Changes in the amount of nutrient of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: A nonexperimental prospective study

Marcela Reyes; Lindsey Smith Taillie; Barry Popkin; Rebecca Kanter; Stefanie Vandevijvere; Camila Corvalán

doi:10.1371/journal.pmed.1003220

. 2020 Jul 28;17(7):e1003220. doi: 10.1371/journal.pmed.1003220

Changes in the amount of nutrient of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: A nonexperimental prospective study

Marcela Reyes ¹, Lindsey Smith Taillie ², Barry Popkin ², Rebecca Kanter ³, Stefanie Vandevijvere ^4,⁵, Camila Corvalán ^1,^*

Editor: Nicholas J Wareham⁶

PMCID: PMC7386631 PMID: 32722710

Abstract

Background

In June 2016, the first phase of the Chilean Food Labelling and Advertising Law that mandated front-of-package warning labels and marketing restrictions for unhealthy foods and beverages was implemented. We assess foods and beverages reformulation after this initial implementation.

Methods and findings

A data set with the 2015 to 2017 nutritional information was developed collecting the information at 2 time periods: preimplementation (T0: January–February 2015 or 2016; n = 4,055) and postimplementation (T1: January–February 2017; n = 3,025). Quartiles of energy and nutrients of concern (total sugars, saturated fats, and sodium, per 100 g/100 mL) and the proportion of products with energy and nutrients exceeding the cutoffs of the law (i.e., products “high in”) were compared pre- and postimplementation of the law in cross-sectional samples of products with sales >1% of their specific food or beverage groups, according to the Euromonitor International Database; a longitudinal subsample (i.e., products collected in both the pre- and postimplementation periods, n = 1,915) was also analyzed. Chi-squared, McNemar tests, and quantile regressions (simple and multilevel) were used for comparing T0 and T1. Cross-sectional analysis showed a significant decrease (T0 versus T1) in the proportion of product with any “high in” (from 51% [95% confidence interval (CI) 49–52] to 44% [95% CI 42–45]), mostly in food and beverage groups in which regulatory cutoffs were below the 75th percentile of the nutrient or energy distribution. Most frequent reductions were in the proportion of “high in” sugars products (in beverages, milks and milk-based drinks, breakfast cereals, sweet baked products, and sweet and savory spreads; from 80% [95% CI 73–86] to 60% [95% CI 51–69]) and in “high in sodium” products (in savory spreads, cheeses, ready-to-eat meals, soups, and sausages; from 74% [95% CI 69–78] to 27% [95% CI 20–35]). Conversely, the proportion of products “high in” saturated fats only decreased in savory spreads (p < 0.01), and the proportion of “high in” energy products significantly decreased among breakfast cereals and savory spreads (both p < 0.01). Quantile analyses showed that most of the changes took place close to the cutoff values, with only few exceptions of overall left shifts in distribution. Longitudinal analyses showed similar results. However, it is important to note that the nonexperimental nature of this study does not allow to imply causality of these findings.

Conclusions

Our results show that, after initial implementation of the Chilean Law of Food Labelling and Advertising, there was a significant decrease in the amount of sugars and sodium in several groups of packaged foods and beverages. Further studies should clarify how food reformulation will impact dietary quality of the population.

Camila Corvalan and colleagues reveal how warning labels and restricted marketing can reduce purchases of unhealthy drinks and packaged food.

Author summary

Why was this study done?

Reformulation of processed foods and beverages has been defined as one of the most cost-effective measures for preventing obesity and cardiometabolic diseases.
In June 2016, Chile implemented the first phase of Food Labelling and Advertising Law that mandates the use of front-of-package warning labels, marketing restrictions for unhealthy foods and beverages, and banning of sales at school.
A previous study showed no relevant reformulation in anticipation to the initial implementation of the law, but Chilean food and beverage companies had claimed they reformulated 20% of packaged foods after implementation.

What did the researchers do and find?

We collected information of the nutrient fact panels of packaged foods and beverages previous to the implementation of the law (2015 and 2016; 4,055 items) and <1 year after initial implementation (2017; 3,025 items).
We found a significant decrease in the proportion of foods and beverages considered as unhealthy (“high in” energy, sugars, saturated fats, or sodium) from 51% to 44%, mostly in food and beverage groups in which regulatory values were below the 75th percentile of the nutrient or energy distribution.
Most frequent reductions were in the proportion of “high-in” sugars products (beverages, milks and milk-based products, breakfast cereals, sweet baked products, and sweet and savory spreads) and “high in” sodium products (savory spreads, cheeses, ready-to-eat meals, sausages, and soups) whereas “high in” saturated fat reductions only took place in savory spreads and “high in” energy among breakfast cereals and savory spreads.
Quantile regression analyses showed that most of the changes occurred around regulation cutoff values, with minor shifts on overall energy or nutrient distributions.

What do these findings mean?

After initial implementation of the first phase of the Chilean Food Labelling and Advertising Law, we observed important decreases in the amount of sugars and sodium in several groups of packaged foods and beverages.
Future follow-ups should address the sustainability of such improvements and whether the reported changes translate into healthier diets.

Introduction

From 1990–2020, packaged foods and beverages with a high degree of processing have become increasingly available worldwide [1, 2]. Those foods and beverages usually have a high amount of energy and nutrients that have been linked to a higher risk of noncommunicable diseases (NCDs; i.e., saturated fats, sugars, and sodium, here forward referred to as nutrients of concern), which contribute to the global burden of disease associated with poor diets [1, 3].

Improving the nutritional quality of packaged foods and beverages, specifically by decreasing the amount of nutrients of concern, has been a major issue in nutrition policy since the second half of the 20th century [4, 5]. Recent reports suggest that reformulation is the most cost-effective measure to improve populations’ diets and health status [4, 6, 7]; although some authors question whether reformulation will result in a significant improvement of the overall nutritional quality of the diet [8]. Different voluntary or regulatory strategies may incentivize reformulation: setting standards for nutrient amount (e.g., banning trans fats or adding upper limits for the amount of sodium) [5, 9–16], implementing fiscal policies, or adding easy-to-understand front-of-package (FOP) nutrition labeling on packaged foods and beverages [17–21], among others. However, as most of these strategies are voluntary, industry is less likely to comply, limiting the impact of the measures [9, 10, 18, 20, 22, 23]. In June 2016, Chile implemented the Law of Food Labelling and Advertising (hereafter, the law) that mandates that packaged foods and beverages with added sugars, saturated fats, or sodium that are above the established cutoffs of nutrients of concern or energy must display up to 4 FOP warning labels that say “high in [nutrient of concern]” (hereafter, products “high in”). The law was implemented in a staggered way, with cutoffs becoming increasingly stricter over 3 phases (S1 Table). Products “high in” cannot be sold or distributed in the school food environment nor be marketed to children under 14 years of age [24]. The Chilean government has introduced a package of strategies to prevent childhood obesity that combines a FOP warning label with actions that discourage the consumption of unhealthy foods by children, which are hypothesized to show a more extended impact on food reformulation than single policies. Therefore, in the current study, we aimed to study changes in the proportion of “high in” products and changes in energy and nutrients of concern in packaged foods and beverages before (2015–2016) and after (2017) the initial implementation (i.e., <1 year of the first phase cutoffs) of the Chilean law.

Methods

Summary of study design

A nutritional information 2015 to 2017 data set was developed with data collected in supermarkets from Santiago (where 30% of the population in Chile lives), Chile, in periods pre- (T0: January–February 2015 or 2016; n = 4,055) and postimplementation (T1: January–February 2017; n = 3,025) of the law. Nutrient information declared on the food labels was compared between T0 and T1. The analytical sample included packaged foods and beverages with sales ≥1% of their specific food groups, according to Euromonitor International Database [25]. The same outcomes were studied in a longitudinal subsample (i.e., products collected in both the pre- and postimplementation periods; n = 1,915). In both analyses, comparisons included the amount of energy and nutrients of concern and the proportion of products “high in” (i.e., foods and beverages with the amount of energy and nutrients above the initial cutoffs). The analyses plan was developed prospectively (S1 Text) with later adjustments according to reviewer’s suggestions (i.e., quantile regressions). This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (See S1 and S2 Checklists).

Collection of nutritional information and ingredients of packaged foods and beverages

Data collection occurred during 3 waves—January to February 2015, January to February 2016, and January to February 2017—at 6 major supermarkets (1 supermarket from each of the 6 major chains in Chile; about 60% of food retail) in high-income neighborhoods in Santiago, Chile, known to have a great variety of food products. Three candy distributors were also included in order to increase the variety of candies and sweet confectioneries. Data were initially collected as part of the multinational collaborative effort for monitoring food environment, INFORMAS [26], upon an agreement with the Chilean National Association for Supermarkets (ASACH). Photos of all packaged foods and beverages available in the store where obtained (i.e., products packaged before being sold, which exclude bulk foods as well as food chosen at the counter). When multiple package sizes were available, only the largest package was collected. The entire package was photographed. After each data collection wave, trained dietitians reviewed the photos and entered general identifying information separately for each product (i.e., barcode, brand, flavor or other important identifier details, manufacturer, etc.), the ingredients list, amount of energy and nutrients (i.e., protein, carbohydrates, total sugars, total fats, fat subtypes if available, and sodium) per 100 g or 100 mL. When implausible information on the amount of energy and nutrients was detected (i.e., addition of grams of macronutrients per 100 g > 100 g or addition of grams of carbohydrates per 100 g × 4 + grams of proteins per 100 g × 4 + grams of total fats per 100 g × 9 < 0.9 or >1.1 declared energy [kcal] per 100 g), pictures were re-reviewed for corrections when possible. When applicable, the instructions for product reconstitution (e.g., powdered milks, condensed juices, etc.) were also entered because they are needed for estimating the amount of energy and nutrients of the product as consumed. In Chile, declaration of nutrients and ingredients are mandatory for packaged foods and beverages, both per serving size and per 100 g or 100 mL.

Food groups

Foods and beverages were assigned to one of 17 mutually exclusive groups, based on a previously used classification [27]. Groups for this analysis were: beverages (sugar-sweetened, non–sugar-sweetened, and unsweetened); milks and milk-based drinks; yogurts; breakfast cereals (ready-to-eat and to be prepared); sweet baked products; desserts and ice creams; candies and sweet confectionery; sweet spreads; savory baked products; savory snacks; savory spreads; cheeses; ready-to-eat meals; sausages; nonsausage meat products; and soups (powder and ready-to-eat). Examples of products included in every group are shown in S2 Table.

Data processing and definition of the analytical sample

Fig 1 shows the number of products that were either included or eliminated from the analytical samples. A total of 26,748 products were photographed during the 3 data collection waves between 2015 and 2017. Data collected in years 2015 and 2016 were pooled for constructing a more comprehensive preimplementation (T0) sample; in the case of duplicate products (intra- or interyear), only the most recent product was retained (i.e., only items collected in 2016 were included). Data collected in 2017 constituted the postimplementation (T1) sample; duplicated products were also eliminated for this period. From both cross-sectional samples (T0 and T1), we excluded items that lacked relevant information (i.e., did not include the ingredients list, any information on the amount of energy and nutrients, reconstitution instructions when needed; 3.3% for T0 and 2.7% for T1), were not under the scope of the regulation (i.e., unprocessed and minimally processed foods and culinary ingredients with no increase in the natural content of nutrient of concern as part of their processing; 14.1% for T0 and 15.5% for T1), and were not included among the best-selling products (i.e., <1% market share within each of the 52 main food groups from the Euromonitor database [25]; 49.4% for T0 and 52.9% for T1). For T0 and T1, market share within a specific food group was computed as Euromonitor sales of <product or brand family of products> during <year> × 100 ÷ addition of sales of <Euromonitor food group>. Products with ≥1% of the market share of their food group were selected manually from the list of Euromonitor products (or brand family of products if product was not directly available).

In line with the Chilean nutrient declaration labeling rules, missing values for saturated fats were replaced by 0 when the amount of total fats was below 3 g per portion size (for missing values other than saturated fats, the missing value was not assigned to any imputed value, nor was the food or beverage item eliminated from the analysis). Additionally, 154 implausible values for the amount of energy and nutrients of concern were omitted prior to the statistical analyses. Details on the exclusion criteria are presented in S3 Table.

Classification of “high in” products was done among those under the scope of the regulation (i.e., products with added sugars, saturated fats or sodium; the list of specific ingredients which could add those nutrients has been reported previously) [28]. Cutoffs for solids or liquids (S1 Table) were used depending on the unit of measure displayed on the label—g or mL, respectively. Given the heterogeneity of unit of measure, however, used among ice creams, some spreads (both sweet and savory) and yogurts, we standardized the nutrient amount to 100 g for these product types by considering the following food specific gravities: 0.5 g/mL for ice creams, 1.15 g/mL for desserts, 1.15 g/mL for mustard, 0.95 g/mL for mayonnaise, and 1.06 g/mL for yogurts and subsequently used the “high in” cutoffs for solids.

A subsample of products that were collected in both T0 and T1 were longitudinally analyzed (approximately 50% of T1 belonged to T0, n = 1,915) to specifically address reformulation (i.e., these analyses exclude the effect of products entering or exiting the food market or potential sampling differences between waves). To construct both the cross-sectional and the longitudinal analytical samples, matching products between and within waves were identified programmatically based on barcode, brand, manufacturer name, and important identifier details (i.e., flavor).

Data analyses

Study outcomes

The main outcomes were changes in (i) the proportion of products exceeding the cutoffs of the initial phase of the law (i.e., “high in” products) for energy, total sugars, saturated fats, sodium, or any “high in” (i.e., products “high in” energy or at least one nutrient of concern) [24], by food or beverage group, between the pre- and postimplementation periods and (ii) the quartiles of energy, total sugars, saturated fats, and sodium (amount per 100 g or 100 mL), by food or beverage group; we used quartiles because variables were not normally distributed.

Statistical analyses

Changes in the proportion of “high in” foods or beverages between T0 and T1 were examined differently depending on the analysis type. The chi-square test was used for the cross-sectional analysis, and the McNemar test was used for the longitudinal analysis. In the cross-sectional analyses, we used quantile regressions with implementation period as the independent variable for estimating changes in every quartile (i.e., 25th, 50th, and 75th percentiles) of energy or the nutrient of concern by food or beverage group; in the longitudinal analyses, we used quantile regression for linear mixed-effect models (fixed or random effect were selected according to the Hausman test and Akaike’s information criterion). Quantile regressions for the overall sample were performed, considering food or beverage groups and the interaction of such groups with the implementation period as covariate. Because interactions were significant (p < 0.1), further analyses were done stratifying by food and beverage groups. Other confounders and effect modifiers were not assessed. Size of the cross-sectional analytical sample was defined by products available at each data collection wave and the inclusion and exclusion criteria. Food groups sizes ranged from 69 to 482 products; the minimum size allows detecting a 50% relative decrease in the proportion of “high in” products and a change of 55% in the standard deviation between the pre- and postimplementation period (alpha error 0.05 and beta error 0.2). All analyses were performed using STATA V15 (release 15; StataCorp LP, College Station, TX; http://www.stata.com), except for the multilevel quantile regressions which were done using R (release 4.0.0; R Foundation for Statistical Computing).

