Potential Health Impact of Increasing Adoption of Sustainable Dietary Practices in Sweden

Potential Health Impact of Increasing Adoption of Sustainable Dietary Practices in Sweden | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Potential Health Impact of Increasing Adoption of Sustainable Dietary Practices in Sweden Emma Patterson, Patricia Eustachio Colombo, James Milner, Rosemary Green, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-96236/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jul, 2021 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background An urgent transition to more sustainable diets is necessary for the improvement of human and planetary health. One way to achieve this is for sustainable practices to become mainstream. We estimated the potential health impact of wider adoption of dietary practices deemed by consumers, researchers and stakeholders in Sweden to be niche, sustainable and with the potential to be scaled up. Methods A life table method was used to estimate the impact - changes in years of life lost (YLL) - over periods of 20 and 30 years in the Swedish population had the practices been adopted in 2010-11, when the last national adult dietary survey was conducted. The practices modelled were reducing red and processed meat (by 25%, 50% and 100%), and assuming, for each stage, replacement by an equal weight of poultry/fish and vegetables +/- legumes; reducing milk intake (by 25%, 50% and 100%); and reducing sugar-sweetened beverage intake (by 25%, 50% and 100%). Using population data together with data on cause-specific mortality and relative risks for diet-disease outcomes, impacts were estimated for each scenario separately and in combination, for the outcomes ischaemic heart disease (IHD), ischaemic stroke, diabetes type 2 and colorectal cancer. Results For a “moderate” combination of scenarios (changes at the 50% level), reductions of 513,200 YLL (lower-upper uncertainty estimate 59,400-797,900) could have been achieved over 20 years and 1,148,500 YLL (135,900-1,786,600) over 30 years. The majority (over 90%) of YLLs prevented were related to IHD, and the majority were in men. The singular practice that had the most impact was reducing the intake of red and processed meat and replacing it with a mixture of vegetables and legumes. Reducing milk intake resulted in an increase in YLL, but this was compensated for by other scenarios. Conclusion If these practices were more widely adopted, they would be expected to lead to improvements in public health in Sweden. Over the long term, this would translate to many premature deaths postponed or prevented from a number of chronic diseases, to the benefit of individuals, society, the climate and the economy. Nutrition & Dietetics Sustainability modelling health impact assessment adults non-communicable diseases diets nutrition climate change greenhouse gas emissions Figures Figure 1 Background An unhealthy dietary pattern is one of the largest contributors to poor health [ 1 ]. The way food is produced, distributed and consumed globally also contributes to about 25–30% of total greenhouse gas emissions (GHGE) [ 2 ], as well as impacting other aspects of environmental sustainability [ 3 ]. Changing the diet therefore has the potential to both improve public health and contribute to reductions in GHGE [ 4 ]. If the internationally agreed sustainability targets, of which the Paris Agreement and the UN Sustainable Development Goals are the most high profile, are to be met, substantial changes to current diets will be required, particularly in industrialised, wealthy nations [ 5 ]. Although the Swedish Food Agency was one of the first to produce food-based dietary guidelines that considered environmental sustainability as well as health [ 6 ], for average Swedish diets to be in line with e.g. the Eat-Lancet Planetary Health Diet [ 4 ] would require a considerable increase in vegetable, fruits, whole grains, legumes and nut intakes, and less red meat, processed meat, added sugar, refined grains, and starchy vegetables. Achieving behaviour change is challenging, and so an urgent question is how best to achieve the major shifts required. The current report is part of a 4-year research programme “Mistra Sustainable Consumption – from niche to mainstream” financed by the Swedish research council Mistra. The programme aims to contribute to the transition to sustainable consumption by generating knowledge on how “niche” sustainable practices, already in place, can become mainstream in Sweden in the areas of food, vacation and home furnishings [ 7 ]. In a previous stage of the programme, a wide range of public and private stakeholders identified a number of dietary practices as being currently niche, sustainable and suitable for scaling up. The definition of sustainability used was broad, but the focus of this analysis is on those practices expected to both benefit health and reduce climate impact. The increasing adoption of practices can be framed in terms of Rogers’ Diffusion of Innovation Theory, where a population can be divided into five different segments based on their propensity to adopt a specific innovation: innovators, early adopters, early majorities, late majorities and laggards [ 8 ]. “Niche” practices can be thought of as those of innovators and early adopters, who can be considered motivated to embrace innovations immediately and without further incentives. In contrast, the early and late majority require more persuasion and/or support in order to change their behaviour, as, until a new norm is reached, powerful negative societal and commercial influences can easily overwhelm the individual consumer’s efforts to take action [ 9 ]. There are many ways to encourage changes in values, norms and practices at a population level, ranging from information to consumers, to more upstream solutions, such as economic instruments (i.e. subventions/taxes) and regulation. A recent report concluded that in order to achieve the considerable dietary changes necessary to reduce Sweden’s GHGE from food - on average 1.5 ton per year for women and 2.0 ton per year for men [ 10 ] – upstream solutions such as incentives or taxes, rather than just information, are necessary [ 11 ]. Such policies are however politically sensitive, so decision makers are often reluctant to use these strategies. For policymakers to take such steps, estimates of the potential health and environmental gains resulting from improvements to diet need to be robustly and consistently demonstrated, and health impact modelling is one way of doing this. This study aims to estimate the long-term public health impacts of adopting the food-related practices identified by the stakeholders referred to above for which health impact data is available. Specifically, the impact of years of life lost due to ischaemic heart disease, stroke, type 2 diabetes and colorectal cancer over 20 and 30 years in Sweden is estimated. Methods Identification of scenarios A previous work package of the main “Mistra Sustainable consumption” programme gathered wide-ranging examples of what were perceived to be niche sustainable practices related to food production and consumption, with potential for scaling up [ 12 ]. Briefly, suggestions were solicited via workshops with representatives from the programme’s 20-odd stakeholder partners and a similar number of researchers, by literature reviews and studying reports, websites, magazines and social media in relation to sustainable consumption practices in Sweden and abroad. They were also gathered using a web-based questionnaire, disseminated in fora for people interested in sustainable consumption between April and October 2018, and interviews with international researchers. Participants were not intended to be representative, and no attempt was made to define sustainability, so suggestions were made from the point of view of climate impact, biodiversity, human health, social impact, animal welfare, community resilience etc. The full list of practices was then compiled [ 12 ], taking no account of their effectiveness to reduce GHGE (this is the focus of other sections of the research programme). From all of the food-related practices suggested, we identified the ones which would plausibly result in improved health as well as lower GHGEs. These were the practices that had been labeled as: 1) “Choose meat with lower climate impact instead of red meat”, 2) “Swap animal-based products for vegetable-based alternatives” and 3) “Reduce ‘unhealthy’ consumption” [ 12 ]. These are practices broadly in line with current national [ 13 ], Nordic [ 14 ] and international [ 15 ] dietary guidelines, as well as with e.g. the Eat-Lancet Commission report [ 4 ], and therefore expected to also have a lower climate impact. In order to conduct the health impact modelling we first operationalised these practices as more specific scenarios, taking into consideration dietary factors for which robust data on potential health impacts was available, as defined by the Global Burden of Disease (GBD) 2017 analysis [ 16 ]. In the end we modelled reductions in red and processed meat and assumed replacement by a) poultry/fish, b) vegetables, and c) a 50:50 mixture of vegetables and legumes. Another animal-based product included in the GBD is milk so we modelled reductions in intake, assuming replacement by a plant-based drink. The practice “Reduce ‘unhealthy ‘consumption” referred to intake of foods with low nutritive value; the most closely related factor in GBD was sugar-sweetened beverages (SSBs) so this was chosen, even if GHGEs associated with SSBs are relatively low. All replacements were by equal weight. For each scenario we modelled partial (25% and 50% decrease with replacement) and full (100% decrease with replacement) implementation in order to illustrate the range of potential impact. In order to be able to summarise the overall potential impact, we also grouped the changes into combinations. These we termed “minor” (changes at the 25% level), “moderate” (the 50% level) or “extensive” (the 100% level). Changes were modelled at the level of food groups rather than individual foods. For some of the assumed compensations it was not possible to model what effect the substitutions would have on health, as these are not considered risk factors according to GBD 2017. As such, in our models, replacements by poultry, fish or plant-based milk were neutral in terms of the health impact. Baseline consumption data Estimates of baseline dietary intake in the adult Swedish population were from the latest nationally representative adult dietary survey, Riksmaten 2010-11, conducted by the Swedish Food Agency. The data is publicly available in fully anonymised form [ 17 ]. Briefly, 1,797 adults aged 18–80 recorded all food and drinks consumed for four consecutive days in a web-based diary, between May 2010 and July 2011. The participants were reasonably representative of the general population, apart from slightly higher education level among participants than non-participants. The method used has been validated with respect to both energy intake and biomarkers [ 18 , 19 ]. From this, the average intakes of red meat, processed meat, vegetables, legumes, milk and SSBs were calculated. Definitions of dietary factors used in the GBD 2017 were followed [ 16 ], namely red meat included beef, pork, lamb and other meats but not poultry or processed meat; processed meat is any meat that has been smoked/cured/salted/chemically preserved; vegetables excluded pickled vegetables, starchy vegetables and legumes; SSBs have ≥ 50 kcal/226.8 ml and excluded juices. Information on ingredients in mixed dishes was also available, allowing for detailed extraction of the dietary factors by summing components consumed both as whole foods and as ingredients. Intakes were calculated for men and women separately. All data from the dietary survey has been anonymised and ethical permission was not required for this analysis according to Swedish law [ 20 ]. Associations between dietary factors and disease risk Relative risks (RR) for associations between dietary factors and disease outcomes were taken from GBD 2017 [ 21 ]. RRs which are expressed in the GBD study in terms of a harmful risk factor (e.g. “diet low in vegetables”) were inverted to create RRs for a positive change in diet. RR in GBD 2017 are modelled and presented per 5-year age intervals, from 25 years and upwards. Rates for 15–24 years were assumed to be the same as for 25–30 and a single RR for the entire population was then calculated by weighting the RRs for each 5-year age interval according to the population structure and taking the average. Population data As the latest national dietary data for Swedish adults is from 2010-11 we used 2011 as the baseline year for our analysis. Data on population size for 2011 was obtained from the national statistics agency, Statistics Sweden [ 22 ] in 1-year age intervals. This was summed to 5-year age intervals and for each interval a sex-specific (weighted) mean age was calculated. Data on all-cause mortality for each age in 2011 was obtained from the same source and neonatal deaths were calculated and excluded. Total deaths were summed to 5-year age intervals for each sex. Disease-specific mortality rates for Sweden in 2011 were taken from the GBD 2017 database [ 23 ]. The diseases for which the dietary risk factors in the scenarios are related to are ischaemic heart disease (IHD), ischaemic stroke, type 2 diabetes and colorectal cancer. These diseases accounted for 19.9%, 5.0%, 1.6% and 3.4% of total deaths in Sweden respectively in 2017 [ 1 ] and are, together with lung cancer, the diseases with a dietary risk factor that account for the most deaths. Data was taken from GBD 2017 rather than directly from the national source, as the grouping of international classification of disease (ICD) cause of death codes made publicly available by the National Board of Health and Welfare do not always overlap perfectly with the groupings used in GBD 2017. Doing so ensured that all deaths for the same ICD codes that the RR are based on were included. Death rates in GBD 2017 are presented for 5-year age intervals. Using the weighted mean age of the Swedish population in each interval, the rate for each 1-year age interval was interpolated using one-way spline interpolation using the programme SRS splines, available as an add-in to Excel [ 24 ]. Based on the population size for each 1-year age interval, the number of deaths at each age for each disease was estimated. Health impact modelling The health impact assessment was performed using the IOMLIFET life table method [ 25 ], adopted in R [ 26 ]. Life table calculations allow for the changes in future population shape that are induced by changes in mortality risks, and the subsequent changes in survival curves can be summarised as e.g. years of life lost (YLL) or changes to life expectancy [ 25 ]. YLL is a summary measure of premature mortality; it represents the years of potential life lost across a population due to premature deaths, taking into account the age at which deaths occur. Life tables were constructed for males and females separately. All data on population size, all-cause mortality and disease-specific mortality as described above were age- and sex-specific. The following assumptions were made: the diet changes are made instantly and then kept constant, and underlying mortality rates remain constant for the duration of follow-up; the exposure–response functions were assumed to be log-linear and, in cases where several dietary exposures affected the same disease, the risks were multiplied together. We modelled the impact on mortality on IHD, stroke, type 2 diabetes and colorectal cancer. As these outcomes are chronic diseases, there is often a cumulative effect of an exposure; this we estimated to reach a maximum after approximately 10 years for IHD, stroke and type 2 diabetes, and 30 years for cancers [ 26 ]. Chronic diseases, particularly cancer, also have long lag times between exposure and onset of outcome, and so no change in cancer risk was assumed for the first 10 years [ 26 ]. To account for these, time-varying functions were used in the models, based on cumulative distribution functions of normally distributed variables (s-shaped curves). An uncertainty interval (UI) was constructed around each estimate based on the lower and upper ranges for the RRs. The output was changes in YLL for the population over time periods of 20 and 30 years. Analysis was conducted in R [ 27 ]. Results The baseline intakes of the dietary factors and the relative risks for each factor and disease outcome used are presented in Table 1 . Men had a higher baseline intake of all selected dietary factors, except for vegetables. Table 1 Average intakes at baseline, size and direction of the unit for the relative risks, and the relative risks (RR) assumed. Baseline intakes Relative risks (95% CI) Dietary factor Men (g/day) Women (g/day) Unit and direction for RR (g) IHD a Ischaemic stroke a Diabetes type 2 a Colorectal cancer a Red meat 67.7 41.9 -100 - - 0.80 (0.97 − 0.68) 0.86 (0.97 − 0.76) Processed meat 43.5 25.5 -50 0.56 (0.97 − 0.4) - 0.59 (0.76 − 0.48) 0.85 (0.91 − 0.79) Vegetables 155.2 169.6 + 100 0.87 (0.95 − 0.79) 0.87 (0.97 − 0.79) - - Legumes 12.3 11.9 + 50 0.76 (0.89 − 0.66) - - - Milk 224.2 171.9 + 226.8 - - - 0.90 (0.96 − 0.83) SSB 110.1 72.7 -226.8 0.81 (1.05–0.66) - 0.83 (0.91 − 0.76) - a Single weighted average of GBD 2017 RRs for dietary risk factors per 5-year intervals, taking into account the Swedish population structure in 2011, and inverted to create RRs for a positive change in diet. SSB, sugar-sweetened beverages IHD, ischaemic heart disease The results of the health impact assessment are presented in Table 2 , as changes in YLL per scenario and per combination of scenarios. The results from the health impact modelling suggest that, had Swedish adults made the “moderate” combination of these dietary changes in 2011 – i.e. a 50% reduction in red and processed meat (with replacement by vegetables), in milk and in SSBs - a reduction of approximately 513,200 YLL could have been achieved over 20 years (Table 2 ). If the more “extensive” combination had been adopted - a 100% reduction in red and processed meat (with replacement by vegetables and legumes), in milk and in SSBs - a reduction of 1,076,900 YLL could have been achieved. Although the uncertainty ranges for the estimates were wide, reflecting the wide ranges of the RRs for many of the dietary factor-disease outcome pairs, even at the lower ranges the estimates for even the “minor” combination of scenarios (changes at the 25% level) were positive. Over 30 years, the impact was more than twice as great (Table 2 ). Table 2 Estimates of the reductions in total years of life lost (YLL) that could be achieved over 20 and 30 years if dietary changes were made in 2011, per scenario and in combination Cumulative reduction in total YLL * Over 20 years Over 30 years Scenarios modelled Estimate (Lower - Upper) Estimate (Lower - Upper) Replacing red & processed meat with poultry/fish # 25% less red & processed meat* 176 300 (16 100 ‒ 269 700) 400 400 (39 600 ‒ 613 600) 50% less red & processed meat 336 200 (31 700 ‒ 501 000) 765 500 (78 300 ‒ 1 144 100) No red or processed meat 611 200 (61 600 ‒ 866 900) 1 397 300 (152 200 ‒ 1 991 100) Replacing red & processed meat with vegetables 25% less red & processed meat 233 600 (37 300 ‒ 361 600) 527 300 (86 600 ‒ 817 300) 50% less red & processed meat** 440 800 (73 800 ‒ 659 900) 997 600 (171 300 ‒ 1 497 900) No red or processed meat 786 100 (144 300 ‒ 1 107 300) 1 787 400 (335 000 ‒ 2 529 000) Replacing red & processed meat with vegetables and legumes 25% less red & processed meat 293 900 (69 000 ‒ 440 200) 661 900 (156 800 ‒ 993 700) 50% less red & processed meat 544 300 (135 800 ‒ 781 900) 1 230 000 (308 800 ‒ 1 774 100) No red or processed meat*** 935 200 (261 800 ‒ 1 248 600) 2 126 100 (596 400 ‒ 2 853 900) Replacing milk consumption with plant-based drink # 25% less milk intake* -700 (-300 ‒ -1 200) -6 200 (-2400 ‒ -11 000) 50% less milk intake** -1 400 (-500 ‒ -2 500) -12 500 (-4800 ‒ -22 500) No milk intake*** -2 900 (-1 100 ‒ -5 300) -25 600 (-9600 ‒ -46 900) Replacing SSB consumption with water # 25% less SSB intake* 37 200 (-6 900 ‒ 71 500) 82 300 (-15200 ‒ 158 200) 50% less SSB intake** 73 800 (-13 900 ‒ 140 600) 163 300 (-30600 ‒ 311 300) No SSB intake*** 144 700 (-27 900 ‒ 270 600) 320 400 (-61600 ‒ 600 200) * Combination “Minor changes” (sum) 212 800 (8 900 ‒ 339 900) 476 600 (22000 ‒ 760 900) ** Combination “Moderate changes” (sum) 513 200 (59 400 ‒ 797 900) 1 148 500 (135900 ‒ 1 786 600) *** Combination “Extensive changes” (sum) 1 076 900 (232 800 ‒ 1 513 900) 2 420 900 (525200 ‒ 3 407 200) SSB, sugar-sweetened beverages. Replacements were of equal weight. Vegetables and legumes were 50:50. Numbers rounded to nearest 100. # Replacement food category was neutral in the health impact model. In general, scenarios involving lower meat intake had greater impacts than those involving milk or SSBs. Reductions in YLL were greater when red and processed meat was replaced with a combination of vegetables and legumes, rather than vegetables alone. One of the scenarios, a reduced intake of milk, resulted in an increase of YLL, as there is evidence that a higher intake of milk is associated with a reduced risk of colorectal cancer. However, other changes, such as reducing red and processed meat can compensate for this: for example the increase in YLL from a 100% reduction in milk intake (2,900 YLL over 20 years) was compensated by the 50% reduction of red and processed meat (reduction of 2,800 YLL over 20 years) (Additional file 1). The results of the combinations of scenarios are presented in Fig. 1 , showing the breakdown by outcome and sex. The disease outcome that was affected most by the changes was IHD mortality, which accounts for the vast majority of the reductions in total YLL (ca 90%), followed by diabetes type 2 (Fig. 1 ). The longer lag time for colorectal cancer means that only very small reductions in YLL would be seen after 20 years; reductions would be seen mainly after 30 years. Reductions in YLL were greater for men (Additional file 1), which was expected as the absolute changes modelled were relative to the baseline intake and intakes were generally higher among men (Table 1 ). Estimates for each change and disease outcome and for each sex are available in Additional file 1. Discussion Wider adoption of the dietary practices identified by the "Mistra Sustainable Consumption” programme as niche, sustainable and with potential to become mainstream would be expected to result in considerable public health benefits, especially for men, in addition to a likely reduction in diet-related GHGEs. Taking 2010-11 as the starting point for implementation, we modelled what the impact in Sweden might have been in terms of deaths prevented or postponed over 20 and 30 years. The results suggest that the gains could have been in the hundreds of thousands of YLL, possibly in the region of a million for the combination of scenarios labelled “moderate” (changes at the 50% level) or “extensive” (changes at the 100% level). To put this in perspective, in Sweden in 2017, the number of YLL from all causes was approximately 1,248,000 according to GBD 2017 [ 21 ]. Approximately 16% of these were due to IHD (ca 203,000), and poor dietary habits (the cumulative effect of all 15 dietary risks included in GBD) was the biggest risk factor. As the biggest change in our dietary scenarios was for YLL due to IHD, it is easy to see how the cumulative figure over a longer time period reaches a substantial number. The practice that had the most impact was reducing the intake of red and processed meat and replacing it with a mixture of vegetables and legumes. The results suggest that this practice alone could prevent about a fifth of YLL due to IHD. As the most recent national dietary data in Sweden is from 2010-11, it is worth considering how relevant the proposed scenarios are today. Changes in per capita supply data between 2010 and 2019 [ 28 ] suggest a 18% decrease in pork, a 7% decrease in beef and a 20% increase in poultry volumes. No clear trend is seen for vegetables using the same source, but the market for “meat alternatives” has increased over 15% year-on-year between 2017 and 2019 [ 29 ]. A internet panel survey of vegetarian practices suggests a similar trend between 2016 and 2019: while the proportion of adults who rarely or never ate vegetarian meals was 48% in 2019, the proportion who never ate vegetarian meals had fallen from 22–15% [ 30 ]. It should be noted that neither per capita supply data nor market analysis is a substitute for individual consumption data but can be useful to observe trends [ 31 ]. These would suggest that the scenario of replacing red meat (with poultry or something else) is already being practiced by some consumer segments – from “innovators” to the “early majority” [ 8 ] – and have the potential to be scaled up. In contrast, the per capita sales volume of SSBs has increased slightly since 2010 [ 32 ]. Rogers lists compatibility with existing values, norms and practices as one of five factors determining the success of an innovation. Others are 2) relative advantage (the greater the perceived relative advantage of an innovation by the user group, the more rapid its rate of adoption is likely to be); 3) simplicity and ease of use; 4) trialability (the degree to which an innovation can be experimented with on a limited basis); and 5) observable results (the easier it is for individuals to see the results of an innovation, the more likely they are to adopt it) [ 8 ]. In order to accelerate the dietary shift required, all these factors could be taken into consideration by stakeholders and decision-makers who want to bring about change. It is always important to take account of several aspects of sustainability simultaneously so that e.g. health is not prioritised at the expense of the environment, or social sustainability. In general, the overlap between foods that have lower environmental impact and also improve health is usually high, with the notable exception of fish and sugar, where health and environmental impacts may act in the opposite directions [ 33 ]. We were not able to consider other impacts that substitutions within food groups could have in more detail. However in another part of the “Sustainable Consumption” project, the impacts of a number of potential “replacement” products relevant for these scenarios (e.g. legume-based products, plant-based drinks) have been quantified from the Swedish perspective [ 34 ], confirming their lower GHGE impact, even when production, transport from abroad, packaging, etc is taken into account. One of the major limitations of simulations is that replacements may have consequences for energy balance and nutritional adequacy, as what and how much replacement occurs in reality is difficult to know. We assumed here replacement by equal weight, not by energy. For example in one scenario we assumed a total reduction in processed and red meat, and compensating for this by increasing vegetables and legumes (in equal amounts) by the same weight. This increase was, on paper, not excessive and would bring average intakes of total fruits, vegetables and legumes up to 384 g for men and 396 g for women, still far below the recommended intakes of 500 g [ 13 ]. If these were consumed in their unprocessed form it would likely mean a shortfall in energy intake, due to the lower energy density of this food group. It is however likely that replacement would also involve more processed vegetable- and legume-based products, which are more energy-dense than unprocessed vegetables and legumes, as the availability of these has increased dramatically in recent years in Sweden [ 29 ]. Given half of all Swedish adults are living with the effects of a prior or ongoing positive energy balance (i.e. are overweight or obese [ 35 ]), for many energy deficits may lead to further health gains. Similarly, the impact associated with reduced SSB consumption are also possibly underestimated as further gains could be mediated through a reduction in obesity, for which high SSB consumption is a risk factor. A more critical issue is if the foods that are reduced are important sources of nutrients that are not compensated for. Some of our modelled scenarios would be almost neutral in terms of impact on micronutrient intake, for example replacing SSB with water. Others are more complex. For example, meat is a rich source of nutrients such as iron, selenium, zinc and some B-vitamins. However, a study from the Nordic region examined this (using, for the Swedish part, the same dietary survey data as in our study) and concluded that the effects on overall dietary quality would be minimal if processed meat was reduced to zero, and if average red meat was reduced to the WCRFs 2007 population-level recommendation of 43 g per day [ 36 ]. They modelled scenarios involving both replacement by other meat, non-meat, and with or without energy compensation. Another way of looking at the replacements at food level and ensuring that the overall diet is nutritionally adequate would be to perform an optimisation analysis using linear programming [ 37 ] and this is planned in a future study. Another limitation of simulations or models is that they remain theoretical. Indeed, the relationship between the health and environmental impacts of self-selected diets is more complex than that between single foods/food groups [ 38 ]. The modelled changes may also be less acceptable to consumers than what is assumed. Vieux et al examined actual dietary