Skip to main content
PLOS ONE logoLink to PLOS ONE
. 2021 Aug 4;16(8):e0254833. doi: 10.1371/journal.pone.0254833

Assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data

Lauren Kate Bandy 1,*, Sven Hollowell 1, Richard Harrington 2, Peter Scarborough 1, Susan Jebb 3, Mike Rayner 1
Editor: Jane Anne Scott4
PMCID: PMC8336824  PMID: 34347807

Abstract

Background

The provision and over-consumption of foods high in energy, saturated fat, free sugars or salt are important risk factors for poor diet and ill-health. In the UK, policies seek to drive improvement through voluntary reformulation of single nutrients in key food groups. There has been little consideration of the overall progress by individual companies. This study assesses recent changes in the nutrient profile of brands and products sold by the top 10 food and beverage companies in the UK.

Methods

The FSA/Ofcom nutrient profile model was applied to the nutrient composition data for all products manufactured by the top 10 food and beverage companies and weighted by volume sales. The mean nutrient profiling score, on a scale of 1–100 with thresholds for healthy products being 62 for foods and 68 for drinks, was used to rank companies and food categories between 2015 and 2018, and to calculate the proportion of individual products and sales that are considered by the UK Government to be healthy.

Results

Between 2015 and 2018 there was little change in the sales-weighted nutrient profiling score of the top 10 companies (49 to 51; p = 0.28) or the proportion of products classified as healthy (46% to 48%; p = 0.23). Of the top five brands sold by each of the ten companies, only six brands among ten companies improved their nutrient profiling score by 20% or more. The proportion of total volume sales classified as healthy increased from 44% to 51% (p = 0.07) driven by an increase in the volume sales of bottled water, low/no calorie carbonates and juices, but after removing soft drinks, the proportion of foods classified as healthy decreased from 7% to 6% (p = 33).

Conclusions

The UK voluntary reformulation policies, setting targets for reductions in calories, sugar and salt, do not appear to have led to significant changes in the nutritional quality of foods, though there has been progress in soft drinks where the soft drink industry levy also applies. Further policy action is needed to incentivise companies to make more substantive changes in product composition to support consumers to achieve a healthier diet.

Introduction

The provision and consumption of foods high in energy, saturated fat, free sugars or salt is an important marker of poor diet and associated with substantial morbidity [1]. To support improvements in public health nutrition, Public Health England (PHE) published a series of voluntary, category-specific reformulation targets for calories, sugar and salt [24] to encourage manufacturers to improve the nutritional quality of everyday products. Progress has been monitored by measuring change in the levels of individual nutrients and does not include a more holistic view of how the nutritional quality of products has changed overall.

The food industry in the UK is powerful and consolidated; in 2018, the retail value sales of packaged food and soft drinks products was £71.3 billion, with the 10 largest companies accounting for nearly a quarter (24%) of the total [5]. In order for PHE’s voluntary reformulation targets to be successful in improving quality of the UK population’s diet, food manufacturers–especially the largest companies whose products dominate the market—must make changes across a range of products. So far, PHE has focused on changes in specific food groups and has published only limited company-level analysis, but progress by company is vital to understanding the industry response to the targets.

Nutrient profiling is “the science of classifying and ranking foods according to their nutritional composition for reasons related to preventing disease and promoting health” [6]. Nutrient profiling generally involves the application of a model that classifies or ranks foods based on their overall nutrition composition, rather than looking at individual nutrients in isolation. It has multiple purposes, including supporting health-related labelling schemes and restricting the marketing of foods to children [7]. The UK Government’s current nutrient profile model was developed by the Food Standards Agency (FSA) to provide the Office for Communications (Ofcom) with a tool to differentiate between foods that can and cannot be advertised to children, based on their nutrition composition [8].

The aim of this study was to assess how the nutritional quality of products offered by the top 10 global food and drink companies has changed over time by applying the FSA/Ofcom nutrient profiling model to a composition database, and weighting it using product sales data.

Methods

Data types and sources

Volume sales data was sourced from Euromonitor and accessed through the Oxford University Library. The top 10 UK food and soft drink manufacturers and their brands were identified based on global company names using 2018 sales data from Euromonitor [5]. A company is defined by Euromonitor as: “the legal entity that produces or distributes an individual or group of brands in the UK”. All of the brands manufactured by these companies between 2015 and 2018 were identified, including those that dropped in or out of the market. Brands were defined as a set of products that have the same generic name and are manufactured by one company.

The composition data were provided by Edge by Ascential (previously Brand View), a private analytics company that collects product information, including nutrient composition data, by scraping the websites of the UK’s three leading retailers: Asda, Sainsbury’s and Tesco. These data were scraped from these three websites on the same date (13th December) for four consecutive years (2015, 2016, 2017 and 2018). The sales data and nutrition composition data were automatically matched in Python based on three identifier variables that were present in both databases: brand name, category and year. A 10% random sample of brands was checked manually for any errors. Of the 20 brands checked, 4 brands were identified as pairing with the correct brand name but incorrect category. All 4 of these errors were brands that appeared in more than one category (e.g. Cadbury is present in five categories, including baked goods and confectionery). The matching code was adjusted so that it first paired based on matching categories, and then brand names, and no errors were identified after further checks.

Applying the FSA/Ofcom nutrient profile model

The FSA/Ofcom nutrient profile model was applied to the individual product composition data. The appropriate points were awarded based on each product’s energy, saturated fat, total sugar and sodium content (“A-points”) and fibre, protein and fruit, nut and vegetable (FNV) content (“C-points”) per 100g, as set out by FSA/Ofcom’s technical guidance [9]. This system was developed for the purposes of restricting advertising of food to children, but here we have used it to classify products as healthy and unhealthy. A food is classified as ‘less healthy’ if it scores four points or more. A drink is classified as ‘less healthy’ if it scores 1 point or more. For the purpose of comparing companies’ entire product portfolios, we converted the nutrient profile score to a 1–100 scale (-2(original score) +70), so that a higher score indicates healthier products. In order to directly compare drink scores with food scores, we also applied a linear adjustment to the distribution of the soft drinks scores (11x – 704, where x is the score for drinks on the 1–100 scale). The linear adjustment was selected so that the 33rd percentile and 66th percentile of both foods and drinks received the same score (44 and 66, respectively). After the scale conversion and linear adjustment, the thresholds for products to be considered healthy according to the FSA/Ofcom nutrient profile model were 62 or more for foods and 66 or more for drinks.

If the nutrient content for a product was missing, then data was imputed by calculating a brand average for foods in the same category, and if this was not possible, an overall category average. FNV content was estimated based on the ingredients list to categorise ingredients into ‘fruit’, ‘nut’, ‘vegetable’ and ‘other’. The percentage composition of ingredients was identified if this information was provided in the ingredients list. For the products where percentage of ingredients were not given, values were imputed based on a brand and category average, or if this was not possible, a category average.

Variables calculated

The total value (£ millions) and volume of food and soft drinks (tonnes) and the sales weighted mean nutrient profiling score (referred to in figure labels as sales-weighted score) were calculated in R for each company and brand, both overall and by category. When one brand had multiple product variants, a simple mean was used. While all brands were included in the analysis, only the top five for each company (n = 50) were presented for the brand-level analyses (Fig 3) for clarity. Bubble and chewing gum and milk formulas for infants, toddlers and children were excluded.

Fig 3. Sales-weighted nutrient profiling score by company and category, 2018.

