multi-faceted including systems that perpetuate the unequal distribution of power and resources along racial lines.5 Overconsumption of added sugars is common, with more than half of the United States population ages two years and older exceeding the daily recommended limit for added sugars put forth in the 2015-2020 Dietary Guidelines for Americans.6 Sugary drinks (defined as all drinks with added caloric sweeteners) are the number one source of added sugars that Americans consume.6In this report, Asian, Black/African American, White, and Other race/ethnicity groups refer to people of non-Latino ethnicity. Other includes people of Native Hawaiian and Pacific Islander, American Indian and Alaska Native, and two or more races.

According to recent estimates, 40% of California residents drink at least one serving of sugary drinks daily.7 Higher than average sugary drink consumption levels are common among Latino and Black/African American Californians.7 In 2018, the beverage industry spent $1 billion to advertise sugary drinks in television, digital platforms (internet and mobile), radio, magazines, newspapers, coupons, and outdoor venues in order to drive preferences and purchases of unhealthy beverages.8 Beverage companies frequently target their sugary drink advertising towards youth, and are more likely to target Black/African American and Latino youth. Additionally, Black/African American and Latino populations are less likely to be the audience for marketing of healthy drinks like water.8

Strong evidence links increased consumption of sugary drinks to higher risk for obesity and other diseases that are tied to what people eat, such as type 2 diabetes,9,10 and the prevalence of these diseases is higher among people with lower income and Latino and Black/African American Californians.7,11 An estimated 37% of adults11 and 21% of youth12 in California have obesity. If current trends continue, 42% of adults in the state will have obesity by 2030.11

Taxes have emerged as a promising strategy to reduce consumption of sugary drinks. In addition to the potential of a sugary drink tax to reduce obesity, it has cost implications as well. This report models the projected effect of a sugary drink excise tax on health, disease outcomes, and health care cost savings over the next decade.

Key Terms
  • Cost-saving: saves more in future health care costs than it costs to implement (not considering the potential revenues)

  • Excise tax: a consumption tax collected from retailers or distributors; it is reflected in the posted price (a sales tax in contrast is applied after purchase of the item)

  • Pass-through rate: how much of the excise tax on distributors is passed on to consumers as an increase in shelf price; a percent ranging from 0% (none of the tax) to 100% (all the tax), or even greater than 100% (more than the amount of the tax)

  • Price elasticity of demand: how much consumer purchasing behavior changes following a change in price of an item

Projected Impact of a Sugary Drink Excise Tax in California

We modeled implementation of a California excise tax on sugary drinks, at a tax rate of $0.02/ounce. All drinks with added caloric sweeteners were considered to be taxed, while 100% juice, milk products, and beverages with fewer than 25 kcals per 12 fluid ounces were considered exempt.

 

Results: What did we find?

We project that implementation of a state excise tax on sugary drinks only, at a tax rate of $0.02/ounce, has a 100% likelihood of being cost-saving. It will prevent more than 195,000 cases of childhood and adult obesity, prevent new cases of diabetes, increase healthy life years, improve health equity, and save more in future health care costs than it will cost to implement. Implementing the tax could also serve as a powerful social signal to reduce sugar consumption. Model results are presented as the most likely estimate as well as a likely range. The likely range is an uncertainty interval that is estimated by considering uncertainty from data sources and population projections and calculating a central range in which 95 percent of these model results fell.

 

How many people would be affected by the tax?

This can be thought of as reach or the number of people affected by the strategy. Based on our modeling, the table below presents the estimated number of people affected by the tax in the first year and the number of people affected by the tax over ten years.

Number of people affected by the tax

Likely Range

First Year Population Reach

38.0 million

37.9 million; 38.1 million

Ten Year Population Reach

42.2 million

42.0 million; 42.5 million

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.

What effect would the tax have on sugary drink consumption and spending?

Compared to projections of sugary drink consumption without a tax, the tax is projected to result in lower levels of sugary drink consumption. We project a 90% likelihood that individuals and households who purchase sugary drinks will spend less on sugary drinks after the tax is implemented, even though sugary drink prices will be higher with the tax. 

Impact of the tax on sugary drink consumption & spending

Likely Range

Decrease in 12-oz Servings of Sugary Drinks per Person in the First Year

69

42; 125

Decrease in Spending on Sugary Drinks in the First Year per Person Consuming Sugary Drinks

$48

-$10; $170

90% likelihood of decrease in spending

Decrease in Spending on Sugary Drinks in the First Year per Household

$142

-$29; $502

90% likelihood of decrease in spending

Decrease in Spending on Sugary Drinks in the First Year Overall in California

$1.09 billion

-$220 million; $3.88 billion

90% likelihood of decrease in spending

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.

Average Annual Pre-Tax Sugary Drink Consumption Per Person in California by Race/Ethnicity

While Californians, on average, consume 251 servings of sugary drinks each in a year, higher than average sugary drink consumption levels are common among Latino and Black/African American Californians. Asian Californians drink an average of 162 12 ounce servings of sugary drinks per person per year; Black/African American Californians drink an average of 372 12 ounce servings of sugary drinks per person per year; Latino Californians drink an average of 312 12 ounce servings of sugary drinks per person per year; White Californians drink an average of 191 12 ounce servings of sugary drinks per person per year; and Californians of Other races drink an average of 254 12 ounce servings of sugary drinks per person per year
DATA SOURCES: California Health Interview Survey 2011-2017, NHANES 2011-2016, UConn Rudd Center for Food Policy & Obesity Sugary Drink Tax Calculator 2020; Analysis by CHOICES Project, 2020.
Each serving is 12 ounces.

While Californians, on average, consume 251 servings of sugary drinks each in a year, higher than average sugary drink consumption levels are common among Latino and Black/African American Californians.

Post-Tax Decrease in Sugary Drink Consumption Per Person in California by Race/Ethnicity*

With a tax, sugary drink consumption would decrease the most among Latino and Black/African American Californians. On average, each Latino Californian would reduce consumption by 85 servings per year and each Black/African American Californian would reduce consumption by 102 servings per year. Asian Californians would reduce consumption an average of 44 12 ounce servings of sugary drinks per person per year; Black/African American Californians would reduce consumption an average of 102 12 ounce servings of sugary drinks per person per year; Latino Californians would reduce consumption an average of 85 12 ounce servings of sugary drinks per person per year; White Californians would reduce consumption an average of 52 12 ounce servings of sugary drinks per person per year; and Californians of Other races would reduce consumption an average of 69 12 ounce servings of sugary drinks per person per year

*In the first year following an excise tax of $0.02/ounce on sugary drinks.

Each serving is 12 ounces.

With a tax, sugary drink consumption would decrease the most among Latino and Black/African American Californians. On average, each Latino Californian would reduce consumption by 85 servings per year and each Black/African American Californian would reduce consumption by 102 servings per year.

Impact of the tax on sugary drink consumption, by race and ethnicity

Outcome

Asian

Mean

Likely Range

Black/African American

Mean

Likely Range

Latino

Mean

Likely Range

White

Mean

Likely Range

Other*

Mean

Likely Range

Decrease in 12-oz Servings of Sugary Drinks per Person in the First Year

44

27; 81

102

62; 186

85

52; 154

52

32; 98

69

42; 124

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
*Other includes people of Native Hawaiian and Pacific Islander, American Indian and Alaska Native, and two or more races.

What effect would the tax have on obesity and related health outcomes, overall and by race/ethnicity?

Compared to projections of obesity and related health outcomes without a tax, the tax is projected to result in fewer cases of obesity and fewer deaths over the 10-year period under consideration. Under the proposed tax, Black/African American Californians will experience a 39% higher than average reduction in obesity prevalence, and Latino Californians will experience a 33% higher than average reduction in obesity prevalence.

Impact of the tax on obesity and related health outcomes

Likely Range

Quality Adjusted Life Years (QALYs) Gained Over 10 Years

58,200

25,000; 130,000

Years of Life Gained Over 10 Years

14,600

5,410; 34,500

Deaths Prevented Over 10 Years*

4,280

1,680; 10,000

Years with Obesity Prevented Over 10 Years

1,410,000

696,000; 2,770,000

Cases of Obesity Prevented in 2030*

198,000

96,700; 394,000

Cases of Childhood Obesity Prevented in 2030*

33,700

12,500; 74,800

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.
*Not discounted.

