Predicted impact of banning nonessential, energy-dense food and beverages in schools in Mexico: A microsimulation study

Ana Basto-Abreu; Martha Carnalla; Francisco Reyes-Sánchez; Alan Reyes-García; Michelle M Haby; Isabel Junquera-Badilla; Lianca Sartoris-Ayala; Juan A Rivera; Barry M Popkin; Tonatiuh Barrientos-Gutiérrez

doi:10.1371/journal.pmed.1004394

. 2024 May 10;21(5):e1004394. doi: 10.1371/journal.pmed.1004394

Predicted impact of banning nonessential, energy-dense food and beverages in schools in Mexico: A microsimulation study

Ana Basto-Abreu ¹, Martha Carnalla ¹, Francisco Reyes-Sánchez ¹, Alan Reyes-García ¹, Michelle M Haby ², Isabel Junquera-Badilla ¹, Lianca Sartoris-Ayala ¹, Juan A Rivera ¹, Barry M Popkin ³, Tonatiuh Barrientos-Gutiérrez ^1,^*

PMCID: PMC11086919 PMID: 38728236

Abstract

Background

Childhood obesity is a growing concern worldwide. School-based interventions have been proposed as effective means to improve nutritional knowledge and prevent obesity. In 2023, Mexico approved a reform to the General Education Law to strengthen the ban of sales and advertising of nonessential energy-dense food and beverages (NEDFBs) in schools and surroundings. We aimed to predict the expected one-year change in total caloric intake and obesity prevalence by introducing the ban of NEDFBs sales in schools, among school-aged children and adolescents (6 to 17 years old) in Mexico.

Methods and findings

We used age-specific equations to predict baseline fat-free mass (FFM) and fat mass (FM) and then estimated total energy intake (TEI) per day. The TEI after the intervention was estimated under 4 scenarios: (1) using national data to inform the intervention effect; (2) varying law compliance; (3) using meta-analytic data to inform the intervention effect size on calories; and (4) using national data to inform the intervention effect by sex and socioeconomic status (SES). We used Hall’s microsimulation model to estimate the potential impact on body weight and obesity prevalence of children and adolescents 1 year after implementing the intervention in Mexican schools. We found that children could reduce their daily energy intake by 33 kcal/day/person (uncertainty interval, UI, [25, 42] kcal/day/person), reducing on average 0.8 kg/person (UI [0.6, 1.0] kg/person) and 1.5 percentage points (pp) in obesity (UI [1.1, 1.9] pp) 1 year after implementing the law. We showed that compliance will be key to the success of this intervention: considering a 50% compliance the intervention effect could reduce 0.4 kg/person (UI [0.3, 0.5] kg/person). Our sensitivity analysis showed that the ban could reduce body weight by 1.3 kg/person (UI [0.8, 1.8] kg/person) and up to 5.4 kg/person (UI [3.4, 7.5] kg/person) in the best-case scenario. Study limitations include assuming that obesity and the contribution of NEDFBs consumed at school remain constant over time, assuming full compliance, and not considering the potential effect of banning NEDFBs in stores near schools.

Conclusions

Even in the most conservative scenario, banning sales of NEDFBs in schools is expected to significantly reduce obesity, but achieving high compliance will be key to its success.

Why was this study done?

- School-based interventions have been recognized as effective means to improve nutritional knowledge and prevent obesity-related diseases.
- In December 2023, the Chamber of Representatives of Mexico approved an amendment that strengthens and updates the General Education Law (Article 75) and nutritional guidelines to ban the sales and advertising of nonessential energy-dense food and beverages (NEDFBs) in schools.

What did the researchers do and find?

- We used age-specific equations to predict baseline fat-free mass (FFM) and fat mass (FM) and total energy intake (TEI) per day.
- We used microsimulation modeling to predict body weight and obesity prevalence of children and adolescents 1 year after implementing the intervention in Mexican schools.
- Our modeling study suggests that an important impact on obesity prevalence can be expected if the law is implemented and enforced as intended.

What do these findings mean?

- If successful, this law could serve as an example beyond Mexico on how to achieve changes in body weight through school food regulation.
- An important limitation of our main scenario is that we assumed full compliance of schools with the law, yet lower compliance will reduce its impact. We also did not consider historical trends on obesity or NEDFBs consumed in schools during our 1 year simulation, and we considered only the ban impact inside schools, excluding effects near and outside schools.

Introduction

Childhood obesity is a growing concern worldwide. In Mexico, the prevalence of obesity in children and adolescents is among the highest in the world, 18.6% and 18.2%, respectively [1]. In 2019, obesity was associated with 1.5% of all-cause mortality among Mexicans aged 10 to 24 years [2]. Children with overweight or obesity are more likely to become adults with obesity [3] increasing their risk of developing chronic diseases such as type-2 diabetes, cardiovascular diseases, and cancer, significantly reducing life expectancy [4–7]. The healthcare costs of chronic diseases increase significantly during adulthood [8]; interventions to address childhood obesity are key for a healthy adulthood to reduce the disease and economic burden associated with obesity.

School-based interventions have been identified as effective policies to influence children’s food consumption, nutritional knowledge, and obesity-related behaviors [9,10]. School-based interventions, such as nutritional standards for food and beverages sold in schools, have shown pooled reductions in unhealthy food intake of −0.17 servings/day and in sugar-sweetened beverages (SSBs) intake of −0.18 servings/day, resulting in reductions of 49 kcal/day in school and 79 kcal/day in total energy intake (TEI) [11]. Countries such as Australia, Bermuda, Chile, France, and Slovenia have implemented restrictions on the accessibility of junk food and sugary beverages within school facilities, including banning vending machines, implementing nutritional guidelines to improve school meals, and promoting water and milk as the only beverage options [12–16]. In Latin America, Chile implemented a package of obesity prevention initiatives that included front-of-package warning labels, child-directed food advertisement bans, and school regulations. Three years after the intervention, total sugars consumed by children at school decreased 11.8 percentage point (pp), saturated fats decreased 1.1 pp, and sodium decreased 10.3 mg/100 kcal. In adolescents, total sugars and saturated fats consumed at school decreased 5.3 pp and 1.5 pp, respectively [17]. In 2022, the Chilean Ministry of Education reported that school-age children decreased their prevalence of obesity by 4.8% relative to 2021 [18], which suggests promising results for countries implementing similar interventions, such as Mexico.

