Cost-effectiveness of calorie labeling at large fast-food chains across the U.S

Roxanne Dupuis; Jason P Block; Jessica L Barrett; Michael W Long; Joshua Petimar; Zachary J Ward; Erica L Kenney; Aviva A Musicus; Carolyn C Cannuscio; David R Williams; Sara N Bleich; Steven L Gortmaker

doi:10.1016/j.amepre.2023.08.012

. Author manuscript; available in PMC: 2025 Jan 1.

Published in final edited form as: Am J Prev Med. 2023 Aug 14;66(1):128–137. doi: 10.1016/j.amepre.2023.08.012

Cost-effectiveness of calorie labeling at large fast-food chains across the U.S.

Roxanne Dupuis ^1,², Jason P Block ³, Jessica L Barrett ¹, Michael W Long ⁴, Joshua Petimar ^3,⁵, Zachary J Ward ⁶, Erica L Kenney ^1,⁷, Aviva A Musicus ⁷, Carolyn C Cannuscio ⁸, David R Williams ¹, Sara N Bleich ⁹, Steven L Gortmaker ¹

^1.Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA

^2.Present address: Department of Population Health, NYU Grossman School of Medicine, New York, NY

^3.Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA

^4.Department of Prevention and Community Health, Milken Institute School of Public Health, the George Washington University, Washington, D.C.

^5.Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA

^6.Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA

^7.Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA

^8.Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA

^9.Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA

Roxanne Dupuis: Validation, Formal analysis, Investigation, Writing-Original Draft, Writing – Review & Editing, Visualization

Jason P. Block: Conceptualization, Validation, Investigation, Writing – Review & Editing, Supervision, Funding acquisition

Jessica L. Barrett: Validation, Investigation, Resources, Writing – Review & Editing, Project administration

Michael W. Long: Methodology, Investigation, Writing – Review & Editing

Joshua Petimar: Formal analysis, Writing – Review & Editing

Zachary J. Ward: Methodology, Software, Writing – Review & Editing

Erica L. Kenney: Validation, Writing – Review & Editing

Aviva A. Musicus: Validation, Formal analysis, Writing – Review & Editing

Carolyn C. Cannuscio: Validation, Writing – Review & Editing

David R. Williams: Conceptualization, Writing – Review & Editing

Sara N. Bleich: Conceptualization, Writing – Review & Editing

Steven L. Gortmaker: Conceptualization, Methodology, Validation, Investigation, Writing – Review & Editing, Supervision, Funding acquisition

Present address: Department of Population Health, NYU Grossman School of Medicine, 180 Madison Avenue, 3^rd floor, New York, NY 10016.

^✉

Corresponding Author: Roxanne Dupuis, PhD, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Kresge Building, 7th floor, Boston, MA 02115. roxanne.dupuis@nyulangone.org

PMCID: PMC10840662 NIHMSID: NIHMS1929380 PMID: 37586572

Abstract

Introduction:

Calorie labeling of standard menu items has been implemented at large restaurant chains across the United States since 2018. The objective of this study was to evaluate the cost-effectiveness of calorie labeling at large U.S. fast-food chains.

Methods:

This study evaluated the national implementation of calorie labeling at large fast-food chains from a modified societal perspective and projected its cost-effectiveness over a ten-year period (2018-2027) using the Childhood Obesity Intervention Cost-Effectiveness Study microsimulation model. Using evidence from over 67 million fast-food restaurant transactions between 2015 and 2019, the impact of calorie labeling on calorie consumption and obesity incidence was projected. Benefits were estimated across all racial, ethnic, and income groups. Analyses were performed in 2022.

Results:

Calorie labeling is estimated to be cost-saving, prevent 550,000 cases of obesity in 2027 alone (95% uncertainty interval (UI): 518,000; 586,000), including 41,500 (95% UI: 33,700; 50,800) cases of childhood obesity, and save $22.60 in health care costs for every $1 spent by society in implementation costs. Calorie labeling is also projected to prevent cases of obesity across all racial and ethnic groups (range between 126-185 cases per 100,000 people) and all income groups (range between 152-186 cases per 100,000 people).