Results

In Table 1, we observed that, in the cross-sectional analyses, the overall proportion of products with any “high in” warnings significantly decreased from 51% (95% confidence interval [CI] 49–52) to 44% (95% CI 42–45) after the initial implementation of the law. Table 1 shows changes in the proportion of “high in” products by food and beverage groups. Decreases were most common in “high in” sugars (for beverages, milks and milk-based drinks, breakfast cereals, sweet baked products, sweet and savory spreads [all p < 0.01]) and “high in” sodium (savory spreads, cheeses, sausages, and soups [all p < 0.01], and ready-to-eat meals [p = 0.03]). Conversely, the proportion of products “high in” saturated fats only decreased in savory spreads (p < 0.01), and the proportion of products “high in” energy significantly decreased among breakfast cereals and savory spreads (both p < 0.01). Changes took place in food and beverage groups in which the regulatory cutoffs were below the 75th percentile of the nutrient or energy distribution; except in a few cases, mostly for saturated fats cutoffs (sweet baked products, candies and sweet confectioneries, cheeses, and sausages) and for sugars cutoffs (desserts and ice creams and candies and sweet confectioneries).

Table 1. Changes between T0 and T1 in the proportion of “high in” energy and nutrients of concern (or any “high in”) by food and beverage group, cross-sectional analysis.

Food or beverage	T0, % (95% CI)	T1, % (95% CI)	p-value	Relative change, % of T0
Beverages	n = 686	n = 482
Any “high in”	26 (23–29)	11 (8–14)	<0.01	−58
High in energy (T0 cutoff: 99th percentile)	0.1 (0.004–0.8)	0 (0–1)	0.40	−100
High in sugars (T0 cutoff: 64th percentile)	26 (23–29)	11 (8–14)	<0.01	−58
High in saturated fats (T0 cutoff: NA)	0	0	NA	NA
High in sodium (T0 cutoff: 99th percentile)	0	0	NA	NA
Milks and milk-based drinks	n = 201	n = 103
Any “high in”	32 (26–39)	2 (0.2–7)	<0.01	−94
High in energy (T0 cutoff: 99th percentile)	0.5 (0.01–2.7)	0	0.47	−100
High in sugars (T0 cutoff: 56th percentile)	32 (25–39)	2 (0.2–7)	<0.01	−94
High in saturated fats (T0 cutoff: 99th percentile)	0	0	NA	NA
High in sodium (T0 cutoff: 96th percentile)	0.5 (0.01–3)	0	0.47	−100
Yogurts	n = 312	n = 272
Any “high in”	0	0	NA	NA
High in energy (T0 cutoff: 99th percentile)	0	0	NA	NA
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 99th percentile)	0	0	NA	NA
High in sodium (T0 cutoff: 99th percentile)	0	0	NA	NA
Breakfast cereals	n = 148	n = 125
Any “high in”	80 (73–86)	61 (52–69)	<0.01	−25
High in energy (T0 cutoff: 14th percentile)	81 (74–87)	61 (52–69)	<0.01	−25
High in sugars (T0 cutoff: 53rd percentile)	46 (38–55)	24 (17–33)	<0.01	−48
High in saturated fats (T0 cutoff: 89th percentile)	8 (4–14)	7 (3–13)	0.65	−13
High in sodium (T0 cutoff: 99th percentile)	0	0.8 (0.2–4)	0.27	NA
Sweet baked products	n = 198	n = 173
Any “high in”	100	94 (89–97)	<0.01	−6
High in energy (T0 cutoff: 6th percentile)	93 (89–96)	91 (86–95)	0.44	−2
High in sugars (T0 cutoff: 6th percentile)	95 (90–97)	83 (77–89)	<0.01	−13
High in saturated fats (T0 cutoff: 23rd percentile)	75 (68–81)	66 (59–73)	0.07	−12
High in sodium (T0 cutoff: 99th percentile)	0	0	NA	NA
Desserts and ice creams	n = 437	n = 333
Any “high in”	43 (38–48)	42 (37–48)	0.90	−2
High in energy (T0 cutoff: 96th percentile)	3 (2–5)	3 (1–5)	0.82	0
High in sugars (T0 cutoff: 65th percentile)	35 (30–40)	33 (28–38)	0.60	−6
High in saturated fats (T0 cutoff: 75th percentile)	24 (20–28)	26 (21–31)	0.58	+8
High in sodium (T0 cutoff: 99th percentile)	0	0	NA	NA
Candies and sweet confectioneries	*n = 391*	n = 445
Any “high in”	87 (83–90)	82 (78–85)	0.04	−6
High in energy (T0 cutoff: 27th percentile)	73 (68–77)	70 (66–74)	0.40	−4
High in sugars (T0 cutoff: 19th percentile)	80 (76–84)	76 (71–80)	0.11	−5
High in saturated fats (T0 cutoff: 47th percentile)	52 (47–57)	47 (42–51)	0.14	−10
High in sodium (T0 cutoff: 99th percentile)	0.3 (0.006–1.4)	0.2 (0.006–1.3)	0.93	−33
Sweet spreads	n = 165	n = 115
Any “high in”	75 (67–81)	62 (52–71)	0.02	−17
High in energy (T0 cutoff: 99th percentile)	1.8 (0.4–5)	5.2 (1.9–11)	0.12	+189
High in sugars (T0 cutoff: 37th percentile)	60 (53–68)	41 (32–51)	<0.01	−32
High in saturated fats (T0 cutoff: 83rd percentile)	17 (12–24)	26 (18–35)	0.08	+53
High in Sodium (T0 cutoff: 99th percentile)	0	0	NA	NA
Savory baked products	n = 100	n = 81
Any “high in”	64 (54–73)	65 (54–76)	0.84	+2
High in energy (T0 cutoff: 32rd percentile)	64 (53–73)	65 (54–76)	0.80	+2
High in sugars (T0 cutoff: 94th percentile)	5 (2–11)	4 (1–11)	0.69	−20
High in saturated fats (T0 cutoff: 86th percentile)	11 (7–19)	7 (3–15)	0.41	−36
High in sodium (T0 cutoff: 93rd percentile)	6 (2–13)	4 (1–11)	0.45	−33
Savory snacks	n = 69	n = 70
Any “high in”	94 (86–98)	100	0.04	+6
High in energy (T0 cutoff: 3rd percentile)	91 (82–97)	99 (92–100)	0.05	+9
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in Saturated fats (T0 cutoff: 80th percentile)	16 (8–27)	7 (2–16)	0.10	−56
High in sodium (T0 cutoff: 94th percentile)	6 (2–14)	3 (0.03–9)	0.39	−50
Savory spreads	n = 210	n = 174
Any “high in”	75 (69–81)	48 (41–56)	<0.01	−36
High in energy (T0 cutoff: 56th percentile)	43 (36–50)	26 (20–33)	<0.01	−38
High in sugars (T0 cutoff: 92nd percentile)	8 (4–12)	2 (0.4–5)	<0.01	−75
High in saturated fats (T0 cutoff: 56th percentile)	44 (37–51)	28 (21–35)	<0.01	−33
High in sodium (T0 cutoff: 65th percentile)	33 (27–40)	18 (13–24)	<0.01	−45
Cheeses	n = 109	n = 117
Any “high in”	81 (72–88)	86 (79–92)	0.26	+6
High in energy (T0 cutoff: 75th percentile)	24 (16–33)	23 (16–32)	0.89	−4
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 20th percentile)	80 (71–87)	85 (78–91)	0.26	6
High in sodium (T0 cutoff: 73rd percentile)	27 (19–37)	12 (7–19)	<0.01	−56
Ready-to-eat meals	n = 242	n = 223
Any “high in”	13 (9–18)	9 (6–14)	0.20	−23
High in energy (T0 cutoff: 91st percentile)	6 (4–10)	5 (3–9)	0.71	0
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 94th percentile)	5 (3–9)	2 (0.7–5)	0.08	−60
High in sodium (T0 cutoff: 90th percentile)	10 (7–15)	5 (3–9)	0.03	−50
Sausages	n = 362	n = 142
Any “high in”	81 (77–85)	32 (25–41)	<0.01	−60
High in energy (T0 cutoff: 85th percentile)	15 (12–19)	9 (5–15)	0.09	−40
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 46th percentile)	17 (14–22)	14 (9–21)	0.35	−18
High in sodium (T0 cutoff: 23rd percentile)	74 (69–78)	27 (20–35)	<0.01	−64
Nonsausage meat products	n = 297	n = 101
Any “high in”	20 (15–25)	25 (17–34)	0.26	+25
High in energy (T0 cutoff: 98th percentile)	0.1 (0.02–0.3)	3 (0.6–9)	0.16	+900
High in sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 81st percentile)	16 (12–21)	20 (13–29)	0.41	+25
High in sodium (T0 cutoff: 95th percentile)	4 (2–7)	2 (0.2–7)	0.27	−50
Soups	n = 125	n = 69
Any “high in”	98 (94–100)	86 (75–93)	<0.01	−12
High in energy (T0 cutoff: 99th percentile)	0	0	NA	NA
High in Sugars (T0 cutoff: 99th percentile)	0	0	NA	NA
High in saturated fats (T0 cutoff: 99th percentile)	0	0	NA	NA
High in sodium (T0 cutoff: 1st percentile)	100	89 (79–96)	<0.01	−11

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Values represent the frequency and 95% CI of “high in” products.

Cutoffs correspond to the limits on the amount of energy or nutrient of concern for the initial implementation of the law (i.e., for solids, per 100g: 350 kcal of energy, 22.5 g of sugars, 6 g of saturated fats, 800 mg of sodium; for liquids, per 100 mL: 100 kcal of energy, 6 g of sugars, 3 g of saturated fats, 100 mg of sodium). The corresponding percentile was calculated according to T0 distribution of energy or nutrient of concern by food or beverage group.

Relative change: delta in the proportion between T0 and T1, relative to proportion in T0 (T0 − T1) × 100 ÷ T0; a negative sign represents a decrease, a positive sign represents an increase).

T0: preimplementation period, January to February 2015 + January to February 2016 (n = 4,055); T1: postimplementation period, January to February 2017 (n = 3,025).

Comparison between T0 and T1 were done using Chi².

Significant p-values (i.e., <0.05) are bolded.

Note that discrepancies in the percentage of “high in” sodium and any “high in” among soups were given by differences in the denominator used in each case; the denominator for the former did not consider food items with missing information of sodium, whereas the latter did.

Figs 2–17 show distributions of energy and nutrients of concern by food or beverage group for T0 and T1 (cross-sectional samples). Food and beverage groups with a significant decrease in the proportion of “high in” products showed a left shift of the T1 distribution (blue curve) below the initial cutoffs (red line). In Table 2, we present the quantile regression analyses by food and beverage groups. We show that, for all food groups in which there were significant decreases in “high-in” sugars, there were also significant decrease in the quantile closer to the regulation cutoffs (p < 0.01 for the 75th percentile in beverages, milk and milk-based drinks, and breakfast cereals; p = 0.03 for median in sweet spreads) but not necessarily in the other quantiles. The exception was given by sweet baked products (p = 0.05 for 25th percentile) and savory spreads (p = 0.77 for 75th percentile). Similarly, food groups in which “high in” sodium significantly decreased, showed a significant decrease in the quantile closer to the cutoff: p < 0.01 for 75th percentile in savory spreads and 25th percentile in sausages; although, there was no significant change in the case of cheeses (p = 0.11 for 75th percentile), ready-to-eat meals (p = 0.21 for 75th percentile), and soups (p = 0.18 for 25th percentile). Same happened for energy in the case of breakfast cereals (p < 0.01 for the 25th percentile decrease), and savory spreads (p < 0.01 for the 50th percentile), as well as in savory spreads (with a decrease in the 50th percentile of saturated fats, p = 0.02, respectively). On the other hand, some food and beverages groups presented significant right shifts in distribution of sugars (sausages and nonsausage meat products), sodium (breakfast cereals and desserts and ice creams), saturated fats (yogurts, desserts and ice creams, sweet spreads, ready-to-eat meals, and nonsausage meat products), and energy (savory spreads).

Fig 2 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 17 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Table 2. Changes in quartiles of energy and nutrients of concern by food/beverages group; cross-sectional analysis.