Mexico does not have a formal school meal program that covers all schools, except for a school breakfast program funded by the government that operates in less than 50% of elementary schools [19]. Instead, most schools have school cafeterias that are operated privately (the appointment and training of the provider is the responsibility of the Secretary of Education). In 2010, Mexico implemented national guidelines as a part of the Sectoral Health Program 2007–2012 [20] to regulate the preparation, sales, and distribution of foods and beverages that exceeded recommended nutritional standards in elementary and middle schools [21]. By 2014, through a reform of the General Education Law these guidelines became mandatory and sanctionable (Table A in S1 Appendix presents a list of prohibited food and beverages) [22]. The law provided an exception for Fridays, when all products were allowed. This law was poorly implemented: between 2012 and 2015, only 27% of food sold in schools met the nutritional criteria [23] and in 2018 in Northwest Mexico only 15% of schools had fully implemented the guidelines [24]. Low compliance has been explained by the poor dissemination and knowledge about the guidelines, lack of monitoring, enforcement and sanctions, failure to ban junk food advertising, and not applying restrictions to venues near schools [23,24]. Valuable lessons were learned from this experience: (1) increase parental involvement; (2) offer healthy substitutes for popular foods and beverages; (3) engage food delivery staff and teachers; (4) provide free drinking water; and (5) regulate advertising of unhealthy food in and around schools [23,24].

In December 2023, the Mexican Senate approved a reform to strengthen the General Education Law (Article 75) [25]. This reform law implied a structural change in the regulatory framework, emphasizing direct government involvement in assuring compliance. The approved legislation mandates the Secretary of Education to issue new General Guidelines for the preparation, sale, and distribution of prepared and processed foods and beverages in schools. Guidelines are under development, but the main changes to the law include the ban on advertising unhealthy food, expand the law to venues near schools, standardize the nutritional criteria to front of package labels proposed in the General Law of Health (Art 212) (Table B in S1 Appendix) [26], promote healthier food alternatives prioritizing plain water and stronger sanctions for noncompliance. Also, to enhance compliance, the Secretary of Education will now monitor and ensure the enforcement of the new guidelines through school directors and supervisors, replacing the previous role of parental committees. With this changes, it is expected the law reform will promote better compliance. In the US, policies restricting SSB consumption in schools had higher impact when mandatory and when an administrative agency was designated as responsible for its implementation [27]. We aimed to estimate the expected impact of banning sales of nonessential energy-dense food and beverages (NEDFBs) in schools on total caloric intake and obesity prevalence of school-aged children and adolescents 1 year after its implementation, considering varying degrees of compliance.

Methods

Fig 1 summarizes each step of the modeling process. Briefly, we estimated baseline fat-free mass (FFM) and fat mass (FM) for the children and adolescents based on body mass index (BMI), age, and sex, using equations developed and validated by Deurenberg and colleagues [28]. This equation estimates the body fat percentage for children and adolescents, derived from a multiple linear regression. Baseline FFM and FM were used to predict the TEI per day depending on sex, age, and BMI category using a nonlinear function proposed by Hall and colleagues [29]. This TEI function considered children’s growth and was derived from the sum of 2 components: energy balance (= energy intake–energy expenditure) and energy expenditure. We estimated the effect of the law on TEI in Mexican children under 4 different scenarios: (1) main scenario, using Mexican data to inform the intervention effect by education level; (2) sensitivity scenario varying law compliance; (3) sensitivity scenario, using meta-analytic data to inform the effect of the intervention; and (4) using Mexican data to inform the intervention effect by sex and socioeconomic status (SES). Considering a business-as-usual scenario (BAU, no intervention) and the estimated intervention effect on TEI, we used Hall’s microsimulation model to estimate the potential impact on body weight and obesity prevalence of children and adolescents that could be achieved 1 year after implementing the intervention in Mexican schools. A more detailed description of the modeling process is presented in the next subsections and in S1 Appendix. Model inputs are presented in Table C in S1 Appendix.

Estimated total energy intake by day (TEI)

We estimated the daily TEI from day 0 (t₀) to day 365 (t₃₆₅) (one-year simulation period) for each individual using a nonlinear equation proposed by Hall and colleagues [29]. This equation accounts for daily increases in energy intake due to children’s growth, dependent on sex, age, and baseline height and body weight. Hall’s TEI equation is presented in S1 Appendix, section 3. We denote TEI_i(t₀, t₃₆₅) as the vector containing the estimated intakes from t₀ to t₃₆₅ for individual i in the sample:

T E I_{i} (t_{0}, t_{365}) = F (t_{0}, t_{365}, s e x_{i}, a g e_{i}, i n i t i a l h e i g h t_{i} a n d b o d y w e i g h t_{i})

(1)

The database needed for the baseline estimation was retrieved from the Mexican Health and Nutrition Survey (ENSANUT, from its Spanish acronym) 2018, specifically from anthropometric data for children and adolescents aged 6 to 17. ENSANUT is a cross-sectional, multistage, stratified, and cluster-sample survey representative at the national, regional, rural, and urban levels [30]. An overview of the baseline dataset with anthropometric sample sizes is described in section 2.1 in S1 Appendix. We calibrated Hall’s TEI equation to our baseline sample. More details about the calibration process are presented in section 3 in S1 Appendix.

