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Nutrient profiling systems (NPS) are used to classify foods according to their nutritional composition. However, investigating their prospective associations with health is key to their validation. The study investigated the associations of the original Food Standards Agency (FSA)-NPS and three variants (Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (NPSC), Health Star Rating NPS and the French High Council of Public Health NPS (HCSP-NPS)), with weight status. Individual dietary indices based on each NPS at the food level were computed to characterise the dietary quality of 71 403 French individuals from the NutriNet-Santé cohort. Associations of these indices with weight gain were assessed using mixed models and with overweight and obesity risks using Cox models. Participants with a higher dietary index (reflecting lower diet nutritional quality) were more likely to have a significant increase in BMI over time (β-coefficients positive) and an increased risk of overweight (hazard ratio (HR) T3 v. T1 = 1·27 (95 % CI 1·17, 1·37)) for the HCSP-Dietary Index, followed by the original FSA-Dietary Index (HR T3 v. T1 = 1·18 (95 % CI 1·09, 1·28)), the NPSC-Dietary Index (HR T3 v. T1 = 1·14 (95 % CI 1·06, 1·24)) and the Health Star Rating-Dietary Index (HR T3 v. T1 = 1·12 (95 % CI 1·04, 1·21)). Whilst differences were small, the HCSP-Dietary Index appeared to show significantly greater association with overweight risk. Overall, these results show the validity of NPS derived from the FSA-NPS, supporting their use in public policies for chronic disease prevention.
Health-related claims (HRCs) are statements found on food packets that convey the nutritional quality of a food (nutrition claims) and/or its impact on a health outcome (health claims). Foods carrying HRCs have a slightly improved nutritional profile than foods without HRCs, however, it's unclear whether this translates into dietary improvements. We conducted a modelling study to measure the effect of HRCs on diet. As HRCs are already present on foods it is assumed that any impact that they have upon diet are already in effect. We modelled the impact on food purchases of removing HRCs, by assuming that the sales boost they receive is neutralised. These results can be inverted to estimate the current dietary impact of HRCs. Using the Living Costs Food (LCF) survey data, we calculate the average purchases and nutrient intake per person, per day. The LCF data is divided into sales of products with HRCs and sales of products without HRCs through solving mathematical equations combining LCF sales data with odds ratios from a meta-analysis examining the impact of HRCs on choices and data from a survey of foods examining the prevalence of HRCs and the nutritional quality of foods that carry them so that the sum of the sales of products with HRCs and without HRCs is equal to the total sales of products. Similarly, mathematical equations are solved that combine nutritional composition data with the sales of foods carrying and not carrying HRCs. In the baseline scenario foods carrying HRCs made-up 37% of the total purchases, and contributed 29% (559kcal) of the total kcals purchased (1907kcal). When HRCs are removed from foods there is an average increase of 18kcal/d (95% Uncertainty Intervals [UI] -15, 52), + 2g/d increase in total fat (95% UI -1, 4) and saturated fat (95% UI 1, 3), smaller changes are seen for protein (+ 0.5g/d, 95% UI -1, 2), total sugar (+ 0.5g/d, 95% UI -4, 7) and carbohydrate (-0.5g/d, 95% UI -5, 7). There is reduction in the amount of fruit (-11g/d, 95% UI -34, 26) but an increase in vegetables (+ 6g/d, 95% UI -6, 19). These results should be interpreted with caution due to the large uncertainty intervals. When HRCs are removed, we see a small deterioration in the quality of the average diet. If we invert these findings we can assume HRCs currently have a positive, albeit small, impact on diet.
There are concerns that price promotions encourage unhealthy dietary choices. This review aims to answer the following research questions (RQ1) what is the prevalence of price promotions on foods in high-income settings, and (RQ2) are price promotions more likely to be found on unhealthy foods?
Systematic review of articles published in English, in peer-review journals, after 1 January 2000.
Included studies measured the prevalence of price promotions (i.e. percentage of foods carrying a price promotion out of the total number of foods available to purchase) in retail settings, in upper-mid to high-income countries.
‘Price promotion’ was defined as a consumer-facing temporary price reduction or discount available to all customers. The control group/comparator was the equivalent products without promotions. The primary outcome for this review was the prevalence of price promotions, and the secondary outcome was the difference between the proportions of price promotions on healthy and unhealthy foods.
