Cost-effectiveness Analysis of Nutrition Facts Added-Sugar Labeling and Obesity-Associated Cancer Rates in the US | Cancer Screening, Prevention, Control | JAMA Network Open | JAMA Network
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Figure 1.  Estimated Health Care–Related Cost Savings Associated With Added-Sugar Labeling by Cancer Type Over a Lifetime
Estimated Health Care–Related Cost Savings Associated With Added-Sugar Labeling by Cancer Type Over a Lifetime
Figure 2.  Probabilistic Sensitivity Analyses for Cost-effectiveness of Added-Sugar Labeling Over 12 Years and a Lifetime
Probabilistic Sensitivity Analyses for Cost-effectiveness of Added-Sugar Labeling Over 12 Years and a Lifetime
Table 1.  Key Input Parameters and Data Sources in the Dietary Cancer Outcome Model
Key Input Parameters and Data Sources in the Dietary Cancer Outcome Model
Table 2.  Estimated Health Gains and Costs of the US Food and Drug Administration’s Added-Sugar Labeling on Reducing Cancer Burdens in the US Over 12 Years and a Lifetimea
Estimated Health Gains and Costs of the US Food and Drug Administration’s Added-Sugar Labeling on Reducing Cancer Burdens in the US Over 12 Years and a Lifetimea
Table 3.  Estimated New Cancer Cases and Deaths Prevented by FDA’s Added-Sugar Labeling Policy Among US Adults Over a Lifetime by Age, Sex, and Race/Ethnicitya
Estimated New Cancer Cases and Deaths Prevented by FDA’s Added-Sugar Labeling Policy Among US Adults Over a Lifetime by Age, Sex, and Race/Ethnicitya
Supplement.

eAppendix 1. Baseline Cancer Incidence and Methods of Cancer Incidence Projections for 13 Types of Cancer

eAppendix 2. Cancer Survival for 13 Types of Cancer

eTable 1. Period Method for Five-Year Relative Survival for 2014

eAppendix 3. Methods of Estimating the Health-Related Quality of Life Among 13 Types of Cancer

eAppendix 4. Estimate the Association Between Added Sugar Labeling Policy and Added Sugar Intake

eAppendix 5. Methods of Estimating Policy Implementation Costs

eTable 2. Implementation Cost Estimates for the Nutrition Facts Added Sugar Labeling Policy (in 2015 US Dollars)

eTable 3. Discounting Factor in Each Year Over a Lifetime

eTable 4. Population Size of People Who are Alive in Each Year Over a Lifetime (in Millions)

eAppendix 6. Annual Health-Related Costs Among Cancer Patients and the General Population Without Cancer

eTable 5. Description of Data Source of Health-Related Expenditures

eTable 6. Defining Population and 32 Subgroups

eTable 7. Characteristics of US Adults Aged 20 Years or Older Participated in the NHANES, 2013-2016

eTable 8. Consumption of Added Sugar From Foods and Beverages Among US Adults Participated in 2013-2016 NHANES, by 32 Subgroups

eTable 9. Relative Risk Estimates of Etiologic Relationships Between Body Mass Index (BMI) and Cancer

eTable 10. Health-Related Quality of Life Among US Cancer Patients Aged 20 Years or Older, by Cancer Type and Phase of Care

eTable 11. Baseline Medical Costs, Productivity Loss, and Patient Time Costs Among US Cancer Patients Aged 20 Years or Older, by Cancer Type

eTable 12. Baseline Medical Costs, Productivity Loss, and Patient Time Costs Among the General Population Aged 20 Years or Older in the US, by 32 Subgroups

eTable 13. Estimated Changes in Health-Related Costs Associated With Nutrition Facts Added Sugar Labeling on Reducing Cancer Burdens in the US Over a Lifetime, by Cancer Type

eTable 14. Estimated New Cancer Cases Averted by Nutrition Facts Added Sugar Labeling Policy in the US by Age, Gender, Race/Ethnicity, and Cancer Type Over a Lifetime