Fig 3

Statistical analysis

Chi-squared tests were performed in R to test if there were any significant changes in the number of brands and products each company manufactured over time (2015–2018). ANOVA tests were used to test for differences over time in the nutrient profiling scores overall and for each company, category and brand.

Results

In 2018, the top 10 food and soft drink companies had total value sales of £17.1 billion (Table 1). The top 10 companies by value were also the largest 10 in terms of volume sales, although there is variation in the ranking between these two measures. Food company Mondelez is the largest in value terms, while Coca Cola is the largest company in volume terms.

Table 1. Number of products, brands and total volume sales by company, 2018.

Company Name Value sales (£mn) Equivalent value sales per person per day (£) Total volume sales (‘000 tonnes)
Mondelez 2903 0.12 286
PepsiCo 2541 0.10 1073
Mars 2228 0.09 155
Coca-Cola 2167 0.09 1948
Nestlé 1531 0.06 92
Danone 1418 0.06 108
Premier Foods 1346 0.06 74
Unilever 1160 0.05 185
Kraft Heinz 930 0.04 61
Kellogg 858 0.04 144
Total 17,081 0.70 4,126

In 2018, there were 3273 individual products produced by these companies and included in the dataset under 222 different brands. Premier Foods had the largest product portfolio in 2018, with 613 individual products. Kellogg had the smallest, with 91 individual products. There was a decline in the total number of products that were manufactured by the top 10 companies over the period of analysis, from 3471 in 2015 to 3273 in 2018, a reduction of 6% (p <0.05). Seven out of ten of the companies reduced the number of products they manufacture.

Between 2015 and 2018 there was little change in the sales-weighted mean nutrient profiling score of all the products manufactured by included companies, moving from 49 to 51 (p = 0.28). The number of individual products that could be classified as healthy also remained relatively unchanged, at 46% in 2015 and 48% in 2018 (p = 0.23) There was an increase from 44% to 51% in the total volume sales classified as healthy (p = 0.07). Once soft drinks were removed, the proportion of volume sales that were classified as healthy decreased from 7% in 2015 to 6% in 2018 (p = 0.33).

The company that saw the largest increase in sales-weighted nutrient profiling score was Coca-Cola (48 to 51), although its score still remained below the FSA/Ofcom threshold (Fig 1). The company with the highest sales-weighted nutrient profiling score was Danone, with a large proportion of sales from dairy and bottled water, followed by Kraft Heinz, which has high volume sales of high-scoring pre-prepared baby foods. Coca-Cola, Mars, Unilever, Nestlé and Mondelez scored poorly, with portfolios dominated by confectionery and snacks.

Fig 1. Total sales-weighted nutrient profiling score by company and year.

Fig 1

Baby food had the healthiest nutrient profiling score in 2018, at 72 (Fig 2) but little change over time. Spreads, confectionery and ice cream and desserts were the categories with the lowest nutrient profiling score. There was weak evidence of increases in score over time of staples, dairy, soft drinks and baked goods.

Fig 2. Total sales-weighted nutrient profiling score by category and year.

Fig 2

There was great heterogeneity between companies within some categories (Fig 3). For example, the company scores within the baked goods category ranged from 22 (Nestlé) to 69 (Premier Foods). In contrast, there was less variation within savoury snacks (39–52) and confectionery (26–42). Coca-Cola was the least diverse company producing only soft drinks, while Mondelez and Nestlé were the most diverse, with their portfolios containing products from six categories.

Of the five top-selling brands of each company, there were increases in the sales-weighted nutrient profiling score over time for Fanta (Coca-Cola), Volvic (Danone), San Pellegrino (Nestlé), Coco-Pops (Kellogg), Maltesers (Mars) and Angel Delight (Premier Foods) (Fig 4). Only Special K (Kellogg) saw its score cross the Ofcom threshold, up from 58 in 2015 to 62 in 2018 (+7%, p = 0.10). The largest increases were seen in soft drink brands San Pellegrino (+88%, p<0.01), Fanta (+28%, p<0.01) and Volvic (+26%, p<0.01) due to reductions in sugar and energy content. Tropicana (PepsiCo) saw a significant decrease in its score (-14%, p<0.01) due to a reduction in the proportion of sales of reduced sugar products, where the number of different products decreased over time. Coco-Pops (Kellogg) improved its score with an increase of 27% (p<0.01) due to a reduction in sugar, energy and salt. There was no strong evidence for changes in the scores of the top 5 brands for Kraft Heinz, Mondelez, PepsiCo and Unilever.

Fig 4. Sales-weighted nutrient profiling score for top 5 brands by company 2015–2018.

Fig 4

Discussion

Between 2015 and 2018, there was no evidence of change in the overall mean sales weighted nutrient profiling score of products sold by the top 10 food and drink companies in the UK. This mean score remained well below the Ofcom threshold for broadcast advertising. There was only one company (Kellogg’s) where there was weak evidence for improvement in its overall company score due to reductions in sugar and salt in two of its leading brands (Coco-Pops and Special K). There was a very small increase in the number of products classified as healthy (46% in 2015 to 47% in 2018) but a greater increase in the proportion of sales that were classified as healthy (44% in 2015 to 51% in 2018). This was largely attributable to a reduction in the sugar content of some soft drink products and an increase in the volume sales of healthy beverages (bottled water, low/no calorie drinks and fruit juices), changes likely driven by the introduction of the Soft Drink Industry Levy in 2018 [10,11]. Once soft drinks were removed, the proportion of healthy sales fell to 6% in 2018, down from 7% in 2015. This suggests that despite PHE’s reformulation targets for calories, sugar and salt, there has been no improvement in the nutritional quality of foods that people are buying.

Strengths and limitations

By pairing composition data with sales data and applying a nutrient profile model, both the relative healthiness of individual foods and drinks available, and the relative healthiness of what is sold have been assessed, and how this has changed over time. This gives an idea of how companies are responding to voluntary reformulation targets to improve the nutritional quality of their products overall, rather than in relation to a single nutrient.

Only 10 companies, based on global company name, were included in the analysis, which represented 24% of total value sales in the UK in 2018 [5]. These companies were selected based on their value sales, although they are also the top 10 companies in terms of volume sales. By selecting companies based on their global, rather than national, names, UK retailers were excluded from the analysis. This is a major limitation given that own-label brands from the top 3 UK retailers (Tesco, Sainsbury’s and Asda) represented a total market share of 21% in 2018 [5]. While this study sets out a useful and important method for ranking companies in terms of healthiness of product portfolios, future studies should include retailers and a wider range of companies. This would give a more comprehensive picture of how food and drink companies and retailers in the UK are changing their products to meet public health targets. There are a number of data-driven limitations. The first is in relation to missing and imputed data. The values for seven nutrients (energy, saturated fat, total sugars, sodium, fibre, protein and FNV content) are needed to calculate the FSA/Ofcom nutrient profile score of a product. 32% of the 13,371 products included in this study had missing values for fibre, and 67% products had insufficient ingredients information and composition detail to be able to calculate %FNV accurately. There was no difference in the proportion of missing values over time. Missing values were imputed with either a category and/or brand average. The high proportion of missing/imputed fibre and FNV was to be expected as the labelling of fibre on foods is not mandatory (unlike other macronutrients) [12] and the percentages for individual ingredients (i.e. FNV ingredients) only have to be stated when the product title includes an ingredient name, or when a claim about the amount of an ingredient has been made on the label [13].