Business as usual scenario (BAU)

We estimated changes in body weight and obesity after 1 year of simulation considering a BAU scenario, which assumed that children would continue to grow in the no-intervention scenario.

Intervention scenarios

The BAU was compared to 3 intervention scenarios to estimate the expected impact of the law’s implementation based on prior evidence.

1. Main scenario using Mexican dietary data to inform the intervention effect

We used the 24-h dietary recall data from ENSANUT 2012 [31] to estimate the energy contribution of NEDFBs to TEI in schools, since this survey obtained information on place of consumption. NEDFBs were identified using the guidelines established in Mexico in 2020 for “excess” calories, added sugars, fats, trans fats, or sodium (Table G in S1 Appendix) [32]. According to ENSANUT 2012, NEDFBs intake in schools represents 4.2%, 5.4%, and 4.1% of the TEI in elementary, junior high, and high school students, respectively. Table C in S1 Appendix shows the contributions with confidence intervals. We simulated our main scenario by assuming 100% compliance, i.e., that these proportions of the TEI would be eliminated if the law effectively bans NEDFBs. We tested this scenario under 2 different assumptions:

No energy compensation, in which all energy consumed from NEDFBs in schools was subtracted from the TEI.
Energy compensation, in which a part of the reduced calories is compensated by the consumption of other food and beverages in schools. We estimated the expected percentage of caloric compensation using the results from a previous study in Chile which estimated the consumption of regulated and nonregulated food and beverages at the household level after implementing a package of interventions, including warning labels, banning sales of unhealthy food and beverages at schools, and marketing restrictions [33]. After implementation, calories from regulated food and beverages decreased 33.1 and 16.3 kcal/capita/day, respectively, while calories from nonregulated items increased 26.8 in food and 6.3 kcal/capita/day in beverages. With these data, we estimated a caloric compensation of 81% for food (= 26.8/33.1) and 38.7% for beverages (= 6.3/16.3). The compensation rate considers compensation inside and outside schools, but this was applied to calories eaten inside schools and does not capture potential differences by age group.

2. Sensitivity scenario varying compliance

In the previous scenario, we assumed that all Mexican schools would fully implement the intervention. However, an evaluation of the implementation of the 2014 NEDFBs regulation guidelines in one Mexican State showed that only 15% of schools fully complied [24]. Due to the inclusion of enforcement and sanction mechanisms for the new law, we expect a higher compliance rate of at least twice the 2014 observed value (30%), with the possibility of reaching 100% depending on enforcement. For this analysis, we assumed that the compliance rate directly modifies weight reduction:

{W e i g h t r e d u c t i o n}_{i} = i \times w e i g h t r e d u c t i o n_{m a i n s c e n a r i o}

Where i is the compliance rate with values = 30%, 40%, … or 100%

3. Sensitivity scenario using meta-analytic data to inform the intervention effect

To provide an alternative estimate the effect of the law, we conducted a meta-analysis based on published observational studies that implemented similar interventions, i.e., nutrition standards or banning NEDFBs in schools, compared to control. We calculated the mean difference in usual intake (kcal/day) using a random effects model and determined the percentage reduction in TEI. Section 4.3 in S1 Appendix details the included studies and the meta-analytic estimation. We estimated a 177 kcal reduction, representing a 9.8% reduction in TEI (95% CI [−13.4, −6.2] %) in children and adolescents after the intervention. As the included studies were observational (mostly before-after and cross-sectional studies), we expected an overestimation of the banning effect compared to experimental designs due to the lack of a suitable control group and potential confounding. Therefore, we considered 3 possible scenarios:

100% of the effect (−9.8% TEI),
50% of the effect (−4.9% TEI),
25% of the effect (−2.45% TEI).

4. Sensitivity scenario using Mexican dietary data to inform the intervention effect by socioeconomic status and sex

For this scenario, the NEDFBs energy contribution was estimated by socioeconomic status (SES) and sex to assess the potential equity impact of the policy. As in the main scenario, we used the 24-h dietary recall data from ENSANUT 2012 to estimate the energy contribution of NEDFBs to TEI in schools and considered both the energy compensation and no energy compensation scenarios. For females, NEDFBs intake in schools represented 4.7%, 5.0%, and 6.5% of the TEI for the low, middle, and high SES, respectively. For males, those values were 2.3%, 3.5%, and 4.7% of the TEI for the low, middle, and high SES, respectively.

Simulation of body weight after 1 year of intervention

Individual’s body weight was simulated under both BAU and intervention scenarios using the Dynamics of Childhood Growth and Obesity Model (DCGO), proposed by Hall and colleagues [29]. Briefly, the DCGO is a nonlinear model that estimates changes in body weight over time (t) using a system of 2 differential equations to predict fat mass (FM(t)) and fat free mass (FFM(t)). To predict body weight (BW(t)), we considered the relation between energy intake rate and energy expenditure, adjusted by a growth term. All these variables consider children’s characteristics (sex, age, initial height, and body weight) and other parameters that account for complex physiological processes during childhood and adolescence. The DCGO model was previously validated with experimental weight data [29] and calibrated to the Mexican population [34]. In this study, we re-calibrated and validated the model using Mexican data from the ENSANUT 2018. An overview of the microsimulation model and the calibration process is presented in sections 5 and 6 in S1 Appendix.

Changes in body weight and obesity

Reductions in weight, BMI, and obesity prevalence were estimated by comparing each intervention scenario with the BAU scenario. To estimate BMI (in kg/m²) after 1 year of intervention, we predicted each individual’s height using the World Health Organization (WHO) growth reference (height for age) [35]. BMI was categorized in underweight, normal, overweight, and obesity based on the WHO’s growth reference pattern for children and adolescents [36].