eTable 15. Estimated Cancer Deaths Reduced by Nutrition Facts Added Sugar Labeling Policy in the US by Age, Gender, Race/Ethnicity, and Cancer Type Over a Lifetime

eTable 16. Estimated Health Gains and Costs Associated With Nutrition Facts Added Sugar Labeling on Reducing Cancer Burdens in the US Over 10 Years

eTable 17. Estimated Health Gains and Costs Associated With Nutrition Facts Added Sugar Labeling on Reducing Cancer Burdens in the US Over a Lifetime, One-Way Sensitivity Analyses at 50% of the Policy Impact and the Diet-BMI Association

eTable 18. Estimated Health Gains and Costs Associated With Nutrition Facts Added Sugar Labeling on Reducing Cancer Burdens in the US Over a Lifetime, Threshold Analyses on Policy Impact

eFigure 1. Diet and Cancer Outcome Model (DiCOM)

eFigure 2. Estimated Reduced New Cancer Cases and Deaths Associated With Nutrition Facts added Sugar Labeling Policy in the US by Age, Gender, Race/Ethnicity, and Cancer Type Over a Lifetime

eFigure 3. Estimated Life Years and QALYs Gained Associated With Nutrition Facts Added Sugar Labeling Policy in the US by Age, Gender, and Race/Ethnicity Over a Lifetime

eFigure 4. Estimated Changes of Health-Related Costs Associated With Nutrition Facts Added Sugar Labeling Policy in the US by Age, Gender, Race/Ethnicity and Cancer Type Over a Lifetime

eFigure 5. Estimated Net Costs From Societal and Healthcare Perspectives Associated With Nutrition Facts Added Sugar Labeling Policy in the US by Age, Gender, and Race/Ethnicity Over a Lifetime

eFigure 6. One-Way Sensitivity Analysis of Net Costs of Nutrition Facts Added Sugar Label and Obesity-Associated Cancer Rates to Varying Assumptions of Key Input Parameters From (A) Societal Perspective and (B) Healthcare Perspective

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    Original Investigation
    Health Policy
    April 27, 2021

    Cost-effectiveness Analysis of Nutrition Facts Added-Sugar Labeling and Obesity-Associated Cancer Rates in the US

    Author Affiliations
    • 1Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
    • 2Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts
    • 3Division of Clinical Decision Making, Tufts Medical Center, Boston, Massachusetts
    • 4Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York
    • 5New York Academy of Medicine, New York
    • 6Now with Division of Substance Abuse & Mental Health, Department of Health and Social Services, State of Delaware, Newcastle
    • 7Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, Massachusetts
    JAMA Netw Open. 2021;4(4):e217501. doi:10.1001/jamanetworkopen.2021.7501
    Key Points

    Question  What is the estimated association between added-sugar labeling and obesity-related cancer rates in the US?

    Findings  This economic evaluation of Nutrition Facts added-sugar labeling and obesity-related cancer rates estimated that implementing the policy was associated with a reduction of 30 000 new cancer cases, 17 100 cancer deaths, and $1600 million in medical costs among US adults over a lifetime. This policy would generate net savings of $704 million from a societal perspective and $1590 million from a health care perspective.

    Meaning  These model findings suggest added-sugar labeling would be associated with reduced costs and lower rates of obesity-related cancers.

    Abstract

    Importance  Obesity-associated cancer burdens are increasing in the US. Nutrition policies, such as the Nutrition Facts added-sugar labeling, may reduce obesity-associated cancer rates.

    Objective  To evaluate the cost-effectiveness of Nutrition Facts added-sugar labeling and obesity-associated cancer rates in the US.

