Associations between trajectories of obesity prevalence in English primary school children and the UK soft drinks industry levy: An interrupted time series analysis of surveillance data

Nina T Rogers; Steven Cummins; Hannah Forde; Catrin P Jones; Oliver Mytton; Harry Rutter; Stephen J Sharp; Dolly Theis; Martin White; Jean Adams

doi:10.1371/journal.pmed.1004160

. 2023 Jan 26;20(1):e1004160. doi: 10.1371/journal.pmed.1004160

Associations between trajectories of obesity prevalence in English primary school children and the UK soft drinks industry levy: An interrupted time series analysis of surveillance data

Nina T Rogers ^1,^*, Steven Cummins ², Hannah Forde ^1,³, Catrin P Jones ¹, Oliver Mytton ^1,⁴, Harry Rutter ⁵, Stephen J Sharp ¹, Dolly Theis ¹, Martin White ¹, Jean Adams ¹

Editor: Barry M Popkin⁶

PMCID: PMC9879401 PMID: 36701272

Abstract

Background

Sugar-sweetened beverages (SSBs) are the primary source of dietary added sugars in children, with high consumption commonly observed in more deprived areas where obesity prevalence is also highest. Associations between SSB consumption and obesity in children have been widely reported. In March 2016, a two-tier soft drinks industry levy (SDIL) on drinks manufacturers to encourage reformulation of SSBs in the United Kingdom was announced and then implemented in April 2018. We examined trajectories in the prevalence of obesity at ages 4 to 5 years and 10 to 11 years, 19 months after the implementation of SDIL, overall and by sex and deprivation.

Methods and findings

Data were from the National Child Measurement Programme and included annual repeat cross-sectional measurement of over 1 million children in reception (4 to 5 years old) and year 6 (10 to 11 years old) in state-maintained English primary schools. Interrupted time series (ITS) analysis of monthly obesity prevalence data from September 2013 to November 2019 was used to estimate absolute and relative changes in obesity prevalence compared to a counterfactual (adjusted for temporal variations in obesity prevalence) estimated from the trend prior to SDIL announcement. Differences between observed and counterfactual estimates were examined in November 2019 by age (reception or year 6) and additionally by sex and deprivation quintile. In year 6 girls, there was an overall absolute reduction in obesity prevalence (defined as >95th centile on the UK90 growth charts) of 1.6 percentage points (PPs) (95% confidence interval (CI): 1.1, 2.1), with greatest reductions in the two most deprived quintiles (e.g., there was an absolute reduction of 2.4 PP (95% CI: 1.6, 3.2) in prevalence of obesity in the most deprived quintile). In year 6 boys, there was no change in obesity prevalence, except in the least deprived quintile where there was a 1.6-PP (95% CI: 0.7, 2.5) absolute increase. In reception children, relative to the counterfactual, there were no overall changes in obesity prevalence in boys (0.5 PP (95% CI: 1.0, −0.1)) or girls (0.2 PP (95% CI: 0.8, −0.3)). This study is limited by use of index of multiple deprivation of the school attended to assess individual socioeconomic disadvantage. ITS analyses are vulnerable to unidentified cointerventions and time-varying confounding, neither of which we can rule out.

Conclusions

Our results suggest that the SDIL was associated with decreased prevalence of obesity in year 6 girls, with the greatest differences in those living in the most deprived areas. Additional strategies beyond SSB taxation will be needed to reduce obesity prevalence overall, and particularly in older boys and younger children.

Trial registration

ISRCTN18042742.

Nina T Rogers and colleagues investigate changes in the levels of obesity in children aged 4-5 years and 10-11 years, 19 months after the implementation of UK soft drinks industry levy.

Author summary

Why was this study done?

In England, childhood obesity rates are high with around 10% of reception age children (4/5 years) and 20% of children in year 6 (10/11 years) recorded as living with obesity in 2020.
Children who are obese are more likely to suffer from serious health problems including high blood pressure, type 2 diabetes, and depression in childhood and in later life.
In March 2016, to tackle childhood obesity, the UK government announced there would be a soft drinks industry levy (SDIL) on manufacturers of soft drinks to incentivize them to reduce the sugar content of drinks.

What did the researchers do and find?

We tracked changes in the levels of obesity in children in England from reception (ages 4/5 years) and year 6 (ages 10/11 years) over time between 2014 and 2020. This analysis involved comparing obesity levels 19 months following the SDIL with predicted obesity levels had the SDIL not happened according to gender of the child and school’s area level of deprivation.
The UK SDIL was associated with an 8% relative reduction in obesity levels in girls aged 10/11 years, equivalent to prevention of 5,234 cases of obesity per year in girls aged 10/11 years, alone. Reductions were greatest in girls whose school was in the 40% most deprived areas.
No associations were found between the SDIL and changes in obesity levels in boys aged 10/11 years or younger children aged 4/5.

What do these findings mean?

Our findings suggest that the UK SDIL led to positive health impacts in the form of reduced obesity levels in girls aged 10/11 years.
Further strategies are needed to reduce obesity prevalence in primary school children overall, and particularly in older boys and younger children.

Introduction

There is strong evidence that consumption of sugar-sweetened beverages (SSBs) increases the risk of serious diseases including type 2 diabetes, cardiovascular disease, dental caries, and obesity [1–3]. Children and adolescents in the United Kingdom are particularly high consumers of added sugars [4] with consumption typically peaking at approximately 70 g/day in late adolescence, equivalent to over twice the recommended maximum intake of 30 g [5]. SSBs are the primary source of free sugar in the diets of children and are associated with weight gain, obesity, and fatness in children [6–8]. Demographic patterns of SSB and added sugar consumption mirror each other with highest consumption in older children [5,9], boys [9,10], and children from lower socioeconomic groups [11–13]. Recently born cohorts of children are much more likely to have obesity than children from older cohorts such that 10-year-olds born after the 1980s are 2 to 3 times more likely to develop obesity than those born before the 1980s [14]. The persistence of obesity from childhood into adulthood [15] and its acute and chronic negative physical [16–19] and mental [16,20] health consequences in children has led to governments around the world focusing on preventive strategies to reduce obesity in early life.

