The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018

Lauren K Bandy; Peter Scarborough; Richard A Harrington; Mike Rayner; Susan A Jebb

doi:10.1371/journal.pmed.1003647

. 2021 May 18;18(5):e1003647. doi: 10.1371/journal.pmed.1003647

The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018

Lauren K Bandy ^1,^*, Peter Scarborough ¹, Richard A Harrington ¹, Mike Rayner ¹, Susan A Jebb ²

Editor: Barry M Popkin³

PMCID: PMC8171925 PMID: 34003863

Abstract

Background

Consumption of free sugars in the UK greatly exceeds dietary recommendations. Public Health England (PHE) has set voluntary targets for industry to reduce the sales-weighted mean sugar content of key food categories contributing to sugar intake by 5% by 2018 and 20% by 2020. The aim of this study was to assess changes in the sales-weighted mean sugar content and total volume sales of sugar in selected food categories among UK companies between 2015 and 2018.

Methods and findings

We used sales data from Euromonitor, which estimates total annual retail sales of packaged foods, for 5 categories—biscuits and cereal bars, breakfast cereals, chocolate confectionery, sugar confectionery, and yoghurts—for 4 consecutive years (2015–2018). This analysis includes 353 brands (groups of products with the same name) sold by 99 different companies. These data were linked with nutrient composition data collected online from supermarket websites over 2015–2018 by Edge by Ascential. The main outcome measures were sales volume, sales-weighted mean sugar content, and total volume of sugar sold by category and company. Our results show that between 2015 and 2018 the sales-weighted mean sugar content of all included foods fell by 5.2% (95% CI −9.4%, −1.4%), from 28.7 g/100 g (95% CI 27.2, 30.4) to 27.2 g/100 g (95% CI 25.8, 28.4). The greatest change seen was in yoghurts (−17.0% [95% CI −26.8%, −7.1%]) and breakfast cereals (−13.3% [95% CI −19.2%, −7.4%]), with only small reductions in sugar confectionery (−2.4% [95% CI −4.2%, −0.6%]) and chocolate confectionery (−1.0% [95% CI −3.1, 1.2]). Our results show that total volume of sugars sold per capita fell from 21.4 g/d (95% CI 20.3, 22.7) to 19.7 g/d (95% CI 18.8, 20.7), a reduction of 7.5% (95% CI −13.1%, −2.8%). Of the 50 companies representing the top 10 companies in each category, 24 met the 5% reduction target set by PHE for 2018. The key limitations of this study are that it does not encompass the whole food market and is limited by its use of brand-level sales data, rather than individual product sales data.

Conclusions

Our findings show there has been a small reduction in total volume sales of sugar in the included categories, primarily due to reductions in the sugar content of yoghurts and breakfast cereals. Additional policy measures may be needed to accelerate progress in categories such as sugar confectionery and chocolate confectionery if the 2020 PHE voluntary sugar reduction targets are to be met.

In a multi-year cross sectional study of sales and nutrient data, Lauren Kate Bandy and colleagues investigate the sugar content and volume of sugar sales of select foods in the UK from 2015-2018.

Author summary

Why was this study done?

Sugar intakes around the world exceed dietary recommendations, and this increases the risk of excess energy intake and weight gain, diabetes, and dental caries.
In an attempt to reduce sugar consumption, the UK government has set voluntary 5% and 20% sugar reduction targets for industry to achieve by 2018 and 2020, respectively.
This study was conducted to see how the sales-weighted mean sugar content of individual companies’ product profiles changed between 2015 and 2018.

What did the researchers do and find?

Researchers analysed the sales-weighted mean sugar content of products in the 5 food categories that contribute the most to sugar intake in the UK: biscuits and cereal bars, breakfast cereals, chocolate confectionery, sugar confectionery, and yoghurts.
Overall, the sales-weighted sugar content of these products fell by 5%, from 28.7 g/100 g in 2015 to 27.2 g/100 g in 2018, with the largest reductions seen in yoghurts (−17%) and breakfast cereals (−13%).
Of the 50 companies representing the top 10 companies in each category, 24 met the 5% sugar reduction targets for 2018.

What do these findings mean?

Our findings show that there has been a small reduction in the sugar content of foods in the UK, and approximately half of companies had not met the 5% sugar reduction target by 2018.
Additional policy measures may be needed to further reduce the sugar content of these foods.

Introduction

One in 5 deaths globally are linked to a poor diet [1]. High consumption of free sugars is associated with increased energy intake and weight gain [2], type 2 diabetes [3], and dental caries [4]. In 2015, the World Health Organization called on countries to reduce the sugar intakes of both adults and children, recommending that the intake of free sugars not exceed 5% [5]. This was followed by similar advice from the UK Scientific Advisory Committee on Nutrition, which recommended a target for dietary energy intake from free sugars of 5% [6]. According to national dietary surveys, the consumption of free sugars in the UK is twice the guideline intake for adults and almost triple for children aged 4–18 years [7]. Citing the success of the salt reduction targets, and in a further effort to reduce the population’s consumption of sugars, in March 2017 Public Health England (PHE), an executive agency of the UK Department of Health and Social Care, outlined a series of voluntary sugar reduction targets for businesses. A 20% sugar reduction target was set for 9 food categories by 2020, with an interim 5% target for 2018, based on the sales-weighted mean sugar content of products in 2015. Sugar reduction may be achieved by a variety of methods, including reformulating existing products, shifting sales between high- and low-sugar products, and launching new products into the marketplace [7]. The 9 categories covered by PHE’s targets represent 54% of sugar consumed by children aged 4–10 years, and 34% for adults aged 19–65 years [7]. This initiative is part of a wider sugar reduction programme, including the introduction of the Soft Drinks Industry Levy in 2018 [8], public health awareness campaigns such as Change4Life [9], and increased attention to free sugar and its health impacts in the mainstream media [10].

In a time when some countries, states, and cities are implementing mandatory nutrition policies, including taxes on soft drinks [11] and front-of-package warning labels [12,13], there is much interest and debate around whether voluntary initiatives are effective. The success of PHE’s voluntary sugar reduction policy is heavily dependent on action by the entire food industry to reduce the sugar content of its products and to encourage changes in consumer behaviour towards purchasing lower sugar alternatives by launching new product lines or focussing marketing and advertising practices on lower sugar products. It is therefore important to analyse the actions of individual companies in relation to sugar reduction. PHE has previously published an evaluation of this policy that analysed category-level changes in sales-weighted mean sugar content over the same time period (2015–2018) [14]. It found that the sugar content of products fell by −2.9%, with total volume sales of sugar increasing by 2.6%. The PHE report included only a limited number of companies, there was no indication of the variability between companies, and the data were not peer-reviewed.

The aim of this study was to use alternative and more comprehensive datasets with information on the nutrient composition of foods and food sales data to analyse how the sales-weighted mean sugar content and total volume of sugars sold from foods covered by PHE’s sugar reduction targets have changed by category and company between 2015 and 2018.

Methods

This study was not conducted as part of any preplanned analyses. It was undertaken as part of a DPhil (PhD) project, with analyses being carried out between March and October 2019. All sensitivity analyses were added during the peer-review process. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist).

Data types and sources

Data on the sugar content of foods were sourced from a commercial third party, Edge by Ascential (previously known as Brand View). Edge by Ascential collects product information, including nutrient composition data, by scraping the websites of 3 leading UK retailers: Tesco, Sainsbury’s, and Asda. The product information used in this study was collected on the same date (13 December) in 4 consecutive years (2015–2018), with 2015 being as far back historically as data were available.

Data were provided for all food and beverage products. Each product contained the following: date and year, retailer name, product name, brand name, company (manufacturer) name, barcode, price, ingredients, pack size, serving size (if stated), and nutrient composition per 100 g for energy, protein, carbohydrate, sugars, fat, saturated fats, fibre, and salt. Each product was also assigned to a product category. These categories were used to identify the relevant products to be included in this study. Only 5 categories from PHE’s sugar reduction targets (biscuits and cereal bars, breakfast cereals, chocolate confectionery, sugar confectionery, and yoghurts) were easily identifiable. Duplicates sold in different supermarkets were removed based on barcode and year.