Beverages and food	Energy (kcal/100g-mL)			Sugars (g/100g-mL)			Saturated fats (g/ 100 g-mL)			Sodium (mg/ 100 g-mL)
Beverages and food	T0	T1	p-value	T0	T1	p-value	T0	T1	p-value	T0	T1	p-value
*Beverages*
25th percentile	1.54	1.00	0.02	0.10	0.07	0.68	-	-	-	5.12	1.93	<0.01
50th percentile	13.00	12.00	0.61	2.91	2.50	0.34	-	-	-	10.00	8.60	0.01
75th percentile	38.00	26.00	<0.01	8.60	5.80	<0.01	-	-	-	18.00	17.00	0.38
*Milks and milk-based drinks*
25th percentile	39.00	37.11	0.28	4.80	4.60	0.04	0.10	0.05	0.03	38.90	38.66	0.92
50th percentile	52.58	46.00	0.05	5.41	4.90	0.03	0.90	0.90	1.00	46.39	49.00	0.28
75th percentile	67.00	58.00	<0.01	7.30	5.10	<0.01	1.14	1.24	0.55	63.70	60.00	0.36
*Yogurts*
25th percentile	50.00	49.00	0.32	5.80	5.00	<0.01	0.06	0.05	0.69	49.00	50.00	0.44
50th percentile	65.00	60.00	0.33	8.00	7.10	0.11	0.48	0.42	0.74	55.00	55.00	1.00
75th percentile	94.00	90.00	0.11	13.30	13.10	0.70	1.41	1.60	0.02	62.00	62.00	1.00
*Breakfast cereals*
25th percentile	362.00	348.00	<0.01	15.00	10.00	0.03	1.10	1.30	0.33	101.0	103.0	0.92
50th percentile	384.00	364.00	<0.01	21.00	18.00	0.06	1.90	2.40	0.20	195.00	200.00	0.86
75th percentile	408.00	404.00	1.00	28.60	22.20	<0.01	3.90	4.30	0.37	320.0	395.00	<0.01
*Sweet baked products*
25th percentile	428.00	437.00	0.43	27.80	25.30	0.05	6.30	5.60	0.37	182.00	188.00	0.73
50th percentile	477.00	474.00	0.59	33.00	32.20	0.51	10.00	9.70	0.63	257.00	268.00	0.36
75th percentile	501.00	500.00	0.82	40.00	38.20	0.35	12.40	12.90	0.51	311.00	331.00	0.17
*Desserts and ice creams*
25th percentile	78.00	87.00	0.08	14.78	14.78	1.00	0.00	0.00	-	14.00	25.26	<0.01
50th percentile	138.00	151.00	0.25	20.00	20.00	1.00	1.30	2.80	<0.01	48.00	63.64	<0.01
75th percentile	229.09	223.64	0.68	24.55	25.45	0.30	5.74	6.00	0.73	80.00	90.91	0.06
*Candies and sweet confectioneries*
25th percentile	343.00	341.00	0.91	43.00	32.00	0.14	0.00	0.00	-	24.00	17.00	0.03
50th percentile	471.00	452.00	0.29	54.00	54.00	1.00	8.10	5.10	0.13	67.00	58.00	0.23
75th percentile	544.00	533.00	0.06	64.00	66.00	0.06	16.80	17.00	0.75	137.00	130.00	0.56
*Sweet spreads*
25th percentile	105.00	81.00	0.19	5.63	4.30	0.25	0.00	0.00	-	12.00	13.00	0.52
50th percentile	197.00	210.00	0.62	28.00	10.00	0.02	0.00	0.00	-	20.00	20.00	1.00
75th percentile	250.00	308.00	<0.01	48.90	49.20	0.90	0.42	8.32	0.02	52.50	51.00	0.94
*Savory baked products*
25th percentile	283.00	336.00	0.13	2.00	2.40	0.26	0.98	1.20	0.46	332.00	335.00	0.93
50th percentile	407.00	413.00	0.67	3.30	3.70	0.47	2.90	3.00	0.84	472.00	463.00	0.84
75th percentile	447.00	437.00	0.44	5.50	6.20	0.70	4.80	5.00	0.76	640.00	661.00	0.68
*Savory snacks*
25th percentile	499.00	483.00	0.05	0.20	0.67	0.13	2.90	2.90	1.00	452.00	404.00	0.31
50th percentile	519.00	508.00	0.13	1.72	2.00	0.62	3.90	3.31	0.01	538.00	492.00	0.06
75th percentile	543.00	532.00	0.11	4.00	3.70	0.81	4.30	4.00	0.69	608.00	590.00	0.70
*Savory spreads*
25th percentile	88.00	50.00	<0.01	0.30	0.24	0.52	0.00	0.00	-	470.00	399.00	0.03
50th percentile	253.00	114.00	<0.01	2.00	1.90	0.88	3.70	0.07	0.02	653.00	511.00	<0.01
75th percentile	516.00	359.00	0.03	5.50	5.70	0.77	18.20	10.70	0.04	1,070.53	745.00	<0.01
*Cheeses*
25th percentile	228.00	273.00	0.21	0.00	0.00	-	10.94	12.80	0.48	364.00	361.00	0.91
50th percentile	305.00	320.00	0.23	0.70	0.20	0.06	14.60	15.40	0.31	492.40	524.30	0.54
75th percentile	350.00	346.00	0.60	2.60	1.80	0.21	18.34	17.92	0.56	842.00	718.00	0.11
*Ready-to-eat meals*
25th percentile	114.00	136.00	0.47	0.00	0.20	0.09	0.00	0.00	-	9.70	5.00	<0.01
50th percentile	329.00	336.00	0.69	0.91	1.20	0.14	0.00	0.30	<0.01	191.00	241.00	0.39
75th percentile	342.00	342.00	1.00	2.80	2.90	0.83	0.60	0.90	0.47	430.00	497.00	0.21
*Sausages*
25th percentile	133.00	116.00	0.37	0.22	0.50	0.01	1.85	0.80	0.22	800.00	730.00	<0.01
50th percentile	236.00	235.00	0.95	0.50	0.50	1.00	6.50	5.99	0.47	929.00	786.00	<0.01
75th percentile	320.00	283.00	0.03	1.50	0.90	<0.01	10.00	9.30	0.17	1,120.00	952.00	0.03
*Nonsausage meat products*
25th percentile	123.00	135.00	0.20	0.00	0.00	-	1.00	1.51	0.11	333.00	320.00	0.72
50th percentile	166.00	188.00	0.05	0.00	0.10	0.02	2.10	3.10	0.03	410.00	429.00	0.39
75th percentile	220.00	238.00	0.17	0.50	0.80	0.27	4.70	5.90	0.13	522.00	560.00	0.31
*Soups*
25th percentile	24.00	22.00	0.08	0.35	0.25	0.24	0.00	0.00	-	298.17	270.00	0.18
50th percentile	25.50	25.00	0.66	0.75	0.58	0.28	0.00	0.00	1.00	334.5	326.5	0.60
75th percentile	32.40	32.90	0.81	1.52	1.51	0.98	0.00	0.11	0.08	381.27	376.67	0.70

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Quartiles and p-values were obtained from quantile regressions models (one model per nutrient per food or beverage group), using implementation period as independent variable (T0 = 0, T1 = 1).

Significant p-values (i.e., <0.05) are bolded.

T0: preimplementation period, January to February 2015 + January to February 2016 (n = 4,055).

T1: postimplementation period, January to February 2017 (n = 3,025).

Fig 3 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 4 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 5 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 6 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 7 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 8 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 9 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 10 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 11 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 12 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 13 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 14 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 15 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 16 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

In Table 3, we observe that in the longitudinal subsample there was also a significant decrease in the proportion of any “high in” from 52% (95% CI 49–54) to 42% (95% CI 40–44; p < 0.01). Food groups in which we observed significant decreases in “high in” sugars, “high in” sodium, “high in” saturated fats, and “high in” energy were all the same than in the cross-sectional analyses, except for sweet baked products and savory spreads in the case of sugars, and ready-to-eat meals and soups, in the case of sodium.

Table 3. Changes between T0 and T1 in the proportion of “high in” energy and nutrients of concern (or any “high in”) by food or beverage group, longitudinal analysis.

Beverages and foods	T0, % (95% CI)	T1, % (95% CI)	p-value	Relative change, % of T0
Beverages, n = 326
Any “high in”	20 (16–25)	9 (6–12)	<0.01	−55
High in energy (T0 cutoff: 99th percentile)	0	0	-	NA
High in sugars (T0 cutoff: 69th percentile)	20 (16–25)	9 (6–12)	<0.01	−55
High in saturated fats (T0 cutoff: NA)	0	0	-	NA
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Milks and milk-based drinks, n = 76
Any “high in”	30 (21–42)	0	<0.01	−100
High in energy (T0 cutoff: 98th percentile)	1 (0.03–7)	0	0.33	−100
High in sugars (T0 cutoff: 63rd percentile)	28 (18–39)	0	<0.01	−100
High in saturated fats (T0 cutoff: 99th percentile)	0	0	-	NA
High in sodium (T0 cutoff: 98th percentile)	1 (0.03–7)	0	0.32	NA
Yogurts, n = 181
Any “high in”	0	0	-	NA
High in energy (T0 cutoff: 99th percentile)	0	0	-	NA
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 99th percentile)	0	0	-	NA
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Breakfast cereals, n = 67
Any “high in”	78 (66–87)	55 (43–67)	<0.01	−29
High in energy (T0 cutoff: 12th percentile)	78 (66–87)	55 (44–67)	<0.01	−29
High in sugars (T0 cutoff: 57th percentile)	42 (30–54)	20 (11–32)	<0.01	−52
High in saturated fats (T0 cutoff: 87th percentile)	8 (3–17)	6 (2–15)	0.75	−25
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Sweet baked products, n = 118
Any “high in”	100	99 (95–100)	0.32	−1
High in energy (T0 cutoff: 4th percentile)	96 (90–99)	97 (93–99)	0.32	−1
High in sugars (T0 cutoff: 6th percentile)	94 (88–97)	89 (81–94)	0.06	−5
High in saturated fats (T0 cutoff: 19th percentile)	78 (69–85)	73 (64–81)	0.08	−6
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Desserts and ice creams, n = 230
Any “high in”	45 (39–52)	38 (32–44)	<0.01	−16
High in energy (T0 cutoff: 97th percentile)	2 (0.1–5)	3 (0.1–6)	0.48	+50
High in sugars (T0 cutoff: 62nd percentile)	37 (31–44)	30 (24–37)	<0.01	−19
High in saturated fats (T0 cutoff: 73rd percentile)	27 (21–33)	25 (19–31)	0.05	−7
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Candies and sweet confectioneries, n = 216
Any “high in”	88 (83–92)	88 (83–92)	0.56	0
High in energy (T0 cutoff: 24th percentile)	75 (69–81)	75 (69–81)	1.00	0
High in sugars (T0 cutoff: 18th percentile)	82 (76–87)	81 (75–86)	1.00	−1
High in saturated fats (T0 cutoff: 46th percentile)	54 (47–60)	50 (43–57)	<0.01	−7
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Sweet spreads, n = 73
Any “high in”	79 (68–88)	71 (59–81)	0.06	−10
High in energy (T0 cutoff: 97th percentile)	3 (0.3–10)	4 (0.9–12)	0.32	+33
High in sugars (T0 cutoff: 45th percentile)	56 (44–68)	45 (34–57)	<0.01	−20
High in saturated fats (T0 cutoff: 70th percentile)	28 (18–40)	30 (19–42)	1.00	+7
High in sodium (T0 cutoff: 99th percentile)	0	0	-	NA
Savory baked products, n = 61
Any “high in”	62 (49–74)	62 (49–74)	-	0
High in energy (T0 cutoff: 32nd percentile)	62 (49–74)	62 (49–74)	-	0
High in sugars (T0 cutoff: 96th percentile)	3 (0.3–11)	2 (0.4–9)	0.32	0
High in saturated fats (T0 cutoff: 87th percentile)	11 (5–22)	5 (1–14)	0.05	−55
High in sodium (T0 cutoff: 94th percentile)	5 (1–14)	3 (0.4–12)	0.32	−40
Savory snacks, n = 29
Any “high in”	90 (73–98)	100	0.08	+11
High in energy (T0 cutoff: 6th percentile)	90 (73–98)	97 (82–100)	0.16	+8
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 83rd percentile)	14 (4–32)	10 (2–27)	0.56	−29
High in sodium (T0 cutoff: 93rd percentile)	7 (0.1–23)	3 (0.01–18)	0.32	−57
Savory spreads, n = 112
Any “high in”	72 (62–80)	55 (46–65)	<0.01	−24
High in energy (T0 cutoff: 55th percentile)	43 (34–53)	34 (25–43)	<0.01	−21
High in sugars (T0 cutoff: 95th percentile)	4 (1–10)	0	0.03	−100
High in saturated fats (T0 cutoff: 52nd percentile)	48 (38–57)	35 (27–45)	<0.01	−27
High in sodium (T0 cutoff: 71st percentile)	29 (20–38)	17 (11–25)	<0.01	−41
Cheeses, n = 60
Any “high in”	82 (70–90)	82 (70–90)	-	0
High in energy (T0 cutoff: 78th percentile)	22 (12–34)	20 (11–32)	0.71	−9
High in sugars (T0 cutoff: 99 percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 20th percentile)	80 (68–89)	80 (68–89)	-	0
High in sodium (T0 cutoff: 67th percentile)	32 (20–45)	18 (10–30)	<0.01	−44
Ready-to-eat meals, n = 109
Any “high in”	16 (9–24)	13 (7–21)	0. 56	−19
High in energy (T0 cutoff: 91st percentile)	8 (4–15)	7 (3–14)	0.80	−13
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 94th percentile)	6 (2–12)	3 (0.1–8)	0.29	−50
High in sodium (T0 cutoff: 89th percentile)	10 (5–17)	7 (3–14)	0.48	−30
Sausages, n = 120
Any “high in”	81 (73–87)	31 (23–40)	<0.01	−62
High in energy (T0 cutoff: 90th percentile)	9 (5–16)	8 (4–15)	0.56	−11
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 52nd percentile)	12 (7–19)	12 (7–19)	1.00	0
High in sodium (T0 cutoff: 23rd percentile)	73 (64–81)	27 (19–36)	<0.01	−63
Nonsausage meat products, n = 77
Any “high in”	31 (21–43)	27 (18–39)	0.37	−13
High in energy (T0 cutoff: 97th percentile)	3 (0.3–9)	3 (0.3–9)	-	0
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 69th percentile)	28 (18–39)	22 (13–33)	0.21	−21
High in sodium (T0 cutoff: 94th percentile)	5 (1–13)	3 (0.3–9)	0.16	−40
Soups, n = 57
Any “high in”	96 (88–100)	95 (85–99)	0.65	−1
High in energy (T0 cutoff: 99th percentile)	0	0	-	NA
High in sugars (T0 cutoff: 99th percentile)	0	0	-	NA
High in saturated fats (T0 cutoff: 99th percentile)	0	0	-	NA
High in sodium (T0 cutoff: 1st percentile)	100	100	-	0

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Values represent the frequency and 95% CI of “high in” products.

Cutoffs correspond to the limits on the amount of energy or nutrient of concern for the initial implementation of the law (i.e., for solids, per 100 g: 350 kcal of energy, 22.5 g of sugars, 6 g of saturated fats, 800 mg of sodium; for liquids, per 100 mL: 100 kcal of energy, 6 g of sugars, 3 g of saturated fats, 100 mg of sodium). The corresponding percentile was calculated according to T0 distribution of energy or nutrient of concern by food or beverage group.

Relative change: delta in the proportion between T0 and T1, relative to proportion in T0 (T0 − T1) × 100 ÷ T0; a negative sign represents a decrease, a positive sign represents an increase).

T0: preimplementation period, January to February 2015 + January to February 2016; T1: postimplementation period, January to February 2017 (n = 1,915).

Comparison between T0 and T1 were done using McNemar test.

Significant p-values (i.e., <0.05) are bolded.

