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Probabilistic Model to Assess the Impact of Sugar Taxation on Low/No-Calorie Sweeteners Displacing Added Sugar Intakes (P13-029-19).

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Objectives In the last few years, the sugar tax, predominantly targeting sugar sweetened beverages, has been introduced in a range of different countries globally. This has led to reformulation of… Click to show full abstract

Objectives In the last few years, the sugar tax, predominantly targeting sugar sweetened beverages, has been introduced in a range of different countries globally. This has led to reformulation of product portfolios around the time of the tax introduction, in order to meet the public health bodies' demands on reducing sugar in specific products but likely also to come in below the levy's sugar threshold. As a result, label and own brand products have been reformulated where low/no-calorie sweeteners (LNCS) are displacing added sugars. The change in product composition from sugar sweetened to products now containing levels of both added sugars and LNCS has had an impact on reducing added sugar intakes and possibly increased exposure to LNCS in populations, a recent shift that has not been captured yet in national health and food consumption surveys and it will be likely a few more years before this will be quantified in the next round of surveys. Methods In order to assess the impact of this change, food consumption survey data may be used in conjunction with more up to date brand composition data and information on LNCS presence and use levels. Results Examples of up-to-date brand specific product composition databases and initiatives include Mintel, GS1 Branded Foods Database, Country specific branded food databases, ILSI Branded Foods Database, Food Switch, Label Insight's Open Data initiative or the collecting industry data. Market representative LNCS occurrence can then be assessed per food product category in order to calculate the probability of presence of different LNCS in each food product categories. Similarly more up-to-date added sugar concentration data can be obtained and substituted for out-of-date data. LNCS use levels from the food categories of interest, or regulatory use levels from the locally relevant additive regulation, can then be applied to the model. Conclusions A probabilistic intake assessment, accounting for presence probability of LNCS and new distributions of added sugar vs LNCS concentrations as well as distribution of food consumption can then help estimate the current intake of added sugars and LNCS, especially post the recent introduction of the sugar tax across different countries. Funding Sources Creme Global.

Keywords: sugar; impact; food; added sugar; lncs; product

Journal Title: Current developments in nutrition
Year Published: 2019

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