Nine studies (239 344 observations) were included for the meta-analysis for RQ1, the prevalence of price promotions ranged from 6 % (95 % CI 2 %, 15 %) for energy-dense nutrient-poor foods to 15 % (95 % CI 9 %, 25 %) for cereals, grains, breads and other starchy carbohydrates. However, the I-squared statistic was 99 % suggesting a very high level of heterogeneity. Four studies were included for the analysis of RQ2, of which two supported the hypothesis that price promotions were more likely to be found on unhealthy foods.
The prevalence of price promotions is very context specific, and any proposed regulations should be supported by studies conducted within the proposed setting(s).
The current paper describes methods of evaluating dietary habits of Sri Lankan adolescents based on the Diet Quality Index–International (DQI-I), which has been used in multiple international studies to describe dietary variety, moderation, adequacy and balance. The paper describes the method for calculating DQI-I scores and examines associations between DQI-I scores and dietary intake, and between DQI-I scores and sociodemographic factors.
The study followed a three-stage cluster randomised sampling method. Dietary intake was collected using a validated FFQ. Estimated micronutrient intakes and number of servings consumed were described according to DQI-I quartiles. DQI-I scores were tabulated according to sociodemographic characteristics. Multilevel modelling was used to examine associations between sociodemographic characteristics and DQI-I scores.
Secondary schools in rural Sri Lanka.
Adolescents (n 1300) aged 12–18 years attending secondary school in rural Sri Lanka.
DQI-I scores increased with consumption of fat (% energy), cholesterol (mg/d), energy (kJ/d), protein (% energy), Na (mg), dietary fibre (g), Fe (mg) and Ca (mg), but decreased according to percentage of energy coming from carbohydrates. DQI-I scores were significantly lower among females and students with lower levels of maternal education.
Policies are needed to increase the availability and affordability of nutrient-rich foods such as fruits, vegetables and high-protein foods, particularly to students from lower socio-economic backgrounds. Significant differences in diet quality according to sex, socio-economic status and district suggest there is potential for targeted interventions that aim to increase access to affordable, nutrient-rich foods among these groups.
The global burden of obesity leads to significant morbidity and has major economic implications. In April 2018, Britain will join a growing number of countries attempting to tackle this using fiscal measures when the UK Soft Drinks Industry Levy is introduced. We review recent evidence from natural experiments of the impact of health-related food and drink taxes on consumer behaviour, and discuss the possible consequences of these approaches on purchases and health. We highlight some of the potential indirect consequences and the importance of robust prospective evaluation.
To prioritise policy actions for government to improve the food environment and contribute to reduced obesity and related diseases.
Cross-sectional study applying the Food Environment Policy Index (Food EPI) in two stages. First, the evidence on all relevant policies was compiled, through an Internet search of government documents, and reviewed for accuracy and completeness by government officials. Second, independent experts were brought together to identify critical gaps and prioritise actions to fill those gaps, through a two-stage rating process.
A total of seventy-three independent experts from forty-one organisations were involved in the exercise.
The top priority policy actions for government identified were: (i) control the advertising of unhealthy foods to children; (ii) implement the levy on sugary drinks; (iii) reduce the sugar, fat and salt content in processed foods (leading to an energy reduction); (iv) monitor school and nursery food standards; (v) prioritise health and the environment in the 25-year Food and Farming Plan; (vi) adopt a national food action plan; (vii) monitor the food environment; (viii) apply buying standards to all public institutions; (ix) strengthen planning laws to discourage less healthy food offers; and (x) evaluate food-related programmes and policies.
Applying the Food EPI resulted in agreement on the ten priority actions required to improve the food environment. The Food EPI has proved to be a useful tool in developing consensus for action to address the obesity epidemic among a broad group of experts in a complex legislative environment.
In this paper, I first provide definitions of nutrient profiling and of a nutrient profile model. I set out the purposes of nutrient profiling: both general and specific. I give two examples of nutrient profile models that have been developed for regulatory purposes by the Food Standards Agency (FSA) in the UK and the WHO for its European Region – the UK FSA/Ofcom and the WHO-Euro models – and compare the way the models are constructed and function, how they have been developed, the extent to which they have been tested and validated and their use in regulation. Finally I draw some conclusions about the future use of nutrient profiling for regulatory purposes. I argue that its full potential has yet to be realised and give some reasons why. I pose some urgent research questions with respect to nutrient profiling.