patterns in six European countries, including Sweden, and concluded that exclusion of entire categories of food is not necessary to achieve health and climate benefits, and a “more sustainable” diet with “moderate” amounts of animal-based products is probably realistic, as it is already adopted by nearly one in five adults [ 38 ] corresponding to the population segments innovators, early adopters and some of the early majority according to the Diffusion of Innovations theory [ 8 ]. We therefore made sure to include scenarios where animal-based products were still included to a large degree, as well as being more extensively reduced. Our results complement those of Saha et al [ 39 ] who estimated 1-year health gains if food and nutrient intakes were in line with Nordic Nutrition Recommendations. They used the PRIME model, which is different from our method and based on other premises. They estimated that 6,405 deaths in a year in Sweden would be prevented/delayed from cardiovascular diseases and diet-related cancers, or 14.4% of the total. In line with our results, the majority of YLL reductions were also from IHD. However other differences make the results difficult to compare: they used the same dietary data but based their model on population data from 2016, not 2011 which was 5.4% smaller; they modelled nutrients (fat, salt, dietary fiber, energy) and only one food group (fruits and vegetables (and not meat)). They also assumed that their health gains occurred in the same year, not allowing for a lag time as we have done, and not taking into account the impact on the population structure over time. Assuming a constant effect over 20 years or 30 years, this would correspond to 128 000 or 192 000 prevented or postponed deaths in their study. Using the same IOMLIFET approach, a scenario for the UK (population 67 million) suggested almost 7 million YLL would be saved over 30 years if diets were in line with WHO dietary recommendations [ 26 ]. A study from Italy using the IOMLIFET model that we used predicted that reducing beef by 63% (to 150 g per week) and processed meat by 80% (to 50 g per week) would reduce YLL by 9 and 20 million respectively over 30 years [ 40 ]. This is a similar range to our numbers, when Italy’s population size (about 6 times larger) is taken into account. Other strengths and limitations are that we included all food-based dietary factors in GBD 2017 that could be connected to the proposed scenarios but did not consider nutrient-based dietary factors. Where several dietary exposures affected the same disease, the risks were multiplied together. It is possible that this leads to an over-estimation, but this is commonly done in other models too, e.g. PRIME, and due to lack of information on mediation/overlap. We only examined YLL, not years of life lived with disability (YLD), another widely used measure of health impact, which means that we have most likely underestimated total health benefits by not accounting for impacts on morbidity. We also did not consider here the effects that any reduction in body mass index (BMI) may have had, either due to negative energy balance as a result of a scenario, or as a scenario in its own right. Reducing excess consumption (and waste) is one obvious way to reduce the environmental impact of a diet, as this is determined by both the quality and quantity of food consumed [ 38 ]. The population has increased since 2011, from 9.48 million in 2011 to 10.33 million in 2019 [ 22 ], an increase of 8.9% so the reductions in YLLs may be underestimated. The population structure has remained similar: the proportion of men increased by less than 1%, life expectancy at birth increased by 1.9% for men and 1.3% for women during the last decade. Data on disease-specific death rates were taken from GBD 2017 rather than directly from the national source to ensure deaths from and RRs for diseases were for exactly the same disease codes, but the differences in deaths from both sources were minimal (< 1%). The expected benefit to the environment was limited to the effect on GHGEs, but other aspects such as water and land use are also important. Although a clear socioeconomic gradient is seen with dietary quality, the impact of dietary changes on economic sustainability was not possible to include in this analysis. Conclusion The widespread adoption of dietary practices identified as being niche today but with the potential to become mainstream, could result in considerable improvements in public health in Sweden, particularly over the long term. Although modeling health impacts requires making assumptions and a level of uncertainty, this potentially translates to many premature deaths postponed or prevented from a number of chronic diseases, primarily IHD, to the benefit of the individual, society and with probable benefits for the economy and the climate. Some motivated consumer segments may be willing to adopt more sustainable practices right away. However, in order to accelerate the transition to a more sustainable diet within the timeframe that meeting commitments to international agreements regarding sustainability requires, policymakers should consider more potent strategies in order to persuade far more of the population to do so. Abbreviations GBD Global Burden of Disease GHGE Greenhouse gas emissions ICD International Classification of Disease IHD Ischaemic heart disease IOMLIFET Institute of Occupational Medicine Life Table RR relative risk SSB Sugar-sweetened beverages YLL Years of life lost Declarations Ethics approval and consent to participate This study involved no personal data. Ethical permission was therefore not required for this in accordance with Swedish law [20]. Consent for publication Not applicable. Availability of data and materials The dietary survey data Riksmaten 2010-11 is available from the Swedish Food Agency, https://www.livsmedelsverket.se/om-oss/psidata/apimatvanor The data on disease specific mortality rates is available from GBD, http://ghdx.healthdata.org/gbd-results-tool The relative risks for diet-disease associations are available from GBD 2017, http://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017 The population data is available from Statistics Sweden, http://www.scb.se/be0101 Other datasets generated during the current analysis are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding EP and LSE were funded by Mistra - The Swedish Foundation for Strategic Environmental Research through the research programme Mistra Sustainable Consumption, grant DIA 2016/3. RG is funded by the Sustainable and Healthy Food Systems (SHEFS) project - Wellcome Trust Our Planet Our Health grant 205200/Z/6/Z. JM is funded by the Complex Urban Systems for Sustainability and Health (CUSSH) project – Wellcome Trust Our Planet Our Health grant 209387/Z/17/Z. PEC is funded by the Swedish Research Council FORMAS grant number 2016-00353. Acknowledgements Not applicable. Authors’ contributions EP formulated the research question, contributed to the study design, gathered the data, performed the analysis, drafted and revised the paper. PEC contributed to the data collection, data analysis and study design and revised the draft paper. JM contributed to the analysis and study design and revised the draft paper. RG contributed to the study design and revised the draft paper, LSE formulated the research question, secured funding for the project, contributed to the study design and drafted and revised the paper. References GBD 2017 Causes of Death Collaborators: Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet 2018(392):1736-1788. Poore J, Nemecek T: Reducing food's environmental impacts through producers and consumers . Science 2018, 360 (6392):987-992. Gerten D, Heck V, Jägermeyr J, Bodirsky BL, Fetzer I, Jalava M, Kummu M, Lucht W, Rockström J, Schaphoff S et al : Feeding ten billion people is possible within four terrestrial planetary boundaries . Nature Sustainability 2020, 3 (3):200-208. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, Garnett T, Tilman D, DeClerck F, Wood A et al : Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems . Lancet 2019, 393 (10170):447-492. Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, Brinsden H, Calvillo A, De Schutter O, Devarajan R et al : The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report . Lancet 2019, 393 (10173):791-846. Mason P, Lang T: Sustainable Diets: How Ecological Nutrition Can Transform Consumption and the Food System : Taylor & Francis; 2017. Mistra Sustainable Consumption - from niche to mainstream https://www.sustainableconsumption.se/en/start-eng Accessed 2020-09-01. Rogers EM: Diffusion of Innovations, 5th Edition : Simon and Schuster; 2003. Mozaffarian D, Angell SY, Lang T, Rivera JA: Role of government policy in nutrition—barriers to and opportunities for healthier eating . BMJ 2018:k2426. Sjörs C, Raposo SE, Sjölander A, Bälter O, Hedenus F, Bälter K: Diet-related greenhouse gas emissions assessed by a food frequency questionnaire and validated using 7-day weighed food records . Environ Health 2016, 15 :15. Röös E, Larsson J, Resare Sahlin K, Jonell M, Lindahl T, André E, Säll S, Harring N, Persson M. Styrmedel för hållbar matkonsumtion – en kunskapsöversikt och vägar framåt [Incentives for sustainable food consumption - an overview of the evidence and ways forward] 2020. Sveriges lantbruksuniversitet, forskningsplattformen SLU Future Food https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/StyrmedelForHallbarMatkonsumtion.pdf Accessed 2020-08-01 Kamb A, Svenfelt Å, Kayama AC, Parekh V, Bradley K. Att äta hållbart? En kartläggning av vad hållbar matkonsumtion kan innebära [To eat sustainably? A survey of what sustainable food consumption can mean] 2019. Mistra Sustainable Consumption Report 1:2 Stockholm, KTH. https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2019/03/Att- äta-hållbart-En-kartläggning-av-vad-hållbar-matkonsumtion-kan-innebära.pdf Accessed 2020-09-16 Livsmedelsverket [Swedish Food Agency]. Find Your Way to Eat Greener, Not Too Much and Be Active 2015. Livsmedelsverket [Swedish Food Agency] http://www.livsmedelsverket.se/globalassets/english/food-habits-health-environment/dietary-guidelines/kostraden-eng-a4-utskriftversion.pdf?id=8199 Accessed Nordic Council of Ministers: Nordic Nutrition Recommendations 2012 - Integrating nutrition and physical activity : Nordic Council of Ministers; 2014. World Cancer Research Fund/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Cancer: A Global Perspective. Continuous Update Project Expert Report 2018 www.dietandcancerreport.org Accessed GBD Diet Collaborators: Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017 . Lancet 2019, 393 (10184):1958-1972. Riksmaten vuxen 2010-11 . Livsmedelsverket [Swedish Food Agency] https://www.livsmedelsverket.se/om-oss/psidata/apimatvanor Access date 2020-09-01 Nybacka S, Lindroos AK, Wirfält E, Leanderson P, Landberg R, Ericson U, Larsson I, Warensjö Lemming E, Bergström G, Hedblad B et al : Carotenoids and alkylresorcinols as objective biomarkers of diet quality when assessing the validity of a web-based food record tool and a food frequency questionnaire in a middle-aged population . BMC Nutrition 2016, 2 (1):53. Nybacka S, Bertéus Forslund H, Wirfält E, Larsson I, Ericson U, Warensjö Lemming E, Bergström G, Hedblad B, Winkvist A, Lindroos AK: Comparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population . J Nutr Sci 2016, 5 :e39. Swedish Parliament: Lag (2003:460) om etikprövning av forskning som avser människor [Law (2003:460) on ethical review of research concerning humans] Global Burden of Disease Study 2017 (GBD 2017) Burden by Risk 1990-2017 . Institute for Health Metrics Evaluation Washington University http://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017 Access date 2020-08-01 Befolkningsstatisik [Population statistics] . Statistiska Centralbyrån [Statistics Sweden] http://www.scb.se/be0101/ Access date Global Burden of Disease Study 2017 (GBD 2017) Cause-Specific Mortality 1980-2017 . Institute for Health Metrics Evaluation Washington University http://ghdx.healthdata.org/gbd-results-tool Access date 2020-08-15 Srs1 Software LLC. SRS1 Cubic Spline for Excel v2.5 . Massachusetts, USA. Miller BG, Hurley JF: Life table methods for quantitative impact assessments in chronic mortality . J Epidemiol Community Health 2003, 57 :200–206. Milner J, Green R, Dangour AD, Haines A, Chalabi Z, Spadaro J, Markandya A, Wilkinson P: Health effects of adopting low greenhouse gas emission diets in the UK . BMJ Open 2015, 5 (4):e007364. R Core Development Team 2020 R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing. Vienna, Austria. Jordbruksverket [Swedish Board of Agriculture]: Konsumtion och förbrukning av kött [Consumption and use of meat] https://djur.jordbruksverket.se/amnesomraden/konsument/livsmedelskonsumtionisiffror/kottkonsumtionen.4.465e4964142dbfe44705198.html Accessed 2020-08-30 Macklean Management Consultancy. Marknadsanalys och potential för växtbaserade proteiner. Rapport för Lantbrukarnas Riksförbund. [Market analysis and potential for plant-based proteins. Report for the Federation of Swedish Farmers] 2020. https://www.lrf.se/globalassets/dokument/mitt-lrf/nyheter/2020/marknadsanalys-och-potential-for-vaxtbaserade-proteiner.pdf Accessed 2020-09-03 Eustachio Colombo P, Elinder LS, Carlsson Kanyama A. Vegobarometern – En undersökning av svenskarnas benägenhet att välja vegetarisk mat under åren 2016 - 2019. [The Vegobarometer - a survey of Swedes willingness to choose vegetarian food between 20136 and 2019] 2020. Mistra Sustainable Consumption Report 1:5 Stockholm, KTH. https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/05/Vegobarometern-rapport-Mistra-SC-Final2.pdf Accessed 2020-09-03 Becker W, Helsing E: Food and health data; their use in nutrition policy-making . In . Volume 34 , edn. Edited by WHO Regional Office for Europe: agris.fao.org; 1991. The Swedish Brewers Association: Soft drink sales volume statistics https://sverigesbryggerier.se/statistik/ Accessed 2020-09-12 Clark MA, Springmann M, Hill J, Tilman D: Multiple health and environmental impacts of foods . Proc Natl Acad Sci U S A 2019, 116 (46):23357-23362. Carlsson Kanyama A, Dunér F. 40% mindre växthusgasutsläpp från konsumtionen här och nu. Beräkningar givet förändrad konsumtion av mat, semestrande och inredning [40% lower greenhouse gas emissions from consumption here and now. Calculations assuming altered consumption of food, vacations and home furnishings.] 