Health co-benefits of sustainable dietary transitions to reduced red and processed meat intake in the United Kingdom: a modelling study

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Previous modelling has outlined how policy interventions could shift dietary patterns to align with meat reduction targets, but the resulting health impacts remain unexplored. Methods We quantified the projected health impacts of the UK Climate Change Committee's (CCC) dietary targets—a 35% or 50% reduction in meat consumption—focusing on red and processed meat and substitution with vegetables and legumes, on incidence and mortality of key diet-related chronic diseases over 30 years. Dietary inputs were derived from an agent-based opinion dynamics model parameterised using the UK National Diet and Nutrition Survey data, and health impacts were estimated using the IOMLIFET life-table model. Results Achieving the red and processed meat reduction components of the CCC's targets was associated with a projected increase in life expectancy of 7·2 (1·1–12·9) and 9·4 (1·3–15·4) months and years of life gained of 8·4 (1·3–14·9) and 10·9 (1·5–17·9) million, in the 35% and 50% scenarios, respectively. An estimated 3·4 (0·5–6·6) and 4·5 (0·6–8·1) million chronic disease cases could be averted. Up to 95% of health gains were attributable to cardiovascular outcomes, with three quarters linked to increased vegetable and legume consumption. Conclusion These exploratory estimates suggest policy-driven reductions in red and processed meat aligned with climate goals could deliver substantial public health benefits. While conditional on modelled behavioural trajectories, findings offer relevant insights for European strategies to reduce cardiovascular disease burden and promote sustainable dietary transitions. Chronic disease prevention Life expectancy Health policy Health impact modelling Policy evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The urgent need to shift population diets toward more sustainable and health-promoting patterns is widely recognised as a cornerstone for addressing both the rising burden of chronic diseases across Europe and globally, and the escalating environmental impacts of food production and consumption ( 1 – 5 ). Similar to dietary patterns observed in many European countries ( 6 ), current dietary patterns in the United Kingdom (UK) are characterised by high consumption of red and processed meat, with average weekly intakes of nearly 590 g in adult men and around 400 g in adult women ( 7 ), compared with the UK-specific recommended maximum intake of 490 g per week (70 g per day) ( 8 ). Such high intakes have been associated with increased risks of cardiovascular disease (CVD), type 2 diabetes, colorectal cancer, and premature mortality ( 9 ), as well as with substantial environmental impacts ( 10 ). In line with broader European climate mitigation pathways outlined by the European Commission’s roadmap for a competitive low-carbon Europe by 2050 ( 11 ), the UK’s Climate Change Committee (UKCCC) has emphasised the necessity of reducing meat consumption as part of comprehensive strategies to mitigate national greenhouse gas emissions (GHGE) and enhance public health ( 12 ). Supporting this, epidemiological modelling studies ( 13 – 16 ) and the recent EAT–Lancet Commission on healthy, sustainable, and just food systems ( 17 ) suggest that a diet low in red and processed meat intake, and high in plant-based foods such as vegetables and pulses could yield substantial health and environmental co-benefits. Despite the robust evidence base that supports a transition towards a reduced consumption of meat and an increased intake of plant-based foods in high-income contexts, understanding how to reach real-world dietary change at scale remains challenging due to the complex interplay of social, cultural, economic, and behavioural factors that shape food choices ( 17 , 18 ). Agent-based and microsimulation models have emerged as powerful tools to predict the potential impact of dietary interventions and policy strategies on population-level dietary behaviours ( 19 , 20 ), enabling researchers and policymakers to estimate the large-scale effects of shifts in food consumption patterns. Our previous work ( 21 ) utilised agent-based opinion dynamics models to simulate the effects of governmental influence (fiscal measures and/or information campaigns) on dietary patterns in the UK, specifically targeting reductions in meat consumption to align with the UKCCC’s climate goals ( 12 ). This modelling demonstrated that achieving targets of 35% and 50% average meat reduction by 2030 and 2050, respectively ( 12 ), could lead to significantly increased intakes of vegetables, pulses, and meat alternatives, alongside decreases in GHGE, land use, and water use ( 21 ). While this prior study quantified the shifts in food group consumption and the environmental benefits, a comprehensive assessment of the associated health impacts was beyond its primary scope. Robust evidence on these health impacts is fundamental for policymakers to effectively weigh the benefits against possible drawbacks of interventions, and to foster their legitimacy and public acceptance. However, despite the recognised value of evidence-informed policymaking, research indicates that policy change in nutrition and diet is often slow and contested, facing barriers such as political priorities, industry pressure, and public scepticism ( 22 ). According to a review by the World Health Organisation on fiscal policies to promote healthy diets (WHO) ( 23 ), including analyses focused on European settings, the provision scientific evidence on potential benefits of new policies is critical not only to support the adoption of dietary interventions but also to sustain them amidst ongoing debate and resistance. This review also highlighted the lack of evidence on long-term health impacts as an important evidence gap to be addressed in future research. Building on our prior work and using the UK as a case study within a wider European policy context, this study therefore aims to estimate the associated health impacts of dietary transitions towards meeting the UKCCC’s targets of 35% and 50% average meat reduction by 2030 and 2050. By quantifying the potential changes in chronic disease incidence and mortality resulting from achieving national meat reduction targets and the likely substitution to other foods this would entail, this research provides actionable insights for policymakers in the UK and other European countries seeking to promote healthier and more environmentally sustainable diets at the population level. Methods Scenarios We employed life table modelling to quantify the health impacts resulting from previously simulated changes in meat intake in the UK ( 21 ). Briefly, to simulate the impact of governmental influence on people’s consumption of total (red and white, processed and non-processed) meat consumption, dietary data derived from the UK National Diet and Nutrition Survey (NDNS) 2019 ( 7 ) served as the baseline input data for agent-based opinion dynamics models. Agents, representing the UK adult population, were connected within a social network and influenced through a sequence of governmental campaigns to meet the UKCCC’s targets of a 35% average reduction in meat consumption by 2030 and a 50% average reduction by 2050 ( 12 ). While the UKCCC's dietary pathways include reduced intakes of both meat and dairy, this study focuses solely on changes in meat consumption, consistent with the upstream agent-based modelling in which only meat reduction was simulated. In a scenario of high governmental influence represented by the implementation of nationwide information campaigns for reduced meat consumption in conjunction with fiscal measures (taxes on meat + subsidies on meat alternatives/legumes/vegetables), meat consumption declined in the simulations, while the intake of vegetables, legumes, and meat alternatives increased isocalorically ( 21 ). No other dietary components were assumed to change. It took approximately 5·2 and 8·1 years for the 35% and 50% reduction targets to be met, respectively. Further details of the methods used for the simulations can be found in Fontan et al, 2025 ( 21 ). We quantified the health impacts of meeting these two targets, specifically analysing the changes in average consumption levels of red meat, processed meat, vegetables, and legumes among adult males and females. Health impacts related to changes in unprocessed white meat and meat alternatives were not modelled due to a lack of robust evidence linking these consumption patterns to health outcomes. Hence, the portion of unprocessed white meat was removed from the total simulated meat reduction proportionally to their share (36% for males and 39% for females) of total meat consumption at baseline, and was assigned no health impact in the model—representing a conservative assumption consistent with the limited epidemiological evidence for this food group. In the 35% meat reduction scenario, we quantified the health impacts associated with achieving a 35% average reduction in red and processed meat consumption within the UK population. In the 50% meat reduction scenario, we evaluated the health impacts related to a 50% reduction in red and processed meat consumption. For brevity, both scenarios are hereafter referred to as the 35% and 50% meat reduction scenarios, where 'meat' refers exclusively to red and processed meat throughout." Disease outcomes and relative risks Exposure-response relationships (i.e. relative risks per unit change in consumption) between dietary intake and chronic disease morbidity and mortality were obtained from the Global Burden of Disease (GBD) study ( 9 ). The GBD study is the most comprehensive ongoing global observational epidemiological study and has assessed 396 diseases and injuries and 87 risk factors across 204 countries since 1990. GBD-derived exposure–response functions were used to ensure internal consistency across outcomes and risk factors and to enable comparability with other global and national burden of disease assessments. The relative risk (RR) for a given dietary risk-disease pair quantifies the change in mortality (or morbidity) risk associated with a change in exposure to that dietary risk. For example, the risk of ischemic heart disease (IHD) is reduced by 24% for each 50 g increase in daily legume intake (Table 2 ). Relative risks from the GBD expressed in terms of a harmful risk factor (e.g. “diet high in red meat”) were inverted (i.e. the reciprocal of the relative risk was taken) to obtain relative risks corresponding to a beneficial dietary change. A total of six disease outcomes linked to the consumption of red meat, processed meat, vegetables, and legumes (Table 2 ) were considered in the model ( 9 ). Table 1 Changes in daily intakes for the dietary scenarios tested, based on previous work ( 21 ). Meat reduction scenario Males + 18y Females + 18y Red meat (g/day) Processed meat (g/day) Legumes (g/day) Vegetables (g/day) RR Red meat (g/day) Processed