Conclusions:

Calorie labeling at large fast-food chains is estimated to be a cost-saving intervention to improve long-term population health. Calorie labeling is a low-cost intervention that is already implemented across the U.S. in large chain restaurants.

Introduction

One in three Americans consumes fast-food on any given day.^1,2 Research has shown that these highly palatable ultra-processed foods may be less satiating and lead to overconsumption.³ Increased consumption of sugary and ultra-processed foods in the last several decades has emerged as a critical contributor to excess weight gain in the U.S.^4,5 On days they eat at fast-food restaurants, children consume 126 more calories compared to days without fast-food, adolescents consume 310 more calories, and adults consume 194 more calories.^6,7 Calorie menu labeling may empower individuals to make healthy choices while eating out by providing information on the healthfulness of menu items at the point of purchase.

The 2010 Patient Protection and Affordable Care Act (ACA) mandated calorie labeling at large restaurant chains across the country, effective May 2018.⁸ Prior to national implementation of calorie labeling, results of evaluations of calorie labeling’s impact on dietary choices were mixed, primarily due to the lack of well-powered studies.^9,10 Recent work, using restaurant transaction data that included time before and after national implementation of calorie labeling—the only such evaluation to date—found small-to-moderate reductions in calories purchased per transaction.¹¹

While prior cost-effectiveness analyses of calorie labeling have been published,^12–14 they relied on effect estimates from meta-analyses of menu labeling studies in various settings (including laboratory settings), none of which evaluated the national implementation of calorie labeling as mandated by the ACA. Additionally, to the authors’ knowledge, none have estimated the impact of calorie labeling for both children and adults and across racial and ethnic groups and income groups, despite concerns that calorie labeling might be less impactful for individuals with lower socioeconomic status (SES).^15,16 Using evidence from the only evaluation of the national implementation of calorie labeling as mandated by the ACA,¹¹ this study evaluates the cost-effectiveness and equity impact of calorie labeling at large fast-food restaurant chains in the U.S. This information can inform federal government decision-making, given that such policies can be rescinded, and similar ones are being proposed (e.g., front-of-package nutrition labels).¹⁷

Methods

The intervention modeled is the Federal calorie labeling regulation, which requires all non-institutional food retail chains with ≥ 20 locations to provide calorie information on their menus, menu boards, or food tags, along with an anchor statement specifying the daily recommended calorie intake for a typical adult.⁸ To evaluate the cost-effectiveness of calorie labeling at large fast-food chains—defined as restaurants with counter service and no wait staff¹⁸—this study used the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) microsimulation model.¹² A 10-year time horizon (2018-2027) was used to ensure the policy relevance of the cost-effectiveness estimates,^12,19 given the lack of good evidence on weight maintenance over longer periods of time. The study does not constitute human subjects research.

Study Population

The CHOICES model was used to create a nationally-representative virtual population ages 2 and above^12,20,21—the assumed intervention target population—using data from the 2010 Census Bureau, American Community Survey, Behavioral Risk Factor Surveillance System (BRFSS), National Health and Nutrition Examination Surveys (NHANES), and National Survey of Children’s Health (NSCH). Data from the U.S. Population Projections, Period Life Tables, and NIH-AARP Diet and Health Study were used to account for population growth. Lifetime height and weight trajectories are based on published analyses.²² Detailed microsimulation model parameters are available in Appendix Table 1.