$0.02 per ounce state excise tax on sugary drinks would lead to 198,000 cases of obesity prevented in 2030 and 33,700 cases of childhood obesity prevented in 2030

 

Pre-Tax Obesity Prevalence in California by Race/Ethnicity

Nearly half of Latino (43.5%) and Black/African American (43.6%) Californians have obesity, while smaller percentages of Asian (16.1%), White (33.2%), and Californians of another race (33.9%) have obesity.
DATA SOURCES: California FitnessGram 2013-2017, NHANES 2011-2016, NSCH 2003-2018; Analysis by CDPH and the CHOICES Project, 2020.

Nearly half of Latino (43.5%) and Black/African American (43.6%) Californians have obesity, while smaller percentages of Asian (16.1%), White (33.2%), and Californians of another race (33.9%) have obesity.

Post-Tax Cases of Obesity Prevented in 2030 in California by Race/Ethnicity*

A tax in California could prevent 198,000 cases of obesity in 2030. 58% of prevented obesity cases will be among Latino Californians, while they represent 42% of the population. 8% of prevented obesity cases will be among Black/African American Californians, while they represent 6% of the population.

 

*With an excise tax of $0.02/ounce on sugary drinks.

A tax in California could prevent 198,000 cases of obesity in 2030. 58% of prevented obesity cases will be among Latino Californians, while they represent 42% of the population. 8% of prevented obesity cases will be among Black/African American Californians, while they represent 6% of the population.

Impact of the tax on behavior and health, by race/ethnicity

Outcome

Asian

Mean

Likely Range

Black/African American

Mean

Likely Range

Latino

Mean

Likely Range

White

Mean

Likely Range

Other

Mean

Likely Range

QALYS Gained Over 10 Years

3,560

1,490; 8,160

5,100

1,990; 11,300

28,700

12,600; 64,200

18,900

7,810; 42,600

1,910

818; 4,230

Years of Life Gained Over 10 Years

893

128; 2,400

1,820

381; 4,470

5,330

1,720; 12,800

6,020

2,040; 14,900

506

0; 1,490

Years with Obesity Prevented Over 10 Years

80,800

39,800; 159,000

110,000

53,300; 218,000

820,000

407,000; 1.6 million

354,000

159,000; 751,000

47,100

23,300; 89,700

Cases of Obesity Prevented in 2030*

12,700

5,960; 26,500

15,100

7,130; 30,700

114,000

56,300; 218,000

49,800

22,400; 106,000

7,050

3,430; 13,500

Cases of Childhood Obesity Prevented in 2030*

1,700

571; 3,980

2,920

1,060; 6,560

24,200

8,920; 52,900

3,610

1,240; 8,740

1,260

428; 2,920

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.
*Not discounted.
Other includes people of non-Latino ethnicity and Native Hawaiian and Pacific Islander, American Indian and Alaska Native, and two or more races.

How much would the tax cost to implement?

There are initial and ongoing costs to implementing the tax. To implement the strategy, the California Department of Tax and Fee Administration would perform several administrative activities, including identifying and notifying taxpayers, revising manuals and tax return documents and systems, processing tax statements, and conducting audits. Businesses would also need to prepare tax statements and participate in audits, which would require labor from private tax accountants. Cost information was drawn from tax programs that were previously implemented in California13 and from planned or implemented excise taxes on sugary drinks in other states and localities.14 The cost and benefit estimates do not include expected tax revenue (discussed below). Below we include annual and 10-year implementation costs.

Costs

Likely Range

Annual Implementation Cost

$3.9 million

$2.69 million; $4.99 million

Annual Implementation Cost per Person

$0.10

$0.07; $0.13

Total Intervention Implementation Cost Over 10 Years

$39.0 million

$26.9 million; $49.9 million

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.

How much would the tax save in health care costs compared to what it costs to implement?

The estimated reduction in obesity attributable to the tax leads to lower projected health care costs, offsetting tax implementation costs and resulting in a net cost savings. The difference between total health care costs with no strategy and lower health care costs with a strategy represents health care costs saved; these savings can be compared to the cost of implementing the tax to arrive at the metric of health care costs saved per $1 invested.

Costs

Likely Range

Health Care Costs Saved Over 10 Years

$1.83 billion

$783 million; $4.06 billion

Net Costs Difference Over 10 Years

-$1.79 billion

-$4.03 billion; -$740 million

Health Care Costs Saved per $1 Invested Over 10 Years

$46.89

$19.82; $118.76

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.

What would the impact of the tax be on Medi-Cal* spending?

Based on the projected reduction in adult obesity prevalence due to the tax (compared to what prevalence would be without the tax), adult obesity-related Medi-Cal expenditures in California are estimated to decrease. This does not include potential reductions in child obesity-related expenditures, and may be an underestimate if adults utilizing Medi-Cal have higher than average health care costs of obesity.15 A previous analysis found that 12.2% of adult Medi-Cal expenditures were due to obesity.16 We estimate that, in California, obesity accounted for $8.9 billion of $73.3 billion total adult Medi-Cal expenditures in 2019.16,17 This assumes that 74% of all Medi-Cal payments are for adults.18 The state paid 37% of total Medi-Cal expenditures in 2019,17 so we estimate that the state paid $3.3 billion in adult obesity-related Medi-Cal expenditures.

*Medi-Cal is California’s Medicaid program

Medi-Cal Spending

Likely Range

Reduction in Annual Adult Obesity-Related Medi-Cal Expenditures (Paid by State)

$17.9 million

$8.19 million; $38.5 million

Reduction in Total Annual Adult Obesity-Related Medi-Cal Expenditures (Paid by State and Federal)

$48.3 million

$22.1 million; $104 million

The Likely Range is a 95% uncertainty interval estimated by running the model 1,000 times, taking into account uncertainty from data sources and population projections, and calculating a central range in which 95 percent of these model results fell.
Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.

What are the key cost-effectiveness metrics?

Since we project that the tax saves more in future health care costs than it costs to implement, there is a 100% likelihood that the tax would be cost-saving. 

Cost-effectiveness metrics

Cost per Year with Obesity Prevented Over 10 Years

Cost-saving*

Cost per QALY Gained Over 10 Years

Cost-saving*

Cost per YL Gained Over 10 Years

Cost-saving*

Cost per Death Averted Over 10 Years

Cost-saving*

Costs and health outcomes are discounted at 3% per year, unless otherwise noted. Discounting estimates the present value of costs and health outcomes that are spent or received in the future, given that they are worth more today than they would be tomorrow.
*There is a 100% likelihood that these metrics are cost-saving.

What effect would the tax have on diabetes?

Economic studies indicate that with a sugary drink tax, consumers will buy less of these products.19 A $0.02/ounce tax will decrease the consumption of sugary drinks and this has other health implications that we can estimate. In California, the proposed sugary drink excise tax would lead to a 7% reduction in diabetes incidence over one year once the tax reaches full effect. We calculated this impact on diabetes incidence using projected declines in sugary drink consumption, not directly via microsimulation.$0.02 per ounce state excise tax on sugary drinks would lead to a 7% reduction in diabetes incidence and 13,900 cases of diabetes prevented

What effect would the tax have on tooth decay?

In California, we estimated that a $0.02/ounce tax would lead to a reduction of 502,000 decayed, missing, and filled teeth among Medi-Cal* recipients and $39.5 million in savings to Medi-Cal over 10 years due to a reduction in treatment of tooth decay. For the entire California population, we estimated the tax would lead to a reduction of 1,620,000 decayed, missing, and filled teeth and $135 million in savings for all payers due to a reduction in treatment of tooth decay. The Medi-Cal reimbursement tax estimates may underestimate the total cost savings of tooth decay treatment projected here as dental providers may charge higher amounts to patients. Impact on tooth decay was calculated using projected declines in sugary drink consumption, not directly via microsimulation.