2020. Mistra Sustainable Consumption Report 1:6 Stockholm. https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/Rapport-40-mindre-va%CC%88xthusgasutsla%CC%88pp-fra%CC%8An-konsumtionen-ha%CC%88r-och-nu-.pdf Accessed 2020-08-15 Folkhälsomyndigheten [Public Health Agency of Sweden]. Folkhälsans utveckling. Årsrapport 2020 [Public health developments. Annual report 2020] 2020. Folkhälsomyndigheten https://www.folkhalsomyndigheten.se/contentassets/9fd952e9014642249164352cd5a3eb50/folkhalsans-utveckling-arsrapport-2020.pdf Accessed 2020-09-14 Tetens I, Hoppe C, Frost Andersen L, Helldán A, Warensjö Lemming E, Trolle E, Holm Totland T, Lindroos AK. Nutritional evaluation of lowering consumption of meat and meat products in the Nordic context 2013. http://dx.doi.org/10.6027/TN2013-506 Accessed 2020-06-16 van Dooren C: A Review of the Use of Linear Programming to Optimize Diets, Nutritiously, Economically and Environmentally . Frontiers in Nutrition 2018, 5 :48. Vieux F, Privet L, Soler LG, Irz X, Ferrari M, Sette S, Raulio S, Tapanainen H, Hoffmann R, Surry Y et al : More sustainable European diets based on self-selection do not require exclusion of entire categories of food . J Clean Prod 2020, 248 :119298. Saha S, Nordstrom J, Gerdtham UG, Mattisson I, Nilsson PM, Scarborough P: Prevention of Cardiovascular Disease and Cancer Mortality by Achieving Healthy Dietary Goals for the Swedish Population: A Macro-Simulation Modelling Study . International journal of environmental research and public health 2019, 16 (5). Farchi S, De Sario M, Lapucci E, Davoli M, Michelozzi P: Meat consumption reduction in Italian regions: Health co-benefits and decreases in GHG emissions . PLoS One 2017, 12 (8):e0182960. Supplementary Files Additionalfile1.pdf Cumulative gains in YLL over 20 or 30 years, per sex and disease outcome. Cite Share Download PDF Status: Published Journal Publication published 06 Jul, 2021 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Minor revision 22 Jan, 2021 Review # 2 received at journal 09 Dec, 2020 Review # 1 received at journal 09 Dec, 2020 Reviewer # 3 agreed at journal 22 Nov, 2020 Reviewer # 2 agreed at journal 18 Nov, 2020 Reviewer # 1 agreed at journal 02 Nov, 2020 Reviewers invited by journal 27 Oct, 2020 Editor assigned by journal 21 Oct, 2020 Submission checks completed at journal 20 Oct, 2020 Editor invited by journal 20 Oct, 2020 First submitted to journal 04 Oct, 2020 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-96236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":3895759,"identity":"0128401a-dd43-415d-a422-e1ca8ea7def8","order_by":0,"name":"Emma Patterson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBAC+wYgwfgHzrcxYCOkxQBEMLYxSPBA+GmkazlsQNBhBmKnEz8wttnV2bP3Pv7wccd5Yz4G9ocP8Gmxl87dLMH4J1mCh+e4meTMM7fN2Bh4jPFaZSCdu0GCsYFZgkcijY2Zt+22DVALmwQBLZt/MDbUS/DIP2P+zNt2DqiF/fkPAlq2AW05DLSFjUGat+0A0GEMZvh0gLVYJDYcl+w5k8YmObMt2ZiNmccYr8PsZ+duvvGxoZqfvf0Y84ePbXaG89vbH37Aaw0IJKDwmAmqHwWjYBSMglFACAAAjSY+FxleEscAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1208-0936","institution":"Karolinska Institutet","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Patterson","suffix":""},{"id":3895760,"identity":"663a6000-6263-449b-aa6a-eaa19dc90ad0","order_by":1,"name":"Patricia Eustachio Colombo","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"Eustachio","lastName":"Colombo","suffix":""},{"id":3895761,"identity":"eec713f3-d6cd-414e-8d16-1e74846b9751","order_by":2,"name":"James Milner","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Milner","suffix":""},{"id":3895762,"identity":"ce566ba0-179b-4f47-99d6-eee264625912","order_by":3,"name":"Rosemary Green","email":"","orcid":"","institution":"London School of Hygiene \u0026 Tropical Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rosemary","middleName":"","lastName":"Green","suffix":""},{"id":3895763,"identity":"b5bba190-64ab-47c5-ac91-0e59c5c5cbb8","order_by":4,"name":"Liselotte Schäfer Elinder","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liselotte","middleName":"Schäfer","lastName":"Elinder","suffix":""}],"badges":[],"createdAt":"2020-10-21 18:46:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-96236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-96236/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-021-11256-z","type":"published","date":"2021-07-06T21:13:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3231269,"identity":"606a6551-ce78-4dc9-b3ed-a878d78aacc8","added_by":"auto","created_at":"2020-10-27 21:46:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55671,"visible":true,"origin":"","legend":"Cumulative reductions in YLL over 20 and 30 years for the different diet scenario combinations. See text/Table 2 for a description of the combinations.","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-96236/v1/fee6fd2f1578b89292950172.JPG"},{"id":13606543,"identity":"8c38f548-005a-4a32-8d07-c63c043236f0","added_by":"auto","created_at":"2021-09-17 06:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1316178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-96236/v1/f0707577-6a07-4f0a-bfee-20a0b554ae5d.pdf"},{"id":3231270,"identity":"a653eb84-5721-4075-99a1-2255b3203309","added_by":"auto","created_at":"2020-10-27 21:46:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":465727,"visible":true,"origin":"","legend":"Cumulative gains in YLL over 20 or 30 years, per sex and disease outcome.","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-96236/v1/34b1a1ec1d79531a22e18ee0.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePotential Health Impact of Increasing Adoption of Sustainable Dietary Practices in Sweden\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAn unhealthy dietary pattern is one of the largest contributors to poor health [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The way food is produced, distributed and consumed globally also contributes to about 25\u0026ndash;30% of total greenhouse gas emissions (GHGE) [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e], as well as impacting other aspects of environmental sustainability [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Changing the diet therefore has the potential to both improve public health and contribute to reductions in GHGE [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. If the internationally agreed sustainability targets, of which the Paris Agreement and the UN Sustainable Development Goals are the most high profile, are to be met, substantial changes to current diets will be required, particularly in industrialised, wealthy nations [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the Swedish Food Agency was one of the first to produce food-based dietary guidelines that considered environmental sustainability as well as health [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e], for average Swedish diets to be in line with e.g. the Eat-Lancet Planetary Health Diet [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] would require a considerable increase in vegetable, fruits, whole grains, legumes and nut intakes, and less red meat, processed meat, added sugar, refined grains, and starchy vegetables.\u003c/p\u003e\n\u003cp\u003eAchieving behaviour change is challenging, and so an urgent question is how best to achieve the major shifts required. The current report is part of a 4-year research programme \u0026ldquo;Mistra Sustainable Consumption \u0026ndash; from niche to mainstream\u0026rdquo; financed by the Swedish research council Mistra. The programme aims to contribute to the transition to sustainable consumption by generating knowledge on how \u0026ldquo;niche\u0026rdquo; sustainable practices, already in place, can become mainstream in Sweden in the areas of food, vacation and home furnishings [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a previous stage of the programme, a wide range of public and private stakeholders identified a number of dietary practices as being currently niche, sustainable and suitable for scaling up. The definition of sustainability used was broad, but the focus of this analysis is on those practices expected to both benefit health and reduce climate impact.\u003c/p\u003e\n\u003cp\u003eThe increasing adoption of practices can be framed in terms of Rogers\u0026rsquo; Diffusion of Innovation Theory, where a population can be divided into five different segments based on their propensity to adopt a specific innovation: innovators, early adopters, early majorities, late majorities and laggards [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u0026ldquo;Niche\u0026rdquo; practices can be thought of as those of innovators and early adopters, who can be considered motivated to embrace innovations immediately and without further incentives. In contrast, the early and late majority require more persuasion and/or support in order to change their behaviour, as, until a new norm is reached, powerful negative societal and commercial influences can easily overwhelm the individual consumer\u0026rsquo;s efforts to take action [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. There are many ways to encourage changes in values, norms and practices at a population level, ranging from information to consumers, to more upstream solutions, such as economic instruments (i.e. subventions/taxes) and regulation. A recent report concluded that in order to achieve the considerable dietary changes necessary to reduce Sweden\u0026rsquo;s GHGE from food - on average 1.5 ton per year for women and 2.0 ton per year for men [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e] \u0026ndash; upstream solutions such as incentives or taxes, rather than just information, are necessary [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Such policies are however politically sensitive, so decision makers are often reluctant to use these strategies. For policymakers to take such steps, estimates of the potential health and environmental gains resulting from improvements to diet need to be robustly and consistently demonstrated, and health impact modelling is one way of doing this.\u003c/p\u003e\n\u003cp\u003eThis study aims to estimate the long-term public health impacts of adopting the food-related practices identified by the stakeholders referred to above for which health impact data is available. Specifically, the impact of years of life lost due to ischaemic heart disease, stroke, type 2 diabetes and colorectal cancer over 20 and 30\u0026nbsp;years in Sweden is estimated.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eIdentification of scenarios\u003c/h2\u003e\n\u003cp\u003eA previous work package of the main \u0026ldquo;Mistra Sustainable consumption\u0026rdquo; programme gathered wide-ranging examples of what were perceived to be niche sustainable practices related to food production and consumption, with potential for scaling up [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Briefly, suggestions were solicited via workshops with representatives from the programme\u0026rsquo;s 20-odd stakeholder partners and a similar number of researchers, by literature reviews and studying reports, websites, magazines and social media in relation to sustainable consumption practices in Sweden and abroad. They were also gathered using a web-based questionnaire, disseminated in fora for people interested in sustainable consumption between April and October 2018, and interviews with international researchers. Participants were not intended to be representative, and no attempt was made to define sustainability, so suggestions were made from the point of view of climate impact, biodiversity, human health, social impact, animal welfare, community resilience etc. The full list of practices was then compiled [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], taking no account of their \u003cem\u003eeffectiveness\u003c/em\u003e to reduce GHGE (this is the focus of other sections of the research programme). From all of the food-related practices suggested, we identified the ones which would plausibly result in improved health as well as lower GHGEs. These were the practices that had been labeled as: 1) \u0026ldquo;Choose meat with lower climate impact instead of red meat\u0026rdquo;, 2) \u0026ldquo;Swap animal-based products for vegetable-based alternatives\u0026rdquo; and 3) \u0026ldquo;Reduce \u0026lsquo;unhealthy\u0026rsquo; consumption\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. These are practices broadly in line with current national [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e], Nordic [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] and international [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] dietary guidelines, as well as with e.g. the Eat-Lancet Commission report [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e], and therefore expected to also have a lower climate impact.\u003c/p\u003e\n\u003cp\u003eIn order to conduct the health impact modelling we first operationalised these practices as more specific scenarios, taking into consideration dietary factors for which robust data on potential health impacts was available, as defined by the Global Burden of Disease (GBD) 2017 analysis [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the end we modelled reductions in red and processed meat and assumed replacement by a) poultry/fish, b) vegetables, and c) a 50:50 mixture of vegetables and legumes. Another animal-based product included in the GBD is milk so we modelled reductions in intake, assuming replacement by a plant-based drink. The practice \u0026ldquo;Reduce \u0026lsquo;unhealthy \u0026lsquo;consumption\u0026rdquo; referred to intake of foods with low nutritive value; the most closely related factor in GBD was sugar-sweetened beverages (SSBs) so this was chosen, even if GHGEs associated with SSBs are relatively low. All replacements were by equal weight. For each scenario we modelled partial (25% and 50% decrease with replacement) and full (100% decrease with replacement) implementation in order to illustrate the range of potential impact. In order to be able to summarise the overall potential impact, we also grouped the changes into combinations. These we termed \u0026ldquo;minor\u0026rdquo; (changes at the 25% level), \u0026ldquo;moderate\u0026rdquo; (the 50% level) or \u0026ldquo;extensive\u0026rdquo; (the 100% level). Changes were modelled at the level of food groups rather than individual foods. For some of the assumed compensations it was not possible to model what effect the substitutions would have on health, as these are not considered risk factors according to GBD 2017. As such, in our models, replacements by poultry, fish or plant-based milk were neutral in terms of the health impact.