Advertisement

Projecting the Influence of Sugar-Sweetened Beverage Warning Labels and Restaurant Menu Labeling Regulations on Energy Intake, Weight Status, and Health Care Expenditures in US Adults: A Microsimulation

Published:August 02, 2021DOI:https://doi.org/10.1016/j.jand.2021.05.006

      Abstract

      Background

      Accurate, readily accessible, and easy-to-understand nutrition labeling is a promising policy strategy to address poor diet quality and prevent obesity.

      Objective

      This study projected the influence of nationwide implementation of sugar-sweetened beverage (SSB) warning labels and restaurant menu labeling regulations.

      Design

      A stochastic microsimulation model was built to estimate the influences of SSB warning labels and menu labeling regulations on daily energy intake, body weight, body mass index, and health care expenditures among US adults.

      Participants/setting

      The model used individual-level data from the National Health and Nutrition Examination Survey, Medical Expenditure Panel Survey, and other validated sources.

      Statistical analyses performed

      The model was simulated using the bootstrapped samples, and the means and associated 95% CIs of the policy effects were estimated.

      Results

      SSB warning labels and restaurant menu labeling regulations were estimated to reduce daily energy intake by 19.13 kcal (95% CI 18.83 to 19.43 kcal) and 33.09 kcal (95% CI 32.39 to 33.80 kcal), body weight by 0.92 kg (95% CI 0.90 to 0.93 kg) and 1.57 kg (95% CI 1.54 to 1.60 kg), body mass index by 0.32 (95% CI 0.31 to 0.33) and 0.55 (95% CI =0.54 to 0.56), and per-capita health care expenditures by $26.97 (95% CI $26.56 to $27.38) and $45.47 (95% CI $44.54 to $46.40) over 10 years, respectively. The reduced per-capita health care expenditures translated into an annual total medical cost saving of $0.69 billion for SSB warning labels and $1.16 billion for menu labeling regulations. No discernable policy effect on all-cause mortality was identified. The policy effects could be heterogeneous across population subgroups, with larger effects in men, non-Hispanic Black adults, and younger adults.

      Conclusions

      SSB warning labels and menu labeling regulations could be effective policy leverage to prevent weight gains and reduce medical expenses attributable to adiposity.