To test what impact the imputed fibre data had on the results, a sensitivity analysis was conducted. 31% (n = 4186) of all products in the original dataset had imputed fibre values, and these were evenly distributed across the four years. For our sensitivity analysis, we adjusted the fibre content for these products to 0.0g/100g, with the FSA/Ofcom points awarded for fibre also then given 0, the lowest score possible. The number of products that were classified as healthy fell from 47% to 46% in 2018, and there were negligible changes in the total sales-weighted nutrient profiling score for 2018, which fell from 51 to 50.

For fruit, nut and vegetable (FNV) content, 8896 (67%) of included products had imputed values, although three-quarters of these (n = 6624) fell into categories that you would not expect to contain enough FNV to score one point: baked goods, confectionery, dairy, ice cream, savoury snacks, soft drinks, spreads and staples. To test what impact the imputed FNV data may have had on the results, the remaining 2272 products (baby food, breakfast cereals, ready meals, and sauces, dressings and condiments) had their %FNV adjusted to 0%. After this adjustment, 25% (n = 564) of the 2272 products saw a change in their final Ofcom score. The overall proportion of products classified as healthy in 2018 fell from 47% to 46%. The results were the same as those found with the fibre sensitivity analysis, with a similar group of products being affected by the lack of fibre and FNV values. These results suggest that while the missing fibre and FNV values is a weakness in the dataset, the interpretation of the data was unchanged, and it has not affected the overall results.

Data restrictions meant that time period covered changes between 2015 and 2018. Previous reformulation efforts made before 2015, for example as part of the salt reduction programme that began in 2006, will have been excluded. Using a wider historic time period may show that some companies who started reformulation efforts promptly have made more signficant changes than recorded here. Applying this method to datasets in multiple countries may offer insight into how companies are responding in countries with varying public health nutrition policies, for example voluntary reformulation targets in the UK compared to taxes on energy dense foods in Mexico [14] and mandatory warning labels in Chile [15].

The FSA/Ofcom nutrient profile model was used because it is designed for and used in the UK market and has been widely validated in terms of how its use may impact on dietary choices [16]. However, its original purpose was for the assessment of whether or not a product should be advertised to children, rather than to assess the nutritonal quality of a company’s product portfolio and classifying products as healthy and unhealthy, as it was used here. It would be possible to conduct similar analyses using other nutrient profiling models such as Health Star Rating [17] and Nutri Score [18], though since all rely on changes in the underlying nutrient composition differences between scoring systems are likley to be modest.

We combined the distributions of food and drink products by using a linear transformation that matched the distributions at two points–the 33rd and 66th percentile. The selection of the two matching points was arbitrary. Matching at different points (e.g. the 25th and 75th percentiles) would have produced a different linear transformation and hence different scores for drinks. This is an inevitable limitation associated with combining scores for companies with both food and drink profiles.

Comparisons with other studies

There are a number of studies that have examined the nutrient content of foods sold in the UK over time. Previous studies have shown that voluntary salt reduction targets in the UK led to gradual and important changes in the salt content of foods between 2008–2011 [19,20], although a more recent report from Public Health England (PHE) suggests that only 28 of 52 of the 2017 salt reduction targets had been met in 2018 [4]. Two studies have shown that there were significant changes in the sugar content of soft drinks in the UK in context of the introduction of the Soft Drink Industry Levy [10,11]. The changes in the sugar content of soft drinks presented in these studies is in line with the results presented here, where the majority of the change in the volume sales of foods classified as healthy was driven by changes in the sugar content of soft drinks. Another study has also looked at the sugar content of foods between 2015 and 2018 and also presented findings by category and company [21].This study showed that 24 out of the top 50 companies (including retailers) in the UK had met Public Health England’s 5% sugar reduction targets, and that companies have made limited progress towards meeting this voluntary policy. Public Health England have themselves published a series of reports that monitor progress being made towards their 20% sugar reduction targets using both sales and composition data [3]. For example, they have shown that there was a -2.9% reduction in the sugar content of foods between 2015 and 2018 [3]. A strength of our study is that it applies a nutrient profiling model, whereas these analyses are based on single nutrients and are therefore not directly comparable. However, they generally show that there has been mixed progress by the food industry towards public health goals.

INFORMAS (International Network on Food and Obesity/NCD Research, Monitoring and Action Support) have produced a series of company scorecards that rank the world’s top 25 food companies, including supermarkets and quick-service restaurants, in a number of different areas, including product formulation [22]. While the scores are not based on quantitative analysis of the nutritional quality of companies’ products, they are based on business practices and companies’ commitments to nutrition-related policies, which is also important for monitoring food industry progress towards public health goals.

In 2019, the Access to Nutrition Initiative (ATNI) published its UK Product Profile [23]. It analysed the nutritional quality of 3069 products from the top five food categories of the world’s top 18 manufacturers in 2016. The ATNI study also applied the HSR nutrient profiling model. Nine companies (excluding Premier Foods, a UK-only company) included here were also included in the ATNI index. ATNI found that 31% of products were classified as healthy enough to advertise to children, compared to 45% in 2016 here. 22% of sales were classified as healthy, as opposed to 55% in this study. These differences are likely to be accounted for by the fact that ATNI had a lower coverage (this study included 3438 products for 10 companies in 2016, compared to 3069 products for 18 companies for ATNI). The main advantage of this study over ATNI’s UK Product Profile is that it includes four years’ worth of data and therefore examines trends over time, whereas ATNI’s study is a snapshot of a single year. The two studies are not directly comparable as the ATNI companies were defined at the global level, rather than UK level, and therefore the brands included under each company vary. However, the general ranking of the companies were similar between the two studies; Kraft Heinz and Danone were the two top scoring companies, and Nestlé, Mars and Mondelez were ranked at the bottom.

Another study similar to this one, conducted in India by Jones et al. 2017, used Euromonitor sales data and nutrition composition data for 943 products, collected from either the packet or company websites [24]. It applied the Health Star Rating (HSR) to analyse the nutritional quality of the top 11 packaged food manufacturers in India. The study found that the overall healthiness of products was low and that only 17% of products were considered healthy [24]. This is lower than the 45% of products classified as healthy in this study in 2016. These differences are to be expected as the Indian study excluded products like staples (bread, pasta, rice), and used a different nutrient profiling model (HSR). Despite covering a very different market, it demonstrates that a high proportion of products sold by leading companies in other countries are also unhealthy, and that this problem is not isolated to the UK.

Implications of research

This study shines a spotlight on the very small changes over time in the nutritional quality of food and drink products from the UKs largest food and beverage companies. While the proportion of volume sales increased from 44% to 53% over time, this change was entirely down to increased volume sales of bottled water, low/no calorie drinks and high-scoring fruit juices. The brands that saw the biggest changes to their scores over time were soft drinks. Once soft drinks were removed, the total volume sales of foods classified as healthy dropped to just 6% in 2018, down from 7% in 2015. This strongly suggests that PHE’s reformulation targets for sugar, salt and calories have not had a substantive impact on the nutritional quality of foods.

This method of ranking food and drink companies based on the nutritional quality of their product portfolios could be used to benchmark companies as a tool for ‘healthier’ impact investment. There is an increasing interest by investment banks and other financial organisations to assess what impact food companies are having on public health and how responsible their business practices are (known as impact investment) [25]. This has already been done in part by ATNI in collaboration with Shared Action [26] and INFORMAS [22].

