Modelling of the impact of universal added sugar reduction through food reformulation

Abstract

Food reformulation has been suggested to be one of the strategies to reduce population added sugar (AS) intake. This study aims to investigate the untested assumption that a reduction in AS through reformulation will result in a reduction in population intakes of AS and energy. Plausible dietary data from 4,140 respondents of an Australian national nutrition survey were used. Dietary modelling was performed at AS reductions of 10%, 15%, and 25% using four strategies: simple removal of AS or replacement with non-nutritive sweeteners (NNS), and replacement of AS with NNS and either: polyols, 50% fibres or 50% maltodextrin. Paired t-tests were conducted to compare the intake of energy, fat, and AS pre- and post-reformulation. The chosen reformulation strategies resulted in a projected reduction in AS and energy, with the greatest reduction found in 25% reformulation which was the highest level modelled. The overall projected mean (SD) reduction in energy and AS after 25% reformulation was 114 (92) kJ/day and 11.73 (7.52) g/day, p < 0.001. To conclude, product reformulation may be a potentially useful strategy for reducing AS intake. Although the magnitude of projected reduction was small at the individual level, the impact may be meaningful at a population level.

The impact of food reformulation on nutrient intakes and health, a systematic review of modelling studies

“…Overall, studies were heterogeneous in the way interventions and outcomes were modelled and reported, limiting between-study comparisons. As regards the type of nutrient reformulated, 20 studies focused exclusively on sodium reduction (60.6%) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], followed by 5 studies considering at least 2 different nutrients (15.1%) [8,[42][43][44][45], 5 studies addressing sugar (15.1%) [46][47][48][49][50] and 3 study addressing fat (9%) [11,51,52].…”

“…Four studies provided estimates for more than one item [8,[28][29][30], whereas in 4 studies target foods were not specified since reformulation was modelled directly through its assumed effect on intake [22,25,32,39]. Ten studies simply estimated changes in intake following reformulation (30.3%) [8,11,26,33,42,43,45,49,50,53], whereas other studies estimated effects on health outcomes (n = 12, 36.4%) [25, 31, 35, 37, 39-41, 44, 46-48, 51] or health related quality of life measures (n = 11, 33.3%) [22-24, 28-30, 32, 34, 36, 38, 52].…”

“…Ten studies used cohort Markov models [22, 23, 28-30, 32, 36-38], or individual level micro-simulations [31]. Finally, 13 records, accounting for almost 40% of the included studies, used mathematical-statistical models to estimate the potential change in intake by linking cross-sectional, individual-level consumption data to the nutrient-density of foods before and after reformulation [8,11,24,26,33,42,43,45,46,[48][49][50]53]. In the majority of this last group, time was not explicitly considered, since the analysis was limited to a re-assessment of past individual intakes after reformulating all or a specific set of products.…”

Consumo de alimentos ultraprocessados, qualidade nutricional da dieta e obesidade na população australiana

“…The extremely high average content of free sugars in ultra-processed food items suggest that reducing the content of sugars in packaged foods through reformulation, as currently in discussion in Australia [25], while potentially useful [26], may not solve the problem of excessive free sugars intake. Reformulation policies may also drive consumers towards consuming other ultra-processed foods [27], such as those that might not be included in the national reformulation program [25], or to artificially sweetened drinks [28].…”

“…The direction, magnitude and the statistical significance of the associations between the dietary share of ultra-processed foods and the risk of non-recommended intake levels of all studied nutrients did not change with adjustment for age, sex, educational attainment, socioeconomic status and geographical location. We also categorised individuals with diet energy density higher than 1.25 kcal/g (World Cancer Research Fund recommends lowering this value as a public health goal) (26) and the proportion of non-recommended intake levels increased from 82% to 98% from the lower to the upper quintile (PR: 1.2; p<0.001data not shown).…”