\u003c/p\u003e\n\u003ch2\u003eBaseline consumption data\u003c/h2\u003e\n\u003cp\u003eEstimates of baseline dietary intake in the adult Swedish population were from the latest nationally representative adult dietary survey, Riksmaten 2010-11, conducted by the Swedish Food Agency. The data is publicly available in fully anonymised form [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Briefly, 1,797 adults aged 18\u0026ndash;80 recorded all food and drinks consumed for four consecutive days in a web-based diary, between May 2010 and July 2011. The participants were reasonably representative of the general population, apart from slightly higher education level among participants than non-participants. The method used has been validated with respect to both energy intake and biomarkers [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. From this, the average intakes of red meat, processed meat, vegetables, legumes, milk and SSBs were calculated. Definitions of dietary factors used in the GBD 2017 were followed [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], namely red meat included beef, pork, lamb and other meats but not poultry or processed meat; processed meat is any meat that has been smoked/cured/salted/chemically preserved; vegetables excluded pickled vegetables, starchy vegetables and legumes; SSBs have \u0026ge;\u0026thinsp;50\u0026nbsp;kcal/226.8\u0026nbsp;ml and excluded juices. Information on ingredients in mixed dishes was also available, allowing for detailed extraction of the dietary factors by summing components consumed both as whole foods and as ingredients. Intakes were calculated for men and women separately. All data from the dietary survey has been anonymised and ethical permission was not required for this analysis according to Swedish law [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch2\u003eAssociations between dietary factors and disease risk\u003c/h2\u003e\n\u003cp\u003eRelative risks (RR) for associations between dietary factors and disease outcomes were taken from GBD 2017 [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. RRs which are expressed in the GBD study in terms of a harmful risk factor (e.g. \u0026ldquo;diet low in vegetables\u0026rdquo;) were inverted to create RRs for a positive change in diet. RR in GBD 2017 are modelled and presented per 5-year age intervals, from 25\u0026nbsp;years and upwards. Rates for 15\u0026ndash;24\u0026nbsp;years were assumed to be the same as for 25\u0026ndash;30 and a single RR for the entire population was then calculated by weighting the RRs for each 5-year age interval according to the population structure and taking the average.\u003c/p\u003e\n\u003ch2\u003ePopulation data\u003c/h2\u003e\n\u003cp\u003eAs the latest national dietary data for Swedish adults is from 2010-11 we used 2011 as the baseline year for our analysis. Data on population size for 2011 was obtained from the national statistics agency, Statistics Sweden [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] in 1-year age intervals. This was summed to 5-year age intervals and for each interval a sex-specific (weighted) mean age was calculated. Data on all-cause mortality for each age in 2011 was obtained from the same source and neonatal deaths were calculated and excluded. Total deaths were summed to 5-year age intervals for each sex.\u003c/p\u003e\n\u003cp\u003eDisease-specific mortality rates for Sweden in 2011 were taken from the GBD 2017 database [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The diseases for which the dietary risk factors in the scenarios are related to are ischaemic heart disease (IHD), ischaemic stroke, type 2 diabetes and colorectal cancer. These diseases accounted for 19.9%, 5.0%, 1.6% and 3.4% of total deaths in Sweden respectively in 2017 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] and are, together with lung cancer, the diseases with a dietary risk factor that account for the most deaths. Data was taken from GBD 2017 rather than directly from the national source, as the grouping of international classification of disease (ICD) cause of death codes made publicly available by the National Board of Health and Welfare do not always overlap perfectly with the groupings used in GBD 2017. Doing so ensured that all deaths for the same ICD codes that the RR are based on were included. Death rates in GBD 2017 are presented for 5-year age intervals. Using the weighted mean age of the Swedish population in each interval, the rate for each 1-year age interval was interpolated using one-way spline interpolation using the programme SRS splines, available as an add-in to Excel [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Based on the population size for each 1-year age interval, the number of deaths at each age for each disease was estimated.\u003c/p\u003e\n\u003ch2\u003eHealth impact modelling\u003c/h2\u003e\n\u003cp\u003eThe health impact assessment was performed using the IOMLIFET life table method [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e], adopted in R [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Life table calculations allow for the changes in future population shape that are induced by changes in mortality risks, and the subsequent changes in survival curves can be summarised as e.g. years of life lost (YLL) or changes to life expectancy [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. YLL is a summary measure of premature mortality; it represents the years of potential life lost across a population due to premature deaths, taking into account the age at which deaths occur. Life tables were constructed for males and females separately. All data on population size, all-cause mortality and disease-specific mortality as described above were age- and sex-specific. The following assumptions were made: the diet changes are made instantly and then kept constant, and underlying mortality rates remain constant for the duration of follow-up; the exposure\u0026ndash;response functions were assumed to be log-linear and, in cases where several dietary exposures affected the same disease, the risks were multiplied together.\u003c/p\u003e\n\u003cp\u003eWe modelled the impact on mortality on IHD, stroke, type 2 diabetes and colorectal cancer. As these outcomes are chronic diseases, there is often a cumulative effect of an exposure; this we estimated to reach a maximum after approximately 10\u0026nbsp;years for IHD, stroke and type 2 diabetes, and 30\u0026nbsp;years for cancers [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Chronic diseases, particularly cancer, also have long lag times between exposure and onset of outcome, and so no change in cancer risk was assumed for the first 10\u0026nbsp;years [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. To account for these, time-varying functions were used in the models, based on cumulative distribution functions of normally distributed variables (s-shaped curves). An uncertainty interval (UI) was constructed around each estimate based on the lower and upper ranges for the RRs. The output was changes in YLL for the population over time periods of 20 and 30\u0026nbsp;years. Analysis was conducted in R [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline intakes of the dietary factors and the relative risks for each factor and disease outcome used are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Men had a higher baseline intake of all selected dietary factors, except for vegetables.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAverage intakes at baseline, size and direction of the unit for the relative risks, and the relative risks (RR) assumed.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBaseline intakes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eRelative risks (95% CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDietary factor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(g/day)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWomen (g/day)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnit and direction for RR (g)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIHD\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIschaemic stroke\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes type 2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eColorectal cancer\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003cp\u003e(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.68)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003cp\u003e(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProcessed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003cp\u003e(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003cp\u003e(0.76\u0026thinsp;\u0026minus;\u0026thinsp;0.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003cp\u003e(0.91\u0026thinsp;\u0026minus;\u0026thinsp;0.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVegetables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003cp\u003e(0.95\u0026thinsp;\u0026minus;\u0026thinsp;0.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003cp\u003e(0.97\u0026thinsp;\u0026minus;\u0026thinsp;0.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLegumes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003cp\u003e(0.89\u0026thinsp;\u0026minus;\u0026thinsp;0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMilk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e224.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e+\u0026thinsp;226.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003cp\u003e(0.96\u0026thinsp;\u0026minus;\u0026thinsp;0.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSSB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-226.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003cp\u003e(1.05\u0026ndash;0.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003cp\u003e(0.91\u0026thinsp;\u0026minus;\u0026thinsp;0.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSingle weighted average of GBD 2017 RRs for dietary risk factors per 5-year intervals, taking into account the Swedish population structure in 2011, and inverted to create RRs for a positive change in diet.\u003c/p\u003e\n\u003cp\u003eSSB, sugar-sweetened beverages\u003c/p\u003e\n\u003cp\u003eIHD, ischaemic heart disease\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the health impact assessment are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, as changes in YLL per scenario and per combination of scenarios. The results from the health impact modelling suggest that, had Swedish adults made the \u0026ldquo;moderate\u0026rdquo; combination of these dietary changes in 2011 \u0026ndash; i.e. a 50% reduction in red and processed meat (with replacement by vegetables), in milk and in SSBs - a reduction of approximately 513,200 YLL could have been achieved over 20\u0026nbsp;years (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). If the more \u0026ldquo;extensive\u0026rdquo; combination had been adopted - a 100% reduction in red and processed meat (with replacement by vegetables and legumes), in milk and in SSBs - a reduction of 1,076,900 YLL could have been achieved. Although the uncertainty ranges for the estimates were wide, reflecting the wide ranges of the RRs for many of the dietary factor-disease outcome pairs, even at the lower ranges the estimates for even the \u0026ldquo;minor\u0026rdquo; combination of scenarios (changes at the 25% level) were positive. Over 30 years, the impact was more than twice as great (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEstimates of the reductions in total years of life lost (YLL) that could be achieved over 20 and 30\u0026nbsp;years if dietary changes were made in 2011, per scenario and in combination\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eCumulative reduction in total YLL *\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOver 20\u0026nbsp;years\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOver 30\u0026nbsp;years\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScenarios modelled\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(Lower - Upper)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(Lower - Upper)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReplacing red \u0026amp; processed meat with poultry/fish\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25% less red \u0026amp; processed meat*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e176 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(16 100 ‒ 269 700)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e400 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(39 600 ‒ 613 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50% less red \u0026amp; processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e336 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(31 700 ‒ 501 000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e765 500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(78 300 ‒ 1\u0026nbsp;144 100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo red or processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e611 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(61 600 ‒ 866 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1 397 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(152 200 ‒ 1 991 100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReplacing red \u0026amp; processed meat with vegetables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25% less red \u0026amp; processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e233 600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(37 300 ‒ 361 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e527 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(86 600 ‒ 817 300)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50% less red \u0026amp; processed meat**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e440 800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(73 800 ‒ 659 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e997 600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(171 300 ‒ 1\u0026nbsp;497 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo red or processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e786 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(144 300 ‒ 1 107 300)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1 787 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(335 000 ‒ 2 529 000)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReplacing red \u0026amp; processed meat with vegetables and legumes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25% less red \u0026amp; processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e293 900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(69 000 ‒ 440 200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e661 900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(156 800 ‒ 993 700)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50% less red \u0026amp; processed meat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e544 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(135 800 ‒ 781 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1 230 000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(308 800 ‒ 1\u0026nbsp;774 100)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo red or processed meat***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e935 