Removing seasonal confectionery from prominent store locations and purchasing behaviour within a major UK supermarket: Evaluation of a nonrandomised controlled intervention study.

BACKGROUND: The proportion of energy from free sugars and saturated fat currently exceeds the UK-recommended intake across all age groups. Recognising the limits of reformulation programmes, the government in England has announced their intention to introduce legislation to restrict the promotion of foods high in free sugars, salt, and saturated fats in prominent store locations. Here, we evaluated a grocery store intervention to remove seasonal confectionery from prominent locations within a major UK supermarket. METHODS AND
FINDINGS: A nonrandomised controlled intervention study with interrupted time series (ITS) analysis was used. Data were analysed from 34 intervention stores located in 2 London boroughs and 151 matched control stores located elsewhere in the UK owned by the same retailer. Stores were matched based on store size and overall sales during the previous year. Between 15 February 2019 and 3 April 2019 (before Easter), stores removed free-standing promotional display units of seasonal confectionery from prominent areas, although these products were available for purchase elsewhere in the store. Store-level weekly sales (units, weight (g), and value (£)) of seasonal chocolate confectionery products were used in primary analyses, with data from 1 January 2018 to 24 November 2019. Secondary outcomes included total energy, fat, saturated fat, and sugars from all in-store purchases. Multivariable hierarchical models were used to investigate pre/post differences in weekly sales of confectionery in intervention versus control stores. ITS analyses were used to evaluate differences in level and trends after intervention implementation. Over a preintervention baseline period (15 February 2018 to 3 April 2018), there were no significant differences in sales (units, weight, and value) of all chocolate confectionery between intervention versus control stores. After intervention implementation, there was an attenuation in the seasonal increase of confectionery sales (units) in intervention stores compared to control (+5% versus +18%; P < 0.001), with similar effects on weight (g) (+12% versus +31%; P < 0.001) and value (£) (-3% versus +10%; P < 0.001). ITS analyses generally showed statistically significant differences in the level at the point of intervention (P ranges 0.010 to 0.067) but also in the trend afterwards (P ranges 0.024 to 0.053), indicating that the initial difference between intervention and control stores reduced over time. There was a significant difference in level change in total energy sold, adjusted for the total weight of food and drink (kcal/g, P = 0.002), and total fat (fat/g) (P = 0.023), but no significant changes in saturated fat or sugars from total sales in ITS models. There was no evidence that the main results varied across store deprivation index. The limitations of this study include the lack of randomisation, residual confounding from unmeasured variables, absolute differences in trends and sales between intervention versus control stores, and no independent measures of intervention fidelity.
CONCLUSIONS: Removal of chocolate confectionery from prominent locations was associated with reduced purchases of these products, of sufficient magnitude to observe a reduction in the energy content of total food purchases. These results from a "real-world" intervention provide promising evidence that the proposed legislation in England to restrict promotions of less healthy items in prominent locations may help reduce overconsumption. TRIAL REGISTRATION: https://osf.io/br96f/.

Introduction

Obesity is a global public health issue [1]. Currently in the UK, 26% of men, 29% of women, and 20% of children aged 10 to 11 years old have obesity, with significantly higher prevalence in the most deprived areas [2]. Despite years of health promotion to encourage and motivate individuals to choose a healthier diet, the proportion of energy coming from free sugars and saturated fat in the UK continues to exceed the recommended levels across all age groups with especially high intakes of free sugars among children [3]. A nutritionally poor diet increases the burden of major chronic diseases, including diabetes and cardiovascular disease, principally through increases in body weight as well as blood cholesterol, blood pressure, and insulin resistance [4-8].

Interventions to change food purchasing habits at the point of choice offer an upstream opportunity to change behaviour rather than relying on influencing consumption at the moment of eating. The World Health Organisation and other groups have advocated for the implementation of health-related taxes [9,10], but the acceptability of these interventions is relatively low [11,12]. Governments are increasingly interested in policy interventions to change supermarket environments because of the potential of these interventions to achieve population-level change in dietary habits and with higher public acceptability ratings [13-16].

Although sugar-sweetened beverages (SSBs) have been a major focus of policy actions [17], confectionery, together with cakes and biscuits, make a greater contribution to free sugars, saturated fat, and total energy intakes in the UK population [3]. Sales of chocolate and sugar confectionery in the UK have increased by 16.3% and 7.3%, respectively, in the last 5 years [18], with the highest purchases in the least affluent households [19]. Despite ambitious sugar reduction targets set by Public Health England there has been almost no change in the period 2015 to 2019, with sugar in sweet and chocolate confectionery reducing by just 0.1% and 0.4%, respectively, against the 20% reduction target [18]. This is perhaps because of the challenges of reformulation for this food category, especially compared to SSBs with their potential to use nonnutritive sweeteners. Instead, any reductions in calories, sugar, and saturated fat from confectionery are likely to depend upon reducing the volume of confectionery consumed. All food is rewarding, but the combination of high energy, fat, and sugar in confectionery is associated with strong and reinforcing biological signals [20]. There are also powerful social norms and cultural traditions that foster the notion of confectionery as a treat, and, thus, people tend to discount the long-term harms in favour of the short-term reward [21]. Few people consider confectionery to be a healthy food, and education alone is unlikely to be successful in reversing these powerful biological and societal drivers of consumption.

The 2020 obesity plan in England proposed new legislation to restrict volume- and location-based promotions on unhealthier products (i.e., those high in fat, salt, and sugar), as well as the placement of these products in prominent locations within supermarkets [16]. Placement and price promotions, together with availability, have been identified in previous systematic reviews of in-store interventions as potentially effective strategies to influence food purchasing behaviours [22-32]. According to the typology of interventions in proximal physical microenvironments (TIPPME) framework, availability and placement strategies work by increasing the range, variety, number, as well as visibility and accessibility of products, and this can stimulate purchases [33].