Weighting composition data by sales volume indicates what was sold, which is a better proxy for consumption and more relevant to public health than analysing the nutrient content of available products only. Therefore, each individual product in the Edge by Ascential database was matched with sales data sourced from Euromonitor via the Bodleian Library, University of Oxford. Euromonitor is a private market research company that provides sales data collected from primary and secondary data sources, including store audits, interviews with companies, publicly available statistics, and company reports [15]. The Euromonitor dataset used in this study includes brands sold through all retail channels, including supermarkets, discount stores, convenience stores, traditional markets, and vending machines. This dataset did not include food service, and only approximately 85% of retail sales are covered, meaning the dataset does not represent the whole food market.

Five categories were identified where the sales database could be directly related to the PHE targets (Table 1). All of the sub-categories presented in Table 1 were included in this analysis. The sales database did not provide enough granularity to identify puddings, cakes, morning goods, and sweet spreads, which were therefore excluded.

Table 1. Number and list of categories and sub-categories included in analysis.

Public Health England food category	Number of Euromonitor sub-categories included in analysis	Euromonitor sub-categories included
Biscuits and cereal bars	7	Cereal bars, chocolate coated biscuits, cookies, filled biscuits, plain biscuits, snack bars, wafers
Breakfast cereals	5	Children’s breakfast cereals, flakes, hot cereals, muesli and granola, other ready to eat cereals
Chocolate confectionery	5	Boxed assortments, chocolate pouches and bags, chocolate with toys, countlines (individual chocolate bars), tablets (large chocolate bars)
Sugar confectionery	10	Boiled sweets, liquorice, lollipops, mints, other sugar confectionery, pastilles, gums, jellies and chews, toffees, caramels and nougat
Yoghurts	5	Drinking yoghurt, flavoured fromage frais and quark, flavoured yoghurt, plain fromage frais and quark, plain yoghurt

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Euromonitor measures sales by brand, rather than by individual products. A brand was defined as a set of products that have the same core name and are manufactured by the same company. For example, the company Mondelez manufacturers multiple brands, including Cadbury Dairy Milk chocolate (one brand) and Oreo biscuits (another brand). Within each brand, there may be multiple individual products, for example Cadbury Dairy Milk Fruit and Nut and Cadbury Dairy Milk Whole Nut.

The brand-level sales data were matched with the product-level nutrient composition data based on the variables brand name, company name, category, and year. Where brands were matched with more than 1 individual product, a mean sugar content was calculated, as demonstrated in the flow chart in Fig 1 below.

Corresponding product-level nutrient composition data could not be found for 62 brands in the sales database for the 5 included categories. Twenty of these brands were not sold in the supermarkets included in the nutrient composition database. This represented 2% of total volume sales. For these cases, the nutrient composition data were sourced online from the brand website in mid-2019, and these data were used for all 4 years. Forty-two brands across the 5 categories, representing 3% of total volume sales, were human errors in the sales database and were not manufactured in the given time period. As these products could not be matched with corresponding nutrient composition data, they were removed from the dataset. Euromonitor classifies a number of small and local brands under the umbrella of ‘others’, and these products, representing 13.5% of total volume sales (ranging from 7% for sugar confectionery to 21.1% for biscuits and cereal bars), were also excluded from the main analyses; a sensitivity analysis was conducted to assess the impact on the results.

Data analysis

The unit of analysis was the brand, and the sales-weighted mean sugar content (g/100 g) was the primary outcome, with total volume of sugars sold (tonnes) also being calculated, given that it is a proxy for consumption and therefore adds important context for public health. The additional variables in the datasets identified the companies that owned the brands and the categories they belonged to, allowing for analyses to be stratified by company and category. Change in sales-weighted mean sugar content was calculated between 2015 and 2018, with all products being included regardless of whether or not they were present in the market in 2015 or 2018. To adjust for population changes and to put the results in the context of dietary recommendations, results for total volume of sugars sold are presented in grams per person per day. This value was calculated using annual population estimates from the Office for National Statistics [16], dividing by annual population size and by 365.

For each category, 5% and 20% sugar reduction targets were calculated using the sales-weighted mean sugar content with 2015 as a baseline.

The change in total volume of sugars sold was split into the change in the absolute mean sugar content and change in total volume sales using a decomposition formula [17]. Briefly, this is derived as follows. Total volume of sugar sold (V) = mean sugar content (S) × volume of foods sold (F). Differentiation (to give change in volume of sugar sold over time) gives V′ = F × S′ + S × F′. Let ΔV = V′/V (i.e., the annual percentage change in V). Then

Δ V= \frac{F \times S' + S \times F'}{S \times F} = \frac{F \times S'}{F \times S} + \frac{S \times F'}{F \times S} = \frac{S'}{S} + \frac{F'}{F} = Δ S+ Δ F

Therefore, the annual percentage change in the total volume of sugar sold is equal to the sum of the annual percentage change in the mean sugar content and the annual percentage change in the volume of the foods sold. This was calculated at the total, category, and company level, and percentage change was used to attribute absolute changes in the volume of sugar to changes in sugar content and changes in total volume sales.

For most categories, 95% confidence intervals were calculated using absolute and sales-weighted standard deviations and means using unique products identified in the nutrient composition database as the units of analysis. For sugar confectionery, the nutrient composition data were not normally distributed, so the mean and median sugar content and interquartile range were calculated. We tested for differences in the sales-weighted mean sugar content of each category in 2018 compared to 2015 using a Kruskal–Wallis test, weighted so that each brand was proportional to total sales.

Sensitivity analysis

We conducted 2 sensitivity analyses. Sensitivity analysis 1 assessed the impact of individual product variation within brands on the overall sales-weighted mean sugar content of foods. To do this, brands within one category and year, e.g., breakfast cereals in 2018, were classified as either ‘product range’ brands, or ‘individual product leader’ brands. This was done based on the researcher’s knowledge of the market and with the aid of product searches on online supermarkets. Product range brands are those that have a wide range of individual products and flavour variations but were judged to have no single product leader in terms of sales. These brands were excluded from the sensitivity analysis as the method of taking a mean sugar content of all individual products within a brand was unlikely to have affected the results. For brands that have a single individual product that is likely to represent the majority of that brand’s sales (e.g., Original Weetabix) compared to other minority variants (e.g., Weetabix High Protein), assuming an equal weighting of sales amongst these individual products when calculating the mean sugar content is likely to have impacted the results. For brands classified as ‘individual product leaders’, we compared the sugar content of the leading individual product with the sugar content values used for the main study (g/100 g) and calculated percentage difference and range.

Sensitivity analysis 2 was conducted to assess the impact of excluding the sales volume that represented small and local businesses under the umbrella term ‘others’. Scenario 1 assumed that this volume had the same sales-weighted mean sugar content as the overall category in 2015 and remained unchanged over time. This scenario was considered most likely as small and local companies represented by the ‘others’ sales volume are less likely to have the capacity for reformulation. Scenario 2 assumed that the sales-weighted mean sugar content of the volume sales represented by ‘others’ changed at the same rate as the overall category between 2015 and 2018.

Results

For 2015, 95 companies were included in this analysis (although, for clarity, only the top 10 companies by volume sales for each category are presented in Fig 3). The 95 companies manufactured 350 brands, with 2,515 products in the 5 included categories (biscuits and cereal bars, breakfast cereals, chocolate confectionery, sugar confectionery, and yoghurts) (Table 2). These figures remained relatively unchanged over the time period. In 2018, data were included for 97 companies producing 353 brands and 2,351 individual products.

Fig 3 — Black marker lines represent the percentage change of total volume of sugars sold by company and category. This total change is split into the percentage change due to changes in volume sales of products (orange) and the percentage change due to changes in the mean sugar content of products (grey).

Table 2. Data points in the nutrient composition and sales datasets 2015–2018.

