{"paper_id":"463e85e9-98bd-4091-8ef6-072bb17cac18","body_text":"1 \n \n \n \nThe estimated impact of mandatory front-of-pack nutrition labelling policies on adult \nobesity prevalence and cardiovascular mortality in England: a modelling study \n \nRunning Title: Mortality impact of nutrition labels in England  \n \nRebecca Evans PhD1, Prof Martin O'Flaherty PhD2, I Gusti Ngurah Edi Putra PhD1, Chris \nKypridemos PhD2, Prof Eric Robinson PhD1, Zoé Colombet PhD2 \n1. Department of Psychology, University of Liverpool, Liverpool, United Kingdom. \n2. Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, \nUnited Kingdom. \n \nCorresponding author: Rebecca Evans, Department of Psychology, University of Liverpool, \nLiverpool, United Kingdom \nEmail: R.K.Evans@liverpool.ac.uk  \n \nWord count: 4236 (including tables) \nAbstract count: 294 \nNumber of tables: 3 \nNumber of figures: 2\n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2 \n \n \n \nAbstract \nObjectives Since 2013, industry-endorsed front-of-pack traffic light labels have been \nimplemented voluntarily on packaged food in the UK. The UK Government is now \nconsidering alternative labelling approaches which may be more effective, such as Chile’s \nmandatory nutrient warning labels. The primary aim of this study was to model the likely \nimpact of implementing mandatory front-of-pack nutrition labels in England on energy intake \nand consequent population-level obesity, and, secondarily, cardiovascular disease (CVD) \nmortality. \nDesign Microsimulation modelling analysis \nSetting England \nModel A microsimulation model (2024-2043) to estimate the impact of changing front-of-\npack nutrition labels in England. The two main policy scenarios tested were mandatory \nimplementation of (i) traffic light labels and (ii) nutrient warning labels. For each scenario, \nthe impact of the policy through assumed changes in energy intake due to consumer \nbehaviour change and reformulation was modelled.  \nMain outcome measures Change in obesity prevalence (%) and CVD deaths prevented or \npostponed. \nResults Compared to the baseline scenario (current voluntary implementation of traffic light \nlabelling), mandatory implementation of traffic light labelling was estimated to reduce obesity \nprevalence in England by 2.28% (95% UI –4.06 to –0.96) and prevent or postpone 17000 \n(95% UI 4700 to 48000) CVD deaths. Mandatory implementation of nutrient warning \nlabelling was estimated to have a larger impact; a 3.68% (95% UI –9.94 to –0.18) reduction in \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n3 \n \n \n \nobesity prevalence and the prevention/postponement of 29000 (95% UI 1200 to 110000) \nCVD deaths.  \nConclusions This work offers the first modelled estimation of the impact of introducing \nmandatory front-of-pack nutrition labels on health outcomes in the adult population in \nEngland. Findings suggest that mandatory implementation of nutrient warning labels would \nreduce rates of obesity and CVD deaths, compared to current voluntary or mandatory \nimplementation of traffic light labelling, and should therefore be considered by the UK \ngovernment. \n \nFunding: European Research Council (Grant reference: PIDS, 8031940). \nKeywords: microsimulation model; policy evaluation; inequalities; food labelling policies \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n4 \n \n \n \nAbbreviations \nBMI: Body mass index \nCVD: Cardiovascular disease  \nIMD: Index of Multiple Deprivation \nNCD: Non-communicable diseases\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n5 \n \n \n \nIntroduction  1 \nDiet-related disease is a major cause of poor population health and social inequalities in 2 \nhealth (1). Many pre-prepared foods and non-alcoholic beverages (hereafter: food) are high in 3 \ncalories, added sugar, salt, and/or saturated fat (2,3). Excessive consumption of these nutrients 4 \nincreases the risk of obesity and other associated non-communicable diseases (NCD) such as 5 \ncardiovascular disease (CVD), and NCD mortality (4).  6 \nIn the UK, the average adult consumes an excess of 200-300 calories per day, and nearly two-7 \nthirds of UK adults are living with overweight or obesity (5,6). Notably, the prevalence of 8 \noverweight and obesity is patterned by deprivation (14 percentage points higher in the most 9 \nrelative to the least deprived areas), and education (12 percentage points higher for those with 10 \nno qualifications compared to those who are degree-level educated) (5). Therefore, there is a 11 \nneed for equitable public health policies that improve dietary quality across the population. 