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Interventions and public health nutrition

The effects of the Danish saturated fat tax on food and nutrient intake and modelled health outcomes: an econometric and comparative risk assessment evaluation

Subjects

Abstract

Background/Objective:

The World Health Organisation recommends governments to consider the use of fiscal policies to promote healthy eating. However, there is very limited evidence of the effect of food taxation in a real-life setting, as most evidence is based on simulation studies. The objective of this study is to evaluate the effect of the Danish tax on saturated fat in terms of changes in nutritional quality of the diet, that is, changes in saturated fat consumption, as well as other non-targeted dietary measures, and to model the associated changes in mortality for different age groups and genders.

Subjects/Methods:

On the basis of household scanner data, we estimate the impact of the tax on consumption of saturated fat, unsaturated fat, salt, fruit, vegetables and fibre. The resultant changes in dietary quality are then used as inputs into a comparative risk assessment model (PRIME (Preventable Risk Integrated ModEl)) to estimate the effect of these changes on non-communicable diseases (NCDs) and mortality.

Results:

The tax resulted in a 4.0% reduction in saturated fat intake. Vegetable consumption increased, and salt consumption increased for most individuals, except younger females. We find a modelled reduction in mortality with 123 lives saved annually, 76 of them below 75 years equal to 0.4% of all deaths from NCDs.

Conclusions:

Modelling the effect of the changes in diet on health outcomes suggests that the saturated fat tax made a positive, but minor, contribution to public health in Denmark

Introduction

Diet-related non-communicable diseases (NCDs) are the leading preventable causes of global morbidity and mortality,1, 2 and among the World Health Organisation (WHO) regions the European region has the highest burden of NCDs.3 Poor diet (i.e. excess consumption of saturated fat, free sugars and salt and low consumption of vegetables and fruits) and related overweight and obesity contribute to a large proportion of NCDs including cardiovascular diseases and cancer.4,5,6,7 This has led the WHO and European Union to recommend national governments to consider the use of fiscal policies as a way of promoting healthy eating.8,9,10 Several countries have recently introduced health-related food taxes, the best publicised being as follows: Mexico, a soft drink and junk food tax;11 Denmark, a tax on saturated fat;12 Hungary, a tax on foods high in fat and sugar; and France, a tax on soft drinks.13 Although experimental studies have demonstrated the effect of changes in food price on purchasing behaviour,14 the literature regarding the effectiveness of health-related food taxes is mostly based on modelling studies as health-related food taxes are not suited to study by randomised controlled trials. In modelling studies, the effect of altering food prices on purchases is calculated using own- and cross-price elasticities.15, 16, 17However, as modelling involves the aggregation of data over products and time, within-group substitution between certain products might not be adequately assessed within these studies, implying that the true effect of the tax will be underestimated. In other cases, the effect of the tax is likely to be overstated in modelling studies, if, for example, consumers substitute to cheaper brands of the same products to avoid changing consumption habits. Furthermore, modelling studies need to take pass-over rates for the tax into account (i.e. the amount of the tax that is passed on from producers to consumers), which may vary between products and store types.18, 19 The evaluation of taxes from natural experiments will take the above-mentioned effects into account but is rarely conducted as health-related food taxes are currently implemented at a low level, and the effect on purchases is difficult to separate from background noise. However, Bahl et al.20 did find a significant decrease in the consumption of soft drink because of changing levels of taxation during the period 1975–1996 in Ireland when the tax was set at 10%.

In October 2011, Denmark introduced a tax on saturated fat, abolished as of 1 January 2013. Before its introduction the tax was debated worldwide, as Denmark was the first country in the world to introduce a tax on saturated fat.12, 21 The introduction of the tax was part of a larger reform with the aim of reducing income tax.12 The tax on saturated fat amounted to 16 DKK/kg saturated fat (2.14€/kg) in the following foods meant for human consumption if the level of saturated fat exceeded 2.3 g/100 g: meat, dairy products, animal fats that were rendered or extracted in other ways, edible oils and fats, margarine and spreads. The resultant price changes were non-trivial, for example the price of a standard package of butter at 500 g where the content of saturated fat was 52 g/100 g increased by 0.33€ (20%). Previous work has shown that the tax resulted in a decrease in consumption of oil, butter and other fats by 10–15%. Furthermore, that work showed some substitution from supermarkets to discount stores and that discount stores passed on >100% of the tax to consumers, whereas supermarkets passed on <100%.18 To date, there have been no evaluations of the effect of the tax on dietary quality or health outcomes.21