Assumptions and other parameters

We assumed that all children and adolescents attend school and that the contribution of NEDFBs consumed at school remains constant over time. According to Hall’s microsimulation model, physical activity progressively decreases with age [29]. For the BAU scenario, we assumed children’s growth as the only factor for TEI daily changes without considering any previous trends or externalities that could cause a change in TEI. Additionally, we assumed that the height of children and adolescents would change according to WHO’s height-for-age curves.

Results

Table 1 presents the model results after 1 year of simulation for the BAU and main intervention scenarios. Considering compensation, children could reduce 33 kcal/person/day, meaning a 0.8 kg reduction in weight (uncertainty interval, UI, [0.6, 1.0] kg), 0.3 kg/m² in BMI (UI [0.2, 0.4] kg/m²), and 1.5 pp in obesity prevalence (UI [1.1, 1.9] pp). That change in obesity prevalence represents a relative reduction of 9.3% compared to BAU (= 1.5*100/16.1). Without energy compensation, children could reduce 105 kcal/person/day (UI [90, 120] kcal/person/day), which translates into a 2.5 kg reduction in weight (UI [2.1, 2.9] kg), 1.1 kg/m² in BMI (UI [0.9, 1.2] kg/m²), and 4.4 pp (UI [3.9, 5.0] pp) in obesity prevalence, representing a 27.3% reduction compared to BAU (= 4.4 *100/16.1).

Table 1. Estimates of caloric, body weight, BMI, and obesity changes 1 year after implementing the banning of NEDFBs in schools in Mexico.

	BAU ^*[Reference]	Main scenario with energy compensation		Main scenario without energy compensation
	Total (UI^**)	Total (UI)	Change respect to BAU (UI)	Total (UI)	Change respect to BAU (UI)
Energy intake (kcal/person/day)	2,316 [2,304, 2,328]	2,283 [2,272, 2,295]	−33 [−42, −25]	2,211 [2,200, 2,223]	−105 [−120, −90]
Body weight (kg/person)	51.1 [50.6, 51.6]	50.3 [50.1, 50.5]	−0.8 [−1.0, −0.6]	48.6 [48.3, 49.0]	−2.5 [−2.9, −2.1]
BMI (kg/m ² )	21.5 [21.3, 21.7]	21.2 [21.1, 21.2]	−0.3 [−0.4, −0.2]	20.4 [20.3, 20.6]	−1.1 [−1.2, −0.9]
Obesity (pp)	16.1 [16.1, 16.1]	14.6 [14.3, 15.0]	−1.5 [−1.9, −1.1]	11.7 [11.1, 12.3]	−4.4 [−5.0, −3.9]

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*Business as usual (simulating 1 year under no intervention).

**UI = Uncertainty interval.

Scenarios are based on energy compensation and non-compensation for the consumption of banned foods inside schools.

BMI, body mass index; NEDFB, nonessential energy-dense food and beverage; pp, percentage points.

Table 2 presents the results of the sensitivity analysis varying the compliance rate of schools. It shows the impact on the changes of 2 outputs of interest: body weight and obesity. We found that, by varying compliance rates between 30% and 100%, weight reductions could range from 0.2 to 0.8 kg with compensation and from 0.7 to 2.5 kg without compensation. Obesity reductions could range from 0.5 to 1.5 pp with compensation and from 1.4 pp to 4.4 pp without compensation.

Table 2. Body weight and obesity reduction 1 year after the intervention varying compliance.

		Body weight (kg/person)		Obesity (pp)
		Considering energy compensation	Not considering energy compensation	Considering energy compensation	Not considering energy compensation
Compliance rate (%)	100	−0.8 [−1.0, −0.6]	−2.5 [−2.9, −2.1]	−1.5 [−1.9, −1.1]	−4.4 [−5.0, −3.9]
	90	−0.7 [−0.9, −0.5]	−2.2 [−2.6, −1.9]	−1.2 [−1.7, −1.0]	−4.1 [−4.5, −3.5]
	80	−0.6 [−0.8, −0.5]	−2.0 [−2.3, −1.7]	−1.1 [−1.5, −0.9]	−3.5 [−4.0, −3.1]
	70	−0.6 [−0.7, −0.4]	−1.7 [−2.0, −1.5]	−1.0 [−1.3, −0.8]	−2.9 [−3.5, −2.7]
	60	−0.5 [−0.6, −0.4]	−1.5 [−1.7, −1.3]	−0.9 [−1.1, −0.7]	−2.6 [−3.0, −2.3]
	50	−0.4 [−0.5, −0.3]	−1.2 [−1.4. −1.0]	−0.8 [−1.0, −0.5]	−2.2 [−2.5, −1.9]
	40	−0.3 [−0.4, −0.2]	−1.0 [−1.2, −0.8]	−0.6 [−0.8, −0.4]	−1.8 [−2.0, −1.6]
	30	−0.2 [−0.3, −0.2]	−0.7 [−1.0, −0.6]	−0.5 [−0.6, −0.3]	−1.4 [−1.5, −1.2]

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Results are presented for body weight (kg) and obesity (percentual points) considering compliance rates from 30% to 100%.

pp, percentage points.

Fig 2 shows the results of the sensitivity analysis using meta-analytic estimates of the reduction in energy intake in Mexican school children compared to the main scenario. It shows the impact of the law banning NEDFBs in schools on the change in 2 outputs of interest: body weight and obesity prevalence. For weight, the sensitivity analysis showed reductions ranging from 1.3 to 5.4 kg in body weight and from 2.3 to 9.3 pp in obesity prevalence. Using the 100% effect of the meta-analysis, we found significantly larger reductions in body weight and obesity than in both main scenarios, with and without compensation. When using 75% and 50% of the effect of the meta-analysis, the results were not different from the main scenario without compensation.