12 \nFront-of-pack nutrition labels are an evidence-based policy tool used to help consumers make 13 \nhealthier food choices and encourage industry to improve the nutritional profile of the 14 \nproducts they sell (7). In the UK, an industry-endorsed traffic light front-of-pack nutrition 15 \nlabel (see Figure 1.A) has been implemented voluntarily since 2013. This traffic light label 16 \nuses green, amber, and red colours to indicate whether a product contains low, moderate, or 17 \nhigh levels of nutrients of concern, alongside guideline daily amount (GDA) percentages for 18 \neach nutrient (typically per serving). However, UK consumers report that the traffic light 19 \nlabel is difficult to interpret, which may widen health inequalities (8). Additionally, less than 20 \nhalf of consumers use the label to determine product calorie content, and calorie content 21 \nspecifically is not designated with a traffic light colour (9). It may be that simpler labels are 22 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n6 \n \n \n \nrequired, as most consumers typically spend no more than a few seconds examining labels 23 \nbefore making a food selection (10).  24 \nIn July 2020, the UK Government launched a consultation considering an alternative front-of-25 \npack nutrition label to the traffic light (11). In the consultation, Chile’s nutrient warning labels 26 \nwere highlighted as a potential alternative, and the benefits of implementing mandatory front-27 \nof-pack labelling were discussed.  28 \nIn 2016, Chile implemented a mandatory policy requiring packaged foods containing ‘high’ 29 \namounts (as defined by thresholds set by the Ministry of Health) of calories, added sugar, 30 \nsodium, and/or saturated fat to display nutrient warning labels (12) (see Figure 1.B). Very 31 \nsimilar policies have since been implemented in other South American countries, including 32 \nArgentina, Brazil, Colombia, Mexico, Peru, and Uruguay (13,14). Mandatory nutrient 33 \nwarnings have also been implemented further afield in Canada and Israel, and policy 34 \ndevelopment is under consideration in several other countries, including the US, India, and 35 \nSouth Africa (15). Evidence indicates that implementation in Chile has reduced the purchase 36 \nof energy (a relative 8.3% decrease, 95% CI: [5.0, 11.6]) and nutrients of concern (ranging 37 \nfrom –9.6% for saturated fat to –20.2% for sugar) (16), and has led to product reformulation 38 \nacross all food groups, leading to reductions in energy content (-3.9%), and other labelled 39 \nnutrients of concern (ranging from –1.5% for saturated fat to –15% for sugar) (17).  40 \nFurthermore, evidence from a meta-analysis of over 100 randomised controlled trials (RCTs) 41 \nand quasi-experimental studies suggests that nutrient warning labels may perform better than 42 \ntraffic light labels in terms of reducing consumers’ purchase of energy (an additional 6.4% 43 \n(95% CI: [0.4; 12.5] reduction) and nutrients of concern, and probability of choosing less 44 \nhealthy products (7). Therefore, it is important to examine the potential impact of their 45 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n7 \n \n \n \nimplementation in the UK on health outcomes such as adult obesity prevalence, to inform 46 \npolicy decision-making.  47 \nThe present study aimed to estimate the likely long-term impacts of implementing (i) 48 \nmandatory nutrient warning labels and (ii) mandatory traffic light labels on packaged in-store 49 \nfoods, relative to the current voluntary implementation of traffic light labels, on energy intake 50 \nand consequent population-level obesity prevalence and cardiovascular mortality due to 51 \nchange in BMI in England.52 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n8 \n \n \n \nMethods 53 \nModel overview  54 \nWe built a dynamic, discrete-time, stochastic, open-cohort microsimulation model to quantify 55 \nthe estimated effects of implementing front-of-pack nutrition labels in England; an adaptation 56 \nof the IMPACT NCD Model based on the IMPACT Food Policy Model (18).  The model 57 \nsimulates the life-course of individuals and their counterfactuals under alternative policy 58 \nscenarios. This enables the detailed simulation of diet policies and their impact on relevant 59 \nexposures, subsequent disease epidemiology, and mortality in a competing risk framework 60 \nthat accounts for different lag-times between exposures and outcomes. In this case, we 61 \nsimulated the effects of implementing mandatory front-of-pack nutrition labels (nutrient 62 \nwarning and traffic light) on daily energy intake from packaged food, and subsequent 63 \npopulation-level obesity prevalence and CVD mortality due to change in BMI. We modelled 64 \nthe population of England, aged 30 to 89 years, over 20 years (2024 to 2043) using a synthetic 65 \npopulation stratified by age, sex and Index of Multiple Deprivation (IMD) that captures the 66 \nreal demographics, energy intakes, and disease epidemiology of the actual population of 67 \nEngland using available national data sources (see below and in Appendix section “Creation 68 \nof our synthetic population”).  69 \nWe evaluated two main policy scenarios: 70 \n1. Traffic light labels are implemented as a mandatory policy 71 \n2. Nutrient warning labels are implemented as a mandatory policy  72 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n9 \n \n \n \nWe compared each scenario with a counterfactual “no intervention” (baseline) scenario, 73 \nwhich corresponds to the current England legislation: continued voluntary implementation of 74 \ntraffic light labels.  