Transparent monitoring of this kind also allows for greater consumer understanding of the work that is, or is not, being undertaken by companies. There is some evidence that pressure from the social environment is a factor influencing corporate behaviour [27], and public benchmarking exercises may increase pressure on companies to make meaningful change.

Conclusion

This study has demonstrated that it is feasible to monitor overall healthiness of company product portfolios over time. It shows that companies have made little change to the nutritional quality of their product portfolios, despite a few individual brand success stories, a factor which needs to be considered by policy makers when reviewing the current focus on single-nutrient reformulation programmes. Implementing a transparent monitoring and evaluation system such as this, would allow for targeted work with the companies to drive improvements in public health nutrition.

Data Availability

This study used data from two commercial sources. The sales data was accessed under licence from Euromonitor International (https://www.euromonitor.com/packaged-food) via the Bodleian Library, University of Oxford, using Euromonitor’s database portal Passport GMID. The product information dataset, including nutrition composition data, was purchased for the purpose of the lead author’s DPhil research project from Edge by Ascential (https://www.ascentialedge.com/our-solutions). Due to licencing restrictions, the Euromonitor and Edge by Ascential datasets can only be requested under licence for the purpose of verification and replication of study’s findings via the research group’s Data Access Committee (contact: Trisha Gordon foodDBaccess@ndph.ox.ac.uk). Further use of these datasets must be negotiated with the data owners (Euromonitor contact: Ashton Moses - passport.support@euromonitor.com, Edge by Ascential contact: David Beech - info@ascentialedge.com). The authors received no special privileges in accessing the data.

Funding Statement

LB, SH and MR are funded by the Nuffield Department of Population Health, University of Oxford. PS is funded by a British Heart Foundation Intermediate Basic Science Research Fellowship (FS/15/34/31656). All authors are part of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). SJ is also funded by the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford at Oxford Health NHS Foundation Trust and is an NIHR senior investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Abbafati C, Machado DB, Cislaghi B, Salman OM, Karanikolos M, McKee M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020. doi: 10.1016/S0140-6736(20)30925-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Tedstone A, Targett V, Mackinlay B, Owtram G, Coulton V, Morgan K, et al. Calorie reduction: The scope and ambition for action [Internet]. 2018. [cited 2018 Mar 12]. Available from: www.gov.uk/phe. [Google Scholar]
  • 3.Tedstone A, Targett V, Owtram G, Pyne V, Allen R, Bathrellou K, et al. Sugar Reduction: Achieving the 20% A technical report outlining progress to date, guidelines for industry, 2015 baseline levels in key foods and next steps [Internet]. 2017 [cited 2017 May 17]. Available from: www.gov.uk/phe. [Google Scholar]
  • 4.Public Health England (PHE). Salt Reduction Targets for 2017 [Internet]. 2017 [cited 2019 Jul 9]. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/604338/Salt_reduction_targets_for_2017.pdf.
  • 5.Euromonitor International. Euromonitor Passport: Packaged food and soft drinks, market statistics [Internet]. 2019 [cited 2019 Dec 3]. Available from: https://www.portal.euromonitor.com/portal/statisticsevolution/index.
  • 6.WHO. WHO | Nutrient Profiling [Internet]. 2011 [cited 2019 Nov 26]. Available from: https://www.who.int/nutrition/topics/profiling/en/.
  • 7.WHO. Nutrient Profiling Report of a WHO/IASO Technical Meeting [Internet]. 2010 [cited 2019 Nov 26]. Available from: https://www.who.int/nutrition/publications/profiling/WHO_IASO_report2010.pdf?ua=1.
  • 8.Department of Health. Nutrient Profiling Technical Guidance [Internet]. 2011 [cited 2018 Aug 9]. Available from: http://www.dh.gov.uk/publications.
  • 9.Department of Health. Nutrient Profiling Technical Guidance [Internet]. 2011 [cited 2019 Nov 26]. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/216094/dh_123492.pdf.
  • 10.Bandy LK, Scarborough P, Harrington RA, Rayner M, Jebb SA. Reductions in sugar sales from soft drinks in the UK from 2015 to 2018. BMC Med. 2020. doi: 10.1186/s12916-019-1477-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Scarborough P, Adhikari V, Harrington RA, Elhussein A, Briggs A, Rayner M, et al. Impact of the announcement and implementation of the UK Soft Drinks Industry Levy on sugar content, price, product size and number of available soft drinks in the UK, 2015–19: A controlled interrupted time series analysis. PLoS Med. 2020. doi: 10.1371/journal.pmed.1003025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Food Standards Agency. Nutrition labelling | Food Standards Agency [Internet]. 2018 [cited 2019 Dec 6]. Available from: https://www.food.gov.uk/business-guidance/nutrition-labelling.
  • 13.UK Government. The Food Information Regulations 2014–2014–1855. 2014.
  • 14.Batis C, Rivera JA, Popkin BM, Taillie LS. First-Year Evaluation of Mexico’s Tax on Nonessential Energy-Dense Foods: An Observational Study. PLoS Med. 2016. Jul;13(7):e1002057. doi: 10.1371/journal.pmed.1002057 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Reyes M, Garmendia ML, Olivares S, Aqueveque C, Zacarías I, Corvalán C. Development of the Chilean front-of-package food warning label. [cited 2020 Nov 3]; Available from: 10.1186/s12889-019-7118-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Cooper SL, Pelly FE, Lowe JB. Construct and criterion-related validation of nutrient profiling models: A systematic review of the literature. Vol. 100, Appetite. Academic Press; 2016. p. 26–40. doi: 10.1016/j.appet.2016.02.001 [DOI] [PubMed] [Google Scholar]
  • 17.Dunford EK, Ni Mhurchu C, Huang L, Vandevijvere S, Swinburn B, Pravst I, et al. A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013–2018. Obes Rev [Internet]. 2019. Nov 1 [cited 2021 May 28];20(S2):107–15. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/obr.12879 [DOI] [PubMed] [Google Scholar]
  • 18.Julia C, Hercberg S. Nutri-Score: evidence of the effective-ness of the French front-of-pack nutrition label. Ernahrungs Umschau. 2017;64(12):181–7. [Google Scholar]
  • 19.He FJ, Brinsden HC, Macgregor GA. Salt reduction in the United Kingdom: A successful experiment in public health. Vol. 28, Journal of Human Hypertension. Nature Publishing Group; 2014. p. 345–52. doi: 10.1038/jhh.2013.105 [DOI] [PubMed] [Google Scholar]
  • 20.Ni Mhurchu C, Capelin C, Dunford EK, Webster JL, Neal BC, Jebb SA, et al. Sodium content of processed foods in the United Kingdom: analysis of 44,000 foods purchased by 21,000 households. Am J Clin Nutr. 2011. Mar;93(3):594–600. doi: 10.3945/ajcn.110.004481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bandy LK, Scarborough P, Harrington RA, Rayner M, Jebb SA. The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015–2018. Popkin BM, editor. PLOS Med [Internet]. 2021. May 18 [cited 2021 May 28];18(5):e1003647. Available from: https://dx.plos.org/10.1371/journal.pmed.1003647. doi: 10.1371/journal.pmed.1003647 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.INFORMAS. BIA-Obesity | INFORMAS [Internet]. INFORMAS—Benchmarking food environments. 2018 [cited 2018 May 31]. Available from: http://www.informas.org/bia-obesity/#BIAObesity%7C0.
  • 23.Access to Nutrition Initiative, Attard J, Cooper K, Gordon K, Chapman E, Vasquez I, et al. UK Product Profile 2019. 2019.
  • 24.Jones A, Dunford E, Crossley R, Thout SR, Rayner M, Neal B. An evaluation of the healthiness of the Indian packaged food and beverage supply. Nutrients. 2017;9(10). doi: 10.3390/nu9101103 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Irving (Schroders) E, Crossman (Rathbone Greenbank) M. Sugar, obesity and noncommunicable disease: Investor expectations. 2017.
  • 26.ATNI. ATNI partnership with ShareAction–Access to Nutrition [Internet]. 2020 [cited 2020 Dec 18]. Available from: https://accesstonutrition.org/project/atni-shareaction/.
  • 27.van Erp J. Naming and Shaming of Corporate Offenders. In: Encyclopedia of Criminology and Criminal Justice. Springer New York; 2014. p. 3209–17. [Google Scholar]