Most reviews have generally highlighted the lack of high-quality evidence in real supermarkets, especially for interventions that disincentive purchases of less healthy options [23,24,26,32]. In collaboration with the Consumer Goods Forum (CGF), a global membership body of 400 major consumer goods retailers and manufacturers, and with agreements enabling access to sales data from a major UK supermarket, we conducted an independent evaluation of an intervention, designed and implemented by a national food retailer, to remove seasonal chocolate confectionery from prominent store locations before the Easter period.

Methods

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Study design and data source

Data from a major UK retailer (comprising 27.7% of the UK grocery market share in January 2019) were used in this project. The study was completely developed and implemented by the retail partner, so we followed the methods suggested for the monitoring and evaluation of natural experiments [34], and a nonrandomised controlled design was used. The study was implemented in 34 stores (hereafter referred to as intervention stores), with a matched sample of 151 unique control stores. Data on store-level weekly sales of seasonal chocolate confectionery (units, weight [g], and value [£] of each eligible product within the category) were obtained for both intervention and control stores, spanning dates from 1 January 2018 to 24 November 2019 (with 4 weeks missing from 26 November 2018 to 30 December 2018 from all stores), which comprised a total of 17,380 aggregated store-week data points (see flowchart of store data in Fig A in S1 Appendix). Data from nutrients in all food-related sales, including total energy, sugar, saturated fat, and total fat, were available from 1 January 2019 to 24 November 2019.

By using aggregated weekly sales data, this study was exempt from ethical review and approval. A preregistered protocol (https://osf.io/br96f/) was completed and fully available from 22 July 2020 before obtaining data for analysis.

Store selection and matching

Retail partner’s finance and data teams used proprietary analytics to select intervention and control stores for this study with no input from the research team. Based on each retailer’s operational considerations and with input from the CGF and the project partner, Impact on Urban Health, intervention stores were selected within London boroughs (Lambeth and Southwark, UK). The sample of intervention stores were located in neighbourhoods covering a range of socioeconomic deprivation strata based on the 2019 English Index of Multiple Deprivation (IMD) income domain, the official measure of relative deprivation in small areas (Lower-layer Super Output Areas) across England [35]. Selected intervention stores were all small supermarkets according to a retail food outlet categorisation system previously defined, which includes stores with 1 to 4 manned cash registers [36,37]. Control stores were selected across each retailer own stores, with store size and overall sales performance over the previous year used as the criteria for matching stores.

Intervention

The intervention aimed to reduce the extra availability of seasonal chocolate confectionery by removing free-standing promotional display units from prominent areas, for example, store entrance, as well as by substituting seasonal confectionery located in end of aisles with other products. A total of 178 uniquely barcoded products were removed from display units or end of aisles, but all these products were still available for purchase elsewhere in the store (although many of these products were seasonal and only available during a short period of time). The intervention was implemented in the run-up to the Easter period, for approximately 7 weeks (15 February 2019 to 3 April 2019) with a phased implementation: 17% of eligible products were removed from 15 February 2019; 53% more were removed from 13 March 2019; and the remaining 30% were removed from 18 March 2019.

Outcome measures

Primary outcome measures included store-level weekly sales data (units, weight, and value) for the whole category of seasonal chocolate confectionery. Secondary outcome measures included nutrient data (i.e., total energy, sugar, fibre, saturated fat, and total fat) from all food-related sales.

Store characteristics

Store characteristics relating to the customer population included the English IMD and ethnicity. The store postcode was matched to the IMD income domain, the official measure of relative deprivation in small areas (Lower-layer Super Output Areas) across England [38], which was used as a proxy for the socioeconomic status (SES) of the customer population. The store sample covered neighbourhoods from deciles 1 to 10; regrouped into IMD 1 to 3 (most deprived), 4 to 6 (mid), and 7 to 10 (least deprived). Ethnicity of the store customer population was classified by the retailer using internal proprietary systems and grouped as predominantly white versus other ethnicities.

Statistical analysis

Power analyses were not conducted, and the retailer chose the number of stores to roll out the interventions.

Descriptive analyses were used to investigate differences in store demographic characteristics between intervention and control stores using χ2 tests. We used data over the year prior to intervention (2018) to define preintervention baseline period (15 February 2018 to 3 April 2018), which matched as much as possible the intervention period. We tested differences in weekly sales of target products over the 2018 baseline periods between intervention and control stores using Student t tests.

The following prespecified statistical models were used for the primary and secondary outcome analyses, using consistent methods for intervention evaluation [39]:

Hierarchical models (negative binomial for unit sales; or linear mixed models for weight and value of purchases) were used with a fixed effect adjustment for store demographic characteristics and average weekly sales over the baseline preintervention period. This model was used to investigate differences in weekly sales of target products in intervention versus control stores over the time period while the intervention was active compared to the preintervention baseline period (2018) [40].

Interrupted time series (ITS) analyses and corresponding plots with fitted linear trends were computed using all available data before and after the intervention for intervention and control stores [41]. To assess whether differences visible in the graphs were statistically significant between intervention and control stores and to account for any preintervention differences between groups in the outcome variable, we used a difference-in-difference approach, calculating the mean difference in weekly sales between intervention and control stores, and testing whether this time series of differences changed after versus before intervention using a linear regression model. We used a Chow-type test for level and trend changes after intervention implementation, and Newey–West standard errors with lag 4 to allow for autocorrelation in the time series. Since intervention implementation was phased, we conducted one model where intervention started on the week of 15 February 2019 when 17% of products were removed, and a second model where intervention started on the week of 13 March when 53% more products were removed.

