In the Netherlands, reformulation strategies have been established for several years, whereas Nutri-Score was implemented in 2024. Besides being a helpful tool for consumers to make healthier food choices, Nutri-Score also aims to stimulate food reformulation by food manufacturers. The present study investigates whether changes in food composition could have led to different calculated Nutri-Score classifications.

Food compositions and Nutri-Score classifications were calculated using the updated Nutri-Score algorithm. Food groups with the largest change in the distribution of Nutri-Score classifications were analysed in-depth by plotting frequency distributions and calculating median contents for nutrient contents that relatively changed the most in 2020.

Food composition data were available from the Dutch Branded Food database in 2018 (n 38 295) and 2020 (n 48 091).

Not applicable.

In general, median nutrient contents and calculated Nutri-Score classifications were similar for 2018 and 2020. The median sugar and SFA contents were lower for some food groups (e.g. breakfast cereals, meat preserves, sweets and sweet goods) in 2020 compared to 2018. The median SFA content for meat preserves and sweets and sweet goods was relatively low in Nutri-Score classification A ascending towards higher median content in Nutri-Score classification E.

Although food reformulation was not substantial in the Dutch food retail supply in 2018 and 2020, some differences in Nutri-Score classifications were observed. When implemented, Nutri-Score may encourage food manufacturers to increase their reformulation efforts. Repeated monitoring of food compositions and Nutri-Score classifications is recommended to establish reformulation efforts by food manufacturers.

To compare the initial and the updated versions of the front-of-pack label Nutri-Score (related to the nutritional content) with the NOVA classification (related to the degree of food processing) at the food level.

Using the OpenFoodFacts database – 129,950 food products – we assessed the complementarity between the Nutri-Score (initial and updated) with the NOVA classification through a correspondence analysis. Contingency tables between the two classification systems were used.

The food offer in France.

Not applicable.

With both versions (i.e. initial and updated) of the Nutri-Score, the majority of ultra-processed products received medium to poor Nutri-Score ratings (between 77·9 % and 87·5 % of ultra-processed products depending on the version of the algorithm). Overall, the update of the Nutri-Score algorithm led to a reduction in the number of products rated A and B and an increase in the number of products rated D or E for all NOVA categories, with unprocessed foods being the least impacted (–3·8 percentage points (–5·2 %) rated A or B and +1·3 percentage points (+12·9 %) rated D or E) and ultra-processed foods the most impacted (–9·8 percentage points (–43·4 %) rated A or B and +7·8 percentage points (+14·1 %) rated D or E). Among ultra-processed foods rated favourably with the initial Nutri-Score, artificially sweetened beverages, sweetened plant-based drinks and bread products were the most penalised categories by the revision of Nutri-Score while low-sugar flavoured waters, fruit and legume preparations were the least affected.

These results indicate that the update of the Nutri-Score reinforces its coherence with the NOVA classification, even though both systems measure two distinct health dimensions at the food level.

To measure the effects of health-related food taxes on the environmental impact of consumer food purchases in a virtual supermarket.

This is a secondary analysis of data from a randomised controlled trial in which participants were randomly assigned to a control condition with regular food prices (n 152), an experimental condition with a sugar-sweetened beverage (SSB) tax (n 131) or an experimental condition with a nutrient profiling tax based on Nutri-Score (n 112). Participants were instructed to undertake their typical weekly grocery shopping for their households. Primary outcome measures were three environmental impact indicators: greenhouse gas (GHG) emissions, land use and blue water use per household per week. Data were analysed using linear regression analyses.

Three-dimensional virtual supermarket.

Dutch adults (≥ 18 years) who were responsible for grocery shopping in their household (n 395).

GHG emissions (–7·6 kg CO2-eq; 95 % CI –12·7, –2·5) and land use (–3·9 m2/year; 95 % CI –7·7, –0·2) were lower for the food purchases of participants in the nutrient profiling tax condition than for those in the control condition. Blue water use was not affected by the nutrient profiling tax. Moreover, the SSB tax had no significant effect on any of the environmental impact indicators.

A nutrient profiling tax based on Nutri-Score reduced the environmental impact of consumer food purchases. An SSB tax did not affect the environmental impact in this study.

To investigate the effects of a sugar-sweetened beverage (SSB) tax and a nutrient profiling tax on consumer food purchases in a virtual supermarket.

A randomised controlled trial was conducted with a control condition with regular food prices (n 152), an SSB tax condition (n 130) and a nutrient profiling tax condition based on Nutri-Score (n 112). Participants completed a weekly grocery shop for their household. Primary outcome measures were SSB purchases (ordinal variable) and the overall healthiness of the total shopping basket (proportion of total unit food items classified as healthy). The secondary outcome measure was the energy (kcal) content of the total shopping basket. Data were analysed using regression analyses.

Three-dimensional virtual supermarket.

Dutch adults aged ≥18 years are being responsible for grocery shopping in their household (n 394).

The SSB tax (OR = 1·62, (95 % CI 1·03, 2·54)) and the nutrient profiling tax (OR = 1·88, (95 %CI 1·17, 3·02)) increased the likelihood of being in a lower-level category of SSB purchases. The overall healthiness of the total shopping basket was higher (+2·7 percent point, (95 % CI 0·1, 5·3)), and the energy content was lower (−3301 kcal, (95 % CI −6425, −177)) for participants in the nutrient profiling tax condition than for those in the control condition. The SSB tax did not affect the overall healthiness and energy content of the total shopping basket (P > 0·05).

A nutrient profiling tax targeting a wide range of foods and beverages with a low nutritional quality seems to have larger beneficial effects on consumer food purchases than taxation of SSB alone.