*Medi-Cal is California’s Medicaid program

$0.02 per ounce state excise tax on sugary drinks would lead to $39.5 million in dental decay treatment cost savings over 10 years (Medi-Cal) and $135 million in dental decay treatment total cost savings over 10 years (Societal)

Key Considerations for Health Equity

Concerns have been raised regarding the impact of the tax on low-income households, because lower-income populations tend to consume more sugary drinks.20 Economic studies indicate that with a sugary drink tax, consumers will buy less of these products.19 This change in purchasing is substantial, so that consumers can be expected to spend less on sugary drinks after a tax is implemented. Using sales data from the Rudd Center Revenue Calculator for Sugary Drink Taxes,21 we project that individuals and households in California will spend less money on sugary drinks after a $0.02 per ounce tax: about $48 less per year per person, and $142 per year less for an average household. This would free up disposable income for other consumer purchases. A typical consumer in California who continues to consume these beverages after the tax is in place would be expected to pay tax of about $1.40 per week, or $73/year.

In addition to these changes in spending, health benefits are projected to be greatest among low-income individuals. We also project that greater health benefits will accrue among Latino and Black/African American California residents compared with White and Asian residents. Using data on sugary drink consumption in the CHOICES model, the average daily consumption of sugary drinks among people in California varies by race/ethnicity group (see pre-tax figures on pages 4 and 6). Under the proposed tax, we project that Black/African American Californians would see a 39% greater reduction in obesity prevalence than average, and Latino Californians would see a 33% greater reduction in obesity prevalence than average. On that basis, the proposed tax could decrease disparities in obesity outcomes and improve health equity.

These expected changes in consumption and health outcomes have led economists to conclude that low-income populations benefit substantially from sugary drink taxes.22

Implementation Considerations

A $0.02/ounce statewide excise tax on sugary drinks in California could raise as much as $1.3 to $1.8 billion in annual revenue.21 Revenue raised from a sugary drink tax could be reinvested in communities with low-income. For instance, in Berkeley, CA, revenue from a municipal sugary drink tax has been allocated for spending on school and community programs, many serving families with low-income or communities of color, to promote healthy eating, diabetes, and obesity prevention.23,24 Public support for such taxes generally increases with earmarking for preventive health activities.24

There is opposition from the food and beverage industry, which spends billions of dollars promoting their products.25 Relatively small beverage excise taxes are currently applied across many states. The proposed tax is likely to be sustainable if implemented based on the successful history of tobacco excise taxes. There is potential for a shift in social norms of sugary drink consumption based on evidence from tobacco control tax and regulatory efforts.26 

 

Modeling Assumptions and Summary of the CHOICES Microsimulation Model

 

How does an excise tax on distributors affect the price paid by consumers?

An excise tax is incorporated directly into a beverage’s shelf price. We assume 100% pass-through of the tax over 10 years and assume the tax rate would be adjusted annually for inflation. Our pass-through rate estimate is supported by empirical studies of excise taxes in Mexico and France that demonstrate near-complete pass-through rates to consumers27 and consistent evidence in the U.S. indicating that beverage taxes increase prices, although there is some variation by store type.28-31   

The expected change in sugary drink price was estimated based on national sugary drink prices32 and regional sales data for several categories of sugary drinks (i.e., soda, sports drinks, fruit drinks, energy drinks, sweetened teas, sweetened coffees, and enhanced water).21 In California, we assume the average price of sugary drinks is almost $0.09/ounce, so a $0.02/ounce tax would raise prices by 23%. This means that, for example, the price of a 12-ounce can of soda would increase from $1.06 to $1.30/can post-tax.

How does increasing the price of sugary drinks change individual sugary drink consumption?

How much consumers will change their purchases in response to price changes is called price elasticity for demand. We assume for every 10% increase in the price of sugary drinks, there will be a 12% reduction in purchases (a mean own-price elasticity of demand of -1.21).19 Recent research on the Berkeley, CA $0.01/ounce tax found a 21% reduction in sugary drink intake among populations with low-income consistent with this estimate.33-37 In California, we assume a $0.02/ounce tax that raises prices by 23% would reduce purchases by 27%. We assume this 27% reduction in purchases results in a 27% reduction in consumption.

To estimate current sugary drink consumption levels in California, we used national estimates of sugary drink consumption from NHANES 2011-2016 adjusted to race- and ethnicity-specific estimates of sugary drink consumption among children, teens, and adults from the California Health Interview Survey38 and estimates of sugary drink sales in California from the UConn Rudd Center for Food Policy & Obesity.21

What are the health effects of decreasing sugary drink consumption?

Research has shown that decreasing sugary drink consumption can have positive effects on health in youth and adults.

Assumptions about sugary drinks and obesity risk

We estimated the impact of a change in sugary drink intake on body mass index (BMI), accounting for dietary compensation, based on rigorous studies identified in evidence reviews.14 The relationship among adults was modeled based on the range of estimated effects from four large, multi-year longitudinal studies, which indicated that a one-serving reduction in sugary drinks was associated with a BMI decrease of 0.21 kg/m2 to 0.57 kg/m2 in adults over a 3-year period.39-42 Among youth, we used evidence from a double-blind randomized controlled trial conducted over 18 months, which found that an additional 8-ounce serving of sugary drinks led to a 2.2 pound greater weight gain.43

Assumptions about sugary drinks and diabetes risk

We estimated the impact of the tax-induced reduction in sugary drink intake on diabetes incidence for adults ages 18-79 years using a published meta-analysis of the relative risk of developing diabetes due to a one-serving change in sugary drink consumption44 as well as state-level estimates of diabetes incidence from the CDC Atlas 201645 and prevalence from the California Health Interview Survey 2017-2018.46 On average, each 8.5-ounce serving of sugary drinks per day increases the risk of diabetes by 18%.44

Assumptions about sugary drinks and tooth decay

We estimated the impact of a sugary drink excise tax on tooth decay and tooth decay treatment costs using a longitudinal analysis of the relationship between intake of sugars and tooth decay in adults. On average, for every 10 grams higher intake of sugar per day, there is an increase in decayed, missing and filled teeth (i.e., tooth decay) of approximately 0.10 over 10 years.47 There are many studies showing a similar relationship between higher intake of sugars and tooth decay in children and youth48 and thus we assume the same relationship as found in adults. We used the Medi-Cal Dental Provider Handbook 201949 to estimate a Medi-Cal cost of treating tooth decay as: $278 for a permanent crown and $58 for a filling. These codes reflect treatment for one to four surfaces but do not reflect the actual frequency of multi-surface treatment among Medi-Cal recipients or for higher reimbursement rates due to temporary crowns or potential flat tax schedules. Based on analysis of data on tooth decay, fillings and crowns for the U.S. population from NHANES 1988-1994 (the last year crowns and fillings were separately reported),50 we estimate that 78.9% of tooth decay in children and 43.5% of tooth decay in adults is treated. Using this same data set, we estimate that 97% of treatment for children is fillings and 82.5% of treatment for adults is fillings. To estimate Medi-Cal-specific savings in costs of dental treatment, we used estimates of the number of people enrolled in the Medi-Cal Dental Program in 2018.51

CHOICES Microsimulation Model

The CHOICES microsimulation model was used to calculate the costs and effectiveness of a tax in California over 10 years (2020-2030). We forecasted what would happen to a virtual population of residents in California with and without a sugary drink tax to model changes in disease and mortality rates and health care costs due to the tax. Cases of obesity prevented were calculated at the end of the model period in 2030. The model was based on peer-reviewed CHOICES methodology.14,52-54 We created a virtual population of residents in California using data from: the U.S. Census, American Community Survey, Behavioral Risk Factor Surveillance System, NHANES, National Survey of Children’s Health,52 the Medical Expenditure Panel Survey, multiple national longitudinal studies, and obesity prevalence data provided by California Department of Public Health. Impacts on diabetes, tooth decay, Medicaid spending, and household spending on sugary drinks were calculated based on summary results from the model, not directly via microsimulation.

Of note, the CHOICES microsimulation model does not include annual revenue generation from a state excise tax on sugary drinks in any of the cost-effectiveness calculations. The Rudd Center Revenue Calculator for Sugary Drink Taxes estimates potential annual revenues from excise taxes on sugary drinks and is “intended to provide a rough estimate” for municipalities to consider.21 According to the Rudd Center,21 a $0.02/ounce excise tax in California could raise as much as $1.8 billion in 2020. Accounting for 10-30% non-compliance as the Rudd Center advises, annual revenue estimates range between $1.3 – $1.8 billion.CHOICES microsimulation model: start in 2020 and simulate to 2030. Start with a virtual population using data from the 2010 U.S. census. Then take into account population factors, such as population growth and BMI trends. Then take into account individual factors, such as body growth, personal characteristics (e.g. dietary intake), and smoking. Then, input the intervention (dietary intake/physical activity). Then, look at health status (obesity) and outcomes (obesity, health care costs, and mortality).