Measure	2015	2016	2017	2018
Number of individual products in nutrient composition dataset	2,515	2,535	2,443	2,351
Number of brands in sales dataset	350	349	355	353
Number of companies	95	99	98	97

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The total volume of sales from the 5 food categories did not change between 2015 and 2018, with small increases in the volume sales of biscuits and confectionery counteracted by declines in the volume sales of breakfast cereals and yoghurts (Table 3).

Table 3. Total volume sales of food category in tonnes, 2015–2018.

Category	Volume sales (tonnes)				Percent change (2015–2018)
Category	2015	2016	2017	2018	Percent change (2015–2018)
Biscuits and cereal bars	383,600	387,500	392,500	395,400	3%
Breakfast cereals	394,500	391,000	384,100	383,100	−3%
Chocolate confectionery	349,900	354,400	352,400	353,600	1%
Sugar confectionery	128,000	127800	126,800	127,400	0%
Yoghurts	511,600	514,900	513,600	503,200	−2%
Total	1,767,600	1,775,600	1,769,400	1,762,700	0%

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The sales-weighted mean sugar content for included food categories fell from 28.7 g/100 g to 27.2/100 g, a reduction of 1.5 g/100 g, or 5.2% (95% CI −9.1%, −1.4%) although this change was not statistically significant (p = 0.52) (Table 4). The greatest change was observed in yoghurts and breakfast cereals, with a reduction of 1.9 g/100 g, or 17.0% (95% CI −26.8%, −7.1%), and 2.5 g/100 g, or 13.3% (95% CI −19.2%, −7.4%), respectively. Biscuit and cereal bar mean sugar content declined by 2.5 g/100 g, or 6.3% (95% CI −10.0%, −2.7%), from 18.8 g to 16.3 g per 100 g. The reductions for chocolate (−1.0%) and sugar confectionery (−2.4%) were small.

Table 4. The sales-weighted mean sugar content of food categories, 2015–2018.

Category	Sales-weighted mean sugar content (g/100 g) (SD, 95% CI)		Absolute (g/100 g) and percentage change (95% CI) 2015–2018	p-Value
Category	2015	2018	Absolute (g/100 g) and percentage change (95% CI) 2015–2018	p-Value
Biscuits and cereal bars	30.0 (9.0, 29.3–30.7)	28.1 (9.9, 27.3–29.0)	−1.9, −6.3% (−10.0%, −2.7%)	0.78
Breakfast cereals	18.8 (9.3, 18.0–19.5)	16.3 (8.6, 15.5–16.9)	−2.5, −13.3% (−19.2%, −7.4%)	0.16
Chocolate confectionery	51.7 (10.6, 50.8–52.7)	51.2 (10.8, 50.2–52.4)	−0.5, −1.0% (−3.1%, −1.2%)	0.91
Sugar confectionery	62.2 (50.0–75.6)^*	60.7 (52.8–69.0)^*	−1.5, −2.4% (−4.2%, −0.6%)	0.92
Yoghurts	11.2 (4.1, 10.9–11.5)	9.3 (4.0, 9.1–9.6)	−1.9, −17.0% (−26.8%, −7.1%)	0.70
Total	28.7 (8.3, 27.2–30.4)	27.2 (8.3, 25.8–28.4)	−1.5, −5.2% (−9.1%, −1.4%)	0.52

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*Interquartile range given for sugar confectionery due to data not being normally distributed.

The total volume of sugar sold decreased from 21.4 g/d to 19.8 g/d, a reduction of 1.6 g/d, or 7.5% (Table 5). Of this, 70% was attributable to a reduction in the mean sugar content of foods and 30% was due to a decrease in volume sales (Fig 2).

Table 5. Total volume of sugars sold by food category in per capita per day terms, 2015–2018.

Category	Total sugar sales, grams/person/day (95% CI)		Absolute (grams/person/day) and percentage change 2015–2018
Category	2015	2018	Absolute (grams/person/day) and percentage change 2015–2018
Biscuits and cereal bars	4.9 (4.8–5.0)	4.6 (4.5–4.7)	−0.3, −6.1%
Breakfast cereals	3.1 (3.0–3.2)	2.6 (2.5–2.7)	−0.5, −16.1%
Chocolate confectionery	7.6 (7.5–7.7)	7.5 (7.4–7.7)	−0.1, −1.3%
Sugar confectionery	3.4 (2.7–4.1)^*	3.2 (2.8–3.6)^*	−0.2, −5.9%
Yoghurts	2.4 (2.3–2.5)	1.9 (1.9–2.0)	−0.5, −20.8%
Total	21.4 (20.3–22.7)	19.8 (18.8–20.7)	−1.6, −7.5%

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*Interquartile range given for sugar confectionery due to data not being normally distributed.

Reductions in the sugar content of products and the total volume of sugars sold varied by company (Fig 3). Company-specific decreases in total sugar volume (represented by the black marker lines) were predominately due to reductions in the mean sugar content (represented by the orange bars), although some companies also had reduced sales volumes (represented by the grey bars). Where we recorded increases in the total volume of sugars sold, this corresponded to increases in sales volumes for individual companies. Companies that manufacture products in more than 1 category are presented separately in each relevant category. In the case of the companies Mondelez (biscuits), Tesco (breakfast cereals), Thorntons (chocolate confectionery), and Haribo and Mondelez (sugar confectionery), there were also increases in the mean sugar content. These were attributable to an increased proportion of sales from brands within these companies with a higher sugar content.

Sensitivity analysis results

Sensitivity analysis 1—heterogeneity of individual products within brands

Forty-five percent (n = 25) of breakfast cereal brands were classified by the researcher as product range brands. These are brands that have a wide range of individual products and flavour variations, and were judged to have no single product leader in terms of sales. As these brands represent a wide range of individual products and flavours, they were excluded from this analysis as it was assumed taking a mean sugar content across the product range would not impact the results. Fifty-five percent (n = 30) of brands were classified as brands with a leading individual product, where taking a mean sugar content across all products might have an impact on the overall sales-weighted mean sugar content. When comparing the sugar content of the leading individual product to the average values used in this study, there was an average variation of −8% (95% CI −88% to 30%), representing a difference of 0.5 g/100 g (95% CI −8.3 to 4.1 g/100 g) in sugar content.

Sensitivity analysis 2—volume represented by ‘others’

We assumed 2 scenarios for assessing what impact excluding the ‘others’ sales volume had on the overall sales-weighted mean sugar content of foods, as this volume represented between 7% and 21% of sales depending on the category. Scenario 1 assumed that the brands represented by the ‘others’ sales volume did not change their sugar content over time. Scenario 2 assumed that the sales-weighted sugar content of ‘others’ brands changed at the same rate as the overall category. The results of both scenarios were similar to the results presented in this study (Table 6).

Table 6. Main analysis results of sales-weighted mean sugar content of foods in 2015 and 2018 compared to the results of 2 sensitivity scenarios.

Year	Overall sales-weighted mean sugar content of foods (g/100 g)
Year	Main analysis¹	Scenario 1²	Scenario 2³
2015	28.7	29.1	29.1
2018	27.2	27.8	27.6

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¹The overall sales-weighted mean sugar content of foods presented in the main analysis in this study for 2015 and 2018.

²Scenario 1 assumed that the brands represented by ‘others’ did not change their sugar content over time, and the sugar content remained the same as in 2015.

³Scenario 2 assumed that the sales-weighted sugar content of brands represented by ‘others’ changed at the same rate over time as the overall category.

Discussion

Nutrient composition and food sales data were combined to calculate the sales-weighted mean sugar content of 5 food categories between 2015 and 2018. The results were compared to PHE’s 5% and 20% sugar reduction targets for 2018 and 2020, respectively. Our results show there was a decrease in the total volume sales of sugars of 7.5% between 2015 and 2018, of which 70% was attributable to a decrease in the sugar content of foods and 30% to changes in sales volume of specific brands and products. The reduction in the sales-weighted mean sugar content of the selected foods was 1.5 g/100 g, equivalent to 5.2%, which was in line with the PHE interim 2018 target for sugar reduction. There was, however, great heterogeneity between categories and companies. The yoghurt and breakfast cereal categories saw the largest reductions in sales-weighted mean sugar content, 17.0% and 13.3%, respectively. In contrast, chocolate confectionery and sugar confectionery saw little change in either the sales-weighted sugar content of products or per capita volume of sugar sold. Of the 50 companies representing the top 10 companies in each category, 24 (48%) met the 2018 category-specific 5% reduction targets. In addition, 4 companies had already met the 20% target for reductions by 2020. There were increases in the sales-weighted mean sugar content of products sold by 10 companies, due to an increase in sales of brands with higher sugar content within those companies.

