We analysed multiple commonly used healthiness measures as part of our assessment.

Box 2: how are we defining healthiness?
Box 2: which metrics for food healthiness did we consider?
  1. Calorie density, also known as energy density, is a measure of the calories per 100g of a product. High calorie dense foods (those with ≥400 kcal/100g) are generally considered unhealthy, for example, confectionery, although there are exceptions such as nuts, which are healthy calorie dense foods.
  2. Converted nutrient profiling model (NPM) score is a holistic measure of health that assigns an integer score to food products based on their nutritional content (energy; sugar; saturated fat; sodium; protein; fruit, vegetables and nuts; and fibre). The NPM was originally developed to determine the suitability of products for advertising to children. In our analysis we have defined products as ‘unhealthy’ if they have a converted NPM score ≤62. [7]
  3. HFSS is a binary measure of a product’s healthiness. Products with a converted NPM ≤62 and in scope of HFSS regulations are subject to this classification in our analysis. Since HFSS scores are based on NPM scores, industry will need NPM and calorie density data to categorise products in this way.

Based on our analysis, we recommend the introduction of an average NPM score-based target.

This measure has the optimal balance between impact and feasibility of implementation, as it is a more holistic measure of the health of food and is already established in legislation (see technical appendix for a full appraisal of health metrics). Retailers are already required to calculate NPM scores for many of their products to comply with existing HFSS legislation. This target would be applied across a retailer’s entire food product portfolio (for branded and own brand) and sales weighted (see Box 1) to ensure that products that have a higher volume (in kg) of sales contribute more to average scores than those that are purchased less frequently and in smaller volumes. 

While we are recommending an average NPM score, we believe that a calorie density measure would also work. A calorie density-based target provides a direct route to tackling obesity by incentivising a reduction in calories sold and modelling shows it can achieve the same impact as the NPM-based target. However, it is an unfamiliar metric to industry. It only considers improvements in a single element of food composition, unlike the NPM score, which captures a more holistic view of ‘healthiness’ and is the basis of existing legislation. It should be noted that if the targets were to be extended to the out-of-home sector, calorie density may be a more viable metric as large businesses are already required to calculate calorie information for their meals to comply with calorie labelling legislation

We ruled out implementing an HFSS-based target. A target aiming to reduce the proportion of a retailer’s HFSS sales by applying a binary classification to products as either HFSS or non-HFSS limits its effectiveness, as it only incentivises improvements in products near the HFSS classification boundary and not in the most unhealthy products. Our modelling demonstrates that retailers would have to enact more extreme sales or reformulation shifts to meet such a target, potentially making the policy either too difficult to implement or not sufficiently impactful to justify its adoption. See the technical appendix for a detailed appraisal of the healthiness metrics.

[7] We transformed raw NPM scores for the converted NPM target with a commonly used formula developed by the University of Oxford, which involves multiplying the raw NPM score by -2 and adding 70. Using this formula, a raw NPM score of 4 is equal to a converted NPM score of 62 (the threshold for a low converted NPM score, HFSS and ‘unhealthy’ classification). We have referred to this scaled NPM score as a ‘converted NPM score’ (see technical appendix for more details).

Authors

Lydia Leon

Lydia Leon

Lydia Leon

Senior Analyst, healthy life mission

Lydia works as a senior analyst in the healthy life mission team.

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Husain Taibjee

Husain Taibjee

Husain Taibjee

Analyst, healthy life mission

Husain joined Nesta in 2022 as an analyst and will help to deliver Nesta’s healthy life mission.

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Lauren Bowes Byatt

Lauren Bowes Byatt

Lauren Bowes Byatt

Deputy Director, healthy life mission

Lauren is the Deputy Director of the healthy life mission.

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Hugo Harper

Hugo Harper

Hugo Harper

Mission Director, healthy life mission

Hugo leads Nesta's healthy life mission.

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Elena Mariani

Elena Mariani

Elena Mariani

Principal Data Scientist, healthy life mission

Elena is a principal data scientist for the healthy life mission.

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Isabel Stewart

Isabel Stewart

Isabel Stewart

Data Scientist, Data Analytics Practice

Izzy is a Data Scientist working in the Data Analytics practice.

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Jessica Jenkins

Jessica Jenkins

Jessica Jenkins

Senior Policy Advisor (Health), Rapid Insights Team

Jess is a senior policy advisor in our Rapid Insights Team (RIT).

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Caitlin Turner

Caitlin Turner

Caitlin Turner

Senior Analyst, healthy life mission

Caitlin joins Nesta as a senior analyst in the healthy life mission.

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