75 \nWe did not model the impact of Nutri-Score, an alternative front-of-pack label which uses a 76 \ncolour spectrum and letter grades to summarise product healthiness, as a main scenario (19), 77 \nThis is because meta-analytic evidence suggests that it does not perform significantly 78 \ndifferently to the traffic light label in terms of reducing energy purchased (7). Instead, results 79 \nfor Nutri-Score are presented in the Appendix (see Appendix Table 4). 80 \nFront-of-pack nutrition labels  81 \nFront-of-pack nutrition labels impact diet through (1) consumer behaviour change, and (2) 82 \nindustry response, i.e., reformulation of the products by industry (see Figure 2). 83 \nEffect on consumer behaviour change 84 \nWe assumed that the traffic light labels and nutrient warning labels would reduce energy 85 \npurchased from packaged food by 6.5% (95% CI: [2.0; 11.0]), 12.9% (95% CI: [8.0; 18.0]), 86 \nand 6% (95% CI: [1.0; 11.0]) respectively, compared to no label, based on the estimates from 87 \nSong et al.’s review and network meta-analysis (7). Based on the same meta-analysis, we 88 \nassume that nutrient warning labels will outperform traffic light labels in reducing the total 89 \namount of energy purchased by 6.4% (95% CI: [0.4; 12.5]). Based on existing literature, we 90 \nassumed no differential policy effects by sex, age or socioeconomic position (7,20). Due to an 91 \nabsence of evidence, we assumed both labels have a consistent effect on consumer behaviour 92 \nover time.  93 \nEffect on energy content reformulation  94 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n10 \n \n \n \nFor nutrient warning labels, we assumed a 3.9% (95% CI: [12.5; 4.95]) reduction in energy 95 \ncontent of labelled packaged foods, based on evidence from Chile post-implementation (17). 96 \nWhile there is no available data specifically in relation to traffic light labelling and product 97 \nreformulation, evidence suggests that a small amount of reformulation does occur in response 98 \nto food labelling, particularly when it is implemented mandatorily (21–23). Therefore, we 99 \nalso assumed the same 3.9% reduction in energy content of packaged foods in response to 100 \nmandatory traffic light labelling. 101 \nLabel coverage 102 \nWe assumed that all packaged products (100%) would feature a traffic light label, as under 103 \nmandatory implementation, this would be required by law (16). Under current voluntary 104 \nimplementation, it is estimated that 75% of packaged products feature the label (24), so 105 \nmandatory implementation would yield an additional 25% coverage. For nutrient warning 106 \nlabels, based on evidence on the proportion of products featuring a “high in” warning in 107 \nChile, we assumed that 51% (95% CI: [49.0; 52.0]) of packaged foods in England would 108 \nfeature the label (i.e., will be above threshold for warning) (25). The nutritional quality of 109 \npackaged food in Chile is relatively similar to the UK; the average Health Star Rating for 110 \npackaged food is 2.44 compared to 2.83 (scores range from 0.5 to 5, with a higher score 111 \nindicating better nutritional quality) (26). Moreover, an analysis of food items from the UK 112 \nNDNS indicated that approximately 40% of UK food items meet requirements for a red traffic 113 \nlight label, and this figure does not include items that would be labelled due to being high in 114 \nenergy (27). Research suggests that 32% of UK supermarket snack foods alone exceed adult 115 \nenergy intake recommendations (3) and therefore it is reasonable to estimate that this would 116 \namount to at least an additional 10% of products being labelled, consistent with the 51% 117 \nfigure derived from Chile. 118 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n11 \n \n \n \nEstimating model uncertainty  119 \nWe used the Monte Carlo approach (100 iterations) to estimate the uncertainty of model 120 \nparameters. The sources of uncertainty we considered were the uncertainty of the relative risk 121 \nof coronary heart disease (CHD) and stroke based on BMI, the uncertainty of mortality 122 \nforecasts, and the uncertainty of the policy (label) effect. We summarised the output 123 \ndistributions by reporting the medians and 95% uncertainty intervals (UIs).  124 \nOne-way sensitivity analyses on key parameters 125 \nChange in nutrient warning labels coverage 126 \nEvidence from Chile suggests that approximately one year after initial implementation of the 127 \nnutrient warning label policy, reformulation resulted in a decrease in the proportion of 128 \nproducts featuring a label from 51% to 44% (95% CI: [42.0 - 45.0]) (25). Reformulation to 129 \nreduce nutrients of concern is consistently observed in response to the introduction of front-130 \nof-pack nutrition labelling policies in various countries, including Australia, Canada, the 131 \nNetherlands, and New Zealand, to avoid a “negative” label (e.g., a low health rating) or the 132 \nabsence of a “positive” label (e.g., a healthy choice indicator) (28). Therefore, in this 133 \nsensitivity analysis we assume that coverage is 51% for the first-year post-implementation, 134 \nand coverage then drops to 44% thereafter. 