Table 1

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

Model inputOutcomeEstimatesDistributionCommentsData source
1. Simulated populationPopulationMean consumption of calories was 332 kcal/day from full-service or fast-food restaurants (online supplemental tables 1, 8 and 9)GammaStratified by age, sex, race/ethnicity; 32 subgroupsNHANES 2013–2016
2. Policy effect*
a. Consumer behaviourPolicy effect7.3% (95% CI 4.4% to 10.1%) (online supplemental appendix 1 and appendix table 1)BetaOne-time effectMeta-analysis of labelling interventions on reducing calorie intake, Shangguan et al, 201915
b. Industry responsePolicy effect5% (online supplemental appendix 1 and appendix table 2)BetaAssumption: 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 contentCalorie 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 effectAmong individuals with:
BMI <25: 0.0015 per kcal
BMI ≥25: 0.003 per kcal
NormalAssumption: 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 futureHall et al, 201830; Hall et al, 201129
4. Etiologic effect of BMI on cancer outcomes*Cancer outcomeRRs ranged from 1.05 to 1.50 (online supplemental table 2)Log normalBMI change and cancer incidenceContinuous 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 BetaStratified by age, sex and race/ethnicityNCI’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 GammaStratified by age and sexNCI’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 GammaAdministration and monitoring costs for government; compliance and reformulation costs for industryFDA’s budget report; Nutrition Review Project; and FDA’s Regulatory Impact Analysis
8. Health-related quality of life (HRQoL)*For 13 types of cancerRanged from 0.64 to 0.86 (online supplemental table 5 and appendix 4)BetaEQ-5D§ data from published literature by cancer typePublished 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.