Analyses were conducted using all intervention and control stores with all available data. A prespecified exploratory subgroup analysis on unit sales was performed by store IMD groups (IMD 1 to 3 high deprivation versus IMD 4 to 10 middle/low deprivation), and likelihood ratio tests were used to test the significance of interactions. Stata version 16 was used for all statistical tests with a 5% significance level.

Results

Differences in store characteristics

A total of 185 stores were analysed, with all the intervention stores located in areas of medium or high deprivation, which is representative of the population of Lambeth and Southwark (London, UK). The control group had 28% of stores in areas of low deprivation (Table 1). There were significant differences in IMD scores (P < 0.001) but not in ethnicity between intervention and control stores.

Table 1

Store demographic characteristics.

	Total stores		Intervention stores		Control stores		χ2 test
	N = 185	%	n = 34	%	n = 151	%	P value
IMD score groups
IMD 1–3 (most deprived)	52	28	18	53	34	23	<0.001
IMD 4–6	91	49	16	47	75	49
IMD 7–10 (least deprived)	42	23	0	0	42	28
Ethnicity
Predominantly white	49	27	7	21	42	28	0.388
Other ethnicities	136	73	27	79	109	72

IMD, Index of Multiple Deprivation.

Primary analysis—Sales of confectionery

Over a preintervention baseline period (15 February 2018 to 3 April 2018), there were no significant differences in sales (units, weight, and value) of all chocolate confectionery between intervention versus control stores (Table 2).

Table 2

Average weekly sales of confectionery in intervention vs. control stores and comparison of changes before/after intervention between intervention vs. control stores.

	Baseline period 15 Feb– 3 April 2018			Intervention period 15 Feb– 3 April 2019					Comparison intervention vs. control stores
	Average sales			Average sales		Absolute difference vs. baseline period		% Change
Units/store/week	Mean	SD	P value *	Mean	SD	Mean	SD		IRR †	95% CI		P value
Intervention stores	894.2	202.9	0.070	938.1	304.8	43.9	162.8	5%	0.861	0.808	0.918	<0.001
Control stores	966.7	267.9		1,137.6	368.6	170.8	195.8	18%	0.864	0.809	0.922	<0.001
Weight (g)/store/week	Mean	SD	P value *	Mean	SD	Mean	SD		β †	95% CI		P value
Intervention stores	97,172.2	25,332.2	0.074	108,650.2	36,058.1	11,478.1	17,434.8	12%	−20,416.5	−28,373.6	−12,459.3	<0.001
Control stores	105,554.9	31,309.0		137,827.8	45,876.4	32,272.9	21,601.4	31%	−21,790.1	−30,228.9	−13,351.3	<0.001
Value (£)/store/week	Mean	SD	P value *	Mean	SD	Mean	SD		β †	95% CI		P value
Intervention stores	1,096.4	287.7	0.058	1,067.7	351.1	−28.7	144.4	−3%	−164.1	−227.2	−101.1	<0.001
Control stores	1,198.9	352.6		1,323.9	415.3	125.0	175.4	10%	−176.7	−241.4	−112.1	<0.001

*Student t tests comparing average sales over the baseline period between intervention vs. control stores.

IRRs from hierarchical negative binomial models (used in the models of unit sales), minimally adjusted for average sales per week over the 2018 period (top row) or fully adjusted (bottom row) with fixed effect adjustment for store ethnicity, IMD, and average sales per week over the 2018 period; Beta (β) coefficients from hierarchical normal mixed models (used in the models of gr and £ sales), minimally adjusted for average sales per week over the 2018 period (top row) or fully adjusted (bottom row) with fixed effect adjustment for store ethnicity, IMD, and average sales per week over the 2018 period.

IMD, Index of Multiple Deprivation; IRR, incidence rate ratio.

After intervention implementation, there was an attenuation in the seasonal increase of confectionery sales (units) in intervention stores compared to control (+5% versus +18%; P < 0.001), with similar effects on weight (+12% versus +31%; P < 0.001) between 15 February 2019 to 3 April 2019. However, there was a decrease in value sales in intervention compared to control stores over the same time period (−3% versus +10%; P < 0.001) (Table 2). There were absolute differences in confectionery sales of approximately 127 units per store per week (+43.9 units in intervention stores versus +170.8 units in control stores over the intervention period compared to the baseline period) or 21 kg (11.5 kg in intervention stores versus 32.3 kg in control stores) (Table 2).

ITS analyses were conducted using 2 different time points for intervention implementation, firstly on 15 February 2019 when 17% of products were removed and secondly on 13 March 2019 when 53% more of products were removed. The trends before intervention implementation were generally consistent between intervention and control stores (Fig 1, Table A in S1 Appendix). After intervention implementation on 15 February 2019, there was a statistically significant difference in the level of weekly sales (Fig 1; P level = 0.026 in units/store/week and P level = 0.044 in £/store/week). There were stronger differences in level after intervention intensification on 13 March 2019 (Fig 1; P level = 0.010 in units/store/week, P level = 0.042 in g/store/week and P level = 0.026 in £/store/week). There were generally significant differences in the downward trends afterwards, indicating that the initial difference between intervention and control stores reduced over time.

Fig 1

ITS analysis showing level and trend changes in weekly sales of confectionery.

*Solid dots (observed) and lines (modelled) represent intervention stores; white dots (observed) and dotted lines (modelled) represent control stores. ITS, interrupted time series.