Fig 3 shows results stratified by sex and SES. We found that the expected impact is estimated to be higher in females, mainly in higher SES, with an expected reduction of 1.1 kg/person considering compensation, which translates into 2.1 pp obesity reduction. Among males, we found a higher expected impact among high SES than low SES with an expected reduction of 0.9 kg/person considering compensation, which translates into 1.8 pp obesity reduction.

Discussion

We aimed to estimate the expected impact of banning NEDFBs inside schools 1 year after implementation. Considering intake compensation, children could reduce their daily energy consumption by approximately 33 kcal/day/person, which translates into an average reduction of 0.8 kg in weight and 1.5 pp in obesity prevalence. The estimated impact could be higher if the effect size of the intervention is equal to the meta-analytic results using observational data but could be lower if not all schools comply with the intervention. Even in the most conservative scenario, we expect the intervention to reduce obesity, but achieving high compliance will be key to its success.

The expected impact on BMI of banning NEDFBs in schools in our analysis is higher compared to prior studies evaluating similar interventions using observational data. The most comparable study was conducted in children aged 12 to 15 years in Canadian schools, where junk food was banned. Five to 8 years after the intervention, they observed a mean reduction in BMI of 0.32 kg/m² (95% CI [0.03, 0.62]) [37]. That change would represent an average yearly change of −0.06 kg/m², although weight change is nonlinear, and a larger decrease is to be expected in the first year. While temporalities are difficult to align, this is lower than our predicted 1-year BMI change of −0.3 kg/m² (UI [−0.4, −0.2] kg/m²) for children aged 6 to 17 years. Baseline caloric consumption and the proportion of calories consumed in school are likely the main reason for this difference, although caloric data was not provided in the Canadian report. A Brazilian study showed reductions in body composition among girls aged 11 to 15 years, although the results are expressed in BMI z-scores for age [38], hindering comparisons. However, the direction of the effect is the same in response to regulations on the sale of processed food and beverages in schools. Both observational studies and our simulation results showed that banning NEDFBs in schools should lead to a reduction in BMI, although the magnitude of the effect relies on baseline caloric consumption, different settings and different assumptions in our simulation model (compliance rate and compensation rate).

Our prediction of −33 kcal/person/day (UI [−42, −25] kcal/person/day) from banning NEDFBs, which translates into a −0.8 kg (UI [−1.0, −0.6] kg), is higher than the expected impact of the 10% SSBs tax and 8% junk food tax on Mexican children, but lower than both tax interventions combined. Taxes on SSBs and junk food were expected to reduce 17.6 kcal/person (UI [16.3, 18.8] kcal/person) and 17.4 kcal/person (UI [16.4, 18.4] kcal/person), respectively, 1 year after their implementation [34,39]. This translates into body weight reductions of 0.42 kg (UI [0.39, 0.45] kg) for the SSBs tax and 0.40 kg (UI not available) for the junk food tax. We used a similar microsimulation model, a similar age group, and targeted the same food as both previous studies, but with a different intervention (tax versus school ban). In all 3 cases, weight reduction is simulated for 1 year, although the nonlinear equation used in all studies suggests that further impacts on body weight after the first year of simulation are to be expected [40]. Empirical evidence from an online survey in adolescents aged 13 to 17 years in Mexico showed a reduction of 38.7% in the purchase of NEDFBs after the implementation of the front-of-package labeling [41]. However, the relative reduction does not allow us to quantify the reduction in kcal to properly compare the results with our study.

Some limitations must be acknowledged. First, the total impact of the intervention may be underestimated because we are using 24-h nutritional recall data from 2012 to estimate the calories from junk food consumed at school, which is known to underestimate unhealthy food consumption. Also, for children under 12 years of age, 24-h nutritional recall data obtains information from parents or guardians, who may not be well informed about their child’s consumption patterns at school. Still, ENSANUT is representative at the national level, allowing us to make population estimates generalizable to the Mexican context. Results may be overestimated because we are assuming that all children and adolescents go to school, but in the 2019 to 2020 cycle, 98.7% of 6- to 12-year-olds, 95.9% of 13- to 15-year-olds, and 75.5% of 16- to 18-year-olds were enrolled in school [42]. Given the uncertainty of the compliance rate, we tested different compliance levels, but we assumed that it would be at least twice the compliance of the 2014 guidelines implemented in Mexico (15%) [24]. We are assuming that the banning effect and compliance rate are equal for public or private schools. Evidence exists that private schools had better compliance with the regulation than public schools [24], but the overall effect is likely to be small, as most Mexican children attend public schools [42]. Overestimation could also arise from energy compensation, either from allowed food and beverages in school or through NEDFBs consumed outside of schools. We considered substantial levels of food compensation (81%) and beverages (39%), based on household-level estimates after the Chilean policies on food labeling, marketing, and sales in schools [33]. Another study from Chile after implementing the same policies did not report caloric compensation, but compensation for specific nutrients. They reported an 80% sugar compensation for children, but complete sugar compensation for adolescents [17]. Our model considers a growth-related increase in TEI (Table I in S1 Appendix) but does not include an increase in TEI due to past obesity trends over time. This assumption should have little effect on estimates, given the one-year span of our simulation. Finally, we are not considering the potential effect of banning junk food near schools, as our study only included the expected impact inside schools.

The school context is very important for implementing and impacting health interventions. Our sensitivity analysis using a meta-analytical input, showed a higher expected effect of the intervention. However, studies included were observational, often lacked a control group and were subject to residual confounding. Also, studies in the meta-analysis were conducted in high-income countries, where infrastructure and organization of school food services is better and where compliance could also be higher than in Mexico. Compliance will play a key role in the success of any law targeting the school food environment. Previous experience with the guidelines issued in 2010 showed that the lack of dissemination and training, the lack of regulations outside and near schools, and the lack of monitoring were key elements for low compliance [23,24]. Furthermore, school cafeterias share a small percentage of profits with schools, which are used to buy essential supplies, improve facilities, or finance school activities; thus, food sales are an economic incentive for schools [24]. With the 2023 modification to the law, some of these limitations are addressed, including the promotion of endemic and natural foods from each region to reduce the incentive to sell processed foods, as well as a mechanism for implementation, evaluation, monitoring, and sanctions for noncompliance. However, considering that the degree of compliance is still uncertain and may vary for different reasons, including possible economic incentives for schools to sell processed foods, we evaluated its impact in our sensitivity analysis, showing that if all schools comply with the law, the intervention is predicted to reduce 0.8 kg/person, but if only 30% of schools comply with it, the intervention is predicted to reduce 0.2 kg/person.