Comparison with other studies

To our knowledge, this is the first peer-reviewed study to report on the changes made by individual companies towards reducing the sugar content of foods in the UK. The findings are similar to the few studies that have looked at the sugar content of individual food categories. One study looking at the sugar content of breakfast cereals in the UK found that the absolute mean sugar content of products, based on data collected from 5 online supermarkets, was 20.8 g/100 g in 2015 [18], compared to our finding of 19.1 g/100 g. Another study reported the sugar content of 921 yoghurts sold from 5 online supermarkets in the UK in 2016 [19]. Results are similar to our findings for drinking yoghurts (9.1 g/100 g compared to 9.0 g/100 g in this study), flavoured yoghurts (12.0 g/100 g versus 11.3 g/100 g), and plain/Greek-style yoghurts (5.0 g/100 g versus 4.3 g/100 g).

PHE has previously published its own analysis of the change in the sales-weighted mean sugar content of different food categories, using sales and nutrient composition data from Kantar Worldpanel [14]. PHE reported that the sales-weighted mean sugar content of foods declined by 2.9% overall between 2015 and 2018, compared to the 5.2% (95% CI −9.1%, −1.4%) decrease reported here. Category-level changes from the PHE report are compared with the results of this study in Table 7. Both studies saw the greatest reductions in sugar content in yoghurts and breakfast cereals, with small changes for chocolate and sugar confectionery.

Table 7. Percentage change in sales-weighted mean sugar content of different food categories between 2015 and 2018 compared to Public Health England findings [14].

Category	Percent change in sales-weighted mean sugar content
Category	Public Health England [14]	This study (95% CI)
Biscuits and cereal bars	−0.6%	−6.3% (−10.0%, −2.7%)
Breakfast cereals	−8.5%	−13.3% (−19.2%, −7.4%)
Chocolate confectionery	−0.3%	−1.0% (−3.1%, 1.2%)
Sugar confectionery	+0.6%	−2.4% (−4.2%, −0.6%)
Yoghurts	−10.3%	−17.0% (−26.8%, −7.1%)

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PHE also reported that the total volume of sugar sold from included categories increased by 2.6% from 2015 to 2018, compared to the reduction of 7.5% reported here [14]. The difference between the results is likely to be due to the different categories included in the 2 analyses, and differences in the datasets used. PHE used sales and nutrient composition data from Kantar Worldpanel for monitoring the sugar content of foods [14]. Kantar sales data are based on the results from a sample of measured weekly household purchases scanned by participating households and report much lower overall sales compared to the total annual sales estimates used here from Euromonitor. This may be due to individuals forgetting to scan products, especially impulse purchases consumed out of the home such as confectionery [20]. Although the absolute values differ, the main patterns were similar, with the greatest reductions in sugar content in yoghurts and breakfast cereals, and little change in chocolate and sugar confectionery.

Strengths and limitations of this study

By pairing nutrient composition data with food sales data, we were able to analyse the sugar content of what was sold, not just of what products were available. This provides insights into how individual companies have reduced the sugar in their products, potentially as a result of PHE’s call to reduce the sales-weighted mean sugar content of their products by 20%. We hope that these results provide transparent information for stakeholders to assess how the food industry is progressing towards public health targets.

The nutrient composition data used here were collected by Edge by Ascential from the websites of the UK’s 3 leading retailers (Tesco, Sainsbury’s, and Asda) on the same date (13 December) in each year included in this study. The total number of products included is therefore likely to be an underestimate, as it is a reflection of what is available online on a single date, and does not include products that are available for purchase from other retailers, independent stores, and markets. Taking data from single time points also means that we have not captured the churn of products that are entering and being removed from the market seasonally and over the course of the year.

This study does not cover the entire food market, nor does it cover every category included in PHE’s sugar reduction targets. Cakes, morning goods, ice cream, puddings, and sweet spreads were excluded due to a lack of granularity or lack of alignment between PHE’s categories and the Euromonitor sales database. According to the National Diet and Nutrition Survey, these missing categories represent an estimated 11% of sugar intake for children aged 11–18 years [7].

Using sales data—as opposed to dietary survey data to estimate intake or household panel data to monitor purchases—has 2 main advantages. First, it avoids reliance on individual recall of consumption, and underreporting in scan data [20], and, second, sales datasets include granular details about the individual brands that have been sold. Euromonitor has wide coverage, including hypermarkets, supermarkets, convenience stores, vending machines, and fast food outlets. This means that it is particularly suited to studying changes at the company level.

However, there are some major limitations of using Euromonitor sales data. Euromonitor does not cover the whole market, and a lack of granularity in the data meant that some high-sugar categories, including ice cream, cakes, and pastries, were not included in this study. Therefore, limited conclusions can be drawn in terms of how the sugar content of all foods has changed in the UK. Another limitation is that Euromonitor groups small and local brands under the umbrella term ‘others’, meaning that a proportion of volume sales could not be paired with nutrient composition data. The results of the sensitivity analyses, which examined 2 different scenarios for estimating the sugar content of these products, demonstrated that excluding the ‘others’ sales volume was unlikely to have had any significant impact on the results. However, it does mean that smaller, local brands are essentially excluded from this study and means the findings are less representative of the whole market.

A more major limitation is that the food sales data were only available at the brand level, not the individual product level, and therefore any heterogeneity that occurred between products under the same brand would been have missed by assuming all products are sold equally. Results of the sensitivity analysis using the breakfast cereal category as an example showed that this is unlikely to have affected around half (45%) of brands, as these represent a broad variety of products. However, for the remaining 55%, the sugar content value used in this study and the sugar content of the leading individual product differed, with an overall percentage difference of −8% (95% CI −88% to 31%), or 0.5 g/100 g (95% CI −8.3 to 4.1 g/100 g). These results suggest that while the study’s overall findings are not likely to be affected by assuming all products within a brand are sold equally, there may be some significant misrepresentations at the brand and potentially company level. Use of individual-product-level sales would improve this, although datasets that have product-level sales data have other limitations, including their cost and limitations in publication of company and brand names [21].

Users of third-party sales databases have no control over the data collection process, and there is limited transparency in the methods of data collection, or the reliability of sources [21]. The sales data used in this study are for the total UK population and are not broken down by any sociodemographic factors; therefore, any variation in the impact sugar reduction might have based on income and brand preference could not be determined in this study. Sales data do not account for waste, but in this time trend analysis, the absolute values are less pertinent than the change, assuming that there have not been major changes in food waste.

Policy implications

Although our analysis suggests the PHE 5% interim target for 2018 was met overall, the majority of this change was driven by just 2 categories, yoghurts and breakfast cereals, with negligible changes in sugar and chocolate confectionery. The difference between categories may be due to differences in the technical ease of reformulating products. It is important to note that PHE’s sugar reduction targets sit alongside long-running public health awareness campaigns, including Change4Life [9], as well as increased attention from the mass media about the sugar content of everyday products, and therefore the results observed are not solely due to PHE’s setting targets. The larger reductions observed in the sugar content of yoghurts and breakfast cereals may also reflect pressure from the public health community about the sugar content of these products that are otherwise considered part of a healthy diet, but where the sugar content is perceived to be ‘hidden’. In contrast, there has been less media attention paid to the sugar content of confectionery, perhaps because it is seen as an indulgence and the sugar content is more overt.