    Design, Setting, and Participants  A probabilistic cohort state-transition model was used to conduct an economic evaluation of added-sugar labeling and 13 obesity-associated cancers among 235 million adults aged 20 years or older by age, sex, and race/ethnicity over a median follow-up of 34.4 years. Policy associations were considered in 2 scenarios: with consumer behaviors and with additional industry reformulation. The model integrated nationally representative population demographics, diet, and cancer statistics; associations of policy intervention with diet, diet change and body mass index, and body mass index with cancer risk; and policy and health-related costs from established sources. Data were analyzed from January 8, 2019, to May 6, 2020.

    Main Outcomes and Measures  Net costs and incremental cost-effectiveness ratio were estimated from societal and health care perspectives. Probabilistic sensitivity analyses incorporated uncertainty in input parameters and generated 95% uncertainty intervals (UIs).

    Results  Based on consumer behaviors, the policy was associated with a reduction of 30 000 (95% UI, 21 600-39 300) new cancer cases and 17 100 (95% UI, 12 400-22 700) cancer deaths, a gain of 116 000 (95% UI, 83 800-153 000) quality-adjusted life-years, and a saving of $1600 million (95% UI, $1190 million-$2030 million) in medical costs associated with cancer care among US adults over a lifetime. The policy was associated with a savings of $704 million (95% UI, $44.5 million-$1450 million) from the societal perspective and $1590 million (95% UI, $1180 million-$2020 million) from the health care perspective. Additional industry reformulation to reduce added-sugar amounts in packaged foods and beverages would double the impact. Greater health gains and cost savings were expected among young adults, women, and non-Hispanic Black individuals than other population subgroups.

    Conclusions and Relevance  These findings suggest that the added-sugar labeling is associated with reduced costs and lower rates of obesity-associated cancers. Policymakers may consider and prioritize nutrition policies for cancer prevention in the US.

    Introduction

    Obesity is associated with an increased risk of 13 cancers.1 In the US, 631 000 individuals were diagnosed with obesity-associated cancers in 2014, accounting for 40% of all incident cancer cases.2 With the prevalence of overweight and obesity remaining high, obesity-associated cancers are contributing to substantial health and economic burdens in the US.

    Evidence supports that high added sugar consumption contributes to obesity.3 Yet, US adults consume more than 14% of daily calories (approximately 300 kcals/d) from added sugars, exceeding the recommendation of the 2020-2025 Dietary Guidelines for Americans (<10% daily calories from added sugar).4 It was estimated that more than 3000 new cancer cases per year among US adults are attributable to increased added-sugar consumption from sugar-sweetened beverages alone5; decreasing added-sugar intake from all sources may be associated with reduced obesity-related cancer burdens.

    In 2016, the US Food and Drug Administration (FDA) announced a mandatory labeling policy for all packaged foods and beverages to include the amount of added sugars on the Nutrition Facts label.6 This policy seeks to help consumers make informed food choices and reduce added sugar consumption.6,7 Manufacturers may also be motivated to reformulate sugar content in their products.8,9 However, the health benefit and economic association between the added-sugar labeling policy and obesity-associated cancer rates have not been evaluated. This study aimed to estimate the cost-effectiveness of the Nutrition Facts added-sugar labeling and obesity-associated cancer rates among US adults and further evaluate the association between implementing the policy and health disparities in population subgroups by age, sex, and race/ethnicity.

    Methods
    Study Overview

    We used the Diet and Cancer Outcome Model,10,11 a probabilistic cohort state-transition model to perform an economic evaluation of the added-sugar labeling policy and obesity-related cancer rates among 235 million US adults aged 20 years or older over a simulated lifetime (eFigure 1 in the Supplement). The model integrated nationally representative population demographics, dietary intake, and cancer statistics; association estimates of policy intervention with diet, diet change with body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared), and BMI with cancer risks; and policy and health-related costs from established sources1,7-9,12-24 (Table 1). Data were analyzed from January 8, 2019, to May 6, 2020. This study used deidentified national data sets and was exempted from review by the Tufts University Health Science Institutional Review Board because it did not involve human subjects as defined by the Department of Health and Human Services and US Food and Drug Administration. The study followed the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline for economic evaluation studies.