Decision Letter 0

Jane Anne Scott

15 Apr 2021

PONE-D-21-01154

Assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data.

PLOS ONE

Dear Dr. Bandy,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by May 30 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Jane Anne Scott, PhD, MPH Grad Dip Dietetics, BSc

Academic Editor

PLOS ONE

Additional Editor Comments:

Line 125 Suggest rewording.’ Products that scored below relevant scores were classified as unhealthy.’ As it was not just foods but also beverages.

Line 145 Did the exclusion of infant formulas include the exclusion of toddler formulas that are not officially classified as breastmilk substitutes?

Line 322 should read 3069 products

Line 296 please provide references for the other nutrient profiling models referred to i.e. Health Stare Rating and Nutriscore

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This was a nicely-written, simple yet effectively presented study examining changes in the nutrient profile score of food and beverage products from 10 major manufacturers in the UK. The study is certainly of interest and timing-wise is important given the attention of late to marketing to children in the UK and EU.

Overall, my main comments and suggestions for improvement relate to the authors interpretation of the results and to the comparison with existing studies. I have split my comments into major versus minor. All I think are easily actionable and I would recommend they all be addressed prior to this being accepted for publication.

Major

1. What about retailers? In the UK retailer own brands/private label dominate the food supply. This is a huge limitation of the paper as it stands and so interpretation of results needs to be made more cautiously given that the largest part of the market has not been included. Authors mention this briefly as a limitation, however I recommend some commentary on what % of the market the retailers represent, and whether any specific retailer has % sales higher than one of the top 10 companies? I personally don’t know is this is the case but it should be mentioned for sure. Perhaps a rewording to say you are looking at global food and beverage companies within the context of the UK market? Only Premier Foods is a UK company, all others in the top 10 are global companies.

2. How did imputation differ between 2015 and 2018 – the authors mention substantial imputation was needed, yet my sense would be that if this differed between years, then some kind of sensitivity analysis would be warranted to see whether changes could be attributed to estimated nutrient values rather than true change. I appreciate sensitivity analyses was done with and without imputation, but understanding how this affected each year’s dataset is also important.

3. I caution the authors to revise the text in the sections comparing results to previous studies to better clarify the likely reasons for differences in results, rather than dismiss the results of the previous studies for being flawed – as it is written it comes across as if the present study is much better/more accurate than previous studies, which I do not believe is the case – each study uses different methodologies (and different companies in some cases) and hence would be completely expected to yield different results (especially when comparing India to the UK – these are VASTLY different packaged food supplies!). PHE I am sure would also include retailers? This would give vastly different results than looking only at global manufacturers. Have the authors looked at previous studies examining changes in the nutritional content of UK foods – there are certainly studies out there on sodium, and likely others too. The discussion section was definitely “thin” on references and seemed to focus on a small number of studies alone.

Minor

1. Line 69/70 – “in 2018” is repeated

2. Line 124 – why? Is this based on something? Need to justify these cut-offs

3. Line 148 – which program was used

4. Line 173-174 – remove the sentence about soft drinks as it was a non-significant result. Or else change the wording to say there was no difference.

5. Line 225 – should be “Coco-Pops” for consistency

6. Line 280 – spelling error – drink

7. Small thing – should be “PepsiCo” with the C capitalised

Reviewer #2: Thank you for the opportunity to review this manuscript – assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data. Overall, the manuscript is written well, and the methods applied are thorough. I have a number of comments that could further improve the manuscript.

Abstract

- in methods, the threshold of nutrient profile score for classifying products as healthy vs. unhealthy hasn’t been provided. Describing briefly the threshold will help readers understand better the results.

- Line 48, is it ‘only 6 brands among the 10 companies improved …’ ?

- Lines 46 and 47 ‘… the number of products classified as healthy (46% to 48%)…’ the statement says ‘the number’ but results are in ‘%’

- Line 49, proportion of total volume sales (classed?) classified as healthier increased from 44% to 51% …; but in line 52, the proportion of foods classified as healthy decreased from 7% to 6%... is confusing. It’s important to be consistent which means the decrease should be the ‘proportion of total volume sales classified as healthier’. Moreover, the use of healthier in some places and healthy in other places is confusing. The authors may choose to use ‘healthy’ whenever ‘healthier’ refers to ‘healthy’ as a binary variable created using the threshold of ≥62 for foods and ≥66 nutrient profiling score for drinks.

Methods

- Line 108, ‘… data were automatically matched based on product name, brand name, category and year, …’ needs to be explained on how the authors created the identifier variable in the sales database and nutrient composition database. By identifier variable I mean the variable that was used to link (match) the two databases.

- Line 111, ‘… The matching code was adjusted, …’ The authors please explain how this adjustment was done.

- Line 142, ‘… weighted mean nutrient profiling scores were calculated for each company and brand, …’. It is well written here; however, in figure3 and other figures it says ‘sales-weighted score’. The authors may choose to clarity it here or may choose to change ‘Variable calculated’ to ‘Sales-weighted score’ in line 140.

- Line 146, … Infant formulas (i.e., breast milk substitutes) were excluded. Were there other products to be excluded such as products not required to display a Nutrition Information Panel (e.g. tea, unflavored coffee, artificial sweeteners, chewing and bubble gums, salt, flour, corn flour, vinegar, herbs and spices, pepper, baking soda, baking powder, tartaric acid, citric acid, cooking ingredients, ice, curry powder, yeast, bicarbonate of soda. Other foods such as special products (baby foods, protein bars, protein powders, and fitness or diet products), and alcoholic beverages may also be excluded. If these products were not available in the database, then the authors may mention about them.

Results

- In Table 1. What does ‘per capita per day’ refer to?

- Line 173, does ‘healthier’ mean ‘healthy’? based on the threshold described in line 125

- Line 173 says ‘… increase from 44% to 51% …’ but in line 174 after removing soft drinks from the analysis, the proportion of volume sales classified as healthier decreased from 7% to 6% (p=0.33). I assume that 7% refers to 51% minus 44%, and 6% refers to 50% minus 44%. I am not sure about it, and it think its needs to be clarified here.

- I am unsure whether ‘bottled water’ should be excluded from the analysis, especially it may favor manufacturing of more healthy beverages by Coca-Cola, and PepsiCo.

