Table 1

Key input parameters and data sources in the Dietary Cancer Outcome Model (DiCOM)

Model input	Outcome	Estimates	Distribution	Comments	Data source
1. Simulated population	Population	Mean consumption of calories was 332 kcal/day from full-service or fast-food restaurants (online supplemental tables 1, 8 and 9)	Gamma	Stratified by age, sex, race/ethnicity; 32 subgroups	NHANES 2013–2016
2. Policy effect*
a. Consumer behaviour	Policy effect	7.3% (95% CI 4.4% to 10.1%) (online supplemental appendix 1 and appendix table 1)	Beta	One-time effect	Meta-analysis of labelling interventions on reducing calorie intake, Shangguan et al, 2019¹⁵
b. Industry response	Policy effect	5% (online supplemental appendix 1 and appendix table 2)	Beta	Assumption: no reformulation in the first year of policy intervention; restaurants will replace the high-calorie menu items with low-calorie options or reformulate the menu items in years 2 to 5 of the intervention to achieve a 5% reduction in calorie content	Calorie changes in large chain restaurants from 2008 to 201518; higher-calorie menu items eliminated in large-chain restaurants19
3. Effect of change in calorie intake on BMI change (kg/m²)*	Dietary effect	Among individuals with: BMI <25: 0.0015 per kcal BMI ≥25: 0.003 per kcal	Normal	Assumption: 55 kcal per day reduction in calorie intake would lead to one pound weight loss within 1 year, with no further weight loss in the future	Hall et al, 201830; Hall et al, 201129
4. Etiologic effect of BMI on cancer outcomes*	Cancer outcome	RRs ranged from 1.05 to 1.50 (online supplemental table 2)	Log normal	BMI change and cancer incidence	Continuous update project (CUP) conducted by the World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR)
5. Cancer statistics*	Cancer incidence‡ and survival	online supplemental tables 3 and 4 and appendices 2 and 3, appendix tables 3 and 4	Beta	Stratified by age, sex and race/ethnicity	NCI’s Surveillance, Epidemiology, and End Results Programme (SEER) Database; CDC’s National Programme of Cancer Registries (NPCR) Database
6. Healthcare-related costs*†	Medical expenditures, productivity loss and patient time costs	online supplemental tables 6 and 7, appendix 6 and appendix table 7	Gamma	Stratified by age and sex	NCI’s cancer prevalence and cost of care projections; published literature
7. Policy costs*†	For government and industry	online supplemental appendix 5 and appendix tables 5 and 6	Gamma	Administration and monitoring costs for government; compliance and reformulation costs for industry	FDA’s budget report; Nutrition Review Project; and FDA’s Regulatory Impact Analysis
8. Health-related quality of life (HRQoL)*	For 13 types of cancer	Ranged from 0.64 to 0.86 (online supplemental table 5 and appendix 4)	Beta	EQ-5D§ data from published literature by cancer type	Published literature

*Uncertainty distributions were incorporated in the probabilistic sensitivity analyses. Uncertainties in each parameter are presented in supplemental materials (online supplemental appendix table 5 and online supplemental tables 2–9).
†If the source did not provide uncertainty estimates, we assumed the standard errors were 20% of the mean estimate to generate gamma distribution.
‡Time-varying input parameter, for which the model accounted for the secular trends. Details are provided in the Supplements.
§EQ-5D is a standardised instrument developed by the EuroQol Group as a measure of health-related quality of life that can be used in a wide range of health conditions and treatments.
BMI, body mass index; CDC, Centers for Disease Control and Prevention; FDA, Food and Drug Administration; NCI, National Cancer Institute; NHANES, National Health and Nutrition Examination Survey.