Citation

Gouck J, Whetstone L, Walter C, Pugliese J, Kurtz C, Seavey-Hultquist J, Barrett J, McCulloch S, Reiner J, Garrone M, Cradock A, Gortmaker, S. California: A Sugary Drink Excise Tax. California Department of Public Health, Sacramento, CA, the County of Santa Clara Public Health Department, San Jose, CA, and the CHOICES Learning Collaborative Partnership at the Harvard T.H. Chan School of Public Health, Boston, MA; March 2021. For more information, please visit www.choicesproject.org

The design for this brief and its graphics were developed by Molly Garrone, MA and partners at Burness.

Funding

This work is supported by The JPB Foundation and the Centers for Disease Control and Prevention (U48DP006376). The findings and conclusions are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention or other funders.

For further information, contact choicesproject@hsph.harvard.edu

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References

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  27. Colchero MA, Salgado JC, Unar-Munguia M, Molina M, Ng SW, Rivera-Dommarco JA. Changes in Prices After an Excise Tax to Sweetened Sugar Beverages Was Implemented in Mexico: Evidence from Urban Areas. PLoS One. 2015;10(12):11.
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  30. Marinello S, Pipito AA, Leider J, Pugach O, Powell LM. The impact of the Oakland sugar-sweetened beverage tax on bottled soda and fountain drink prices in fast-food restaurants. Preventive Medicine Reports. 2020;17:101034.
  31. Ng S, Silver L, Ryan-Ibarra S, et al. Berkeley Evaluation of Soda Tax (BEST) Study Preliminary Findings. Presentation at the annual meeting of the American Public Health Association. Paper presented at: Presentation at the annual meeting of the American Public Health Association; November, 2015; Chicago, IL.
  32. Powell L, Isgor z, Rimkus L, Chaloupka F. Sugar-sweetened beverage prices: Estimates from a national sample of food outlets. Chicago, IL: Bridging the Gap Program, Health Policy Center, Institute for Health Research and Policy, University of Illinois at Chicago; 2014.
  33. Falbe J, Thompson HR, Becker CM, Rojas N, McCulloch CE, Madsen KA. Impact of the Berkeley Excise Tax on Sugar-Sweetened Beverage Consumption. American Journal of Public Health. 2016;106(10):1865-1871.
  34. Cawley J, Frisvold D, Hill A, Jones D. The impact of the Philadelphia beverage tax on purchases and consumption by adults and children. Journal of Health Economics. 2019;67:102225.
  35. Lee MM, Falbe J, Schillinger D, Basu S, McCulloch CE, Madsen KA. Sugar-Sweetened Beverage Consumption 3 Years After the Berkeley, California, Sugar-Sweetened Beverage Tax. American Journal of Public Health. 2019;109(4):637-639.
  36. Zhong Y, Auchincloss AH, Lee BK, Kanter GP. The Short-Term Impacts of the Philadelphia Beverage Tax on Beverage Consumption. American Journal of Preventive Medicine. 2018;55(1):26-34.
  37. Silver LD, Ng SW, Ryan-Ibarra S, et al. Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study. PLoS Medicine. 2017;14(4):e1002283
  38. 2011-2017 California Health Interview Survey data, analysis by the Harvard T.H. Chan School of Public Health, data provided by the UCLA Center for Health Policy Research, available at: http://healthpolicy.ucla.edu/chis/Pages/default.aspx
  39. Chen L, Caballero B, Mitchell DC, et al. Reducing Consumption of Sugar-Sweetened Beverages Is Associated with Reduced Blood Pressure: A Prospective Study among U.S. Adults. Circulation. 2010;121(22):2398-2406.
  40. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in Diet and Lifestyle and Long-Term Weight Gain in Women and Men. New England Journal of Medicine. 2011;364(25):2392-2404.
  41. Palmer JR, Boggs DA, Krishnan S, Hu FB, Singer M, Rosenberg L. Sugar-Sweetened Beverages and Incidence of Type 2 Diabetes Mellitus in African American Women. Archives of Internal Medicine. 2008;168(14):1487-1492.
  42. Schulze MB, Manson JE, Ludwig DS, et al. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. Journal of the American Medical Association. 2004;292(8):927-934.
  43. de Ruyter JC, Olthof MR, Seidell JC, Katan MB. A trial of sugar-free or sugar-sweetened beverages and body weight in children. New England Journal of Medicine. 2012;367(15):1397-1406.
  44. Imamura F, O’Connor L, Ye Z, et al. Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. British Journal of Sports Medicine. 2016;50(8):496-U484.
  45. Centers for Disease Control and Prevention. Diabetes Atlas. Accessed 10/21/20 at: https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html#
  46. 2017-2018 California Health Interview Survey data, analysis by the Harvard T.H. Chan School of Public Health, data provided by the UCLA Center for Health Policy Research, available at: http://healthpolicy.ucla.edu/chis/Pages/default.aspx
  47. Bernabe E, Vehkalahti MM, Sheiham A, Lundqvist A, Suominen AL. The Shape of the Dose-Response Relationship between Sugars and Caries in Adults. Journal of Dental Research. 2016;95(2):167-172.
  48. Sheiham A, James WPT. A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public Health Nutrition. 2014;17(10):2176-2184.
  49. California Department of Health Care Services, Medi-Cal Dental Provider Handbook 2019. Accessed October 19, 2020 at: https://dental.dhcs.ca.gov/Dental_Providers/Denti-Cal/Provider_Handbook
  50. Ward Z, et al. NHANES III Dental Examination: An Incisive Report. unpublished report; 2018.
  51. California Department of Health Care Services, Multi Year Medi-Cal Dental Measures Data by Age Groups Calendar Year 2013 to 2018. Accessed October 19, 2020 at: https://data.chhs.ca.gov/dataset/test-dhcs-multi-year-dental-measures-data-by-age-groups-calendar-year-2013-to-2015
  52. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of Growth Trajectories of Childhood Obesity into Adulthood. New England Journal of Medicine. 2017;377(22):2145-2153.
  53. Ward ZJ, Long MW, Resch SC, et al. Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence. PLoS One. 2016;11(3):13.
  54. Long MW, Ward Z, Resch SC, et al. State-level estimates of childhood obesity prevalence in the United States corrected for report bias. International Journal of Obesity. 2016;40(10):1523-1528.

Impact Of The Healthy, Hunger-Free Kids Act On Obesity Trends

Kids eating healthy food at lunch time

A CHOICES study examined the impact of the Healthy, Hunger-Free Kids Act of 2010 on child obesity risk, and found that policies that strengthen nutritional standards for meals and beverages at schools may be effective tools for reducing obesity among children living in poverty.

Kenney EL, Barrett JL, Bleich SN, Ward ZJ, Cradock AL, Gortmaker SL. Impact Of The Healthy, Hunger-Free Kids Act On Obesity Trends. Health Aff. 2020;39(7). doi:10.1377/hlthaff.2020.00133

Children eating lunch in a classroom.

The study’s research team, led by Erica Kenney, examined the impact of the Healthy, Hunger-Free Kids Act of 2010 on child obesity risk. The legislation strengthened nutritional standards for meals and beverages provided through the National School Lunch, Breakfast, and Smart Snacks programs. The Act’s whole grain standards were relaxed under the Trump administration, but this change was struck down in federal court. Additional rollbacks of the Act’s standards have been proposed.

The researchers reviewed data for 173,013 youths taken from the National Survey of Children’s Health from 2003–2018, prior to when rollbacks went into effect.

While they found no significant association between the legislation and childhood obesity trends overall, they did find significant reductions in obesity risk among children living in poverty—a population that is particularly reliant on school meals. Among these children, the risk of obesity, which had been trending steadily upwards prior to the legislation going into effect, declined substantially each year following the act’s implementation, translating to a 47% reduction in obesity prevalence in 2018 from what would have been expected without the legislation.

The researchers conclude that the Healthy, Hunger-Free Kids Act’s science-based nutritional standards should be maintained to support healthy growth, especially among children living in poverty. They also suggest that policymakers consider strategies to increase participation in school meals programs.