135 \nChile’s black octagon specifically (as opposed to nutrient warning labels more generally) 136 \nIn this sensitivity analysis, we test based on evidence from Chile specifically, post-137 \nimplementation (as opposed to meta-analytic data on nutrient warning labels in general from 138 \nexperimental studies), which suggests an overall 8.8% (95% CI: [-7.1 to –10.5]) reduction in 139 \nenergy purchased (16). Notably, nutrient warning labels were introduced in Chile as part of a 140 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n12 \n \n \n \nset of policies, including restrictions on food marketing to children, and therefore this 141 \nreduction in energy purchase may not me wholly attributable to nutrient warning label 142 \nimplementation. 143 \nLower reformulation due to traffic light labels 144 \nIt is possible that reformulation of energy content may be lower in response to traffic light 145 \nlabelling relative to nutrient warning labelling. This is because calories are not colour-coded 146 \nin traffic light labels and therefore food companies may be less inclined to reformulate energy 147 \ncontent of products. We assumed there would be a smaller 0.9% (95% CI [-3.1, 4.9]) 148 \nreduction in energy content, based on a meta-analysis of food labelling effects on product 149 \nenergy reformulation (23).   150 \nTable 1: Summary of key model assumptions 151 \n Traffic light label  Nutrient warning label \nMain assumptions   \nEffect on energy intake -6.5% [-11%; -2%] (7) -12.9% [-18%; -8%] \n(outperforms the traffic light \nlabel by 6.4% [0.4; 12.5] (7) \nEffect on reformulation in \nterms of energy content \n-3.9% [-12.5; 4.95] (17) -3.9% [-12.5; 4.95] (17) \nLabel coverage on \npackaged products \n100% (currently 75% \nunder voluntary \nimplementation) (24) \n51% [49%; 52%] (25) \nSensitivity assumptions   \nChanges in label coverage \nover time due to \nreformulation \n- Drops to 44% [42.0; 45.0] 4 \nyears post-implementation (25) \nChile’s black octagon \nnutrient warning label \neffectiveness on energy \nintake from labelled \nproducts \n- -8.8% [7.1.; -10.5] (16) \nEffect on reformulation in \nterms of energy content \n-0.9% [-3.1, 4.9] (23) - \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n13 \n \n \n \nA further detailed description of the model, input sources, and key assumptions are provided 152 \nin the Appendix. 153 \nModel engine 154 \nFront-of-pack nutrition labels are hypothesised to reduce energy intake, which will 155 \nsubsequently impact the body weight of the population (i.e., BMI), and, in turn, change CVD 156 \nmortality risk. This pathway is described in Figure 2 and detail in Appendix (section 157 \n“Estimating the effect of change in energy intake upon obesity prevalence and CVD 158 \nmortality”). In short, the change in energy intake is calculated by subtracting intake post-159 \nintervention from baseline intake for each year. Changes in energy intake are then converted 160 \ninto changes in body weight, based on principles of energy conservation, using the 161 \nChristiansen & Garby prediction formula (29) (detail in Appendix section “Estimating the 162 \neffect of change in energy intake on BMI”). The estimated change in BMI is then calculated 163 \nbased on the estimated change in body weight, which allows us to estimate the change in 164 \nobesity prevalence. Next, these changes in BMI are used to estimate changes in CVD 165 \nmortality risk, with a 6-year lag time (30) (see details in Appendix section “Estimating the 166 \neffect of change in BMI upon CVD mortality”). Using this information, new mortality rates 167 \nand, consequently, the number of deaths projected can be estimated.  168 \nModel outputs 169 \nThe model produced the change in obesity prevalence and the total number of deaths 170 \nprevented or postponed (DPPs) for each scenario. The equity impact of the intervention was 171 \nexamined by calculating the ratio between the most and least deprived quintile groups (using 172 \nthe IMD). Results are presented for English adults aged 30 to 89 years from 2024 to 2043, 173 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n14 \n \n \n \nrounded to 2 significant figures for mortality and rounded to 2 decimal places for obesity 174 \nprevalence.  175 \nData sources 176 \nWe constructed a synthetic population of England to simulate the population-level impact of 177 \nthe policy scenarios. This is described in the Appendix section “Data sources used in our 178 \nmodel” and Appendix Table 1. The England population projections were derived from the 179 \nOffice for National Statistics (ONS), and mortality trend projections were based on the CVD 180 \ndeaths observed in England from 1981 to 2016.  