This analysis raises concerns about the likelihood of achieving the more stretching 20% reduction target set for 2020. Over half of companies included in this analysis had not met the 5% reduction target, and since companies that made the greatest reductions and already achieved the 2020 target may now slow or pause their sugar reduction efforts, there will need to be a considerable acceleration in reductions by other companies, especially in the confectionery categories. It is also important to note that the categories that have seen the greatest changes (breakfast cereals and yoghurts) are also those that had the lowest levels of sugar to start with. Further research using more recent data could be conducted to assess further changes in sugar content between 2018 and 2020.

It is notable that the level of change in the sugar content of foods is smaller than that seen in soft drinks. Using similar methodology, we previously showed that the sales-weighted mean sugar content of soft drinks fell by 34% over the same time period [22], compared to 8% for the food categories studied here. The relative success of sugar reduction in drinks over that in foods may partly be due to the greater technical challenges in reformulation of foods. In drinks the sweetness delivered by sugar and other caloric sweeteners can be replaced with high-intensity sweeteners [23]. Foods containing starches and/or fats combined with sugars are harder to reformulate [23]. In food categories such as those included in this study, sugar not only delivers sweetness but has other technical properties, including water retention, browning, texture modification, and structure [23]. The consumer acceptability of reduced-sugar foods is also thought to be lower than that of their regular counterparts [24], meaning that food companies may be reluctant to reduce the sugar content of their products [24,25], especially for indulgent products such as confectionery and biscuits.

Alternatively, the small changes in the sugar content of foods compared to drinks may reflect differences in the policy context. In the UK, sugar reduction in soft drinks has been driven in part by the Soft Drinks Industry Levy, with high-sugar products (>8 g/100 ml) being subject to a tax of 24 pence per litre and mid-sugar products (5–8 g/100 ml) being taxed at 18 pence per litre [26]. Evidence shows that the introduction of the levy led to companies reformulating their products to less than 5 g sugar per 100 ml in order to avoid the levy, leading to significant reductions in the sugar content [27] and the total volume of sugar sold from soft drinks [22]. In contrast, the food categories included here are subject only to a voluntary programme of sugar reduction targets monitored by PHE, but with no penalties for lack of progress. Without sanctions, food companies may be less motivated to engage with this voluntary programme.

The PHE sugar reduction targets are designed to help the population achieve dietary recommendations for free sugar intake, which in the UK are no more than 19 g for children aged 4–6 years, 24 g for children aged 7–10 years, and 30 g for those aged 11 years and above [5,6]. Assuming a direct relationship between sales and consumption, this analysis suggests a per capita intake of free sugars of 19.7 g/d from these food categories alone. Given that these categories provide only 40%–60% of total free sugar intake [7], it is clear that much greater reductions in these and other categories of foods and drinks will be required to meet dietary recommendations.

The UK claims to be the first country in the world that has implemented a structured sugar reduction programme with incremental targets [28]. However, other countries have implemented a range of other policies to reduce the availability and affordability of high-sugar foods. For example, in Chile, high-sugar products have had to display a ‘high in sugar’ front-of-package warning label since June 2016 [29]. A prospective study has shown that the proportion of breakfast cereal products that are classified as high sugar fell from 46% in 2015–2016 to 24% in 2017, with little change in sweet confectionery. In Mexico in 2013, a tax on nonessential energy-dense foods that contain >275 kcal/100 g was implemented, alongside a nationwide public health campaign. An initial evaluation showed that purchases of these products fell by 5% 2 years after the implementation [30]. Hungary introduced a tax in 2011 on a range of processed foods based on their sugar content, including confectionery and snacks. An initial evaluation of this policy showed that prices of tax-eligible products increased by 29% and consumption declined by 3% [31]. These case studies may provide examples of other actions that could be taken in the UK to accelerate progress on sugar reduction.

In conclusion, our findings suggest there has been a mixed response by companies to reducing the sugar content of foods in the UK between 2015 and 2018. The greatest reductions in sales-weighted mean sugar content were observed in breakfast cereals and yoghurts, with minimal change in sugar and chocolate confectionery. The majority of companies had not met the 5% sugar reduction target by 2018, suggesting that additional policy measures may be needed to further reduce the sugar content of foods.

Supporting information

S1 STROBE Checklist

(DOCX)

Click here for additional data file.^{(31KB, docx)}

Abbreviation

PHE: Public Health England

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).

Funding Statement

LB 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.

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PLoS Med. doi: 10.1371/journal.pmed.1003647.r001

Decision Letter 0

Adya Misra

20 Jul 2020

Dear Dr Bandy,

Thank you for submitting your manuscript entitled "Assessing progress by UK companies to reduce total volume sales of sugar in selected food categories" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff [as well as by an academic editor with relevant expertise] and I am writing to let you know that we would like to send your submission out for external peer review.

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PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003647.r002

Decision Letter 1

Adya Misra

15 Oct 2020

Dear Dr. Bandy,

Thank you very much for submitting your manuscript "Assessing progress by UK companies to reduce total volume sales of sugar in selected food categories" (PMEDICINE-D-20-03288R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Notes from the Academic Editor

Please clearly state the weaknesses and flaws in the nutrient data base collected. In particular in the weights used to give all foods the same weights. This potentially means that obscure foods in one category that are truly niche foods would have the same weight as a popular food item [e.g. a product with no reformulation].

By using weights of sales instead of longitudinally following specific products, this is not true reformulation at all but rather an attempt to examine sugar reductions. This can only be done by looking at each product. Therefore, weighted by product and not by category with the same weights for all. These inherent limitations must be discussed clearly in a revision.

Requests from the editors:

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Abstract

Abstract- please replace “similar period” with the exact timeline

Last sentence of the methods and findings must outline 2-3 limitations of your study design/methodology

Please add exact p-values, unless p<0.001 as needed

Please temper all results and conclusions by adding “our results show ..” or similar.

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Please use square brackets for references and Vancouver style for bibliography

Discussion

Please begin this section with 1-2 sentences summarising what was done

Several sentences need toning down, for example “Most progress was made in the yoghurt and breakfast cereal categories ..”. I suggest revising to “our results show most sugar reductions were observed in xxx categories” or similar

Line 191- I’m not sure consumer behaviour can be inferred from this data, at least not without major caveats so I suggest removing

Please avoid assertions of primacy and add “to our knowledge” at line 221

Throughout- while you present the findings as a direct result of PHE voluntary targets, there is no reason to believe that these changes in sugar content were independent of the PHE. I suggest de-emphasizing this aspect of your manuscript and noting that the results “could be” due to PHE directives.

Please ensure that the study is reported according to the [STROBE] guideline, and include the completed [STROBE or other] checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Comments from the reviewers:

Reviewer #1: The study by Brandy LK assessed the progress made by UK food companies in sugar reduction in five food categories covered by PHE's voluntary sugar targets (biscuits and cereal bars, breakfast cereals, chocolate confectionery, sugar confectionery and yoghurts) using public available sales data and nutrient composition information of 2515 products under 250 brands nested in 95 companies. They found a 7.5% reduction in total volume of sugars sold per capita and 5.2% in sales-weighted mean sugar content. The sugar reduction was primarily observed in yogurts and breakfast cereals, and more than half the companies did not meet the PHE's 2018 target. This study provided the latest evidence on how the UK food industry responded to PHE's voluntary sugar reduction program and highlighted the issue of accelerating the sugar reduction progress in categories such as confectionery for most UK companies.

The paper is well written. However, there are several points that need further clarification:

1. Can you explain how you generated the percentage (line 152-153) and absolute value (Figure 1) that "reduction in the mean sugar content" or "decrease in volume sales" contributed to the total sales-weighted volume of sugar sold?

2. This study calculated the mean sugar content for each brand by simply averaging out the sugar content of all the product variants (line 98-101). However, the sales might differ greatly among product variants within the same brand (for example, the regular chocolate spread might be more popular than sugar-reduced ones), thus we suggested that the mean sugar content of each brand should be weighted by product-level sales if data available.

3. Does the Euromonitor covers the sales data of the five food categories in the UK? Can you provide more information about the coverage of Euromonitor sales data?