“Based on our study, as well as research that USDA and other researchers have conducted showing improvements in diet, the improved school meals standards have been a great public health success story,” said first author Kenney. “These healthier school meals are helping to protect the health of the children who have been placed at highest risk for poor health, and they reduce hunger while also reducing their risk of chronic diseases later in life.”

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Stories from the Field: Oklahoma Takes Action to Improve Child Health

The information in this Story from the Field is intended only to provide educational information.

In this story from the field, partners in Oklahoma worked with the CHOICES Team to see what would happen if they took actions in the Special Supplemental Program for Women, Infants, and Children (WIC) in Oklahoma that could help families reduce the time their young children spent watching TV, where they are frequently exposed to ads for unhealthy foods.

Identifying Priorities in Oklahoma

In 2014, Oklahoma Governor Mary Fallin requested a plan for using data and evidence to inform public health decision-making and minimize costs. Leaders from the state health and human services agencies came together and prioritized obesity as the most important issue to address.1

The Oklahoma State Department of Health (OSDH) and the Oklahoma Department of Human Services (OKDHS) identified the Special Supplemental Program for Women, Infants, and Children (WIC) as a key opportunity for focusing on obesity prevention because more than 40,000 Oklahoma children ages 2 to 4 (27%) participate in WIC – and nearly 15% of those children have obesity.2

Gathering Information for Action

In 2016, OSDH and OKDHS partnered with the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) Project at the Harvard T.H. Chan School of Public Health’s Learning Collaborative Partnership (LCP). The CHOICES Project works with health agencies to create new evidence to inform decision-making. Using local data, state and local health agencies learn how to apply evaluations of effectiveness, reach, and cost to understand the relative cost-effectiveness of strategies to prevent and treat childhood obesity.

Strategy Selection

The Oklahoma CHOICES Team (OSDH and OKDHS) sought best-value-for-money strategies for preventing obesity among Oklahoma’s young children. The Oklahoma CHOICES Team attended a CHOICES training where they reviewed potential strategies with a strong evidence base. Limiting screen time is a recommended strategy to promote healthy weight among children.

The Intervention

In this scenario, WIC clinic staff would need to ask caregivers how much screen time their children view and talk with them about how to reduce that time. Prior research has shown this method to be effective.3 Reducing screen time can reduce the risk for obesity because of decreased exposure to unhealthy foods and drinks.

The Change Needed

The Oklahoma CHOICES Team, along with the OK WIC Service Team, determined that the WIC online electronic participant record needed modification so that WIC clinic staff could ask caregivers how much screen time their children view during WIC recertification visits, and provide tailored counseling to caregivers on how to reduce that time.

An infographic titled, "How much screen time should my child get?" It outlines screen time as television, computers, video games, and hand held devices like tablets, ipads, and smart phones).

Handout created by Oklahoma WIC Service given to participants during counseling sessions

 

Helping Change Happen

As part of the LCP, the CHOICES Team works with local stakeholders to make projections about what may happen when a program or policy is implemented. Through this collaboration, the partners determined that these program changes could result in 149,000 Oklahoma children adopting healthy screen time behaviors over 10 years and the prevention of 660 cases of childhood obesity in the final year. For every dollar spent putting this strategy into effect, $20.90 would be saved in obesity-related health care costs over 10 years. After reviewing these data and realizing how easy it would be to modify the WIC software, the Oklahoma Team decided to make the change. Screen time counseling in the Oklahoma WIC program was rolled out in 2017.

Steps Taken for Implementation

  • Modified the online system Public Health Oklahoma Client Information System (PHOCIS) to prompt WIC counselors to ask screen time question
  • Conducted staff training for WIC counselors
  • Created educational materials to use with families during counseling sessions
  • Asked participating families about their children’s screen time habits
  • Provided families the option to discuss screen time habits during the counseling session and/or to receive educational materials

Impact & Lessons Learned

Since 2017:
An infographic stating: "nearly 30,000 families received screentime guidance" and "75% are taking steps to reduce screentime."

We learned two key things: the very positive result of reducing screen time, and that this strategy was something we could not only model but implement, and we could do it fairly quickly.” – Terry Bryce, State WIC Director

References

  1. Oklahoma State Department of Health. Oklahoma Health 360° – Obesity Report. Retrieved from: https://www. ok.gov/health2/documents/Health%20360%20_OBESITY%20Final%20Report%2011.3.17.pdf 
  2. Communication with Oklahoma State Department of Health: WIC Service. (2017).
  3. Whaley SE, McGregor S, Jiang L, Gomez J, Harrison G, Jenks, E. A WIC-Based Intervention to Prevent Early Childhood Overweight. J Nutr Educ Behav. 2010;42(3 Suppl):S47-51.
Suggested Citation:

CHOICES Stories from the Field: Oklahoma Takes Action to Improve Child Health. Oklahoma State Department of Health & Oklahoma Department of Human Services, Oklahoma City, OK, and the CHOICES Project Team at the Harvard T.H. Chan School of Public Health, Boston, MA; May 2020.

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Brief: Healthy Incentives Program within the SNAP in Harris County, Texas

The information in this brief is intended only to provide educational information.

This brief summarizes a CHOICES Learning Collaborative Partnership simulation model of a Healthy Incentives Program (HIP) in Harris County, TX that aims to increase fruit and vegetable consumption among Supplemental Nutrition Assistance Program (SNAP) recipients by providing a financial incentive of $0.30 for every $1 spent on fruits and vegetables.

The Issue

Over the past three decades, more and more people have developed obesity.1 The health care costs of treating obesity-related conditions in adulthood were $147 billion in 2008.2 As a strategy to reduce the risk of obesity and chronic disease, public health authorities emphasize the goal of increasing fruit and vegetable intake.3

In Harris County, nearly 69% of adults have too much excess weight.4 Nearly 265,000 adult Harris County residents are eligible for SNAP benefits.5 SNAP is a key strategy for reducing food insecurity among low-income populations. Regardless of SNAP participation, household purchases of fruits and vegetables are low.6 And, compared to eligible nonparticipants, SNAP participants may have a lower diet quality. Interventions may be needed that support the consumption of healthy foods like fruits and vegetables.7 Financial incentives are a proven strategy in increasing fruit and vegetable purchases.8,9

About the Healthy Incentives Program

Under this program, SNAP participants will receive an incentive of $0.30 for every $1 of SNAP benefits that they spend on targeted fruits and vegetables in SNAP-authorized grocery stores and farmers markets. Targeted fruits and vegetables include fresh, canned, frozen, and dried fruits and vegetables without added sugars, fats, oils, or salt, but exclude white potatoes and 100% fruit juice. SNAP participants will be automatically enrolled in the program; then, the incentive will be automatically credited back to the participants’ SNAP account after each household purchase of these products. The incentive will be capped at $60 per household per month to prevent misuse.

Comparing Costs and Outcomes

CHOICES cost-effectiveness analysis compared the costs and outcomes of providing SNAP participants with an incentive of $0.30 for every $1 of SNAP benefits that they spend on targeted fruits and vegetables in SNAP-authorized grocery stores and farmers markets over a 10-year time horizon with the costs and outcomes expected if no incentive is provided.

Implementing a Healthy Incentives Program in Harris County could increase fruit and vegetable purchases and improve health. By the end of 2027:
An infographic about the obesity and financial benefits of the fruit and vegetable incentive.

Conclusions and Implications

Providing SNAP participants with an incentive of $0.30 for every $1 of SNAP benefits that they spend on targeted fruits and vegetables in SNAP-authorized grocery stores and farmers markets could have a positive impact on health of the SNAP participants. This strategy could increase purchases of fruits and vegetables by more than 3 servings per week per person (a 36% increase)10 and promote better diet quality among participants. While not quantified here, this strategy may also increase fruit and vegetable consumption among children in households participating in SNAP.

A program incentivizing the purchase of fruits and vegetables among SNAP participants would require an investment of $479 per adult. For every $1.00 invested in implementing a Healthy Incentives Program, $0.03 in obesity-related health care costs would be saved. This strategy would prevent 393 cases of obesity in Harris County in 2027. Evidence is growing about how food pricing and incentive programs may help promote healthy diets and reduce the prevalence of obesity. These findings reinforce the importance of investing in preventive efforts that could have widespread community impact.