181 \nWe used generalised additive models for location, shape and scale (GAMLSS) to estimate (i) 182 \nBMI and (ii) energy intake distributions dependent on age, sex, and IMD. GAMLSS can 183 \nhandle complex relationships between the response variable and its predictors and numerous 184 \ntypes of distributions (31). Trends in energy intake daily energy intakes and BMI were 185 \nobtained from the nationally representative National Diet and Nutrition Survey (NDNS) 186 \n2009-2019. These trends in energy intake and BMI observed in the last 10 years in England 187 \nwere assumed to continue in the future. To obtain the daily energy from packaged food 188 \nbought from grocery retail stores, we assumed that 55% of all food and beverage expenditure 189 \n(including alcoholic beverages) was for at-home consumption (vs. 45% spent on restaurants 190 \nand other out-of-home food services) (32) and that 80% of the products purchased are 191 \npackaged (vs. 20% fresh) (8) (see details in Appendix section “Modelling approach and 192 \nscenarios”). 193 \nR (version 4.3.0) was used to conduct all data management and statistical analyses. We used 194 \nthe “demography” package (33) for forecasting mortality and the “gamlss” package to fit the 195 \ndistribution (34). For code, see https://github.com/zoecolombet/FoPLabels_code  196 \n197 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n15 \n \n \n \nResults 198 \nMaintaining current voluntary traffic light labelling would result in obesity prevalence of 199 \n28.03% (95% UI 27.74 - 28.30) by 2043. 200 \nThe implementation of mandatory traffic light labelling in England was estimated to reduce 201 \nobesity prevalence by 1.49 percentage points (absolute; 95% UI –2.44 to -0.76; Table 2) in 202 \nthe next 20 years when only considering consumer behaviour change (i.e., change in energy 203 \nintake). Reformulation of the energy content of the packaged products sold was estimated to 204 \nlower obesity prevalence by 0.66 percentage points (95% UI –2.79 to 0.00; Table 2). 205 \nCombining these factors would result in a decrease of 2.28 percentage points in obesity 206 \nprevalence among adults (95% UI –4.06 to –0.96; Table 2). 207 \nImplementing mandatory nutrient warning labels on packaged products was estimated to have 208 \na larger impact and reduce obesity prevalence by 2.31 percentage points (95% UI –6.79 to –209 \n0.02; Table 2) when only considering consumer behaviour change. Reformulation of the 210 \nenergy content of the packaged products sold was estimated to lower obesity prevalence by 211 \n0.96 percentage points (95% UI –6.10 to 0; Table 2). Combining these factors would result in 212 \na decrease of 3.68 percentage points in obesity prevalence among adults (95% UI -9.94 to –213 \n0.18; Table 2). 214 \nMaintaining current voluntary implementation of traffic light labelling in England, the current 215 \ncardiovascular mortality trends were estimated to result in approximately 1,900,000 deaths 216 \n(95% UI 1,100,000 – 3,300,000) in English adults by 2043.  217 \n 218 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n16 \n \n \n \nImplementing traffic light labelling mandatorily would prevent or postpone approximately 219 \n7300 deaths (95% UI 2500 to 21000; Table 2) attributable to BMI-related CVD, based on 220 \nconsumer behaviour change alone. Reformulation was estimated to avert 2500 deaths (95% 221 \nUI 0 to 17000; Table 2). Combined, this would result in 17000 deaths (95% UI 4700 to 222 \n48000; Table 2) prevented or postponed. 223 \nAgain, implementing mandatory nutrient warning labels was estimated to have a larger 224 \nimpact, resulting in the prevention or postponement of an estimated 14300 (95% UI 240 to 225 \n54000) deaths based on consumer behaviour change, 4300 deaths (95% UI 0 to 42000; Table 226 \n2) based on reformulation, and 29000 deaths (95% UI 1200 to 110000; Table 2) based on the 227 \ntwo combined. 228 \n 229 \n 230 \n 231 \n 232 \n 233 \n 234 \n 235 \n 236 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n17 \n \n \n \nTable 2: Estimated change in obesity prevalence and CVD mortality due to change in BMI in 237 \nadults in England (2024–43), according to different front-of-pack labelling implementation 238 \nscenarios 239 \n Change in prevalence of \nobesity (%) \nCVD deaths prevented or \npostponed* \nConsumer behaviour \nchange \n  \nTraffic light labelling \n(mandatory) \n-1.49 (- 2.44, -0.76) \n \n7300 (2500, 21000)\n \nNutrient warning labelling \n(mandatory) \n-2.31 (-6.79, -0.02) \n \n14300 (240, 54000) \nReformulation   \nTraffic light labelling \n(mandatory) \n-0.66 (-2.79, 0) 2500 (0, 17000) \n \nNutrient warning labelling \n(mandatory) \n-0.96 (-6.10, 0) 4300 (0, 42000) \nCombined   \nTraffic light labelling \n(mandatory) \n-2.28 (-4.06, -0.96) 17000 (4700, 48000) \n \nNutrient warning labelling \n(mandatory) \n-3.68 (-9.94, -0.18) 29000 (1200, 110000) \n*Results from 2024 to 2043. 