      Keywords

      The Continuing Professional Education (CPE) quiz for this article is available for free to Academy members through the MyCDRGo app (available for iOS and Android devices) and through www.jandonline.org (click on “CPE” in the menu and then “Academy Journal CPE Articles”). Log in with your Academy of Nutrition and Dietetics or Commission on Dietetic Registration username and password, click “Journal Article Quiz” on the next page, then click the “Additional Journal CPE quizzes” button to view a list of available quizzes. Non-members may take CPE quizzes by sending a request to [email protected] There is a $45 fee per quiz (includes quiz and copy of article) for non-members. CPE quizzes are valid for 3 years after the issue date in which the articles are published.
      Research Question: What are the estimated policy impacts of nationwide implementation of sugar-sweetened beverage warning labels and menu labeling regulations applied to chain restaurants?
      Key Findings: Sugar-sweetened beverage warning labels and restaurant menu labeling regulations were estimated to reduce daily energy intake by 19.13 and 33.09 kcal, body weight by 0.92 and 1.57 kg, body mass index by 0.32 and 0.55, and per-capita health care expenditures by $26.97 and $45.47 over 10 years, respectively. The reduced per-capita health care expenditures translated into an annual total medical cost saving of $0.69 billion for sugar-sweetened beverage warning labels and $1.16 billion for menu labeling regulations. The policy effects could be heterogeneous, with larger effects in men, non-Hispanic Black adults, and younger adults.
      The obesity epidemic has continued to worsen in the United States over the past few decades, despite growing public awareness of the problem.
      • An R.
      Prevalence and trends of adult obesity in the US, 1999-2012.
      The obesity rate among US adults increased from 30.5% in 1999-2000 to 42.4% in 2017-2018.
      • Hales C.M.
      • Carroll M.D.
      • Fryar C.D.
      • Ogden C.L.
      Prevalence of Obesity And Severe Obesity Among Adults: United States, 2017-2018.
      Mounting evidence shows that obesity increases the risk of noncommunicable chronic diseases, such as type 2 diabetes, cardiovascular disease, and certain types of cancer,
      • Pi-Sunyer X.
      The medical risks of obesity.
      and is associated with an estimated 2 to 20 years of reduced life expectancy.
      • Fontaine K.R.
      • Redden D.T.
      • Wang C.
      • Westfall A.O.
      • Allison D.B.
      Years of life lost due to obesity.
      ,
      Prospective Studies Collaboration
      Body mass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies.
      The physical toll of obesity is accompanied by the substantial economic burden on individuals, families, and society, which was estimated to be 9.3% of the nation’s gross domestic product. Obesity was declared a chronic disease by the World Obesity Federation and other authoritative health organizations after decades of controversy.
      • Bray G.A.
      • Kim K.K.
      • Wilding J.P.H.
      World Obesity Federation
      Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation.
      At the population level, obesity is believed to be the consequence of a chronic energy imbalance in calorie intake and expenditure.
      • Hill J.O.
      • Wyatt H.R.
      • Peters J.C.
      Energy balance and obesity.
      ,
      • Romieu I.
      • Dossus L.
      • Barquera S.
      • et al.
      Energy balance and obesity: what are the main drivers?.
      However, individual-level weight-loss interventions are frequently unsuccessful in the long term, likely due to lacking supportive policies in the environment.
      • Swinburn B.A.
      • Sacks G.
      • Hall K.D.
      • et al.
      The global obesity pandemic: shaped by global drivers and local environments.
      The World Health Organization acknowledges that population-based policies that make healthier dietary choices available are among the fundamental strategies in supporting individual behavioral changes.
      World Health Organization
      Obesity and overweight.
      Despite the recognition of the importance of government actions in combating obesity, few obesity prevention policies have been implemented in the United States. Related to this void is a lack of consensus on effective policy strategies.
      • Gortmaker S.L.
      • Swinburn B.A.
      • Levy D.C.
      • et al.
      Changing the future of obesity: science, policy, and action.
      A low-cost but potentially controversial form of nutrition labeling is sugar-sweetened beverage (SSB) warning labels. SSBs are a primary source of added sugars in the American diet. About half of US adults drink SSBs on a given day, with an average consumption of 145 kcal from SSBs daily.
      • Rosinger A.
      • Herrick K.
      • Gahche J.
      • Park S.
      Sugar-Sweetened Beverage Consumption Among US Youth, 2011-2014.
      SSB consumption is closely linked to obesity and other adverse health conditions (eg, diabetes and dental decay) from large-scale epidemiologic studies.
      • Malik V.S.
      • Popkin B.M.
      • Bray G.A.
      • Després J.P.
      • Hu F.B.
      Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk.
      • Bleich S.N.
      • Vercammen K.A.
      The negative impact of sugar-sweetened beverages on children’s health: an update of the literature.
      • Malik V.S.
      • Schulze M.B.
      • Hu F.B.
      Intake of sugar-sweetened beverages and weight gain: a systematic review.
      Over the past 5 years, multiple state legislatures have attempted to apply lessons learned from the tobacco warning labels to develop and implement warning labels for SSBs.
      • Popova L.
      Sugar-sweetened beverage warning labels: lessons learned from the tobacco industry.
      Both text- and image-based tobacco warning labels demonstrated effectiveness at educating consumers about smoking risks and reducing tobacco consumption, likely through increased consumer knowledge about health risks of smoking and negative emotional responses to the warnings.
      • Ngo A.
      • Cheng K.W.
      • Shang C.
      • Huang J.
      • Chaloupka F.J.
      Global evidence on the association between cigarette graphic warning labels and cigarette smoking prevalence and consumption.
      ,
      • Hammond D.
      • Fong G.T.
      • McNeill A.
      • Borland R.
      • Cummings K.M.
      Effectiveness of cigarette warning labels in informing smokers about the risks of smoking: findings from the International Tobacco Control (ITC) Four Country Survey.
      The legislation has met with resistance from the food and beverage industry.
      • Popova L.
      Sugar-sweetened beverage warning labels: lessons learned from the tobacco industry.
      In February 2014, the Sugar-Sweetened Beverages Safety Warning Act was introduced in California. The Act proposed to prohibit the sales of SSBs in a sealed beverage container or as a multipack unless carrying a prescribed safety warning message about the adverse health risks of SSB consumption.
      California Legislative Information
      SB-1000 public health: sugar-sweetened beverages: safety warnings.
      However, this bill failed to advance. Another attempt to implement the SSB warning labels occurred in early 2019 with the reintroduction of the Sugar-Sweetened Beverages Safety Warning Act, but to date, this bill had not yet been passed into law.
      California Legislative Information
      SB-347 sugar-sweetened beverages: safety warnings.
      Similar bills have been introduced in other states (eg, New York, Vermont, Hawaii, and Washington), but none have been passed so far.
      • VanEpps E.M.
      • Roberto C.A.
      The influence of sugar-sweetened beverage warnings: a randomized trial of adolescents’ choices and beliefs.
      Nevertheless, emerging literature suggests that SSB warning labels could be effective in reducing SSB purchases and consumption.
      • VanEpps E.M.
      • Roberto C.A.
      The influence of sugar-sweetened beverage warnings: a randomized trial of adolescents’ choices and beliefs.
      • Roberto C.A.
      • Wong D.
      • Musicus A.
      • Hammond D.
      The influence of sugar-sweetened beverage health warning labels on parents’ choices.
      • Bollard T.
      • Maubach N.
      • Walker N.
      • Ni Mhurchu C.
      Effects of plain packaging, warning labels, and taxes on young people’s predicted sugar-sweetened beverage preferences: an experimental study.
      • Mantzari E.
      • Pechey R.
      • Codling S.
      • Sexton O.
      • Hollands G.J.
      • Marteau T.M.
      The impact of ‘on-pack’ pictorial health warning labels and calorie information labels on drink choice: a laboratory experiment.
      • Acton R.B.
      • Hammond D.
      The impact of price and nutrition labelling on sugary drink purchases: results from an experimental marketplace study.
      • Acton R.B.
      • Jones A.C.
      • Kirkpatrick S.I.
      • Roberto C.A.
      • Hammond D.
      Taxes and front-of-package labels improve the healthiness of beverage and snack purchases: a randomized experimental marketplace.
      • Grummon A.
      • Taillie L.
      • Golden S.
      • Hall M.
      • Ranney L.
      • Brewer N.
      Sugar-sweetened beverage health warnings and purchases: a randomized controlled trial.
      • Caro J.C.
      • Corvalan C.
      • Reyes M.
      • Silva A.
      • Popkin B.
      • Taillie L.S.
      Chile’s 2014 sugar-sweetened beverage tax and changes in prices and purchases of sugar-sweetened beverages: an observational study in an urban environment.
      These studies, conducted in a lab, online, or naturalistic environment, found that warning labels changed participants’ perceptions of SSB products, raised awareness of the health risks, and modified their purchase preferences.
      Critical drivers of increased energy intake include a food system that has produced massive, highly processed foods and marketed them effectively.
      • Gortmaker S.L.
      • Swinburn B.A.
      • Levy D.C.
      • et al.
      Changing the future of obesity: science, policy, and action.
      Nutrition labels that contain accurate, readily accessible, and easy-to-understand nutritional and energy information are a simple, low-cost, potentially effective way to counteract the obesogenic food environment.
      • Roberto C.A.
      • Khandpur N.
      Improving the design of nutrition labels to promote healthier food choices and reasonable portion sizes.
      The presumption is that nutrition information will guide individuals to select healthier foods and consume the appropriate amount of energy for weight management.
      • Dumoitier A.
      • Abbo V.
      • Neuhofer Z.T.
      • McFadden B.R.
      A review of nutrition labeling and food choice in the United States.
      Although nutrition labeling has been mandated on packaged foods in the United States for decades, it was not generally made available to consumers for foods in chain restaurants and similar retail food establishments until very recently.
      US Food and Drug Administration
      Menu labeling requirments.
      Americans consume more than one-third of their calories away from home, and frequently eating out is associated with increased energy intake.
      • Cohen D.A.
      • Story M.
      Mitigating the health risks of dining out: the need for standardized portion sizes in restaurants.
      ,
      US Dept of Agriculture
      America’s eating habits: food away from home.
      The federal menu labeling provisions of Section 4205 of the Patient Protection and Affordable Care Act required certain chain restaurants and other similar retail food establishments with 20 or more locations to offer energy and other nutrition information for menu items.
      US Dept of Health and Human Services
      Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments.
      This requirement, finalized by the Food and Drug Administration (FDA), took effect May 7, 2018.
      US Dept of Health and Human Services
      Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments.
      According to four systematic reviews and meta-analyses, evidence on the effectiveness of the FDA menu labeling has been mixed, with an overall small reduction in calories ordered or purchased per meal under some contexts and the effect may differ by individual sociodemographics.
      • Long M.W.
      • Tobias D.K.
      • Cradock A.L.
      • Batchelder H.
      • Gortmaker S.L.
      Systematic review and meta-analysis of the impact of restaurant menu calorie labeling.
      • Sinclair S.E.
      • Cooper M.
      • Mansfield E.D.
      The influence of menu labeling on calories selected or consumed: a systematic review and meta-analysis.
      • VanEpps E.M.
      • Roberto C.A.
      • Park S.
      • Economos C.D.
      • Bleich S.N.
      Restaurant menu labeling policy: review of evidence and controversies.
      • Bleich S.N.
      • Economos C.D.
      • Spiker M.L.
      • et al.
      A systematic review of calorie labeling and modified calorie labeling interventions: impact on consumer and restaurant behavior.
      Dietary habits and obesity risk differ substantially across population subgroups. For instance, on average, men and younger adults consume more calories per day than women and older adults.
      US Dept of Health and Human Services and US Department of Agriculture
      2015-2020 Dietary Guidelines for Americans. 8th Edition.
      Young adults and non-Hispanic Black adults consume more SSBs and chain restaurant foods than older adults and non-Hispanic White adults.
      • Lundeen E.A.
      • Park S.
      • Pan L.
      • Blanck H.M.
      Daily intake of sugar-sweetened beverages among us adults in 9 states, by state and sociodemographic and behavioral characteristics, 2016.
      ,
      • Fryar C.D.
      • Hughes J.P.
      • Herrick K.A.
      • Ahluwalia N.
      Fast Food Consumption Among Adults in the United States, 2013-2016.
      The obesity prevalence among non-Hispanic Black and Hispanic adults is higher than among non-Hispanic White and Asian adults.
      • Petersen R.
      • Pan L.
      • Blanck H.M.
      Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action.
      Given those profound sociodemographic disparities, it is likely that SSB warning labels and menu labeling regulations exert heterogeneous influences on population subgroups.
      The present study aimed to project the policy impacts of a nationwide adoption of SSB warning labels and menu-labeling regulations in US adults. To our knowledge, this study is the first head-to-head comparison of the projected impacts of two promising nutrition labeling strategies on the national scale. We focused on these two policy strategies because they were low-cost, scalable, and potentially effective, making them ideal candidates for population-level obesity interventions. The projected impacts of the two nutrition labeling policies on daily energy intake, body weight status, and health care expenditures were estimated using a microsimulation model, based on individual-level data from two nationally representative surveys and other validated data sources. The strength of this systems science approach was that it used real rather than simulated individual-level data. We estimated the policy effects specific to population subgroups by sex, age group, and race/ethnicity. Findings from this study could facilitate evidence building concerning policy leverages for reducing obesity and fueling the momentum to promote a healthier food environment.