Secondary analyses—Changes in energy and nutrients

Data on total energy (calories), total sugars, total fat, and saturated fat from all food-related sales for the 2019 year were used in ITS models to evaluate the impact of the intervention on the overall healthiness of grocery shopping (Fig 2). There was a significant level change in total energy sold, adjusted for the total weight of food and drink (kcal/g, P level = 0.002), and total fat (fat/g, P level = 0.023), but no significant changes in saturated fat or sugars from total sales in ITS models. There were no significant differences in the trends afterwards for any of the nutrients studied.

Fig 2

ITS analysis showing level and trend changes in calories and nutrients from all sales (averages per store/week) during the implementation of the chocolate confectionery availability study from 1 January 2019 to 24 November 2019.

*Solid dots (observed) and lines (modelled) represent intervention stores; white dots (observed) and dotted lines (modelled) represent control stores. ITS, interrupted time series.

Differences by store deprivation

There was no evidence that the results varied across store IMD group for any of the metrics reported, with significant reductions in units, weight, and value in intervention stores located in high deprivation areas as well as those in mid or lower deprivation areas, compared to control stores (Table 3).

Table 3

Comparison of changes in sales of confectionery before/after intervention between intervention vs. control stores, across store IMD groups.

	Comparison intervention vs. control stores
Units/store/week	IRR *	95% CI		P value	P interaction †
IMD 1–3 high deprivation	0.90	0.83	0.98	0.010	0.795
IMD 4–10 medium/low deprivation	0.86	0.79	0.95	0.001
Weight (g)/store/week	β *	95% CI		P value	P interaction †
IMD 1–3 high deprivation	−18,571.0	−29,205.5	−7,936.4	0.001	0.775
IMD 4–10 medium/low deprivation	−20,935.8	−32,566.5	−9,305.1	<0.001
Value (£)/store/week	β *	95% CI		P value	P interaction †
IMD 1–3 high deprivation	−103.0	−175.3	−30.8	0.005	0.156
IMD 4–10 medium/low deprivation	−204.5	−298.2	−110.7	<0.001

*IRRs from hierarchical negative binomial models (used in the models of unit sales), with fixed effect adjustment for store ethnicity, IMD, and average sales per week over the 2018 period; Beta (β) coefficients from hierarchical normal mixed models (used in the models of gr and £ sales), with fixed effect adjustment for store ethnicity, IMD, and average sales per week over the 2018 period.

†P interaction from likelihood ratio tests.

IMD, Index of Multiple Deprivation; IRR, incidence rate ratio.

Discussion

This intervention to remove chocolate confectionery in prominent areas of the store showed a significant attenuation in the seasonal increase of confectionery sales (units) in intervention stores compared to control (+5% versus +18%; P < 0.001). Similar results were observed with weight (g) of confectionery (+12% versus +31%; P < 0.001) and value (£) (−3% versus +10%; P < 0.001), with an overall absolute difference between intervention and control stores of approximately 127 units (approximately 21 kg) of confectionery per store per week. In parallel, we observed significant reductions in total energy and fat from all food-related sales, but no significant differences in saturated fat or total sugars. There was no evidence that the intervention results varied according to the level of deprivation in the area in which the store was sited.

Systematic reviews of grocery store interventions have reported that positioning products in prominent locations, such as near checkouts or the end of an aisle, increases visibility of products and stimulates purchases [22-25]. A previous natural experiment across 12 stores in the US found that prominent positioning of sweet snacks at the end-of-aisles had a greater effect on sales of less healthier options compared to prominent positioning of the healthier ones and copositioning of both significantly increased sales of the unhealthier options only [42]. An observational study using sales from a UK grocery store estimated a 52% higher weekly volume sales of carbonated drinks when these were displayed in end of aisles [43]. But there is an important gap in the evidence with regard to interventions that can reduce the prominent positioning of unhealthy food, as most of the literature has focused on selling more healthy foods. A recent cluster RCT in Australian supermarkets tested a complex intervention to limit in-store promotional and marketing activities targeting high-fat/high-sugar products, including removal of price promotions, signage, and removal of products from prominent areas, and showed significant reductions in total sugars without affecting supermarket profit [44]. By just removing confectionery from prominent store locations, our study showed a significant attenuation in pre-Easter sales (units, weight, and value) of confectionery.

In the context of the increasing gap in dietary inequalities and long-term health outcomes, it is also important to understand if supermarket interventions help reduce, or at least do not exacerbate, dietary inequalities. It has been postulated that, compared to individual-level interventions, population-level approaches that trigger automatic (rather than conscious) behavioural responses [45,46] may be less likely to increase health inequalities. But evidence from systematic reviews is limited and most studies testing positioning interventions have not specifically looked at differential effects across sociodemographic groups [23,32]. Our results showed no evidence of differences in intervention results, and the intervention appeared to work equally well regardless of the area deprivation score of the store.

The 2020 obesity plan in England has laid out plans to introduce legislation to restrict the promotion of foods high in fat, sugar, or salt (HFSS), by restricting volume-based promotions such as “Buy One Get One Free” as well as restrictions to placement in prominent locations intended to encourage purchasing, both online and in physical stores [16]. Our results provide direct evidence on the reduced availability of chocolate confectionery in prominent locations, which will be of interest to policymakers and could help shape effective policies for confectionery and potentially other items.