240 \n 241 \n 242 \n 243 \n 244 \n 245 \n 246 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n18 \n \n \n \nTable 3: Estimated change in obesity prevalence and CVD mortality due to change in BMI in 247 \nadults in England (2024–43), according to IMD quintile groups and different front-of-pack 248 \nlabelling implementation scenarios 249 \n Prevalence of obesity, \npercentage points \nCVD deaths  \n Predicted obesity \nprevalence  \nCVD deaths predicted \nCurrent voluntary traffic light \nlabelling  \n  \nQ1 (most deprived) 32.53 (32.00, 33.04) 470,000 (270,000 – \n830,000) \nQ5 (least deprived) 24.29 (23.55, 24.85) 290,000 (170,000 – 500, \n000) \n Predicted change in \nobesity prevalence \nCVD deaths prevented or \npostponed \nMandatory traffic light labelling – \nconsumer behaviour change \n  \nQ1 -1.46 (-2.24, -0.71) 2000 (240, 5500) \nQ5 -1.48 (-2.46, -0.75) 1000 (0, 4500) \nMandatory traffic light labelling - \nreformulation \n  \nQ1 -0.66 (-2.85, 0) 500 (0, 6500) \nQ5 -0.65 (-2.73, 0) 250 (0, 2000) \nMandatory traffic light labelling - \ncombined \n  \nQ1 -2.14 (-3.96, -0.91) 4000 (740, 14000) \nQ5 -2.28 (-4.08, -0.93) 2500 (500, 8000) \nMandatory nutrient warning \nlabelling – consumer behaviour \nchange \n  \nQ1 -2.25 (-6.25, -0.01) 3500 (0, 13000) \nQ5 -2.31 (-6.81, -0.03) 2000 (0, 8800) \nMandatory nutrient warning \nlabelling - reformulation \n  \nQ1 -0.90 (-5.58, 0) 1000 (0, 12000) \nQ5 -1.05 (-6.20, 0) 500 (0, 5500) \nMandatory nutrient warning \nlabelling - combined \n  \nQ1 -3.61 (-9.58, -0.19) 7500 (0, 30000) \nQ5 -3.59 (-9.80, -0.20) 4500 (0, 18000) \n 250 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n19 \n \n \n \nThe introduction of either front-of-package label as a mandatory policy is estimated to reduce 251 \nobesity prevalence and relative CVD deaths to a similar extent across socioeconomic 252 \ndeprivation levels (see Table 3).  253 \nSee Appendix Table 3 for sensitivity analysis results relating to nutrient warning label 254 \ncoverage, Chile’s nutrient warning label specifically, and traffic light label reformulation. 255 \nBriefly, nutrient warning labels with reduced coverage, and Chile’s warning label specifically 256 \nstill outperformed traffic light labels. Traffic light labels saw a notable decrease in 257 \nperformance using the more conservative reformulation estimate. See Appendix Table 4 for 258 \nresults relating to Nutri Score. As expected, results for Nutri Score were very similar to those 259 \nfor traffic light labelling. 260 \n 261 \n 262 \n 263 \n 264 \n 265 \n 266 \n 267 \n 268 \n 269 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n20 \n \n \n \nDiscussion 270 \nThis work offers the first modelled estimation of the impact of changing front-of-pack 271 \nnutrition label policy on obesity prevalence and CVD mortality in the adult population in 272 \nEngland. Our findings indicate that, in place of current voluntary traffic light labelling, the 273 \nintroduction of mandatory nutrient warning labels would reduce obesity prevalence and CVD 274 \ndeaths substantially more than making traffic light labels mandatory, with no differential 275 \neffects on health inequalities. 276 \nOur findings are largely consistent with the existing limited evidence in this area. One 277 \nprevious study modelled the impact of nutrient warning labels in Mexico (35). The study 278 \nestimated a mean caloric reduction of 36.8 kcal/day/person, and, 5 years post-implementation, 279 \n1.3 million fewer cases of obesity (5% reduction). A handful of studies have modelled the 280 \nimpact of traffic light labelling on NCD mortality. One study modelling impact in Canada 281 \n(36) estimated that 11715 deaths per year due to diet-related NCDs, and 10490 deaths per 282 \nyear due to energy intake alone would be prevented. However, this was contingent on 283 \nCanadians using the traffic light labelling to avoid foods labelled with red lights. Another 284 \nstudy estimated the impact of Nutri-Couleurs (traffic light label) across 27 EU nations and 285 \nfound no significant effect on NCD mortality (37). However, the effect estimate for change in 286 \nenergy intake was derived from a large-scale randomised controlled trial in French 287 \nsupermarkets which only covered four product types (bread, ready meals, fresh catering, and 288 \npastries) (38), as opposed to the use of meta-analytic evidence in the present research. 289 \nAlthough the current research provides important insights into the likely impact of changing 290 \nfront-of-pack nutrition label policy in England, there are limitations to be acknowledged. We 291 \nassumed that reductions in energy intake would be in response to labelled products, which 292 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n21 \n \n \n \nmay be an overestimate for traffic light labels as not all products would feature a “red” 293 \nindicator. We also assumed that energy intake trends from NDNS will continue, but it is 294 \npossible that COVID-19 and/or the cost-of-living crisis may result in long-term changes. Our 295 \nresults will also underestimate total policy benefits as we did not include changes in 296 \nchildhood obesity in our model. 297 \nIt is also important to acknowledge that the present research underestimates the impact of the 298 \nlabelling policies on total CVD mortality as due to model design we do not model effects of 299 \npolicies due to changes in intake of nutrients of concern (salt, sugar, saturated fat) and instead 300 \nmodel change via energy intake and reductions to BMI. Excess intake of salt, sugar, and 301 \nsaturated fat is associated with CVD risk (39). Evidence suggests that labelling policies 302 \ndecrease the purchase of nutrients of concern, especially nutrient warning labels relative to 303 \ntraffic light labels, so impacts on CVD mortality are likely to be particularly underestimated 304 \nfor nutrient warning labels (7,20).  