Our modeling is based on the best available evidence of the potential impact of the new law. It suggests that an important impact on obesity prevalence can be expected if the law is implemented and enforced as intended, but that lower effects will be expected if enforcement is suboptimal. Our study highlights the need for better evidence about the actual impact of government laws and policies such as these to determine their effectiveness and whether adjustments are needed. If the predicted impact of the new law is achieved it could serve as an example beyond Mexico of how to achieve changes in childhood obesity.

Policy actions based on improving the nutritional quality of foods available in schools have been recognized as key strategies for improving children’s diet [43,44]. The estimated obesity reduction ranged from 1.0 to 3.3 pp on average 1 year after the law’s implementation in our main scenario. Establishing laws to limit the consumption of NEDFBs in schools promotes healthy food choices and the early adoption of healthy eating habits. This will allow students to develop a lifelong appreciation for nutritious foods, improve health outcomes, and reduce the risk of chronic diseases (such as type-2 diabetes, hypertension, hyperlipidemia, and depression) [45–47] that have major economic implications for health systems and society. This law could be part of a long-term strategy to prevent and treat childhood overweight and obesity, leading to healthy, productive adults and reducing the predicted healthcare burden worldwide. By creating a healthier school environment, we can help shape the future health and well-being of our children and communities.

Supporting information

S1 Appendix. Additional information on the simulation model and assumptions.

(PDF)

pmed.1004394.s001.pdf^{(508.4KB, pdf)}

Acknowledgments

We would like to thank to Gabriela García Chávez for her valuable insights on this manuscript.

Abbreviations

BAU: business-as-usual scenario
BMI: body mass index
FFM: fat-free mass
FM: fat mass
NEDFB: nonessential energy-dense food and beverage
pp: percentage points
SES: socioeconomic status
SSB: sugar-sweetened beverage
TEI: total energy intake
UI: uncertainty interval
WHO: World Health Organization

Data Availability

The data can be found in the following repository: https://zenodo.org/doi/10.5281/zenodo.8386075.

Funding Statement

This work was funded by a Bloomberg Philanthropies grant (71206 to JAR; https://www.bloomberg.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004394.r001

Decision Letter 0

Katrien G Janin

14 Nov 2023

Dear Dr Barrientos-Gutierrez,

Thank you for submitting your manuscript entitled "Expected impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study" for consideration by PLOS Medicine.

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PLoS Med. doi: 10.1371/journal.pmed.1004394.r002

Decision Letter 1

Katrien G Janin

16 Dec 2023

Dear Dr. Barrientos-Gutierrez,

Thank you very much for submitting your manuscript "Expected impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study" (PMEDICINE-D-23-03317R1) for consideration at PLOS Medicine.

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

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Comments from the Academic Editor:

For the meta-analysis, I'm surprised by an intervention that reduces total energy intake by 10% - this is substantial and I think unrealistic. But I think that the authors do a good job of not over-emphasising this finding from sensitivity analysis. It would be nice to have some discussion of how realistic the estimation from the meta-analysis really is. It just seems unlikely to me that children would reduce energy intake by 10%long term.

The paper is poorly contextualised. I'd like to see more discussion of: limitations of meta-analysis as above, why compliance with the existing regulations is low and why it might be expected to improve with the new regulations (compliance seems the key to the whole paper - both in terms of the justification for the intervention and results of sensitivity analysis), how the impacts of this intervention compare to other potential approaches to obesity (there is a little bit of this, but I'd suggest expanding further). Related, I'd also like to see the UI in table 2 - it's not clear at what point the effect is indistinguishable from null.

Comments from the Editorial Team:

We would like to ask the authors the following:

i) In line with the academic editor and reviewer comments, please improve the contextualisation of the presented research

ii) for the ‘Sensitivity scenario using meta-analytic data to inform the intervention effect’ in the method section please detail your search criteria and search period, inclusion and exclusion criteria and the basis upon which you selected studies to be included in the meta analysis. We note that studies were mainly sourced from ‘Effectiveness of school food environment policies on children’s dietary behaviors: A systematic review and metaanalysis.’ publication. We also note you have added one study, but how was this study sourced? Did you perform a systematic literature review to update the Micha study? This is unclear to us. Please provide more details (beyond those details in Section 3.3 in S1 Appendix) and ask you to move pertinent information inot the main manuscript. We also like more details on how you arrived at the estimated 177-kcal reduction (9.8% reduction in TEI), especially given that this substantially differs from the data derived from ENAUD. In the discussion, please add more details on why these estimates differ so much, and how realistic this scenario (or not) may be. Once again, we appreciate you have added that this is likely an overestimate, but ask a bit more detail and ask why you think this model - if likely an overestimate - adds to the discussion).

iii) In the Discussion, be more explicit about how the effect sizes compare to prior studies; and contextualise findings including with regard to compensatory calorie intake and weight regain.

iv) In the Discussion, also clearly point out what this adds to prior knowledge and say something about what the findings are useful for (and what they are not useful for), and expand on what these findings mean beyond Mexico.