References

  1. Flegal, K.M., Kruszon-Moran, D., Carroll, M.D., Fryar, C.D., Ogden, C.L. (2016). Trends in Obesity Among Adults in the United States, 2005 to 2014. JAMA, 315(21), 2284-91.
  2. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. (2009). Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates. Health Affairs. 28(5).
  3. U.S. Department of Health and Human Services. (2010). Healthy people 2020 (2nd ed.). Washington, DC: U.S. Government Printing Office. U.S. Department of Agriculture and U.S. Department of Health and Human Services. (2010). Dietary guidelines for Americans, 2010 (7th ed.). Washington, DC: U.S. Government Printing Office.
  4. Texas Department of State Health Services. Texas Behavioral Risk Factor Surveillance System. 2014
  5. Texas Health and Human Services Commission. (2019)
  6. Grummon, A., Taillie L. (2017). Nutrition profile of Supplemental Nutrition Assistance Program household food and beverages purchases. Am J Clin Nutr, 105(6). 1433-1442.
  7. Andreyeva T., Tripp A., Schwartz, M. (2015). Dietary Quality of Americans by Supplemental Nutrition Assistance Program Participation Status: A Systematic Review. Am J Prev Med. 49(4):594-604.
  8. Sturm R, An R, Segal D, Patel D. (2013). A cash-back rebate program for healthy food purchases in South Africa: results from scanner data. Am J Prev Med. 44(6):567-572.
  9. Gittelsohn J, Trude ACB, Kim H. Pricing Strategies to Encourage Availability, Purchase, and Consumption of Healthy Foods and Beverages: A Systematic Review. Prev Chronic Dis 2017;14:170213. DOI: https://doi.org/ 10.5888/pcd14.170213.
  10. Zhang, F. F., Liu, J., Rehm, C. D., Wilde, P., Mande, J. R., & Mozaffarian, D. (2018). Trends and disparities in diet quality among US adults by Supplemental Nutrition Assistance Program participation status. JAMA network open, 1(2), e180237-e180237.
Suggested Citation:

Flax C, Barrett J, Cradock A. Houston Healthy Incentives Program within the Supplemental Nutrition Assistance Program in Harris County, TX [Issue Brief]. Houston Health Department, Houston, TX and the CHOICES Learning Collaborative Partnership at the Harvard T.H. Chan School of Public Health, Boston, MA; December 2019.

This issue brief was developed at the Harvard T.H. Chan School of Public Health in collaboration with the Houston Health Department through participation in the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) Learning Collaborative Partnership. This brief is intended for educational use only.

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Report: Utah: Sugary Drink Tax

Sugary drinks

The information in this brief is intended to provide educational information on the cost-effectiveness of sugary drink excise taxes.

Executive Summary

Continually rising rates of obesity represent one of the greatest public health threats facing the United States. Obesity has been linked to excess consumption of sugary drinks. Federal, state, and local governments have considered implementing excise taxes on sugary drinks to reduce consumption, reduce obesity, and provide a new source of government revenue.1-4

We modeled implementation of a state excise tax on sugary drinks, at a tax rate of $0.02/ounce. Powdered drink mixes were modeled at a tax rate of $0.0025 per reconstituted fluid ounce according to the package instructions.

The tax modeled is projected to be cost-saving and result in lower levels of sugary drink consumption, thousands of cases of obesity prevented, and hundreds of millions of dollars in health care cost savings. For every dollar invested, this tax is projected to save $28.88 in health care costs.

Background 

Although sugary drink consumption has declined in recent years, adolescents and young adults in the United States consume more sugar than the Dietary Guidelines for Americans 2015-2020 recommends, with persistent racial and ethnic disparities.5-9 According to recent estimates, 27% of adults and 14% of youth in Utah drink at least one soda or other sugary drink per day.10,11 Public health researchers have suggested that excess intake of sugary drinks may be one of the single largest drivers of the obesity epidemic in the U.S.12 An estimated 26% of adults13 and 10% of youth14 in Utah have obesity.

Consumption of sugary drinks increases the risk of chronic diseases through changes in body mass index (BMI), insulin regulation, and other metabolic processes.15-17 Randomized intervention trials and longitudinal studies have linked increases in sugary drink consumption to excess weight gain, diabetes, cardiovascular disease, and other health risks.15-16 There are persistent racial and ethnic disparities across both sugary drink consumption levels and rates of obesity and chronic disease.5-8 In light of this evidence, the Dietary Guidelines for Americans 2015-20209 recommends that individuals limit sugary drink intake in order to manage their body weight and reduce their risk of chronic disease.

Targeted taxation has emerged as one recommended strategy to reduce consumption of sugary drinks.18 This strategy has been studied by public health experts, who have drawn on the success of tobacco taxation and decades of economic research to model the estimated financial and health impact of a sugary drink excise tax.19-22 Sugary drinks include all drinks with added caloric sweeteners. Proposed and enacted sugary drink excise taxes typically do not apply to 100% juice, milk products, or diet drinks. This report aims to model the projected effect of a sugary drink excise tax on projected health and disease outcomes over the next decade.

Modeling Framework: How excise taxes can lead to better health

State excise tax is linked to change in BMI through change in sugary drink price and consumption

Logic Model for Sugar Sweetened Beverage Tax

 

Key Terms
  • Excise tax: a consumption tax collected from retailers or distributors; it is reflected in the posted price (a sales tax in contrast is applied after purchase of the item)
  • Pass-through rate: how much of the excise tax on distributors is passed on to consumers as an increase in shelf price; a percent ranging from 0% (none of the tax) to 100% (all the tax)
  • Price elasticity of demand: how much consumer purchasing behavior changes following a change in price of an item
How does an excise tax work?

An infographic displaying the connection between tax, price, and demand.

How does an excise tax on distributors affect the price paid by consumers?

The first portion of the logical model above, highlighting excise tax and drink price.Since the cost of a sugary drink excise tax is incorporated directly into the beverage’s sticker price, an excise tax will likely influence consumer purchasing decisions more than a comparable sales tax that is added onto the item at the register. We assume 100% pass-through of the tax over 10 years and assume the tax rate would be adjusted annually for inflation. Our pass-through rate estimate is supported by empirical studies of excise taxes in Mexico and France that demonstrate near-complete pass-through rates to consumers.23 Short term studies for the local tax in Berkeley, CA indicate imperfect (or less than 100%) pass-through.3,24,25 More recent analyses from Philadelphia, PA indicate that over all the sales studied, pass-through was close to complete. The pass-through seen in pharmacies was higher than in supermarkets and mass merchandise settings.26 The expected change in sugary drink price was estimated using an average of $0.06/ounce based on national sugary drink prices.27 The price per ounce in this study was based on a weighted average of sugary drink consumption across stores, restaurants, and other sources according to the estimates from the National Health and Nutrition Examination Survey (NHANES) 2009-2010. The price per ounce of sugary drinks purchased in stores was calculated using weighted averages of two-liter bottles, 12-can cases, and single-serve containers based on 2010 Nielsen Homescan data.27 For example, a $0.02/ounce tax would raise the price of a 12-ounce can of soda from $0.72 to $0.96/can post-tax.

The middle portion of the Logic Model above, highlighting drink price and and drink consumption.How does increasing the price of sugary drinks change individual sugary drink consumption?

We used national estimates of sugary drink consumption from NHANES 2011-2016 adjusted to local race- and ethnicity-specific estimates of adult sugary drink consumption from the Utah Behavioral Risk Factor Surveillance System28 and youth sugary drink consumption from the Youth Behavioral Risk Surveillance System10 to estimate current sugary drink consumption levels in Utah. The mean own-price elasticity of demand for sugar-sweetened soft drinks (not including diet) is -1.21.29 The own-price elasticity reflects how much consumers will change their purchases in response to price changes. For example, an elasticity of -1.21 estimates that a 16% price increase would lead to a 19% reduction in purchases. Recent research on the Berkeley, CA tax found a 21% reduction in sugary drink intake among low-income populations consistent with this estimate.24

The final part of the Logic Model, highlighting drink consumption and BMI.

What are the individual health effects of decreasing sugary drink consumption?