This research was made possible through collaboration with food retailers, facilitated by an established industry programme led by the CGF to encourage healthier and more sustainable retail practices. This evaluation provides proof-of-concept that it is possible to establish these collaborations and has led to useful lessons for future collaborations, especially in relation to contractual agreements, and the design of larger and more definitive intervention studies. For example, the duration of intervention was limited here because of the seasonal nature of the products, but future studies of positioning interventions should aim to try implement the intervention for a longer time period. This is important since the trends from ITS models after implementation suggested that the effect of the intervention may be short-lived, though this may be related to the seasonal nature of the products targeted (i.e., a large proportion of the products targeted in the intervention are not available the rest of the year). Our analysis of the nutrient content of the total sales showed some evidence of no compensatory behaviours at least within the same retailer, although other research would need to investigate if customers are purchasing confectionery in different stores where no restrictions are imposed. Future research should also seek to analyse changes in purchases at a household rather than store level using data from customer loyalty cards rather than store-level sales to better study any potential impact of interventions on health inequalities [47,48].

A major strength of this study is the use of a large dataset of objectively collected sales data, which is generalizable to all customers of the participating stores over the studied time period. Data were available over an extended time period and drawn from an intervention conducted in real supermarkets, which can provide important insights to inform population-level interventions to encourage healthier food purchasing. However, this “real-world” intervention study presents analytical challenges. Adjustment for confounding and other sources of heterogeneity was approached in several ways. Firstly, control stores were matched to intervention stores, with more than one control store per intervention store. Matching was done using store demographic factors and overall sales over the previous periods, which, in this case, resulted in nonsignificant differences in baseline sales between intervention and control stores. However, there were significant differences by IMD due to the fact that stores in less deprived areas were underrepresented in the intervention group, though we adjusted for deprivation in the models. The difference-in-difference approach used in ITS models also helps to remove the effect of any small absolute differences in sales between the intervention and control stores. Finally, with access to extended periods of time (2018 and 2019), we were able to use the 2018 period as a control in the models. Other limitations to note include the lack of randomisation, residual confounding from unmeasured variables, and absolute differences in trends and sales between intervention versus control stores. There could have been other interventions in stores running alongside the one evaluated here, which could have influenced the observed effects, but the use of control stores could potentially adjust for this. In addition, we have no independent measures of intervention fidelity and we had to rely on the retailer implementation plans, which means suboptimal implementation may have diminished the apparent effects of this intervention. The intervention was selected, developed, and implemented by the retailer, without the direct involvement of the research group. It is not possible to know the extent to which this was influenced by behavioural theory, prior commercial insights, or awareness of government thinking, though it is probable that all contributed to greater or lesser extent. Finally, there was limited data on store characteristics, and the retailer provided only restricted data on the ethnicity of the customer population. The very broad categorisation of ethnicity is unlikely to have removed all of the confounding related to ethnicity in our results, although there were no significant differences in the distribution of ethnicity between intervention and control stores. Similarly, the IMD used as a measure of store deprivation may also be a very crude proxy for the SES status of the customer population, particularly when people drive to larger out-of-town supermarkets or for smaller stores located in city centres with a large proportion of nonlocal customers.

There is limited evidence for effective interventions to discourage food options that contribute the most energy, saturated fat, and free sugars [3], particularly confectionery, biscuits, or cakes. These results showed that removal of seasonal confectionery in prominent locations is a promising strategy to reduce unhealthy food purchasing behaviours, with changes in just one subcategory of foods of sufficient magnitude to observe reductions in the energy content of total food purchases. These results provide promising evidence that the proposed legislation in England to restrict promotions of less healthy items in prominent locations may help reduce overconsumption.

STROBE Checklist.

(DOCX)

Click here for additional data file.

Supplementary table and figure.

Fig A. Flowchart of store data. Table A. Model-based estimates of the difference-in-difference interrupted time series (β coefficients (95% CI)) of mean baseline trend, post-implementation level change, and post-implementation trend change.

(DOCX)

Click here for additional data file.

16 Sep 2021

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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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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Requests from the editors:

The editorial team would like to suggest combining the content of manuscript PMEDICINE-D-21-03910 and PMEDICINE-D-21-03912 into a single manuscript. You will receive a separate letter informing you that PMEDICINE-D-21-03912 has been rejected, however this is only to ensure the combined manuscript is processed as a single submission within Editorial Manager. The requests from the editors below, therefore, pertain to both of your submissions (henceforth referred to as 3910/3912).

Please use the reviewers’ comments to guide your structuring of the revised manuscript. It was generally felt that a more consistent and systematic presentation of the various interventions would facilitate interpretation of your findings. Reviewer 1 suggests that Supplementary tables 1 and 2 from the 3912 manuscript could be combined and presented as Table 1 in the main text to facilitate reader comprehension - this will also now require the inclusion of the study presented in 3910.

Please revise your title according to reflect the combining of the two manuscripts. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).

Please add a final line to your abstract ‘Methods and findings’ which summarises the main limitations (it should begin ‘The limitations of this study include’ or similar). Given that the manuscript covers a variety of studies, it may be best to focus on the limitations relating to study design more generally as opposed to detailing limitations specific to any one of the experiments presented.

Please include line numbering in the margins throughout.

Citations should be in square brackets and occur before punctuation.

As reflected in the reviewers’ reports, please provide further clarification as to the definition/classification of ethnicity in your studies and consider discussing it as a limitation.

The PDF conversion process appears to have introduced some minor formatting errors – an example can be found in line 1 of page 8 of 3910 and also line 3 of 3912 (a boxed question mark appears where it should not). Please check throughout for other errors.

Please remove the funding information from the acknowledgements section (in the event of publication this will be published as metadata based on your responses to the submission form).

Please also remove the Contributors, Competing interests, and Data sharing sections from the main text for the same reason as above.