Measures

Prevention of excess weight gain is the primary outcome used to estimate population health and healthcare cost impacts; the impact of the intervention on quality-adjusted life years (QALYs) is also reported (see Appendix Table 1 for methods used to estimate QALY weights). The effect of calorie labeling at large fast-food chains on body-mass index (BMI) was modeled (Appendix Table 2). In the absence of a direct estimate of the impact of calorie labeling or of a change in fast-food consumption on BMI, the following logic was used (Appendix Figure 1). First, it was assumed that calorie labeling would reduce the average calories purchased per fast-food meal, which would directly translate into a reduction in calories consumed per meal. Using over 67 million fast-food restaurant transactions between 2015 and 2019, Petimar et al. found that, pre-implementation of calorie labeling, consumers purchased 1486 calories per transaction on average. Post-implementation, consumers purchased on average 4.7% fewer calories—equivalent to 73 calories—per transaction across all restaurants, with a greater reduction at restaurants in census tracts with higher income compared to those with lower income.¹¹ This study applied a stratified estimate for the effect of calorie labeling on individual fast-food purchases by census tract income quartile using data from supplemental analyses of the fast-food transaction data published by Petimar,¹¹ under the assumption that people are more likely to dine at restaurants in their census tracts or in census tracts with similar demographic profiles.^23–25 For those living in census tracts where the median household income is in the lowest quartile (<$35,800), calorie labeling is associated with a 2.3% reduction in calories purchased per transaction; for those living in census tracts where the median household income is in the highest quartile (≥$66,120), the reduction is 8.1%.

Using evidence from prior studies,^26–28 it was assumed that, due to compensation (i.e., adjustments in energy intake in response to dietary changes in order to feel satiated²⁹), only 25% of the reduction in calories consumed per fast-food meal would translate into a change in daily energy intake (75% compensation). From this reduced energy intake, reductions in weight using the Hall et al. energy balance models for children and adults were used.^12,30,31 A time-to-effect of 24 months for children and 36 months for adults was used and maintenance of the intervention and its effects over the 10-year time horizon was assumed.^30,31

Using the FDA’s analysis of the food retail market, it was assumed that 69.4% of fast-food calories were obtained from large chains that were required to implement calorie labeling.³² Mean daily calories consumed from fast-food sources by age, sex, race, and ethnicity were estimated using data from the 2011-2016 NHANES to account for baseline differences in consumption by group (Appendix Table 3). Self-reported race and ethnicity categories were collapsed into four separate groups to ensure a large enough sample size to estimate calorie consumption for each strata. The impact of calorie labeling on the prevention of excess weight gain (cases of obesity prevented) is reported for the overall population, as well as stratified by racial and ethnic and income groups (defined using percent of the federal poverty level (FPL)), in the year 2027 only given that individuals move in and out of obesity over time. Differences in the effect of the intervention are based on baseline differences in fast-food consumption and differential effects estimated by census tract income quartile; there are no direct estimates of change in calories purchased by race and ethnicity of the consumer.

Implementation cost estimates were extracted from the FDA’s food retail market analysis and updated.³² Cost categories included 1) federal government policy dissemination; 2) restaurant industry nutritional assessment, menu design and replacement, and legal review; and 3) local government compliance monitoring. Costs were limited to those at the chain level. Omitted costs included those associated with 1) analyzing reformulated menu items given that reformulation is not required by the final rule³² and 2) training restaurant staff in using calorie labels, which are expected to be very small or non-existent. The cost-effectiveness analysis used a modified societal perspective,¹² 2019 as the reference year, a 3% discount rate,³³ and a cost-effectiveness threshold of $150,000 per QALY.³⁴ Healthcare costs saved per $1 invested in the intervention and the cost per QALY gained are reported. Healthcare costs associated with BMI are based on published analyses.³⁵ Following cost-effectiveness analysis guidelines, cost-per-QALY gained values are not reported when they are negative or cost-saving.³⁶ Additional details about model inputs are available in Table 1 and Appendix Table 5.

Table 1.