Research has shown that decreasing sugary drink consumption can have positive effects on health in adults and youth. We conducted evidence reviews for the impact of a change in sugary drink intake on BMI, accounting for dietary compensation.22 Four large, multi-year longitudinal studies in adults16,30-32 were identified. The relationship was modeled using a uniform distribution based on the range of estimated effects on BMI due to reducing sugary drink intake; a one-serving reduction was associated with a BMI decrease of 0.21 kg/m2 to 0.57 kg/m2 in adults over a 3-year period. Among youth, a double-blind randomized controlled trial conducted over 18 months found that an additional 8-ounce serving of sugary drinks led to a 2.2 pound greater weight gain.33

Reach

The intervention applies to all youth and adults in Utah. However, the model estimates the health effects on those 2 years of age and older.*

*BMI z-scores were used in our analyses, which are not defined for children under 2 years of age.

Implementation Costs

We assume the tax will incur start-up and ongoing labor costs for tax administrators in the Utah State Tax Commission.34 To implement the intervention, the Utah State Tax Commission would need to process tax statements and conduct audits. Businesses would also need to prepare tax statements and participate in audits, which would require labor from private tax accountants. Cost information was drawn from localities with planned or implemented excise taxes on sugary drinks.22 The cost and benefit estimates do not include expected tax revenue.

CHOICES Microsimulation Model

The CHOICES microsimulation model for Utah was used to calculate the costs and effectiveness over 10 years (2017–27). Cases of obesity prevented were calculated at the end of the model period in 2027. The model was based on prior CHOICES work,22,35-37 and created a virtual population of Utah residents using data from: U.S. Census, American Community Survey, Behavioral Risk Factor Surveillance System, NHANES, National Survey of Children’s Health,37 the Medical Expenditure Panel Survey, multiple national longitudinal studies, and obesity prevalence data provided by Utah Department of Health. Using peer-reviewed methodology, we forecasted what would happen to this virtual population with and without a sugary drink tax to model changes in disease and mortality rates, and health care costs due to the tax.

An infographic outline the study's basic methodology.

 

Results: $0.02/ounce State Excise Tax on Sugary Drinks

Overall, the model shows that a sugary drink excise tax is cost-saving. Compared to the simulated natural history without a tax, the tax is projected to result in lower levels of sugary drink consumption, fewer cases of obesity, fewer deaths, and $132 million dollars in health care savings over the 10-year period under consideration.

The estimated reduction in obesity attributable to the tax leads to lower projected health care costs, offsetting tax implementation costs and resulting in net cost savings. The difference between total health care costs with no intervention and lower health care costs with an intervention represents health care costs saved; these savings can be compared to the cost of implementing the tax to arrive at the metric of health care costs saved per $1 invested.

The CHOICES microsimulation model does not include annual revenue generation from a state excise tax on sugary drinks in any of the cost-effectiveness calculations. Revenue is likely substantial. The Rudd Center Revenue Calculator for Sugary Drink Taxes estimates potential annual revenues from excise taxes on sugary drinks only and is “intended to provide a rough estimate” for municipalities to consider.38 According to the Rudd Center,38 a $0.02/ounce excise tax in Utah could raise as much as $181 million in 2019. Actual tax revenue may be lower than these projected estimates; the Rudd Center advises to adjust the revenues down by 10-30% to account for non-compliance. These would result in annual revenues of $127 – $163 million.

An infographic outlining the results of the 2 cent excise tax on sugary drinks.

 

Outcome $0.02/ounce excise tax
Mean
(95% uncertainty interval)
10 Year Reach* 3,430000
(3,420,000; 3,440,000)
First Year Reach* 2,950,000
(2,940,000; 2,940,000)
Decrease in 12-oz Servings of Sugary Drinks per Person in the First Year* 90
(55; 159)
Mean Reduction in BMI Units per Person* -0.176
(-0.330; -0.090)
10 Year Intervention Implementation Cost per Person $1.33
($1.30; $1.36)
Total Intervention Implementation Cost Over 10 Years $4.56 million
($4.48 million; $4.65 million)
Annual Intervention Implementation Cost $456,000
($448,000; $465,000)
Health Care Costs Saved Over 10 Years $132 million
($59.6 million; $271 million)
Net Costs Difference Over 10 Years -$127 million
(-$267 million; -$55.1 million)
Quality Adjusted Life Years (QALYs) Gained Over 10 Years 5,410
(2,470; 11,200)
Years of Life Gained Over 10 Years 1,190
(473; 2,750)
Deaths Prevented Over 10 Years* 354
(141; 791)
Years with Obesity Prevented Over 10 Years 137,000
(69,700; 259,000)
Health Care Costs Saved per $1 Invested Over 10 Years $28.88
($13.07; $58.76)
Cases of Obesity Prevented in 2027* 19,600
(9,900; 36,700)
Cases of Childhood Obesity Prevented in 2027* 2,670
(1,110; 6,150)
Cost per Year with Obesity Prevented Over 10 Years Cost-saving
Cost per QALY Gained Over 10 Years Cost-saving
Cost per YL Gained Over 10 Years Cost-saving
Cost per Death Averted Over 10 Years Cost-saving

Uncertainty intervals are estimated by running the model 1,000 times, taking into account both uncertainty from data sources and virtual population projections, and calculating a central range in which 95% of the model results fell.

All metrics reported for the population over a 10-year period and discounted at 3% per year, unless otherwise noted.

*Not discounted.

Results: $0.02/ounce State Excise Tax on Sugary Drinks by Race and Ethnicity

Outcome White, Non-Hispanic
Mean
(95% uncertainty interval)
Other, Non-Hispanic 
Mean
(95% uncertainty interval)
Hispanic
Mean
(95% uncertainty interval)
Decrease in 12-oz Serving of Sugary Drinks per Person in the First Year* 85
(52; 151)
89
(55; 156)
119
(73; 209)
Reduction in Obesity Prevalence in 2027* 1.00** 1.02
(0.87; 1.22)
1.74
(1.52; 1.99)
Health Care Costs Saved Over 10 Years $98.1 million
($44.6; $201 million)
$7.46 million
($3.37; $15.6 million)
$26.3 million
($11.9; $54.9 million)
QALYS Gained Over 10 Years 4,030
(1,830; 8,330)
324
(146; 682)
1,060
(491; 2,180)
Years of Life Gained Over 10 Years 963
(371; 2,200)
69
(11; 170)
163
(50; 384)
Years with Obesity Prevented Over 10 Years 96,100
(48,600; 180,000)
9,410
(4,770; 17,700)
31,500
(15,900; 58,900)
Cases of Obesity Prevented in 2027* 13,800
(6,890; 25,900)
1,320
(675; 2,520)
4,430
(2,180; 8,300)
Cases of Childhood Obesity Prevented in 2027* 1,690
(686; 3,780)
298
(119; 691)
765
(297; 1,740)

Uncertainty intervals are estimated by running the model 1,000 times, taking into account both uncertainty from data sources and virtual population projections, and calculating a central range in which 95 percent of the model results fell.

All metrics reported for the population over a 10-year period and discounted at 3% per year, unless otherwise noted.

*Not discounted.

*Reference category.

Pre-tax Sugary Drink Consumption in Utah by Race and EthnicityA bar graphing showing the Pre-tax Sugary Drink Consumption in Utah by Race and Ethnicity. White, Non-hispanic averages 7.7 ounces. Other, Non-Hispanic averages 8.0 ounces. Hispanic averages 10.7 ounces. The overall average is 8.1 ounces.

Pre-tax Obesity Prevalence in Utah by Race and Ethnicity

A bar graph of the Pre-tax Obesity Prevalence in Utah by Race and Ethnicity. White, Non-Hispanic shows 28.5%. Other, Non-White Hispanic shows 26.0%. Hispanic shows 33.3%. The overall average shows 28.9%.

Cases of Obesity Prevented in Utah in 2027*A graph showing the Cases of Obesity Prevented in Utah in 2027 if the 2 cent excise tax is implemented. Of the cases of obseity prevented in 2027 in Utah, 30% qualify as Other, including Hispanic. The Other, including Hispanic group comprises 21% of the total Utah population.

Decrease in Sugary Drink Consumption by Race and Ethnicity*A graph of the decrease in sugary drink consumption after the 2 cent excise tax. Each serving is considered 12 ounces. White, Non-Hispanic shows a decrease of 85 servings. Other, Non-Hispanic shows a decrease of 89 servings. Hispanic shows a decrease of 119 servings.