Please update your Data Availability Statement (in the submission form) to include your statement regarding availability of statistical code. Note that a study author cannot be the main contact for researchers wishing to obtain this code, it would be better if this can be shared in a public repository or handled by an alternative data management contact. Please see our policy here (https://journals.plos.org/plosmedicine/s/data-availability) and get in touch if you have any questions.

Please remove the Ethical approval statement from the end of the main text as this is already stated in the methods section.

In your references, please remove all formatting (italics) and only use et al. after listing the first six authors. Please also ensure you have provided the data accessed for any URLs provided. Our guidelines can be found here: https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

Please ensure you define all abbreviations used in your tables (e.g. IMD, IRR).

Thank you for providing a STROBE checklist. Please update the checklist to reflect the combined manuscript and please use section names and paragraph numbers rather than page numbers (these are likely to change during the revision process).

Please reference the checklist in your methods ("This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

Thank you for providing an author summary, please update this to reflect the combined manuscript.

When reporting p-values, please use P<0.001 rather than P<0.0001 (please also check your figures and tables to ensure P<0.001 is used consistently).

Comments from the reviewers:

Reviewer #1: This is a well-conducted non-randomised controlled trial testing removal of seasonal confectionery from prominent store locations in a major UK supermarket. The study design, sample size, datasets, statistical methods and analyses, and presentation (tables and figures) and interpretation of the results are mostly adequate and of a good standard. The key issue in the non-randomised controlled trial setting is to control the potential confounding factors and minimise the biases which the authors have done well by adequate store selection and matching, and also adjusting in the analyses for store demographic characteristics such as deprivation score IMD and ethnicity and average weekly sales over the baseline pre-intervention period. However, there are still a few relatively minor issues needing attention.

1) Adjustment for ethnicity. It's well known that London boroughs of Lambeth and Southwark have an urban multi-ethnic population with nearly half of population from ethnic minorities background, which is very different from other parts of the UK. In the paper, ethnicity is classified as predominantly white vs other ethnicities, which is a bit simplistic and non-specific. It is likely to have residual confounding in ethnicity even after such adjustment in the paper. It would be good if the authors can discuss this as a limitation in the paper.

2) In the abstract, it says "ITS analyses showed statistically significant differences in the level at the point of intervention (P<0·05 for confectionery sales)". As the authors are using 3 digits for P-values in the paper, it would be good to be consistent throughout. I am aware here P<0.05 refers to a range of p-values but can replace with, for example, P ranges from 0.024 to 0.064. Also, in the next sentence "but also in the trend afterwards, indicating that the initial difference between intervention and control stores reduced over time", p-value is needed to support the statement.

3) In the first paragraph in the discussion, it says "This intervention to remove chocolate confectionery in prominent areas of the store attenuated the seasonal increase in confectionery sales, with an overall absolute difference between intervention and control stores of approximately 127 units (~21 kg) of confectionery per store per week". However, this 127 units just appeared out of blue and I had to figure it out myself. It's not mentioned in table or the results section at all, and it's not the primary outcome either. It would be good to give a message consistent with what's in the abtract which is on percentage differences in sales and etc. The absolute difference can be described as secondary but can't replace the primary outcome.

Reviewer #2: I was asked to review these papers together. Given their similarity many of my comments apply equally to both and so I have provided a combined report. I've noted where comments refer particularly to one or the other paper and described these as the 'combined' or 'easter' papers. Overall, I wasn't convinced by the value of separating these papers. Whilst the combined paper is certainly complicated, I don't think that adding the easter analyses would make it appreciably more so. I think perhaps the only value of the distinct easter paper is that it speaks very specifically to current UK public health policy decisions. On the other hand, the specific high value placement message could be drawn out in post-publication dissemination and, arguably, UK public health policy needs to be thinking beyond just the current policy decisions on the table to some of the strategies presented in the combined paper.

These papers report on a rather disparate set of interventions in supermarkets aiming to improve the healthiness of purchases. I applaud the authors on the partnership they have developed with the respective retailers and agree that this represents a substantial achievement both in terms of interventions achieved and in data made available. Unfortunately I found the report (particularly in the combined paper) of, what is undoubtedly a complicated collection of work, rather hard to follow and a little underdeveloped in terms of both theoretical introduction and interpretation of findings.

The nature of the partnership between the researchers and the retailers is a little unclear. Although there are a number of different definitions of natural experiments, and natural experimental evaluations, key is that the nature and implementation of the intervention is outwith control of the researchers. It is not clear if this was the case here. If there is uncertainty in definitions, non-randomised controlled trials is an unambiguous description of what was done.

I'd also like to understand more about why this particular group of interventions was selected for study. Was this informed by theory, pragmatic considerations or something else? A key limitation on how the findings can be interpreted is the possibility of an intervention modality by food category interaction - as different food categories were selected for each intervention modality, we can't tell if the effects reported are specific to the food category, the intervention modality, or the interaction of the two. I'd like to see more consideration given to this throughout.

I also thought there was a missed opportunity to raise the issue of industry versus government led public health initiatives in this paper. Again, it wasn't clear to me whether the decision to implement these interventions was driven by retailers, the industry body mentioned, the charity mentioned, or the researchers. Shedding some further light on this may help illuminate how such interventions might be implemented more consistently.

Whilst a lot of information about the interventions and data used is provided in supplementary material (particularly in the combined paper), I would have liked to see this brought into the main text. The scant descriptions of the interventions on p6 of the combined paper left we many unanswered questions that I didn't feel were appropriate to be left to supplementary material. This includes exactly what products were impacted; how eg lower fat and low sugar are defined; whether products are added or moved from elsewhere in store etc.