      Materials and Methods

      Overview of Microsimulation Model

      We used stochastic microsimulation modeling to estimate the effects of nationwide implementation of SSB warning labels and menu labeling regulations on daily energy intake, body weight, body mass index (BMI), and health care expenditures. Microsimulation is a systems science approach that examines behaviors and outcomes resulting from interactions among multiple system components over time.
      • Maglio P.P.
      • Sepulveda M.J.
      • Mabry P.L.
      Mainstreaming modeling and simulation to accelerate public health innovation.
      ,
      • Schofield D.J.
      • Zeppel M.
      • Tan O.
      • Lymer S.
      • Cunich M.
      • Shrestha R.
      A brief, global history of microsimulation models in health: past applications, lessons learned and future directions.
      Originated from applications to problems in the physical sciences,
      • Santow G.
      Microsimulation in demographic research.
      the microsimulation model has been increasingly applied to other areas primarily due to its capacity to integrate large amounts of data from diverse sources and model complex interactions among determinants of policy outcomes at different levels, such as macro-determinants (eg, demographic, social, political, or economic trends), institutional determinants (eg, government regulations or programs), local determinants (eg, neighborhood physical environment), and individual determinants (eg, personal characteristics, choices, or actions).
      • Rutter C.M.
      • Zaslavsky A.M.
      • Feuer E.J.
      Dynamic microsimulation models for health outcomes: a review.
      ,
      • Harland K.
      • Birkin M.
      Microsimulation modelling for social scientists.
      Microsimulation models are well positioned to simulate the health and social outcomes of a policy or phenomenon that has not manifested in a specific setting.
      • Harland K.
      • Birkin M.
      Microsimulation modelling for social scientists.
      ,
      • Belanger A.
      • Sabourin P.
      Microsimulation and Population Dynamics: An Introduction to Modgen 12.
      Figure 1 shows the flowchart of the microsimulation model. The model consisted of two experimental conditions (ie, SSB warning labels and menu labeling regulations) and one control condition (ie, without an intervention). Simulated study participants came from multiple waves of the National Health and Nutrition Examination Survey (NHANES). The three main outcomes were change in daily calorie intake, cumulative change in body weight and BMI, and cumulative change in health care expenditures. The model simulated a 10-year horizon, in which study participants were subject to an annual sex-, age-, and race/ethnicity-specific mortality risk moderated by their adiposity status. Under the two experimental conditions, study participants’ daily consumption of SSBs or chain restaurant foods decreased due to the policy interventions, resulting in a reduction in daily energy intake, and subsequently, body weight, BMI, and health care expenditures attributable to adiposity. Under the control condition, study participants’ daily consumption of SSBs or chain restaurant foods remain unchanged, and their body weight trajectory only evolved with age. Contrasting the outcomes between the experimental and the control conditions yielded the estimated effects of the policy interventions.
      Figure thumbnail gr1
      Figure 1Flowchart of the microsimulation model.

      Agents

      NHANES is a program of studies conducted by the National Center for Health Statistics to evaluate the nutritional and health status of children and adults.
      Centers for Disease Control and Prevention
      National Health and Nutrition Examination Survey.
      NHANES adopted a multistage probability sampling design to recruit a nationally representative sample of civilian, noninstitutionalized US children and adults, with oversampling of specific population subgroups. NHANES participants who satisfied the following criteria served as agents in the microsimulation model: being aged 18 years and older, not being pregnant at the time of the interview, having completed at least one of the two 24-hour dietary recalls, and being interviewed in the NHANES 2003-2018 waves.

      Daily Energy Intake from SSBs and Chain Restaurant Foods

      During the 2003-2018 waves, NHANES participants were administered two 24-hour dietary recalls. Both recalls asked about all food items and the associated quantities consumed from midnight to midnight the day before the interview. Trained dietary interviewers conducted the first recall interview in-person during the health examination in the mobile examination center. After the interview, participants were provided with measuring cups, spoons, a ruler, and a food model booklet containing two-dimensional drawings of the various measuring guides. The second recall was collected by telephone approximately 3 to 10 days after the mobile examination center exam. The calorie and nutrient contents of each food item were coded using the US Department of Agriculture Food and Nutrient Database for Dietary Studies (FNDDS). The NHANES dietary recall data recorded energy intake for each consumed food or beverage item based on the quantity of food/beverage reported and the corresponding energy contents.
      According to the Centers for Disease Control and Prevention and the Dietary Guidelines for Americans 2015-2020,
      US Dept of Health and Human Services and US Department of Agriculture
      2015-2020 Dietary Guidelines for Americans. 8th Edition.
      ,
      Centers for Disease Control and Prevention
      Get the facts: sugar-sweetened beverages and consumption.
      SSBs include sugar-sweetened carbonated beverages, fruit drinks, energy drinks, sports drinks, and sweetened bottled waters. By contrast, diet drinks are sugar-free, zero-calorie, or low-calorie drinks commonly sweetened by nonnutritive sweeteners.
      • Fakhouri T.H.I.
      • Kit B.K.
      • Ogden C.L.
      Consumption of Diet Drinks in the United States, 2009-2010.
      To identify SSB consumption and differentiate it from the consumption of diet drinks, we systematically reviewed all FNDDS codes used in An
      • An R.
      Beverage consumption in relation to discretionary food intake and diet quality among US Adults, 2003 to 2012.
      and the Center for Health Metrics and Evaluation of the American Heart Association.
      American Heart Association Center for Health Metrics and Evaluation
      Definitions—low calorie and sugar-sweetened beverages.
      We identified a total of 37 unduplicated FNDDS codes concerning SSBs and another 154 concerning diet drinks. We used those 37 FNDDS codes to classify SSB consumption in NHANES participants.
      Since 2002, the dietary interviews included a question on the source of each food or beverage item consumed (ie, where it was obtained, such as “store,” “restaurant with waiter/waitress,” “restaurant fast food/pizza,” “from someone else/gift,” “cafeteria at school,” or “cafeteria not at school”). Because the question was not asked in 2001, National Center for Health Statistics decided not to include its response data in the public version of NHANES 2001-2002 wave. Nevertheless, the data were released to the public for all subsequent waves. The dietary interviews also asked whether a food or beverage item was consumed at home or away from home. Following An
      • An R.
      Fast-food and full-service restaurant consumption and daily energy and nutrient intakes in US adults.
      and Powell and Nguyen,
      • Powell L.M.
      • Nguyen B.T.
      Fast-food and full-service restaurant consumption among children and adolescents: effect on energy, beverage, and nutrient intake.
      food items consumed at chain restaurants were defined as those obtained from “fast-food/pizza restaurant” and consumed away from home. We did not include restaurant foods consumed at home because takeout-food consumers might not be exposed to the FDA-mandated calorie labels.
      We averaged daily energy intake from SSBs and chain restaurant foods for each agent (ie, NHANES participant) on both 24-hour dietary recall days. For those who took only a single dietary recall, we used data of that recall day for analyses.