This paper uses data from a representative panel of over 2500 Danish households during the period before and after the introduction of the Danish saturated fat tax to estimate the impact of the tax on consumption of saturated fat and other non-targeted dietary measures (e.g. salt intake, fruit and vegetable consumption). The resultant changes in dietary quality that were observed are then used as inputs into a comparative risk assessment model to estimate the effect of these changes on NCD mortality.

Methods and data

Data

The data we used to estimate changes in consumption cover the years 1 January 2009 to 31 December 2012 and are from GfK Panel Services Scandinavia (hereafter abbreviated GfK) that maintains a representative consumer panel of ~2500 distinct households. Approximately 20% of the households leave the panel each year to be replaced with a similar household, that is, the panel is unbalanced. For each shopping trip, the households’ diary keeper reports purchases of foods and other staples including the date of purchase, the store and total expenditure on the trip. The data have been concatenated with information on nutrient content using data from the Danish Food Composition Databank, maintained by the National Food Institute (http://www.foodcomp.dk/v7/fcdb_search.asp) containing information about the nutrient content (e.g. saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, sugar, sodium and calcium) per 100 g of 1049 different foods (as of January 2013). The concatenation has been performed at the most detailed level possible, and, as the purchase data are on the barcode level and cover all purchases for the households, the level of detail is set by the items covered in the Food Composition databank. This covers, for example, 11 different types of drinking milk, 7 different types of ryebread and 113 types of fish and fish products. For 123 processed foods that are not covered in the food composition databank, the nutrient composition is found using supplier websites.

Hence, the data used for the estimations contained information about the amount of nutrients purchased, as well as the prices paid for various products and total expenditure on each shopping trip. These were supplemented with sociodemographic information about the households. Data have been aggregated to a weekly level for each household. To ensure robust parameter estimates, the data set was restricted to households that report food purchases for at least 52 weeks during the data period, which reduced the length of the panel slightly. Both nutrient purchase and food expenditure are estimated per person by dividing the purchase data with the weighted number of members in the household. The weights are individual for each household and based on gender and age-dependent energy intake data from the official national dietary survey of Denmark. This survey has energy intake for females and males in the age groups 0–5, 6–9, 10–14, 15–17, 18–24, 25–34, 35–44, 45–54, 55, 64 and 65+ years. Hence, the weight for a woman in the age group 18–24 years living in a household with a male in the age group 25–34 years and a child in the age group 6–9 years will consist of her average energy intake divided by the total energy intake in the household and hence be different from a woman in the same age group but living together with a male in the age group 35–44 year and a child in the age group 0–5 year.

Estimation of the consumption effects of the tax

We developed a parametric model with nutrient intake changes taking into account that the reaction to the tax might differ for singles and couples of each sex, by estimating the model independently for females and males. The estimated changes in purchases due to the implementation of the tax are used to predict changes in consumption.

First, the data set was separated into singles and couples (i.e. households with either one or two adult members), and the single group has been divided into single men and single women. The part of the data set that consists of couples was used in both the male and the female estimations. Adolescents above 15 years appeared in the estimations as well, for example, if a household consists of a woman aged 51 years, a male aged 56 years and a male adolescent aged 19 years, this household will appear once in the estimation for females and twice in the estimation for males but with different amount of nutrients and/or energy consumed determined by the weights described earlier. The aggregated change in nutrient consumption for men and women separately was then weighted using the relative number of singles and couples within each gender and age group for the whole population using weights from Statistics Denmark. The tax effect was interacted with age and single status.