Key intervention implementation parameters: Reach, effect, and cost

Model parameter	Mean values	Sources
Reach parameters
Benefiting population	American children ages 2-19; American adults ages 20-100 in all 50 states + Washington, D.C.	n/a
Effect parameters
Proportion of fast-food meals from large chain restaurants	69.4%	FDA Regulatory Impact Assessment³²
Mean daily calories consumed from fast-food	Children ages 2-19: 267.7 kcals (standard error (SE): 10.3) Adults ages 20-100: 301.1 kcals (SE: 7.6)	2011-2016 National Health and Nutrition Examination Survey
Percentage change in calories purchased per transaction, stratified by census tract income quartile	Quartile 1 (lowest income): −2.3% (95% CI: −3.2%, −1.3%) Quartile 2: −4.1% (95% CI: −5.1%, −3.1%) Quartile 3: −4.2% (95% CI: −5.2%, −3.2%) Quartile 4 (highest income): −8.1% (95% CI: −8.9%, −7.2%)	Petimar et al. 2021,¹¹ supplemental analyses
Cost parameters, by payer
Federal government (FDA)
Menu labeling guideline communication	$1,900,000 in year 1 only • FDA administrative staff labor (10 fulltime equivalents (FTEs))	FDA Regulatory Impact Assessment;³² U.S. Bureau of Labor Statistics
Restaurant industry
Number of fast-food chains in year 1	502	Authors own calculations based on FDA Regulatory Impact Assessment³² and County Business Patterns 2018 data
Proportion of chains without prior menu labeling in 2018	47%	FDA Regulatory Impact Assessment³²
Percent growth in new eligible chains and establishments per year	2%	Authors own calculations based on FDA Regulatory Impact Assessment³² and County Business Patterns 2018 data
Number of fast-food establishments in year 1	109,152	Authors own calculations based on FDA Regulatory Impact Assessment³² and County Business Patterns 2018 data
Number of menu items per chain	180	Analysis of MenuStat data (https://www.menustat.org/)
Number of new menu items per year per chain	12	Supplementary analysis of MenuStat data https://www.menustat.org/
Number of menu boards per establishment	3	FDA Regulatory Impact Assessment³²
Number of printed menus per establishment	360	FDA Regulatory Impact Assessment³²
Menu item analysis	$25,900,000 over 10 years • Analysis of current and new menu items • Nutrition database cost per menu item ($64) • Dietician labor per menu item (4 hours)	FDA Regulatory Impact Assessment;³²U.S. Bureau of Labor Statistics
Menu and menu board design and replacement	$218,000,000 in year 1 only • Redesign cost per menu ($4,200) • Menu board replacement cost ($625) • Restaurant labor per menu board replacement (1.5 hours) • Printed individual menu ($0.07)	FDA Regulatory Impact Assessment;³²U.S. Bureau of Labor Statistics
Industry legal review	$600,000 in years 1-10 • Lawyer labor per chain (10 hours)	FDA Regulatory Impact Assessment;³²U.S. Bureau of Labor Statistics
Local government
Compliance monitoring	$58,600,000 in years 1-10 • Public health inspector labor per establishment (0.0005 FTE)	Previous cost-effectiveness analysis of menu labeling;¹²U.S. Bureau of Labor Statistics

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Statistical Analysis

The CHOICES model is a stochastic, discrete-time, individual-level microsimulation model of the U.S. population.¹² All models are reported with 95% uncertainty intervals (UI) using 1,000 iterations of the model using Monte Carlo simulations.^12,37 Probabilistic sensitivity analyses were conducted by simultaneously sampling from parameter distributions. Analyses were performed in 2022.

A set of one-way sensitivity analyses were also conducted (Appendix Table 2). In the first set of analyses, instead of stratifying the effect of calorie labeling on fast-food purchases by census tract income quartile, an overall effect (−4.7% (95% CI: −5.2%, −4.2%)) was applied to all individuals in the population (sensitivity model 1).¹¹ Additionally, a threshold analysis was conducted for the amount of compensation and assumed that the change in total daily energy intake was only 5% (instead of 25%) of the change in calories consumed from fast-food (sensitivity model 2).

In the second set of analyses (3-5), the effect of calorie labeling on changes in intake of sugar-sweetened beverages (SSBs) intake only was modeled, which incorporated direct evidence on the expected change in weight from a change in SSB consumption from randomized trials and change-in-change studies and accounted for a differential effect of the intervention by baseline BMI (Appendix Figure 2).^27,38–44 Mean daily calories consumed from SSBs from fast-food sources by age, sex, and obesity status were estimated from NHANES to account for baseline differences in intake by group (Appendix Table 4).