      Effect of SSB Warning Labels

      An and colleagues
      • An R.
      • Liu J.
      • Liu R.
      Impact of sugar-sweetened beverage warning labels on consumer behaviors: a systematic review and meta-analysis.
      conducted a systematic review and meta-analysis concerning the influence of SSB warning labels on consumer behaviors. SSB warning labels were classified into six categories: symbol with nutrient profile, symbol with health effect, text with nutrient profile, text of health effect, graphic with health effect, and graphic with nutrient profile. Compared with the no-label control group, overall SSB warning label use was associated with reduced SSB purchases (Cohen’s d = –0.18, 95% CI –0.31 to –0.06). Across alternative label categories, the graphic with health effect (odds ratio [OR] = 0.34), text of health effect (OR = 0.47), graphic with nutrient profile (OR = 0.58), and symbol with health effect (OR = 0.67) were associated with a reduced odds of SSB purchases. Grummon and Hall
      • Grummon A.H.
      • Hall M.G.
      Sugary drink warnings: a meta-analysis of experimental studies.
      conducted a meta-analysis on the experimental studies assessing the influence of SSB warning labels on beverage purchases. Fairly close to the effect in An and colleagues
      • An R.
      • Liu J.
      • Liu R.
      Impact of sugar-sweetened beverage warning labels on consumer behaviors: a systematic review and meta-analysis.
      (ie, Cohen’s d = –0.18), Grummon and Hall
      • Grummon A.H.
      • Hall M.G.
      Sugary drink warnings: a meta-analysis of experimental studies.
      found that SSB warning labels led to fewer calories purchased (Cohen’s d = –0.16, 95% CI –0.24 to –0.07). To be conservative, we adopted Grummon and Hall’s estimate in the microsimulation model.
      • Grummon A.H.
      • Hall M.G.
      Sugary drink warnings: a meta-analysis of experimental studies.
      Cohen’s d measures the distance between two sample means in the unit of pooled standard deviation. To translate Cohen’s d to a percentage change for the policy effect, Cohen’s d was multiplied by the standard deviation of daily calorie intake from SSBs, and then divided by the mean of daily calorie intake from SSBs. The translated policy effect of SSB warning labels was 24.39%, with a standard error of 6.22%.

      Effect of Menu Labeling Regulations

      To our knowledge, four systematic reviews and meta-analyses have estimated the quantitative relationship between menu labeling regulations and daily calorie intake from chain restaurant foods.
      • Long M.W.
      • Tobias D.K.
      • Cradock A.L.
      • Batchelder H.
      • Gortmaker S.L.
      Systematic review and meta-analysis of the impact of restaurant menu calorie labeling.
      • Sinclair S.E.
      • Cooper M.
      • Mansfield E.D.
      The influence of menu labeling on calories selected or consumed: a systematic review and meta-analysis.
      • VanEpps E.M.
      • Roberto C.A.
      • Park S.
      • Economos C.D.
      • Bleich S.N.
      Restaurant menu labeling policy: review of evidence and controversies.
      • Bleich S.N.
      • Economos C.D.
      • Spiker M.L.
      • et al.
      A systematic review of calorie labeling and modified calorie labeling interventions: impact on consumer and restaurant behavior.
      We extracted data from unduplicated studies included in those four reviews and one recent original study not included in those reviews,
      • Petimar J.
      • Zhang F.
      • Cleveland L.P.
      • et al.
      Estimating the effect of calorie menu labeling on calories purchased in a large restaurant franchise in the southern United States: quasi-experimental study.
      and performed a meta-analysis to estimate the pooled effect size. Due to the high heterogeneity (I2 = 99.97%), a random-effect model was used. Menu labeling regulations were found to lead to a reduction in daily calorie intake from chain restaurant foods (Cohen’s d = 0.20, 95% CI 0.04 to 0.36). The same procedure (documented in details in the preceeding subtitle) was adopted to translate Cohen’s d to the policy effect of menu labeling regulations (33.45%, with a standard error of 8.53%).

      Policy Effect on Body Weight and BMI

      Longitudinal studies found that body weight gradually increased from young to middle adulthood.
      • Malhotra R.
      • Ostbye T.
      • Riley C.M.
      • Finkelstein E.A.
      Young adult weight trajectories through midlife by body mass category.
      ,
      • Stenholm S.
      • Vahtera J.
      • Kawachi I.
      • et al.
      Patterns of weight gain in middle-aged and older US adults, 1992-2010.
      Dutton and colleagues
      • Dutton G.R.
      • Kim Y.
      • Jacobs Jr., D.R.
      • et al.
      25-year weight gain in a racially balanced sample of U.S. adults: the CARDIA study.
      examined the trajectory in weight gain of a large, racially balanced sample of US adults over 25 years. Based on their data, we fitted a parametric model to simulate the increase in adults’ weight with respect to age. The rate of growth gradually leveled off and approached zero when people reached age 55 years.
      Hall and colleagues
      • Hall K.D.
      • Sacks G.
      • Chandramohan D.
      • et al.
      Quantification of the effect of energy imbalance on bodyweight.
      ,
      • Hall K.D.
      Metabolic adaptations to weight loss.
      developed a mathematical model to project dynamic, long-term weight change resulting from energy imbalance. In general, their model predicted the rate of weight change to be substantially attenuated after the first 1 to 2 years due to metabolic adaptations.
      In the control condition, agents’ weight evolved only with age. In the two experimental conditions, agents’ weight was affected by both aging and policy interventions. Specifically, Hall’s model
      • Hall K.D.
      • Sacks G.
      • Chandramohan D.
      • et al.
      Quantification of the effect of energy imbalance on bodyweight.
      ,
      • Hall K.D.
      Metabolic adaptations to weight loss.
      was used to project agents’ weight reduction induced by decreased daily calorie intake due to the SSB warning labels and menu labeling regulations. We calculated the cumulative reduction in weight and BMI attributable to the policy interventions by summing up the year-end differences in weight and BMI between the control and experimental conditions.

      Policy Effect on Health Care Expenditures

      We estimated changes in annual health care expenditures due to changes in BMI, using data from the Medical Expenditure Panel Survey 2003-2016 waves. The Medical Expenditure Panel Survey is a nationally representative survey of families and individuals focusing on health care costs and utilization.
      Agency for Healthcare Research and Quality
      Medical Expenditure Panel Survey.
      Information collected included specific health services that people used, frequency of utilization, costs, and pay sources. Health care expenditures were the sum of direct payments for care provided during the year from all payer sources, including out-of-pocket payments and payments by private insurance, Medicaid, Medicare, and other sources. Changes in annual health care expenditures in response to a unit reduction in BMI were estimated in a 2-part model, given that total health care expenditure data were skewed in the dataset, with 21% of $0 expenditure, a median of $909, and a mean of $4,603. The first part of the model involved a probit probability function and the second part a generalized linear regression with a natural logarithmic link and a gamma error distribution. The cumulative reduction in healthcare expenditures equaled the sum of annual reduction in health care expenditures across the 10-year study period. For agents who were not overweight or obese (BMI ≤ 25), a reduction in BMI was assumed to result in no reduction in health care expenditures. We performed the 2-part model separately by sex, age group, and race/ethnicity to generate subgroup-specific estimates. All expenditures were converted to 2016 US dollars using the medical care consumer price index by the US Bureau of Labor Statistics.
      US Bureau of Labor Statistics
      Consumer price index.
      The 2-part models were estimated using Stata MP Version 16.1.

      Stata MP [software program]. Version 16.1. College Station, TX: StataCorp; 2021.

      Mortality Risk

      In the microsimulation model, each agent was observed for age 10 years, at the beginning of each year, was subject to age-, sex-, and racial/ethnic-specific all-cause mortality risk, based on the United States Life Tables, 2017.
      • Arias E.
      • Xu J.Q.
      United States Life Tables, 2017.
      Agents’ mortality risk was further moderated by their adiposity status. The Global BMI Mortality Collaboration conducted a meta-analysis based on 239 prospective cohort studies with a total sample size of more than 10 million.
      • Di Angelantonio E.
      • Bhupathiraju ShN
      • et al.
      Global BMI Mortality Collaboration
      Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents.
      Mortality hazard ratios associated with underweight; overweight; and grade 1, 2, and 3 obesity were estimated, with normal weight (20 ≤ BMI < 25) as the reference group. We used their estimated hazard ratios to adjust the all-cause mortality risk from the life tables.