Impact on Diabetes

We estimated the impact of the tax-induced reduction in sugary drink intake on diabetes incidence for adults ages 18-79 years using a published meta-analysis of the relative risk of developing diabetes due to a one-serving change in sugary drink consumption39 as well as local estimates of diabetes. On average, each 8.5-ounce serving of sugary drinks per day increases the risk of diabetes by 18%.39
An infographic about diabetes prevention and dental health results after the 2 cent excise tax.

In Utah, we estimated that the proposed sugary drink excise tax would lead to a 7% reduction in diabetes incidence in the sugary drink tax model. Impact on diabetes incidence was calculated over one year once the tax reaches its full effect. Impact on diabetes was calculated based on summary results from the model, not directly via microsimulation.

Impact on Tooth Decay

We estimated the impact of a sugary drink excise tax on tooth decay cost using a longitudinal analysis of the relationship between intake of sugars and tooth decay in adults. On average, for every 10 grams higher intake of sugar per day, there is an increase in decayed, missing and filled teeth (DMFT) of approximately 0.10 over 10 years.40 As described above, we assume that the excise tax will result in a reduction in sugary drink intake. There are many studies showing a similar relationship between higher intake of sugars and tooth decay in children and youth41 and thus we assume the same relationship as found in adults.

We used the Bureau of Coverage and Reimbursement Policy Coverage and Reimbursement Fee Schedule42 data to estimate a Medicaid cost of treating DMFT as: $307.90 for a permanent crown and $45.54 for a filling. These codes reflect treatment for one surface and do not reflect higher reimbursement rates for multi-surface treatment, temporary crowns, or potential flat tax schedules. Based on analysis of data on tooth decay, fillings and crowns for the U.S. population from NHANES 1988-1994 (the last year crowns and fillings were separately reported),43 we estimate that 78.9% of tooth decay in children and 43.5% of tooth decay in adults is treated. Using this same data set, we estimate that 97% of treatment for children is fillings and 82.5% of treatment for adults is fillings.

To estimate Medicaid-specific dental caries cost savings, we used local estimates of the number of people enrolled in Medicaid and the proportion of people receiving Medicaid dental services. Because of limited Medicaid dental coverage for adults in Utah, only children are included in the Medicaid-specific calculations. In Utah, we estimated that a $0.02/ounce tax would lead to a total of $464,000 in Medicaid savings over a period of 10 years due to a reduction in treatment of DFMT. The Medicaid reimbursement tax estimates may underestimate the total cost savings of tooth decay treatment projected here as dental providers may charge higher amounts to patients.

Considerations for Health Equity

An infographic about individual and household savings on sugary drinks after the excise tax..Concerns have been raised regarding the impact of the tax on low-income households. For many goods, including cigarettes, low-income households are more price-sensitive than high-income peers. If this is also true for low-income sugary drink consumers, these households would spend less on sugary drinks after the tax goes into effect, which would free up disposable income for other consumer purchases.44 Using sales data from the Rudd Center Revenue Calculator for Sugary Drink Taxes,38 we project that individuals and households in Utah will spend less money on sugary drinks after the tax is implemented.

In addition, low-income consumers, on average, consume more sugary drinks than higher-income consumers. We project that greater health benefits from this policy will accrue to low-income consumers. We also project that greater health benefits will accrue among Hispanic Utah residents compared with non-Hispanic residents of white or other race. Using data from NHANES and Utah on sugary drink consumption in the CHOICES model, the average daily consumption of sugary drinks by people in Utah varies by race and ethnic group (see pre-tax graphs above). Under the proposed tax, Hispanic Utahns will experience a 74% greater reduction in obesity prevalence compared to White non-Hispanic residents. On that basis, the proposed tax could decrease disparities in obesity outcomes.

Implementation Considerations

Revenue raised from a sugary drink tax could also be reinvested in low-income communities. For instance, in Berkeley, CA, sugary drink tax revenue has been allocated for spending on school and community programs to promote healthy eating, diabetes, and obesity prevention; many serve low-income or minority populations.45,46 Public support for such taxes generally increases with earmarking for preventive health activities.46

There is opposition from the food and beverage industry, which spends billions of dollars promoting their products.47 Relatively small beverage excise taxes are currently applied across many states. The proposed tax is likely to be sustainable if implemented based on the successful history of tobacco excise taxes. There is potential for a shift in social norms of sugary drink consumption based on evidence from tobacco control tax and regulatory efforts.48 This shift in norms can be facilitated by taxing sugary drinks, which reduces the attractiveness of non-caloric drink options and discourages consumers from selecting any sugary drink options when making beverage decisions.

Conclusion

We project that implementation of a state excise tax on sugary drinks only, at a tax rate of $0.02/ounce, will prevent thousands of cases of childhood and adult obesity, prevent new cases of diabetes, increase healthy life years, and save more in future health care costs than it will cost to implement. Implementing the tax could also serve as a powerful social signal to reduce sugar consumption.

Results prepared by the Salt Lake County Health Department and the CHOICES Project Team at the Harvard T.H. Chan School of Public Health: McKinnon A, Ward Z, Barrett J, Cradock A, Resch S, Flax C, and Gortmaker S. December 2019. Funded by The JPB Foundation. Results are those of the authors and not the funders. For further information, contact choicesproject@hsph.harvard.edu

Appendices

Appendices A and B showing the health effects of a $0.02/ounce excise tax on sugary drinks in Salt Lake County, UT, may be viewed starting on page 15 in the full report PDF.

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References

  1. American Public Health Association Taxes on Sugar-Sweetened Beverages. 2012.
  2. Hakim D, Confessore N. Paterson seeks huge cuts and $1 billion in taxes and fees. New York Times. January 19, 2010.
  3. Falbe J, Rojas N, Grummon AH, Madsen KA. Higher Retail Prices of Sugar-Sweetened Beverages 3 Months After Implementation of an Excise Tax in Berkeley, California. American Journal of Public Health. 2015;105(11):2194-2201.
  4. Leonhardt D. The battle over taxing soda. The New York Times. May 19, 2010.
  5. Wang YC, Bleich SN, Gortmaker SL. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among US children and adolescents, 1988-2004. Pediatrics. 2008;121(6):E1604-E1614.
  6. Bleich SN, Wang YC, Wang Y, Gortmaker SL. Increasing consumption of sugar-sweetened beverages among US adults: 1988-1994 to 1999-2004. American Journal of Clinical Nutrition. 2009;89(1):372-381.
  7. Kit BK, Fakhouri THI, Park S, Nielsen SJ, Ogden CL. Trends in sugar-sweetened beverage consumption among youth and adults in the United States: 1999-2010. American Journal of Clinical Nutrition. 2013;98(1):180-188.
  8. Bleich SN, Vercammen KA, Koma JW, Li ZH. Trends in Beverage Consumption Among Children and Adults, 2003-2014. Obesity. 2018;26(2):432-441.
  9. U.S. Department of Health and Human Services, U.S. Department of Agriculture. 2015 – 2020 Dietary Guidelines for Americans. December 2015.
  10. Centers for Disease Control and Prevention (CDC), Utah Department of Health. Youth Risk Behavior Surveillance System. 2017.
  11. Park S, Xu F, Town M, Blanck H. Prevalence of Sugar-Sweetened Beverage Intake Among Adults – 23 States and the District of Columbia, 2013. MMWR Morb Mortal Wkly Report 2016. 2016;65(7):169-174.
  12. Brownell KD, Frieden TR. Ounces of Prevention – The Public Policy Case for Taxes on Sugared Beverages. New England Journal of Medicine. 2009;360(18):1805-1808.
  13. Utah Department of Health. (2018). Public Health Indicator Based Information System (IBIS) Utah’s Public Health Data Source. Health Indicator of Overweight or Obese. https://ibis.health.utah.gov/ibisph-view/indicator/view/OvrwtObe.UT_US.html
  14. Utah Department of Health. (2018). Public Health Indicator Based Information System (IBIS) Utah’s Public Health Data Source. Health Indicator of Obese Among Children and Adolescents. https://ibis.health.utah.gov/ibisph-view/indicator/view/OvrwtChild.LHD.html
  15. Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. American Journal of Clinical Nutrition. 2013;98(4):1084-1102.
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