Results

Compared to what would have happened had calorie labeling not been implemented (the counterfactual), calorie labeling is projected to reach 349M Americans over the period 2018-2027 and prevent 550,000 (95% UI: 518,000, 586,000) cases of obesity in 2027, including 41,500 (95% UI: 33,700, 50,800) cases of childhood obesity (Table 2). It is also projected to prevent 17,700 (95% UI: 13,600, 22,200) deaths over the period 2018-2027 and to lead to a gain of 267,000 (95% UI: 251,000, 283,000) QALYs.

Table 2.

Projected reach, cost, and cost-effectiveness outcomes, 2018-2027

Outcome	Mean (95% UI)
10-year population reach (2018-2027)	349,000,000 (348,000,000, 350,000,000)
First year population reach (2018)	314,000,000 (314,000,000, 315,000,000)
10-year intervention implementation cost (2018-2027)	$305,000,000
Annual intervention implementation cost	$30,500,000
First-year intervention implementation cost (2018)	$236,000,000
Annual intervention implementation cost per benefiting person	$0.10
Healthcare costs saved over 10 years (millions)	$6,880 ($6,670, $7,090)
Net cost difference (millions)	−$6,580 (−$6,790, −$6,370)
Healthcare costs saved per $1 invested	$22.60 ($21.90, $23.30)
Deaths averted	17,700 (13,600, 22,200)
QALYs gained	267,000 (251,000, 283,000)
Cost per QALY gained^a	Cost-saving
Cases of adult + childhood obesity prevented in 2027 alone	550,000 (518,000, 586,000)
Cases of adult + childhood obesity prevented per 100,000 people in 2027 alone	175 (165, 186)
Reduction in adult + childhood obesity prevalence (overall) in 2027 alone (%)^b	0.175 (0.165, 0.186)
Cases of childhood obesity prevented in 2027 alone	41,500 (33,700, 50,800)
Cases of childhood obesity prevented per 100,000 people in 2027 alone	59 (48, 72)
Reduction in childhood obesity prevalence in 2027 alone (%)^b	0.059 (0.048, 0.072)

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95% UI: 95% uncertainty interval

QALY: Quality-adjusted life years

Negative values mean the intervention is cost-saving (dominant)

100% of microsimulation iterations were cost-saving (dominant)

This is a reduction compared to what would have occurred in the absence of the national implementation of calorie labeling.

Calorie labeling is also projected to prevent cases of obesity across all racial and ethnic groups and all income groups (Table 3). However, cases of obesity prevented per 100,000 people among Hispanic or Latino individuals is 0.887 times (95% UI: 0.805-0.990) that projected among non-Hispanic/Latino white individuals. Similarly, cases of obesity prevented per 100,000 people among the lowest income group is 0.817 (95% UI: 0.748, 0.882) that projected for the group with highest income.

Table 3.

Intervention effect and health equity metrics in final model year (2027) with 95% uncertainty intervals

Population group	Cases of adult + childhood obesity prevented	Cases of adult + childhood obesity prevented per 100,000 people	Relative reduction in cases of adult + childhood obesity prevented per 100,000 people^b	Cases of childhood obesity prevented	Cases of childhood obesity prevented per 100,000 people	Relative reduction in cases of childhood obesity prevented per 100,000 people^b
By race and ethnicity
Black or African American, not Hispanic or Latino	70,600 (62,400, 79,200)	179 (160, 200)	0.968 (0.835, 1.137)	6,260 (4,480, 8,470)	65 (47, 87)	1.121 (0.781, 1.685)
Hispanic or Latino	101,000 (93,400, 109,000)	164 (151, 177)	0.887 (0.805, 0.990)	11,200 (8,410, 14,300)	61 (47, 79)	1.057 (0.754, 1.498)
All other races, not Hispanic or Latino^a	34,200 (29,900, 39,800)	126 (111, 147)	0.681 (0.581, 0.796)	3,850 (2,440, 5,580)	52 (33, 74)	0.894 (0.552, 1.430)
White, not Hispanic or Latino	343,000 (315,000, 372,000)	185 (170, 201)	reference	20,200 (14,600, 26,400)	58 (42, 76)	reference
By household income as a % of the federal poverty level (FPL)
0-130% FPL	110,000 (104,000, 118,000)	152 (144, 161)	0.817 (0.748, 0.882)	12,500 (9,900, 15,200)	57 (45, 70)	0.982 (0.815, 1.230)
131-185% FPL	52,800 (48,100, 57,700)	164 (150, 180)	0.885 (0.804, 0.975)	5,100 (3,520, 6,510)	60 (42, 75)	1.028 (0.778, 1.396)
186-350% FPL	148,000 (137,000, 163,000)	183 (169, 201)	0.983 (0.912, 1.074)	11,100 (8,720, 14,300)	62 (49, 80)	1.071 (0.851, 1.312)
351-1000% FPL	238,000 (219,000, 258,000)	186 (171, 200)	reference	12,800 (9,780, 16,400)	58 (45, 75)	reference