The model was estimated using SAS version 9.4 (SAS Institute Inc., NC, USA) as a fixed-effects model taking individual heterogeneity into account. A fixed-effects model controls for the individual’s initial level of consumption focusing on how a change in the explanatory variables influences the explained variable. Furthermore, we controlled for seasonality and holiday periods to smooth the trends in purchase behaviour and isolate the independent effect of the tax on saturated fat. The model appears as

where yit is purchase (in g/pers for fibre, fat (total, saturated, monounsaturated and polyunsaturated), salt, fruits and vegetables and in kJ/pers for energy); tax is a dummy, equal to 0 before the tax is introduced (1 October 2011) and 1 thereafter; pretax is a dummy for the 2 weeks preceding the introduction of the fat tax to account for hoarding; age is the age in years of the considered individual; tot_food exp pers is expenditure on food per person deflated with the consumer price index, Christmas is a dummy for the 2 weeks around Christmas, t is a weekly trend and Month is a monthly dummy accounting for seasonality in consumption. αi is a parameter for each individual that is fixed over time and that works like an individual intercept for each individual; and ɛit accounts for random noise. The estimated changes in fats purchased have been transformed into changed energy shares for total fat and saturated and unsaturated fat after the estimation.

Modelled estimates of the effect of tax on mortality from NCDs

PRIME model description

The PRIME (Preventable Risk Integrated ModEl) model is a scenario model that links behavioural risk factors with NCD mortality either directly or mediated by body mass index (BMI), blood cholesterol or blood pressure. The PRIME model has previously been used to explore the impact on health of population-level changes in diet in the UK,12, 23, 24, 25,26 Ireland,27 Canada28 and France.29 With a few exceptions, each of the links in the PRIME model is parameterised by published meta-analyses of epidemiological studies: prospective cohort studies for links that terminate with NCD mortality, and randomised controlled trials for links that terminate with either blood cholesterol or blood pressure. The model contains 12 behavioural risk factors covering the domains of diet, physical inactivity, alcohol consumption and tobacco consumption. There are 24 health outcomes included in the PRIME model, primarily cardiovascular diseases and cancers, and also kidney disease, liver disease and chronic obstructive pulmonary disease. For the analyses reported in this paper, the following dietary risk factors were included: fruit, vegetables, fibre, total fat, saturated fat, polyunsaturated fat, monounsaturated fat, dietary cholesterol (the health effects of fats are mediated via blood cholesterol) and salt (mediated via blood pressure). The health outcomes were cerebrovascular disease (ICD-10 codes: I60–69), ischaemic heart disease (I20–25), hypertensive disease (I10–15), heart failure (I50), aortic aneurysm (I71), pulmonary embolism (I26), rheumatic heart disease (I05–09), lung cancer (C34), colorectal cancer (C18–20), pancreas cancer (C25), kidney cancer (C64), diabetes (E11, E14) and kidney disease (N18). The primary analyses reported here assumed that total calories consumed would be the same after introduction of the tax, despite small changes in calorie purchasing estimated by the regression models. A full description of the PRIME model, including all of the parameters used in the model, is available in Scarborough et al.22

Data used for the PRIME model

To use the PRIME model, it had to be calibrated using Danish baseline data on current dietary consumption. Means and standard deviations of energy, salt, fruit, vegetables, total fat, saturated fat, mono- and polyunsaturated fat intakes (all averaged over the period 2003–2008) were taken from the official national dietary survey of Denmark.30 Height and weight estimates were based on self-reported data from the GfK panel. Age- and sex-specific mortality and population data were taken from Statistics Denmark for the year 2008. Ninety-five percent confidence intervals were constructed based on a Monte Carlo simulation with 100 000 draws based on standard deviations from the baseline consumption and the standard errors from the estimation model.

Results

Descriptive statistics

The sociodemographic variables for the panel participants are shown in Table 1 below. There are more households located in the capital and fewer located in Eastern regions compared with official numbers from Statistics Denmark. Furthermore, the panel members are less educated than the general population.

Table 1 Sociodemographic characteristics of the panel (average 2009–012)

Consumption changes

Table 2 shows the percentage change in consumption compared with the baseline consumption for men and women within each age group. These changes were calculated using the model parameters, that is, after the effect of the introduction of the saturated fat tax had been isolated. Goodness-of-fit values (R2) and estimated parameters for the saturated fat equation are shown in Supplementary Appendix A. The parameters for the other estimated equations are available from the authors upon request.

