Objectives Since 2013, industry-endorsed front-of-pack traffic light labels have been implemented voluntarily on packaged food in the UK. The UK Government is now considering alternative labelling approaches which may be more effective, such as Chile's mandatory nutrient warning labels. The primary aim of this study was to model the likely impact of implementing mandatory front-of-pack nutrition labels in England on energy intake and consequent population-level obesity, and, secondarily, cardiovascular disease (CVD) mortality. Design Microsimulation modelling analysis Setting England Model A microsimulation model (2024-2043) to estimate the impact of changing front-of-pack nutrition labels in England. The two main policy scenarios tested were mandatory implementation of (i) traffic light labels and (ii) nutrient warning labels. For each scenario, the impact of the policy through assumed changes in energy intake due to consumer behaviour change and reformulation was modelled. Main outcome measures Change in obesity prevalence (%) and CVD deaths prevented or postponed. Results Compared to the baseline scenario (current voluntary implementation of traffic light labelling), mandatory implementation of traffic light labelling was estimated to reduce obesity prevalence in England by 2.28% (95% UI -4.06 to -0.96) and prevent or postpone 17000 (95% UI 4700 to 48000) CVD deaths. Mandatory implementation of nutrient warning labelling was estimated to have a larger impact; a 3.68% (95% UI -9.94 to -0.18) reduction in obesity prevalence and the prevention/postponement of 29000 (95% UI 1200 to 110000) CVD deaths. Conclusions This work offers the first modelled estimation of the impact of introducing mandatory front-of-pack nutrition labels on health outcomes in the adult population in England. Findings suggest that mandatory implementation of nutrient warning labels would reduce rates of obesity and CVD deaths, compared to current voluntary or mandatory implementation of traffic light labelling, and should therefore be considered by the UK government.

The authors have declared no competing interest.

Funding Salaries for ZC and ER were fully and part-funded, respectively, by the European Research Council under the European Union's Horizon 2020 research and innovation programme (Grant reference: PIDS, 803194). ER and RE are funded by the National Institute for Health and Care Research (NIHR) Oxford Health Biomedical Research Centre (BRC) (Grant reference: NIHR203316). Role of the funding source The funder played no role in the study design, data collection, data analysis, data interpretation, writing of the paper, or the decision to submit this work for publication.

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ONS and NDNS data are available online. The demography package for R software has been used for forecasting mortality and the gamlss package has been used to fit the distribution. Syntax for the generation of derived variables and for the analysis used in this study are available publicly: https://github.com/zoecolombet/FoPLabels_code