4. The number and the subcategories listed in table 1 for "sugar confectionery"? are not consistent. Please double check.

5. Please double check the percentage figure of total volume sales for the 62 and 42 brands that can't be matched for their nutrient composite data (line 103 and 106). Should it be 3% in line 103 and 2% in line 106?

6. Where does the percentage "five categories which together provide 40-60% of free sugar intake in the UK" (line 175) come from? Can you provide a reference here?

7. Can you explain more about the potential reason why the %change in sales-weighted mean sugar content between 2015 to 2018 was much smaller in PHE's findings compared to this study? Why the sales weighted sugar content of sugar confectionery category showed a reduction (2.4% [-0.6%, -4.2%]) in this study, whereas there was a small increase (+0.6%) in PHE's report?

Reviewer #2: Comments

Generally, I think this is an interesting paper that provides a useful contribution to public health. Reformulation of the food supply is a topic of major interest with regards to population-level strategies to improve diets, prevent obesity, and related NCDs, and in particular it is of great interest whether voluntary pledges vs. mandatory regulations are needed to improve the nutritional quality of the food supply. This paper focused in the UK is useful, especially in comparing and contrasting the changes that have been observed in response to this voluntary initiative compared to UK's mandatory sugary drink tax.

Although I think my comments below are readily addressable, I did find the overall quality of the paper difficult to assess due to some confusion in the methods section. I think that adding some more detail on what exactly was done, as well as the rationale, would help tremendously. I look forward to reading future revisions.

Major queries:

* Clarity around brand-company-product. It was at times difficult to follow what you meant by each term, and they seemed to be used interchangeably in some cases.

* Clarity around sales weighting: providing rationale for why to do the sales-weighting and how exactly it was done (seemingly at the brand level). It could be useful to provide an example.

* Clarity around the sample: did products move in and out? Or you followed specific products over time?

* Additional consideration and potentially sensitivity analyses around a) the implications of missing data (e.g. missing categories, other missing data); but also the key limitation that you are weighting at the brand level, which doesn't incorporate the considerable heterogeneity that likely occurs for products within brands. Even if you could do a sensitivity analysis of one specific brand and products within it to show how much variation you might expect to see if one product happened to be sold much more than another product, that would be helpful.

* This article is framed as a pre/post study to address the question, did companies respond to PHE's initiative, but it's impossible to know whether they were already reformulating their products and PHE's initiative just happened to coincide with it. Have you considered adding additional years prior to PHE's announcement?

Intro

It would be useful to link sugar to health, and particular free sugar, as there is a slight but important difference between the concept of free sugar and total sugars, which it seems like PHE is targeting.

you might explain briefly what is PHE for non-UK readers. It would be very relevant to know if this is a government org, ngo, industry-backed group, etc.

Consider adding more rationale about why we care if this voluntarily initiative works or not? Particularly in the 2nd to last paragraph, why is it important to understand variability between companies?

How does this link to global efforts (mandatory or voluntary policy actions) to reduce sugar?

Consider more rationale about why it is important to consider sales-weighted data (vs. simply looking at changes in the nutrient composition of products)

When did PHE actually release their guidelines? It would be useful to understand to provide context for time window that was used in this study.

Methods

Please state what were the other 4 categories you could not analyze? Do you know what % of sales these categories account for? This is an important point, particularly as I noted that beverages were not included at all in this analysis. Was that b/c of Euromonitor or because they weren't included in PHE's initiative? This is important considering all the other sugar reduction efforts going on in beverages. Also, if this does focus on foods, it would be useful to strengthen in the intro why it is important to consider foods in the diet, not just sugary drinks.

In your explanation of the brand, it's not totally clear what you're saying. It sounds like Cadbury Dairy Milk and Oreos are considered two separate brands, right?

Why only consider such a small set of years (2015-2018)? Isn't it useful to consider additional years prior to PHE's announcement to understand if trends accelerated?

With regards to understanding reformulation, did you include products in the analysis that could not be reformulated because they did not contain added sugar originally (e.g. plain oatmeal)?

Please be clear if Euromonitor was providing brand-level or product-level data. It sonds like it was at the brand level. How did you identify all the product within the brand? Secondly, you note that the data is "sales-weighted," but it is sales weighted at the brand level, right? So if there is variability within the brand by individual product, you won't see that here.

How frequently is the nutrient composition data updated?

I got confused between brands, products, and companies. For example, in Line 102, you note you couldn't find nutrient data for 62 brands- but in many cases a brand does not equal a product, which is what the nutrient data is on?

It would be helpful to understand the total scope of missing data, at least in the discussion. Sounds like you have about ~17% volume sales missing from missing nutrient data, plus the 4 other categories you could not find in Euromonitor. Would you get a different picture if purchases data were used instead?

Analysis: would be useful to understand if brand and company are synonymous. I was a little surprised to see this reported by company when previously you were talking mainly about brands. Also, I'm assuming brands are followed longitudinally over time? Or are these repeated cross sections? What is the sample size.

I didn't understand the structure of the data. Was it at the product-level, with covariates that included year, category, and company?

Please explain how the sales-weighting by brand level was done in the context of a dataset on products.

Results:

Please include units in table 3

Please include some measure of statistical significance in the text when you describe results (Line 140)

Line 140- the absolute reduction in biscuits was quite similar to that ovserved in yogurt and cereal, which had higher relative declines mainly because they started out at lower amounts. You might just note this by highlighting the absolute vs. relative change.

How did you conduct the analysis that gave rise to the result about product-level changes vs. sales-level changes reported in Line 152-153?

Figure 1 is confusing. The bars look as though they represent the total sugar sales in each year, but then the stacked bar in 2018 represents change? I think you would want to find some way to represent that the 1.2 and 0.5 represent sugar that is no longer in the food supply (whereas currently it looks like part of a bar that represents sugar currently observed in the food supply).

On the company analysis, did some companies product products across categories? If so, how did you handle this in analysis?

Line 164- when parsing out the change due to sugar content and change due to sales, it's not clear why you would want the sugar content to be sales weighted. Isn't the point to show specifically what is going on at the product level, not accounting for changes in behavior (sales)? This is where it is also useful to understand if you only included products that remained in the data over time, since it is unclear how new product entry or product exit affected these numbers (a different concept from reformulation, which is changing the nutrient content of a specific product).

Figure 2: I found it hard to understand whether within each company it has a net increase or decrease in sugar sales. This could benefit from a sentence or two to help the reader interpret what is happening in this figure.

Worth noting in the discussion that several of the categories that changed most had the smallest levels of starting sugar levels (and particularly in yogurt, some of this is natural sugar).

Maybe not surprising not to see a reduction in confectionary. Could you discuss how the changes observed might also reflect demand? It seems like if someone is making the choice to have a candy bar, they don't necessarily want it to have reduced sugar levels, whereas this could be a more desired attribute for seemingly healthy products like cereal or yogurt, or even for products that have more variability in sweetness, such as biscuits/cookies.

Discussion

Were you able to look at non-sugar sweeteners? (Is this mandatorily reported in the UK)? It woudl be useful to know if these sugar reductions resulted in a reduction in overall sweetness of the products, or if they were just being replaced with NNS. I would also look more into UK/global trends in NNS. My understanding is that NNS-containing foods are increasing, not just for drinks but in foods as well (in fact, yogurt is a popular category for this).

The paragraph stating line 294- I think it would be more useful to compare to countries that expicitly addressed sugar as part of their policy. In particular, consider the countries that are explicitly including a warning on high-sugar foods. Those are currently: Chile, Peru, Israel; currently being implemented in Mexico and Uruguay. Chile has some results, including a paper on reformulation that was published in Plos Med (Reyes et al)- this may actually be the most relevant comparison for this paper.

Limitations section- it would be useful to discuss more the implications of missing data and the fact that you had to (I think) weight all products within a brand equally. For example, what if one product within a brand accounts for most of the sales in that brand? Like if there was a low-sugar Twix that was developed, for example, but everyone continued purchasing the main Twix, this would show a misleading reduction in sugar for this brand.