    Simulated US Population and Added Sugar Consumption

    The 2013-2016 National Health and Nutrition Examination Survey data were used to simulate the US adult population aged 20 years or older in 32 subgroups by age (20-44, 45-54, 55-64, and ≥65 years), sex (men and women), and race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other) (eTable 6 and eTable 7 in the Supplement), assuming a closed cohort starting in 2015.

    The mean added sugar consumption from packaged foods and beverages was estimated using data collected from participants with at least 1 valid 24-hour diet recall, stratified by 32 population subgroups (eTable 8 in the Supplement). Because added sugars are ubiquitously consumed in the US, to correct measurement errors associated with dietary intake estimated using 1 or 2 days of diet recalls, the amount-only National Cancer Institute method was applied to estimate the usual intake distribution of added sugars by fitting a linear regression on a transformed scale with person-specific outcomes.25 The prevalence of overweight or obesity in 32 subgroups was estimated based on the objectively measured BMI. The complex survey design was incorporated in all statistical analyses by using appropriate sampling weights, strata, and units to ensure the representativeness of the noninstitutionalized US adult population.

    Added Sugar Consumption and Obesity-Associated Cancer Risk

    To estimate the relative risks of obesity-associated cancer risk with high added sugar consumption, we associated the multivariate-adjusted association of change in added sugar consumption (grams per day) with change in BMI (the diet-BMI associations) and the estimates of BMI and cancer risk (BMI-cancer relative risks). The diet-BMI estimates were obtained from a pooled analysis from 120 997 US men and women followed up over 12 to 20 years in 3 prospective cohort studies (Nurses’ Health Study, Nurses’ Health Study II, and Health Professionals Follow-up Study) (Table 1).12,26 The estimation accounted for potential calorie compensation (ie, compensating daily calories from other sources), facilitating more realistic and conservative estimates. The relative risks of cancer associated with each 5-point increase in BMI for 13 cancers were based on systematic reviews and meta-analyses of prospective cohort studies conducted by the World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project and the International Agency for Research on Cancer (eTable 9 in the Supplement).1,13

    Cancer Incidence, Mortality, and Health-Related Quality of Life

    Age-adjusted cancer incidence for 13 obesity-associated cancers in 2015 in each of the 32 population subgroups was obtained from the National Program of Cancer Registries and the Surveillance, Epidemiology, and End Results program.27 We projected the cancer incidence from 2015 to 2030 based on the incidence of cancer from 2006 to 2014 using the average annual percent change method (eAppendix 1 in the Supplement).14 We extracted and converted the 5-year relative survival rates for each cancer to an annual probability of death (eAppendix 2 and eTable 1 in the Supplement).28 Health-related quality of life was obtained from publications that reported the utility weights using EuroQol-5 Dimension for each cancer among the US patient population (eAppendix 3 and eTable 10 in the Supplement).

    Policy Association With Dietary Intake

    Potential policy association was considered in 2 scenarios: consumers’ consumption of added sugars (ie, consumer behavior) and additional industry reformulation (ie, consumer behavior plus industry reformulation). The association with consumer behavior was obtained from a meta-analysis of food labeling interventions that reported a 6.6% (95% CI, 4.4%-8.8%) reduction in calories from menu calorie labeling.7,29 We assumed that the policy would result in a 1-time reduction in added sugar consumption during the first year of policy implementation, with no further increases or decreases thereafter. We further assumed no policy association with cancer risk within the first 5 years of implementation.

    The association with industry reformation was estimated from the FDA’s regulatory impact analysis8 on the added-sugar labeling policy and the findings from the UK sugar reduction strategy.9 Based on these reports, we assumed no reformulation during the first year of implementation. In each of the subsequent 2 to 5 years, 7.5% to 9.0% of sugar-containing products would be reformulated to achieve a cumulative reduction of 25% in added sugar content of reformulated products,8,9 with no further changes thereafter. In total, an average 8.25% reduction in the added sugars would result from industry reformulation over 5 years (eAppendix 4 in the Supplement).