      Modeling Uncertainty

      To account for the cross-sectional variations in the policy effects among agents, we assumed that the policy effects of SSB warning labels and menu labeling regulations followed two distinctive normal distributions with means and standard deviations determined by the corresponding estimates from meta-analyses.

      Energy Compensation

      Empirical studies documented that a decrease in calorie intake due to reduced SSB or fast-food consumption could be partially offset by additional calorie intake of other food or beverage items, resulting in a smaller reduction in daily total energy intake. However, findings on the exact proportion of energy compensated were mixed and inconclusive.
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      We adopted a relatively conservative estimate (37%) used by Grummon and colleagues
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      for the energy compensation rate in both policy interventions.

      Modeling Policy Effects by Population Subgroup

      We estimated the effects of SSB warning labels and menu labeling regulations on daily energy intake, body weight, BMI, and health care expenditures among all agents and by sex (ie, men and women), age group (ie, young adults aged 18 to 39 years, middle-aged adults aged 40 to 64 years, and older adults aged 65 years and older), and race/ethnicity (ie, non-Hispanic White adults, non-Hispanic Black adults, non-Hispanic other race, and Hispanic adults).

      Simulation Setting

      The estimation of the microsimulation models utilized R version 4.0.2.
      Each model was simulated 1,000 times using the bootstrapped sample of the same size as the original NHANES sample (N = 39,774), based on which means, standard errors, and 95% CIs of the policy effects were estimated. The NHANES survey design (ie, sampling strata, primary sampling units, and sampling weights) was incorporated in the estimates.

      Human Subjects Protection

      This study used publicly available, de-identified data without involvement of human subjects, and was exempt from institutional review board review.

      Results

      The Table reports the survey-design-adjusted descriptive statistics of the study sample. The agents were split roughly equally between men and women. The mean age was 46.5 years, and agents 18 to 39 years, 40 to 64 years, and 65 years and older occupied 38.3%, 43.7%, and 18.0%, respectively. Non-Hispanic White adults, non-Hispanic Black adults, non-Hispanic other race, and Hispanic adults occupied 67.8%, 11.2%, 7.1%, and 13.9%, respectively. Agents had an average of 125 kcal daily energy intake from SSBs and 158 kcal from chain restaurant foods. Agents’ body weight averaged 80.7 kg, and BMI averaged 28.8.
      TableDescriptive statistics of the National Health and Nutrition Examination Survey (NHANES) 2003-2018 waves
      Descriptive statistics were estimated through incorporating the NHANES survey design (ie, sampling strata, primary sampling unit, and sampling weight).
      VariableMean ± standard error
      Sex (%)
      Men0.495 ± 0.003
      Women0.505 ± 0.003
      Age (y)
      All age46.51 ± 0.221
      18-390.383 ± 0.006
      40-640.437 ± 0.004
      ≥ 650.180 ± 0.004
      Race/ethnicity (%)
      Non-Hispanic White adults0.678 ± 0.012
      Non-Hispanic Black adults0.112 ± 0.007
      Non-Hispanic other race0.071 ± 0.003
      Hispanic adults0.139 ± 0.008
      Diet (kcal)
      Daily energy intake from SSBs
      Sugar-sweetened beverages.
      124.95 ± 0.955
      Daily energy intake at chain restaurants157.70 ± 1.653
      Weight outcomes
      Body weight (kg)80.7 ± 0.105
      Body mass index28.75 ± 0.033
      a Descriptive statistics were estimated through incorporating the NHANES survey design (ie, sampling strata, primary sampling unit, and sampling weight).
      b Sugar-sweetened beverages.
      In comparison to the control condition, both experimental conditions were found to lead to reduced daily calorie intake, body weight, BMI, and health care expenditures. Specifically, SSB warning labels and menu labeling regulations were projected to reduce daily energy intake by 19.13 kcal (95% CI 18.83 to 19.43 kcal) and 33.09 kcal (95% CI 32.39 to 33.80 kcal), body weight by 0.92 kg (95% CI 0.90 to 0.93 kg) and 1.57 kg (95% CI 1.54 to 1.60 kg), BMI by 0.32 (95% CI 0.31 to 0.33) and 0.55 (95% CI =0.54 to 0.56), and per-capita health care expenditures by $26.97 (95% CI $26.56 to $27.38) and $45.47 (95% CI $44.54 to $46.40) over 10 years, respectively.
      Figure 2 shows the projected trajectories for body weight (Figure 2A), BMI (Figure 2B), and health care cost savings over 10 years (Figure 2C). In Figure 2A, the solid line depicts, under the control condition, a gradual increase in body weight due to the aging process among agents. Following the implementation of the SSB warning labels, the dashed line illustrates a sharp decrease in body weight over the first 2 years. The rate of decline slowed down considerably starting from the third year, and subsequently, a rise in body weight was observed as the aging effect began to dominate that of the SSB warning labels. The body weight trajectory under the menu labeling regulations, denoted by the dotted line, was similar to that of the SSB warning labels. However, the initial reduction in body weight over the first 2 years under the menu labeling regulations was larger. The trajectories of BMI, illustrated in Figure 2B, resembled those of body weight. Figure 2C shows the trajectory of healthcare cost savings attributable to SSB warning labels and menu labeling regulations through preventing unhealthy weight gain. The majority of savings were accumulated over the first two years, and the rate of accumulation slowed down substantially afterward. After the sixth year, the cumulative health care savings remained largely unchanged for both interventions.
      Figure thumbnail gr2
      Figure 2Projected trajectories for body weight, body mass index (BMI), and health care cost savings over 10 years. A, Projected trajectory for body weight over 10 years. B, Projected trajectory for BMI over 10 years. C, Projected trajectory for health care cost savings over 10 years. The grey-shaded areas represent 95% CI.
      Figure 3 shows the projected BMI distributions by the end of the tenth year. The solid, dashed, and dotted lines depict the BMI distributions among agents under the control condition, SSB warning labels, and menu labeling regulations, respectively. Compared with the control condition, the BMI distributions under the SSB warning labels and menu labeling regulations were increasingly shifting to the left, indicating the effectiveness of both policy interventions in preventing weight gain.
      Figure thumbnail gr3
      Figure 3Projected body mass index (BMI) distributions by the end of the 10th year. SSB = sugar-sweetened beverage.
      Figure 4 contrasts the survival curves between the control condition and the two policy interventions over 10 years. Relative to the control condition, SSB warning labels and menu labeling regulations appeared to marginally improve agents’ survival rate by preventing unhealthy weight gain. However, the improvement was not statistically significant, indicated by the overlapping 95% CIs of the interventions and the control.
      Figure thumbnail gr4
      Figure 4Projected 10-year survival curve. SSB = sugar-sweetened beverage.
      Figure 5 shows the differential policy effectiveness on body weight (Figure 5A), BMI (Figure 5B), and health care expenditures (Figure 5C) by sex, age group, and race/ethnicity. The reduction in body weight (Figure 5A) and BMI (Figure 5B), and health care cost savings (Figure 5C) were higher under the menu labeling regulations than under the SSB warning labels for all subgroups. Compared with women, non-Hispanic White adults and Hispanic adults, and older adults, men, non-Hispanic Black adults, and younger adults had a more substantial reduction in body weight attributable to the SSB warning labels and menu labeling regulations (Figure 5A). Compared with women, non-Hispanic White adults, and older adults, men, non-Hispanic Black adults and Hispanic adults, and younger adults had a more substantial reduction in BMI attributable to the SSB warning labels and menu labeling regulations (Figure 5B). Compared with women, Hispanic adults, and older adults, men, non-Hispanic White adults and Black adults, and younger adults had higher health care cost savings attributable to the SSB warning labels and menu labeling regulations (Figure 5C).
      Figure thumbnail gr5
      Figure 5Estimated policy effectiveness on weight reduction, body mass index (BMI) reduction, and health care cost savings by sex, age group, and race/ethnicity. A, Policy effectiveness on weight reduction by sex, age group, and race/ethnicity. B, Policy effectiveness on health care cost savings by sex, age group, and race/ethnicity. The error bars represent 95% CI.