GENERAL:

Please provide 95% CIs and p values for all results where appropriate (including the abstract), check and amend throughout. We suggest reporting statistical information in the following format: ‘x’; (95% CI [‘y’,’ z’] p value) and use commas as opposed to hyphens (as these can be confused with negative values) to separate upper and lower bounds. For p values, please report as p<0.001 and where higher as 'p=0.002'. Please add the statistical method used to your method section. We also invite you to report p values to consistently to the third decimal digit - thousandths.

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Please ensure all the items listed below are included with your manuscript. Please review the list below and confirm/revise as necessary:

i) Please provide a diagram that shows the model structure, including how the disease natural history is represented, the process and determinants of disease acquisition, and how the putative intervention could affect the system.

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vi) Please discuss the scientific rationale for this choice of model structure and identify points where this choice could influence conclusions drawn. Please also describe the strength of the scientific basis underlying the key model assumptions.

TITLE: We suggest you replace ‘expected’ by ‘predicted’ (Predicted impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study).

AUTHORS 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

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Comments from the reviewers:

Reviewer #1: The manuscript "Expected impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study" describes the expected effect of a new law (which is currently in the legislative process) banning the sale and advertising of unhealthy foods inside schools and surrounding, in Mexico. Using previously proposed equations for estimating total daily energy intake, and nationally representative data on body weight, weight status and intake of unhealthy foods/beverages at schools, authors modeled the effect of the policy on energy intake, body weight, body mass index and prevalence of obesity among school-age children, considering different scenarios (i.e., dietary compensation, school compliance, effect size of the decrease in energy intake). Simulating the effect of a policy which is under legislative discussion is a good strategy to inform policy-makers' decision, and a good example of the potential use of scientific investigation when it is well conducted, in a relevant topic, and timely done/published. Moreover, policies aiming at improving school food environments are a especially relevant strategy to address the obesity pandemic. Therefore, this manuscript is highly relevant.

The manuscript is well written, the methodology is clearly explained and the details are presented largely in the supplementary file; results are clear and relevant. The document reflects a well conducted analysis. From my perspective, authors fail in presenting the current situation of school food environments in the introduction, and contrasting it with the new scenario (the one expected after the policy implementation). I believe one difference between the current and the expected situation is that in the former nutritional standard are optional, whereas the latter implies a mandatory situation (i.e., a ban). Additionally, currently the standards apply to preparation, sale and distribution of foods, whereas the new scenario will apply just to food sales and marketing (is there a food feeding program in Mexico?) Finally, I believe the nutritional criteria for identifying unhealthy foods are different between both scenarios, given -I understood- the new regulation uses the same cutoffs used for defining foods that should have a warning label, which were developed in the last few years. All these aspects should be clearly stated in the introduction (of course, I might be wrong in my interpretation).

Specific comments

The quality of Figure 1 should be improved.

"BAU" should be presented earlier in the text, the first time wen "business as usual" is mentioned.

Data from ENSANNUT 2012 were used for estimating the dietary share of unhealthy foods eaten at schools, whereas ENSANNUT 2018 was used for obtaining the weight status data and estimating total energy intake; please provide the rationale for doing so.

Authors estimated a compensation scenario based on compensation data from household food purchases obtained after implementing the Chilean food labelling regulation. A different article evaluating the Chilean regulation showed a greater compensatory effect (for energy from total sugars and saturated fats) for the dietary improvement obtained at schools among adolescents (dietary compensation made at home and other locations): https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-023-01445-x. Given total energy intake is not reported in the article, data cannot be used for the current analyses; however, authors could discuss these data, acknowledging a potential underestimation of the dietary compensation.

Legend could be improved for Figure 2 (the squares are too little to see the colors).

The second paragraph of discussion reads: "The Canadian model assumes the intervention increases linearly over time; our model assumes a non-linear function of calories on body mass over time, and a significant proportion (around 50%) of the intervention effect will be achieved during the first year and then decrease progressively." I am confused with this statement. I assume "our model" refers to the one used in the current analysis; in that case, it is relevant the non-linear simulation model is presented earlier in the text and in a clearer way.

In the third paragraph, authors commented on modeling analyses performed previously for Mexican taxes on foods and sugary-sweetened beverages. It would be relevant to discuss whether these estimations were confirmed with real

PLoS Med. 2024 May 10;21(5):e1004394. doi: 10.1371/journal.pmed.1004394.r003

Author response to Decision Letter 1

19 Feb 2024

Attachment

Submitted filename: Response to reviewers. Banning.docx

pmed.1004394.s002.docx^{(657.9KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004394.r004

Decision Letter 2

Katrien G Janin

19 Mar 2024

Dear Dr. Barrientos-Gutierrez,

Thank you very much for re-submitting your manuscript "Predicted impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study" (PMEDICINE-D-23-03317R2) for review by PLOS Medicine.

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

Given the minor nature of the remaining comments (see comment from reviewer #3), we expect to receive your revised manuscript within the next few days week (Friday, 22nd of March, 2024). Please email me (kjanin@plos.org) if you have any questions or concerns.

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

[LINK]

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

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

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

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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

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

We look forward to receiving the revised manuscript by Mar 22 2024 11:59PM.

Sincerely,

Katrien Janin, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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Note from the editors:

We like to thank the authors for addressing fully our comments, and for the the additional clarity you have provided provided in the manuscript. (but please do see the outstanding comment from reviewer #3)

As a general comment, please note that supplementary materials are not checked and will be posted as supplied by the authors. Therefore, please double check and ensure you appropriate cite the Supplementary Information in your manuscript. Please cite your Supporting Information as outlined here: https://journals.plos.org/plosmedicine/s/supporting-information - Please note you may use almost any description as the item name of your supporting information as long as it contains an "S" and number. For example, “S1 Appendix” and “S2 Appendix,” “S1 Table” and “S2 Table.

I look forward to receiving the revised version of your manuscript, and issuing the editorial accept for you paper,

Best wishes,

Katrien Janin

(kjanin@plos.org)

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

Comments from Reviewers:

Reviewer #2: In my opinion, the authors have adequately addressed my and other reviewers' comments and concerns. Congratulations!