The application of IMD and ethnicity to stores was poorly explained and, I felt, crude. Was this using lower level super output area of location? If so, how likely is it that supermarket customers are drawn only from the surrounding LSOA? At best, I think the limitations of this aspect of the work need further consideration. Perhaps it should be cast in an 'exploratory' light?

I'd recommend both manuscripts are reviewed by a statistician. I wasn't clear what the hierarchical vs ITS analyses were contributing and how I should reconcile the information provided by both. More information on what specific questions these two different strategies addressed would be helpful. I found the inconsistent presentation of different information on the results for each intervention in the combined paper additionally confusing. Eg information on nutritional information and differences in pre-intervention sales are sometimes presented, sometimes not.

There were some apparent inconsistencies in the results that could do with more explanation and discussion. For example, in the ITS, I wasn't clear why there seemed to be substantial increases in the level for control stores at the point of intervention in many cases - even if not statistically significant, what do these reflect? I also wasn't clear if seasonal differences had been taken into account (both eg seasonal events such as Christmas, and seasonal patterns such as temperature changes in summer). This seems particularly important in the easter paper. Finally, the changes in some, but not all, nutrients of interest in line with sales (in the easter paper particularly, but I think in the combined paper too, but reporting of nutrients here was less consistent) could be explored further - there appears to be some displacement of purchases to other foods, but not total?

I would like to see more consideration in the discussion section (of the combined paper) of why some intervention modalities seemed more promising than others. The authors present a general background of inconsistent previous literature. What do these findings add? Are we any further forward in working out in what contexts different intervention modalities may be more or less effective? How could we make progress on this?

Overall the manuscripts need attention to terminology (and consistency) of this throughout - eg cost vs value; natural experiment vs natural experimental evaluation; interventions being short-lived vs intervention effects being short lived etc; the 'opposite' of the population approach is the high-risk approach, not the individual approach; some assertions are without reference (eg socioceoncomic inequalities exist); 'hypotheses' are referred to in the results, but none were presented in the aims.

In relation to the consideration of Adams et al (2016), please be clearer that the hypothesis concerning 'agency' is proposed, not stated; and that the agentic effort referred to is specifically of individual potential beneficiaries of interventions - interventions don't have agency, they require individual recipients to use their agency and mobilise their resources in order to benefit.

Reviewer #3: The authors present a nice study on reducing the availability of seasonal confectionary items from prominent grocery store locations (end of aisle) in an effort to curb over consumption of calorically dense foods. While the authors could not conduct a full RTC, they have baseline data from the prior year and a good number of stores designated as treatment vs. control in the intervention year.

Methods and Statistics: I found the stats to be a bit messy. The authors went through a pretty complicated procedure to try and match control and treatment grocery stores but there are some obvious limitations. First, even if they could be perfectly matched or randomly assigned, there are store-level differences that are inevitably unaccounted for. The authors acknowledge this, but there's still an element of preciseness to their results that seems unwarranted given the limitations of the sample and the data. It's also noteworthy that the intervention weakened over time -- even though the intervention intensified (pg 9) -- where the authors report that the initial difference between the treatment and control groups reduced over time. I don't believe the authors fully address this point in the discussion, but if I understand this correctly it has big implications for their findings.

Summary and conclusions: One question that lingers in any study where food/grocery options are limited by design is compensation (e.g., Schwartz, Riis, Elbel & Ariely 2012; Schwartz, Mochon, et al 2014). While it's great that the confectionary sales decreased in the intervention grocery stores, how do we know that shoppers didn't simply buy their Easter chocolate someplace else? The fact that the strength of the intervention weakened over time suggests that people will go out of their way to find these items when they aren't conveniently placed at the end of the aisle. Recent research also suggests this could be the case -- consumers in the Philadelphia area were shown to go to another county to buy SSB in order to avoid the soda tax. Denmark's fat tax had the unintended consequence of driving an increase in sales on dairy items in neighboring Germany. It would be nice to hear how the authors addressed this - did they follow up with shoppers to determine whether they purchased fewer confectionary items overall (regardless of where they shopped) compared to the prior year? I'm not sure how comprehensive the UK plan is for restricting access or promotions, but it seems that there's little incentive for grocery chains to cooperate with these promotional restrictions if they drive sales to competitors who don't cooperate. It's also possible that people will go deeper into the grocery store to find the items they want -- particularly around holidays when even disciplined shoppers are inclined to indulge. To that end, why would this intervention be better than some others, such as those that focus on reducing the portion sizes or sugar content? I would encourage the authors to think a bit beyond their initial results to determine the robustness of these findings and whether they are really going to achieve the policy goal of reducing confectionary consumption. I would also encourage them to scale back their claims -- fewer calories sold doesn't necessarily equate to fewer calories consumed if customers bought their confectionary items someplace else.

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

[LINK]

5 Dec 2021

Submitted filename: Response to reviewers CGF Conf trial 02 12 2021.docx

Click here for additional data file.

17 Jan 2022

Dear Dr. Piernas,

Thank you very much for submitting your revised manuscript "A natural experiment removing seasonal confectionery from prominent store locations within in a major UK supermarket: A non-randomised controlled trial" (PMEDICINE-D-21-03910R2) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent back to the reviewers. 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 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. 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.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. 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. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the 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. 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 us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Feb 07 2022 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. 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. If new competing interests are declared later in the revision process, this may also hold up the submission. 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. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

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

Requests from the editors:

Please revise your title to ‘Removing seasonal confectionery from prominent store locations within a major UK supermarket: Evaluation of a non-randomised controlled intervention study’.

The final line in your Financial Disclosure (‘We thank the consumer goods…’) would be better placed in an Acknowledgements section at the end of the main text (as it does not pertain to funding received).