      Discussion

      This study utilized a stochastic microsimulation model based on nationally representative health survey data to project and contrast the impacts of nationwide implementation of SSB warning labels and menu labeling regulations on daily energy intake, weight status, and health care expenditures among US adults. Four key findings emerged. First, SSB warning labels and menu labeling regulations were estimated to reduce daily calorie intake by approximately 19 and 33 kcal, body weight by about 1.0 and 1.6 kg, and BMI by about 0.3 and 0.6, and save per-capita health care cost of approximately $27 and $45 over 10 years, respectively. Second, no discernable policy effect on all-cause mortality was identified. Finally, the policy effects could be heterogeneous across population subgroups, with larger effects in men, non-Hispanic Black adults, and younger adults.
      The estimated policy effects of SSB warning labels are comparable to findings from a previous modeling study. Using microsimulation modeling, Grummon and colleagues
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      found that SSB warning label use would reduce daily energy intake from SSBs by 25.3 kcal (vs 19.13 kcal in our study) and BMI by 0.64 (vs 0.32 in our study). The discrepancy in the estimated BMI reduction between Grummon and colleagues
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      and this study likely stemmed from differences in time horizon (NHANES 2005-2014 vs 2003-2018), model configurations, and assumptions. Each agent in our model represented a real-world NHANES participant with predetermined characteristics. In contrast, Grummon and colleagues
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      used simulated individuals and assigned them prepolicy attributes (eg, height, weight, and SSB intake) based on the demographic-specific distributions estimated from the NAHNES data. Moreover, Grummon and colleagues
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      assumed no within-individual relationship between body weight and age, whereas our model assumed nonlinear, positive, but diminishing weight gains during an agent’s aging process.
      To our knowledge, a single study, so far, has estimated the influence of menu labeling regulations on the national scale, focusing on type 2 diabetes mellitus and cardiovascular disease events.
      • Grummon A.H.
      • Smith N.R.
      • Golden S.D.
      • Frerichs L.
      • Taillie L.S.
      • Brewer N.T.
      Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
      On the other hand, several studies estimated the policy effect at the local level. For example, Kuo and colleagues
      • Kuo T.
      • Jarosz C.J.
      • Simon P.
      • Fielding J.E.
      Menu labeling as a potential strategy for combating the obesity epidemic: a health impact assessment.
      conducted a health impact assessment to quantify the influence of menu labeling law in Los Angeles County, CA. They reported an average reduction of 100 kcal per meal from chain restaurants. A recent quasiexperimental study traced purchases from 104 chain restaurants in three southern US states for 3 years.
      • Petimar J.
      • Zhang F.
      • Cleveland L.P.
      • et al.
      Estimating the effect of calorie menu labeling on calories purchased in a large restaurant franchise in the southern United States: quasi-experimental study.
      They found a 60 kcal per transaction reduction in foods purchased following the menu-labeling regulations. By contrast, Petimar and colleagues
      • Petimar J.
      • Ramirez M.
      • Rifas-Shiman S.L.
      • et al.
      Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: a natural experiment.
      collected data from McDonald’s in four New England cities. They found that menu labeling regulations increased the probability of noticing calorie information but did not reduce calories purchased. This study assumed a modest reduction (33.09 kcal, on average) in daily calories consumed at a chain restaurant, based on a comprehensive meta-analysis.
      The reduction in annual health care expenditures attributable to the policy-induced weight loss was sizable. The SSB warning label and menu labeling regulations were estimated to reduce cumulative health care expenditures by $27 and $45 per person over ten years, respectively. This per-capita reduction would translate into an annual total medical cost savings of $0.69 and $1.16 billion. The initial cost of complying with the FDA’s menu labeling mandate was estimated to be $388.4 million among all chain restaurants in the United States, with a recurring cost of $55.1 million.
      US Dept of Health and Human Services
      Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments.
      Averaging these expenses over the course of 10 years yielded an annual cost <8.5% of the annual medical savings from the menu labeling regulations.
      Significant heterogeneities in the policy impacts were present across population subgroups. Previous studies have consistently documented an average of higher consumption of daily calories from SSBs and chain restaurant foods among men, younger adults, and non-Hispanic Black adults compared with women, older adults, and other racial and ethnic groups.
      US Dept of Health and Human Services and US Department of Agriculture
      2015-2020 Dietary Guidelines for Americans. 8th Edition.
      • Lundeen E.A.
      • Park S.
      • Pan L.
      • Blanck H.M.
      Daily intake of sugar-sweetened beverages among us adults in 9 states, by state and sociodemographic and behavioral characteristics, 2016.
      • Fryar C.D.
      • Hughes J.P.
      • Herrick K.A.
      • Ahluwalia N.
      Fast Food Consumption Among Adults in the United States, 2013-2016.
      Therefore, a proportional reduction in SSB and chain restaurant food consumption due to the policy interventions would have a more substantial influence on daily energy consumption, body weight, BMI, and health care expenditures among men, younger adults, and non-Hispanic Black adults.
      Several limitations should be noted concerning the modeling study. Unlike real-world experimentation (eg, a randomized controlled trial or natural experiment), results from a microsimulation model make projections based on a set of assumptions rather than empirical observations. Although we obtained parameter values from recent, rigorous, large-scale studies or meta-analyses, the real-world contains a multitude of complexity in which many competing and supplementary policies intertwine and jointly influence people’s dietary behaviors. An implicit assumption of the microsimulation model was that individual agents made their own dietary decisions independently. In reality, people’s dietary choices are under the influence of others or made jointly in a social network (eg, family and friends). Also, individual dietary choices may be influenced by the social and physical environment where people spend time, such as home, neighborhood, or workplace. SSB warning labels are diverse and may exert differential influence on consumers’ SSB purchases and consumption. The meta-analyses conducted by An and colleagues
      • An R.
      • Liu J.
      • Liu R.
      Impact of sugar-sweetened beverage warning labels on consumer behaviors: a systematic review and meta-analysis.
      and Grummon and Hall
      • Grummon A.H.
      • Hall M.G.
      Sugary drink warnings: a meta-analysis of experimental studies.
      pooled different types of SSB warning labels, as studies testing a particular type or format of label remain limited, which warrants future research. Chain restaurants in NHANES were classified as “restaurant fast food/pizza,” which was arguably a crude measure. Measures that better match the FDA’s definition for “chain restaurants and similar retail food establishments” subject to menu labeling regulations are called for future experimental and modeling studies.
      US Dept of Health and Human Services
      Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments.
      This study focused on the policy impacts on energy consumption and weight loss. We did not assess the potential fuller influence of the two labeling policies, such as improved health awareness, nutrition knowledge, or diet quality. The microsimulation model assumed changes in health care costs to immediately follow changes in BMI (both occurred during the same year). Obviously, in reality, health care cost savings attributable to a healthier weight status may take months or years to realize and accumulate. However, no sufficient data allowed us to make reasonable and sensitive assumptions on the distribution of the time interval between weight change and health care cost accumulation, as relevant parameters may be strongly influenced by various individual characteristics.