meat (g/day) Legumes (g/day) Vegetables (g/day) RR -35% a -15.6 -14.3 + 59.3 + 168.9 Central -10.4 -8.0 + 35.5 + 114.8 Central -35% b -27.5 -25.1 + 92.3 + 288.0 Lower -19.3 -14.9 + 49.2 + 174.8 Lower -35% c -3.7 -3.4 + 16.5 + 49.8 Upper -1.4 -1.1 + 15.5 + 54.8 Upper -50% a -22.5 -20.5 + 83.0 + 242.7 Central -15.5 -11.9 + 50.1 + 171.8 Central -50% b -38.6 -35.2 + 137.2 + 427.6 Lower -27.8 -21.4 + 75.4 + 277.3 Lower -50% c -6.4 -5.9 + 19.0 + 57.8 Upper -3.2 -2.4 + 18.5 + 66.3 Upper a The central exposure-response estimate combined with the average change in consumption. b The lower exposure-response estimate combined with the average change in consumption + 1 standard deviation. c The upper exposure-response estimate combined with the average change in consumption – 1 standard deviation. RR = Relative risk estimate. Table 2 Dietary exposure-response pathways (including upper and lower 95% confidence intervals) used in the health impact modelling. Dietary exposure Health outcome Unit Relative risk a 95% CI Vegetables Ischaemic heart disease 100 g increase 0·86 0.78 - 0·94 Ischaemic stroke 100 g increase 0·87 0·79 - 0·97 Intracerebral haemorrhage 100 g increase 0·90 0·83 - 0·97 Subarachnoid haemorrhage 100 g increase 0·90 0·83 - 0·97 Legumes Ischaemic heart disease 50 g increase 0·76 0·65 - 0·89 Red meat Colorectal cancer 100 g decrease 0·86 0·76 - 0·97 Diabetes type 2 100 g decrease 0·80 0·68 - 0.97 Processed meat Ischaemic heart disease 50 g decrease 0·56 0·39 - 0·97 Colorectal cancer 50 g decrease 0·85 0·79 - 0·91 Diabetes type 2 50 g decrease 0·58 0·47 - 0·76 a Relative risks from the Global Burden of Disease study ( 9 ). b CI = Confidence interval. Baseline health data Age- and sex-specific data for the UK population in 2021, including population-size estimates, all-cause mortality, and disease-specific mortality (mortality rates) and morbidity (incidence rates) for relevant outcomes (Table 2 ) were downloaded from the GBD results tool ( 24 ). Health impact modelling Health impacts from the dietary scenarios were quantified following a previously developed approach ( 25 , 26 ) using the IOMLIFET life table model ( 27 ) implemented in R version 4.5.1 ( 28 ). This model projects population survival patterns by applying changes in mortality risk from hypothetical dietary changes to age-specific mortality rates. By inputting hypothetical dietary changes (representing altered risk exposures) and applying known exposure-response functions (i.e. relative risks), the model quantifies subsequent changes in life expectancy and years of life lost (mortality outcomes) across time. Years of life lost represent the years of life lost for an individual (or a population) as a result of premature avertable mortality, considering the age at which deaths occurred. Since the dietary modifications were expected to reduce mortality rates, years of life lost were translated to years of life gained (YLG). Changes in morbidity (new cases of disease) were quantified using the same principles. Morbidity calculations used the life table population output as the baseline, to which changes in risk exposures were applied. Each morbidity calculation was performed independently based on incidence rates and then aggregated across the entire UK adult population. Changes in YLG and disease incidence were aggregated over a 30-year period. This duration was selected to allow the full observation of latent health impacts, as discussed further below. Changes in life expectancy at birth were determined by calculating the difference between the baseline life expectancy (total expected life years divided by the initial population) and the modelled life expectancy (impacted expected life years divided by the impacted starting population) across the full health projection. Our modelling strategy incorporated several key assumptions regarding dietary changes and their health outcomes (Supplementary Table 1). Where multiple dietary exposures influenced the same disease outcome, relative risks were multiplied together to avoid double counting of overlapping pathways. To account for the time required for dietary changes to manifest as health impacts, we integrated time lags based on established epidemiological evidence ( 29 , 30 ) and a previously applied modelling approach ( 31 ). Specifically, we assumed that the time to full effect (TTFE) on all disease outcomes was approximately 10 years in the main analyses. More information regarding data management and assumptions for the health impact modelling may be found in the supplementary materials (full list of assumptions in Supplementary Table 1; lag functions in Supplementary Figs. 1–3). Sensitivity analysis To assess the sensitivity of our results to key parameters, we generated upper and lower health impact estimates (i.e. confidence intervals) for the modelled outcomes. These were based on a dispersion measure of ± 1 standard deviation around the mean for changes in the consumption of red meat, processed meat, vegetables and legumes. These estimates were combined with the upper and lower 95% Confidence Intervals (CIs) for the RRs provided by the GBD study ( 9 ) (Table 2 ). In addition, we explored different lags between reduction in disease risk factors and potential health gains, as per previous health impact modelling research ( 31 ). The time perspective for health gains varies by disease: cardiovascular disease mortality may respond within a few years following risk factor reduction, while cancer outcomes are associated with longer exposure durations ( 31 ). To balance these time perspectives while accounting for the morbidity burden of both disease groups, we followed the approach of Fadnes et al. in assuming a TTFE of 10 years with a gradual, linear increase in effect as the main analysis. Sensitivity analyses with 5 and 30 years to full effect were also conducted to reflect this uncertainty ( 31 ). Results The health impact modelling results indicated that reaching the UKCCC’s meat reduction targets would have positive impacts on average life expectancy in the UK. Specifically, life expectancy would increase by 7·2 (1·1–12·9) months in the 35% meat reduction scenario, and by 9·4 (1·3–15·4) months in the 50% meat reduction scenario (Table 3 ). A major share (95%) of the estimated increased life expectancy was due to reduced mortality from CVD (ischemic heart disease, ischemic stroke, subarachnoid haemorrhage and intracerebral haemorrhage, combined) (Fig. 1 ). Furthermore, total YLG accumulated over 30 years from avoided deaths ranged between 8.4 (1·3–14·9) million and 10.9 (1·5–17·9) million in the 35% and 50% meat reduction scenarios, respectively. These gains were largely driven by reduced mortality from CVD (Fig. 2 ). Table 3 Changes in life expectancy, years of life gained and incidence of disease, accumulated over 30 years, 10 years into the health impact projection for the 35% and 50% meat reduction scenarios, respectively. Outcome 35% meat reduction scenario a 50% meat reduction scenario a LE in months (CI) 7·2 (1·1 to 12·9) 9·4 (1·3 to 15·4) YLG a in million (CI) 8·4 (1·3 to 14·9) 10·9 (1·5 to 17·9) Disease incidence Colorectal dancer in nr (CI) -47,046 (-2,782 to -156,240) -72,207 (-7,841 to -225,156) Type-2 diabetes in nr (CI) -799,197 (-78,970 to -1,890,263) -1,122,824 (-144,260 to -2,474,253) CVD a in nr (CI) -2,545,353 (-363,482 to -4,538,914) -3,299,217 (-428,209 to -5,406,950) a Time to full effect of 10 years. LE = Life expectancy. YLG = Years of Life Gained. CVD = Cardiovascular disease. a Ischemic heart disease, ischemic stroke, subarachnoid haemorrhage and intracerebral haemorrhage, combined. Similar impacts were observed through reduced morbidity (Table 3 ). The largest share (approximately 75%) of reductions in new disease cases over 30 years was attributable to CVD, with an estimated 2·5 (0·4–4·5) and 3·3 (0·4–5·4) million fewer cases under the 35% and 50% meat reduction scenarios, respectively (Table 3 , Fig. 3 ). This was followed by type 2 diabetes—which according to the GBD estimates is affected only by changes in red and processed meat consumption—with an estimated 0·8 (0·08 − 1·9) and 1·1 (0·1–2·5) million fewer cases under the two scenarios. (Table 3 , Fig. 3 ). New cases of colorectal cancer over 30 years, also only impacted by the reduction in consumption of red and processed meat as per GBD estimates, constituted the lowest share of total reductions in disease incidence (Fig. 3 ) but still achieved reductions of up to 72 (7·8-225) thousand fewer cases in the 50% meat reduction scenario (Table 3 ). Overall, about three quarters of mortality gains could be attributed to changes in the consumption of vegetables and pulses (Fig. 4 ) with the same patterns for morbidity outcomes (no data shown). The sensitivity analyses, which tested two alternative TTFEs (5 and 30y), showed similar health impacts, with slightly larger gains observed for TTFE 5 years and somewhat lower impacts for TTFE 30y compared to TTFE 10 years (Supplementary Table 2, Supplementary Figs. 4–7). More specifically, applying a TTFE of 5 years resulted in gains that were 7–10% larger than those obtained with the main model (TTFE of 10 years), depending on the scenario and outcome. In contrast, applying a TTFE of 30 years resulted in gains that were 28–35% smaller than those of the main model. Discussion This modelling study demonstrated that achieving the UKCCC’s meat reduction targets through strong government-led interventions—specifically reductions in red and processed meat and their substitution with vegetables and legumes—could deliver substantial public health benefits, complementing previously reported environmental gains ( 21 ). Over a 30-year horizon, the projected reduction in meat intake and corresponding increase in the consumption of vegetables and legumes was associated with a projected reduction of up to 4·5 million cases of chronic disease and gains of up to 9·4 months in population life expectancy in the UK under the assumptions of the model. Importantly, the health gains reported here are conditional on the behavioural dynamics simulated in the agent-based model and should be interpreted as exploratory scenario estimates rather than predictions of real-world outcomes. Given the broadly similar dietary patterns, chronic disease profiles, and shared climate mitigation commitments across many high-income European countries, these findings are likely to be informative for wider European policy discussions on sustainable diets, including those occurring at EU and WHO European Region levels. The main part of these gains can be attributed to reduced mortality and morbidity from CVD, which is the most prominent cause of death and disability in the UK and across Europe, and is also the disease category most strongly influenced by dietary risk factors ( 9 ). These projected health gains align directly with national public health priorities outlined in recent UK strategies such as the NHS Long Term Plan ( 32 ) and the 10 Year Health Plan for England ( 33 ), which emphasise reducing the burden of cardiometabolic diseases. Similar priorities are reflected across European public health strategies, including WHO Europe targets for non-communicable disease reduction and EU-level prevention frameworks, where cardiovascular disease remains a leading cause of preventable mortality and a major contributor to rising health system expenditure. The anticipated gains are also likely to reduce the economic burden of cardiovascular disease, which is estimated to cost the wider UK economy £29 billion annually ( 34 ). These results are consistent with—and extend—the findings of previous health modelling studies in the European Region and other high income settings, which have demonstrated that dietary shifts away from meat, particularly red and processed meats, are associated with substantial reductions in mortality and incidence of chronic diseases ( 14 , 35 – 40 ). They also tally with the findings of a recent policy modelling study ( 41 ) that found significant reductions in chronic disease mortality following the hypothetical implementation of a tax on meat at a global scale. Similarly, in a high-tax scenario in Germany ( 42 ), comparable reductions in processed meat intake were projected to yield decreases of 126,000 cases of diabetes and 7,200 cases of colorectal cancer by year 10 of the simulation. These estimates are somewhat higher than the outcomes in our study, which is likely attributable to methodological differences, particularly the use of different exposure-response functions as well as the absence of time-lag assumptions between changes in dietary exposure and subsequent disease impacts in their modelling approach. Moreover, Milner et al. ( 38 ) assessed the health impacts of multiple measures to achieve net zero GHGEs, including reductions in meat consumption according to the UKCCC’s targets, and reported similar trends in gains in YLG and life expectancy. However, their estimated health gains were somewhat lower than those identified in our study. Again, this difference may stem from methodological choices: their modelling of dietary change did not incorporate opinion-dynamics or social network-based substitutions and did not include reductions in processed meat consumption. Crucially, our results build on a novel agent-based modelling approach that explicitly explored the time required to achieve the 35% and 50% meat reduction targets. We found that these targets could be reached in a shorter timeframe—approximately 5·2 years for reaching a 35% reduction and 8·1 years for a 50% reduction—compared to previous studies, which assumed the targets would only be met by 2030 and 2050, respectively. This suggests that earlier research may have underestimated the potential health gains associated with achieving these dietary targets. A large share of the modelled gains in our study was due to reduced mortality and morbidity from CVD. This tallies with findings from previous research ( 37 , 39 , 43 ) and reflects the fact that nearly all dietary risk factors considered in our model (the intake of vegetables, legumes and processed meat) are linked to cardiovascular outcomes, whereas cancers and type 2 diabetes are influenced by only two risk factors (red and processed meat). Furthermore, changes in CVD mortality and morbidity are largely impacted by changes in the intake of vegetables and legumes, for which the absolute dietary changes were considerably larger than those for red and processed meat. A key strength is that the dietary changes underpinning our health impact modelling were informed by a novel agent-based opinion dynamics modelling approach, capturing more realistic patterns of dietary transition rather than assuming abrupt or uniform shifts. However, the model relies on stylised behavioural assumptions and synthetic social network structures, and the health outputs should therefore be interpreted as conditional on these modelled trajectories rather than empirically observed dietary transitions. While parameterised for the UK context, the modelling framework used here is readily adaptable to other European countries, where social norms, peer effects, and policy-driven food environment changes similarly shape dietary behaviour. Combined with actual population estimates, underlying mortality and morbidity data, and robust epidemiological evidence on specific risk associations, this allows us to provide policy-relevant estimates of avoided chronic disease burden associated with large-scale dietary change scenarios. Sensitivity analyses around time lags to benefit realization further address a critical area of uncertainty often overlooked in health impact modelling. At the same time, our estimates carry some uncertainty due to the assumptions and data used in the model. First, while the model accounts for different underlying mortality and morbidity by age and sex, it assumes uniform dietary change and health response across the UK population. It does not capture the considerable heterogeneity and nuance in health status and dietary patterns across socioeconomic groups. Evidence suggests that health inequalities are widening in the UK, and that the burden of chronic disease remains disproportionately high among the most deprived households ( 44 ). Our model does not reflect these gradients, and thus likely underestimates both the challenges and the opportunities for addressing health inequities in real-world settings. Furthermore, our model assumes that age- and sex-specific mortality and incidence rates remain constant throughout follow-up. This approach does not account for uncertainty in future trends—including the current increase in use of GLP-1 receptor agonists prescribed for weight loss and diabetes management in the UK ( 45 ). These changes could lower chronic disease rates in the population and, consequently, alter the absolute health gains associated with dietary reductions in meat intake. Thus, our estimates should be interpreted in the context of uncertainty regarding future changes in both baseline risk and health determinants. Our risk–benefit assessment was focused on the primary chronic diseases linked to red and processed meat, vegetables, and legumes, as classified in the GBD. We did not consider the risk of nutrient deficiencies or unintended consequences such as increased exposure to chemical contaminants such as heavy metals from higher vegetable and legume consumption. This leaves a considerable “black box” relating to nutritional adequacy, particularly in subpopulations or under sustained dietary transitions, and to food safety risks. Nutritional adequacy was not modelled because available relative risks for some nutrients are tightly linked to the food groups already included (e.g., fibre and vegetable/legume consumption) and including them separately would risk double-counting effects. Additionally, large reductions in meat intake may affect micronutrient adequacy, particularly for nutrients for which meat is a primary dietary source, such as iron and vitamin B12. While this represents a limitation of the current analysis, previous optimisation modelling has demonstrated that nutritionally adequate diets with substantially reduced meat intake and increased plant-based foods are achievable within current dietary patterns ( 46 ), suggesting that the health gains modelled here may not come at the expense of micronutrient adequacy. Chemical contaminant exposure risks were similarly not considered in the present analysis, as comprehensive estimation would require accounting for a wide range of chemicals, including heavy metals and pesticides, and would necessitate substantial modifications to the current modelling framework. While dose–response functions exist and have been used for many individual chemicals ( 47 , 48 ), integrating them into our health impact model would require a considerably expanded approach. Future work could draw on risk–benefit assessment studies that incorporate multiple contaminants using alternative methodologies, allowing for a more complete evaluation of dietary risks. Furthermore, potential health effects arising from shifts in other parts of the diet, such as changes in refined carbohydrate, dairy, or total fat intake, were not captured, which may have influenced the magnitude of estimated health gains. We didn’t model these other potential foo groups because the substitution patterns were determined by the agent-based opinion dynamics model ( 21 ), in which fiscal measures incentivised these specific food groups as replacements for meat. Finally, the confidence intervals around our results were notably wide. This reflects an inherent feature of modelling dietary impacts: a combination of variation in dietary intake across the population and uncertainty arising from limitations in self-reported dietary data, for which no alternative high-resolution data exist. In the sensitivity analyses, ± 1 standard deviation adjustments captured a range of modelled changes—from very small to very large—in consumption of red meat, processed meat, legumes, and vegetables. These wide intervals therefore represent both the heterogeneity in actual dietary patterns in the UK and the unavoidable imprecision of current dietary surveillance and reporting systems. As with all modelling studies of this type, results should be interpreted as indicative of potential, rather than guaranteed, population-level effects. They should also be complemented by research investigating the broader social and economic impacts of food policy changes in the UK, as such shifts could have wider unintended consequences ( 49 ). This study set out to estimate the population health impacts of achieving the UKCCC's meat reduction targets—specifically a 35% and 50% reduction in red and processed meat consumption—through policy-driven dietary transitions in the UK. Our findings suggest that meeting these targets could be associated with substantial reductions in chronic disease burden and meaningful gains in population life expectancy, driven predominantly by reductions in cardiovascular mortality. While these estimates are exploratory and conditional on modelled behavioural trajectories, they provide a relevant indication of the potential scale of public health gains achievable through climate-aligned food policies, with implications extending beyond the UK to broader European efforts to reduce cardiometabolic disease burden and advance sustainable dietary transitions. Abbreviations CVD Cardiovascular disease CIs Confidence Intervals GBD Global Burden of Disease IHD Ischemic heart disease NDNS National Diet and Nutrition Survey RR Relative risk SD Standard deviation TTFE Time to full effect UK United Kingdom UKCCC United Kingdom’s Climate Change Committee WHO World Health Organisation YLG Years of Life Gained Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. PEC had financial support from the Swedish Research Council (VR, grant nr. 2022-00344) for the submitted work. The Swedish Research Council had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication Ethics statement This study utilised publicly available national dietary data and population statistics, including mortality and morbidity statistics. Since the data used in this research was obtained from secondary sources and was anonymised, there was no direct interaction with human subjects. Thus, specific ethical approvals for human subject research were not required. Author contributions All authors contributed to the conceptualisation of this manuscript. PEC conducted and interpreted the research, analysed and visualised the data, drafted the paper and had primary responsibility for the final content. OA analysed and visualised the data, interpreted the research and critically revised the paper. JM contributed to design of the methodology, interpreted the research and critically revised the paper. AF, SP, SMP, AH and ML, interpreted the research and critically revised the paper. RG interpreted the research, critically revised the paper and maintained study oversight. Both PEC and OA directly accessed and verified the underlying data reported in the manuscript. All authors confirm that they had full access to all the data in the study and accept responsibility to submit for publication. Data sharing statement All data used in this study are publicly available from the original sources cited in the manuscript. No individual participant data were collected. The datasets used for the analyses, together with the data dictionary defining each variable, can be accessed from the original public repositories. The analytical code used to process the data and generate the results will be made available with publication. No additional, related documents are available. The analytical code will be available from the date of publication and can be obtained via request to the corresponding author ( [email protected] ). Access will be granted for academic and non-commercial research purposes, subject to appropriate citation of the original data sources. 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Available from: http://ghdx.healthdata.org/gbd-results-tool Milner J, Green R, Dangour AD, Haines A, Chalabi Z, Spadaro J et al (2015) Health effects of adopting low greenhouse gas emission diets in the UK. BMJ Open 5(4):e007364–e007364. 10.1136/bmjopen-2014-007364 Eustachio Colombo P, Milner J, Pastorino S, Green R (2025) Population health impacts from the taxation of salt and sugar in the United Kingdom. Public Health Nutr 28(1):e153. 10.1017 /S1368980025100967 PubMed PMID: 40859903 Miller BG (2003) Life table methods for quantitative impact assessments in chronic mortality. J Epidemiol Community Health 57(3):200–206. 10.1136/jech.57.3.200 R Core Team. R: A language and environment for statistical computing [Internet]. Vienna: R Foundation for Statistical Computing (2021) Available from: http://www.R-project.org/ Harashima E, Nakagawa Y, Urata G, Tsuji K, Shirataka M, Matsumura Y (2007) Time-lag estimate between dietary intake and breast cancer mortality in Japan. Asia Pac J Clin Nutr 16(1):193–198 PubMed PMID: 17215198 Capewell S, O’Flaherty M (2011) Can dietary changes rapidly decrease cardiovascular mortality rates? Eur Heart J 32(10):1187–1189. 10.1093/eurheartj/ehr049 Fadnes LT, Økland JM, Haaland ØA, Johansson KA (2022) Estimating impact of food choices on life expectancy: A modeling study. PLOS Med 19(2):e1003889. 10.1371/journal.pmed.1003889 England NHS NHS England » 2025/26 priorities and operational planning guidance [Internet]. [cited 2025 Nov 10]. Available from: https://www.england.nhs.uk/long-read/2025-26-priorities-and-operational-planning-guidance/ GOV.UK [Internet] [cited 2025 Nov 10]. Fit for the future: 10 Year Health Plan for England - executive summary (accessible version). Available from: https://www.gov.uk/government/publications/10-year-health-plan-for-england-fit-for-the-future/fit-for-the-future-10-year-health-plan-for-england-executive-summary Shih K, Herz N, Sheikh A, O’Neill C, Carter P, Anderson M (2025) Economic burden of cardiovascular disease in the United Kingdom. Eur Heart J - Qual Care Clin Outcomes 11(5):678–690. 10.1093/ehjqcco/qcaf011 Clark MA, Springmann M, Hill J, Tilman D (2019) Multiple health and environmental impacts of foods. Proc Natl Acad Sci 116(46):23357–23362. 10.1073/pnas.1906908116 Springmann M, Wiebe K, Mason-D’Croz D, Sulser TB, Rayner M, Scarborough P (2018) Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet Health 2(10):e451–e461. 10.1016/S2542-5196 (18)30206-7 PubMed PMID: 30318102 Springmann M, Godfray HCJ, Rayner M, Scarborough P (2016) Analysis and valuation of the health and climate change cobenefits of dietary change. Proc Natl Acad Sci 113(15):4146–4151. 10.1073/pnas.1523119113 Milner J, Turner G, Ibbetson A, Eustachio Colombo P, Green R, Dangour AD et al (2023) Impact on mortality of pathways to net zero greenhouse gas emissions in England and Wales: a multisectoral modelling study. Lancet Planet Health 7(2):e128–e136. 10.1016/S2542-5196(22)00310-2 Eustachio Colombo P, Milner J, Scheelbeek PF, Taylor A, Parlesak A, Kastner T et al (2021) Pathways to 5-a-day: modeling the health impacts and environmental footprints of meeting the target for fruit and vegetable intake in the United Kingdom. Am J Clin Nutr 114(2):530–539. 10.1093/ajcn/nqab076 Pastorino S, Milojevic A, Green R, Beck R, Carnell E, Colombo PE et al (2024) Health impact of policies to reduce agriculture-related air pollutants in the UK: The relative contribution of change in PM2.5 exposure and diets to morbidity and mortality. Environ Res 262:119923. 10.1016/j.envres.2024.119923 Springmann M, Mason-D’Croz D, Robinson S, Wiebe K, Godfray HCJ, Rayner M et al (2018) Health-motivated taxes on red and processed meat: A modelling study on optimal tax levels and associated health impacts. PLoS ONE 13(11):e0204139. 10.1371/journal.pone.0204139 Schönbach JK, Thiele S, Lhachimi SK (2019) What are the potential preventive population-health effects of a tax on processed meat? A quantitative health impact assessment for Germany. Prev Med 118:325–331. 10.1016/j.ypmed.2018.11.011 Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJL et al (2009) The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors. PLOS Med 6(4):e1000058. 10.1371/journal.pmed.1000058 Inequalities in mortality involving common physical health conditions England - Office for National Statistics [Internet]. [cited 2025 Nov 12]. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthinequalities/bulletins/inequalitiesinmortalityinvolvingcommonphysicalhealthconditionsengland/21march2021to31january2023 Analyse | OpenPrescribing [Internet] [cited 2025 Nov 17]. Available from: https://openprescribing.net/analyse/#org=regional_team&numIds=0601023AW,0601023AZ,0601023AWBB&denom=nothing&selectedTab=data Eustachio Colombo P, Elinder LS, Nykänen EPA, Patterson E, Lindroos AK, Parlesak A (2024) Developing a novel optimisation approach for keeping heterogeneous diets healthy and within planetary boundaries for climate change. Eur J Clin Nutr 78(3):193–201. 10.1038/s41430-023-01368-7 Thomsen ST, Pires SM, Devleesschauwer B, Poulsen M, Fagt S, Ygil KH et al (2018) Investigating the risk-benefit balance of substituting red and processed meat with fish in a Danish diet. Food Chem Toxicol 120:50–63. 10.1016/j.fct.2018.06.063 Mihalache OA, Elliott C, Dall’Asta C (2024) Human Health Impact Based on Adult European Consumers’ Dietary Exposure to Chemical Contaminants and Consumption of Unprocessed Red Meat, Processed Meat, and Legumes. Expo Health 16(6):1421–1433. 10.1007/s12403-024-00634-8 Lee MRF, Domingues JP, McAuliffe GA, Tichit M, Accatino F, Takahashi T (2021) Nutrient provision capacity of alternative livestock farming systems per area of arable farmland required. Sci Rep 11(1):14975. 10.1038/s41598-021-93782-9 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials20260325.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9224499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630424132,"identity":"e86ab406-1f2d-4c03-981e-cce0c57efa97","order_by":0,"name":"Patricia Eustachio 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expectancy by disease outcome across the health impact projection for the 35% and 50% meat reduction scenarios, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/5394a2452d2fce97c5e6f28f.png"},{"id":109203361,"identity":"2edba34b-be26-45f6-9149-99984c212597","added_by":"auto","created_at":"2026-05-13 14:30:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108538,"visible":true,"origin":"","legend":"\u003cp\u003eShare of changes in years of life gained by disease outcome 10 years into the health impact projection for the 35% and 50% meat reduction scenarios, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/a346f5f6cb3f3c5b07f0521f.png"},{"id":108013441,"identity":"bcc22467-6adf-4e5d-a9f8-f59dc84adc19","added_by":"auto","created_at":"2026-04-28 13:21:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125251,"visible":true,"origin":"","legend":"\u003cp\u003eShare of changes in disease incidence by disease outcome 10 years into the health impact projection for the 35% and 50% meat reduction scenarios, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/ed61ef48ae524760ebcb2420.png"},{"id":108181300,"identity":"91da7fe2-582c-43c7-850e-6e1d649568c7","added_by":"auto","created_at":"2026-04-30 08:58:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73148,"visible":true,"origin":"","legend":"\u003cp\u003eShare of health gains by dietary risk 10 years into the health impact projection for the 35% and 50% meat reduction scenarios, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/c85dd600f40b9825e46f6286.png"},{"id":109204724,"identity":"5c1a7b56-36e4-414c-917f-1f46c9d1bd40","added_by":"auto","created_at":"2026-05-13 15:01:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":622576,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/cced4810-b90e-4968-ba6b-241970d0775d.pdf"},{"id":108013438,"identity":"9b05eb72-fc82-435c-83db-ae676501e570","added_by":"auto","created_at":"2026-04-28 13:21:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":711492,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials20260325.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224499/v1/c259630b3cf0ab326037c774.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health co-benefits of sustainable dietary transitions to reduced red and processed meat intake in the United Kingdom: a modelling study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe urgent need to shift population diets toward more sustainable and health-promoting patterns is widely recognised as a cornerstone for addressing both the rising burden of chronic diseases across Europe and globally, and the escalating environmental impacts of food production and consumption (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similar to dietary patterns observed in many European countries (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), current dietary patterns in the United Kingdom (UK) are characterised by high consumption of red and processed meat, with average weekly intakes of nearly 590 g in adult men and around 400 g in adult women (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), compared with the UK-specific recommended maximum intake of 490 g per week (70 g per day) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Such high intakes have been associated with increased risks of cardiovascular disease (CVD), type 2 diabetes, colorectal cancer, and premature mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), as well as with substantial environmental impacts (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In line with broader European climate mitigation pathways outlined by the European Commission\u0026rsquo;s roadmap for a competitive low-carbon Europe by 2050 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), the UK\u0026rsquo;s Climate Change Committee (UKCCC) has emphasised the necessity of reducing meat consumption as part of comprehensive strategies to mitigate national greenhouse gas emissions (GHGE) and enhance public health (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Supporting this, epidemiological modelling studies (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and the recent EAT\u0026ndash;Lancet Commission on healthy, sustainable, and just food systems (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) suggest that a diet low in red and processed meat intake, and high in plant-based foods such as vegetables and pulses could yield substantial health and environmental co-benefits. Despite the robust evidence base that supports a transition towards a reduced consumption of meat and an increased intake of plant-based foods in high-income contexts, understanding how to reach real-world dietary change at scale remains challenging due to the complex interplay of social, cultural, economic, and behavioural factors that shape food choices (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgent-based and microsimulation models have emerged as powerful tools to predict the potential impact of dietary interventions and policy strategies on population-level dietary behaviours (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), enabling researchers and policymakers to estimate the large-scale effects of shifts in food consumption patterns. Our previous work (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) utilised agent-based opinion dynamics models to simulate the effects of governmental influence (fiscal measures and/or information campaigns) on dietary patterns in the UK, specifically targeting reductions in meat consumption to align with the UKCCC\u0026rsquo;s climate goals (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This modelling demonstrated that achieving targets of 35% and 50% average meat reduction by 2030 and 2050, respectively (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), could lead to significantly increased intakes of vegetables, pulses, and meat alternatives, alongside decreases in GHGE, land use, and water use (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). While this prior study quantified the shifts in food group consumption and the environmental benefits, a comprehensive assessment of the associated health impacts was beyond its primary scope. Robust evidence on these health impacts is fundamental for policymakers to effectively weigh the benefits against possible drawbacks of interventions, and to foster their legitimacy and public acceptance. However, despite the recognised value of evidence-informed policymaking, research indicates that policy change in nutrition and diet is often slow and contested, facing barriers such as political priorities, industry pressure, and public scepticism (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). According to a review by the World Health Organisation on fiscal policies to promote healthy diets (WHO) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), including analyses focused on European settings, the provision scientific evidence on potential benefits of new policies is critical not only to support the adoption of dietary interventions but also to sustain them amidst ongoing debate and resistance. This review also highlighted the lack of evidence on long-term health impacts as an important evidence gap to be addressed in future research.\u003c/p\u003e \u003cp\u003eBuilding on our prior work and using the UK as a case study within a wider European policy context, this study therefore aims to estimate the associated health impacts of dietary transitions towards meeting the UKCCC\u0026rsquo;s targets of 35% and 50% average meat reduction by 2030 and 2050. By quantifying the potential changes in chronic disease incidence and mortality resulting from achieving national meat reduction targets and the likely substitution to other foods this would entail, this research provides actionable insights for policymakers in the UK and other European countries seeking to promote healthier and more environmentally sustainable diets at the population level.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eScenarios\u003c/h2\u003e\u003cp\u003eWe employed life table modelling to quantify the health impacts resulting from previously simulated changes in meat intake in the UK (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Briefly, to simulate the impact of governmental influence on people\u0026rsquo;s consumption of total (red and white, processed and non-processed) meat consumption, dietary data derived from the UK National Diet and Nutrition Survey (NDNS) 2019 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) served as the baseline input data for agent-based opinion dynamics models. Agents, representing the UK adult population, were connected within a social network and influenced through a sequence of governmental campaigns to meet the UKCCC\u0026rsquo;s targets of a 35% average reduction in meat consumption by 2030 and a 50% average reduction by 2050 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). While the UKCCC's dietary pathways include reduced intakes of both meat and dairy, this study focuses solely on changes in meat consumption, consistent with the upstream agent-based modelling in which only meat reduction was simulated. In a scenario of high governmental influence represented by the implementation of nationwide information campaigns for reduced meat consumption in conjunction with fiscal measures (taxes on meat\u0026thinsp;+\u0026thinsp;subsidies on meat alternatives/legumes/vegetables), meat consumption declined in the simulations, while the intake of vegetables, legumes, and meat alternatives increased isocalorically (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). No other dietary components were assumed to change. It took approximately 5\u0026middot;2 and 8\u0026middot;1 years for the 35% and 50% reduction targets to be met, respectively. Further details of the methods used for the simulations can be found in Fontan et al, 2025 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe quantified the health impacts of meeting these two targets, specifically analysing the changes in average consumption levels of red meat, processed meat, vegetables, and legumes among adult males and females. Health impacts related to changes in unprocessed white meat and meat alternatives were not modelled due to a lack of robust evidence linking these consumption patterns to health outcomes. Hence, the portion of unprocessed white meat was removed from the total simulated meat reduction proportionally to their share (36% for males and 39% for females) of total meat consumption at baseline, and was assigned no health impact in the model\u0026mdash;representing a conservative assumption consistent with the limited epidemiological evidence for this food group. In the 35% meat reduction scenario, we quantified the health impacts associated with achieving a 35% average reduction in red and processed meat consumption within the UK population. In the 50% meat reduction scenario, we evaluated the health impacts related to a 50% reduction in red and processed meat consumption. For brevity, both scenarios are hereafter referred to as the 35% and 50% meat reduction scenarios, where 'meat' refers exclusively to red and processed meat throughout.\"\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDisease outcomes and relative risks\u003c/h2\u003e \u003cp\u003eExposure-response relationships (i.e. relative risks per unit change in consumption) between dietary intake and chronic disease morbidity and mortality were obtained from the Global Burden of Disease (GBD) study (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The GBD study is the most comprehensive ongoing global observational epidemiological study and has assessed 396 diseases and injuries and 87 risk factors across 204 countries since 1990. GBD-derived exposure\u0026ndash;response functions were used to ensure internal consistency across outcomes and risk factors and to enable comparability with other global and national burden of disease assessments.\u003c/p\u003e \u003cp\u003eThe relative risk (RR) for a given dietary risk-disease pair quantifies the change in mortality (or morbidity) risk associated with a change in exposure to that dietary risk. For example, the risk of ischemic heart disease (IHD) is reduced by 24% for each 50 g increase in daily legume intake (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Relative risks from the GBD expressed in terms of a harmful risk factor (e.g. \u0026ldquo;diet high in red meat\u0026rdquo;) were inverted (i.e. the reciprocal of the relative risk was taken) to obtain relative risks corresponding to a beneficial dietary change. A total of six disease outcomes linked to the consumption of red meat, processed meat, vegetables, and legumes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were considered in the model (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in daily intakes for the dietary scenarios tested, based on previous work (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMeat reduction scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMales +\u0026thinsp;18y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eFemales +\u0026thinsp;18y\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed meat\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProcessed meat\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLegumes\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRed meat\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProcessed meat\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLegumes\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003cp\u003e(g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-35%\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;168.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;114.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-35%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;288.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;49.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;174.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-35%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;49.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-50%\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;83.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;242.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;171.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-50%\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;137.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;427.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;75.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;277.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-50%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e+\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e+\u0026thinsp;66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003eThe central exposure-response estimate combined with the average change in consumption.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003eThe lower exposure-response estimate combined with the average change in consumption\u0026thinsp;+\u0026thinsp;1 standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003eThe upper exposure-response estimate combined with the average change in consumption \u0026ndash; 1 standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eRR\u0026thinsp;=\u0026thinsp;Relative risk estimate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDietary exposure-response pathways (including upper and lower 95% confidence intervals) used in the health impact modelling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary exposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative risk\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschaemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschaemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntracerebral haemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubarachnoid haemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLegumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschaemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 g increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRed meat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes type 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 g decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProcessed meat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschaemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 g decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 g decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes type 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 g decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eRelative risks from the Global Burden of Disease study (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eCI = Confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline health data\u003c/h3\u003e\n\u003cp\u003eAge- and sex-specific data for the UK population in 2021, including population-size estimates, all-cause mortality, and disease-specific mortality (mortality rates) and morbidity (incidence rates) for relevant outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were downloaded from the GBD results tool (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHealth impact modelling\u003c/h3\u003e\n\u003cp\u003eHealth impacts from the dietary scenarios were quantified following a previously developed approach (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) using the IOMLIFET life table model (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) implemented in R version 4.5.1 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This model projects population survival patterns by applying changes in mortality risk from hypothetical dietary changes to age-specific mortality rates. By inputting hypothetical dietary changes (representing altered risk exposures) and applying known exposure-response functions (i.e. relative risks), the model quantifies subsequent changes in life expectancy and years of life lost (mortality outcomes) across time. Years of life lost represent the years of life lost for an individual (or a population) as a result of premature avertable mortality, considering the age at which deaths occurred. Since the dietary modifications were expected to reduce mortality rates, years of life lost were translated to years of life gained (YLG). Changes in morbidity (new cases of disease) were quantified using the same principles. Morbidity calculations used the life table population output as the baseline, to which changes in risk exposures were applied. Each morbidity calculation was performed independently based on incidence rates and then aggregated across the entire UK adult population. Changes in YLG and disease incidence were aggregated over a 30-year period. This duration was selected to allow the full observation of latent health impacts, as discussed further below. Changes in life expectancy at birth were determined by calculating the difference between the baseline life expectancy (total expected life years divided by the initial population) and the modelled life expectancy (impacted expected life years divided by the impacted starting population) across the full health projection.\u003c/p\u003e \u003cp\u003eOur modelling strategy incorporated several key assumptions regarding dietary changes and their health outcomes (Supplementary Table\u0026nbsp;1). Where multiple dietary exposures influenced the same disease outcome, relative risks were multiplied together to avoid double counting of overlapping pathways. To account for the time required for dietary changes to manifest as health impacts, we integrated time lags based on established epidemiological evidence (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and a previously applied modelling approach (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Specifically, we assumed that the time to full effect (TTFE) on all disease outcomes was approximately 10 years in the main analyses. More information regarding data management and assumptions for the health impact modelling may be found in the supplementary materials (full list of assumptions in Supplementary Table\u0026nbsp;1; lag functions in Supplementary Figs.\u0026nbsp;1\u0026ndash;3).\u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the sensitivity of our results to key parameters, we generated upper and lower health impact estimates (i.e. confidence intervals) for the modelled outcomes. These were based on a dispersion measure of \u0026plusmn;\u0026thinsp;1 standard deviation around the mean for changes in the consumption of red meat, processed meat, vegetables and legumes. These estimates were combined with the upper and lower 95% Confidence Intervals (CIs) for the RRs provided by the GBD study (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, we explored different lags between reduction in disease risk factors and potential health gains, as per previous health impact modelling research (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The time perspective for health gains varies by disease: cardiovascular disease mortality may respond within a few years following risk factor reduction, while cancer outcomes are associated with longer exposure durations (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To balance these time perspectives while accounting for the morbidity burden of both disease groups, we followed the approach of Fadnes et al. in assuming a TTFE of 10 years with a gradual, linear increase in effect as the main analysis. Sensitivity analyses with 5 and 30 years to full effect were also conducted to reflect this uncertainty (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe health impact modelling results indicated that reaching the UKCCC\u0026rsquo;s meat reduction targets would have positive impacts on average life expectancy in the UK. Specifically, life expectancy would increase by 7\u0026middot;2 (1\u0026middot;1\u0026ndash;12\u0026middot;9) months in the 35% meat reduction scenario, and by 9\u0026middot;4 (1\u0026middot;3\u0026ndash;15\u0026middot;4) months in the 50% meat reduction scenario (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A major share (95%) of the estimated increased life expectancy was due to reduced mortality from CVD (ischemic heart disease, ischemic stroke, subarachnoid haemorrhage and intracerebral haemorrhage, combined) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, total YLG accumulated over 30 years from avoided deaths ranged between 8.4 (1\u0026middot;3\u0026ndash;14\u0026middot;9) million and 10.9 (1\u0026middot;5\u0026ndash;17\u0026middot;9) million in the 35% and 50% meat reduction scenarios, respectively. These gains were largely driven by reduced mortality from CVD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in life expectancy, years of life gained and incidence of disease, accumulated over 30 years, 10 years into the health impact projection for the 35% and 50% meat reduction scenarios, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35% meat reduction scenario\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50% meat reduction scenario\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLE in months (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026middot;2 (1\u0026middot;1 to 12\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026middot;4 (1\u0026middot;3 to 15\u0026middot;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYLG\u003csup\u003ea\u003c/sup\u003e in million (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026middot;4 (1\u0026middot;3 to 14\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026middot;9 (1\u0026middot;5 to 17\u0026middot;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal dancer in nr (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-47,046 (-2,782 to -156,240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-72,207 (-7,841 to -225,156)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType-2 diabetes in nr (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-799,197 (-78,970 to -1,890,263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,122,824 (-144,260 to -2,474,253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003csup\u003ea\u003c/sup\u003e in nr (CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,545,353 (-363,482 to -4,538,914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3,299,217 (-428,209 to -5,406,950)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eTime to full effect of 10 years.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eLE\u0026thinsp;=\u0026thinsp;Life expectancy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eYLG\u0026thinsp;=\u0026thinsp;Years of Life Gained.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCVD\u0026thinsp;=\u0026thinsp;Cardiovascular disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eIschemic heart disease, ischemic stroke, subarachnoid haemorrhage and intracerebral haemorrhage, combined.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar impacts were observed through reduced morbidity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The largest share (approximately 75%) of reductions in new disease cases over 30 years was attributable to CVD, with an estimated 2\u0026middot;5 (0\u0026middot;4\u0026ndash;4\u0026middot;5) and 3\u0026middot;3 (0\u0026middot;4\u0026ndash;5\u0026middot;4) million fewer cases under the 35% and 50% meat reduction scenarios, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This was followed by type 2 diabetes\u0026mdash;which according to the GBD estimates is affected only by changes in red and processed meat consumption\u0026mdash;with an estimated 0\u0026middot;8 (0\u0026middot;08\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;9) and 1\u0026middot;1 (0\u0026middot;1\u0026ndash;2\u0026middot;5) million fewer cases under the two scenarios. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). New cases of colorectal cancer over 30 years, also only impacted by the reduction in consumption of red and processed meat as per GBD estimates, constituted the lowest share of total reductions in disease incidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) but still achieved reductions of up to 72 (7\u0026middot;8-225) thousand fewer cases in the 50% meat reduction scenario (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, about three quarters of mortality gains could be attributed to changes in the consumption of vegetables and pulses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) with the same patterns for morbidity outcomes (no data shown).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sensitivity analyses, which tested two alternative TTFEs (5 and 30y), showed similar health impacts, with slightly larger gains observed for TTFE 5 years and somewhat lower impacts for TTFE 30y compared to TTFE 10 years (Supplementary Table\u0026nbsp;2, Supplementary Figs.\u0026nbsp;4\u0026ndash;7). More specifically, applying a TTFE of 5 years resulted in gains that were 7\u0026ndash;10% larger than those obtained with the main model (TTFE of 10 years), depending on the scenario and outcome. In contrast, applying a TTFE of 30 years resulted in gains that were 28\u0026ndash;35% smaller than those of the main model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis modelling study demonstrated that achieving the UKCCC\u0026rsquo;s meat reduction targets through strong government-led interventions\u0026mdash;specifically reductions in red and processed meat and their substitution with vegetables and legumes\u0026mdash;could deliver substantial public health benefits, complementing previously reported environmental gains (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Over a 30-year horizon, the projected reduction in meat intake and corresponding increase in the consumption of vegetables and legumes was associated with a projected reduction of up to 4\u0026middot;5\u0026nbsp;million cases of chronic disease and gains of up to 9\u0026middot;4 months in population life expectancy in the UK under the assumptions of the model. Importantly, the health gains reported here are conditional on the behavioural dynamics simulated in the agent-based model and should be interpreted as exploratory scenario estimates rather than predictions of real-world outcomes. Given the broadly similar dietary patterns, chronic disease profiles, and shared climate mitigation commitments across many high-income European countries, these findings are likely to be informative for wider European policy discussions on sustainable diets, including those occurring at EU and WHO European Region levels. The main part of these gains can be attributed to reduced mortality and morbidity from CVD, which is the most prominent cause of death and disability in the UK and across Europe, and is also the disease category most strongly influenced by dietary risk factors (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These projected health gains align directly with national public health priorities outlined in recent UK strategies such as the NHS Long Term Plan (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and the 10 Year Health Plan for England (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), which emphasise reducing the burden of cardiometabolic diseases. Similar priorities are reflected across European public health strategies, including WHO Europe targets for non-communicable disease reduction and EU-level prevention frameworks, where cardiovascular disease remains a leading cause of preventable mortality and a major contributor to rising health system expenditure. The anticipated gains are also likely to reduce the economic burden of cardiovascular disease, which is estimated to cost the wider UK economy \u0026pound;29\u0026nbsp;billion annually (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results are consistent with\u0026mdash;and extend\u0026mdash;the findings of previous health modelling studies in the European Region and other high income settings, which have demonstrated that dietary shifts away from meat, particularly red and processed meats, are associated with substantial reductions in mortality and incidence of chronic diseases (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). They also tally with the findings of a recent policy modelling study (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) that found significant reductions in chronic disease mortality following the hypothetical implementation of a tax on meat at a global scale. Similarly, in a high-tax scenario in Germany (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), comparable reductions in processed meat intake were projected to yield decreases of 126,000 cases of diabetes and 7,200 cases of colorectal cancer by year 10 of the simulation. These estimates are somewhat higher than the outcomes in our study, which is likely attributable to methodological differences, particularly the use of different exposure-response functions as well as the absence of time-lag assumptions between changes in dietary exposure and subsequent disease impacts in their modelling approach. Moreover, Milner et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) assessed the health impacts of multiple measures to achieve net zero GHGEs, including reductions in meat consumption according to the UKCCC\u0026rsquo;s targets, and reported similar trends in gains in YLG and life expectancy. However, their estimated health gains were somewhat lower than those identified in our study. Again, this difference may stem from methodological choices: their modelling of dietary change did not incorporate opinion-dynamics or social network-based substitutions and did not include reductions in processed meat consumption. Crucially, our results build on a novel agent-based modelling approach that explicitly explored the time required to achieve the 35% and 50% meat reduction targets. We found that these targets could be reached in a shorter timeframe\u0026mdash;approximately 5\u0026middot;2 years for reaching a 35% reduction and 8\u0026middot;1 years for a 50% reduction\u0026mdash;compared to previous studies, which assumed the targets would only be met by 2030 and 2050, respectively. This suggests that earlier research may have underestimated the potential health gains associated with achieving these dietary targets.\u003c/p\u003e \u003cp\u003eA large share of the modelled gains in our study was due to reduced mortality and morbidity from CVD. This tallies with findings from previous research (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and reflects the fact that nearly all dietary risk factors considered in our model (the intake of vegetables, legumes and processed meat) are linked to cardiovascular outcomes, whereas cancers and type 2 diabetes are influenced by only two risk factors (red and processed meat). Furthermore, changes in CVD mortality and morbidity are largely impacted by changes in the intake of vegetables and legumes, for which the absolute dietary changes were considerably larger than those for red and processed meat.\u003c/p\u003e \u003cp\u003eA key strength is that the dietary changes underpinning our health impact modelling were informed by a novel agent-based opinion dynamics modelling approach, capturing more realistic patterns of dietary transition rather than assuming abrupt or uniform shifts. However, the model relies on stylised behavioural assumptions and synthetic social network structures, and the health outputs should therefore be interpreted as conditional on these modelled trajectories rather than empirically observed dietary transitions. While parameterised for the UK context, the modelling framework used here is readily adaptable to other European countries, where social norms, peer effects, and policy-driven food environment changes similarly shape dietary behaviour. Combined with actual population estimates, underlying mortality and morbidity data, and robust epidemiological evidence on specific risk associations, this allows us to provide policy-relevant estimates of avoided chronic disease burden associated with large-scale dietary change scenarios. Sensitivity analyses around time lags to benefit realization further address a critical area of uncertainty often overlooked in health impact modelling.\u003c/p\u003e \u003cp\u003eAt the same time, our estimates carry some uncertainty due to the assumptions and data used in the model. First, while the model accounts for different underlying mortality and morbidity by age and sex, it assumes uniform dietary change and health response across the UK population. It does not capture the considerable heterogeneity and nuance in health status and dietary patterns across socioeconomic groups. Evidence suggests that health inequalities are widening in the UK, and that the burden of chronic disease remains disproportionately high among the most deprived households (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Our model does not reflect these gradients, and thus likely underestimates both the challenges and the opportunities for addressing health inequities in real-world settings. Furthermore, our model assumes that age- and sex-specific mortality and incidence rates remain constant throughout follow-up. This approach does not account for uncertainty in future trends\u0026mdash;including the current increase in use of GLP-1 receptor agonists prescribed for weight loss and diabetes management in the UK (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). These changes could lower chronic disease rates in the population and, consequently, alter the absolute health gains associated with dietary reductions in meat intake. Thus, our estimates should be interpreted in the context of uncertainty regarding future changes in both baseline risk and health determinants.