Many children’s food products highlight positive attributes on their front-of-package labels in the form of nutrient claims. This cross-sectional study investigated all retailed packaged foods (n 5620) in a major Brazilian supermarket, in order to identify the availability of products targeted at children, and to compare the nutritional content of products with and without nutrient claims on labels. Data on energy, carbohydrate, protein, fibre, Na and total and SFA content, along with the presence and type of nutrient claims, were obtained in-store from labels of all products. Products targeted at children were identified, divided into eight food groups and compared for their nutritional content per 100 g/ml and the presence of nutrient claims using the Mann–Whitney U test (P<0·05). Of the 535 food products targeted at children (9·5 % of all products), 270 (50·5 %) displayed nutrient claims on their labels. Children’s products with nutrient claims had either a similar or worse nutritional content than their counterparts without nutrient claims. The major differences among groups were found in Group 8 (e.g. sauces and ready meals), in which children’s products bearing nutrient claims had higher energy, carbohydrate, Na and total and SFA content per 100 g/ml than products without nutrient claims (P<0·05). This suggests that, to prevent misleading parents who are seeking healthier products for their children, the regulation on the use of nutrient claims should be revised, so that only products with appropriate nutrient profiles are allowed to display them.
The present study aimed to measure the prevalence of different types of health and nutrition claims on foods and non-alcoholic beverages in a UK sample and to assess the nutritional quality of such products carrying health or nutrition claims.
A survey of health and nutrition claims on food packaging using a newly defined taxonomy of claims and internationally agreed definitions of claim types.
A national UK food retailer: Tesco.
Three hundred and eighty-two products randomly sampled from those available through the retailer’s website.
Of the products, 32 % (95 % CI 28, 37 %) carried either a health or nutrition claim; 15 % (95 % CI 11, 18 %) of products carried at least one health claim and 29 % (95 % CI 25, 34 %) carried at least one nutrition claim. When adjusted for product category, products carrying health claims tended to be lower in total fat and saturated fat than those that did not, but there was no significant difference in sugar or sodium levels. Products carrying health claims had slightly higher fibre levels than products without. Results were similar for comparisons between products that carry nutrition claims and those that do not.
Health and nutrition claims appear frequently on food and beverage products in the UK. The nutrient profile of products carrying claims is marginally healthier than for similar products without claims, suggesting that claims may have some but limited informational value. The implication of these findings for guiding policy is unclear; future research should investigate the ‘clinical relevance’ of these differences in nutritional quality.
Higher variety of recommended foods, identified arbitrarily based on dietary guidelines, has been associated with better health status. Nutrient profiling is designed to identify objectively, based on nutrient content, healthier foods whose consumption should be encouraged. The objective was to assess the prospective associations between total food variety (food variety score, FVS) and variety from selected recommended and non-recommended foods (RFV and NRFV, respectively) and risk of chronic disease and mortality. In 1991–3, 7251 participants of the Whitehall II study completed a 127-item FFQ. The FVS was defined as the number of foods consumed more than once a week. (N)RFV(Ofcom) and (N)RFV(SAIN,LIM) were similarly derived selecting healthier (or less healthier) foods as defined by the UK Ofcom and French SAIN,LIM nutrient profile models, respectively. Multi-adjusted Cox regressions were fitted with incident CHD, diabetes, CVD, cancer and all-cause mortality (318, 754, 137, 251 and 524 events, respectively – median follow-up time 17 years). RFV and NRFV scores were mutually adjusted. The FVS (fourth v. first quartile) was associated with a 39 and 26 % reduction of prospective CHD and all-cause mortality risk, respectively. The RFV(Ofcom) (third v. first quartile) was associated with a 27 and 35 % reduction of all-cause mortality and cancer mortality risk, respectively; similar associations were suggested, but not significant for the RFV(SAIN,LIM). No prospective associations were observed with NRFV scores. The results strengthen the rationale to promote total food variety and variety from healthy foods. Nutrient profiling can help in identifying those foods whose consumption should be encouraged.
Obesity levels are rising in almost all parts of the world, including the UK. School food offers children in Great Britain between 25 % and 33 % of their total daily energy, with vending typically offering products high in fat, salt or sugar. Government legislation of 2007 to improve the quality of school food now restricts what English schools can vend. In assessing the effect of this legislation on the quality of English secondary-school vending provision, the response of schools to these effects is explored through qualitative data.