- Results in page 8 showing significant improvement in nutrient profiling score for soft drink brands give the impression that these brands (i.e., San Pellegrino, Fanta, Volvic, Tropicana, Kellogg) are doing good in manufacturing more healthy beverages. I’m not sure whether inclusion of ‘bottled water’ in the analysis has affected the results or whether reductions in salt, sugar and energy have led to these results.

Discussion

- Line 238 ‘… foods and drinks available, I understand the authors studied sales; but did the authors examine availability too?

- Line 240 ‘… responding to public health calls to improve …’ is not specific. The authors may choose to write ‘… responding to the voluntary reformulation initiatives to improve’

- Lines 246 to 274 mainly describe and discuss ‘missing values’ and ‘imputation’. They can be divided between methods, results and discussion. If the authors think the results related to missing values, imputation and sensitivity analysis can distract the readers, the authors may choose to provide them as an appendix.

- Line 261, … (-0.5%) refers to what?

- Line 288, please cite the references in ‘… energy dense foods in Mexico [reference?] and mandatory warning labels in Chile [reference?]’.

One of the strengths of this study can be use of nutrient profiling score that the authors may want to highlight it in the discussion. In this regard lines 312 and 313 say … these analyses consider only single nutrients and do not apply a nutrient profile model …’ but these are not emphasizing on this strength of this study.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Essa Tawfiq

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment

Submitted filename: ETsReview.docx

PLoS One. 2021 Aug 4;16(8):e0254833. doi: 10.1371/journal.pone.0254833.r002

Author response to Decision Letter 0


24 Jun 2021

[PONE-D-21-01154] Reviewer response letter

Editor Comments:

Line 125 Suggest rewording.’ Products that scored below relevant scores were classified as unhealthy.’ As it was not just foods but also beverages.

Thanks, this has been corrected

Line 145 Did the exclusion of infant formulas include the exclusion of toddler formulas that are not officially classified as breastmilk substitutes?

Line 151 now reads: “Milk formulas for infants, toddlers and children were excluded.” We hope this has clarified this.

Line 322 should read 3069 product

Thanks, this has been corrected (line 349)

Line 296 please provide references for the other nutrient profiling models referred to i.e. Health Star Rating and Nutriscore

Thanks, these have been added (line 307)

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Funding statement:

LB, SH and MR are funded by the Nuffield Department of Population Health, University of Oxford. PS is funded by a British Heart Foundation Intermediate Basic Science Research Fellowship (FS/15/34/31656). All authors are part of the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). SJ is also funded by the NIHR Collaboration for Leadership in Applied Health Research and Care Oxford at Oxford Health NHS Foundation Trust and is an NIHR senior investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Please see the updated data availability statement below:

This study used data from two commercial sources. The sales data was accessed under licence from Euromonitor International (https://www.euromonitor.com/packaged-food) via the Bodleian Library, University of Oxford, using Euromonitor’s database portal Passport GMID. The product information dataset, including nutrition composition data, was purchased for the purpose of the lead author’s DPhil research project from Edge by Ascential (https://www.ascentialedge.com/our-solutions). Due to licencing restrictions, the Euromonitor and Edge by Ascential datasets can only be requested under licence for the purpose of verification and replication of study’s findings via the research group’s Data Access Committee (contact: Trisha Gordon foodDBaccess@ndph.ox.ac.uk). Further use of these datasets must be negotiated with the data owners (Euromonitor contact: Ashton Moses - passport.support@euromonitor.com, Edge by Ascential contact: David Beech - info@ascentialedge.com).

Reviewer #1: This was a nicely-written, simple yet effectively presented study examining changes in the nutrient profile score of food and beverage products from 10 major manufacturers in the UK. The study is certainly of interest and timing-wise is important given the attention of late to marketing to children in the UK and EU.

Overall, my main comments and suggestions for improvement relate to the authors interpretation of the results and to the comparison with existing studies. I have split my comments into major versus minor. All I think are easily actionable and I would recommend they all be addressed prior to this being accepted for publication.

Major

1. What about retailers? In the UK retailer own brands/private label dominate the food supply. This is a huge limitation of the paper as it stands and so interpretation of results needs to be made more cautiously given that the largest part of the market has not been included. Authors mention this briefly as a limitation, however I recommend some commentary on what % of the market the retailers represent, and whether any specific retailer has % sales higher than one of the top 10 companies? I personally don’t know is this is the case but it should be mentioned for sure. Perhaps a rewording to say you are looking at global food and beverage companies within the context of the UK market? Only Premier Foods is a UK company, all others in the top 10 are global companies.

We thank the reviewer for their comment and agree that the exclusion of retailers from the list of companies is a central limitation. Euromonitor classifies company names in two ways, by Global Brand Owner (GBO – the ultimate global owner of the brand) and National Brand Owner (NBO – the UK registered producer (including under licence) or distributor of the brand). We selected the top 10 companies based on GBO rather than NBO. Under GBO, all retailers are grouped under the umbrella term ‘Private Label’ and individual retailer names are not given. When the top 10 companies in the UK are viewed by NBO, the individual retailers are named and 5 out of 10 of the top 10 companies are indeed retailers. Top 10 by GBO was chosen for the main reason that we used composition data from Brand View, which only provided data for three of these five retailers (Tesco, Sainsbury’s and Asda), and so we wouldn’t be able to compare all 5 retailers that featured in the top 10 companies (Morrisons and Marks & Spencer being the other two). We also thought global companies might be more recognisable for a global audience.

We have edited the methods section to make it clear that the top 10 UK companies were selected based on global name (line xx) and the strengths and limitations section of the discussion (line xx) now reads: “Only 10 companies, based on global company name, were included in the analysis, which represented 24% of total value sales in the UK in 2018 [5]. These companies were selected based on their value sales, although they are also the top 10 companies in terms of volume sales. By selecting companies based on their global, rather than national, names, UK retailers were excluded from the analysis. This is a major limitation given that own-label brands from the top 3 UK retailers (Tesco, Sainsbury’s and Asda) represented a total market share of 21% in 2018 [5]. While this study sets out a useful and important method for ranking companies in terms of healthiness of product portfolios, future studies should include retailers and a wider range of companies. This would give a more comprehensive picture of how food and drink companies and retailers in the UK are changing their products to meet public health targets.”

Our research group has developed a tool that collects product information data (including nutrition composition and ingredients) from 10 supermarket websites in the UK and therefore we are planning to undertake a similar study that will score and rank UK retailers based on the healthiness of their own-label products. We anticipate that this will be a complimentary paper to this one, and hope that the corrections we have made here have highlighted the limitations in excluding retailers from our analysis.

2. How did imputation differ between 2015 and 2018 – the authors mention substantial imputation was needed, yet my sense would be that if this differed between years, then some kind of sensitivity analysis would be warranted to see whether changes could be attributed to estimated nutrient values rather than true change. I appreciate sensitivity analyses was done with and without imputation, but understanding how this affected each year’s dataset is also important.

There was no difference in the proportion of values that were imputed over time, and a line has been added to the methods section (line 263) to make this clear to the reader. The proportion of imputed FNV values was 29% (n=994) in 2015 and 30% (n=1010) in 2018, and the proportion of imputed fibre values was 68% (n=2336) in 2015 and 71% (n=2132) in 2018. Given the lack of difference over time, a sensitivity analysis to test the impact of imputed data on each year’s dataset was not deemed necessary.