      Conclusions

      This study projected the impacts of nationwide implementation of SSB warning labels and restaurant menu labeling mandates. SSB warning labels and menu labeling regulations were estimated to reduce daily energy intake by 20 and 32 kcal, body weight by 1.0 and 1.5 kg, and BMI by 0.3 and 0.5, and save per-capita health care cost of $28 and $43 over 10 years, respectively. The policy effects could be heterogeneous across population subgroups, with larger effects in men, non-Hispanic Black adults, and younger adults. In sum, SSB warning labels and restaurant menu labeling regulations could be effective policy leverage to prevent weight gains and reduce medical expenses attributable to adiposity.

      Author Contributions

      R. An designed the study and wrote the manuscript. J. Zheng and X. Xiang revised the manuscript.

      References

        • An R.
        Prevalence and trends of adult obesity in the US, 1999-2012.
        ISRN Obes. 2014; 2014: 185132
        • Hales C.M.
        • Carroll M.D.
        • Fryar C.D.
        • Ogden C.L.
        Prevalence of Obesity And Severe Obesity Among Adults: United States, 2017-2018.
        National Center for Health Statistics, Hyattsville, MD2020 (NCHS Data Brief No. 360)
        • Pi-Sunyer X.
        The medical risks of obesity.
        Postgrad Med. 2009; 121: 21-33
        • Fontaine K.R.
        • Redden D.T.
        • Wang C.
        • Westfall A.O.
        • Allison D.B.
        Years of life lost due to obesity.
        JAMA. 2003; 289: 187-193
        • Prospective Studies Collaboration
        Body mass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies.
        Lancet. 2009; 373: 1083-1096
        • Waters H.
        • Graf M.
        America’s obesity crisis.
        • Bray G.A.
        • Kim K.K.
        • Wilding J.P.H.
        • World Obesity Federation
        Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation.
        Obes Rev. 2017; 18: 715-723
        • Hill J.O.
        • Wyatt H.R.
        • Peters J.C.
        Energy balance and obesity.
        Circulation. 2012; 126: 126-132
        • Romieu I.
        • Dossus L.
        • Barquera S.
        • et al.
        Energy balance and obesity: what are the main drivers?.
        Cancer Causes Control. 2017; 28: 247-258
        • Swinburn B.A.
        • Sacks G.
        • Hall K.D.
        • et al.
        The global obesity pandemic: shaped by global drivers and local environments.
        Lancet. 2011; 378: 804-814
        • World Health Organization
        Obesity and overweight.
        • Gortmaker S.L.
        • Swinburn B.A.
        • Levy D.C.
        • et al.
        Changing the future of obesity: science, policy, and action.
        Lancet. 2011; 378: 838-847
        • Rosinger A.
        • Herrick K.
        • Gahche J.
        • Park S.
        Sugar-Sweetened Beverage Consumption Among US Youth, 2011-2014.
        National Center for Health Statistics, Hyattsville, MD2017 (NCHS Data Brief No 271)
        • Malik V.S.
        • Popkin B.M.
        • Bray G.A.
        • Després J.P.
        • Hu F.B.
        Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk.
        Circulation. 2010; 121: 1356-1364
        • Bleich S.N.
        • Vercammen K.A.
        The negative impact of sugar-sweetened beverages on children’s health: an update of the literature.
        BMC Obes. 2018; 5: 6
        • Malik V.S.
        • Schulze M.B.
        • Hu F.B.
        Intake of sugar-sweetened beverages and weight gain: a systematic review.
        Am J Clin Nutr. 2006; 84: 274-288
        • Popova L.
        Sugar-sweetened beverage warning labels: lessons learned from the tobacco industry.
        J Calif Dent Assoc. 2016; 44: 633-640
        • Ngo A.
        • Cheng K.W.
        • Shang C.
        • Huang J.
        • Chaloupka F.J.
        Global evidence on the association between cigarette graphic warning labels and cigarette smoking prevalence and consumption.
        Int J Environ Res Public Health. 2018; 15: E421
        • Hammond D.
        • Fong G.T.
        • McNeill A.
        • Borland R.
        • Cummings K.M.
        Effectiveness of cigarette warning labels in informing smokers about the risks of smoking: findings from the International Tobacco Control (ITC) Four Country Survey.
        Tob Control. 2006; 15: 19-25
        • California Legislative Information
        SB-1000 public health: sugar-sweetened beverages: safety warnings.
        • California Legislative Information
        SB-347 sugar-sweetened beverages: safety warnings.
        • VanEpps E.M.
        • Roberto C.A.
        The influence of sugar-sweetened beverage warnings: a randomized trial of adolescents’ choices and beliefs.
        Am J Prev Med. 2016; 51: 664-672
        • Roberto C.A.
        • Wong D.
        • Musicus A.
        • Hammond D.
        The influence of sugar-sweetened beverage health warning labels on parents’ choices.
        Pediatrics. 2016; 137e20153185
        • Bollard T.
        • Maubach N.
        • Walker N.
        • Ni Mhurchu C.
        Effects of plain packaging, warning labels, and taxes on young people’s predicted sugar-sweetened beverage preferences: an experimental study.
        Int J Behav Nutr Phys Act. 2016; 13: 95
        • Mantzari E.
        • Pechey R.
        • Codling S.
        • Sexton O.
        • Hollands G.J.
        • Marteau T.M.
        The impact of ‘on-pack’ pictorial health warning labels and calorie information labels on drink choice: a laboratory experiment.
        Appetite. 2020; 145: 104484
        • Acton R.B.
        • Hammond D.
        The impact of price and nutrition labelling on sugary drink purchases: results from an experimental marketplace study.
        Appetite. 2018; 121: 129-137
        • Acton R.B.
        • Jones A.C.
        • Kirkpatrick S.I.
        • Roberto C.A.
        • Hammond D.
        Taxes and front-of-package labels improve the healthiness of beverage and snack purchases: a randomized experimental marketplace.
        Int J Behav Nutr Phys Act. 2019; 16: 46
        • Grummon A.
        • Taillie L.
        • Golden S.
        • Hall M.
        • Ranney L.
        • Brewer N.
        Sugar-sweetened beverage health warnings and purchases: a randomized controlled trial.
        Am J Prev Med. 2019; 57: 601-610
        • Caro J.C.
        • Corvalan C.
        • Reyes M.
        • Silva A.
        • Popkin B.
        • Taillie L.S.
        Chile’s 2014 sugar-sweetened beverage tax and changes in prices and purchases of sugar-sweetened beverages: an observational study in an urban environment.
        PLoS Med. 2018; 15: e1002597
        • Roberto C.A.
        • Khandpur N.
        Improving the design of nutrition labels to promote healthier food choices and reasonable portion sizes.
        Int J Obes (Lond). 2014; 38: S25-S33
        • Dumoitier A.
        • Abbo V.
        • Neuhofer Z.T.
        • McFadden B.R.
        A review of nutrition labeling and food choice in the United States.
        Obes Sci Pract. 2019; 5: 581-591
        • US Food and Drug Administration
        Menu labeling requirments.
        • Cohen D.A.
        • Story M.
        Mitigating the health risks of dining out: the need for standardized portion sizes in restaurants.
        Am J Public Health. 2014; 104: 586-590
        • US Dept of Agriculture
        America’s eating habits: food away from home.
        • US Dept of Health and Human Services
        Food labeling: nutrition labeling of standard menu items in restaurants and similar retail food establishments.
        https://www.fda.gov/media/90450/download
        Date accessed: May 17, 2020
        • Long M.W.
        • Tobias D.K.
        • Cradock A.L.
        • Batchelder H.
        • Gortmaker S.L.
        Systematic review and meta-analysis of the impact of restaurant menu calorie labeling.
        Am J Public Health. 2015; 105: e11-e24
        • Sinclair S.E.
        • Cooper M.
        • Mansfield E.D.
        The influence of menu labeling on calories selected or consumed: a systematic review and meta-analysis.
        J Acad Nutr Diet. 2014; 114: 1375-1388
        • VanEpps E.M.
        • Roberto C.A.
        • Park S.
        • Economos C.D.
        • Bleich S.N.
        Restaurant menu labeling policy: review of evidence and controversies.
        Curr Obes Rep. 2016; 5: 72-80
        • Bleich S.N.
        • Economos C.D.
        • Spiker M.L.
        • et al.
        A systematic review of calorie labeling and modified calorie labeling interventions: impact on consumer and restaurant behavior.
        Obesity. 2017; 25: 2018-2044
        • US Dept of Health and Human Services and US Department of Agriculture
        2015-2020 Dietary Guidelines for Americans. 8th Edition.
        • Lundeen E.A.
        • Park S.
        • Pan L.
        • Blanck H.M.
        Daily intake of sugar-sweetened beverages among us adults in 9 states, by state and sociodemographic and behavioral characteristics, 2016.
        Prev Chronic Dis. 2018; 15: 180335
        • Fryar C.D.
        • Hughes J.P.
        • Herrick K.A.
        • Ahluwalia N.
        Fast Food Consumption Among Adults in the United States, 2013-2016.
        National Center for Health Statistics, Hyattsville, MD2018 (NCHS Data Brief No 322)
        • Petersen R.
        • Pan L.
        • Blanck H.M.
        Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action.
        Prev Chronic Dis. 2019; 16: 180579
        • Maglio P.P.
        • Sepulveda M.J.
        • Mabry P.L.
        Mainstreaming modeling and simulation to accelerate public health innovation.
        Am J Public Health. 2014; 104: 1181-1186
        • Schofield D.J.
        • Zeppel M.
        • Tan O.
        • Lymer S.
        • Cunich M.
        • Shrestha R.
        A brief, global history of microsimulation models in health: past applications, lessons learned and future directions.
        IJM. 2018; 11: 97-142
        • Santow G.
        Microsimulation in demographic research.
        in: International Encyclopedia of the Social & Behavioral Sciences. Elsevier, Kidlington, Oxford2001: 9780-9785
        • Rutter C.M.
        • Zaslavsky A.M.
        • Feuer E.J.
        Dynamic microsimulation models for health outcomes: a review.
        Med Decis Making. 2011; 31: 10-18
        • Harland K.
        • Birkin M.
        Microsimulation modelling for social scientists.
        in: Brunsdon C. Singleton A. Geocomputation. SAGE Publications, London, UK2015: 78-96
        • Belanger A.
        • Sabourin P.
        Microsimulation and Population Dynamics: An Introduction to Modgen 12.
        Springer International Publishing, New York, NY2017
        • Centers for Disease Control and Prevention
        National Health and Nutrition Examination Survey.
        • Centers for Disease Control and Prevention
        Get the facts: sugar-sweetened beverages and consumption.
        • Fakhouri T.H.I.
        • Kit B.K.
        • Ogden C.L.
        Consumption of Diet Drinks in the United States, 2009-2010.
        National Center for Health Statistics, Hyattsville, MD2012 (NCHS Data Brief, No 109)
        • An R.
        Beverage consumption in relation to discretionary food intake and diet quality among US Adults, 2003 to 2012.
        J Acad Nutr Diet. 2016; 116: 28-37
        • American Heart Association Center for Health Metrics and Evaluation
        Definitions—low calorie and sugar-sweetened beverages.
        https://healthmetrics.heart.org/definitions-3/
        Date accessed: September 17, 2020
        • An R.
        Fast-food and full-service restaurant consumption and daily energy and nutrient intakes in US adults.
        Eur J Clin Nutr. 2016; 70: 97-103
        • Powell L.M.
        • Nguyen B.T.
        Fast-food and full-service restaurant consumption among children and adolescents: effect on energy, beverage, and nutrient intake.
        JAMA Pediatr. 2013; 167: 14-20
        • An R.
        • Liu J.
        • Liu R.
        Impact of sugar-sweetened beverage warning labels on consumer behaviors: a systematic review and meta-analysis.
        Am J Prev Med. 2021; 60: 115-126
        • Grummon A.H.
        • Hall M.G.
        Sugary drink warnings: a meta-analysis of experimental studies.
        PLOS Med. 2020; 17e1003120
        • Petimar J.
        • Zhang F.
        • Cleveland L.P.
        • et al.
        Estimating the effect of calorie menu labeling on calories purchased in a large restaurant franchise in the southern United States: quasi-experimental study.
        BMJ. 2019; 367: l5837
        • Malhotra R.
        • Ostbye T.
        • Riley C.M.
        • Finkelstein E.A.
        Young adult weight trajectories through midlife by body mass category.
        Obesity (Silver Spring). 2013; 21: 1923-1934
        • Stenholm S.
        • Vahtera J.
        • Kawachi I.
        • et al.
        Patterns of weight gain in middle-aged and older US adults, 1992-2010.
        Epidemiology. 2015; 26: 165-168
        • Dutton G.R.
        • Kim Y.
        • Jacobs Jr., D.R.
        • et al.
        25-year weight gain in a racially balanced sample of U.S. adults: the CARDIA study.
        Obesity (Silver Spring). 2016; 24: 1962-1968
        • Hall K.D.
        • Sacks G.
        • Chandramohan D.
        • et al.
        Quantification of the effect of energy imbalance on bodyweight.
        Lancet. 2011; 378: 826-837
        • Hall K.D.
        Metabolic adaptations to weight loss.
        Obesity (Silver Spring). 2018; 26: 790-791
        • Agency for Healthcare Research and Quality
        Medical Expenditure Panel Survey.
        https://www.meps.ahrq.gov/mepsweb/
        Date accessed: May 17, 2020
        • US Bureau of Labor Statistics
        Consumer price index.
      1. Stata MP [software program]. Version 16.1. College Station, TX: StataCorp; 2021.