A longitudinal postal and visit-based inventory survey of schools collected vending data during the academic year 2006–2007 (pre-legislation), 2007–2008 and 2008–2009 (both post-legislation). Interviews with school staff explored issues of compliance. Product categorisation and analysis were carried out by product type, nutrient profiling and by categories of foods allowed or prohibited by the legislation.
English secondary schools.
A representative sample of 279 schools including sixty-two researcher-visited inventory schools participated in the research.
School vending seems to have moved towards compliance with the new standards – now drinks vending predominates and is largely compliant, whereas food vending is significantly reduced and is mostly non-compliant. Sixth form vending takes a disproportionate share of non-compliance. Vending has declined overall, as some schools now perceive food vending as uneconomic. Schools adopting a ‘whole-school’ approach appeared the most successful in implementing the new standards.
Government legislation has achieved significant change towards improving the quality of English school vending, with the unintended consequence of reducing provision.
There is debate over the casual factors for the rise in body weight in the UK. The present study investigates whether increases between 1986 and 2000 for men and women were a result of increases in mean total energy intake, decreases in mean physical activity levels or both. Estimates of mean total energy intake in 1986 and 2000 were derived from food availability data adjusted for wastage. Estimates of mean body weight for adults aged 19–64 years were derived from nationally representative dietary surveys conducted in 1986–7 and 2000–1. Predicted body weight in 1986 and 2000 was calculated using an equation relating body weight to total energy intake and sex. Differences in predicted mean body weight and actual mean body weight between the two time points were compared. Monte Carlo simulation methods were used to assess the stability of the estimates. The predicted increase in mean body weight due to changes in total energy intake between 1986 and 2000 was 4·7 (95 % credible interval 4·2, 5·3) kg for men and 6·4 (95 % credible interval 5·9, 7·1) kg for women. Actual mean body weight increased by 7·7 kg for men and 5·4 kg for women between the two time points. We conclude that increases in mean total energy intake are sufficient to explain the increase in mean body weight for women between 1986 and 2000, but for men, the increase in mean body weight is likely to be due to a combination of increased total energy intake and reduced physical activity levels.
1.A new Nutrition Committee for the European Union
1.1 A new Nutrition Committee for the European Union, should be created to give independent scientific and policy advice on nutrition, diets and physical activity to the Commission. This should be supported by a strengthened Nutritional Unit within the Commission.
2.1 There needs to be a comprehensive and coherent nutritional policy for the EU
2.2 The development of European dietary goals should continue after the completion of the Eurodiet Project.
2.3 The European Commission should revise its Recommended Daily Allowances for vitamins and minerals using a systematic, evidence-based approach. Recommended Daily Allowances should be set at a level which would prevent deficiencies and lower the risk of disease.
2.4 The European Commission should produce, preferably every four years, a report on the state of nutrition, diet and physical activity in the EU. This report should contain proposals for action
3.Components of a nutrition policy
3.1 The European Commission should not be involved in the direct delivery of lifestyle advice to the public.
3.2 The European Commission should continue to support networks whose members are involved in educating the public and in training professionals about nutrition, diets and physical activity.Research
3.3 European Community funding of health-related research should better reflect the Community's public health priorities.
3.4 The European Community should ear-mark funds for large, multi-centre studies into nutrition, diet and physical activity with a duration of up to 10 years.
3.5 The European Commission should draw up proposals for the regulation of health claims.
3.6 The European Community should agree rules for the use of nutrition claims along the lines agreed by the Codex Alimentarius Commission.
3.7 The European Commission should review the 1990 Nutrition Labelling Directive particularly with a view to making nutrition labelling more comprehensible and it should encourage the development of other ways of providing consumers with information about the nutrient content of foods though, for example, the Internet.
3.8 The European Commission should review the Novel Food Regulations, particularly with a view to ensuring that the nutritional consequences of consuming novel foods are better assessed and to making approval procedures more efficient.
3.9 European Community rules on food fortification and on food supplements should be harmonised but in such a way that the interests of consumers are paramount.
3.10 The Common Agriculture Policy should be subject to a regular and systematic health impact assessment.
3.11 Given that there are subsidies under the Common Agricultural Policy designed to increase consumption of surplus food, these should be directed towards promoting the consumption of foods for which there is strong evidence of a need for increased consumption in the EU for health reasons.
