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Regulating health and nutrition claims in the UK using a nutrient profile model: an explorative modelled health impact assessment.

ABSTRACT: BACKGROUND: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). The EU stated that HRCs should be regulated such that they can only appear on foods that meet a specified nutrient profile (NP). A NP model has been proposed, but not agreed by the European Commission. METHODS:To model the impact of HRCs on health impacts in the UK, we built a front-end model to a pre-established non-communicable-disease (NCD) scenario model, the Preventable Risk Integrated ModEl (PRIME) by combining data from a meta-analysis examining the impact of HRCs on dietary choices and a survey of pre-packaged foods examining the prevalence of HRCs and the nutritional quality of foods that carry them. These data are used to model the impact of regulating HRCs on the nutritional quality of the diet and PRIME is used to model the health outcomes associated with these changes. Two scenarios are modelled: regulating HRCs with a NP model (the FSANZ NPSC and a draft EU model) so that only foods that pass the model are eligible to carry HRCs, and reformulating HRC-carrying foods that fail the model. RESULTS:Regulating the use of HRCs with a NP model (the FSANZ NPSC) would have unclear impacts on population health and could potentially lead to less healthy diets. This is because HRCs are currently more likely to be found on products with a better nutritional profile and restricting their use could shift consumers to less healthy diets. Two hundred fifty-eight additional deaths (95% Uncertainty Intervals [UI] -6509, 8706) were predicted if foods did not change in their nutrient composition. If all foods that currently carry HRCs were reformulated to meet the NP model criteria then there would be a positive impact of using the model: (4374 deaths averted (95%UI -2569, 14,009)). The largest contributor to the uncertainty is the underpowered estimates of nutritional quality of foods with and without claims. CONCLUSIONS:Regulating HRCs could result in negative health impacts, however the wide uncertainty intervals from this analysis demonstrate that a larger health impact assessment is necessary.

SUBMITTER: Kaur A

PROVIDER: S-EPMC6367824 | biostudies-other | 2019 Feb

REPOSITORIES: biostudies-other

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Regulating health and nutrition claims in the UK using a nutrient profile model: an explorative modelled health impact assessment.

Kaur Asha A Scarborough Peter P Rayner Mike M

The international journal of behavioral nutrition and physical activity 20190207 1

<h4>Background</h4>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). The EU stated that HRCs should be regulated such that they can only appear on foods that meet a specified nutrient profile (NP). A NP model has been proposed, but not agreed by the European Commission.<h4>Methods</h4>To model the impact of HRCs on health impacts in the UK, we built a front-end ...[more]

PMID: 30732626

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Project description:ObjectivesTo review available health and nutrition claims for infant formula products in multiple countries and to evaluate the validity of the evidence used for substantiation of claims.DesignInternational cross sectional survey.SettingPublic facing and healthcare professional facing company owned or company managed formula industry websites providing information about products marketed for healthy infants delivered at full term in 15 countries: Australia, Canada, Germany, India, Italy, Japan, Nigeria, Norway, Pakistan, Russia, Saudi Arabia, South Africa, Spain, the United Kingdom, and the United States in 2020-22.Main outcome measuresNumber and type of claims made for each product and ingredient. References cited were reviewed and risk of bias was assessed for registered clinical trials using the Cochrane risk of bias tool, and for systematic reviews using the Risk Of Bias in Systematic reviews tool.Results757 infant formula products were identified, each with a median of two claims (range from 1 (Australia) to 4 (US)), and 31 types of claims across all products. Of 608 products with ≥1 claims, the most common claim types were "helps/supports development of brain and/or eyes and/or nervous system" (323 (53%) products, 13 ingredients), "strengthens/supports a healthy immune system" (239 (39%) products, 12 ingredients), and "helps/supports growth and development" (224 (37%) products, 20 ingredients). 41 groups of ingredients were associated with ≥1claims, but many claims were made without reference to a specific ingredient (307 (50%) products). The most common groups of ingredients cited in claims were long chain polyunsaturated fatty acids (278 (46%) products, 9 different claims); prebiotics, probiotics, or synbiotics (225 (37%) products, 19 claims); and hydrolysed protein (120 (20%) products, 9 claims). 161/608 (26%) products with ≥1 claims provided a scientific reference to support the claim-266 unique references were cited for 24 different claim types for 161 products. The reference types most frequently cited were clinical trials (50%, 134/266) and reviews (20%, 52/266). 28% (38/134) of referenced clinical trials were registered, 14% (19/134) prospectively. 58 claims referred to 32 registered clinical trials, of which 51 claims (27 trials) related to a randomised comparison. 46 of 51 claims (90%) referenced registered clinical trial outcomes at high risk of bias, and all cited systematic reviews and pooled analyses, carried a high risk of bias.ConclusionsMost infant formula products had at least one health and nutrition claim. Multiple ingredients were claimed to achieve similar health or nutrition effects, multiple claims were made for the same ingredient type, most products did not provide scientific references to support claims, and referenced claims were not supported by robust clinical trial evidence.