Table 2 Consumption changes (% change from baseline consumption)

The tax on saturated fat had a substantial effect on the consumption of saturated fat, with reductions in consumption across all age-sex groups, ranging from 4.9% for middle-aged women to 1.6% for older men (on average the decrease in consumption of saturated fat was 4%). Furthermore, a decrease in the consumption of total fat was seen for almost all age groups ranging from 4.9% for middle-aged females to a decrease between 1.6% for older males and 4.4% for younger males (on average fat consumption decreased by 4.0%). Vegetable consumption increased by 7.9% on average and fibre consumption increased by 3.7%. However, substitutions as a result of the tax also produced some undesired consequences. For example, salt consumption increased for all age groups, except younger females, and fruit consumption decreased for younger men and women and for older women.

Modelled results on NCD mortality

Table 3 shows the modelled changes in NCD mortality that is estimated as a result of the changes in diet produced by the saturated fat tax. The results suggest that the total changes to the diet as a result of the saturated fat tax would reduce mortality from ischaemic heart disease, primarily because of a more favourable fatty acid profile and increases in fibre and vegetable consumption, but lead to an increase in stroke deaths and heart failures because of increases in salt consumption (Table 4). In total, the tax is estimated to produce a small reduction in NCD mortality, almost exclusively in men and younger women. The lack of impact of the tax in older women is because of increases in salt consumption and decreases in fruit and fibre consumption, offsetting the positive effect of reductions in saturated fat.

Table 3 Yearly deaths averted or delayed (95% confidence interval)
Table 4 Deaths averted or delayed by behavioural risk factor and by cause (95% confidence interval)

We conducted sensitivity analyses to test the following two assumptions: (1) that changes in purchased calories would not result in changes in consumed calories and (2) that the panel data are representative of the Danish population. The first sensitivity analysis used the same nutritional quality data reported above but included changes in calories in the comparative risk assessment model. We found that, when including energy change in the comparative risk assessment modelling, the overall results were reversed resulting in a small increase in NCD mortalities except for females where the results are improved. Results from this estimation are shown in Supplementary Appendix B. This is due to an implausible increase in energy intake for men resulting in increased BMI, which outweighs the positive impact of other nutritional changes. The energy increases are mainly due to increased consumption of fruit and vegetables, which make it reasonable to assume that this increased energy intake will not increase body weight.31

The second sensitivity analysis reworked the econometric modelling, with no restriction on panel membership and then to those who provided at least 26 weeks of panel data. Varying the length of panel membership, we found that generally the central tendencies in consumption changes are similar across panels. Results of the sensitivity analyses for consumption are provided in Supplementary Appendix C.

Discussion

This is the first study to evaluate the health impact of the Danish saturated fat tax. We used observed data to estimate the impact of the tax on nutritional quality of the diet and then a comparative risk assessment model to estimate how those dietary changes would impact on NCD mortality in Denmark. This allows for an assessment of whether the competing effects on individual dietary variables (e.g. reducing saturated fat but increasing salt) have an overall positive or negative impact on population health. We find that the saturated fat tax had a small positive influence on dietary quality in Denmark. In particular, consumption of the targeted nutrient (saturated fat) reduced by 4% as a result of the tax. Because of substitution effects the tax had some unintended consequences in some age and sex groups, most notably an increase in salt consumption for men and older females and a decrease in fruit consumption in younger men and women and older women. However, substitution also resulted in positive dietary results such as a considerable increase in the consumption of vegetables. Overall, the tax had a positive health impact in men, resulting in a modelled decrease in deaths mainly from cardiovascular disease and a neutral health impact in women (positive in younger women). These age and gender differences in substitution effects are likely to originate from differences in current dietary habits and difference in price sensitivities and substitution patterns.