The World Health Organization recommends taxes on SSBs to reduce consumption of added sugars to improve health [21]. Over 50 jurisdictions have implemented taxes on soft drinks, although they differ in terms of how much tax is passed through to the consumer, the types of soft drink targeted and the structure of the tax (including banded structure [22] and taxes levied in terms of volume sold [23] or as a proportion of the price [24]). In March 2016, the UK government proposed a number of strategies, including a soft drinks industry levy (SDIL) on manufacturers, importers, and bottlers of SSBs, to reduce prevalence of obesity in childhood [25]. The two-tier SDIL, implemented in April 2018, differed from most other tax structures in that it was designed to incentivise manufacturers to reformulate higher sugar soft drinks to move them to a lower tax tier. Manufacturers and importers were subject to a charge of £0.24/litre on soft drinks containing ≥8 g of sugar per 100 ml, £0.18/litre on soft drinks containing between ≥5 to <8 g of sugar per 100 ml, and no levy on drinks containing <5 g sugar per 100 ml [26]. Levy exempt drinks include milk, milk-based drinks, 100% fruit juice, and powders used to make drinks. As part of the broader health strategy for young people, the UK government indicated they would use revenues raised through the SDIL to fund physical education in schools and breakfast and after-school clubs [27].

Evidence suggests that the UK SDIL led to substantial reformulation of the UK soft drinks market. The percentage of drinks containing >5 g sugar/100 ml fell from 49% to just 15% between September 2015 and February 2019, with reformulation accelerating after announcement of the UK SDIL [28]. Overall, the UK SDIL was associated with a reduction in sugar purchased from soft drinks [29]. While the price of soft drinks increased following implementation of the SDIL, the levy was only partially passed on to the consumer. For example, in drinks containing between ≥5 to <8 g of sugar per 100 ml, approximately one-third of the levy was passed on [28]. A number of modelling studies [30–33] have predicted that the introduction of SSB taxes would lead to a modest reduction in obesity in children and adults at the population level, but no study to date has used empirical data to examine whether the response of the SSB industry to the UK SDIL was associated with a subsequent change in the prevalence of childhood obesity. A few studies have used empirical data to estimate associations between SSB taxes and weight-related outcomes in children and adolescents and have either shown no overall association [23,34–36] or small to modest associations in specific subgroups such as low-income households [36] children with higher body mass indices (BMIs) [36,37] or in adolescent girls but not boys [38]. Different findings from these discrete studies may be related to use of different outcome measures (in particular, one study relied on subjective measures of self-reported weight [34]), differences in change in SSB prices achieved by taxes (some were associated with small average increases in prices of SSB (<5%) [34,35]) or differences in substitutions to high-calorie untaxed food [23] and drinks [23,35].

In this study, we use cross-sectional data on monthly prevalence of objectively assessed obesity in children when they enter (reception class; ages 4 to 5) and exit (year 6; ages 10 to 11) English primary schools to examine whether 19 months following the implementation of the UK SDIL there were changes in the trajectory of prevalence of obesity (1) overall and (2) by sex and deprivation.

Methods

The study was registered (ISRCTN18042742) and the study protocol published [39]. This study is reported as per the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement (S1 Checklist).

Data source

We used population level data from the National Child Measurement Programme (NCMP). This surveillance programme began in 2006 and measures the height and weight of approximately 1 million children from English state-maintained primary schools in reception (ages 4 to 5 years) and year 6 (ages 10 to 11 years) annually, with the aim of monitoring national rates of overweight and obesity in children. Local authorities oversee the data collection, and letters are sent to the parents of eligible children where they are informed about why the data are collected and how these are stored. There is also an opportunity to opt out of measurement. Approximately 99% of eligible schools (approximately 17,000 schools) take part each year and individual response rates are high with over 90% of eligible pupils taking part [40].

Surveillance data provided by NCMP include prevalence of children with overweight or obesity by school class (reception or year 6), sex (male or female), school year (e.g., 2013/14), month of measurement, and the index of multiple deprivation (IMD) quintile of the location of the primary school that the child attends. The NCMP measures the height and weight of children in England throughout the academic school year (September to July); hence, there was no available data for the month of August when the long summer holiday takes place. IMD scores are commonly used in England as measures of multiple deprivation by considering seven distinct domains including income, employment, education, barriers to housing, health and disability, crime, and living environment [41]. The BMI thresholds used to derive overweight and obesity prevalence values were based on the 85th and 95th centiles, respectively, of a reference sample of measures taken in the UK in 1990 taking account of height, weight, sex, and age, reflecting the definitions used by Public Health England for population surveillance [42]. The study period was initially planned to end 2 years following the implementation of SDIL, but follow-up was curtailed in November 2019 (4 months prior to the proposed end date) to avoid any influence of potential household storing of food and drink in preparation for (i) the UK leaving the European Union (December 2019) and (ii) national lockdown because of the COVID-19 pandemic (March 2020) [43] to avoid contamination with documented changes in weight status occurring in the pandemic [44].

Statistical analysis

Interrupted time series (ITS) analyses were conducted to assess obesity prevalence in relation to the UK SDIL in children attending primary school reception or year 6 classes, overall and by sex and IMD quintile. The ITS used monthly data from September 2013 (study month 1) until November 2019 (study month 69), including the months of the SDIL announcement (March 2016; study month 29) and implementation (April 2018; study month 52).

Generalised least squares (GLS) models were used. Autocorrelation in the time series was examined visually using plots of autocorrelation and partial autocorrelation and statistically using Durbin–Watson tests; an autocorrelation-moving average (ARIMA) correlation structure was used, with the order (p) and moving average (q) parameters chosen to minimise the Akaike information criterion (AIC) in each model. School holidays are reported to influence weight-related outcomes in school children [45]. To take account of this and other key events in the academic calendar year that might impact weight, we used calendar months as a proxy. Following a standard data-driven approach, to identify which calendar months might predict significant changes in obesity prevalence, we ran a series of GLS models in which a single calendar month was added to the equation. After all, calendar months were tested individually; models were finalised by including all the months that showed significant changes in obesity prevalence. Adding all months as dummy variables was avoided to restrict the number of variables to those that were informative, to reduce error, and to increase the precision of our estimates. The months of September, October, June, and February were significant for reception class children, and September and July were significant for year 6 children. Models for year 6 and reception age children were examined separately because reception age children in England typically start school full time, a few weeks after older children have returned. Model specifications for year 6 and reception class children are included (S1 Text). Counterfactual scenarios were estimated based on pre-announcement trends (S1 Fig). Absolute and relative differences in prevalence of obesity between observed and counterfactual values were estimated at month 69 (November 2019). Confidence intervals were calculated from standard errors estimated using the delta method [46]. All statistical analyses were performed in R version 4.1.0.

Sensitivity analysis 1: Inclusion of two alternative interruption points

The main analysis included a counterfactual based on the pre-announcement trend (i.e., a scenario where neither the announcement nor implementation happened); however, previous research suggests that reformulation of drinks began some months after the announcement of SDIL but before implementation [28]. Therefore, as well as capturing the earliest possible time when reformulation could come into effect, in sensitivity analyses (S1 Fig), we used two alternative interruption points. First (sensitivity analysis 1a), we used a counterfactual based on the trend from September 2013 to November 2016 (equivalent to 8 months post-announcement and the point at which reformulation increased rapidly) [28]. Second (sensitivity analysis 1b), we used a counterfactual based on the pre-implementation trend, i.e., from September 2013 to April 2018.