    Policy and Health-Related Costs

    Policy costs were incorporated for all stakeholders, including costs for the government to administer, monitor, and evaluate the policy, costs for industry to comply with the policy, and additional costs for industry to reformulate their products if reformulation occurs. Government costs were estimated based on the FDA’s budget report and Nutrition Review Project (eAppendix 5 and eTables 2-4 in the Supplement),15,16 and industry costs were estimated based on the FDA’s regulatory impact analysis that accounted for variations in factors including product formula complexity, company size, reformulation type, and compliance period, providing a more accurate cost estimate than a standard per-product cost approach.8

    Direct medical costs for cancer care were extracted from the Surveillance, Epidemiology, and End Results–Medicare linked database for 3 phases of cancer care: initial, continuing, and end-of-life (eAppendix 6, eTable 5, eTable 11, and eTable 12 in the Supplement).14,17 Indirect costs included productivity loss due to missed workdays or disability and patient time costs associated with receiving care, derived from published studies using the Medical Expenditure Panel Survey data.18-21

    Cost-effectiveness and Sensitivity Analyses

    Following the recommendations from the Second Panel on Cost-effectiveness in Health and Medicine,22 we evaluated the policy association with health outcomes by projecting the number of new cancer cases and cancer deaths averted and quality-adjusted life-years (QALYs) gained and cost-effectiveness from health care and societal perspectives. The health care perspective assessed net costs as the government costs of implementing the policy minus savings in the direct medical costs for cancer care. The societal perspective assessed net costs as the difference between the total policy costs (including government and industry costs) and the health-related cost savings (including direct and indirect costs for cancer care). All costs were inflated to 2015 US dollars using the Consumer Price Index or Personal Health Care Index, with all costs and QALYs discounted at 3% annually. Incremental cost-effectiveness ratios (ICERs) were calculated as the difference in costs divided by the difference in QALYs between policy vs no policy. ICERs falling below a willingness-to-pay threshold of $150 000 per QALY gained were considered to be cost-effective.23,24 Cost-effectiveness analysis was further conducted among population subgroups by age, sex, and race/ethnicity to evaluate policy associations with health disparities.

    We performed 1-way sensitivity analyses by varying key input parameters, including policy association, diet-BMI association, changes in medical expenditures associated with cancer care, policy implementation costs, and discounting rate. In addition, we conducted threshold analyses to identify the minimum policy change needed for achieving cost-saving or cost-effective status, evaluated the cost-effectiveness at a 10-year horizon for stakeholders interested in near-term returns, and further identified the time frame during which the policy reached cost-effectiveness. Probabilistic sensitivity analyses were conducted to incorporate the probability distributions around the point estimates of all parameters jointly (Table 1). A total of 1000 Monte Carlo simulations were performed, and 95% uncertainty intervals (UIs) were estimated based on the 2.5 and 97.5 percentiles of the 1000 simulations. All analyses were conducted using SAS, version 9.4 (SAS Institute Inc) and R, version 3.3.1 (R Foundation).

    Results
    Population Characteristics

    The simulated cohort of US adults in 2015 had a mean age of 47.8 years, comprising 65.0% non-Hispanic White individuals and 71.4% overweight or obese adults (eTable 7 in the Supplement). The mean consumption of added sugars was 73.0 g/d from all foods (13.9% of daily calories) and 52.6 g/d from packaged foods and beverages (10.0% of daily calories). Higher amounts of added sugars were consumed by individuals aged 65 years or older (56.9 g/d), women (57.7 g/d), and non-Hispanic Black individuals (55.1 g/d) compared with other groups. Implementing the added-sugar labeling policy was associated with a 3.46-g/d decrease in added sugar intake, corresponding to reductions of BMI in healthy-weight (0.02 points) and overweight/obese (0.04 points) individuals.