Fruit and vegetable consumption
3.12 The promotion of increased fruit and vegetable consumption across the EU should be a key aspect of the European Union's proposed nutrition policy.
3.13 The European Union should review its policy on breast feeding including assessing and, if necessary, improving its legislation on breast milk substitutes and maternity leave.
3.14 The European Union should have a policy for promoting physical activity in Europe. This should be part of, or at least closely integrated with, the European Union's proposed nutritional policy.
To describe four different methods of identifying indicator foods that are high, medium or low in fat with reference to dietary patterns and to use these indicator foods to test three sets of definitions of ‘high’, ‘medium’ and ‘low’ in fat from ‘banding schemes’ developed by the Coronary Prevention Group (CPG), the Food Standards Agency (FSA) and Sainsbury’s.
Indicator foods were developed using food intake data from the UK National Diet and Nutrition Survey and two parameters: (i) probability of the food being consumed by an individual with a high-fat diet (Method 1); and (ii) the contribution of the food to the fat intake of the average diet of consumers (Methods 3 and 4). Method 2 used both parameters. The three banding schemes were tested by assessing their levels of agreement with methods in categorising indicators.
Sensitivity in identifying high, medium and low fat indicators was highest with the CPG banding scheme (high and medium fat indicators) and Sainsbury’s scheme (low fat indicators) (Methods 2, 3 and 4). The levels of agreement (kappa coefficient) were 0·68 for the CPG scheme; 0·51 for the Sainsbury’s scheme; and 0·41 for the FSA scheme (Method 3).
It is possible to use indicator foods related to dietary patterns of a specific population to generate more rational definitions of ‘high’, ‘medium’ and ‘low’ in fat. This could be the starting point for the development of indicator foods for testing more complex nutrient profile models (i.e. those that consider more than one nutrient).
To assess the validity of nutrient profiling Model WXYfm – developed for the purpose of regulating the promotion of ‘less healthy’ foods to children. The model ranks foods according to their healthiness and categorises foods into ‘healthier’ and ‘less healthy’ foods.
Convergent and discriminant validity was tested by comparing the way Model WXYfm categorises foods with the way the UK’s national food guide – the Balance of Good Health (BGH) – categorises foods. Construct validity was assessed by testing a hypothesis relating the constructs of ‘healthiness’ of foods (as measured by Model WXYfm) and the ‘healthiness’ of diets (measured using the Diet Quality Index) and assessing whether this hypothesis was confirmed or refuted by using data on the dietary patterns of subjects (n = 1117) of the National Diet and Nutrition Survey of adults carried out in Great Britain in 2000–01.
Model WXYfm showed good convergent and discriminant validity: the level of agreement between the way the model categorises foods and the way the BGH categorises foods was good (κ = 0.69). Model WXYfm also showed good construct validity: the energy intake from ‘less healthy’ foods amongst subjects with the least healthy diets was nearly twice the energy intake from ‘less healthy’ foods amongst the subjects with the healthiest diets.
Model WXYfm demonstrated good validity in categorising foods in a way that is related to the healthiness of diets both recommended and achieved. The methods for assessing the validity of a nutrient profile model used in this paper have not, to our knowledge, been used before.
This paper describes the development of an online questionnaire for testing nutrition professionals' perceptions of the ‘healthiness’ of individual foods and the results of administering that questionnaire. The questionnaire was designed to produce a standard ranking of foods that can be used as a tool for testing nutrient profile models.
The questionnaire asked respondents to categorise 40 foods (from a master list of 120) in one of six positions, ranging from less to more healthy. The 120 foods were selected to be representative of the British diet. The questionnaire was sent via email to nutrition professionals from the British Dietetic Association and the (British) Nutrition Society.
Eight hundred and fifty responses were received. These responses were used to rank the 120 foods by the average score which they received from the nutrition professionals. A regression analysis was also carried out to examine the relationship between the scores awarded by the nutrition professionals and various features of the foods: their nutritional content, their average serving size, their frequency of consumption, whether they were drinks or foods, etc. Nearly 50% of the variance in the average scores was explained by the nutritional content of the foods. When other variables were included in the analysis the percentage of variance that was explained increased to 64%.
The average scores of the foods produce a standard ranking, which can be used as a tool for validating and comparing nutrient profile models. The regression analysis provides some information about how nutrition professionals rank the ‘healthiness’ of individual foods.