Line 23: Please refer to the study as a ‘non-randomised controlled intervention study’ rather than a non-randomised controlled trial (please check throughout the manuscript for consistency).

Lines 45, 75, 278: Please avoid the term “effectiveness”.

Line 51: Please update to “… was associated with reductions in purchases …” or similar.

Line 62: Update ‘governments’ to ‘the UK Government’.

Thank you for providing the STROBE checklist, please update to remove page numbers and use only section names and paragraph numbers (page numbers will change during revisions and may confuse the reader if inaccurate).

line 51, suggest “… was associated with reductions in purchases …” or similar

Line 252: Should this be ‘the level of weekly sales.’?

Was there any attempt made to control for repeated measures in the data (i.e. the same individuals visiting the store multiple times during the study period)?

Please provide the unadjusted comparisons as well as the adjusted comparisons in Table 2 (these can be presented separately in the supplementary materials if easier).

Comments from the reviewers:

Reviewer #1: Thanks authors for their great effort to improve the manuscript. I am satisfied with the response and revision. No further issues needing attention.

Reviewer #2: Thanks for responding to my previous comments. There is one issue that I still think deserve further attention.

1. IMD. I am still not clear at what geographical level IMD was calculated. Was is lower level super output area, middle level super output area, ward, local authority? All of these would involve linking postcode to another geography, but it is not clear from your ms which other geography you have used. I suspect you have used lower level super output area. The have an average population of just 1500 people. Hence the IMD of LSOA of location is likely to be a very poor marker of the deprivation of the people who shop in a store - particularly large out-of-town stores which most people drive to, and city center stores with high non-local usage. I still don't see this point discussed in consideration of the equity of effects seen.

Reviewer #3: Thanks to the authors for a thoughtful revision and set of comments on both papers. I have read the revised manuscripts, the revision notes, and my previous reviews. I remain positive about both papers, but many of my initial concerns remain. First, I don't think the authors can make the causal claims their titles or discussions of the results suggest. Other reviewers have also pointed this out. In my view, these papers are about seeing how various nudges are playing out in the field not about testing interventions. I equate this to non-experimental data that comes in after a drug that did well in clinical trials has been on the market for a while. You cannot overcome a lack of randomization with statistical massaging and/or a pre-post design. You cannot overcome underpowered studies with statistical massaging either. I don't think the word experiment should appear anywhere these papers. There is no natural experiment, as it would require some kind of exogenous shock that resulted in equivalent stores with equivalent populations be essentially randomized to treatment vs. control. I also have reservations about treating these studies as experimental given how little access the researchers had to interventions that were "selected, developed, and implemented" by the retailers -- who I assume are not experimentalists or data scientists. I understand the authors' desire to make a strong policy argument here and it's impressive to have so much data from several retailers trying to implement various nudges simultaneously. However, given the lack of data that's available for controls and robustness checks, I am very hesitant to sign off on a paper that makes causal claims about the viability of certain interventions. I think there are many interesting insights to be gleaned from the data but I think the authors should tone down their language that these results provide any sort of definitive test. I belabor this point because it's easy for keywords that appear in titles and abstracts to become sound bites for journalists and agenda-driven policy makers who never dig a little deeper into the work to realize that the claims cannot be substantiated by the data.

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

[LINK]

20 Jan 2022

Submitted filename: Response to reviewers CGF Conf trial 20 01 2022.docx

Click here for additional data file.

15 Feb 2022

Dear Dr. Piernas,

Thank you very much for re-submitting your manuscript "Removing seasonal confectionery from prominent store locations within a major UK supermarket: Evaluation of a non-randomised controlled intervention study" (PMEDICINE-D-21-03910R3) 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]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

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 hope 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.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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 Feb 22 2022 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

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

Requests from Editors:

Please update the manuscript title to ‘Removing seasonal confectionery from prominent store locations and purchasing behaviour within a major UK supermarket: Evaluation of a non-randomised controlled intervention study’ (my apologies for omitting this suggestion in my previous revision letter).

In the Data Availability Statement, please provide further details as to how an interested researcher could establish contact with the retailer to request permission to access the data (this could be via an intermediary such as the University Research Office or the Institutional Review Board – please note that it cannot be via a study author). Feel free to contact me directly to discuss (cdavidson@plos.org).

Line 55 – please remove the protocol registration details from the abstract.

Please remove subheadings from the Discussion section.

Please check all references for accuracy against our Submission Guidelines (https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references), in particular those for official reports (there are some issues with the authors listed for references 3, 10, 13, 14, 17, and references 18 and 47 are also not presented consistently).

For the panels in Figure 2, please either show the y-axes beginning at zero or show a break in the axes.

To help us extend the reach of your research, please provide any Twitter handle(s) that would be appropriate to tag, including your own, your coauthors’, your institution, funder, or lab.

Comments from Reviewers:

Reviewer #2: Thanks for responding to my remaining comment. I've not further comments. Congratulations on some great work.

Reviewer #3: Thanks for being responsive to all the comments and questions. Nice work!

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

[LINK]

17 Feb 2022

Submitted filename: Response to reviewers CGF Conf trial 17 02 2022.docx

Click here for additional data file.

23 Feb 2022

Dear Dr Piernas,

On behalf of my colleagues and the Academic Editor, Dr Jean Adams, I am pleased to inform you that we have agreed to publish your manuscript "Removing seasonal confectionery from prominent store locations and purchasing behaviour within a major UK supermarket: Evaluation of a non-randomised controlled intervention study" (PMEDICINE-D-21-03910R4) 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.

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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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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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Callam Davidson

Associate Editor

PLOS Medicine