305 \nWe did not model a scenario where nutrient warning labels are implemented voluntarily, as 306 \nthere are no examples of such implementation. Moreover, the current evidence suggest that 307 \nvoluntary, industry-endorsed initiatives in the context of front-of-package labelling are likely 308 \nto be ineffective for several reasons, such as industry manipulation of label design, 309 \nnoncompliance (particularly as nutrient warning labels are known to deter purchase of 310 \nlabelled products), and a lack of independent target setting, monitoring, and enforcement 311 \n(40,41)Finally, while nutrient warning labels appear effective in reducing purchase and intake 312 \nof energy and nutrients of concern, it may be that alternative/additional labels are required to 313 \nencourage consumers to select health protective food options (i.e., those that contain nutrients 314 \nthat the population do not consume enough of, e.g., fiber, vitamin D).  315 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n22 \n \n \n \nSeveral assumptions in our model were constrained by a lack of available evidence and these 316 \nareas might benefit from further research. Firstly, there was no available data on how the 317 \neffect of the label on consumer behaviour change may change over time. Theoretically, if 318 \npeople become habituated to front of pack labels, then the effect may decrease, or conversely, 319 \nif nutrient literacy and awareness strengthen over time then the effect may increase (8). 320 \nSecondly, there was no available data on compensatory effects from intake of fresh food in 321 \nplace of packaged food, or intake from out-of-home eating. Thirdly, although there is some 322 \nself-report evidence to suggest that age, education, and ethnicity may impact understanding 323 \nof, and therefore response to traffic light labels (8), there was no consistent evidence that 324 \ndemographic factors moderate the effect of labels on product choice (7,20). 325 \nThe World Health Organization (WHO) does not at present recommend the use of any 326 \nspecific labelling scheme but encourages research institutions and member states to continue 327 \nanalysing information to inform decisions (42).  This new modelled evidence supports the use 328 \nof nutrient warning labels to reduce population-level obesity. While such labels are gaining 329 \nglobal popularity, the UK and Europe are yet to adopt this policy approach. It is 330 \nrecommended that the UK Government replaces its current voluntary traffic light labelling 331 \nsystem with mandatory nutrient warning labelling to reduce rates of obesity and related CVD 332 \ndeaths. 333 \nConclusion 334 \nMandatory implementation of nutrient warning labels appears to be the most favorable policy 335 \noption for the UK government to substantially reduce rates of obesity, compared to current 336 \nvoluntary or mandatory implementation of traffic light labelling.337 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n23 \n \n \n \nDeclarations \nData sharing \nONS and NDNS data are available online. The “demography” package for R software has \nbeen used for forecasting mortality and the “gamlss” package has been used to fit the \ndistribution. Syntax for the generation of derived variables and for the analysis used in this \nstudy are available publicly: https://github.com/zoecolombet/FoPLabels_code\n  \nFunding \nSalaries for ZC and ER were fully and part-funded, respectively, by the European Research \nCouncil under the European Union’s Horizon 2020 research and innovation programme \n(Grant reference: PIDS, 803194). ER and RE are funded by the National Institute for Health \nand Care Research (NIHR) Oxford Health Biomedical Research Centre (BRC) (Grant \nreference: NIHR203316). \nRole of the funding source \nThe funder played no role in the study design, data collection, data analysis, data \ninterpretation, writing of the paper, or the decision to submit this work for publication. \nCompeting interest statement \nAll authors have completed the Unified Competing Interest Form and declare: no support \nfrom any organisation for the submitted work; no financial relationships with any \norganisations that might have an interest in the submitted work in the previous three years; no \nother relationships or activities that could appear to have influenced the submitted work. \nTransparency declaration \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n24 \n \n \n \nThe lead author (R.E) affirms that the manuscript is an honest, accurate, and transparent \naccount of the study being reported; that no important aspects of the study have been omitted; \nand that any discrepancies from the study as planned have been explained. \nCopyright statement \nThe Corresponding Author (R.E.) has the right to grant on behalf of all authors and does grant \non behalf of all authors, an exclusive licence on a worldwide basis to the BMJ Publishing \nGroup Ltd to permit this article to be published in BMJ editions and any other BMJPGL \nproducts and sublicences such use and exploit all subsidiary rights, as set out in our licence. \nEthical approval \nEthical approval was not required for this study. \n \nAuthors’ contributions \nZC, RE, ER, MO'F, and EP designed the study. 