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This category includes people who identify as American Indian/Alaska Native, Asian, Native Hawaiian or Pacific Islander, Multi-racial, or another race/ethnicity not represented in the other three categories. The results for “relative rate of obesity cases prevented” among this group are not interpretable given the heterogeneity within this group, which were consolidated given their relatively small population sizes.

The relative reduction metric is a ratio of cases of obesity prevented due to intervention between each group and the reference category in 2027.

Calorie labeling is projected to cost $305M (95% UI: 348M, 350M), equivalent to $0.10 per exposed person per year. Most costs ($236M) were incurred in the first year when restaurants were required to analyze menu items, redesign menus, and replace menu boards. The intervention is projected to save a total of $6,880M (95% UI: ($6,670M, $7,090M) or $22.60 (95% UI: $21.90, $23.30) in healthcare costs per $1 invested in its implementation. Overall, calorie labeling is projected to be a cost-saving intervention.

Results from all five sensitivity analyses project that calorie labeling is cost-saving under multiple different assumptions, including when an overall effect of calorie labeling on calories purchases was used, when the change in daily energy intake was assumed to be only 5% of the change in calories consumed from fast-food, and when the effect of calorie labeling on fast-food SSB purchases only was evaluated (Appendix Tables 6–15). Unlike the results from the main model, however, results from the sensitivity models suggest similar relative impact of calorie labeling across all racial and ethnic groups, with a slightly larger relative impact among Hispanic or Latino individuals in sensitivity models 3-5 and a slightly larger relative impact among non-Hispanic/Latino Black or African American individuals in sensitivity model 1, compared to non-Hispanic/Latino white individuals. Results further suggest a similar relative impact of calorie labeling across all income groups, with a slightly larger relative impact among the group with the second highest income compared to the group with the highest income in sensitivity models 4-5.

Discussion

Calorie labeling at large fast-food chains in the U.S. is projected to be a cost-saving intervention that improves population health. In the main model, calorie labeling is projected to prevent 550,000 cases of obesity, including 41,500 cases of childhood obesity, in 2027. Similar benefits are expected across all racial and ethnic and income groups, with slightly smaller rates of obesity prevented among Hispanic or Latino individuals and among individuals living at are below 185% FPL. These differences were not seen for children. This is a highly successful public health intervention, especially compared to other obesity prevention interventions, which typically cost more to implement.¹²

These results are consistent with those of previously published cost-effectiveness analyses of calorie labeling.^12,13 A 2015 study of calorie labeling at both fast-food and full-service chain restaurants projected the intervention to be cost-saving but with smaller estimated effects on the prevention of weight gain; the study only examined childhood obesity.¹² The study predated the national implementation of calorie labeling and, therefore, relied on a weaker estimate for the effect of calorie labeling on calories purchased from restaurants available at the time (−7.63 calories per meal).¹⁰ A more recent cost-effectiveness analysis of calorie labeling also found the intervention to be cost-saving, over a 5-year period (2018-2023), with benefits for cardiovascular health.¹³ That study, however, used an estimate for the effect of calorie labeling from a meta-analysis that included multiple types of study designs and settings that predated national implementation of calorie labeling.⁴⁵