Reviewer #3: Dear authors,

Thank you for taking the time to address my comments. Your responses are well considered and I thank you for the additional clarity provided in the manuscript to address these points.

I would like to ask for one further clarification please, regarding "A brief overview of the school food system in Mexico to provide a picture of any potential commercial influences that might influence compliance with the new restrictions on sales and advertising of unhealthy food inside schools and surroundings e.g. is school food prepared by in-house caterers, centrally provided (e.g. local or national catering provider) or by private companies?" I am still not clear on the school food system in Mexico and potential additional motives for low compliance - for example, is the food sold in schools done so through private contracts with catering companies (in which case there may be a strong profit motive for non-compliance via increased sales) or is food sold via local/national/school catering services (i.e. not-for-profit)? This may help to provide some context around Mexico's school food system for those unfamiliar with it.

Unfortunately I am still finding the figure 1 to be of low resolution - but perhaps this can be fixed at the editorial stage.

I am satisfied that my other comments have been addressed satisfactorily.

Best wishes, Dr Marie Murphy

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

[LINK]

PLoS Med. 2024 May 10;21(5):e1004394. doi: 10.1371/journal.pmed.1004394.r005

Author response to Decision Letter 2

1 Apr 2024

Attachment

Submitted filename: Comments banning R2.docx

pmed.1004394.s003.docx^{(18.2KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004394.r006

Decision Letter 3

Katrien G Janin

2 Apr 2024

Dear Dr Barrientos-Gutierrez,

On behalf of my colleagues and the Academic Editor, I am pleased to inform you that we have agreed to publish your manuscript "Predicted impact of banning non-essential, energy-dense food and beverages in schools in Mexico: a microsimulation study" (PMEDICINE-D-23-03317R3) in PLOS Medicine.

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

We also have one editorial request, which I will outline below.

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

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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

Sincerely,

Katrien G. Janin, PhD

Senior Editor

PLOS Medicine

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

editorial request:

Apologies for having not picked up on this earlier, but I do find your authors' summary rather long and too result detailed. (Keep in mind that the Authors' Summary is intended for research to make findings accessible to a wide audience that includes non-scientists). I like to suggest to following summary to you. Please have a look and see if you feel that is an accurate non-technical reflection of your study. Please amend as needed.

Why was this study done?

• School-based interventions have been recognized as effective means to improve nutritional knowledge and prevent obesity-related diseases.

• In December 2023, the Chamber of Representatives of Mexico approved an amendment that strengthens and updates the General Education Law and nutritional guidelines to ban the sales and advertising of non-essential energy-dense food and beverages (NEDFBs) in schools.

What did the researchers do and find?

• The predicted impact of the intervention was modelled under different scenarios.

• We used age-specific equations to predict baseline fat-free mass and fat mass and total energy intake per day. We used microsimulation modelling to predict body weight and obesity prevalence of children and adolescents one year after implementing the intervention in Mexican schools.

• Our modelling study suggests that an important impact on obesity prevalence can be expected if the law is implemented and enforced as intended.

What do these findings mean?

• If successful, this law could serve as an example beyond Mexico on how to achieve changes in obesity in children.

• The main limitation of our modelling study is that the study relies on the compliance of the schools. We also did not consider historical trends on obesity or NEDFBs consumed in schools during one year of simulation, and we considered only the ban impact inside schools, excluding effects near and outside schools. "

Associated Data

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

Supplementary Materials

S1 Appendix. Additional information on the simulation model and assumptions.

(PDF)

pmed.1004394.s001.pdf^{(508.4KB, pdf)}

Attachment

Submitted filename: Response to reviewers. Banning.docx

pmed.1004394.s002.docx^{(657.9KB, docx)}

Attachment

Submitted filename: Comments banning R2.docx

pmed.1004394.s003.docx^{(18.2KB, docx)}

Data Availability Statement

The data can be found in the following repository: https://zenodo.org/doi/10.5281/zenodo.8386075.

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PERMALINK

Predicted impact of banning nonessential, energy-dense food and beverages in schools in Mexico: A microsimulation study

Ana Basto-Abreu

Martha Carnalla

Francisco Reyes-Sánchez

Alan Reyes-García

Michelle M Haby

Isabel Junquera-Badilla

Lianca Sartoris-Ayala

Juan A Rivera

Barry M Popkin

Tonatiuh Barrientos-Gutiérrez

Roles

Abstract

Background

Methods and findings

Conclusions

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Fig 1. Step-by-step modeling process, showing inputs and outputs of the model.

Estimated total energy intake by day (TEI)

Business as usual scenario (BAU)

Intervention scenarios

1. Main scenario using Mexican dietary data to inform the intervention effect

2. Sensitivity scenario varying compliance

3. Sensitivity scenario using meta-analytic data to inform the intervention effect

4. Sensitivity scenario using Mexican dietary data to inform the intervention effect by socioeconomic status and sex

Simulation of body weight after 1 year of intervention

Changes in body weight and obesity

Assumptions and other parameters

Results

Table 1. Estimates of caloric, body weight, BMI, and obesity changes 1 year after implementing the banning of NEDFBs in schools in Mexico.

Table 2. Body weight and obesity reduction 1 year after the intervention varying compliance.

Fig 2. Uncertainty captured by the sensitivity analysis using the meta-analytic estimated reduction of the impact of banning NEDFBs in schools.

Fig 3. Predicted impact in body weight, and obesity by SES and sex 1 year after implementing the banning of NEDFBs in schools in Mexico.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Katrien G Janin

Roles

Decision Letter 1

Katrien G Janin

Roles

Author response to Decision Letter 1

Decision Letter 2

Katrien G Janin

Roles

Author response to Decision Letter 2

Decision Letter 3

Katrien G Janin

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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