Also talk about how well you can say that these changes were attributable to PHE's target. You didn't use a counterfactual here. Is

Also consider discussing limitations for what we can understand on public health impact from this study. For example, the effect of sugar reformulation could be greater or lesser for different socio-demographic populations, depending on what brands (and products within the brand), they buy.

Reviewer #3:

This review relates to manuscript PMEDICINE-D-20-03288R1 titled "Assessing progress by UK companies to reduce total volume sales of sugar in selected". The manuscript is well-written and easy to follow.

My main concern relates to the fact that about 20% of data was excluded from the analysis (cf. lines 102-109) without any sensitivity analysis conducted to assess the potential impact of missing data on the results. I would encourage the authors to consider additional analyses under various assumptions about the missing data.

Minor comments

* On line 33, the abstract mentions 99 different manufacturers but online 42, "50 companies" are mentioned (line 42). Then at the start of the results section (line 128) 95 companies are mentioned. Please clarify/correct as relevant.

* It is not always clear which metric is the primary outcome. Given that targets were set for mean sales weighted sugar content, it seems that this should be the primary outcome

* Please clarify whether all existing sub-categories were included in the 5 categories analysed. For example: 7 sub-categories were included under the "biscuits and cereal bars" category. Are these 7 sub-categories all existing categories under "biscuits and cereal bars"?

* Please provide additional details about the calculation of "sales-weighted mean sugar content" explaining the rationale for weighting the data and the details of the calculation.

* Given that the same brands/products were analysed in 2015 and 2018, did the analysis consider the lack of independence ("pairing") between the two periods?

* Please clarify whether the unit of analysis was the product or the brand.

* Please explain in more details the rationale for weighting the data so that each brand is proportional to total sales and having the sum of the unit of analysis equal to the number of brands.

* Estimates are presented with confidence intervals as per usual practice; however, it strikes me that the data presented represents the entire "population" of products in the database and is therefore not subject to sampling uncertainty. I am not sure that confidence intervals are particularly meaningful in this case. To give some idea of the variability, I would suggest instead presenting standard deviations and/or quartiles.

* I suspect that changes in mean sugar content were only calculated for products present in both 2015 and 2018. Please clarify.

* Please consider displaying histograms of % change in sugar content by product for products present in 2015 and 2018.

* Please consider displaying all 5 categories on Figure 1. In doing so, one could show only the 2018 bar per category since it shows both the 2015 and 2018 levels together with where the change occurred (mean sugar content vs volume sales).

* Figure 2 is a nice way to summarize changes by company. I believe additional text would however help the reader understand the Figure. A complementary figure could be a histogram of Percentage change in the total volume of sugar sold for all companies (without indentifying companies)

-Laurent Billot

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 May 18;18(5):e1003647. doi: 10.1371/journal.pmed.1003647.r003

Author response to Decision Letter 1

13 Nov 2020

Attachment

Submitted filename: reviewers_response_letter_final.docx

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PLoS Med. doi: 10.1371/journal.pmed.1003647.r004

Decision Letter 2

Caitlin Moyer

10 Mar 2021

Dear Dr. Bandy,

Thank you very much for submitting your revised manuscript "The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018" (PMEDICINE-D-20-03288R2) for consideration at PLOS Medicine.

I apologize for the delay in getting back to you. Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to two of the original reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we would like to consider an additional revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we may seek re-review by one or more of the reviewers.

We expect to receive your revised manuscript by Mar 31 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

We look forward to receiving your revised manuscript.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

1.Please completely address the comments from the two reviewers.

2. Data availability statement: Thank you for the links to the Euromonitor and Edge by Ascential webpages. For each data source used in your study:

a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number).

b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.

c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author).

3. Abstract: Line 27: Please revise “progress made by UK companies” as it doesn’t seem to reflect the observational nature of the study, and could be rephrased as, “The aim of this study was to assess the sales-weighted mean sugar content and total volume sales of sugar in selected food categories among UK companies between 2015 and 2018” or similar.

4. Abstract: Methods and Findings: Please clarify brand, company, and manufacturer- if any of these terms indicate the same thing, please use consistent terms here and throughout.

5. Abstract: Methods and Findings: Please clearly emphasize that you are noting the key limitations of the study in the final sentence: “The main limitations are that this study does not encompass…”

6. Author summary: Under “Why was this study done?” please revise the third bullet point to make it clear that the study is not directly examining and evaluating progress attributable to the targets.

7. Author summary: What did the researchers do and find? It might be helpful to mention the sugar content measure was weighted by sales volume.

8. Author summary: What do these findings mean? Please remove “...but it’s unlikely the 20% sugar reduction target will be reached by 2020.”

9. Methods: Analysis plan: Thank you for clarifying the study had no pre-planned analysis protocol. However, please indicate all changes to the analysis after the data were examined, such as those arising from peer-review (such as adding sensitivity analyses).

10. Results: Line 194-195: Please clarify what is meant by “top 10” (i.e. in size, in sales volume, etc) in the sentence “...although for clarity, only the top 10 companies for each category are presented in the figures”

11. Discussion: Strengths and Limitations section: The results of sensitivity analyses presented here should be presented in detail in the Results section. More detail would be helpful in classifying the “product range” vs “one product leader” brands, and in addition to the 8% average variation in sugar content for the “leader” product and brand as a whole, the ranges should be presented. Details of how the sensitivity analyses were done should also be included in the Methods.

12. Discussion: Please re-organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

13. Discussion: Line 412-413: Please revise to avoid any implication of a causal relationship between your observations and the PHE targets: “In conclusion, our findings suggest there has been a mixed response by companies to the PHE voluntary sugar reduction targets.”

14. Discussion: Line 415-417: Please revise as the study results do not inform on 2020 data. “Our results show that it is unlikely that the selected categories will meet PHE’s 20% reduction targets by 2020…”

15. Table 3: Please indicate that % change is a comparison between 2015 and 2018

16. Table 4: Please indicate if there is a negative sign missing under percentage change for chocolate confectionery (1.2% vs -1.2%)

17. Figure 2: Please indicate in the title or x axis label that this reflects 2015 vs 2018.

18. References: Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines: https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

19. Checklist: Thank you for including the STROBE checklist. Please revise the checklist, using section and paragraph numbers rather than page and line numbers to indicate locations in text.

Comments from the reviewers:

Reviewer #2: The authors have improved the presentation of results in response to reviewer comments. They are clear about the limitations of using brand-level data in this paper. While this lack of granular product-level data presents limitations, ultimately I do think that because of the challenges and expense of obtaining product-level sales data, with additional clarity in the methods section, this paper could serve as an example to other researchers wishing to analyze category/brand level data using Euromonitor. In addition, this is an interesting and useful paper to understand the extent of sugar reductions in the UK in recent years. A couple of outstanding comments remain:

In my view, the sensitivity analyses are helpful but incomplete. For example- one reviewer pointed out the concern w/missing data (uup to 21.8% for cereal bars). To address this, authors just imputed the same reduction changes as the rest of the category. However, given that these are small and local companies, there is no reason to assume that they would be as able as larger companies to reduce their sugar. A conservative sensitivity analyses would at least assume zero sugar reduction in this group. In addition, it's not clear why you did the sensitivity analyses only for the "umbrella/other" brands and not also the 42 brands for which you couldn't find nutritional data. I don't think simply assuming that the missing brands were the same as included brands is very helpful.

Why not also present non-weighted data at the product level? This would be a useful sensitivity check that would allow you to avoid the problem that you can't weight by sales at the product level. Then you can come up with a plausible range of estimates for how much sugar reduction occurred.

Finally, the methods section is clearer, but still could use a bit more work (see detailed suggestions below). I still also did not understand entirely how you decomposed the total changes into sugar reductions vs. behavioral changes (sales), since my understanding is those two components have different units of measure. More detail and perhaps an example could help here as well. This also might benefit from creating a specific "outcomes" section which explains in more detail how outcomes are operationalized.

Minor:

Abstract

Line 32 Note that these are linked at the brand level

Line 41 Please state earlier what the analysis on companies was and if this was different than from the brand-level analysis.