Calorie labeling is a population-wide policy aimed at educating, empowering, and nudging individuals to make healthy choices while eating out by providing information on calorie content of items at the point of purchase. Evidence regarding differential effects of labeling by SES is mixed; if there is a difference in response by SES, it could potentially be mediated by lower health or nutrition literacy in this population.^16,46 In this cost-effectiveness analysis, calorie labeling at large fast-food chains is projected to reduce cases of obesity for all income groups but may widen health disparities between some groups, especially among adults. However, it is also possible that these results may underestimate the effect among those with lower SES since the main model could not account for differential effects of the intervention by baseline BMI. Evidence indicates that individuals in lower-income groups have, on average, a higher prevalence of obesity,^47,48 which may counteract the smaller effect of calorie labeling. At the same time, the estimates used were based off differential effects seen at the census-tract level, not on estimates of differential purchasing by income; it is possible that true differences by income at the individual level could differ from these neighborhood-level effects.

As a large-scale public health intervention to improve the food environment, calorie labeling is low-cost, feasible, and sustainable. While calorie labeling is projected to be cost-saving, the implementation costs were likely overestimated. Most fast-food chains made nutritional information available (on their websites or inside the stores) prior to labeling;⁴⁹ thus, they would not have accrued additional costs to produce this information. Additionally, the costs of monitoring compliance with calorie labeling by local health departments were modeled over the 10-year intervention period. To date, however, there is little evidence that compliance with labeling is monitored or enforced. Enforcing compliance with (and accuracy of) calorie labeling could potentially maximize population health benefit and cost-savings. Finally, while some concerns have been raised about the potential role of calorie labeling in exacerbating eating disorders,⁵⁰ there is currently only very limited evidence to support this finding or recommend de-implementation of calorie labeling,⁵¹ especially given its potential benefits.

Limitations

This study has multiple strengths and limitations. First, cost-effectiveness modeling studies inherently involve making varied assumptions on relationships for which data are sparse. Multiple scenarios were modeled adjusting those assumptions and found that calorie labeling was cost-saving even under the most conservative scenarios. Second, the 10-year modeling period overlaps with the COVID-19 pandemic, which produced (and continues to produce) substantial disruptions to the economy, food system, and health. The impact of the pandemic was not accounted for in the model. An analysis of food-away-from-home spending during the period of December 2019-April 2021 indicated that while there was a small dip in spending at quick-service restaurants (including fast-food restaurants) during March-May 2020, spending steadily increased over time after May 2020, surpassing pre-pandemic spending.⁵² Third, the estimate for the effect of calorie labeling on changes in food purchases and consumption comes from one study using a single fast-food restaurant’s transaction data; no individual customer data was collected. The effect estimate stratified by income quartile used the restaurant’s census tract information was applied to individuals in the virtual population, potentially introducing bias. People almost certainly visit restaurants outside of census tracts that are similar to their own.²³ These results can be updated as new data becomes available. However, the study used a robust quasi-experimental study design. The estimate for the effect of calorie labeling on consumption is based on evidence using over 67 million fast-food transactions over the period that spanned the implementation of calorie labeling nationally,¹¹ which distinguishes this study from previously published cost-effectiveness analyses of calorie labeling.

To the authors’ knowledge, there are no studies directly linking changes in fast-food consumption with changes in weight, highlighting an area for future research. The energy balance models developed by Hall et al.^30,31 were therefore used, assuming different levels of translation between calories purchased/consumed from fast-food and change in total daily energy intake. An experiment by Roberto et al. found no compensation later in the evening from calorie labeling,⁵³ suggesting the modeling approach used in study may have been conservative by assuming 75% compensation. Additionally, evidence suggests that ultra-processed foods have a strong impact on weight, leading to a possible underestimation of the policy’s effect on obesity.⁵

Finally, only the effects of calorie labeling at fast-food restaurants were estimated, but the policy was implemented at all chain retail food establishments serving prepared foods, including full-service restaurants and supermarkets. A recent study evaluating the effect of calorie labeling at a large supermarket chain found small declines in calories purchased for bakery and deli items,⁵⁴ which have the potential to translate to health benefits. This study likely underestimates the population health effect of calorie labeling as mandated by ACA.

Conclusions

Calorie labeling at large fast-food chains is projected to slow excess weight gain over the next decade. Benefits can only be fully realized if this cost-saving intervention is sustained.