The saturated fat tax was abolished after a unanimous act of the Danish parliament of 10 November 2012, before the health impact of the tax had been evaluated. The primary reason presented for the abolition of the tax was the impact that it had had on cross-border trade with concerns that Danes were travelling to Germany and Sweden to buy butter and other high-saturated fat products at reduced prices as well as concerns about the effect that the tax-induced increase in consumer prices might have on competitiveness due to wage pressure.32

The strength of this study is that it is based on observed purchase data that take all substitution possibilities into account, as well as the real pass-over rates from producers to consumers. Furthermore, the data at hand cover all purchases and the full diet. One weakness of using a natural experiment is that we have no control group, and hence despite the fact that we controlled for trend and unobserved heterogeneity we cannot be totally sure that the observed changes in diet were solely due to the saturated fat tax. The heavy debate surrounding the implementation of the tax might also have influenced behaviour.32 Another possible weakness of the study is that we might have selection bias, that is, that the panel members might be unrepresentative of consumers in Denmark. Partly to analyse whether this is the case, we conducted a sensitivity analysis with varying restrictions on panel membership and found that the results were robust. Another weakness is that we use household purchase data NOT consumption data; hence, we do not know whether the observed changes in purchases will result in changes in consumption, and we do assume that individuals living together in a household consume the same diet composition, even though the amounts differ because of the weights. This also implies that we cannot calculate whether the amount of waste and therefore total energy intake has changed before and after the tax. The sensitivity analysis shows that our results are very sensitive to whether energy change is included in the modelling or not. However, we chose in our primary analysis to assume that energy consumption is unchanged after the introduction of the tax because we assumed that the population would react to small changes in energy purchases by small adjustments to food waste to retain an energy balance. This is supported by two sources of evidence. First, it has been shown in randomised controlled trials of fruit and vegetable supplementation that adding fruit and vegetables to the diet, without removing other foods, does not lead to increases in either energy intake33 or body weight31, 34, 32, 33, 34, 35, 36 and may actually reduce both. As a major substitution effect observed in the panel data is an increase in vegetable consumption, this suggests our assumption that energy intake did not change and may actually be conservative. Second, food waste patterns differ by food category, with large amounts of food waste in fresh vegetables,37 which means that our observed increases in energy purchases may not translate into increases in energy consumption.

Our comparative risk assessment modelling assumes that reductions in saturated fat intake will improve coronary heart disease outcomes. Recent meta-analyses of observational studies have suggested that there is no association between saturated fat intake and coronary heart disease;38,39 however, the conclusions from these meta-analyses have been questioned.40, 41, 42 Associations between saturated fat intake and coronary heart disease may be obscured in observational studies by inaccuracies in measuring dietary intake. Other meta-analyses of cohort studies43 and randomised trials44 have shown that what saturated fat is replaced with in the diet is also important, with the most cardiovascular protection being provided when saturated fat is replaced by polyunsaturated fatty acids. The comparative risk assessment model used in this paper uses changes in the total fatty acid profile to estimate changes in blood cholesterol levels, using data from meta-analyses of experimental data,45 and then translates changes in cholesterol levels into changes in coronary heart disease events using data from a meta-analysis of prospective cohort studies.46

Conclusion

This is the first assessment of the health impact of the Danish saturated fat tax that uses panel data to measure how the tax affected dietary quality. The analysis suggests that the tax successfully reduced saturated fat intake, as well as the intake of other fats, for all combinations of gender and age. The tax also resulted in other dietary improvements including increases in vegetable, fruit and fibre consumption in most groups. However, the analysis also found adverse dietary impacts for some groups in terms of, for example, increased salt consumption in older individuals. Modelling the effect of the changes in diet on health outcomes suggests that the saturated fat tax made a positive contribution to public health in Denmark, with an estimated 123 averted deaths per year, of which more than 80% could be attributed to averted deaths from cardiovascular disease. The analysis, however, also demonstrates that it is important to consider unexpected adverse health effects due to substitution. Health impact assessments of this kind are important for monitoring the success of public health policies and preferably should be conducted before such policies were discontinued.

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Correspondence to S Smed.

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None of the authors have financial relationships with any organisation that might have had an interest in the submitted work in the previous 3 years and no other relationships or activities that could appear to have influenced the submitted work. The authors declare no conflict of interest.

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SS and JDJ were responsible for the econometric modelling. MR and PS were responsible for the NCD modelling. All authors contributed equally to the design and writing of the paper.

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Smed, S., Scarborough, P., Rayner, M. et al. The effects of the Danish saturated fat tax on food and nutrient intake and modelled health outcomes: an econometric and comparative risk assessment evaluation. Eur J Clin Nutr 70, 681–686 (2016). https://doi.org/10.1038/ejcn.2016.6

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