Methods:

In absence of a pre-registered protocol, you should note any analytic changes. These would include sensitivity analyses requested by reviewers, so you can just note that.

Line 138 what does it mean that the brands were "grouped" by company? Simply that you had an additional variable that was company, or products were at the company level, or that you adjusted your SEs for correlation within companies?

The description of products, brands, and companies has improved, and the example in Line 152 is helpful. However, additional clarity is needed to help the reader follow the line of thought. In part, this is because information about how the dataset was created is spread across the methods and data anlaysis section. I recommend starting out with the most disaggregated form of data (product) and then talk step by step how you arrive to the category level. I would keep the data analysis sectoin purely for the actual analysis. Prior to this, you could also create an "outcomes" section that provides detail on the outcomes you used and why (e.g., why sugar vol, the per capita outcome, the sugar-reduction targets).

For example, in Line 147 it is confusing how you mention sales-weighting right away. I think you need to make it explicit here that brands were matched to products at the brand level. Within brand, all unique product types contributed equally (were averaged?). Here is where I would mention that products could fall in or out of the sample, depending on their availability in the market in the respective year (it is confusing to mention this not until Line 174).

And then note that, within a food category, the *brand* level data were weighted by sales.

Finally, it is not clear how the analyses were done incorporating both brands and companies. Did you weight brands within companies, as well as in categories?

You could consider a flow chart as well to help readers visualize this.

Line 175- how did you use ONS stats to calculate per capita sales? did you just divide sales by # of people?

Line 180- it still is not clear to me how you split the change into mean change in sugar content vs. change in total volume sales. In your formula of total sales, what is the outcome? Expenditures, in dollars or sales? From the figure, it looks like it is in grams (of product? of sugar?). I don't understand how you can add sugar content (presumably in grams of sugar) to change in volume.

Sensitivity analyses should be reported in the results section.

Reviewer #3: My previous comments have been adequately addressed. I am just not sure that the sensitivity analysis conducted to account for missing data is sufficient. The authors have assumed the same reduction in sugar content in the 'other' category than overall. I wonder whether a more conservative assumption should be tested as well e.g. by assuming a smaller change in the 'other' category. Otherwise, I would like the authors to provide additional justification supporting the assumption of a similar reduction.

-Laurent Billot

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 May 18;18(5):e1003647. doi: 10.1371/journal.pmed.1003647.r005

Author response to Decision Letter 2

1 Apr 2021

Attachment

Submitted filename: Reviewer response letter_final.docx

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PLoS Med. doi: 10.1371/journal.pmed.1003647.r006

Decision Letter 3

Caitlin Moyer

28 Apr 2021

Dear Dr. Bandy,

Thank you very much for re-submitting your manuscript "The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018" (PMEDICINE-D-20-03288R3) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

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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.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by May 05 2021 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Data availability statement: For clarity, please remove “from the authors” from the sentence: “Due to licencing restrictions, the Euromonitor and Edge by Ascential datasets can only be requested from the authors under licence for the purpose of verification and replication of study’s findings via the research group’s Data Access Committee (contact: Trisha Gordon

trisha.gordon@ndph.ox.ac.uk).” as it seems that the request would go to the Data Access Committee contact person (importantly, the contact for data access request cannot be the authors of the study).

2. Abstract: Methods and Findings: Please briefly describe how sales information in Euromonitor represents all sales in the UK (a fraction of the total sales).

3. Abstract: Methods and Findings: Please mention the total number of companies here: “Of the companies presented in this analysis…”

4. Author summary: Why was this study done? First bullet point: Please change “intakes” to “intake” in the first point.

5. Author summary: What do these findings mean? In the first point, it seems as if this could also be described as “approximately half of the companies” although technically a majority. “...and the majority of companies had not met the 5% sugar reduction target by 2018.”

6. Throughout: For in-text citations, please use square brackets. For multiple references, please do not use spaces within brackets, for example [12,13].

7. Methods: Line 138-139: Please briefly describe the fraction of total sales covered in Euromonitor.

8. Methods: Lines 195-196: Please indicate where these results are shown. Please describe results with p values (for example, in Figure 2 or Table 5). “Chi-squared tests were used to test for differences between the mean sugar content of categories in 2018 compared to 2015.”

9. Results: Line 276: Please briefly mention again the definition for ‘product range’ brands.

10. Results: Line 284: For the second sensitivity analysis, please briefly mention again the significance of the volume represented as “other” in the analysis. Please briefly mention the two scenarios.

11. Discussion: Limitations: Line 364: In the paragraph describing Euromonitor data, please mention that Euromonitor does not cover all sales and the implications of this.

12. References: Please check the formatting of each reference. For example, information is missing from #2, 3, and 4. Please check journal abbreviations (for example, PLoS Med in Reference 29). Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

13. Table 6: Please provide a legend, with a description that clearly describes what is shown in the table. The table should be able to be interpreted on its own.

14. Table 7: It may be helpful to include the reference number/citation for this study in the table or legend.

15. Figure 3: Please include a descriptive legend for this figure.

Comments from Reviewers:

Reviewer #2: Nice job on incorporating the changes. In particular, I really liked the flow chart- this could be used in future studies of nutrition and sales/purchases data. My only comment is that your very final bubble in the flow chart could include what the outcome was (for example, a mean of means).

Reviewer #3: I have no further comments.

-Laurent Billot

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2021 May 18;18(5):e1003647. doi: 10.1371/journal.pmed.1003647.r007

Author response to Decision Letter 3

30 Apr 2021

Attachment

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PLoS Med. doi: 10.1371/journal.pmed.1003647.r008

Decision Letter 4

Caitlin Moyer

7 May 2021

Dear Dr Bandy,

On behalf of my colleagues and the Academic Editor, Barry M. Popkin, I am pleased to inform you that we have agreed to publish your manuscript "The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018" (PMEDICINE-D-20-03288R4) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

Please also make the following two changes to the text:

- When formatting in-text references for multiple references, please include all references within a single set of brackets, but do not use spaces between references, for example [12,13] on page 4.

- Please check the formatting of each reference. Please check NLM abbreviations of journal names, for example: Reference 4, Journal of Dental Research should be J Dent Res and in Reference 13, PLOS Med should be PLoS Med.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

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Supplementary Materials

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Data Availability Statement

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PERMALINK

The sugar content of foods in the UK by category and company: A repeated cross-sectional study, 2015-2018

Lauren K Bandy

Peter Scarborough

Richard A Harrington

Mike Rayner

Susan A Jebb

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Data types and sources

Table 1. Number and list of categories and sub-categories included in analysis.

Fig 1. Data and analysis flow chart.

Data analysis

Sensitivity analysis

Results

Fig 3. Percentage change in the total volume of sugar sold by the top 10 companies in each category between 2015 and 2018.

Table 2. Data points in the nutrient composition and sales datasets 2015–2018.

Table 3. Total volume sales of food category in tonnes, 2015–2018.

Table 4. The sales-weighted mean sugar content of food categories, 2015–2018.

Table 5. Total volume of sugars sold by food category in per capita per day terms, 2015–2018.

Fig 2. Change in the total volume of sugars from all food categories combined, split by change in sales and change in mean sugar content, 2015 and 2018.

Sensitivity analysis results

Sensitivity analysis 1—heterogeneity of individual products within brands

Sensitivity analysis 2—volume represented by ‘others’

Table 6. Main analysis results of sales-weighted mean sugar content of foods in 2015 and 2018 compared to the results of 2 sensitivity scenarios.

Discussion

Comparison with other studies

Table 7. Percentage change in sales-weighted mean sugar content of different food categories between 2015 and 2018 compared to Public Health England findings [14].

Strengths and limitations of this study

Policy implications

Supporting information

Abbreviation

Data Availability

Funding Statement

References

Decision Letter 0

Adya Misra

Roles

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Caitlin Moyer

Roles

Author response to Decision Letter 2

Decision Letter 3

Caitlin Moyer

Roles

Author response to Decision Letter 3

Decision Letter 4

Caitlin Moyer

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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