    Health Association

    Based on consumer behavior alone, implementing the FDA added-sugar labeling policy was associated with a decrease of 30 000 (95% UI, 21 600-39 300) new cancer cases (0.09% reduction of incident cancer) and 17 100 (95% UI, 12 400-22 700) cancer deaths (0.08% reduction of total cancer deaths), and an increase of 116 000 (95% UI, 83 800-153 000) QALYs among 235 million US adults over a median follow-up of 34.4 years (Table 2). With additional industry reformulation, the policy was associated with a reduction of 65 000 (95% UI, 51 400-79 800) new cancer cases (0.19% reduction) and 36 300 (95% UI, 28 700-44 900) cancer deaths (0.18% reduction), and a gain of 252 000 (95% UI, 199 000-312 000) QALYs. By cancer type, the greatest number of new cancer cases averted were endometrial, postmenopausal breast, and kidney cancers and the greatest number of cancer deaths decreased were liver, postmenopausal breast, and endometrial cancers.

    Economic Impact

    Implementing the policy would cost the government an estimated $9.24 million ($7.18 million-$12.8 million) and the industry $1660 million ($1410 million-$1960 million) in compliance costs over a lifetime of the current US population (Table 2). The policy was associated with savings of $1600 (95% UI, $1190 million-$2030 million) direct medical costs, $669 million (95% UI, $478 million-$890 million) productivity loss costs, and $114 million (95% UI, $80.7 million-$151 million) patient time costs for cancer care. With industry reformulation, the policy would cost the industry an additional $869 million ($718 million-$1060 million) and was associated with greater health care cost savings, including $3400 (95% UI, $2720-$4080) in direct medical costs, $1480 (95% UI, $1170-$1820) in direct productivity loss costs, and $252 (95% UI, $200-$311) million in direct and patient time costs. The top 3 cancers with the highest medical cost savings were kidney, liver, and postmenopausal breast cancers (Figure 1 and eTable 13 in the Supplement).

    The policy intervention was cost-saving and associated with savings of $704 million (95% UI, $45 million-$1450 million) from a societal perspective and $1590 million (95% UI, $1180 million-$2020 million) a health care perspective (Table 2). Further industry reformulation would increase the saving to $2570 million (95% UI, $1450 million-$3730 million) from the societal perspective and $3390 million (95% UI, $2710 million-$4070 million) from the health care perspective.

    Policy Associations Among Subgroups

    The policy was associated with greater health gains per 100 000 individuals among young adults (age, 20-44 years; 15 new cancer cases averted) than middle-aged adults (age, 45-54 years; 9 new cancer cases averted), women than men (14 vs 12 new cancer cases averted), and non-Hispanic Black individuals (15 new cancer cases averted) than Hispanic individuals (10 new cancer cases averted) (Table 3). The number of cancer deaths averted, life-years and QALYs gained, health-related costs saved, and net costs followed the same pattern. For example, the policy was associated with more cancer deaths prevented among young adults (age, 20-44 years; 10 deaths per 100 000 individuals) than older adults (age, ≥65 years; 4 deaths per 100 000 individuals), and non-Hispanic Black individuals (9 deaths per 100 000 individuals) than Hispanic individuals (6 deaths per 100 000 individuals). Industry reformulation doubled the outcomes across age, sex, and race/ethnicity groups, with similar disparity patterns observed, such as 32 new cancer cases averted per 100 000 young adults vs 28 cases per 100 000 middle-aged adults, and 21 cancer deaths prevented per 100 000 non-Hispanic Black individuals vs 16 deaths per 100 000 Hispanic individuals (eFigures 2-5 in the Supplement). Findings were also broadly similar within each cancer type (eTable 14 and eTable 15 in the Supplement).

    Sensitivity Analyses

    The cost-effectiveness of the policy was most sensitive to varying assumptions of policy impact, diet-BMI association, and discounting rate (eTable 17 and eFigure 6 in the Supplement). From the societal perspective, the minimal policy impact needed for consumer behavior for the policy to be cost-saving was 4.55%; with additional industry reformulation, the minimal policy impact for reformulation of consumer behavior was 3.23% and, for industry reformulation, 4.04% for the policy to be cost-saving (eTable 18 in the Supplement). When a different time horizon was used, the associated health gains were smaller over 10 to 12 years compared with those over a lifetime; however, the policy was still cost-saving from the health care perspective (Table 2 and eTable 16 in the Supplement).