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S e par at e and combin ed as s oc ia tion s o f body -ma ss ind ex a nd abd omin al \nadip o s ity with c ardiova sc ula r di sea s e: co l labora tive  anal y s i s  of 5 8 pro s p e ctive  s tu die s. L anc et.  \n2011  Mar 26;377(9 771) :1085– 95.  \n3 1. Sta sinopou l o s  MD , Rig by RA, Hell e r G Z , V oudouri s V , Ba sti ani F D.  Flex ible Regr e ssi on a nd \nSmoo t hi ng: U s in g G AM L SS in R. N ew Yor k: Cha pman an d Ha ll/ CR C; 2017.  571 p.  \n3 2. Stati s ta. Sh a r e  o f hou s ehold f ood  and dr i nk  expe nditure  in the Uni ted K ingdom ( U K) from 2000  \nto 1s t quar te r  2020, b y at-h ome a nd ou t- of -home c on s u mpt i on. Avai l able f rom: \nhttp s : / / w ww .s t ati s ta.com / st a t i stic s /941699/in-h ome - v er s u s- out -o f-ho me -f ood -a nd-drink -\ns p en di n g - u nit ed - k i n g dom- uk /  \n3 3. Hyndman  R, Booth H , Ti ckle  L, Ma indona ld  J. Pac kag e  ‘demog r a phy’ fo r R [I nte rne t] . [c i t e d 2022 \nNov 9]. A v ail a ble from: h ttp s : / / g ithub .co m/robjhy ndman/d emography  \n3 4. Sta sinopou l o s  M, Rigby R, Vou douri s V, A kantzilio tou C,  En ea M ,  Kio se D, e t al.  Pac k age ‘gamls s’ : \nGene raliz ed Addi t iv e Model s  for L oca tio n Sca le and Shap e  [ Inte r ne t ] . 2024 [c it ed 2024 Apr  2 4]. \nAvai lable f rom: h t tp s: //cran . r-projec t .or g / w eb/p ack age s/ g aml s s/gaml ss .pdf  \n3 5. Bas to -A b reu A, To rr e s-Alv are z  R,  R eye s-S ánc hez F, G onzále z- Mor a l e s R, C anto - Osorio F , C olc hero \nMA, e t al . P redic ting  obe sity r educ tion a f ter imple m enting w arning l ab el s in Mex ico : A modeli ng \nst u dy . Clé m ent K, edi tor . PL oS M ed . 202 0 Jul  28;17(7) :e1 003221.   \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n28 \n \n \n \n3 6. La bonté ME, E mr i ch TE , Sc arbo rough P , Rayner M, L ’Ab bé MR . T r a ffi c lig ht lab el li ng c ould \npreve nt  mo r tal i ty from nonc ommunic abl e di sea s e s in C a nada : A  s c ena rio mode lli ng s tudy.  \nVadiv elo o MK, edit or. PLoS  ON E . 2019 D ec  27;14(12 ): e022697 5.  \n3 7. Deva ux M, Alde a A, L ero uge A, Vui k S, C e cc hini M. E s t abli s hin g an E U -w id e fr ont -o f -p a ck \nnutriti on lab el : Rev iew o f optio ns  a nd mode l- ba s e d eva lu atio n. Ob e sity Rev iew s. 20 24 \nJun;25 (6): e1 3719.  \n3 8. Allai s  O, Albu qu erqu e P , Bonn e t  C , Duboi s P . Év aluation  Expé r im e nta tion Lo go s  Nutriti onnel s \nRappor t pour le  FF AS  [ Int er n e t]. 2017. Av ailab le from: \nhttp s : / / s ant e.g ouv. fr / IM G /pdf / r appor t_ f inal _group e_ trai temen t_ e valua t i on_log os .pdf  \n3 9. Bowe n KJ, Sul liva n VK, Kri s - E t h e r t on P M,  Pet er s e n  KS. Nut ritio n and C ardiova scu la r Di s ea se—an \nUpdat e. Cu rr A the ro s c ler R ep. 2 018 Jan 3 0;20(2) :8.  \n4 0. La verty AA, Ky pr i demo s C, Se f e rid i P, Va mo s  E P, Pea r son -Stu tta r d J ,  Collin s B, e t  a l. Quan tifying  \nthe imp act o f th e P ublic  He alth R e spon si bility Dea l o n s alt int ak e, c ardi ova s c ul ar dise a se and \ngast ric c anc er bur den s : in terrupt ed t i me se r i e s  a nd mic r o simul a tion st u dy . J Epi de miol \nCommuni t y  Heal th. 20 19 Se p ;73(9 ):881– 7.   \n4 1. Knai  C, P ett icrew  M, D ougla s  N, Durand MA, E a s tmur e E , N o l t e  E, e t a l. The  Publi c  Health \nRes pon sibility Dea l : Us i ng  a S yste ms-L ev el A na ly sis t o  U nde rst and  th e L ac k of Im pa ct on Alc ohol , \nFood, Phy sica l Ac tiv ity, an d  Wo r k plac e H ealth S ub -Sy ste ms. IJER PH. 2018 Dec  17; 15 ( 1 2):2895 .  \n4 2. World He alth O rgani sa tion. Guidi ng prin c iples an d fr ame work ma nual for fron t-o f-p a ck labell ing \nfor pr omoting he althy di e ts [I nte rne t] . 2 019  [cited 2 024 Jul 1 5 ]. Avai l able from: \nhttp s : / / w ww .who.int /public a tion s/ m /it e m/guidin gprincip le s-la b elling -p r om ot i ng - healthy di et  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n \n \n \nFigure 1. Front-of-pack nutrition labels examples \n \nA. Traffic light label (UK) \n \nB. Nutrient warning label – black octagons (Chile) \n \nIn English, the labels would read (left to right): [HIGH IN] SUGAR, \nCALORIES, SATURATED FAT, SODIUM [Ministry of Health].  \n \n \n \n \n \n \n29 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint \n\n \n \n \nFigure 2. Logic diagram of the impact of front-of-pack labelling on obesity prevalence and cardiovascular disease (CVD) mortality. \nAbbreviation: BMI: Body Mass Index \n  \n30 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 16, 2024. ; https://doi.org/10.1101/2024.10.14.24315283doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}