3. I caution the authors to revise the text in the sections comparing results to previous studies to better clarify the likely reasons for differences in results, rather than dismiss the results of the previous studies for being flawed – as it is written it comes across as if the present study is much better/more accurate than previous studies, which I do not believe is the case – each study uses different methodologies (and different companies in some cases) and hence would be completely expected to yield different results (especially when comparing India to the UK – these are VASTLY different packaged food supplies!). PHE I am sure would also include retailers? This would give vastly different results than looking only at global manufacturers. Have the authors looked at previous studies examining changes in the nutritional content of UK foods – there are certainly studies out there on sodium, and likely others too. The discussion section was definitely “thin” on references and seemed to focus on a small number of studies alone.

We recognise that the references in the comparison with previous studies section were light, and have added six studies that look at the sugar and salt content of foods over time. We have also tried to rephrase this section to ensure it’s not overly critical and focuses on how the comparative study adds important context to the findings we have set out. The “Comparison with other studies” section (lines 319-375) now reads:

“There are a number of studies that have examined the nutrient content of foods sold in the UK over time. Previous studies have shown that voluntary salt reduction targets in the UK led to gradual and important changes in the salt content of foods between 2008-2011 [19][20], although a more recent report from Public Health England (PHE) suggests that only 28 of 52 of the 2017 salt reduction targets had been met in 2018 [4]. Two studies have shown that there were significant changes in the sugar content of soft drinks in the UK in context of the introduction of the Soft Drink Industry Levy [10][11]. The changes in the sugar content of soft drinks presented in these studies is in line with the results presented here, where the majority of the change in the volume sales of foods classified as healthy was driven by changes in the sugar content of soft drinks. Another study has also looked at the sugar content of foods between 2015 and 2018 and also presented findings by category and company [21].This study showed that 24 out of the top 50 companies (including retailers) in the UK had met Public Health England’s 5% sugar reduction targets, and that companies have made limited progress towards meeting this voluntary policy. Public Health England have themselves published a series of reports that monitor progress being made towards their 20% sugar reduction targets using both sales and composition data [3]. For example, they have shown that there was a -2.9% reduction in the sugar content of foods between 2015 and 2018 [3]. A strength of our study is that it applies a nutrient profiling model, whereas these analyses are based on single nutrients and are therefore not directly comparable. However, they generally show that there has been mixed progress by the food industry towards public health goals.

INFORMAS (International Network on Food and Obesity/NCD Research, Monitoring and Action Support) have produced a series of company scorecards that rank the world’s top 25 food companies, including supermarkets and quick-service restaurants, in a number of different areas, including product formulation [22]. While the scores are not based on quantitative analysis of the nutritional quality of companies’ products, they are based on business practices and companies’ commitments to nutrition-related policies, which is also important for monitoring food industry progress towards public health goals.

In 2019, the Access to Nutrition Initiative (ATNI) published its UK Product Profile [23]. It analysed the nutritional quality of 3069 products from the top five food categories of the world’s top 18 manufacturers in 2016. The ATNI study also applied the HSR nutrient profiling model. Nine companies (excluding Premier Foods, a UK-only company) included here were also included in the ATNI index. ATNI found that 31% of products were classified as healthy enough to advertise to children, compared to 45% in 2016 here. 22% of sales were classified as healthy, as opposed to 55% in this study. These differences are likely to be accounted for by the fact that ATNI had a lower coverage (this study included 3438 products for 10 companies in 2016, compared to 3069 products for 18 companies for ATNI). The main advantage of this study over ATNI’s UK Product Profile is that it includes four years’ worth of data and therefore examines trends over time, whereas ATNI’s study is a snapshot of a single year. The two studies are not directly comparable as the ATNI companies were defined at the global level, rather than UK level, and therefore the brands included under each company vary. However, the general ranking of the companies were similar between the two studies; Kraft Heinz and Danone were the two top scoring companies, and Nestlé, Mars and Mondelez were ranked at the bottom.

Another study similar to this one, conducted in India by Jones et al. 2017, used Euromonitor sales data and nutrition composition data for 943 products, collected from either the packet or company websites [24]. It applied the Health Star Rating (HSR) to analyse the nutritional quality of the top 11 packaged food manufacturers in India. The study found that the overall healthiness of products was low and that only 17% of products were considered healthy [24]. This is lower than the 45% of products classified as healthy in this study in 2016. These differences are to be expected as the Indian study excluded products like staples (bread, pasta, rice), and used a different nutrient profiling model (HSR). Despite covering a very different market, it demonstrates that a high proportion of products sold by leading companies in other countries are also unhealthy, and that this problem is not isolated to the UK.”

Minor

1. Line 69/70 – “in 2018” is repeated

Thanks, this has been corrected.

2. Line 124 – why? Is this based on something? Need to justify these cut-offs

This section has been rephrased to make this clearer and line 133-135 now reads: “After the scale conversion and linear adjustment, the thresholds for products to be considered healthy according to the FSA/Ofcom nutrient profile model were 62 or more for foods and 66 or more for drinks.”

3. Line 148 – which program was used

Analyses were conducted in R and we have added corrections to line 148 and 155 to make this clear

4. Line 173-174 – remove the sentence about soft drinks as it was a non-significant result. Or else change the wording to say there was no difference.

We have kept this line, as while the change over time wasn’t significant, removing soft drinks from the results did result in a very different result in terms of proportion of volume sales classified as health (51% in 2018 compared to 6% once soft drinks were removed). This result is important to the narrative that the majority of any change seen was from soft drinks, where mandatory policy was applied, compared to voluntary reformulation targets for packaged foods.

5. Line 225 – should be “Coco-Pops” for consistency

Thanks, this has been corrected

6. Line 280 – spelling error – drink

Thanks, this has been corrected

7. Small thing – should be “PepsiCo” with the C capitalised

Thanks, this has been corrected

Reviewer #2: Thank you for the opportunity to review this manuscript – assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data. Overall, the manuscript is written well, and the methods applied are thorough. I have a number of comments that could further improve the manuscript.

Abstract

- in methods, the threshold of nutrient profile score for classifying products as healthy vs. unhealthy hasn’t been provided. Describing briefly the threshold will help readers understand better the results.

Thanks, the thresholds have been added as suggested and line 141 onwards reads: “The mean nutrient profiling score, on a scale of 1-100 with thresholds for healthy products being 62 for foods and 68 for drinks, was used to rank companies and food categories between 2015 and 2018..”

- Line 48, is it ‘only 6 brands among the 10 companies improved …’ ?

Thanks, this has been corrected

- Lines 46 and 47 ‘… the number of products classified as healthy (46% to 48%)…’ the statement says ‘the number’ but results are in ‘%’

Thanks, this has been corrected to ‘proportion’

- Line 49, proportion of total volume sales (classed?) classified as healthier increased from 44% to 51% …; but in line 52, the proportion of foods classified as healthy decreased from 7% to 6%... is confusing. It’s important to be consistent which means the decrease should be the ‘proportion of total volume sales classified as healthier’. Moreover, the use of healthier in some places and healthy in other places is confusing. The authors may choose to use ‘healthy’ whenever ‘healthier’ refers to ‘healthy’ as a binary variable created using the threshold of ≥62 for foods and ≥66 nutrient profiling score for drinks.

Thanks for your comment – we have changed the word ‘healthier’ to ‘healthy’ throughout the manuscript wherever we refer to a binary variable created using the thresholds for food and drink scores.