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(261 800 ‒ 1 248 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2 126 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(596 400 ‒ 2 853 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReplacing milk consumption with plant-based drink\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25% less milk intake*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-300 ‒ -1 200)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-6 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-2400 ‒ -11 000)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50% less milk intake**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-500 ‒ -2 500)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-12 500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-4800 ‒ -22 500)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo milk intake***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-2 900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-1 100 ‒ -5 300)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-25 600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-9600 ‒ -46 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReplacing SSB consumption with water\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25% less SSB intake*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-6 900 ‒ 71 500)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e82 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-15200 ‒ 158 200)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50% less SSB intake**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e73 800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-13 900 ‒ 140 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e163 300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-30600 ‒ 311 300)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNo SSB intake***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e144 700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(-27 900 ‒ 270 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e320 400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(-61600 ‒ 600 200)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e* Combination \u0026ldquo;Minor changes\u0026rdquo; (sum)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e212 800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(8 900 ‒ 339 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e476 600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(22000 ‒ 760 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e** Combination \u0026ldquo;Moderate changes\u0026rdquo; (sum)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e513 200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(59 400 ‒ 797 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1 148 500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(135900 ‒ 1 786 600)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e*** Combination \u0026ldquo;Extensive changes\u0026rdquo; (sum)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 076 900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(232 800 ‒ 1\u0026nbsp;513 900)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e2 420 900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e(525200 ‒ 3\u0026nbsp;407 200)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003eSSB, sugar-sweetened beverages.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003eReplacements were of equal weight. Vegetables and legumes were 50:50. Numbers rounded to nearest 100.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003e\u003csup\u003e#\u003c/sup\u003eReplacement food category was neutral in the health impact model.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn general, scenarios involving lower meat intake had greater impacts than those involving milk or SSBs. Reductions in YLL were greater when red and processed meat was replaced with a combination of vegetables and legumes, rather than vegetables alone. One of the scenarios, a reduced intake of milk, resulted in an increase of YLL, as there is evidence that a higher intake of milk is associated with a reduced risk of colorectal cancer. However, other changes, such as reducing red and processed meat can compensate for this: for example the increase in YLL from a 100% reduction in milk intake (2,900 YLL over 20\u0026nbsp;years) was compensated by the 50% reduction of red and processed meat (reduction of 2,800 YLL over 20\u0026nbsp;years) (Additional file 1).\u003c/p\u003e\n\u003cp\u003eThe results of the combinations of scenarios are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, showing the breakdown by outcome and sex. The disease outcome that was affected most by the changes was IHD mortality, which accounts for the vast majority of the reductions in total YLL (ca 90%), followed by diabetes type 2 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The longer lag time for colorectal cancer means that only very small reductions in YLL would be seen after 20 years; reductions would be seen mainly after 30\u0026nbsp;years. Reductions in YLL were greater for men (Additional file 1), which was expected as the absolute changes modelled were relative to the baseline intake and intakes were generally higher among men (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Estimates for each change and disease outcome and for each sex are available in Additional file 1.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eWider adoption of the dietary practices identified by the \"Mistra Sustainable Consumption\u0026rdquo; programme as niche, sustainable and with potential to become mainstream would be expected to result in considerable public health benefits, especially for men, in addition to a likely reduction in diet-related GHGEs. Taking 2010-11 as the starting point for implementation, we modelled what the impact in Sweden might have been in terms of deaths prevented or postponed over 20 and 30\u0026nbsp;years. The results suggest that the gains could have been in the hundreds of thousands of YLL, possibly in the region of a million for the combination of scenarios labelled \u0026ldquo;moderate\u0026rdquo; (changes at the 50% level) or \u0026ldquo;extensive\u0026rdquo; (changes at the 100% level). To put this in perspective, in Sweden in 2017, the number of YLL from all causes was approximately 1,248,000 according to GBD 2017 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Approximately 16% of these were due to IHD (ca 203,000), and poor dietary habits (the cumulative effect of all 15 dietary risks included in GBD) was the biggest risk factor. As the biggest change in our dietary scenarios was for YLL due to IHD, it is easy to see how the cumulative figure over a longer time period reaches a substantial number. The practice that had the most impact was reducing the intake of red and processed meat and replacing it with a mixture of vegetables and legumes. The results suggest that this practice alone could prevent about a fifth of YLL due to IHD.\u003c/p\u003e \u003cp\u003eAs the most recent national dietary data in Sweden is from 2010-11, it is worth considering how relevant the proposed scenarios are today. Changes in per capita supply data between 2010 and 2019 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] suggest a 18% decrease in pork, a 7% decrease in beef and a 20% increase in poultry volumes. No clear trend is seen for vegetables using the same source, but the market for \u0026ldquo;meat alternatives\u0026rdquo; has increased over 15% year-on-year between 2017 and 2019 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A internet panel survey of vegetarian practices suggests a similar trend between 2016 and 2019: while the proportion of adults who rarely or never ate vegetarian meals was 48% in 2019, the proportion who never ate vegetarian meals had fallen from 22\u0026ndash;15% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It should be noted that neither per capita supply data nor market analysis is a substitute for individual consumption data but can be useful to observe trends [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These would suggest that the scenario of replacing red meat (with poultry or something else) is already being practiced by some consumer segments \u0026ndash; from \u0026ldquo;innovators\u0026rdquo; to the \u0026ldquo;early majority\u0026rdquo; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] \u0026ndash; and have the potential to be scaled up. In contrast, the per capita sales volume of SSBs has increased slightly since 2010 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Rogers lists compatibility with existing values, norms and practices as one of five factors determining the success of an innovation. Others are 2) relative advantage (the greater the perceived relative advantage of an innovation by the user group, the more rapid its rate of adoption is likely to be); 3) simplicity and ease of use; 4) trialability (the degree to which an innovation can be experimented with on a limited basis); and 5) observable results (the easier it is for individuals to see the results of an innovation, the more likely they are to adopt it) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In order to accelerate the dietary shift required, all these factors could be taken into consideration by stakeholders and decision-makers who want to bring about change.\u003c/p\u003e \u003cp\u003eIt is always important to take account of several aspects of sustainability simultaneously so that e.g. health is not prioritised at the expense of the environment, or social sustainability. In general, the overlap between foods that have lower environmental impact and also improve health is usually high, with the notable exception of fish and sugar, where health and environmental impacts may act in the opposite directions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We were not able to consider other impacts that substitutions within food groups could have in more detail. However in another part of the \u0026ldquo;Sustainable Consumption\u0026rdquo; project, the impacts of a number of potential \u0026ldquo;replacement\u0026rdquo; products relevant for these scenarios (e.g. legume-based products, plant-based drinks) have been quantified from the Swedish perspective [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], confirming their lower GHGE impact, even when production, transport from abroad, packaging, etc is taken into account.\u003c/p\u003e \u003cp\u003eOne of the major limitations of simulations is that replacements may have consequences for energy balance and nutritional adequacy, as what and how much replacement occurs in reality is difficult to know. We assumed here replacement by equal weight, not by energy. For example in one scenario we assumed a total reduction in processed and red meat, and compensating for this by increasing vegetables and legumes (in equal amounts) by the same weight. This increase was, on paper, not excessive and would bring average intakes of total fruits, vegetables and legumes up to 384\u0026nbsp;g for men and 396\u0026nbsp;g for women, still far below the recommended intakes of 500\u0026nbsp;g [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. If these were consumed in their unprocessed form it would likely mean a shortfall in energy intake, due to the lower energy density of this food group. It is however likely that replacement would also involve more processed vegetable- and legume-based products, which are more energy-dense than unprocessed vegetables and legumes, as the availability of these has increased dramatically in recent years in Sweden [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Given half of all Swedish adults are living with the effects of a prior or ongoing positive energy balance (i.e. are overweight or obese [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]), for many energy deficits may lead to further health gains. Similarly, the impact associated with reduced SSB consumption are also possibly underestimated as further gains could be mediated through a reduction in obesity, for which high SSB consumption is a risk factor.\u003c/p\u003e \u003cp\u003eA more critical issue is if the foods that are reduced are important sources of nutrients that are not compensated for. Some of our modelled scenarios would be almost neutral in terms of impact on micronutrient intake, for example replacing SSB with water. Others are more complex. For example, meat is a rich source of nutrients such as iron, selenium, zinc and some B-vitamins. However, a study from the Nordic region examined this (using, for the Swedish part, the same dietary survey data as in our study) and concluded that the effects on overall dietary quality would be minimal if processed meat was reduced to zero, and if average red meat was reduced to the WCRFs 2007 population-level recommendation of 43\u0026nbsp;g per day [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. They modelled scenarios involving both replacement by other meat, non-meat, and with or without energy compensation. Another way of looking at the replacements at food level and ensuring that the overall diet is nutritionally adequate would be to perform an optimisation analysis using linear programming [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and this is planned in a future study.\u003c/p\u003e \u003cp\u003eAnother limitation of simulations or models is that they remain theoretical. Indeed, the relationship between the health and environmental impacts of self-selected diets is more complex than that between single foods/food groups [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The modelled changes may also be less acceptable to consumers than what is assumed. Vieux et al examined actual dietary patterns in six European countries, including Sweden, and concluded that exclusion of entire categories of food is not necessary to achieve health and climate benefits, and a \u0026ldquo;more sustainable\u0026rdquo; diet with \u0026ldquo;moderate\u0026rdquo; amounts of animal-based products is probably realistic, as it is already adopted by nearly one in five adults [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] corresponding to the population segments innovators, early adopters and some of the early majority according to the Diffusion of Innovations theory [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We therefore made sure to include scenarios where animal-based products were still included to a large degree, as well as being more extensively reduced.\u003c/p\u003e \u003cp\u003eOur results complement those of Saha et al [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] who estimated 1-year health gains if food and nutrient intakes were in line with Nordic Nutrition Recommendations. They used the PRIME model, which is different from our method and based on other premises. They estimated that 6,405 deaths in a year in Sweden would be prevented/delayed from cardiovascular diseases and diet-related cancers, or 14.4% of the total. In line with our results, the majority of YLL reductions were also from IHD. However other differences make the results difficult to compare: they used the same dietary data but based their model on population data from 2016, not 2011 which was 5.4% smaller; they modelled nutrients (fat, salt, dietary fiber, energy) and only one food group (fruits and vegetables (and not meat)). They also assumed that their health gains occurred in the same year, not allowing for a lag time as we have done, and not taking into account the impact on the population structure over time. Assuming a constant effect over 20\u0026nbsp;years or 30 years, this would correspond to 128 000 or 192 000 prevented or postponed deaths in their study. Using the same IOMLIFET approach, a scenario for the UK (population 67\u0026nbsp;million) suggested almost 7\u0026nbsp;million YLL would be saved over 30\u0026nbsp;years if diets were in line with WHO dietary recommendations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A study from Italy using the IOMLIFET model that we used predicted that reducing beef by 63% (to 150\u0026nbsp;g per week) and processed meat by 80% (to 50\u0026nbsp;g per week) would reduce YLL by 9 and 20\u0026nbsp;million respectively over 30\u0026nbsp;years [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This is a similar range to our numbers, when Italy\u0026rsquo;s population size (about 6 times larger) is taken into account.\u003c/p\u003e \u003cp\u003eOther strengths and limitations are that we included all food-based dietary factors in GBD 2017 that could be connected to the proposed scenarios but did not consider nutrient-based dietary factors. Where several dietary exposures affected the same disease, the risks were multiplied together. It is possible that this leads to an over-estimation, but this is commonly done in other models too, e.g. PRIME, and due to lack of information on mediation/overlap. We only examined YLL, not years of life lived with disability (YLD), another widely used measure of health impact, which means that we have most likely underestimated total health benefits by not accounting for impacts on morbidity. We also did not consider here the effects that any reduction in body mass index (BMI) may have had, either due to negative energy balance as a result of a scenario, or as a scenario in its own right. Reducing excess consumption (and waste) is one obvious way to reduce the environmental impact of a diet, as this is determined by both the quality and quantity of food consumed [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe population has increased since 2011, from 9.48\u0026nbsp;million in 2011 to 10.33\u0026nbsp;million in 2019 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], an increase of 8.9% so the reductions in YLLs may be underestimated. The population structure has remained similar: the proportion of men increased by less than 1%, life expectancy at birth increased by 1.9% for men and 1.3% for women during the last decade. Data on disease-specific death rates were taken from GBD 2017 rather than directly from the national source to ensure deaths from and RRs for diseases were for exactly the same disease codes, but the differences in deaths from both sources were minimal (\u0026lt;\u0026thinsp;1%). The expected benefit to the environment was limited to the effect on GHGEs, but other aspects such as water and land use are also important. Although a clear socioeconomic gradient is seen with dietary quality, the impact of dietary changes on economic sustainability was not possible to include in this analysis.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThe widespread adoption of dietary practices identified as being niche today but with the potential to become mainstream, could result in considerable improvements in public health in Sweden, particularly over the long term. Although modeling health impacts requires making assumptions and a level of uncertainty, this potentially translates to many premature deaths postponed or prevented from a number of chronic diseases, primarily IHD, to the benefit of the individual, society and with probable benefits for the economy and the climate. Some motivated consumer segments may be willing to adopt more sustainable practices right away. However, in order to accelerate the transition to a more sustainable diet within the timeframe that meeting commitments to international agreements regarding sustainability requires, policymakers should consider more potent strategies in order to persuade far more of the population to do so.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eGBD Global Burden of Disease\u003c/p\u003e\n\u003cp\u003eGHGE Greenhouse gas emissions\u003c/p\u003e\n\u003cp\u003eICD International Classification of Disease\u003c/p\u003e\n\u003cp\u003eIHD Ischaemic heart disease\u003c/p\u003e\n\u003cp\u003eIOMLIFET Institute of Occupational Medicine Life Table\u003c/p\u003e\n\u003cp\u003eRR relative risk\u003c/p\u003e\n\u003cp\u003eSSB Sugar-sweetened beverages\u003c/p\u003e\n\u003cp\u003eYLL Years of life lost\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThis study involved no personal data. Ethical permission was therefore not required for this in accordance with Swedish law [20].\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe dietary survey data Riksmaten 2010-11 is available from the Swedish Food Agency, \u003ca href=\"https://www.livsmedelsverket.se/om-oss/psidata/apimatvanor\"\u003ehttps://www.livsmedelsverket.se/om-oss/psidata/apimatvanor\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe data on disease specific mortality rates is available from GBD, \u003ca href=\"http://ghdx.healthdata.org/gbd-results-tool\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe relative risks for diet-disease associations are available from GBD 2017, \u003ca href=\"http://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017\"\u003ehttp://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe population data is available from Statistics Sweden, \u003ca href=\"http://www.scb.se/be0101\"\u003ehttp://www.scb.se/be0101\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eOther datasets generated during the current analysis are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eEP and LSE were funded by Mistra - The Swedish Foundation for Strategic Environmental Research through the research programme Mistra Sustainable Consumption, grant DIA 2016/3. RG is funded by the Sustainable and Healthy Food Systems (SHEFS) project - Wellcome Trust Our Planet Our Health grant 205200/Z/6/Z. JM is funded by the Complex Urban Systems for Sustainability and Health (CUSSH) project \u0026ndash; Wellcome Trust Our Planet Our Health grant 209387/Z/17/Z. PEC is funded by the Swedish Research Council FORMAS grant number 2016-00353.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026rsquo; contributions\u003c/h3\u003e\n\u003cp\u003eEP formulated the research question, contributed to the study design, gathered the data, performed the analysis, drafted and revised the paper. PEC contributed to the data collection, data analysis and study design and revised the draft paper. JM contributed to the analysis and study design and revised the draft paper. RG contributed to the study design and revised the draft paper, LSE formulated the research question, secured funding for the project, contributed to the study design and drafted and revised the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 Causes of Death Collaborators: \u003cstrong\u003eGlobal, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2018(392):1736-1788.\u003c/li\u003e\n\u003cli\u003ePoore J, Nemecek T: \u003cstrong\u003eReducing food's environmental impacts through producers and consumers\u003c/strong\u003e. \u003cem\u003eScience \u003c/em\u003e2018, \u003cstrong\u003e360\u003c/strong\u003e(6392):987-992.\u003c/li\u003e\n\u003cli\u003eGerten D, Heck V, J\u0026auml;germeyr J, Bodirsky BL, Fetzer I, Jalava M, Kummu M, Lucht W, Rockstr\u0026ouml;m J, Schaphoff S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFeeding ten billion people is possible within four terrestrial planetary boundaries\u003c/strong\u003e. \u003cem\u003eNature Sustainability \u003c/em\u003e2020, \u003cstrong\u003e3\u003c/strong\u003e(3):200-208.\u003c/li\u003e\n\u003cli\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, Springmann M, Lang T, Vermeulen S, Garnett T, Tilman D, DeClerck F, Wood A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFood in the Anthropocene: the EAT\u0026ndash;Lancet Commission on healthy diets from sustainable food systems\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2019, \u003cstrong\u003e393\u003c/strong\u003e(10170):447-492.\u003c/li\u003e\n\u003cli\u003eSwinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, Brinsden H, Calvillo A, De Schutter O, Devarajan R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2019, \u003cstrong\u003e393\u003c/strong\u003e(10173):791-846.\u003c/li\u003e\n\u003cli\u003eMason P, Lang T: \u003cstrong\u003eSustainable Diets: How Ecological Nutrition Can Transform Consumption and the Food System\u003c/strong\u003e: Taylor \u0026amp; Francis; 2017.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMistra Sustainable Consumption - from niche to mainstream \u003c/strong\u003e\u003ca href=\"https://www.sustainableconsumption.se/en/start-eng\"\u003ehttps://www.sustainableconsumption.se/en/start-eng\u003c/a\u003e Accessed 2020-09-01.\u003c/li\u003e\n\u003cli\u003eRogers EM: \u003cstrong\u003eDiffusion of Innovations, 5th Edition\u003c/strong\u003e: Simon and Schuster; 2003.\u003c/li\u003e\n\u003cli\u003eMozaffarian D, Angell SY, Lang T, Rivera JA: \u003cstrong\u003eRole of government policy in nutrition\u0026mdash;barriers to and opportunities for healthier eating\u003c/strong\u003e. \u003cem\u003eBMJ \u003c/em\u003e2018:k2426.\u003c/li\u003e\n\u003cli\u003eSj\u0026ouml;rs C, Raposo SE, Sj\u0026ouml;lander A, B\u0026auml;lter O, Hedenus F, B\u0026auml;lter K: \u003cstrong\u003eDiet-related greenhouse gas emissions assessed by a food frequency questionnaire and validated using 7-day weighed food records\u003c/strong\u003e. \u003cem\u003eEnviron Health \u003c/em\u003e2016, \u003cstrong\u003e15\u003c/strong\u003e:15.\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;\u0026ouml;s E, Larsson J, Resare Sahlin K, Jonell M, Lindahl T, Andr\u0026eacute; E, S\u0026auml;ll S, Harring N, Persson M. \u003cstrong\u003eStyrmedel f\u0026ouml;r h\u0026aring;llbar matkonsumtion \u0026ndash; en kunskaps\u0026ouml;versikt och v\u0026auml;gar fram\u0026aring;t [Incentives for sustainable food consumption - an overview of the evidence and ways forward]\u003c/strong\u003e 2020. Sveriges lantbruksuniversitet, forskningsplattformen SLU Future Food \u003ca href=\"https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/StyrmedelForHallbarMatkonsumtion.pdf\"\u003ehttps://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/StyrmedelForHallbarMatkonsumtion.pdf\u003c/a\u003e Accessed 2020-08-01\u003c/li\u003e\n\u003cli\u003eKamb A, Svenfelt \u0026Aring;, Kayama AC, Parekh V, Bradley K. \u003cstrong\u003eAtt \u0026auml;ta h\u0026aring;llbart? En kartl\u0026auml;ggning av vad h\u0026aring;llbar matkonsumtion kan inneb\u0026auml;ra [To eat sustainably? \u003c/strong\u003e\u003cstrong\u003eA survey of what sustainable food consumption can mean]\u003c/strong\u003e 2019. Mistra Sustainable Consumption Report 1:2 Stockholm, KTH. \u003ca href=\"https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2019/03/Att-\"\u003ehttps://www.sustainableconsumption.se/wp-content/uploads/sites/34/2019/03/Att-\u003c/a\u003e\u0026auml;ta-h\u0026aring;llbart-En-kartl\u0026auml;ggning-av-vad-h\u0026aring;llbar-matkonsumtion-kan-inneb\u0026auml;ra.pdf Accessed 2020-09-16\u003c/li\u003e\n\u003cli\u003eLivsmedelsverket [Swedish Food Agency]. \u003cstrong\u003eFind Your Way to Eat Greener, Not Too Much and Be Active\u003c/strong\u003e 2015. Livsmedelsverket [Swedish Food Agency] \u003ca href=\"http://www.livsmedelsverket.se/globalassets/english/food-habits-health-environment/dietary-guidelines/kostraden-eng-a4-utskriftversion.pdf?id=8199\"\u003ehttp://www.livsmedelsverket.se/globalassets/english/food-habits-health-environment/dietary-guidelines/kostraden-eng-a4-utskriftversion.pdf?id=8199\u003c/a\u003e Accessed\u003c/li\u003e\n\u003cli\u003eNordic Council of Ministers: \u003cstrong\u003eNordic Nutrition Recommendations 2012 - Integrating nutrition and physical activity\u003c/strong\u003e: Nordic Council of Ministers; 2014.\u003c/li\u003e\n\u003cli\u003eWorld Cancer Research Fund/American Institute for Cancer Research. \u003cstrong\u003eDiet, Nutrition, Physical Activity and Cancer: A Global Perspective. Continuous Update Project Expert Report 2018\u003c/strong\u003e\u003ca href=\"https://kise.sharepoint.com/teams/GRP_Mistra-HIA3/Delade%20dokument/General/Submission%20BMC%20Public%20Health/www.dietandcancerreport.org\"\u003ewww.dietandcancerreport.org\u003c/a\u003e Accessed\u003c/li\u003e\n\u003cli\u003eGBD Diet Collaborators: \u003cstrong\u003eHealth effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2019, \u003cstrong\u003e393\u003c/strong\u003e(10184):1958-1972.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRiksmaten vuxen 2010-11\u003c/strong\u003e. Livsmedelsverket [Swedish Food Agency] \u003ca href=\"https://www.livsmedelsverket.se/om-oss/psidata/apimatvanor\"\u003ehttps://www.livsmedelsverket.se/om-oss/psidata/apimatvanor\u003c/a\u003e Access date 2020-09-01\u003c/li\u003e\n\u003cli\u003eNybacka S, Lindroos AK, Wirf\u0026auml;lt E, Leanderson P, Landberg R, Ericson U, Larsson I, Warensj\u0026ouml; Lemming E, Bergstr\u0026ouml;m G, Hedblad B\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCarotenoids and alkylresorcinols as objective biomarkers of diet quality when assessing the validity of a web-based food record tool and a food frequency questionnaire in a middle-aged population\u003c/strong\u003e. \u003cem\u003eBMC Nutrition \u003c/em\u003e2016, \u003cstrong\u003e2\u003c/strong\u003e(1):53.\u003c/li\u003e\n\u003cli\u003eNybacka S, Bert\u0026eacute;us Forslund H, Wirf\u0026auml;lt E, Larsson I, Ericson U, Warensj\u0026ouml; Lemming E, Bergstr\u0026ouml;m G, Hedblad B, Winkvist A, Lindroos AK: \u003cstrong\u003eComparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population\u003c/strong\u003e. \u003cem\u003eJ Nutr Sci \u003c/em\u003e2016, \u003cstrong\u003e5\u003c/strong\u003e:e39.