    From the societal perspective, the policy, based on consumer responses alone, would achieve cost-saving for nearly 100% of the 1000 lifetime simulations because nearly all results decreased to below zero (Figure 2). At 12 years, the policy would have a 78% probability of having an ICER less than $150 000/QALY (78% of simulated results decreased below the threshold line). When adding industry reformulation, this probability would increase to 100% over a lifetime and by 12 years. From the health care perspective, the policy would be cost-saving in 1000 simulations, based on either consumer responses alone or additional industry reformulations over a lifetime and when limiting the analytic time horizon to 12 years for both scenarios.

    Discussion

    Using a probabilistic simulation model, our study estimated that implementing the added-sugar labeling policy was associated with a 3.46-g/d decrease in added sugar intake, corresponding to reductions of BMI in healthy-weight (0.02 points) and overweight/obese (0.04 points) individuals. The policy was estimated to be associated with approximately 30 000 new cancer cases and 17 100 cancer deaths among US adults over a lifetime and with substantial health care savings, resulting in net savings of $704 million from the societal perspective and $1590 million from the health care perspective. With additional industry reformulation, the policy was associated with approximately doubled health gains and economic benefits. Greater health gains and cost savings were expected among young adults, women, and non-Hispanic Black individuals compared with other population subgroups.

    Our findings suggest that the added-sugar labeling policy could be a cost-saving strategy for cancer prevention. Earlier simulation studies reported that medical screenings were cost-effective interventions in the primary or secondary prevention of specific cancers.30-32 For example, mammography screening starting at age 45 years was estimated to have an ICER of $40 135/QALY among a large cohort of women born in the 1960s over a lifetime.30 Colonoscopy screening starting at age 45 years achieved an ICER of $33 900/QALY among US men and women,31 and prostate-specific antigen screening had an ICER of $70 831 to $136 332/QALY among a cohort of US men beginning at age 40 years over a lifetime.32 These cancer screening strategies are cost-effective, but none of them achieved net savings. Our results suggest that a population-based nutrition intervention, such as the added-sugar labeling policy, is not only cost-effective but also cost-saving. These findings underscore the need to consider and prioritize nutrition-related policy interventions as cost-effective or cost-saving strategies for cancer prevention.

    Modest industry responses to the labeling policy could be associated with substantial additional health gains and economic benefits for obesity-related cancers. Earlier studies reported that, after the US Congress passed a national law requiring chain restaurants with 20 or more outlets to list calories on menus in 2010, the mean calorie content per menu item fell from 327 kcal in 2008 to 318 kcal in 2015 in 44 of the 100 largest chain restaurants.33 A meta-analysis of interventional studies found that package, menu, and other point-of-purchase labeling policies were associated with industry reformulations, including an 8.9% reduction in sodium content.7 Thus, with the passage of the added-sugar labeling, government and advocacy strategies should be prioritized to encourage industry reformulations for achieving larger benefits.

    Limitations

    The study had limitations. First, our investigation cannot prove a causal relationship between the implementation of this policy and cancer outcomes. Yet, cost-effectiveness analysis provides flexibility to test and evaluate different policy scenarios and reasoned assumptions. Second, we did not include potential health benefits on other sugar- or obesity-associated diseases (eg, diabetes, cardiovascular diseases) or the potential associations with reducing cancer burdens through reduced childhood obesity. Thus, the health benefits and cost savings of the policy could be underestimated. Third, in the absence of direct evidence, we used the estimated association of menu calorie labeling and calorie reduction as a proxy for added-sugar labeling, and limited evidence has suggested an association between menu calorie labeling or nutrition fact information and eating behaviors. However, in the sensitivity analyses with more conservative estimates, the policy remained cost-effective from both health care and societal perspectives. Fourth, insufficient data were available to incorporate potentially differing responses to labeling policy by socioeconomic status. Some studies,34-36 but not others,7,37 suggest that individuals with lower educational levels or income may respond less to nutrition labeling than those with greater educational levels or income. However, such a varying response might alter the relative benefits within the subpopulation but would be unlikely to alter the robust overall cost-savings.

