Nutri-Score 2023 update

In 2023, the algorithm underlying the Nutri-Score front-of-pack label was updated to better align with food-based dietary guidelines (FBDGs) across countries engaged in the system. On the basis of a comparison of FBDGs and literature reviews with the current Nutri-Score classification, modification scenarios were developed and tested in nutritional composition databases of branded products in four countries. The updated Nutri-Score nutrient profile model allows a better discrimination between products, in closer alignment with FBDGs, while the updated algorithm adopts a stricter approach for products that are high in components of concern (including non-nutritive sweeteners) and low in favourable dietary components. The updated Nutri-Score algorithm increases the alignment between the front-of-pack label system and FBDGs, strengthening its potential as a complementary public health tool in an international perspective.

School entrance examinations as a small-scale data source for health monitoring of children using obesity as an example

Background

In the scope of the nationwide obligatory school entrance examinations (SEE), a standardised assessment of the preparedness for school of preschool children takes place in the federal states of Germany. For this purpose, height and weight of the children are determined. These data are available in aggregated form at county level, but are not yet being regularly compiled and processed at national level for use in policy and research.

Methods

In a pilot project, the indexing and merging of SEE data from 2015-2019 was tested in collaboration with six federal states. This was done using obesity prevalence at the time of the school entrance examination. In addition, prevalences were linked to small-scale indicators on settlement structure and sociodemographics from public databases, differences in obesity prevalence at county level were identified, and correlations to regional influencing factors were visualised.

Results

It was feasible to merge SEE data from the federal states with little effort. The majority of the selected indicators were freely available in public databases. In an interactive, easily comprehensible and user-friendly Tableau dashboard for visualising the SEE data, it can be seen that obesity prevalences differ significantly between counties that are similar in terms of settlement structure or sociodemographics.

Conclusions

Providing federal state SEE data and linking them to small-scale indicators enables region-based analyses and cross-state comparisons of similar counties and provides a data basis for continuous monitoring of the prevalence of obesity in early childhood.