\u003c/p\u003e \u003cp\u003eOur risk\u0026ndash;benefit assessment was focused on the primary chronic diseases linked to red and processed meat, vegetables, and legumes, as classified in the GBD. We did not consider the risk of nutrient deficiencies or unintended consequences such as increased exposure to chemical contaminants such as heavy metals from higher vegetable and legume consumption. This leaves a considerable \u0026ldquo;black box\u0026rdquo; relating to nutritional adequacy, particularly in subpopulations or under sustained dietary transitions, and to food safety risks. Nutritional adequacy was not modelled because available relative risks for some nutrients are tightly linked to the food groups already included (e.g., fibre and vegetable/legume consumption) and including them separately would risk double-counting effects. Additionally, large reductions in meat intake may affect micronutrient adequacy, particularly for nutrients for which meat is a primary dietary source, such as iron and vitamin B12. While this represents a limitation of the current analysis, previous optimisation modelling has demonstrated that nutritionally adequate diets with substantially reduced meat intake and increased plant-based foods are achievable within current dietary patterns (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), suggesting that the health gains modelled here may not come at the expense of micronutrient adequacy. Chemical contaminant exposure risks were similarly not considered in the present analysis, as comprehensive estimation would require accounting for a wide range of chemicals, including heavy metals and pesticides, and would necessitate substantial modifications to the current modelling framework. While dose\u0026ndash;response functions exist and have been used for many individual chemicals (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), integrating them into our health impact model would require a considerably expanded approach. Future work could draw on risk\u0026ndash;benefit assessment studies that incorporate multiple contaminants using alternative methodologies, allowing for a more complete evaluation of dietary risks. Furthermore, potential health effects arising from shifts in other parts of the diet, such as changes in refined carbohydrate, dairy, or total fat intake, were not captured, which may have influenced the magnitude of estimated health gains. We didn\u0026rsquo;t model these other potential foo groups because the substitution patterns were determined by the agent-based opinion dynamics model (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), in which fiscal measures incentivised these specific food groups as replacements for meat.\u003c/p\u003e \u003cp\u003eFinally, the confidence intervals around our results were notably wide. This reflects an inherent feature of modelling dietary impacts: a combination of variation in dietary intake across the population and uncertainty arising from limitations in self-reported dietary data, for which no alternative high-resolution data exist. In the sensitivity analyses, \u0026plusmn;\u0026thinsp;1 standard deviation adjustments captured a range of modelled changes\u0026mdash;from very small to very large\u0026mdash;in consumption of red meat, processed meat, legumes, and vegetables. These wide intervals therefore represent both the heterogeneity in actual dietary patterns in the UK and the unavoidable imprecision of current dietary surveillance and reporting systems. As with all modelling studies of this type, results should be interpreted as indicative of potential, rather than guaranteed, population-level effects. They should also be complemented by research investigating the broader social and economic impacts of food policy changes in the UK, as such shifts could have wider unintended consequences (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study set out to estimate the population health impacts of achieving the UKCCC's meat reduction targets\u0026mdash;specifically a 35% and 50% reduction in red and processed meat consumption\u0026mdash;through policy-driven dietary transitions in the UK. Our findings suggest that meeting these targets could be associated with substantial reductions in chronic disease burden and meaningful gains in population life expectancy, driven predominantly by reductions in cardiovascular mortality. While these estimates are exploratory and conditional on modelled behavioural trajectories, they provide a relevant indication of the potential scale of public health gains achievable through climate-aligned food policies, with implications extending beyond the UK to broader European efforts to reduce cardiometabolic disease burden and advance sustainable dietary transitions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Confidence Intervals\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGBD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Global Burden of Disease \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIHD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ischemic heart disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNDNS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Diet and Nutrition Survey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Relative risk\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTFE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Time to full effect\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;United Kingdom\u003c/p\u003e\n\u003cp\u003eUKCCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;United Kingdom’s Climate Change Committee\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organisation\u003c/p\u003e\n\u003cp\u003eYLG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Years of Life Gained\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. PEC had financial support from the Swedish Research Council (VR, grant nr. 2022-00344) for the submitted work. The Swedish Research Council had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilised publicly available national dietary data and population statistics, including mortality and morbidity statistics. Since the data used in this research was obtained from secondary sources and was anonymised, there was no direct interaction with human subjects. Thus, specific ethical approvals for human subject research were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conceptualisation of this manuscript. PEC conducted and interpreted the research, analysed and visualised the data, drafted the paper and had primary responsibility for the final content. OA analysed and visualised the data, interpreted the research and critically revised the paper. JM contributed to design of the methodology, interpreted the research and critically revised the paper. AF, SP, SMP, AH and ML, interpreted the research and critically revised the paper. RG interpreted the research, critically revised the paper and maintained study oversight. Both PEC and OA directly accessed and verified the underlying data reported in the manuscript. All authors confirm that they had full access to all the data in the study and accept responsibility to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available from the original sources cited in the manuscript. No individual participant data were collected. The datasets used for the analyses, together with the data dictionary defining each variable, can be accessed from the original public repositories. The analytical code used to process the data and generate the results will be made available with publication. No additional, related documents are available. The analytical code will be available from the date of publication and can be obtained via request to the corresponding author ([email protected]). Access will be granted for academic and non-commercial research purposes, subject to appropriate citation of the original data sources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S et al (2019) Food in the Anthropocene: the EAT\u0026ndash;Lancet Commission on healthy diets from sustainable food systems. 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Sci Rep 11(1):14975. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-93782-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-93782-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chronic disease prevention, Life expectancy, Health policy, Health impact modelling, Policy evaluation","lastPublishedDoi":"10.21203/rs.3.rs-9224499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eReducing meat intake and increasing plant-based food consumption are priorities for chronic disease prevention and environmental sustainability across Europe. Previous modelling has outlined how policy interventions could shift dietary patterns to align with meat reduction targets, but the resulting health impacts remain unexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe quantified the projected health impacts of the UK Climate Change Committee's (CCC) dietary targets\u0026mdash;a 35% or 50% reduction in meat consumption\u0026mdash;focusing on red and processed meat and substitution with vegetables and legumes, on incidence and mortality of key diet-related chronic diseases over 30 years. Dietary inputs were derived from an agent-based opinion dynamics model parameterised using the UK National Diet and Nutrition Survey data, and health impacts were estimated using the IOMLIFET life-table model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAchieving the red and processed meat reduction components of the CCC's targets was associated with a projected increase in life expectancy of 7\u0026middot;2 (1\u0026middot;1\u0026ndash;12\u0026middot;9) and 9\u0026middot;4 (1\u0026middot;3\u0026ndash;15\u0026middot;4) months and years of life gained of 8\u0026middot;4 (1\u0026middot;3\u0026ndash;14\u0026middot;9) and 10\u0026middot;9 (1\u0026middot;5\u0026ndash;17\u0026middot;9) million, in the 35% and 50% scenarios, respectively. An estimated 3\u0026middot;4 (0\u0026middot;5\u0026ndash;6\u0026middot;6) and 4\u0026middot;5 (0\u0026middot;6\u0026ndash;8\u0026middot;1) million chronic disease cases could be averted. Up to 95% of health gains were attributable to cardiovascular outcomes, with three quarters linked to increased vegetable and legume consumption.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese exploratory estimates suggest policy-driven reductions in red and processed meat aligned with climate goals could deliver substantial public health benefits. While conditional on modelled behavioural trajectories, findings offer relevant insights for European strategies to reduce cardiovascular disease burden and promote sustainable dietary transitions.\u003c/p\u003e","manuscriptTitle":"Health co-benefits of sustainable dietary transitions to reduced red and processed meat intake in the United Kingdom: a modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 13:21:08","doi":"10.21203/rs.3.rs-9224499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e611f9f1-8a32-4029-ada6-9d91bae3368f","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"128580422287190097752234751896591923237","date":"2026-05-07T07:10:27+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T13:21:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 13:21:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224499","identity":"rs-9224499","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}