Sensitivity analysis 2: Combining overweight and obesity prevalence

In addition to examining prevalence of obesity, the main analysis was repeated and broadened to examine trajectories of excess weight prevalence, in relation to the SDIL, using monthly measures of overweight in addition to obesity.

Results

Table 1 summarises the mean obesity prevalence in the study period (i) before the SDIL announcement and (ii) after the SDIL announcement, in primary school children in reception and year 6, overall and by sex and IMD quintile. Highest levels of obesity were observed in the most deprived areas regardless of age and sex; pupils in schools from the most deprived IMD quintiles had nearly twice the prevalence of obesity as those in the least deprived IMD quintiles.

Table 1. Mean obesity prevalence (standard deviation) in the pre- and post-announcement periods of the UK SDIL, by school class, sex, and IMD quintiles.

Mean (standard deviation) obesity prevalence in primary school children in reception¹ and year 6² class
	Total population		Boys		Girls
	Pre-announcement³	Post-announcement⁴	Pre-announcement	Post-announcement	Pre-announcement	Post-announcement
School class: Reception¹
All IMD	9.5(0.9)	9.8(0.9)	9.8(1.9)	10.0(2.2)	9.0(1.9)	9.4(2.1)
IMD 1 (most deprived)	11.9(0.6)	12.5(0.8)	12.2(0.6)	12.9(0.8)	11.5(0.7)	12.2(1.0)
IMD 2	10.6(0.8)	11.0(0.8)	11.1(0.8)	11.4(1.0)	10.1(1.0)	10.6(0.9)
IMD 3	9.1(0.7)	9.5(1.0)	9.4(0.6)	9.8(1.0)	8.8(0.9)	9.3(1.1)
IMD 4	8.3(0.9)	8.5(0.7)	8.8(1.1)	8.6(0.7)	7.8(0.9)	8.3(1.0)
IMD 5 (least deprived)	7.0(0.8)	7.1(0.8)	7.4(0.8)	7.3(0.8)	6.8(1.1)	6.7(0.9)
School class: Year 6²
All IMD	19.2(0.5)	20.1(0.6)	20.8(3.9)	22.1(4.6)	17.3(3.8)	17.9(4.0)
IMD 1	24.3(1.0)	26.0(0.7)	26.1(1.1)	28.4(1.0)	22.5 (1.1)	23.5(0.8)
IMD 2	21.8(0.7)	23.1(0.7)	23.4(0.9)	25.4(1.1)	20.1(0.9)	20.7(0.8)
IMD 3	19.0(0.6)	19.7(0.9)	20.7(0.8)	21.7(1.3)	17.2(0.9)	17.6(0.8)
IMD 4	16.6(0.7)	17.2(0.8)	18.3(0.8)	19.2(1.0)	14.8(0.9)	15.2(0.9)
IMD 5	13.8(0.7)	14.2(0.6)	15.4(1.0)	15.9(0.9)	12.2(0.7)	12.3(0.7)

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¹Reception class–ages 4/5.

²Year 6 class–ages 10/11.

³Pre-announcement period = September 2013–March 2016.

⁴Post-announcement period = April 2016–November 2019.

IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

Changes in obesity prevalence in relation to SDIL

Unless stated otherwise below, all estimates of changes in prevalence of obesity are based on values from November 2019 with respect to the counterfactual scenario of no SDIL announcement or implementation having occurred.

Across all year 6 children, there was a 0.8-percentage point (PP) (95% confidence interval (CI): 0.3, 1.3) absolute reduction or 3.6% (95% CI: 1.2, 5.9) relative reduction in obesity prevalence compared to the counterfactual (see Table 2). Year 6 children in schools from the most deprived IMD quintiles (IMD1 and 2) had the greatest (relative) reductions in obesity prevalence of 4.1% (95% CI: 1.8, 6.3) and 5.5% (95% CI: 3.3, 7.7), respectively; however, large differences between year 6 girls and boys were observed. In year 6 girls, there was an overall relative reduction in obesity prevalence of 8.0% (95% CI: 5.4, 10.5). Analysis by IMD revealed greatest reductions in the two most deprived IMD quintiles (1 and 2) of 9.0% (95% CI: 5.9, 12.1) and 11.0% (95% CI: 9.2, 12.7), respectively, where a clear break in trend was observed graphically some months following the SDIL implementation (Fig 1). In year 6 boys, there was no overall change in obesity prevalence and no obvious pattern in changes in prevalence by IMD quintile, although there was a large relative increase in obesity prevalence of 10.1% (95% CI: 4.3, 15.9) in the least deprived IMD quintile and a small reduction in prevalence of obesity in IMD2 of 3.30% (95% CI: 0.4, 6.2) (Fig 2).

Table 2. Absolute and relative changes in prevalence of obesity (95% CIs), compared to the counterfactual¹, in reception and year 6 boys and girls, by IMD at 19 months post-implementation of the UK SDIL.

	Total population		Boys		Girls
Interruption–SDIL announcement	PP change	Relative change (%)	PP change	Relative change (%)	PP change	Relative change (%)
Reception
All IMD	0.3(0.9, −0.3)	3.0(−3.1, 9.1)	0.5(1.0, −0.1)	4.5(−1.0, 10.0)	0.2(0.8, −0.3)	2.4(−3.6, 8.4)
IMD 1 (most deprived)	−0.5 (0.1, −1.1)	−3.9(−8.4, 0.6)	−0.4(0.2, −0.9)	−2.6(−6.7, 1.4)	−0.6(0.1, −1.2)	−4.3(−9.0, 0.4)
IMD 2	0.7(1.2, 0.2)	6.7(2.0, 11.4)	1.2(2.1, 0.4)	11.1(3.3, 18.9)	0.3(0.9, −0.3)	2.6(−3.2, 8.4)
IMD 3	0.9(1.7, 0.2)	9.7(1.6, 17.9)	0.7(1.7, −0.3)	7.3(−2.7, 17.4)	1.2(1.8, 0.5)	13.0(5.4, 20.5)
IMD 4	0.5(1.0, 0.1)	6.3(1.0, 11.6)	0.5(1.1, −0.2)	5.4(−2.2, 12.9)	0.3(0.6, −0.1)	3.5(−0.6, 7.6)
IMD 5 (least deprived)	0.6(1.1, 0.1)	10.0(2.2, 17.9)	0.6(1.1, 0.1)	9.7(2.0, 17.4)	0.6(1.2, 0.003)	10.8(0.1, 21.5)
Year 6
All IMD	−0.8(−0.3, −1.3)	−3.6(−5.9, −1.2)	−0.04(0.6, −0.6)	−0.2(−2.7, 2.4)	−1.6 (−1.1, −2.1)	−8.0 (−10.5, −5.4)
IMD 1	−1.1(−0.5, −1.8)	−4.1(−6.3, −1.8)	0.2(0.9, −0.5)	0.6(−1.8, 3.0)	−2.4(−1.6, −3.2)	−9.0(−12.1, −5.9)
IMD 2	−1.4(−0.8, −1.9)	−5.5(−7.7, −3.3)	−0.9(−0.1, −1.7)	−3.3(−6.2, −0.4)	−2.5(−2.1, −2.9)	−11.0(−12.7, −9.2)
IMD 3	0.01(0.6, −0.6)	0.04(−3.0, 3.1)	1.0(2.4, −0.5)	4.5(−2.1, 11.1)	−0.5(0.2, −1.2)	−2.8(−6.5, 0.9)
IMD 4	0.2(0.8, −0.4)	1.1(−2.1, 4.4)	0.3(1.0, −0.5)	1.3(−2.3, 4.8)	0.2(0.9, −0.5)	1.2(−3.40, 5.9)
IMD 5	0.3(0.8, −0.3)	1.9(−1.8, 5.6)	1.6(2.5, 0.7)	10.1(4.3, 15.9)	−0.9(−0.3, −1.5)	−7.0(−11.6, −2.3)