\u003c/li\u003e\n\u003cli\u003eSwedish Parliament: \u003cstrong\u003eLag (2003:460) om etikpr\u0026ouml;vning av forskning som avser m\u0026auml;nniskor [Law (2003:460) on ethical review of research concerning humans]\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGlobal Burden of Disease Study 2017 (GBD 2017) Burden by Risk 1990-2017\u003c/strong\u003e. Institute for Health Metrics Evaluation Washington University \u003ca href=\"http://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017\"\u003ehttp://ghdx.healthdata.org/record/ihme-data/gbd-2017-burden-risk-1990-2017\u003c/a\u003e Access date 2020-08-01\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBefolkningsstatisik [Population statistics]\u003c/strong\u003e. Statistiska Centralbyr\u0026aring;n [Statistics Sweden] \u003ca href=\"http://www.scb.se/be0101/\"\u003ehttp://www.scb.se/be0101/\u003c/a\u003e Access date\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGlobal Burden of Disease Study 2017 (GBD 2017) Cause-Specific Mortality 1980-2017\u003c/strong\u003e. Institute for Health Metrics Evaluation Washington University \u003ca href=\"http://ghdx.healthdata.org/gbd-results-tool\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/a\u003e Access date 2020-08-15\u003c/li\u003e\n\u003cli\u003eSrs1 Software LLC. \u003cstrong\u003eSRS1 Cubic Spline for Excel v2.5\u003c/strong\u003e. Massachusetts, USA.\u003c/li\u003e\n\u003cli\u003eMiller BG, Hurley JF: \u003cstrong\u003eLife table methods for quantitative impact assessments in chronic mortality\u003c/strong\u003e. \u003cem\u003eJ Epidemiol Community Health \u003c/em\u003e2003, \u003cstrong\u003e57\u003c/strong\u003e:200\u0026ndash;206.\u003c/li\u003e\n\u003cli\u003eMilner J, Green R, Dangour AD, Haines A, Chalabi Z, Spadaro J, Markandya A, Wilkinson P: \u003cstrong\u003eHealth effects of adopting low greenhouse gas emission diets in the UK\u003c/strong\u003e. \u003cem\u003eBMJ Open \u003c/em\u003e2015, \u003cstrong\u003e5\u003c/strong\u003e(4):e007364.\u003c/li\u003e\n\u003cli\u003eR Core Development Team 2020 \u003cstrong\u003eR: A Language and Environment for Statistical Computing\u003c/strong\u003e. R Foundation for Statistical Computing. Vienna, Austria.\u003c/li\u003e\n\u003cli\u003eJordbruksverket [Swedish Board of Agriculture]: \u003cstrong\u003eKonsumtion och f\u0026ouml;rbrukning av k\u0026ouml;tt [Consumption and use of meat]\u003c/strong\u003e\u003ca href=\"https://djur.jordbruksverket.se/amnesomraden/konsument/livsmedelskonsumtionisiffror/kottkonsumtionen.4.465e4964142dbfe44705198.html\"\u003ehttps://djur.jordbruksverket.se/amnesomraden/konsument/livsmedelskonsumtionisiffror/kottkonsumtionen.4.465e4964142dbfe44705198.html\u003c/a\u003e Accessed 2020-08-30\u003c/li\u003e\n\u003cli\u003eMacklean Management Consultancy. \u003cstrong\u003eMarknadsanalys och potential f\u0026ouml;r v\u0026auml;xtbaserade proteiner. Rapport f\u0026ouml;r Lantbrukarnas Riksf\u0026ouml;rbund. [Market analysis and potential for plant-based proteins. Report for the Federation of Swedish Farmers]\u003c/strong\u003e 2020. \u003ca href=\"https://www.lrf.se/globalassets/dokument/mitt-lrf/nyheter/2020/marknadsanalys-och-potential-for-vaxtbaserade-proteiner.pdf\"\u003ehttps://www.lrf.se/globalassets/dokument/mitt-lrf/nyheter/2020/marknadsanalys-och-potential-for-vaxtbaserade-proteiner.pdf\u003c/a\u003e Accessed 2020-09-03\u003c/li\u003e\n\u003cli\u003eEustachio Colombo P, Elinder LS, Carlsson Kanyama A. \u003cstrong\u003eVegobarometern \u0026ndash; En unders\u0026ouml;kning av svenskarnas ben\u0026auml;genhet att v\u0026auml;lja vegetarisk mat under \u0026aring;ren 2016 - 2019. \u003c/strong\u003e\u003cstrong\u003e[The Vegobarometer - a survey of Swedes willingness to choose vegetarian food between 20136 and 2019]\u003c/strong\u003e 2020. Mistra Sustainable Consumption Report 1:5 Stockholm, KTH. \u003ca href=\"https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/05/Vegobarometern-rapport-Mistra-SC-Final2.pdf\"\u003ehttps://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/05/Vegobarometern-rapport-Mistra-SC-Final2.pdf\u003c/a\u003e Accessed 2020-09-03\u003c/li\u003e\n\u003cli\u003eBecker W, Helsing E: \u003cstrong\u003eFood and health data; their use in nutrition policy-making\u003c/strong\u003e. In\u003cem\u003e. Volume 34\u003c/em\u003e, edn. Edited by WHO Regional Office for Europe: agris.fao.org; 1991.\u003c/li\u003e\n\u003cli\u003eThe Swedish Brewers Association: \u003cstrong\u003eSoft drink sales volume statistics\u003c/strong\u003e\u003ca href=\"https://sverigesbryggerier.se/statistik/\"\u003ehttps://sverigesbryggerier.se/statistik/\u003c/a\u003e Accessed 2020-09-12\u003c/li\u003e\n\u003cli\u003eClark MA, Springmann M, Hill J, Tilman D: \u003cstrong\u003eMultiple health and environmental impacts of foods\u003c/strong\u003e. \u003cem\u003eProc Natl Acad Sci U S A \u003c/em\u003e2019, \u003cstrong\u003e116\u003c/strong\u003e(46):23357-23362.\u003c/li\u003e\n\u003cli\u003eCarlsson Kanyama A, Dun\u0026eacute;r F. \u003cstrong\u003e40% mindre v\u0026auml;xthusgasutsl\u0026auml;pp fr\u0026aring;n konsumtionen h\u0026auml;r och nu. Ber\u0026auml;kningar givet f\u0026ouml;r\u0026auml;ndrad konsumtion av mat, semestrande och inredning [40% lower greenhouse gas emissions from consumption here and now. \u003c/strong\u003e\u003cstrong\u003eCalculations assuming altered consumption of food, vacations and home furnishings.]\u003c/strong\u003e 2020. Mistra Sustainable Consumption Report 1:6 Stockholm. \u003ca href=\"https://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/Rapport-40-mindre-va%CC%88xthusgasutsla%CC%88pp-fra%CC%8An-konsumtionen-ha%CC%88r-och-nu-.pdf\"\u003ehttps://www.sustainableconsumption.se/wp-content/uploads/sites/34/2020/06/Rapport-40-mindre-va%CC%88xthusgasutsla%CC%88pp-fra%CC%8An-konsumtionen-ha%CC%88r-och-nu-.pdf\u003c/a\u003e Accessed 2020-08-15\u003c/li\u003e\n\u003cli\u003eFolkh\u0026auml;lsomyndigheten [Public Health Agency of Sweden]. \u003cstrong\u003eFolkh\u0026auml;lsans utveckling. \u0026Aring;rsrapport 2020 [Public health developments. Annual report 2020]\u003c/strong\u003e 2020. Folkh\u0026auml;lsomyndigheten \u003ca href=\"https://www.folkhalsomyndigheten.se/contentassets/9fd952e9014642249164352cd5a3eb50/folkhalsans-utveckling-arsrapport-2020.pdf\"\u003ehttps://www.folkhalsomyndigheten.se/contentassets/9fd952e9014642249164352cd5a3eb50/folkhalsans-utveckling-arsrapport-2020.pdf\u003c/a\u003e Accessed 2020-09-14\u003c/li\u003e\n\u003cli\u003eTetens I, Hoppe C, Frost Andersen L, Helld\u0026aacute;n A, Warensj\u0026ouml; Lemming E, Trolle E, Holm Totland T, Lindroos AK. \u003cstrong\u003eNutritional evaluation of lowering consumption of meat and meat products in the Nordic context\u003c/strong\u003e 2013. \u003ca href=\"http://dx.doi.org/10.6027/TN2013-506\"\u003ehttp://dx.doi.org/10.6027/TN2013-506\u003c/a\u003e Accessed 2020-06-16\u003c/li\u003e\n\u003cli\u003evan Dooren C: \u003cstrong\u003eA Review of the Use of Linear Programming to Optimize Diets, Nutritiously, Economically and Environmentally\u003c/strong\u003e. \u003cem\u003eFrontiers in Nutrition \u003c/em\u003e2018, \u003cstrong\u003e5\u003c/strong\u003e:48.\u003c/li\u003e\n\u003cli\u003eVieux F, Privet L, Soler LG, Irz X, Ferrari M, Sette S, Raulio S, Tapanainen H, Hoffmann R, Surry Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMore sustainable European diets based on self-selection do not require exclusion of entire categories of food\u003c/strong\u003e. \u003cem\u003eJ Clean Prod \u003c/em\u003e2020, \u003cstrong\u003e248\u003c/strong\u003e:119298.\u003c/li\u003e\n\u003cli\u003eSaha S, Nordstrom J, Gerdtham UG, Mattisson I, Nilsson PM, Scarborough P: \u003cstrong\u003ePrevention of Cardiovascular Disease and Cancer Mortality by Achieving Healthy Dietary Goals for the Swedish Population: A Macro-Simulation Modelling Study\u003c/strong\u003e. \u003cem\u003eInternational journal of environmental research and public health \u003c/em\u003e2019, \u003cstrong\u003e16\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eFarchi S, De Sario M, Lapucci E, Davoli M, Michelozzi P: \u003cstrong\u003eMeat consumption reduction in Italian regions: Health co-benefits and decreases in GHG emissions\u003c/strong\u003e. \u003cem\u003ePLoS One \u003c/em\u003e2017, \u003cstrong\u003e12\u003c/strong\u003e(8):e0182960.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sustainability, modelling, health impact assessment, adults, non-communicable diseases, diets, nutrition, climate change, greenhouse gas emissions","lastPublishedDoi":"10.21203/rs.3.rs-96236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-96236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eAn urgent transition to more sustainable diets is necessary for the improvement of human and planetary health. One way to achieve this is for sustainable practices to become mainstream. We estimated the potential health impact of wider adoption of dietary practices deemed by consumers, researchers and stakeholders in Sweden to be niche, sustainable and with the potential to be scaled up.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eA life table method was used to estimate the impact - changes in years of life lost (YLL) - over periods of 20 and 30 years in the Swedish population had the practices been adopted in 2010-11, when the last national adult dietary survey was conducted. The practices modelled were reducing red and processed meat (by 25%, 50% and 100%), and assuming, for each stage, replacement by an equal weight of poultry/fish and vegetables +/- legumes; reducing milk intake (by 25%, 50% and 100%); and reducing sugar-sweetened beverage intake (by 25%, 50% and 100%). Using population data together with data on cause-specific mortality and relative risks for diet-disease outcomes, impacts were estimated for each scenario separately and in combination, for the outcomes ischaemic heart disease (IHD), ischaemic stroke, diabetes type 2 and colorectal cancer.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eFor a “moderate” combination of scenarios (changes at the 50% level), reductions of 513,200 YLL (lower-upper uncertainty estimate 59,400-797,900) could have been achieved over 20 years and 1,148,500 YLL (135,900-1,786,600) over 30 years. The majority (over 90%) of YLLs prevented were related to IHD, and the majority were in men. The singular practice that had the most impact was reducing the intake of red and processed meat and replacing it with a mixture of vegetables and legumes. Reducing milk intake resulted in an increase in YLL, but this was compensated for by other scenarios. \u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eIf these practices were more widely adopted, they would be expected to lead to improvements in public health in Sweden. Over the long term, this would translate to many premature deaths postponed or prevented from a number of chronic diseases, to the benefit of individuals, society, the climate and the economy.\u003c/p\u003e","manuscriptTitle":"Potential Health Impact of Increasing Adoption of Sustainable Dietary Practices in Sweden","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-27 21:42:01","doi":"10.21203/rs.3.rs-96236/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2021-01-23T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-12-10T00:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable pending editorial decision\n"},{"type":"editorInvitedReview","content":"","date":"2020-12-10T00:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after discretionary revisions\nForm responses:\n---\n\nComments to Author:\n---\nLines 229-231. Table 1. The RR for vegetables with IHD and Stroke seems counter-intuitive. Can this be due to the inclusion of starchy vegetables. In lines 75-77 the authors indicate that the Eat-Lancet Planetary Health Diet would require a considerable increase in vegetable…and less starchy vegetables. If so, can the vegetables be split into starchy and non-starchy with different RRs?\n\n\n* Publons Reviewer Recognition. Springer Nature can send verification of this review directly to Publons (a subsidiary of Clarivate Analytics). If you would like to take advantage of this service, please click on the “Yes” option below. Your name, email address, title of the reviewed manuscript, name of the journal, and date of your review submission (the “Review Data”) will then be transmitted to Publons upon publication of the manuscript. If you have already registered at Publons, they will notify you of the receipt of this review and update your profile as per your settings and their policy. If you are not registered with Publons, you will receive an email from them asking you to register in order for them to be able to recognize your review on your new profile page. Publons may use the Review Data to generate derivative metadata for the benefit of Publons and you as a reviewer, carefully considering the sensitivity of such information. For example, Publons may verify your record as a reviewer by updating your profile published on its webservice if you have registered for such service or help editors to identify candidate reviewers. Please find the details of processing in Publons’ privacy policy https://publons.com/about/terms: **No**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n* Is the study design appropriate to answer the research question (including the use of appropriate controls), and are the conclusions supported by the evidence presented?: **Yes**\n* Are the methods sufficiently described to allow the study to be repeated?: **Yes**\n* Is the use of statistics and treatment of uncertainties appropriate?: **Yes**\n* Is the presentation of the work clear?: **Yes**\n* Are the images in this manuscript (including electrophoretic gels and blots) free from apparent manipulation?: **Yes**\n"},{"type":"reviewerAgreed","content":"","date":"2020-11-23T00:00:00+00:00","index":3,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-11-19T00:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-11-03T00:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-10-27T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-10-21T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-10-20T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-10-20T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-10-04T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8200012-3726-4693-be11-c502aafe921c","owner":[],"postedDate":"October 27th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":1089606,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2021-07-27T21:13:18+00:00","versionOfRecord":{"articleIdentity":"rs-96236","link":"https://doi.org/10.1186/s12889-021-11256-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2021-07-06 21:13:18","publishedOnDateReadable":"July 6th, 2021"},"versionCreatedAt":"2020-10-27 21:42:01","video":"","vorDoi":"10.1186/s12889-021-11256-z","vorDoiUrl":"https://doi.org/10.1186/s12889-021-11256-z","workflowStages":[]},"version":"v1","identity":"rs-96236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-96236","identity":"rs-96236","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}