    Conclusions

    This economic evaluation is, to our knowledge, among the first to suggest that the added-sugar labeling policy is cost-saving and associated with reduced costs and lower cancer rates. Nutrition policies can play important roles in reducing cancer burdens and disparities in the US.

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    Article Information

    Accepted for Publication: March 5, 2021.

    Published: April 27, 2021. doi:10.1001/jamanetworkopen.2021.7501

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Du M et al. JAMA Network Open.

    Corresponding Author: Fang Zhang, MD, PhD, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Ave, Boston, MA 02111 (fang_fang.zhang@tufts.edu).

    Author Contributions: Ms Du and Dr Zhang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Du, Eom, Kim, Wilde, Wong, Wang, Mozaffarian, Zhang.

    Acquisition, analysis, or interpretation of data: Du, Griecci, Cudhea, Eom, Wong, Michaud.

    Drafting of the manuscript: Du, Wong.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Du, Griecci, Cudhea, Eom, Kim, Wilde, Wong.

    Obtained funding: Wong, Zhang.

    Administrative, technical, or material support: Wong, Zhang.

    Supervision: Wong, Wang, Mozaffarian, Zhang.

    Conflict of Interest Disclosures: Dr Wang reported receiving personal fees from State of Delaware (full-time employment as associate deputy director, Division of Substance Abuse & Mental Health, Department of Health and Social Services) outside the submitted work and as an uncompensated adjunct faculty appointment, Columbia University Mailman School of Public Health. Dr Mozaffarian reported receiving grants from the NIH, the Gates Foundation, and the Rockefeller Foundation; personal fees from the Global Organization for EPA and DHA Omega-3s, Danone, Motif FoodWorks, Barilla, Amarin, Acasti Pharma, Cleveland Clinic Foundation, and America's Test Kitchen; participating on scientific advisory boards of start-up companies focused on innovations for health, including Beren Therapeutics, Brightseed, Calibrate, DayTwo, Elysium Health, Filtricine, Foodome, HumanCo, January Inc, and Tiny Organics; and receiving UpToDate chapter royalties outside the submitted work. No other disclosures were reported.

    Funding/Support: This study was supported by National Institutes of Health National Institute on Minority Health and Health Disparities grant 1R01MD011501 (Dr Zhang).

    Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    The Food-PRICE Project: Robert Greenwald, JD (Center for Health Law and Policy Innovation at Harvard Law School), Renata Micha, PhD, RD (Tufts University Friedman School of Nutrition Science and Policy), Emily Broad Leib, JD (Center for Health Law and Policy Innovation at Harvard Law School), Sean B. Cash, PhD (Tufts University Friedman School of Nutrition Science and Policy), Sarah Downer, JD (Center for Health Law and Policy Innovation at Harvard Law School), Jennifer Pomeranz, JD, MPH (New York University School of Global Public Health), Thomas Andrew Gaziano, MD (Bigham and Women’s Hospital), Rogan Kersh, PhD (Wake Forest University), Junxiu Liu, PhD (Tufts University Friedman School of Nutrition Science and Policy), and Yujin Lee, PhD (Tufts University Friedman School of Nutrition Science and Policy).

    Additional Contributions: Colin Rehm, PhD, MPH (Montefiore Medical Center), and Mengyuan Ruan, MS (Department of Public Health and Community Medicine at Tufts University School of Medicine), helped with estimating the added-sugar consumption using National Health and Nutrition Examination Survey data. No financial compensation was provided.

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