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¹Estimated from pre-announcement trends.

CI, confidence interval; IMD, index of multiple deprivation; PP, percentage point; SDIL, soft drinks industry levy.

Fig 1 — Observed and modelled prevalence of obesity is shown by IMD quintile and overall. Dark blue points show observed data and dark blue lines (with grey shadows) shows modelled data (and 95% CIs) of obesity prevalence. The red line indicates the counterfactual line based on the pre-SDIL announcement trend (assuming the announcement and implementation had not occurred). The first and second dashed vertical lines indicate the announcement and implementation of the SDIL, respectively. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

Fig 2 — Observed and modelled prevalence of obesity is shown by IMD quintile and overall. Dark blue points show observed data and dark blue lines (with grey shadows) shows modelled data (and 95% CIs) of obesity prevalence. The red line indicates the counterfactual line based on the pre-SDIL announcement trend (assuming the announcement and implementation had not occurred. The first and second dashed vertical lines indicate the announcement and implementation of the SDIL, respectively. NB: The scales used in Figs 2–4 differ to maximise resolution of the image. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

In reception children, compared to the counterfactual, there was no absolute change in obesity prevalence overall in girls (0.2 PP (95% CI: 0.8, −0.3)) and boys (0.5 PP (95% CI: 1.0, −0.1)). Examination by IMD and sex showed a consistent increase in prevalence of obesity, compared to the counterfactual, in the least deprived IMD groups in both girls (0.6 PP (95% CI: 1.2, 0.003)) (Fig 3) and boys (0.6 PP (95% CI: 1.1, 0.1)) (Fig 4) in reception class.

Fig 3 — Observed and modelled prevalence of obesity is shown by IMD quintile and overall. Dark blue points show observed data and dark blue lines (with grey shadows) shows modelled data (and 95% CIs) of obesity prevalence. The red line indicates the counterfactual line based on the pre-SDIL announcement trends (assuming the announcement and implementation had not occurred). The first and second dashed vertical lines indicate the announcement and implementation of the SDIL, respectively. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

Fig 4 — Observed and modelled prevalence of obesity is shown by IMD quintile and overall. Dark blue points show observed data and dark blue lines (with grey shadows) shows modelled data (and 95% CIs) of obesity prevalence. The red line indicates the counterfactual line based on the pre-SDIL announcement trends (assuming the announcement and implementation had not occurred). The first and second dashed vertical lines indicate the announcement and implementation of the SDIL, respectively. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

When the interruption point was changed to December 2016 (8 months post-SDIL announcement, the point at which reformulation began, sensitivity analysis 1a), changes in obesity prevalence were consistent with the main findings, with reductions in obesity prevalence evident in year 6 children, specifically girls from schools in the most deprived areas (IMD 1 and 2) (S1 Table), and increases in obesity prevalence in year 6 boys from the least deprived areas (IMD 4 and 5). When the interruption point was changed to April 2018 (month of SDIL implementation, sensitivity analysis 1b, S2 Table) findings varied from the main analysis, with an overall absolute increase in the prevalence of obesity in reception age children by 0.7 PP (95% CI: 0.1, 1.3). Compared to the counterfactual, there were few significant changes in obesity prevalence in the different year 6 groups, although reductions (e.g., 3.8% (95% CI 5.7, 2.0) in year 6 girls from IMD 2) and increases (e.g., 3.8% (95% CI 0.2, 7.4) in boys in IMD4) were observed in some groups.

Changes in prevalence of excess weight (overweight or obesity) in relation to the UK SDIL were comparable to the main findings on changes in trends in prevalence of obesity, with greatest reductions in excess weight observed in girls from schools in IMD quintiles 1 and 2 and no change in prevalence of excess weight overall in year 6 boys or reception age children (S3 Table). However, compared to the counterfactual scenario of no announcement or implementation, there was an observed absolute reduction in excess weight of reception age girls from the most deprived IMD (1) of 1.6 PP (95% CI 1.1, 2.1).

Discussion

Summary of findings

This is the first study that we are aware of that uses empirical data to examine changes in childhood obesity prevalence in England in relation to the UK SDIL. After accounting for prior trends in obesity, there was a 0.8-PP absolute reduction in year 6 children living with obesity, 19 months after the implementation of the SDIL. These reductions in year 6 children were predominantly driven by changes in girls, where there was a 1.6-PP absolute or 8.0% relative reduction in obesity prevalence. Assuming, based on our 2019 data, that there are 337,658 year 6 girls in England (of whom 18.4% have obesity), this reduction is equivalent to 5,234 averted cases of obesity in year 6 girls. Relative to the counterfactual, no overall change was observed in year 6 boys. We observed that for year 6 girls, reductions in obesity were greatest in the 40% most deprived IMD areas, with a 2.4-PP absolute or 9.0% relative reduction in the most deprived IMD quintile. Overall, the prevalence of obesity in reception class children was unchanged, compared to the counterfactual.

Comparison with other studies and implications

In this section, we draw on evidence from other studies and compare our findings with them, while also providing some potential explanations for our results and their implications.

First, our findings are plausible since associations between SSB consumption and risk of obesity are well described in the literature [6–8]. Furthermore, a relationship between the UK SDIL and an overall reduction in sugar purchased from soft drinks across the population has previously been reported [29]. Several modelling studies have also predicted that SSB taxes are likely to be most effective at targeting sugar intake in children and younger adults [47,48].

Second, the magnitude and pattern of associations in our results are consistent with recent findings from Mexico that report a modest reduction in overweight or obesity prevalence in adolescent girls (aged 10 to 18) with a 1.3-PP absolute decrease 2 years after a 10% SSB price increase (compared to a 1.6-PP absolute decrease observed in this study in 10- to 11-year-old girls 19 months after the levy was introduced) [38]. Moreover, similar to the findings of this study, no significant reductions in weight-related outcomes were observed in adolescent boys in Mexico. We note, however, that the tax implemented in Mexico is not directly comparable with the UK SDIL; in Mexico; the tax had a different design aimed at increasing the price to consumers resulting in 100% of the SSB tax being passed through to consumers, equating to a 14% increase in prices [49], and, importantly, the tax was included as a wider package of anti-obesity measures, which included charging 8% on high-energy foods [23]. We note the importance of the finding that the tax in Mexico was more effective in girls who were heavier. Similar analysis was not possible here because we only had access to repeated cross-sectional data, which cannot be linked over time.

Third, we found that reductions in obesity in relation to the levy were greatest in children who were older and from the most deprived areas. Previous studies have reported the same children are more likely to be higher consumers of SSBs [5,9,11–13]. This suggests a possible dose–response gradient between consumption levels and effectiveness of the levy in reducing obesity. This also adds to the growing international evidence that SSB taxes may reduce inequalities in diet-related health outcomes. For example, some studies from other countries have shown that lower-income households were more likely to reduce their purchases or intake of sugar from SSBs following introduction of SSB taxes [36,50,51], although this is not always the case [22,52,53].

In this study, we also demonstrate that the UK SDIL is not associated with a change in obesity prevalence in children in the first year of primary school. This result is congruous with findings from a cohort of British children showing that SSB consumption at ages 5 or 7 are not related to adiposity at age 9 years [54]. Added sugars from drinks make up 30% of all added sugars in the diet of young children (aged 1 to 3 years), but this increases to more than 50% by late adolescence [5]. The lower intake of sugars from soft drinks at very young ages may lower the potential of a tax on SSBs, making it harder to observe health effects at the population level. Fruit juices, which are not included in the levy, are thought to contribute similar amounts of sugar in young children’s diets as SSBs and may explain why the levy alone is not sufficient to reduce weight-related outcomes in reception age children. In addition to drinks, confectionery, biscuits, desserts, and cakes are also important high-added sugar items, which are regularly consumed by young children and could be a target of additional obesity reduction strategies [5].

While our finding that the SDIL had greater impacts on obesity prevalence in girls than boys is consistent with previous studies [38], it is unclear why this might be the case, especially since boys were higher baseline consumers of SSBs [13]. One explanation is that there were factors (e.g., in food advertising and marketing) at work around the time of the announcement and implementation of the levy that worked against any associations of the SDIL among boys. There is evidence that soft drink manufacturers altered their marketing strategies in different ways in response to the SDIL including repackaging and rebranding products [55]. Numerous studies have found that boys are often exposed to more food advertising content than girls [56–59], both through higher levels of TV viewing [59] and through the way in which adverts are framed. Physical activity is often used to promote junk food, and boys, compared to girls, have been shown to be more likely to believe that energy-dense junk foods depicted in adverts will boost physical performance [56] and thus they are more likely to choose energy-dense, nutrient-poor products following celebrity endorsements. There is also evidence that girls tend to make healthier choices when it comes to diet (e.g., consuming more fruit and vegetables and less energy-dense foods) and other health behaviours (e.g., brushing teeth) [60]. One possibility for the observed differences between boys and girls may be that girls were more responsive to public health signalling arising from discussions around the SDIL or that they were more likely to choose drinks that had been reformulated to contain less sugar following the SDIL announcement.

Even the strongest association of the SDIL among the most levy-responsive groups (e.g., year 6 girls) reflected only a dampening of the rate of increase in obesity prevalence compared to the counterfactual rather than a reversal in trends. This highlights that alongside the SDIL, additional evidence-informed obesity reduction strategies need to be in place to improve weight-related outcomes, especially in boys and younger children, as they enter primary school education.

Strengths and limitations of the study

This study makes use of a unique and well-powered ongoing nationally representative sample covering over 90% of children aged 4 to 5 and 10 to 11 years in state-run primary schools over the study period and tracks the prevalence of overweight and obesity in over 1 million school children annually. Obesity prevalence data were based on objective measures of height and weight rather than parental self-report, where there is a tendency to underestimate overweight [61]. The NCMP uses 85th and 95th centiles of the UK1990 growth reference to monitor overweight and obesity in children (accounting for age and sex), respectively [42,62]. However, other cut points are sometimes used [63], and there is some debate over whether this is the best measure of adiposity, particularly in younger children [64].

Parental consent in NCMP involves a selective opt-out, which is designed to increase participation rates. However, it has been suggested that girls with obesity are less likely to participate [65]. This may have led to underestimation of the association between SDIL on obesity prevalence in girls. These effects are, however, likely to be small given that obesity levels in girls have not changed dramatically and participation in the sample overall remained high throughout our study period. Socioeconomic disadvantage was assessed using an area-level indicator (IMD) of the school that each child attended, a less sensitive measure than capturing socioeconomic disadvantage at the household level. However, there is a strong correlation between school-level IMD and the proportion of pupils eligible for free school meals, a measure of the number of children attending a school with a low household income, [66] suggesting that the measure used here is a suitable proxy measure of household deprivation.

Data on time trends of expected childhood weight loss in relation to diet interventions are sparse with studies not monitoring weight-related outcomes with regularity and from early in the intervention. This makes it particularly challenging to estimate how long from the SDIL announcement we would expect to observe changes in obesity prevalence in children. However, there is evidence that changes in energy balance in children can lead to rapid changes in weight loss, for example, seasonal differences in BMI are observed in school children, with weight gain typically occurring during the summer periods especially in children with overweight or obesity [45]. Consistent with these observations, our statistical models and ITS graphs reveal spikes in obesity prevalence in the months following the summer holidays (September in reception and year 6 children, and October in children in reception) and dips in other months (e.g., in June and July) in some subgroups. These require further investigation that could contribute to understanding of seasonal variations in childhood obesity. Furthermore, our ITS graphs reveal that in some groups, there may be continued improvement in the longer term with a widening between counterfactual and observed values in, for example, year 6 girls (IMD 1, 2, and 5).

The ITS approach used modelled counterfactuals on the obesity prevalence trends immediately prior to the SDIL announcement. Given that estimates of the overall difference between observed and counterfactual obesity prevalence can be sensitive to the time points at which the counterfactuals are modelled, as part of a sensitivity analysis, we included two extra interruption points. The first additional interruption was 8 months post-announcement of SDIL, a time when reformulation of SSBs was visibly starting to increase; here we observed very similar findings to the main analysis indicating that they are robust. The second additional interruption was assigned to the date of the SDIL implementation; using this model, we observed fewer significant changes in obesity prevalence compared to the counterfactual (for example, no significant difference was observed in year 6 girls overall). This finding may be explained by the fact that companies had already reformulated most of their products prior to the implementation date and trajectories of obesity prevalence had responded rapidly. Furthermore, examining trajectories of “excess weight” prevalence rather than prevalence of obesity as the outcome of interest led to findings broadly consistent with the main analysis.

Conclusions

The UK SDIL was proposed by the UK government to tackle childhood obesity. The pattern of findings of this study suggests that the SDIL can contribute to reducing obesity prevalence in older primary school children. The SDIL announcement and implementation was associated with an overall relative decrease in obesity prevalence in year 6 girls aged 10 to 11 years of approximately 8% compared to the counterfactual scenario based on pre-announcement trends. These associations were even greater in girls from schools in the 40% most deprived areas, suggesting the SDIL could help to reduce inequalities in child obesity. Further obesity reduction policies are needed alongside taxes on SSBs to improve and reverse the current obesity prevalence in children.

Supporting information

S1 Checklist. RECORD checklist.

RECORD, Reporting of studies Conducted using Observational Routinely-collected Data.

(DOCX)

Click here for additional data file.^{(27.9KB, docx)}

S1 Fig. Schematic diagram of the interrupted time series.

Blue solid lines indicate observed data. Dashed red lines represent counterfactuals. Counterfactual for (1) main analysis based on obesity prevalence trends from 09/2013–03/2016; (2)sensitivity analysis (a) based on obesity prevalence trends from 09/2013–12/2016; and (3)sensitivity analysis (b) based on obesity trends from 09/2013–04/2018.

(DOCX)

Click here for additional data file.^{(47.7KB, docx)}

S1 Text. Model specifications for children in year 6 and reception class.

(DOCX)

Click here for additional data file.^{(20.7KB, docx)}

S1 Table. Changes in obesity prevalence compared to a counterfactual scenario based on trends prior to 8 months post-announcement.

Absolute and relative changes in prevalence of obesity (95% CIs), compared to a counterfactual scenario¹ based on trends prior to 8 months post-announcement, overall and by IMD in reception and year 6 children, 19 months post-implementation of UK SDIL. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

(DOCX)

Click here for additional data file.^{(18.9KB, docx)}

S2 Table. Changes in obesity prevalence compared to a counterfactual scenario based on trends prior to the SDIL implementation.

Absolute and relative changes in prevalence of obesity (95% CIs), compared to a counterfactual scenario¹ based on pre-SDIL implementation trends, overall and by IMD in reception and year 6 children, 19 months post-implementation of UK SDIL. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S3 Table. Changes in excess weight prevalence compared to a counterfactual scenario based on trends prior to the SDIL announcement.

Absolute and relative changes in prevalence of excess weight (overweight or obesity) and 95% CIs, compared to a counterfactual scenario¹, based on pre-SDIL announcement trends, overall and by IMD in reception and year 6 children, 19 months post-implementation of UK SDIL. CI, confidence interval; IMD, index of multiple deprivation; SDIL, soft drinks industry levy.

(DOCX)

Click here for additional data file.^{(15.2KB, docx)}

Abbreviations

AIC: Akaike information criterion
BMI: body mass index
CI: confidence interva
GLS: generalised least squares
IMD: index of multiple deprivation
ITS: interrupted time series
NCMP: National Child Measurement Programme
PP: percentage point
SSB: sugar-sweetened beverage
SDIL: soft drinks industry levy

Data Availability

Data are available from NHS Digital (https://digital.nhs.uk/data-and-information/publications/statistical/national-child-measurement-programme) for researchers who meet the criteria for access to confidential data.

Funding Statement

NTR, OM, MW, and JA were supported by the Medical Research Council (grant Nos MC_UU_00006/7). This project was funded by the NIHR Public Health Research programme (grant Nos 16/49/01 and 16/130/01) to MW. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care, UK. 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.1004160.r001

Decision Letter 0

Beryne Odeny

21 Sep 2022

Dear Dr Rogers,

Thank you for submitting your manuscript entitled "Associations between trajectories of obesity prevalence in English primary school children and the UK soft drink industry levy: an interrupted time series analysis of surveillance data" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff 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.1004160.r002

Decision Letter 1

Callam Davidson

3 Nov 2022

Dear Dr. Rogers,

Thank you very much for submitting your manuscript "Associations between trajectories of obesity prevalence in English primary school children and the UK soft drink industry levy: an interrupted time series analysis of surveillance data" (PMEDICINE-D-22-03100R1) 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 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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We look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

PLOS Medicine

plosmedicine.org

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

Requests from the editors:

Please add this statement to the manuscript's Competing Interests: "JA is an Academic Editor on PLOS Medicine's editorial board."

Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Please include continuous line numbering throughout your manuscript to facilitate future reviews.

Please ensure that the study is reported according to the RECORD guideline, and include the completed RECORD checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement (S1 Checklist)."

The RECORD guideline can be found here: https://www.equator-network.org/reporting-guidelines/record/

When completing the checklist, please use section and paragraph numbers, rather than page numbers.

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.

Please present and 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. The vast majority of this content is already present so it is largely a matter of restructuring rather than revising.

Your study is observational and therefore causality cannot be inferred. Please remove language that implies causality, such as ‘effects’ (Conclusions section). Refer to associations instead.

Please provide date accessed for References labelled [Internet].

In Figures 1-4, please show the axis beginning at zero. If this is not possible, please show a break in the axis. Please also ensure that the y axis is identical for all figures to facilitate comparison.

Comments from the reviewers:

Reviewer #1: See attachment

Michael Dewey

Reviewer #2: Overview and general recommendation:

This study estimates trajectories in the prevalence of obesity for children at ages 4-5 years and 10-11 years 19 months after the implementation of the sugar-sweetened beverages (SSBs) tax in the UK. It describes these trajectories overall, by sex and deprivation using an interrupted time series approach or ITSA. It is one of the first to examine changes in unhealthy weight among children in relation to the SSBs tax. Except for one study in Mexico, previous evidence was largely based on microsimulations or was linking relatively small changes in SSBs prices to health outcomes. Overall, this paper is clearly written, uses a valid empirical approach and research design given the setting, novel data, and presents and interprets the results well. Below are some comments that I hope will help improve it.

1. I suggest describing SSBs tax in more detail. While the tax is briefly described in "levels", it would be helpful to have a better understanding what it means in terms of %. How does it compare to the Mexican SSBs tax? And to sales SSBs taxes in the US? How is it similar/different? Why was it designed as a function of sugar content? Was it levied on manufacturers? What was the pass-through to consumer prices or is there another mechanism at play? Are there any previous studies estimating SSBs demand elasticities (overall, by sex and deprivation) in the UK? All these things will help the reader understand better why SSBs tax might be working and how.

2. To this end, the Introduction Is heavily focused on describing historic obesity trends in the UK. I suggest adding information on sugar as well as SSBs intake because that is essentially what SSBs tax is targeting. What is the consumption among children of interest overall, by sex and deprivation? You describe obesity rates, but what is important to understand are baselines of the mechanism through which SSBs tax is likely to operate.

3. Briefly explain what the evidence on tax & reformulation says exactly, so the reader doesn't have to go to reference [20] to find out.

4. Statements referring to the existing evidence (and therefore the gap that this study is filling) are not fully correct. The study published in JAMA Pediatrics that studies unhealthy weight changes among adolescents post SSBs tax in Mexico does not use subjective measures, but objectively measured clinical data. Also, in Mexico, SSBs price changes post tax varied, but on average went up about 10 %. Therefore, your rationale that evidence across studies is mixed cannot be that data relies on self-reported weight and that minimal price increases in prices of SSBs are below 5% - those studies mostly study sales SSBs taxes in the US. So, while that may be the case in most previous work studies, it is not true for the studies you are describing just above that explanation. While I understand what you are trying to say, I suggest rewriting the paragraph for correctness. Also, I suggest to more clearly explain how these papers are different from this study in particular, not only how they are different between each other. For instance, you don't link SSBs price changes to health, but do a before/after SSBs tax comparison - using a very novel and well-powered data tracking children's weight over time, which is quite hard to come by and unique, so I suggest stressing that. I also suggest you also look at the paper by Aguilar et al. (2021) for before/after tax comparison in the context of Mexico - I did not see this study on the reference list. You can discuss (here or in other sections) differences in approaches between studies (e.g., regression discontinuity design vs ITSA).

5. I suggest including calendar month indicators instead of including only selected months by hand. Let the data decide what is needed.

6. It is not clear at what level did you cluster your standard errors. I suggest clustering them at the school level, as you have more than 17000 schools and enough students within each.

7. Discussion of the absolute results is confusing - you explain absolute differences in %? I believe you should state absolute changes in percentage points not percent.

8. Were differences in IMD quintiles significantly different from each other? It does not seem to be the case from looking at the confidence intervals, but please, test this formally and report whether that is the case. You can achieve this by including an interaction term in one regression - from what I understand, you estimate each regression model separately for each subgroup. Make sure to explain this clearly in the methods section as well.

9. Why did obesity prevalence increase among reception children? Report results with numbers for that too to better understand the magnitude and statistical significance of the effects. If magnitudes are small, fine, if not, these results deserve a detailed discussion, too.

10. You carefully apply several sensitivity analyses and mention your limitation, including the potential issue of selection bias. Can you check whether selection bias is present in the data? Provide results in the appendix. For instance, do you see smaller opting out pre- post-tax in poorer areas? How would this bias your results given that these are the groups responding the most to the tax?

11. I think comparison of your results to those in Mexico are interesting (e.g., 1.3pp change) - however, it is important to understand how SSBs taxes compare as well. That is why including more details on this and other SSBs taxes in the Introduction (or a bigger discussion in the Discussion) will be very helpful. Also, study in Mexico finds larger effects for heavier girls at baseline - did you check for that in your study? Given that the literature has previously reported higher health elasticities for heavier individuals I suggest you check that in your data as well (overall and by sex). It could also help explaining why your estimates are larger in more deprived areas where obesity seems to be the highest.

12. This study does not provide causal estimates, so I suggest that you do not refer to them as such. There is no control group (besides the projected counterfactual trend), and this is not a clinical trial. Just because something was reported previously, it does not make it causal - especially if previous literature is heavily observational. Labeling your estimates associations is perfectly fine - nothing is wrong with that, if they are robust. However, study study's research design does not allow us to call them causal.

13. In the Discussion, you mention that you "…found that reductions in sugar intake in relation to the levy were greatest in children who were likely to be higher consumers of SSBs…" This is confusing - nowhere is this mentioned in the data, analyses or results. Is that from another paper of yours? I suggest rewriting so that it is clear you did not analyze diet data in this study.

14. Perhaps you want to include a brief discussion that these are short term effects and may increase over time as sugar may be addictive long term. So, in that sense, your estimates are a lower bound.

15. I like the figures - the break in trend is visible. I suggest you stress the break in trend more in your results discussion as this supports their robustness. I also strongly suggest writing out an equation/regression model you estimate in the appendix, and describe a coefficient of interest, along with controls included etc… Though quite straightforward, I think it will help to understand your methodology better.

16. As mentioned above, in tables, include interactions to test for difference across subgroups. Otherwise, it is hard to say whether one group had a bigger effect than the other.

Reviewer #3: This is a well thought-out and well written piece of work. It provides a valuable evaluation of an important public health strategy, in a population with fairly high intakes of SSBs (particularly the year 6 children), providing a good rationale for the analysis. The methods are well described and sound, and you have included sensitivity analyses with various interruption points to account for varied implementation of the SDIL. Incorporating sex and deprivation into the analyses was a useful addition and has allowed for a more thorough understanding of the impact of SDIL. The discussion draws reasonable conclusions and considers the findings in relation to existing work, including regions with a similar strategy in place e.g. Mexico. You have considered key limitations of the work and have tempered your findings by drawing attention to the relatively small, albeit significant, impact of the strategy and the need for additional strategies to tackle the obesity crisis in children. I have suggested some minor additions to the write-up below, where I would have liked some clarification or some consideration of these points.

Statistical analysis:

1. Interrupted time series (ITS) analyses were conducted to assess obesity prevalence in relation to the UK SDIL in children attending primary school reception or year 6 classes, overall and by sex and index of multiple deprivation (IMD) quintile - please confirm here that the analyses related to deprivation was by school IMD rather than pupil-level IMD

2. models included adjustment for months that showed significant changes in obesity prevalence (September, October, June and February for reception class and September and July for year 6) - was this for every year within the period? (Are you assuming that these significant changes occur/are the same every year or did you only select the months and years where significant changes were observed?)

Discussion:

1. Are the study team aware of any:

* pre-emptive reformulation within major drinks brand prior to announcement, that may have provided an additional interruption point, in addition to the two already explored?

* any other obesity prevention strategies over this time period, that correlate with the interruption points, that may have impacted on overweight/obesity trends, e.g. introduction of revised school food standards, roll out of universal infant free school meals? Have these other strategies been considered in your thinking around the attribution of these changes in obesity to the SDIL?