        • Arias E.
        • Xu J.Q.
        United States Life Tables, 2017.
        National Center for Health Statistics, Hyattsville, MD2019 (National Vital Statistics Reports Vol 68 No 7)
        • Di Angelantonio E.
        • Bhupathiraju ShN
        • et al.
        • Global BMI Mortality Collaboration
        Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents.
        Lancet. 2016; 388: 776-786
        • Grummon A.H.
        • Smith N.R.
        • Golden S.D.
        • Frerichs L.
        • Taillie L.S.
        • Brewer N.T.
        Health warnings on sugar-sweetened beverages: simulation of impacts on diet and obesity among US adults.
        Am J Prev Med. 2019; 57: 765-774
      2. [computer R. R Foundation for Statistical Computing, Vienna, Austria2021
        Version: Version 4.0
        • Kuo T.
        • Jarosz C.J.
        • Simon P.
        • Fielding J.E.
        Menu labeling as a potential strategy for combating the obesity epidemic: a health impact assessment.
        Am J Public Health. 2009; 99: 1680-1686
        • Petimar J.
        • Ramirez M.
        • Rifas-Shiman S.L.
        • et al.
        Evaluation of the impact of calorie labeling on McDonald’s restaurant menus: a natural experiment.
        Int J Behav Nutr Phys Act. 2019; 16: 99

      Biography

      R. An is an assistant professor, Brown School, Washington University, St Louis, MO.
      J. Zheng is a professor, School of Sport Leisure, Recreation and Arts, Shanghai University of Sport, Shanghai, China.
      X. Xiang is an assistant professor, School of Social Work, University of Michigan, Ann Arbor.