Supplementary Material

Appendix Table 1. Microsimulation model parameters, CHOICES v4.6.1

Appendix Table 1.1. Health-related quality of life weights for adults and children by body mass index category based on Muennig et al. and Kwon et al.

Appendix Table 2. Key parameters used to estimate the effect of calorie labeling on weight in the cost-effectiveness analysis, by model

Appendix Table 2.1: Median BMI for Dutch Children ages 2-19 years in 2009, by Age and Sex

Appendix Table 2.2: Estimates of change in weight (kg) per 1-serving change in sugary drinks among adults, by weight category

Appendix Table 3. Daily calories from fast-food at baseline, stratified by age, sex, race, and ethnicity

Appendix Table 4. Daily sugar-sweetened beverages (SSBs) ounces from fast-food at baseline, stratified by age, sex, and binary obesity status

Appendix Table 5. Detailed intervention costs, by payer

Appendix Table 6. Mean reach, cost, effect, and cost-effectiveness outcomes for sensitivity model 1

Appendix Table 7: Intervention effect and health equity metrics in final model year (2027) alone, with 95% uncertainty intervals, for sensitivity model 1

Appendix Table 8. Mean reach, cost, effect, and cost-effectiveness outcomes for sensitivity model 2

Appendix Table 9: Intervention effect and health equity metrics in final model year (2027) alone, with 95% uncertainty intervals, for sensitivity model 2

Appendix Table 10. Mean reach, cost, effect, and cost-effectiveness outcomes for sensitivity model 3

Appendix Table 11: Intervention effect and health equity metrics in final model year (2027) alone, with 95% uncertainty intervals, for sensitivity model 3

Appendix Table 12. Mean reach, cost, effect, and cost-effectiveness outcomes for sensitivity model 4

Appendix Table 13: Intervention effect and health equity metrics in final model year (2027) alone, with 95% uncertainty intervals, for sensitivity model 4

Appendix Table 14. Mean reach, cost, effect, and cost-effectiveness outcomes for sensitivity model 5

Appendix Table 15: Intervention effect and health equity metrics in final model year (2027) alone, with 95% uncertainty intervals, for sensitivity model 5

Appendix Figure 1. Logic model for the effect of calorie labeling on weight

Appendix Figure 2. Logic model for the effect of calorie labeling on sugar-sweetened beverage (SSB) consumption and weight

NIHMS1929380-supplement-1.pdf^{(572.1KB, pdf)}

Acknowledgments

The authors would like to thank Stephanie McCulloch, Ben Rohrer, Matt Lee, Amy Bolton, and Mark Soto for their support.

Research reported in this publication was supported by the National Heart, Lung, and Blood Institute under Award Number R01HL146625, by the National Institute of Diabetes and Digestive and Kidney Diseases under Award Number R01DK115492, by the Centers for Disease Control and Prevention under Award Number U48DP006376, and by the JPB Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The funders had any role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.

The authors have no conflicts of interest to disclose. No financial disclosures were reported by the authors of this paper.

R. Dupuis collected and prepared intervention parameter data for the microsimulation model, reviewed and interpreted model results, wrote the first draft of the manuscript, and reviewed and revised the manuscript. J.P. Block and S.L. Gortmaker conceptualized and designed the study and obtained funding. S.L. Gortmaker led the Childhood Obesity Intervention Cost-Effectiveness Study (CHOICES) as principal investigator. J. Petimar led the evaluation of the effectiveness of calorie labeling at fast-food restaurants and conducted supplemental analyses for this study. Z.J. Ward created the microsimulation model and virtual population. J.P. Block, S.L. Gortmaker, M.W. Long, J.L. Barrett, and A.A. Musicus advised and contributed to the collection and preparation of model intervention parameters. J.P. Block, S.L. Gortmaker, E.L. Kenney, and C.C. Cannuscio reviewed and interpreted model results. S.N. Bleich and D.R. Williams provided critical input to frame and contextualize the intervention and results. All authors provided substantive input on the manuscript and approved the final manuscript as submitted.

Footnotes

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