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

[LINK]

Attachment

Submitted filename: rogers.pdf

Click here for additional data file.^{(51.8KB, pdf)}

PLoS Med. 2023 Jan 26;20(1):e1004160. doi: 10.1371/journal.pmed.1004160.r003

Author response to Decision Letter 1

18 Nov 2022

Attachment

Submitted filename: response to reviewers_only.doc

Click here for additional data file.^{(209KB, doc)}

PLoS Med. doi: 10.1371/journal.pmed.1004160.r004

Decision Letter 2

Callam Davidson

13 Dec 2022

Dear Dr. Rogers,

Thank you very much for re-submitting your manuscript "Associations between trajectories of obesity prevalence in English primary school children and the UK soft drinks industry levy: an interrupted time series analysis of surveillance data" (PMEDICINE-D-22-03100R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three 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 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 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.

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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 Dec 20 2022 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

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

Comments from the Academic Editor:

I am in agreement with Reviewer 1 that the authors ought to have added month dummies and I am unclear as to why they chose not to. At a minimum, the authors need to justify their approach versus all-inclusive month dummies in the paper.

Requests from Editors:

At this stage, we ask that you include 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. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please define abbreviations in your Figure legends and also include the age of children in year 6/reception for the benefit of readers unfamiliar with the English school system.

Comments from Reviewers:

Reviewer #1: The authors have addressed mu points but I still feel there is a remaining issue. The data-driven fitting of month dummies seems to me to be undesirable. Deciding on the model in the light of the data means that the usual statements about confidence intervals and p-values become unreliable. It would be simpler just to add in every month.

Michael Dewey

Reviewer #2: Thank you for carefully revising the manuscript. I have two additional comments:

1. formal testing between income subgroups: just because something is monotonically increasing, it does not mean the effects between subgroups are statistically significantly different from zero. If you describe results separately (i.e., not something is bigger than the other), not testing the difference is fine. If, however, you would like to conclude that associations between the tax and weight changes in lower income groups are larger than associations observed in higher income groups, a formal test is, I believe, necessary. Looking at the table 2, confidence intervals seem to be overlapping and it is not obvious to me why a monotonic relationship alone would imply significant differences between groups; so I recommend an empirical test. If you don't apply it, however, I recommend making sure that results are interpreted within each income groups separately (i.e., without between groups comparison).

2. I understand you do not claim this study's results are causal. However, I recommend that you do not motivate the section "Comparison with other studies and implications" with a sentence on causality either. Your association may be robust, but it is not causal, no matter how in line it is with other correlational studies. Your study is valuable on its own; no need to overstretch its findings.

Reviewer #3: Thank you for responding to these comments. I am satisfied that the response has dealt with my concerns and I have no further issues to raise.

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

[LINK]

PLoS Med. 2023 Jan 26;20(1):e1004160. doi: 10.1371/journal.pmed.1004160.r005

Author response to Decision Letter 2

19 Dec 2022

Attachment

Submitted filename: year6-table2 (2).pdf

Click here for additional data file.^{(6.6KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1004160.r006

Decision Letter 3

Callam Davidson

21 Dec 2022

Dear Dr Rogers,

On behalf of my colleagues and the Academic Editor, Professor Barry Popkin, I am pleased to inform you that we have agreed to publish your manuscript "Associations between trajectories of obesity prevalence in English primary school children and the UK soft drinks industry levy: an interrupted time series analysis of surveillance data" (PMEDICINE-D-22-03100R3) in PLOS Medicine.

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

Sincerely,

Callam Davidson

Associate Editor

PLOS Medicine

Associated Data

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

Supplementary Materials

S1 Checklist. RECORD checklist.

RECORD, Reporting of studies Conducted using Observational Routinely-collected Data.

(DOCX)

Click here for additional data file.^{(27.9KB, docx)}

S1 Fig. Schematic diagram of the interrupted time series.

(DOCX)

Click here for additional data file.^{(47.7KB, docx)}

S1 Text. Model specifications for children in year 6 and reception class.

(DOCX)

Click here for additional data file.^{(20.7KB, docx)}

S1 Table. Changes in obesity prevalence compared to a counterfactual scenario based on trends prior to 8 months post-announcement.

(DOCX)

Click here for additional data file.^{(18.9KB, docx)}

S2 Table. Changes in obesity prevalence compared to a counterfactual scenario based on trends prior to the SDIL implementation.

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S3 Table. Changes in excess weight prevalence compared to a counterfactual scenario based on trends prior to the SDIL announcement.

(DOCX)

Click here for additional data file.^{(15.2KB, docx)}

Attachment

Submitted filename: rogers.pdf

Click here for additional data file.^{(51.8KB, pdf)}

Attachment

Submitted filename: response to reviewers_only.doc

Click here for additional data file.^{(209KB, doc)}

Attachment

Submitted filename: year6-table2 (2).pdf

Click here for additional data file.^{(6.6KB, pdf)}

Data Availability Statement

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PERMALINK

Associations between trajectories of obesity prevalence in English primary school children and the UK soft drinks industry levy: An interrupted time series analysis of surveillance data

Nina T Rogers

Steven Cummins

Hannah Forde

Catrin P Jones

Oliver Mytton

Harry Rutter

Stephen J Sharp

Dolly Theis

Martin White

Jean Adams

Roles

Abstract

Background

Methods and findings

Conclusions

Trial registration

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Data source

Statistical analysis

Sensitivity analysis 1: Inclusion of two alternative interruption points

Sensitivity analysis 2: Combining overweight and obesity prevalence

Results

Table 1. Mean obesity prevalence (standard deviation) in the pre- and post-announcement periods of the UK SDIL, by school class, sex, and IMD quintiles.

Changes in obesity prevalence in relation to SDIL

Table 2. Absolute and relative changes in prevalence of obesity (95% CIs), compared to the counterfactual1, in reception and year 6 boys and girls, by IMD at 19 months post-implementation of the UK SDIL.

Fig 1. Prevalence (%) of obesity in year 6 girls (aged 10/11) between September 2013 and November 2019.

Fig 2. Prevalence (%) of obesity in year 6 boys (aged 10/11) between September 2013 and November 2019.

Fig 3. Prevalence (%) of obesity in girls in reception class (aged 4/5) between September 2013 and November 2019.

Fig 4. Prevalence (%) of obesity in boys in reception class (aged 4/5) between September 2013 and November 2019.

Discussion

Summary of findings

Comparison with other studies and implications

Strengths and limitations of the study

Conclusions

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Beryne Odeny

Roles

Decision Letter 1

Callam Davidson

Roles

Author response to Decision Letter 1

Decision Letter 2

Callam Davidson

Roles

Author response to Decision Letter 2

Decision Letter 3

Callam Davidson

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 2. Absolute and relative changes in prevalence of obesity (95% CIs), compared to the counterfactual¹, in reception and year 6 boys and girls, by IMD at 19 months post-implementation of the UK SDIL.