Project description:In Australia, manufacturers can use two government-endorsed approaches to advertise product healthiness: the Health Star Rating (HSR) front-of-pack nutrition labelling system, and health claims. Related, but different, algorithms determine the star rating of a product (the HSR algorithm) and eligibility to display claims (the Nutrient Profiling Scoring Criterion (NPSC) algorithm). The objective of this study was to examine the agreement between the HSR and NPSC algorithms. Food composition information for 41,297 packaged products was extracted from The George Institute's FoodSwitch database. HSR and the NPSC scores were calculated, and the proportion of products in each HSR category that were eligible to display a health claim under the NPSC was examined. The highest agreement between the HSR scoring algorithm and the NPSC threshold to determine eligibility to display a health claim was at the HSR cut-off of 3.5 stars (k = 0.83). Overall, 97.3% (n = 40,167) of products with star ratings of 3.5 or higher were also eligible to display a health claim, and 94.3% (n = 38,939) of products with star ratings less than 3.5 were ineligible to display a health claim. The food group with greatest divergence was "edible oils", with 45% products (n = 342) with HSR >3.5, but 64% (n = 495) eligible to display a claim. Categories with large absolute numbers of products with HSR <3.5, but eligible to display a claim, were "yoghurts and yoghurt drinks" (335 products, 25.4%) and "soft drinks" (299 products, 29.7%). Categories with a large number of products with HSR ≥3.5, but ineligible to display a claim, were "milk" (260 products, 21.2%) and "nuts and seeds" (173 products, 19.7%). We conclude that there is good agreement between the HSR and the NPSC systems overall, but divergence in some food groups is likely to result in confusion for consumers, particularly where foods with low HSRs are eligible to display a health claim. The alignment of the NPSC and HSR scoring algorithms should be improved.

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Project description:BACKGROUND:Nutrition-related apps are commonly used to provide information about the user's dietary intake, but limited research has been performed to assess how well their outputs agree with those from standard methods. OBJECTIVE:The objective of our study was to evaluate the level of agreement of popular nutrition-related apps for the assessment of energy and available macronutrients and micronutrients against a UK reference method. METHODS:We compared dietary analysis of 24-hour weighed food records (n=20) between 5 nutrition-related apps (Samsung Health, MyFitnessPal, FatSecret, Noom Coach, and Lose It!) and Dietplan6 (reference method), using app versions available in the United Kingdom. We compared estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat, and fiber), and micronutrients (sodium, calcium, iron, vitamin A, and vitamin C) using paired t tests and Wilcoxon signed-rank tests, correlation coefficients, and Bland-Altman plots. We obtained 24-hour weighed food records from 20 participants (15 female, 5 male participants; mean age 36.3 years; mean body mass index 22.9 kg/m2) from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, Reading, UK. Participants had recorded their food consumption over a 24-hour period using standard protocols. RESULTS:The difference in estimation of energy and saturated fat intake between Dietplan6 and the diet apps was not significant. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than using Dietplan6. Lose It! also gave significantly lower estimates for other reported outputs (carbohydrate, fat, fiber, and sodium) than did Dietplan6. Samsung Health and MyFitnessPal significantly underestimated calcium, iron, and vitamin C compared with Dietplan6, although there was no significant difference for vitamin A. We observed no other significant differences between Dietplan6 and the apps. Correlation coefficients ranged from r=-.12 for iron (Samsung Health vs Dietplan6) to r=.91 for protein (FatSecret vs Dietplan6). Noom Coach was limited to energy output, but it had a high correlation with Dietplan6 (r=.91). Samsung Health had the greatest variation of correlation, with energy at r=.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. CONCLUSIONS:The findings suggest that the apps provide estimates of energy and saturated fat intake comparable with estimates by Dietplan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat, and fiber. FatSecret and Lose It! tended to underestimate protein and sodium. Estimates of micronutrient intake (calcium, iron, vitamin A, and vitamin C) by 2 apps (Samsung Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the app least comparable with Dietplan6. As the use and availability of apps grows, this study helps clinicians and researchers to make better-informed decisions about using these apps in research and practice.

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Regulating health and nutrition claims in the UK using a nutrient profile model: an explorative modelled health impact assessment.

Publications

Regulating health and nutrition claims in the UK using a nutrient profile model: an explorative modelled health impact assessment.

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