Figs 18–33 show distributions of energy and nutrients of concern by food or beverage group for T0 and T1 for the longitudinal subsample. Figures show a left shift of the T1 distribution below the initial cutoffs in the distribution of energy or the specific nutrients of concern in food and beverage groups listed above. Similar to the cross-sectional analysis, improvements in the proportion of “high in” products were also shown in the decrease of the quartile closest to the respective cutoff (i.e., 75th percentile of sugars in beverages [p = 0.02], 25th percentile of energy in breakfast cereals [p = 0.03], and 75th percentile of saturated fats in savory spreads [p = 0.04]). However, there were more exceptions than in the cross-sectional sample. On the other hand, there were some significant decreases in the amount of energy or nutrients of concern that were not associated with a decrease in the proportion of “high in” foods and beverages, as sugars and energy for yogurts, or saturated fats among savory spreads, among others (Table 4). In the longitudinal analyses, we did not confirm any of the right shift changes on energy or nutrient content distribution observed in the cross-sectional analyses.

Fig 18 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 33 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Table 4. Changes in quartiles of energy and nutrients of concern by food or beverages group, longitudinal analysis.

Beverages and foods	Energy (kcal/ 100 g-mL)			Sugars (g/ 100 g-mL)			Saturated fats (g/ 100 g-mL)			Sodium (mg/ 100 g-mL)
Beverages and foods	T0	T1	p-value	T0	T1	p-value	T0	T1	p-value	T0	T1	p-value
Beverages
25th percentile	1.39	0.99	0.21	0.27	0.00	0.51	-	-	-	6.09	6.00	0.88
50th percentile	15.65	14.80	0.22	1.34	0.01	0.06	-	-	-	8.37	8.00	0.39
75th percentile	24.85	23.80	0.34	6.03	5.07	0.02	-	-	-	20.10	19.85	0.54
Milks and milk-based drinks
25th percentile	41.92	41.29	0.63	5.00	4.40	<0.01	1.00	0.93	0.21	46.15	45.85	0.79
50th percentile	47.00	44.81	0.21	6.00	5.00	0.01	1.00	0.94	0.34	49.98	49.20	0.48
75th percentile	55.47	50.51	0.04	6.99	5.55	<0.01	1.04	1.00	0.51	56.60	55.89	0.60
Yogurts
25th percentile	75.43	69.43	0.03	6.99	6.95	0.92	0.99	0.95	0.21	52.87	52.28	0.58
50th percentile	75.81	73.29	0.40	9.00	8.81	0.43	1.00	0.96	0.14	56.05	55.54	0.31
75th percentile	90.00	89.53	0.76	11.97	11.10	0.03	1.03	0.98	0.25	56.14	55.00	0.35
Breakfast cereals
25th percentile	358.99	348.99	0.03	13.38	12.99	0.75	2.00	2.00	1.00	153.54	141.46	0.47
50th percentile	388.16	376.81	0.04	17.00	15.78	0.12	3.00	2.85	0.33	214.12	200.80	0.36
75th percentile	411.51	393.61	<0.01	22.00	19.32	0.06	4.02	3.52	0.02	284.82	267.43	0.31
Sweet baked products
25th percentile	454.65	454.50	0.93	29.95	29.71	0.49	7.00	6.99	1.00	240.00	237.25	0.26
50th percentile	473.51	472.49	0.36	34.01	33.95	0.83	10.11	10.00	0.48	248.00	247.29	0.94
75th percentile	481.99	480.53	0.33	35.50	35.29	0.43	11.50	11.46	0.80	264.92	263.92	0.58
Dessert and ice creams
25th percentile	129.76	128.00	0.44	19.12	18.80	0.44	0.02	0.00	0.90	45.53	44.02	0.14
50th percentile	160.18	153.78	0.07	19.99	19.41	0.09	3.60	3.54	0.79	52.96	52.78	0.86
75th percentile	185.89	175.00	0.22	22.31	22.00	0.46	10.36	8.69	0.02	73.99	73.00	0.33
Candies and sweet confectionaries
25th percentile	347.55	344.77	0.39	32.56	31.92	0.26	6.12	6.05	0.92	90.50	89.59	0.66
50th percentile	431.24	425.38	0.04	47.75	47.61	0.73	8.50	8.50	1.00	97.66	97.44	0.82
75th percentile	450.46	449.54	0.72	50.14	49.72	0.44	11.31	10.74	0.36	117.74	114.77	0.07
Sweet spreads
25th percentile	118.03	116.59	0.88	3.92	3.26	0.37	0.00	0.00	1.00	14.53	14.00	0.60
50th percentile	225.03	221.36	0.47	27.45	27.44	0.99	7.05	6.14	0.26	30.99	30.81	0.79
75th percentile	239.92	237.30	0.60	54.82	54.28	0.75	11.98	10.52	0.49	50.40	50.01	0.66
Savory baked products
25th percentile	337.07	334.79	0.34	2.00	2.00	1.00	1.00	0.99	1.00	392.86	386.16	0.07
50th percentile	406.00	404.52	0.60	3.00	3.00	0.99	2.90	3.00	1.00	448.00	445.07	0.62
75th percentile	416.12	414.05	0.28	8.12	7.96	0.62	3.75	3.44	0.17	571.01	565.99	0.30
Savory snacks
25th percentile	481.98	472.49	0.36	0.14	0.72	0.12	2.99	2.99	1.00	485.00	484.99	1.00
50th percentile	507.02	504.00	0.50	2.62	3.60	<0.01	3.79	3.50	0.44	538.16	509.99	0.17
75th percentile	513.87	513.84	0.99	4.37	5.00	0.07	5.18	5.18	1.00	636.17	609.39	0.24
Savory spreads
25th percentile	69.00	67.66	0.84	0.09	0.00	0.54	1.47	0.00	0.03	471.35	399.00	0.01
50th percentile	298.93	283.60	0.07	3.00	3.00	1.00	11.20	11.20	1.00	596.52	576.44	0.56
75th percentile	422.16	415.05	0.47	6.33	6.33	1.00	13.10	12.90	0.04	933.19	744.99	0.06
Cheeses
25th percentile	223.52	222.49	0.75	0.03	0.00	0.81	12.05	11.71	0.55	484.96	483.96	0.97
50th percentile	269.14	266.00	0.48	1.34	1.27	0.66	13.99	13.98	0.91	596.61	596.52	0.98
75th percentile	338.00	336.98	0.79	1.95	1.65	0.12	17.02	17.00	0.97	794.00	770.65	0.62
Ready-to-eat meals
25th percentile	191.05	177.95	1.00	1.27	1.00	0.60	0.01	0.00	0.28	107.88	5.00	0.80
50th percentile	243.35	234.38	0.81	2.01	1.98	0.80	1.00	0.98	0.86	281.92	259.11	0.65
75th percentile	342.45	337.99	0.10	2.50	2.50	1.00	1.34	1.16	0.52	451.70	441.94	0.84
Sausages
25th percentile	195.81	193.77	0.50	0.99	0.99	1.00	4.88	4.40	0.05	817.26	713.00	<0.01
50th percentile	219.21	218.80	0.89	1.00	1.00	0.98	5.50	5.35	0.51	931.28	781.98	<0.01
75th percentile	262.65	257.64	0.16	1.53	1.34	0.14	9.07	8.97	0.70	1,041.19	927.98	0.05
Nonsausage meat products
25th percentile	176.61	168.43	0.05	0.00	0.00	1.00	2.00	2.00	0.99	390.00	360.00	0.03
50th percentile	189.56	181.50	0.07	0.00	0.00	1.00	4.66	4.55	0.99	466.96	462.00	0.65
75th percentile	248.17	247.02	0.82	1.00	1.00	1.00	7.16	6.33	0.02	491.10	480.00	0.44
Soups
25th percentile	23.04	22.97	0.86	0.99	0.97	0.76	0.00	0.00	0.98	318.99	312.54	0.16
50th percentile	27.00	25.99	0.04	0.99	0.98	0.77	0.00	0.00	0.20	372.11	368.90	0.48
75th percentile	32.02	32.01	0.98	1.19	0.99	0.21	0.01	0.00	0.13	377.73	374.68	0.50

Open in a new tab

Quartiles and p-values were obtained from quantile regression for the linear mixed-effect models (one model per nutrient per food or beverage group), using implementation period as independent variable (T0 = 0, T1 = 1).

Significant p-values (i.e., <0.05) are bolded.

T0: preimplementation period, January to February 2015 + January to February 2016 (n = 1,915).

T1: postimplementation period, January to February 2017 (n = 1,915).

Fig 19 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 20 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 21 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 22 — **Density curves for the amount of energy and nutrients of concern in sweet baked products, longitudinal subsample.** The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 23 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 24 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 25 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 26 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 27 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 28 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 29 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 30 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 31 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Fig 32 — The blue line represents the distribution in T0 (preimplementation), the green line represents the distribution in T1 (postimplementation), and the red line represents the cutoff for the amount of energy or nutrients of concern.

Discussion

To our knowledge, this is the first study to evaluate changes in the amount of energy and nutrients of concern in packaged foods and beverages available in the market after the initial implementation of the Chilean Law of Food Labelling and Advertising. Our results indicate that, in the cross-sectional analysis, compared to the preimplementation period, after <1 year of the law (i.e., June 2016 to January-February 2017) the proportion of any “high in” product decreased from 51% to 44%, mostly in food and beverage groups in which the regulatory cutoffs were below the 75th percentile of the nutrient or energy distribution. Decreases in the proportion of products “high in” were higher in sugars (6 out of the 16 food and beverage groups) and sodium (5 out of the 16 groups), whereas the proportion of “high in” saturated fats and “high in” energy decreased only in 1 and 2 food groups, respectively. In most cases, the energy and nutrient of concern distribution of the food and beverage food groups in which we observed decreases moved just below the regulatory cutoff. Several findings were confirmed in the longitudinal analysis.

Although food and beverage reformulation has been suggested as a key strategy for obesity prevention, there is scarce evidence on how real-life initiatives can encourage reformulation. In Australia and New Zealand, significant improvements were seen in the amount of energy (1.5% decrease) and sodium (6.7% decrease) of products that adopted the Health Star Ratings (HSR) FOP label, and reformulation may have been even higher among food products targeted to children [18, 19]. However, HSR was a voluntary initiative implemented by less than 5% of local food suppliers. The nutritional quality of children’s menus in fast food restaurants was reported to have improved after the implementation of an ordinance prohibiting toy incentives to children together with food of low nutritional quality in one county of California, United States [29]. There are also reports of improvement in the nutritional quality of foods driven by other kinds of voluntary actions [9, 10, 16, 30–33]. However, the overall impact of these initiatives on the whole food supply has not been well characterized.

In the Chilean law, regulatory cutoffs were defined based on natural foods and liquids considered gold standards of a healthy diet [24]. Therefore, the same cutoffs were used for all food and beverage groups, only considering differences for liquids and solids. Regulatory policies that are more oriented to promoting reformulation might prefer considering specific cutoffs for each of the food and beverage groups [34]. Our results suggest that setting up cutoffs that are below the 75th percentile of the nutrient distribution would allow to achieve the change looked for; conversely, cutoffs defined on the top end of the distribution would not promote significant changes on the food supply. The Chilean law had 2 other phases of implementation in which regulatory cutoffs became increasingly stricter; therefore, further analyses of changes on the food supply throughout the implementation of these phases will allow to test this hypothesis. Several bodies have also claimed that using cutoffs that are too strict (i.e., that leave most of a food category as regulated) would not incentivize food industry reformulation [35]; interestingly, we did not find that this was the case in Chile because we observed significant changes in food categories in which cutoffs were below the 25th percentile (i.e., regulation affected 75% of the products of that category) such as sugars in sweet baked products, energy in breakfast cereals, and sodium in sausages and soups.

We believe our results suggest that industrial production of several packaged foods and beverages was affected by the initial implementation of the Chilean law. Although our design does not allow us to test causality, we do not believe the observed changes are due to previous trends of food reformulation because we have formerly shown that in the years prior to the implementation of the law there were no relevant changes on the amount of nutrients of concern of the food supply [28] and in the current analyses we do not observe significant changes in food categories in which cutoffs are at the top end of nutrient distribution. Moreover, the fact that most of the changes in the amount of energy or any nutrient of concern were around the first phase cutoffs is also suggestive of a promoting role of the regulatory process. The mandatory nature of the Chilean law and the fact that the regulation considered a set of diverse regulatory measures (i.e., FOP warning labels, marketing restrictions to children, and the healthy school food environment) may have resulted in this fast and significant reformulation of packaged food and beverage products. We believe our results show that food industry has the ability to reduce sugars and sodium amounts in major food and beverage groups, and therefore, efforts should be made to achieve these improvements as recently suggested [4]. At the same time, it is clear that the current efforts are limited in their effect on shifting the food supply from unhealthy saturated fats to healthier fats. In fact, the amount of saturated fats has increased in several food and beverage groups, which could reflect the technical challenges associated to replacement by, i.e., polyunsaturated fats that have a lower melting point and are less stable to oxidation, among other factors. Implication of this finding in population’s health need to be elucidated and could depend on the source of the saturated fats involved [36].

These results can be useful to other countries that are on their way to implementing or have already implemented the use of warning labels for unhealthy packaged foods to prevent obesity, such as Peru, Uruguay, Israel, Mexico, Canada, and Brazil. A comparative study of the healthiness of packaged foods and beverages from 12 countries reported a poor HSR score for the packaged food supply in Chile in 2015 (only Hong Kong and India had poorer scores) [37], suggesting the external validity of our findings could vary from country to country.

Recent reports indicate that food reformulation has a profound impact on diet quality; although this may be an area of debate [4, 8]. In the case of Chile, according to the National Dietary Survey (2010), beverages, milks and milk-based drinks, breakfast cereals, sweet baked products, and sweet spreads represent approximately 15% of the calories and approximately 40% of the added sugars consumed daily by the Chilean population, whereas savory spreads and sausages account for 22% and 8% of sodium intake, respectively [38, 39]. Therefore, the observed decrease in median amount of energy and sugars have the potential to help mitigate the excessive dietary intakes in added sugars [38]. Moreover, in a sample of Chilean preschoolers, it has been described that desserts, dairy products, and beverages cover approximately 23% to 30% of the intake of calories [40], therefore suggesting that the impact could even be higher among children. In the case of sodium, no information is available on the share of the sodium intake represented by the food groups in the study in the Chilean diet; thus, we cannot estimate the potential dietary impact of these changes on sodium intake.

There are limitations in this study. We cannot truly show a direct linkage between the initial implementation of the law and the changes in the amount of energy and nutrients of concern of packaged foods; however, the fact that most left shifts of the distributions were around the first phase cutoffs suggest the regulation might be a relevant driver. Also, we cannot disentangle the effect on reformulation of the different policies implemented simultaneously. Other limitations are that our results are not sales-weighted nor consider the dietary share in Chilean diets. However, our analytical sample included only foods and beverages representing ≥1% of the sales of the specific category in order to obtain more meaningful results [25]. We primarily conducted cross-sectional analyses in which best-selling products were compared before and after the implementation of the law. However, by taking this approach, we were unable to differentiate whether changes are due to reformulation of existing products or to exit of old products or entry of new products; moreover, we are unable to fully exclude sampling differences between years. Therefore, we also conducted longitudinal analyses including only food and beverage products that were available before and after the implementation of the law, which allows us to assess reformulation. We believe consistency between cross-sectional and longitudinal analyses increases the validity of our findings. Also, our analysis did not include confounders or modifiers such as manufacturer company. Finally, our analyses rely on the nutrient amount reported by the food manufacturer on the nutrition facts panel printed on the package and is not based on laboratory assessment. A major strength of our study, however, is that all study data were collected prospectively versus one that could be obtained retrospectively from food industry or retailers’ databases, therefore decreasing its error.

In conclusion, our results show that several food and beverage groups available in the Chilean market decreased the proportion of products “high in” after a short-time period (<1 year) of implementing a mandatory set of regulations including simple FOP warning labels for energy, sodium, total sugars, and saturated fats. These changes happened mainly for sugars and sodium and were reflected in significant decrease in the amount of such nutrients close to the initial cutoff for defining unhealthy foods and beverages. It remains to be seen how consumers react to these food composition changes and whether this short-term reformulation is sustained over time, especially with the complete implementation of the law. Also, future studies should investigate whether the reported reformulation either positively or negatively impacted the quality of the food supply more broadly, considering, i.e., other nutrients or food components, such as nonnutritive sweeteners. More importantly, future studies need to focus on whether the improvements in sugars or sodium observed among foods and beverages ultimately impacts the quality of the overall Chilean diet.

Supporting information

S1 Checklist. STROBE statement: Checklist of items that should be included in reports of cross-sectional studies.

(DOC)

Click here for additional data file.^{(88KB, doc)}

S2 Checklist. STROBE statement: Checklist of items that should be included in reports of cohort studies.

(DOC)

Click here for additional data file.^{(92KB, doc)}

S1 Table. Cutoffs for defining products “high in” in every implementation phase.

(DOCX)

Click here for additional data file.^{(13.1KB, docx)}

S2 Table. Type of foods classified in each food or beverage group.

T0: preimplementation period, January to February 2015 + January to February 2016 (cross-sectional n = 4,055, longitudinal n = 1,915). T1: postimplementation period, January to February 2017 (cross-sectional n = 3,025, longitudinal n = 1,915).

(DOCX)

Click here for additional data file.^{(18.3KB, docx)}

S3 Table. Outliers and implausible values for the amount of energy and nutrients of concern.

(DOCX)

Click here for additional data file.^{(15.1KB, docx)}

S1 Text. Concept note including first draft of data analysis.

(DOCX)

Click here for additional data file.^{(50.2KB, docx)}

Acknowledgments

We thank the Chilean National Association for Supermarkets (ASACH) and all the supermarkets and candy distributors involved for authorizing the data collection. We also thank the research teams at CIAPEC (Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases) at INTA (Institute of Nutrition and Food Technology), University of Chile, and at the Global Food Research Program, University of North Carolina at Chapel Hill.

Abbreviations

FOP: front of package
NCD: noncommunicable disease

Data Availability

The datasets used and/or analyzed during the current study cannot be made available. The 2015-2017 nutritional information dataset was obtained upon a legal agreement made with the supermarket association ASACH. Such agreement includes a clause of not making publicly available the data. Contact: https://www.supermercadosdechile.cl/contacto/. The Euromonitor International Database set is a commercial database that can be obtained upon payment from https://www.euromonitor.com/.

Funding Statement

Funding for this study was obtained from Bloomberg Philanthropies (PI: BP), IDRC (#107731‐002 PI: CC), and Fondecyt (#3150183; PI: RK). Authors have not received payment to write this article by a pharmaceutical company or other agency. The corresponding author had full access to all the data in the study and all authors shared final responsibility for the decision to submit for publication. IDRC URL: https://www.idrc.ca/ Bloomberg: https://www.bloomberg.org/ CONICYT: https://www.conicyt.cl/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC, et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019: 10–9. 10.1111/obr.12860 . [DOI] [PubMed] [Google Scholar]
2.Popkin B, Reardon T. Obesity and the food system transformation in Latin America. Obes Rev. 2018;19(8):1028–64. 10.1111/obr.12694 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958–72. 10.1016/S0140-6736(19)30041-8 . [DOI] [PMC free article] [PubMed] [Google Scholar]
4.OECD. The heavy burden of obesity: the economics of prevention. Paris, France: OECD Health Policy Studies, 2019. [Google Scholar]
5.World Cancer Research Fund International. Improve nutritional quality of the whole food supply: World Cancer Research Fund International; 2018. Available from: https://www.wcrf.org/sites/default/files/Improve-food-supply.pdf. [cited 2019 October 1].
6.Mantilla Herrera AM, Crino M, Erskine HE, Sacks G, Ananthapavan J, Mhurchu CN, et al. Cost-effectiveness of product reformulation in response to the Health Star Rating Food Labelling System in Australia. Nutrients. 2018;10(5):1–16. 10.3390/nu10050614 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Federeci C, Detzel P, Petracca F, Dainelli L, Fattore G. The impact of food reformulation on nutrient intakes and health, a systematic review of modelling studies. BMC Nutrition. 2019;5(1):1–21. 10.1186/s40795-018-0263-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Scrinis G, Monteiro CA. Ultra-processed foods and the limits of product reformulation. Public Health Nutr. 2018;21(1):247–52. 10.1017/S1368980017001392 . [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Nilson EAF, Spaniol AM, Goncalves VSS, Moura I, Silva SA, L'Abbe M, et al. Sodium reduction in processed foods in Brazil: analysis of food categories and voluntary targets from 2011 to 2017. Nutrients. 2017;9(7):1–12. 10.3390/nu9070742 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Health Canada. Sodium reduction in processed foods in Canada: an evaluation of progress toward voluntary targets from 2012 to 2016. Health Canada, 2018-01-15.
11.Kloss L, Meyer J, Graeve L, Vatter W. Sodium intake and its reduction by food reformulation in the European Union—A review. NFS Journal. 2015;1:9–19. 10.1016/j.nfs.2015.03.001 [DOI] [Google Scholar]
12.Curtis CJ, Clapp J, Niederman SA, Ng SW, Angell SY. US food industry progress during the National Salt Reduction Initiative: 2009–2014. Am J Public Health. 2016;106(10):1815–9. 10.2105/AJPH.2016.303397 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Hyseni L, Bromley H, Kypridemos C, O’Flaherty M, Lloyd-Williams F, Guzman-Castillo M, et al. Systematic review of dietary trans-fat reduction interventions. Bull World Health Organ. 2017;95(12):821–30G. 10.2471/BLT.16.189795 . [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Knai C, James L, Petticrew M, Eastmure E, Durand MA, Mays N. An evaluation of a public-private partnership to reduce artificial trans fatty acids in England, 2011–16. Eur J Public Health. 2017;27(4):605–8. 10.1093/eurpub/ckx002 . [DOI] [PubMed] [Google Scholar]
15.Restrepo BJ, Rieger M. Denmark's policy on artificial trans fat and cardiovascular disease. Am J Prev Med. 2016;50(1):69–76. 10.1016/j.amepre.2015.06.018 . [DOI] [PubMed] [Google Scholar]
16.Allemandi L, Tiscornia MV, Guarnieri L, Castronuovo L, Martins E. Monitoring sodium content in processed foods in Argentina 2017–2018: Compliance with national legislation and regional targets. Nutrients. 2019;11(7):1–12. 10.3390/nu11071474 . [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Sacks G, Rayner M, Swinburn B. Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK. Health Promot Int. 2009;24(4):344–52. 10.1093/heapro/dap032 . [DOI] [PubMed] [Google Scholar]
18.Mhurchu CN, Eyles H, Choi YH. Effects of a voluntary Front-of-Pack nutrition labelling system on packaged food reformulation: the Health Star Rating System in New Zealand. Nutrients. 2017;9(8):1–16. 10.3390/nu9080918 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Morrison H, Meloncelli N, Pelly FE. Nutritional quality and reformulation of a selection of children's packaged foods available in Australian supermarkets: Has the Health Star Rating had an impact? Nutr Diet. 2018;76(3):296–304. 10.1111/1747-0080.12486 . [DOI] [PubMed] [Google Scholar]
20.Vandevijvere S, Vanderlee L. Effect of formulation, labelling, and taxation policies on the nutritional quality of the food supply. Curr Nutr Rep. 2019;8(3):240–9. 10.1007/s13668-019-00289-x . [DOI] [PubMed] [Google Scholar]
21.World Cancer Research Fund. Nutrition label standards and regulations on the use of claims and implied claims on food London, UK: World Cancer Research Fund; 2019. Available from: https://www.wcrf.org/sites/default/files/1_Nutrition_labels.pdf. [cited 2019 October 1]. [Google Scholar]
22.Briggs ADM, Mytton OT, Kehlbacher A, Tiffin R, Elhussein A, Rayner M, et al. Health impact assessment of the UK soft drinks industry levy: a comparative risk assessment modelling study. Lancet Public Health. 2017;2(1):e15–e22. 10.1016/S2468-2667(16)30037-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.NYC Health. National Salt Reduction Initiative. Sodium reformulation in top U.S. chain restaurant foods: 2009–2014. New York: 2016.
24.Corvalan C, Reyes M, Garmendia ML, Uauy R. Structural responses to the obesity and non-communicable diseases epidemic: Update on the Chilean law of food labelling and advertising. Obes Rev. 2019;20(3):367–74. 10.1111/obr.12802 . [DOI] [PubMed] [Google Scholar]
25.Euromonitor International. Euromonitor International Database. Available from: https://www.euromonitor.com/. [cited 2019 November 9].
26.Swinburn B, Sacks G, Vandevijvere S, Kumanyika S, Lobstein T, Neal B, et al. INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support): overview and key principles. Obes Rev. 2013;14 Suppl 1:1–12. 10.1111/obr.12087 . [DOI] [PubMed] [Google Scholar]
27.Dunford E, Webster J, Metzler AB, Czernichow S, Ni Mhurchu C, Wolmarans P, et al. International collaborative project to compare and monitor the nutritional composition of processed foods. Eur J Prev Cardiol. 2012;19(6):1326–32. 10.1177/1741826711425777 . [DOI] [PubMed] [Google Scholar]
28.Kanter R, Reyes M, Corvalan C, Vandevijvere S, Swinburn B. Anticipatory effects of the implementation of the Chilean Law of Food Labeling and Advertising on food and beverage product reformulation. Obes Rev. 2019;20 Suppl 2:129–40. 10.1111/obr.12870 [DOI] [PubMed] [Google Scholar]
29.Otten JJ, Hekler EB, Krukowski RA, Buman MP, Saelens BE, Gardner CD, et al. Food marketing to children through toys: response of restaurants to the first U.S. toy ordinance. Am J Prev Med. 2012;42(1):56–60. 10.1016/j.amepre.2011.08.020 . [DOI] [PubMed] [Google Scholar]
30.Ning SX, Mainvil LA, Thomson RK, McLean RM. Dietary sodium reduction in New Zealand: influence of the Tick label. Asia Pac J Clin Nutr. 2017;26(6):1133–8. 10.6133/apjcn.032017.06 . [DOI] [PubMed] [Google Scholar]
31.Savio S, Mehta K, Udell T, Coveney J. A survey of the reformulation of Australian child-oriented food products. BMC Public Health. 2013;13:1–13. 10.1186/1471-2458-13-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Thomson RK, McLean RM, Ning SX, Mainvil LA. Tick front-of-pack label has a positive nutritional impact on foods sold in New Zealand. Public Health Nutr. 2016;19(16):2949–58. 10.1017/S1368980016001208 . [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Vyth EL, Steenhuis IH, Roodenburg AJ, Brug J, Seidell JC. Front-of-pack nutrition label stimulates healthier product development: a quantitative analysis. Int J Behav Nutr Phys Act. 2010;7:1–7. 10.1186/1479-5868-7-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.World Health Organization (WHO). Guiding principles and framework manual for front-of-pack labeling for promoting healthy diet. Geneva: World Health Organization; 2019. [Google Scholar]
35.Ares G, Bove I, Díaz R, Moratorio X, Benia W, Gomes F. [Food industry arguments against front-of-package nutrition labels in UruguayArgumentos da indústria alimentícia contra a rotulagem frontal de advertência nutricional no Uruguai]. Rev Panam Salud Publica. 2020;44:e20–e28. 10.26633/RPSP.2020.20 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.de Oliveira Otto MC, Mozaffarian D, Kromhout D, Bertoni AG, Sibley CT, Jacobs DR, et al. Dietary intake of saturated fat by food source and incident cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr. 2012;96(2):397–404. 10.3945/ajcn.112.037770 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Dunford EK, Ni Mhurchu C, Huang L, Vandevijvere S, Swinburn B, Pravst I, et al. A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013–2018. Obes Rev. 2019;20 Suppl 2:107–15. 10.1111/obr.12879 . [DOI] [PubMed] [Google Scholar]
38.Cediel G, Reyes M, da Costa Louzada ML, Martinez Steele E, Monteiro CA, Corvalan C, et al. Ultraprocessed foods and added sugars in the Chilean diet (2010). Public Health Nutr. 2018;21(1):125–33. 10.1017/S1368980017001161 . [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Agostini C, Corvalán C, Cuadrado C, Martínez C, Paraje G. Evaluación y Aplicación de Impuestos a los Alimentos con Nutrientes Dañinos para la Salud en Chile Final Report from the Expert Committee. Santiago de Chile: Ministry of Finance, Ministry of Health and Inter-American Development Bank (IADB), 2018. [Google Scholar]
40.Rebolledo N, Reyes M, Corvalán C, Popkin BM, Smith Taillie L. Dietary intake by food source and eating location in low- and middle-income Chilean preschool children and adolescents from Southeast Santiago. Nutrients. 2019;11(7):1–18. 10.3390/nu11071695 . [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS Med. doi: 10.1371/journal.pmed.1003220.r001

Decision Letter 0

Adya Misra

7 Feb 2020

Dear Dr Corvalan,

Thank you for submitting your manuscript entitled "Changes on the nutrient content of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: a non-experimental prospective study." for consideration by PLOS Medicine.

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

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Kind regards,

Adya Misra, PhD,

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003220.r002

Decision Letter 1

Adya Misra

30 Apr 2020

Dear Dr. Corvalan,

Thank you very much for submitting your manuscript "Changes on the nutrient content of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: a non-experimental prospective study." (PMEDICINE-D-20-00319R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. We feel that your study would be well suited to our special issue focussing on obesity which is due to publish in July 2020. Please let me know if you would be interested in being included in this issue. 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,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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Requests from the editors:

Abstract

Please combine methods and findings section to adhere to PLOS Medicine style

Please mention the data sources used

The last sentence of the methods and findings section must include a limitation of your study design/methodology

Please provide 95% CI and p -values as needed when reporting numerical results or results of “significance”

Please replace the interpretation section with conclusions and begin this section with “our results show” or similar to avoid overreaching

Please remove funding information and provide this information in the funding section within the metadata

Data Availability

The Data Availability Statement (DAS) requires revision. For each data source used in your study:

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

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

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

On page 3 please replace the summary sub heading with “Abstract”

Author summary

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

Introduction

Line 53 please revise to “In the last few decades …” or similar

Please format all references according to Vancouver style and use square brackets within text

Lines 81-82 could you mention the years studied here to avoid ambiguity

Methods

Please mention names of all data sources used

Role of the funding source should be moved to the funding disclosure section in the article metadata

Results

Please ensure all numerical results are accompanied by p values and 95% CI when noting “significance” or the lack of it. For example Lines 200-201

Please provide exact p values, unless p<0.001

Discussion

Please avoid assertions of primacy (Line 243) and add ” to our knowledge..”

I note that potential confounders and effect modifiers were not assessed- should this be added as a limitation to the discussion?

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

Please report your study according to the relevant guideline, which can be found here: http://www.equator-network.org/

Comments from the reviewers:

Reviewer #1: This paper studies the changes in the proportion of food/beverages exceeding the cut-off of the Chilean Food Labelling and Advertising Law as well as the changes median content of energy and nutrients of these products after the implementation of this Law. It offers important contribution to literature as it provides the highly needed evidence on the potential effectiveness of labelling regulations on improving the nutritional quality of packaged foods and beverages. Attention to the following would improve the manuscript:

1. A large part of the analysis mentions the relative percentage changes of the outcomes between the two periods. However, related figures are not given in tables 1 and 2, making it difficult for readers to follow. The analysis will therefore be clearer if additional columns are added for both samples to provide these percentage change figures.

2. One key finding of the paper is that the content of nutrients of most food groups decreased just below the cut-off from the visual analysis. However, this finding is not clearly explained in the paper, such as how to determine if the shift is just below the cut-off. Given the large numbers of graphs presented, it would also be helpful to have some indications on which graphs display such left-shifts.

3. Some food groups displayed a significant increase in saturated fats and energy after the implemented the law. The authors should provide some discussions on the potential reasons for these increases and their implications on the overall nutritional quality of packaged foods or beverages.

4. Line 131 - where is the sales data sourced from?

5. The titles for figures 1 and 2 are missing.

6. Line 224 - is the "5% change" an increase or a decrease?

7. Line 247 - which nutrient is "the proportion of 'high-in' products" refereed to?

Reviewer #2: I confine my remarks to statistical aspects of this paper. Unfortunately, I think that the wrong variable was analyzed.

When I first read the description of the project, I thought that, if I ran a food company, I would just lower all my numbers to just below the cutoffs. Then the product would not be marked as "high" in anything and more people than ever would buy my products.

Looking at Table 2 and the figures (which were excellent) confirms that that is what happened. The medians did not change much at all, but some of the top quartiles went down. In addition, the p values in this table seem to be off. Some tiny differences were highly sig. (e.g. sugars in desserts).

But I propose a fundamentally different analysis: I think quantile regression should be used, with time as the main iV and good group as a covariate. For the longitudinal analysis, a multilevel quantile model could be used.

Peter Flom

Reviewer #3: Changes on the nutrient content of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: a nonexperimental prospective study.

The impact of regulation of foods is currently of great public health interest and this piece of work examining the introduction of mandatory labelling on processed foods and drinks is therefore welcome. However, I have some concerns over the analysis and manuscript. The chosen analyses methods could be stronger as the results presented show simply pre- and post-law differences that fail to consider existing trends in nutrient content. It is possible that these differences would have been seen if the law had not come into effect not least because the Chilean government signalled its intent to use regulation through the modification of their tax on SSBs. This should be clearly stated in the limitations section.

Title.

Change "on" to "in" i.e. "Changes in the nutrient content of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: a nonexperimental prospective study".

Abstract.

Background

The first sentence of the background section doesn't make sense; suggest changing to something like: "In June 2016, the Chilean Food Labelling and Advertising Law that mandated front-of-package warning labels and marketing restrictions for unhealthy foods/beverages was implemented.

Methods

Sometimes the time periods are referred to as T0 sometimes as T0 please be consistent.

Although later specified it is not immediately clear what T0: Jan-Feb 2015/2016 refers to. Suggest clarifying this earlier. Perhaps reword the first sentence in the methods to state that data collection was carried out at two time periods.

N= is written twice - remove one.

There is a semicolon before N= in T1 but not in T0, change to be consistent.

First sentence of the methods should include a hyphen after pre.

The first sentence is too long. Suggest starting a new sentence after T1. As it stands it doesn't make sense particularly. Change to something like "A longitudinal subsample was also studied to explore changes in the median content of energy and nutrients of concern (total sugars, saturated fats and sodium, per 100g/100mL) and in the proportion of products with energy and nutrients exceeding the cutoffs of the Law (i.e., 'high in' products).

Findings

"High in" has not been defined, suggest changing to "product high in unhealthy nutrients".

"Significant decrease" should be changed to "Significant decreases".

Change the latter part of the penultimate sentence to "whereas changes in the amount of energy and saturated fats were uncommon." writing "content of energy" suggests a binary state of energy or no energy - which is unlikely to occur.

Interpretation.

Remove the final "s" from "sugars".

Author Affiliations

What are the numbers in the author affiliations? Are they postcodes / zip codes?

Line 21 why is "Stefanie Vandevijvere Senior Public Health Nutrition Scientist" written?

The author affiliation for institute 1 does not match that of the corresponding author.

Introduction

Line 53 "In the last decades is too ambiguous, if this manuscript is being read in 20 years time it will not make sense. Please be more precise e.g. "From 1990-2020, packaged foods..."

Personally I find the use of ultra processed foods unhelpful. That is, the implicit suggestion that simply processing the food makes it worse than not processing the food. The law referred to in this manuscript addresses nutrients rather than the number of processes that are required to produce the final food. The second sentence states that it is the high content of energy and nutrients linked to NCDs that is the issue. I am aware of the two positions of investigating diet either by examining total foods or looking at nutrient composition. But the ultra-processing part feels too poorly defined. Sure sausages and processed meat are linked to increased risk of NCDs but then this law focuses on the nutrient composition. Line 59 further discusses improving nutritional quality by reducing nutrients of concern rather making foods less processed. In short could you not write ultra-processed foods in the first sentence of the introduction as you appear to be interested in nutrients and should therefore write that the package foods and beverages contain more nutrients of concern rather than are "processed".

Line 60 change content to level or amount. Again content is like contains - does it contain nutrients of concern? Yes - it has energy.

Line 61. I think we have been interested in the nutritional quality of packaged foods for longer than "since the 2000s".

Line 65 change contents to content.

Line 65 change i.e. to e.g. as these are examples of possible strategies rather than the exhaustive list of all possible strategies implied with i.e.. Also include "adding" (or something similar) before "upper limits for sodium content" as it can be read as banning trans fats or banning upper limits for sodium content.

Line 67 "However, as most of these strategies are voluntary the impact on the nutritional quality of the overall food supply has been limited". I disagree. There are lots of mandatory strategies that incentivise reformulation such as mandatory back of pack labelling, for example, fortification of wheat with calcium, folate and iodine fortification, SSB and fat taxes and VAT/GST. Similarly there are restrictions on more classical food safety grounds like the lead and arsenic content of foods for example. Recommend that this sentence is amended to make the point I think you are trying to make which is that without mandating these strategies industry is less likely to do what is wanted and therefore government intervention is required.

Line 70 feels a bit cumbersome to refer to the law by its full title every time. Could you use an abbreviation?

Line 73 I suspect the implementation was not agreed in a staggered way rather the law was to be implemented in a staggered way, please amend accordingly.

Line 75 sentence starting "Such products..." which products? This seems out of context or at least needs changing to fit.

Line 76 Not sure what the "Chilean experience" is. Should this be something like "The Chilean government has introduced a wide range of strategies to improve public health and tackle childhood obesity that combines a FOP label in combination with child obesity prevention strategies..."

Line 76 child obesity should not be hyphenated.

Line 79 sentence starting "Therefore, in..." this could do with tightening up. Suggest something like "Therefore, in the current study we aimed to study changes in the proportion of 'high in' products and changes in energy and nutrients of concern in packaged foods and beverages, before and after the initial implementation (i.e., <1 year of the first phase cutoffs) of the Chilean Law.

Line 81 1 year does not need hyphenation.

Methods

Line 87 pre does need a hyphen

Line 87 as above T0 and T1 have a comma before N=... Please be consistent.

Line 87 the colon after T0 is subscripted it should not be.

Line 89 was the Law only implemented in Santiago or are you only looking at Santiago? Clarify the interest in Santiago ideally give context of Santiago with the rest of Chile.

Line 89 new sentence after Chile.

Line 90 hyphen after pre

Line 94 are you interested in ingredients? Is the amount of each ingredient given on the label otherwise you won't be able to see reductions in the amount other than perhaps a change in the order of the ingredient (any way I suspect without amounts/percentages it's going to be inexact).

Line 94 why were you taking photographs of products before the law was introduced? Was this work for something else or were you aware that the law was going to be introduced? If it is the former then it's not a big deal and perhaps just include a sentence addressing this if it's the latter, that is, the authors (and manufacturers) were aware in 2015 the law was going to be introduced then your baseline sample could be biased.

Line 96 This had to be read a couple of times to get the meaning perhaps "(one supermarket from each of the 6 major chains in Chile)"

Line 96 could you give an indication of the market share of these 6 chains for those unfamiliar with Chile?

Line 96 are the 3 candy distributors localised to the high income areas? Could you add more clarity to this as I can only think of shops that distribute to a local area and that any distributor would be at a wider geographic area.

Line 99 could you define packaged foods? Is that essentially all foods? Does it include packaged fruit and vegetables?

Line 99 did you explore alternatives to visiting stores such as manufacturer or supermarket websites? The wayback machine can be useful for retrospective website access.

Line 105 did you carry out any validation of the data, for example we saw a temporal changes in product labelling when manufacturers went from providing nutritional information per 100g to providing nutritional information as made up with semi-skimmed milk. This resulted in dramatic reduction in sugar and increases in protein for the same unreformulated product. Possible checks could be to examine the atwater factors (e.g. fat = 9kcal per 1g) and see if based on the macronutrient content energy is as expected.

Line 116 i.e. should be changed to e.g. Also the list provided (nuggets and others) does not provide a lot of clarity, simply writing non-sausage meat products seems like a rather odd classification where I am unsure what is special about sausages but at least seems easy to understand whereas the examples of nuggets and others make it more not less ambiguous.

Line 121 I am unclear why you included two years of data in the baseline sample. The issue with this is - and I'll come to this in the analysis section - is that when you carry out a simple pre post study like this you don't know why the changes occured. They may be due to the law but they may simply reflect on-going reformulation in the food market and that these differences would have appeared if the law had not come into effect. Comparing data from 2015 to 2017 means that the changes are more likely to have come about due to the general reformulation towards healthy products. In short could you explain the benefit for including two years of baseline data as it is not immediately apparent.

If the product was available in 2015 and 2017 but not in 2016 then either it was not captured in 2016 or it was withdrawn and relaunched. If it is the former then it casts doubt on the completeness of the sample.

Also why not do two years post implementation i.e. 2018? Including two years pre-implementation data seems to confuse things, raise questions and add little. It feels like the 2015 data have been included simply because they were collected and are available.

Line 123 (i.e., 2016 items were included) suggested rewriting to something like "only items collected in 2016 were included" to remove ambiguity

Line 129 As I understand it you are only interested in individual products with greater than 1% market share. What percentage of products have less than <1% market share? Having 100 products on the market means that all of these (except for a couple) would be excluded - or have I missed something?

Line 145-146 line spacing looks smaller than the remainder of the document

Line 149 food densities, more technically known as specific gravity.

Line 152 Would help if the two analyses that are presented (i.e. cross-sectional and longitudinal) had their own subsections

Line 161 law should be lower case.

Line 163 hypen for pre

Line 166 "distributions were non-parametric". This is incorrect and should be changed. I suspect what is meant here is that the distributions were not normally distributed and therefore non-parametric tests were used. Distributions have parameters, tests that do not rely on these parameters are non-parametric.

Line 167 Was there any adjustment for multiple testing? If one component of the food changes then the others are more likely to do so. Similarly manufacturers may make the decision to reformulate across their entire product range and therefore these tests are not independent and this should be addressed accordingly - typically be lowering the alpha threshold.

Line 167 Analyses should not have a capital letter as the second word in the subtitles does not anywhere else.

Line 174 insert "at" between available and each.

Line 174 remove space after /

Line 175 see earlier point. For food groups with 482 products the market share for the majority will be <1% whereas the group with only 69 will have a greater number of products with 1+% market share (probably) therefore the results will be biased towards categories with fewer groups.

Line 176 should it be "in" before "the standard..."?

Line 180 should be analyses

Line 184182 error

Results

Is it possible to have a summary of all changes carried out? For example if 5 groups have shown a decline but 5 have increased then there may be no overall benefit. Or perhaps not all food groups are equal as small changes in foods that are highly consumed may have a greater impact on health than large changes in foods that are rarely consumed. Similarly is there a hierarchy in the changes that were observed that is: are changes in energy more important than changes in sodium for example?

Line 187 would prefer % after each percentage value e.g. 51% to 44%.

Line 188 "left columns" is unclear please use column numbers or change.

Line 189 can you provide confidence intervals?

Line 206 change left column to figures on the left or something similar.

Line 209 see above. It is unlikely that foods will be reformulated for a single nutrient. If reformulation occurs it is likely to change energy and nutrients.

Line 214 should be "Changes in".

Line 214 It is unclear what is meant by this sentence, changes...were observed for products in which there was no...change.

Line 219 please write Figures in full each time.

Line 220 remove "overall".

Line 228 change "on" and "among" to "in" and as appropriate elsewhere.

Line 229 change "among" to "in" and as appropriate elsewhere.

Line 223 change to (by between 1% and 6%, depending on the food group)

Line 214237 error.

Discussion

Line 243 change to "This is the first study to evaluate changes..."

Line 247 what is meant by "relative" decrease?

Line 250 this is written to appear that the groups were the driver of the change whereas the groups are passive and had change done to them.

Line 255 "significant improvements were seen".

Line 257remove has been described (or change to have)

Line 259 change "supply" to "suppliers" if appropriate.

Line 259 change "...menus of fast..." to "menus in fast..."

Line 261 remove "the distribution of".

Line 262 remove comma after USA.

Line 262 change first "on" to "of" second "on" to "in".

Line 263 references after the end of the sentence.

Line 268 "given a previous report..." this sentence is not clear please rewrite.

Line 271 add "the" before "healthy school food environment".

Line 272 agree that changes can be brought about with incentives however this cannot be continued indefinitely.

Line 277 change "in their way to implement" to "on their way to implementing".

Line 280 remove "between".

Line 281 change "2015 Chilean packaged food supply in Chile" to "packaged food supply in Chile in 2015" or something similar.

Line 284 change "have" to "has".

References

Some of the journals are abbreviated and some are not. For example Obesity Reviews is given in full and as Obes Rev

American journal of public health requires capital letters for all words.

Some article titles have capital letters for each word some do not.

Page numbers are not included for all articles e.g. Ref 2 includes page numbers Ref 6 does not.

Page number for Ref 1 should read 10-19 not 10-9

Refs 7, 30, 32 only have 1 page number (2/836/65)

Table 1

Remove extra line space in Savory baked products - High in Sodium and Sausages - High in Sodium.

Table 2

Remove space between 100 and mL

Footnote "n" is lower case - change to upper case for consistency.

Why are there sometimes [ and sometimes (?

Sometimes there is a space and a long dash sometimes no space and hyphen. Please be consistent.

Please use consistent number of decimal places.

Figure 1

Not sure why the title is all in capitals,

Subsample and exclusions are spelled incorrectly.

Sometimes the thousands digits are seperated by a comma sometimes they are not - please be consistent.

Some of the lines do not join into the middle of the boxes, sometimes there are spaces between the boxes and the lines.

Recommend that the final boxes are aligned.

Sometimes there are spaces after the = sign, sometimes there are not - please be consistent.

The first horizontal line looks slightly skewed, that is not perfectly horizontal as it has a step in it.

Sometimes the description is given before the N sometimes after - please be consistent.

Change the boxes with N T0=10,081 and N T1=8,563 to match the others by changing the position of the N i.e. T0: N=10,081 ...

In the final boxes T0 and T1 are not sub(sub)scripted

Figure 2

Please include all plots (Energy, Sugar, Sat Fat, Sodium) for each category

n= has become lower case here, please use the same case throughout.

Use T0 and T1 rather than Pre and Post

Include a footnote to state why the numbers (n) aren't the same for energy and sugars

Why do the density curves begin at different points <0?

Remove space between 100 and mL for consistency.

The resolution of these plots is not great - can you change to vectorised images?

The contrast between the green and blue is not great - consider more contrasting colours though maybe this would be improved with resolution changes.

Consider shading the area under the plot

Why is there white space above some of the curves in some plots and not others? Ideally the same scale would be used but failing that at least make them look similar.

Same with the x axis - some curves fill the x axis whereas others have lots of space.

Breakfast cereals - it appears that some breakfast cereals have (close to) 0kcal of energy. Is this correct?

Should be non-sausage meat products throughout the manuscript.

Use "Cheese" or "Cheeses" throughout the manuscript for consistency.

Where are the plots for Ready-to-eat meals?

Supplementary Table 1

Please make line spacing the same for both solids and liquids.

Why are square brackets used here for the first time - please be consistent.

No spaces after 100

Supplementary Table 2

The foods seem to be ordered by random - could you make it alphabetical or are they by number of products?

These categories need lots of revision.

Unclear what the difference between "flavored water with sugar" and "aromatized/flavored waters with sugar" is.

Are "fantasy beverages" a Chilean product?

The "other" group should go at the end of the list to catch those products not categorised previously.

There are three categories for "fantasy beverages" with sugar, without sugar and other. The first two categories cover all possible options - what goes in the final category?

What is the difference between "100% fruit juices" and "100% fruit juices (no added ingredients"? Surely if it is 100% fruit juice then it is only fruit juice and has nothing else.

Flavoured water with sugar is repeated but spelled differently.

What are milk drinks? That seems to encompass all of the subsequent categories.

Change "skim" to "skimmed" for consistency.

Yogurt is in the Milk and milk-based drinks category and in the yogurt category.

Remove spaces around /.

Light or diet yogurt includes light or diet yogurt with fruits and/or nuts.

Breakfast cereals - change to "dry fruit cereal bars".

The three cereals categories seem very specific i.e. balls, honey stars and flakes. Are there a lot of honey stars cereals in Chile?

Dessert & ice cream - change to vegetable chips (remove s)

Candies & sweet confectionery - why are nuts in here?

Remove s from popcorns

Confectionery is spelled two ways - confectionery and confectionary.

Why are chocolate chips in sweet spreads not "Candies and sweet confectionery"?

What is "glazed"?

Jam (all kinds) includes the other jam category.

What is "other products used for pastry"?

Savory baked products - two spaces after "sopaipillas,"

What is the difference between "packaged white bread" and "packaged white bread loaf"?

What is the difference between " packaged whole wheat bread loaf (regular, light or diet)" and "packaged whole wheat bread loaf (all kinds)"?

"Corn torillas" is repeated.

Should be "vegetable broth" not "vegetables broth".

"Other dressings" and "other dressings for salads" do not appear to be mutually exclusive - please amend.

Savory spreads - remove spaces after / to be consistent with elsewhere.

What is the difference between "fresh cheeses and light cheeses" and "fresh cheeses"?

Why is bacon and ham in the sausage category?

Sweet baked products: are bizcochos and biscochos different things or is that a typo?

"Frozen breaded meat" and "frozen marinated or seasoned meat" are written twice

What is the difference between "nuggets (chicken or turkey)" and "nuggets".

Unclear what the difference between "Soups for one or other type of instant soups" and "instant soups" is.

Supplementary Table 3

The first sentence isn't clear, "outlier" should be changed to "outliers" and the final part " considered unlikely to happen for the specific group were omitted" needs rewriting.

Would be helpful to state that these values are per 100g or serving or whatever the units are

N is lower case unlike in the majority of the document - please be consistent

ii should be "Sugar" not "sugars".

iii should be "fat" not "fats".

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

[LINK]

PLoS Med. 2020 Jul 28;17(7):e1003220. doi: 10.1371/journal.pmed.1003220.r003

Author response to Decision Letter 1

22 May 2020

Attachment

Submitted filename: Response to reviewers, R1, 05.21.20.docx

Click here for additional data file.^{(43.7KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003220.r004

Decision Letter 2

Adya Misra

5 Jun 2020

Dear Dr. Corvalan,

Thank you very much for re-submitting your manuscript "Changes in the amount of nutrient of packaged foods and beverages after the initial

implementation of the Chilean Law of Food Labelling and Advertising: a non-experimental

prospective study." (PMEDICINE-D-20-00319R2) 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]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

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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.

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 Jun 10 2020 11:59PM.

Sincerely,

Adya Misra, PhD

Senior Editor

PLOS Medicine

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

Requests from Editors:

Comments from AE:

I think to understand the impact of a labeling process, one needs to look carefully at the specific distributions and where the line is set for something to be declared "high". In the case of sugars in milk drinks, for example, the distribution is fairly flat and the line for being declared high is in the middle of the distribution. There is a clear shift in the distribution post-introduction of labeling. Other distributions are fundamentally different and the line of where "high" is are sometimes way above the top end of the distribution pre-labeling. It is no wonder that there is little impact. In addition to making the changes that have been proposed in this paper, I would rather hope that any resubmission might highlight more clearly some things like the above, which are messages that don't seem to be coming through very strongly at the moment.

Please remove the original submission file from the submission system as can erroneously be used to generate the full submission PDF by the system

In the abstract- its not clear what you mean by a longitudinal subsample that was also analysed. Please clarify and revise as needed

Line 114- perhaps you can revise to 20th century using roman numerals?

Line 143- I think you mean 30% of the population in Chile lives. Please revise

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 remove page numbers and line numbers from STROBE checklists provided as these are likely to change. Please use paragraphs/sections instead.

The financial information can be removed from the word document as it is automatically pulled from the article meta-data. Please only provide the information in the financial disclosure section

Same goes for the data availability information, which can be provided in the data statement

In the discussion you say that your results suggest that food and beverage reformulation occurred rapidly. I’m not sure if this can be directly linked, as you have noted in the limitations section, so I recommend removing this sentence and carefully toning down the discussion to avoid overstating your conclusions

Please add a sentence in the Competing Interests statement to acknowledge that Barry Popkin is an Academic Editor for PLOS Medicine.

Comments from Reviewers:

Reviewer #1: The authors have addressed my previous comments. The quantile analysis has strengthened the paper and made it easier for readers to understand the changes occurred around the cut-off. Only a few minor comments left:

Line 114 what is XX?

Line 167 "small packages where not collected" It is unclear what it is meant.

Line 257 How are "food/beverage groups" controlled/ assessed in the quantile regression?

Reviewer #2: The authors have addressed my concerns and I now recommend publication

Peter Flom

Reviewer #3: The manuscript is much improved. I have a few, minor comments.

Line 76 - school sales at school. Seems like the first "school" is not required

Line 114 - add in years instead XX

Line 165 - no comma after i.e.

Line 166 - add for after asked or change asked to chosen

Line 167 where should be were

Line 173 no comma after i.e.

Line 274 two spaces after all

Line 273 remove hyphen from sweet baked

Line 337 associated with a decrease not associated to

Line 380 reports of improvement not to improvement

Line 403 change in to on.

Line 412 add reference for the National Dietary Survey

Line 405 put such in front of as

Lines 410-415 417-424 combine in to single paragraph

Line 446 remove the

Line 642/670/736 Cutoff corresponds - add s

Line 643/671/737 comma after i.e.

Line 681/747 McNemar tests

Table 1 - What do the confidence intervals represent? Should this be interpreted as, for example, 26% of beverages were "high in" any category in T0? If so this doesn't require a confidence interval as it is a single value with no variance. Also some of the point estimates do not lie exactly between the confidence intervals, e.g. sweet baked products - high in sugars 95 (90-97).

Table 2

Savoury snacks sodium T1 - remove bracket

Sometimes one or two decimal places are given sometimes there are none.

Desserts & ice creams sugars - for p25 and p50 the values at T0 and T1 are the same and the p-value is 1 whereas for p75 T0 and T1 are the same but the p-value is 0.30. Is this because of rounding?

Table 3

Beverages Any 'High in' the dash is different to the others

Ready-to-eat meals, please check n as elsewhere it is 109

Table 4

Candies & sweet confectionary is used, whereas confectioneries is used elsewhere.

Savory spread sugars p25 T0 = 0, T1 = 0 but p!= 1 please add decimal places to explain.

Fig 1

Include a line linking total products collected to products collected in 2015

Some of the arrows are not exactly horizontal

Left hand side exclusion box

Change n to lower case

change semi-colon to comma for consistency

Right hand side exclusion box - remove colon and change semi-colon to comma

Fig 2

Unclear why the x axis start below 0. Are there negative numbers given or is this the default plot?

Kcal does not begin with a capital K elsewhere in the document

In some instances the n provided in the plot does not match that given in table S2, for example Fig 2C n for yogurt is 181 whereas Table S2 states 184. Similarly n for cheeses = 60 and 61 in S2, Fig 2M T0 n=242 Table S2 states 243. This may be due to some products not providing all information if this is the case then perhaps provide a footnote.

S2 Table.

Beverages T0 n is 686 whereas Table S2 states 688

Flavoured is spelled both with and without a "u" suggest the American English spelling without a "u" to be consistent.

Milks & milk-based drinks - too many uses of the word "powder".

Yogurts - no n after Longitudinal.

Breakfast cereals - flakes not lakes.

Desserts & ice cream - vegetable not vegetables and chips not chip.

Candies & sweet confectioneries - change carcass for coating.

Sweet spreads - Unclear on the difference between "honey" and honey (all kinds).

Savory baked products - suggest starting all sections with a capital letter but if not Christmas should have one. Is Christmas bread savory?

Savory spreads - should hicken be chicken? Unclear why broth is in the spread category not soups.

Ready to eat meals - Longitudinal should be on new line

S3 Table

There is a semi-colon in the first list (after ice creams) that makes it unclear which groups you are referring to. Perhaps a value is missing? Also a full stop after sausages that should not be there. Oxford comma after confectionery in iv. No space after > before 8000mg

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

[LINK]

PLoS Med. 2020 Jul 28;17(7):e1003220. doi: 10.1371/journal.pmed.1003220.r005

Author response to Decision Letter 2

23 Jun 2020

Attachment

Submitted filename: Response to reviewers, R2.docx

Click here for additional data file.^{(25.2KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003220.r006

Decision Letter 3

Adya Misra

24 Jun 2020

Dear Dr. Corvalan,

On behalf of my colleagues and the academic editor, Dr. Nicholas J Wareham, I am delighted to inform you that your manuscript entitled "Changes in the amount of nutrient of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: a non-experimental prospective study." (PMEDICINE-D-20-00319R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

Associated Data

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

Supplementary Materials

S1 Checklist. STROBE statement: Checklist of items that should be included in reports of cross-sectional studies.

(DOC)

Click here for additional data file.^{(88KB, doc)}

S2 Checklist. STROBE statement: Checklist of items that should be included in reports of cohort studies.

(DOC)

Click here for additional data file.^{(92KB, doc)}

S1 Table. Cutoffs for defining products “high in” in every implementation phase.

(DOCX)

Click here for additional data file.^{(13.1KB, docx)}

S2 Table. Type of foods classified in each food or beverage group.

(DOCX)

Click here for additional data file.^{(18.3KB, docx)}

S3 Table. Outliers and implausible values for the amount of energy and nutrients of concern.

(DOCX)

Click here for additional data file.^{(15.1KB, docx)}

S1 Text. Concept note including first draft of data analysis.

(DOCX)

Click here for additional data file.^{(50.2KB, docx)}

Attachment

Submitted filename: Response to reviewers, R1, 05.21.20.docx

Click here for additional data file.^{(43.7KB, docx)}

Attachment

Submitted filename: Response to reviewers, R2.docx

Click here for additional data file.^{(25.2KB, docx)}

Data Availability Statement

[pmed.1003220.ref001] 1.Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC, et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019: 10–9. 10.1111/obr.12860 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref002] 2.Popkin B, Reardon T. Obesity and the food system transformation in Latin America. Obes Rev. 2018;19(8):1028–64. 10.1111/obr.12694 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref003] 3.GBD 2017 Diet Collaborators. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393(10184):1958–72. 10.1016/S0140-6736(19)30041-8 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref004] 4.OECD. The heavy burden of obesity: the economics of prevention. Paris, France: OECD Health Policy Studies, 2019. [Google Scholar]

[pmed.1003220.ref005] 5.World Cancer Research Fund International. Improve nutritional quality of the whole food supply: World Cancer Research Fund International; 2018. Available from: https://www.wcrf.org/sites/default/files/Improve-food-supply.pdf. [cited 2019 October 1].

[pmed.1003220.ref006] 6.Mantilla Herrera AM, Crino M, Erskine HE, Sacks G, Ananthapavan J, Mhurchu CN, et al. Cost-effectiveness of product reformulation in response to the Health Star Rating Food Labelling System in Australia. Nutrients. 2018;10(5):1–16. 10.3390/nu10050614 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref007] 7.Federeci C, Detzel P, Petracca F, Dainelli L, Fattore G. The impact of food reformulation on nutrient intakes and health, a systematic review of modelling studies. BMC Nutrition. 2019;5(1):1–21. 10.1186/s40795-018-0263-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref008] 8.Scrinis G, Monteiro CA. Ultra-processed foods and the limits of product reformulation. Public Health Nutr. 2018;21(1):247–52. 10.1017/S1368980017001392 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref009] 9.Nilson EAF, Spaniol AM, Goncalves VSS, Moura I, Silva SA, L'Abbe M, et al. Sodium reduction in processed foods in Brazil: analysis of food categories and voluntary targets from 2011 to 2017. Nutrients. 2017;9(7):1–12. 10.3390/nu9070742 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref010] 10.Health Canada. Sodium reduction in processed foods in Canada: an evaluation of progress toward voluntary targets from 2012 to 2016. Health Canada, 2018-01-15.

[pmed.1003220.ref011] 11.Kloss L, Meyer J, Graeve L, Vatter W. Sodium intake and its reduction by food reformulation in the European Union—A review. NFS Journal. 2015;1:9–19. 10.1016/j.nfs.2015.03.001 [DOI] [Google Scholar]

[pmed.1003220.ref012] 12.Curtis CJ, Clapp J, Niederman SA, Ng SW, Angell SY. US food industry progress during the National Salt Reduction Initiative: 2009–2014. Am J Public Health. 2016;106(10):1815–9. 10.2105/AJPH.2016.303397 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref013] 13.Hyseni L, Bromley H, Kypridemos C, O’Flaherty M, Lloyd-Williams F, Guzman-Castillo M, et al. Systematic review of dietary trans-fat reduction interventions. Bull World Health Organ. 2017;95(12):821–30G. 10.2471/BLT.16.189795 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref014] 14.Knai C, James L, Petticrew M, Eastmure E, Durand MA, Mays N. An evaluation of a public-private partnership to reduce artificial trans fatty acids in England, 2011–16. Eur J Public Health. 2017;27(4):605–8. 10.1093/eurpub/ckx002 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref015] 15.Restrepo BJ, Rieger M. Denmark's policy on artificial trans fat and cardiovascular disease. Am J Prev Med. 2016;50(1):69–76. 10.1016/j.amepre.2015.06.018 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref016] 16.Allemandi L, Tiscornia MV, Guarnieri L, Castronuovo L, Martins E. Monitoring sodium content in processed foods in Argentina 2017–2018: Compliance with national legislation and regional targets. Nutrients. 2019;11(7):1–12. 10.3390/nu11071474 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref017] 17.Sacks G, Rayner M, Swinburn B. Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK. Health Promot Int. 2009;24(4):344–52. 10.1093/heapro/dap032 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref018] 18.Mhurchu CN, Eyles H, Choi YH. Effects of a voluntary Front-of-Pack nutrition labelling system on packaged food reformulation: the Health Star Rating System in New Zealand. Nutrients. 2017;9(8):1–16. 10.3390/nu9080918 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref019] 19.Morrison H, Meloncelli N, Pelly FE. Nutritional quality and reformulation of a selection of children's packaged foods available in Australian supermarkets: Has the Health Star Rating had an impact? Nutr Diet. 2018;76(3):296–304. 10.1111/1747-0080.12486 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref020] 20.Vandevijvere S, Vanderlee L. Effect of formulation, labelling, and taxation policies on the nutritional quality of the food supply. Curr Nutr Rep. 2019;8(3):240–9. 10.1007/s13668-019-00289-x . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref021] 21.World Cancer Research Fund. Nutrition label standards and regulations on the use of claims and implied claims on food London, UK: World Cancer Research Fund; 2019. Available from: https://www.wcrf.org/sites/default/files/1_Nutrition_labels.pdf. [cited 2019 October 1]. [Google Scholar]

[pmed.1003220.ref022] 22.Briggs ADM, Mytton OT, Kehlbacher A, Tiffin R, Elhussein A, Rayner M, et al. Health impact assessment of the UK soft drinks industry levy: a comparative risk assessment modelling study. Lancet Public Health. 2017;2(1):e15–e22. 10.1016/S2468-2667(16)30037-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref023] 23.NYC Health. National Salt Reduction Initiative. Sodium reformulation in top U.S. chain restaurant foods: 2009–2014. New York: 2016.

[pmed.1003220.ref024] 24.Corvalan C, Reyes M, Garmendia ML, Uauy R. Structural responses to the obesity and non-communicable diseases epidemic: Update on the Chilean law of food labelling and advertising. Obes Rev. 2019;20(3):367–74. 10.1111/obr.12802 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref025] 25.Euromonitor International. Euromonitor International Database. Available from: https://www.euromonitor.com/. [cited 2019 November 9].

[pmed.1003220.ref026] 26.Swinburn B, Sacks G, Vandevijvere S, Kumanyika S, Lobstein T, Neal B, et al. INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support): overview and key principles. Obes Rev. 2013;14 Suppl 1:1–12. 10.1111/obr.12087 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref027] 27.Dunford E, Webster J, Metzler AB, Czernichow S, Ni Mhurchu C, Wolmarans P, et al. International collaborative project to compare and monitor the nutritional composition of processed foods. Eur J Prev Cardiol. 2012;19(6):1326–32. 10.1177/1741826711425777 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref028] 28.Kanter R, Reyes M, Corvalan C, Vandevijvere S, Swinburn B. Anticipatory effects of the implementation of the Chilean Law of Food Labeling and Advertising on food and beverage product reformulation. Obes Rev. 2019;20 Suppl 2:129–40. 10.1111/obr.12870 [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref029] 29.Otten JJ, Hekler EB, Krukowski RA, Buman MP, Saelens BE, Gardner CD, et al. Food marketing to children through toys: response of restaurants to the first U.S. toy ordinance. Am J Prev Med. 2012;42(1):56–60. 10.1016/j.amepre.2011.08.020 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref030] 30.Ning SX, Mainvil LA, Thomson RK, McLean RM. Dietary sodium reduction in New Zealand: influence of the Tick label. Asia Pac J Clin Nutr. 2017;26(6):1133–8. 10.6133/apjcn.032017.06 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref031] 31.Savio S, Mehta K, Udell T, Coveney J. A survey of the reformulation of Australian child-oriented food products. BMC Public Health. 2013;13:1–13. 10.1186/1471-2458-13-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref032] 32.Thomson RK, McLean RM, Ning SX, Mainvil LA. Tick front-of-pack label has a positive nutritional impact on foods sold in New Zealand. Public Health Nutr. 2016;19(16):2949–58. 10.1017/S1368980016001208 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref033] 33.Vyth EL, Steenhuis IH, Roodenburg AJ, Brug J, Seidell JC. Front-of-pack nutrition label stimulates healthier product development: a quantitative analysis. Int J Behav Nutr Phys Act. 2010;7:1–7. 10.1186/1479-5868-7-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref034] 34.World Health Organization (WHO). Guiding principles and framework manual for front-of-pack labeling for promoting healthy diet. Geneva: World Health Organization; 2019. [Google Scholar]

[pmed.1003220.ref035] 35.Ares G, Bove I, Díaz R, Moratorio X, Benia W, Gomes F. [Food industry arguments against front-of-package nutrition labels in UruguayArgumentos da indústria alimentícia contra a rotulagem frontal de advertência nutricional no Uruguai]. Rev Panam Salud Publica. 2020;44:e20–e28. 10.26633/RPSP.2020.20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref036] 36.de Oliveira Otto MC, Mozaffarian D, Kromhout D, Bertoni AG, Sibley CT, Jacobs DR, et al. Dietary intake of saturated fat by food source and incident cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis. Am J Clin Nutr. 2012;96(2):397–404. 10.3945/ajcn.112.037770 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref037] 37.Dunford EK, Ni Mhurchu C, Huang L, Vandevijvere S, Swinburn B, Pravst I, et al. A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013–2018. Obes Rev. 2019;20 Suppl 2:107–15. 10.1111/obr.12879 . [DOI] [PubMed] [Google Scholar]

[pmed.1003220.ref038] 38.Cediel G, Reyes M, da Costa Louzada ML, Martinez Steele E, Monteiro CA, Corvalan C, et al. Ultraprocessed foods and added sugars in the Chilean diet (2010). Public Health Nutr. 2018;21(1):125–33. 10.1017/S1368980017001161 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003220.ref039] 39.Agostini C, Corvalán C, Cuadrado C, Martínez C, Paraje G. Evaluación y Aplicación de Impuestos a los Alimentos con Nutrientes Dañinos para la Salud en Chile Final Report from the Expert Committee. Santiago de Chile: Ministry of Finance, Ministry of Health and Inter-American Development Bank (IADB), 2018. [Google Scholar]

[pmed.1003220.ref040] 40.Rebolledo N, Reyes M, Corvalán C, Popkin BM, Smith Taillie L. Dietary intake by food source and eating location in low- and middle-income Chilean preschool children and adolescents from Southeast Santiago. Nutrients. 2019;11(7):1–18. 10.3390/nu11071695 . [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Changes in the amount of nutrient of packaged foods and beverages after the initial implementation of the Chilean Law of Food Labelling and Advertising: A nonexperimental prospective study

Marcela Reyes

Lindsey Smith Taillie

Barry Popkin

Rebecca Kanter

Stefanie Vandevijvere

Camila Corvalán

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Summary of study design

Collection of nutritional information and ingredients of packaged foods and beverages

Food groups

Data processing and definition of the analytical sample

Fig 1. Flow chart describing products excluded from the analytical sample.

Data analyses

Study outcomes

Statistical analyses

Results

Table 1. Changes between T0 and T1 in the proportion of “high in” energy and nutrients of concern (or any “high in”) by food and beverage group, cross-sectional analysis.

Fig 2. Density curves for the amount of energy and nutrients of concern in beverages, cross-sectional samples.

Fig 17. Density curves for the amount of energy and nutrients of concern in soups, cross-sectional samples.

Table 2. Changes in quartiles of energy and nutrients of concern by food/beverages group; cross-sectional analysis.

Fig 3. Density curves for the amount of energy and nutrients of concern in milk and milk-based drinks, cross-sectional samples.

Fig 4. Density curves for the amount of energy and nutrients of concern in yogurts, cross-sectional samples.

Fig 5. Density curves for the amount of energy and nutrients of concern in breakfast cereals, cross-sectional samples.

Fig 6. Density curves for the amount of energy and nutrients of concern in sweet baked products, cross-sectional samples.

Fig 7. Density curves for the amount of energy and nutrients of concern in desserts and ice creams, cross-sectional samples.

Fig 8. Density curves for the amount of energy and nutrients of concern in candies and sweet confectioneries, cross-sectional samples.

Fig 9. Density curves for the amount of energy and nutrients of concern in sweet spreads, cross-sectional samples.

Fig 10. Density curves for the amount of energy and nutrients of concern in savory baked products, cross-sectional samples.

Fig 11. Density curves for the amount of energy and nutrients of concern in savory snacks, cross-sectional samples.

Fig 12. Density curves for the amount of energy and nutrients of concern in savory spreads, cross-sectional samples.

Fig 13. Density curves for the amount of energy and nutrients of concern in cheeses, cross-sectional samples.

Fig 14. Density curves for the amount of energy and nutrients of concern in ready-to-eat meals, cross-sectional samples.

Fig 15. Density curves for the amount of energy and nutrients of concern in sausages, cross-sectional samples.

Fig 16. Density curves for the amount of energy and nutrients of concern in nonsausage meat products, cross-sectional samples.

Table 3. Changes between T0 and T1 in the proportion of “high in” energy and nutrients of concern (or any “high in”) by food or beverage group, longitudinal analysis.

Fig 18. Density curves for the amount of energy and nutrients of concern in beverages, longitudinal subsample.

Fig 33. Density curves for the amount of energy and nutrients of concern in soups, longitudinal subsample.

Table 4. Changes in quartiles of energy and nutrients of concern by food or beverages group, longitudinal analysis.

Fig 19. . Density curves for the amount of energy and nutrients of concern in milk and milk-based drinks, longitudinal subsample.

Fig 20. Density curves for the amount of energy and nutrients of concern in yogurts, longitudinal subsample.

Fig 21. Density curves for the amount of energy and nutrients of concern in breakfast cereals, longitudinal subsample.

Fig 22.

Fig 23. Density curves for the amount of energy and nutrients of concern in desserts and ice creams, longitudinal subsample.

Fig 24. Density curves for the amount of energy and nutrients of concern in candies and sweet confectioneries, longitudinal subsample.

Fig 25. Density curves for the amount of energy and nutrients of concern in sweet spreads, longitudinal subsample.

Fig 26. Density curves for the amount of energy and nutrients of concern in savory baked products, longitudinal subsample.

Fig 27. Density curves for the amount of energy and nutrients of concern in savory snacks, longitudinal subsample.

Fig 28. Density curves for the amount of energy and nutrients of concern in savory spreads, longitudinal subsample.

Fig 29. Density curves for the amount of energy and nutrients of concern in cheeses, longitudinal subsample.

Fig 30. Density curves for the amount of energy and nutrients of concern in ready-to-eat meals, longitudinal subsample.

Fig 31. Density curves for the amount of energy and nutrients of concern in sausages, longitudinal subsample.

Fig 32. Density curves for the amount of energy and nutrients of concern in nonsausage meat products, longitudinal subsample.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Adya Misra

Roles

Decision Letter 1

Adya Misra

Roles

Author response to Decision Letter 1

Decision Letter 2

Adya Misra