Methods

- Line 108, ‘… data were automatically matched based on product name, brand name, category and year, …’ needs to be explained on how the authors created the identifier variable in the sales database and nutrient composition database. By identifier variable I mean the variable that was used to link (match) the two databases.

This section has now been updated as suggested and reads: “The sales data and nutrition composition data were automatically matched in Python based on three identifier variables that were present in both databases: brand name, category and year.” (lines 109-111)

- Line 111, ‘… The matching code was adjusted, …’ The authors please explain how this adjustment was done.

This section has now been updated as suggested and reads: “A 10% random sample of brands checked manually for any errors. Of the 20 brands checked, 4 brands were identified as pairing with the correct brand name but incorrect category. All 4 of these errors were brands that appeared in more than one category (e.g. Cadbury is present five categories, including baked goods and confectionery). The matching code was adjusted so that it first paired based on matching categories, and then brand names, and no errors were identified after further checks.” (Lines 111-117)

- Line 142, ‘… weighted mean nutrient profiling scores were calculated for each company and brand, …’. It is well written here; however, in figure3 and other figures it says ‘sales-weighted score’. The authors may choose to clarity it here or may choose to change ‘Variable calculated’ to ‘Sales-weighted score’ in line 140.

We agree that ‘sales-weighted mean nutrient profiling scores’ is clearer, but it was too long to use for the title of the figure key (character limit), therefore we have kept the figure key title the same, but added the following line (line 146) to the methods section: “The total value (£ millions) and volume of food and soft drinks (tonnes) and the sales weighted mean nutrient profiling score (referred to in figure labels as sales-weighted score) were calculated in R for each company and brand, both overall and by category.”

- Line 146, … Infant formulas (i.e., breast milk substitutes) were excluded. Were there other products to be excluded such as products not required to display a Nutrition Information Panel (e.g. tea, unflavored coffee, artificial sweeteners, chewing and bubble gums, salt, flour, corn flour, vinegar, herbs and spices, pepper, baking soda, baking powder, tartaric acid, citric acid, cooking ingredients, ice, curry powder, yeast, bicarbonate of soda. Other foods such as special products (baby foods, protein bars, protein powders, and fitness or diet products), and alcoholic beverages may also be excluded. If these products were not available in the database, then the authors may mention about them.

We only selected products manufactured by the 10 companies presented in the studies. The only products we excluded were infant formulas and bubble and chewing gum (now updated in line 151).

Results

- In Table 1. What does ‘per capita per day’ refer to?

This refers to the equivalent value spend per person and the column heading as been updated to make this clearer.

- Line 173, does ‘healthier’ mean ‘healthy’? based on the threshold described in line 125

Thanks for your comment – we have changed the word ‘healthier’ to ‘healthy’ throughout the manuscript wherever we refer to a binary variable created using the thresholds for food and drink scores.

- Line 173 says ‘… increase from 44% to 51% …’ but in line 174 after removing soft drinks from the

analysis, the proportion of volume sales classified as healthier decreased from 7% to 6% (p=0.33). I assume that 7% refers to 51% minus 44%, and 6% refers to 50% minus 44%. I am not sure about it, and it think its needs to be clarified here.

We have added the years to this sentence and hope that this clarifies the meaning. Lines 179-181 now read: “Once soft drinks were removed, the proportion of volume sales that were classified as healthy decreased from 7% in 2015 to 6% in 2018 (p = 0.33).”

- I am unsure whether ‘bottled water’ should be excluded from the analysis, especially it may favor manufacturing of more healthy beverages by Coca-Cola, and PepsiCo.

We decided to include bottled water in the analysis, as shifting sales from sugary drinks to bottled water would be considered to be a positive industry health behaviour. Additionally, we also adjusted the soft drink scores to make them directly comparable to food scores, so that soft drink companies are not favoured.

- Results in page 8 showing significant improvement in nutrient profiling score for soft drink brands give the impression that these brands (i.e., San Pellegrino, Fanta, Volvic, Tropicana, Kellogg) are doing good in manufacturing more healthy beverages. I’m not sure whether inclusion of ‘bottled water’ in the analysis has affected the results or whether reductions in salt, sugar and energy have led to these results.

San Pellegrino and Volvic are bottled water brands and so increased sales of these will have affected the results. Fanta, Kellogg and Tropicana will not be affected by changes to bottled water sales. Please see above regarding the reason for including bottled water in the anaylsis.

Discussion

- Line 238 ‘… foods and drinks available, I understand the authors studied sales; but did the authors examine availability too?

Where results are presented as number of individual products classified as healthy (i.e. not weighted by sales), then this is a marker of availability.

- Line 240 ‘… responding to public health calls to improve …’ is not specific. The authors may choose to write ‘… responding to the voluntary reformulation initiatives to improve’

Thanks, this has been updated as recommended.

- Lines 246 to 274 mainly describe and discuss ‘missing values’ and ‘imputation’. They can be divided between methods, results and discussion. If the authors think the results related to missing values, imputation and sensitivity analysis can distract the readers, the authors may choose to provide them as an appendix.

Thanks for your comment - we decided that even with the missing values/imputation sections in the main body of the text it was succinct enough, and therefore we have not moved anything to an appendix

- Line 261, … (-0.5%) refers to what?

Thanks, this appears to be a mistake and has been removed.

- Line 288, please cite the references in ‘… energy dense foods in Mexico [reference?] and mandatory warning labels in Chile [reference?]’.

Thanks, these references have been added (line 298).

One of the strengths of this study can be use of nutrient profiling score that the authors may want to highlight it in the discussion. In this regard lines 312 and 313 say … these analyses consider only single nutrients and do not apply a nutrient profile model …’ but these are not emphasizing on this strength of this study.

Thanks, we have updated this line as recommended and lines 336-339 now read: “A strength of our study is that it applies a nutrient profiling model, whereas these analyses are based on single nutrients and are therefore not directly comparable. However, they generally show that there has been mixed progress by the food industry towards public health goals.”

Attachment

Submitted filename: Response to Reviewers (3).docx

Decision Letter 1

Jane Anne Scott

5 Jul 2021

Assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data.

PONE-D-21-01154R1

Dear Dr. Bandy,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jane Anne Scott, PhD, MPH Grad Dip Dietetics, BSc

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Jane Anne Scott

9 Jul 2021

PONE-D-21-01154R1

Assessing the healthiness of UK food companies’ product portfolios using food sales and nutrient composition data.

Dear Dr. Bandy:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Jane Anne Scott

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: ETsReview.docx

    Attachment

    Submitted filename: Response to Reviewers (3).docx

    Data Availability Statement

    This study used data from two commercial sources. The sales data was accessed under licence from Euromonitor International (https://www.euromonitor.com/packaged-food) via the Bodleian Library, University of Oxford, using Euromonitor’s database portal Passport GMID. The product information dataset, including nutrition composition data, was purchased for the purpose of the lead author’s DPhil research project from Edge by Ascential (https://www.ascentialedge.com/our-solutions). Due to licencing restrictions, the Euromonitor and Edge by Ascential datasets can only be requested under licence for the purpose of verification and replication of study’s findings via the research group’s Data Access Committee (contact: Trisha Gordon foodDBaccess@ndph.ox.ac.uk). Further use of these datasets must be negotiated with the data owners (Euromonitor contact: Ashton Moses - passport.support@euromonitor.com, Edge by Ascential contact: David Beech - info@ascentialedge.com). The authors received no special privileges in accessing the data.


    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES