Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: A modeling study

Ana Basto-Abreu; Rossana Torres-Alvarez; Tonatiuh Barrientos-Gutierrez; Paula Pereda; Ana Clara Duran

doi:10.1371/journal.pmed.1004399

. 2024 Jul 17;21(7):e1004399. doi: 10.1371/journal.pmed.1004399

Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: A modeling study

Ana Basto-Abreu ¹, Rossana Torres-Alvarez ^2,³, Tonatiuh Barrientos-Gutierrez ^1,^*, Paula Pereda ^4,⁵, Ana Clara Duran ^6,^*

Editor: Jean Adams⁷

PMCID: PMC11253955 PMID: 39018346

Abstract

Background

The consumption of sugar-sweetened beverages (SSBs) is associated with obesity, metabolic diseases, and incremental healthcare costs. Given their health consequences, the World Health Organization (WHO) recommended that countries implement taxes on SSB. Over the last 10 years, obesity prevalence has almost doubled in Brazil, yet, in 2016, the Brazilian government cut the existing federal SSB taxes to their current 4%. Since 2022, a bill to impose a 20% tax on SSB has been under discussion in the Brazilian Senate. To simulate the potential impact of increasing taxes on SSB in Brazil, we aimed to estimate the price-elasticity of SSB and the potential impact of a new 20% or 30% excise SSB tax on consumption, obesity prevalence, and cost savings.

Methods and findings

Using household purchases data from the Brazilian Household Budget Survey (POF) from 2017/2018, we estimated constant elasticity regressions. We used a log-log specification by income level for all beverage categories: (1) sugar-sweetened beverages; (2) alcoholic beverages; (3) unsweetened beverages; and (4) low-calorie or artificially sweetened beverages. We estimated the adult nationwide baseline intake for each beverage category using 24-h dietary recall data collected in 2017/2018. Taking group one as the taxed beverages, we applied the price and cross-price elasticities to the baseline intake data, we obtained changes in caloric intake. The caloric reduction was introduced into an individual dynamic model to estimate changes in weight and obesity prevalence. No benefits on cost savings were modeled during the first 3 years of intervention to account for the time lag in obesity cases to reduce costs. We multiplied the reduction in obesity cases during 7 years by the obesity costs per capita to predict the costs savings attributable to the sweetened beverage tax. SSB price elasticities were higher among the lowest tertile of income (−1.24) than in the highest income tertile (−1.13), and cross-price elasticities suggest SSB were weakly substituted by milk, water, and 100% fruit juices. We estimated a caloric change of −17.3 kcal/day/person under a 20% excise tax and −25.9 kcal/day/person under a 30% tax. Ten years after implementation, a 20% tax is expected to reduce obesity prevalence by 6.7%; 9.1% for a 30% tax. These reductions translate into a −2.8 million and −3.8 million obesity cases for a 20% and 30% tax, respectively, and a reduction of $US 13.3 billion and $US 17.9 billion in obesity costs over 10 years for a 20% and 30% tax, respectively. Study limitations include using a quantile distribution method to adjust self-reported baseline weight and height, which could be insufficient to correct for reporting bias; also, weight, height, and physical activity were assumed to be steady over time.

Conclusions

Adding a 20% to 30% excise tax on top of Brazil’s current federal tax could help to reduce the consumption of ultra-processed beverages, empty calories, and body weight while avoiding large health-related costs. Given the recent cuts to SSB taxes in Brazil, a program to revise and implement excise taxes could prove beneficial for the Brazilian population.

Using the context of the proposed Sugar-Sweetened-Beverages tax in Brazil, Ana Basto-Abreu and colleagues model the estimated reduction in obesity prevalence and savings in health-related costs.

Author summary

Why was this study done?

The consumption of sugar-sweetened beverages (SSBs) is associated with obesity, metabolic diseases, and incremental healthcare costs.
The World Health Organization (WHO) recommends governments to tax SSB to improve diet and reduce chronic diseases.
Since 2022, a bill to impose a 20% tax on SSB is under discussion in the Brazilian Senate.

What did the researchers do and find?

We estimated SSB own- and cross-price elasticities by socioeconomic status and simulated the potential effects of introducing a 20% excise tax to SSB in consumption. If a 20% tax is introduced, we would expect a 16.9 kcal/day/person caloric reduction among Brazilian adults.
We used an individual dynamic weight change model to translate caloric reductions into obesity reductions. We estimated an expected 6.7% reduction in obesity, which could lead to USD 13.3 billion healthcare cost savings over 10 years.
Larger benefits of the tax are expected to be experienced by younger adults and by people from high-income groups.

What do these findings mean?

The implementation of the SSB tax is expected to produce important reductions in population weight and obesity prevalence, while saving health-related costs.
Limitations of our analysis include assuming no change in weight under the no-intervention scenario and using a quantile distribution method to adjust self-reported baseline weight and height.

Introduction

Consumption of sugar-sweetened beverages (SSBs) is associated with increased caloric intake, weight gain, and the development of multiple chronic diseases, such as diabetes, metabolic syndrome, and cancer [1–3]. Governments worldwide are establishing structural interventions to improve consumers’ choices. Following the experience of alcohol and tobacco, taxes on SSB have gained international attention as an effective measure to reduce sugar consumption and improve health [4]. A systematic review analyzed the impact of SSB taxes in the United States, finding on average that a 10% tax on SSBs produces a 10% decrease in consumption [5]. Recently, the World Health Organization (WHO) recommended that countries implement a 20% tax on sugary beverages to improve diet and reduce chronic diseases [6].

Latin America has been at the forefront of SSB taxes [7]. In 2014, Mexico implemented a 10% SSB tax that reduced consumption by 9.7% 2 years after implementation [8]. The 10% SSB tax in Mexico was simulated over 10 years, predicting a 2.5% reduction in the prevalence of obesity among adults [9]. Further simulation studies estimated important reductions in cardiovascular disease [10], child body weight [10,11], and obesity-related cancers [12]. In 2016, Chile implemented an integral package of interventions to reduce SSB consumption, including increasing an existing 13% tax to 18% [13,14]. By 2017, the consumption of taxed beverages decreased by 23.4%, while caloric content decreased by 27.5% [14]. SSB taxes and regulations in the Latin American region have proven to reduce consumption and are expected to produce important health benefits.

In 2010, Brazil’s SSB per capita consumption was estimated at 142 ml/day/person [15]. Between 2009 and 2019, the prevalence of obesity in the country increased from 11.8% to 20.3% across all ages, but particularly among young adults [16], where SSB consumption is more frequent [15]. The rise in obesity has been concurrent with the increase in SSB consumption [17]. Despite having high consumption levels, Brazil reduced SSB federal taxes in 2016 by 23 percentage points, and the current SSB federal tax in the country is 4% [18]. Since 2022, a bill to impose a 20% tax on SSB is under discussion in the Brazilian Senate [19].

We aimed to estimate the potential impact of an SSB tax in Brazil to disincentivize SSB consumption. First, we estimated own- and cross-price elasticities for SSB and other beverages assuming a new 20% and 30% excise taxes were added to the existing federal tax. Then, we used a dynamic simulation model to estimate the expected impact of the taxes on body weight in the adult Brazilian population. Prevented obesity cases were then linked to obesity costs to estimate the obesity costs averted by such intervention over a 10-year period.

Methods

Model overview

We used a simulation model to estimate the potential impact of an SSB tax on obesity and obesity costs in Brazil (Fig 1) as the country is discussing its implementation. First, we used purchases data to estimate own- and cross-price elasticities for SSB in Brazil; we assumed that the expected reduction in purchases is translated into a proportional reduction in consumed calories. Using a dynamic weight change model, we estimated changes in body mass index (BMI) and obesity prevalence over 10 years, then linked them to per capita obesity costs to estimate the expected obesity costs averted. We simulated 2 tax scenarios—a 20% and a 30% SSB tax—, and we considered changes in SSB intake only and the potential caloric substitution by other beverages based on the cross-price elasticities. All steps are further explained in the next subsections.

Price elasticities by income level

To estimate the price elasticities of SSB by income level, we used the Brazilian Household Budget Survey (Pesquisa de Orçamentos Familiares—POF) from 2017 to 2018 (POF 2017–2018) collected by the Brazilian Institute of Geography and Statistics (IBGE) [20]. POF 2017–2018 is representative of Brazil, its states, and metropolitan regions. Within POF, we used the purchase database, which registers all food purchases of a sample of 57,920 Brazilian households within a week (representing 59,783,430 Brazilian households). We considered POF survey sampling weights in all estimates.

POF data include food acquisition—in total quantity (in kg)—and monetary value (in Brazilian reais). We divided beverages into 4 categories: (1) sugar-sweetened beverages; (2) alcoholic beverages; (3) unsweetened beverages; and (4) low-calorie or artificially sweetened beverages (light/diet). Table A in S1 Appendix describes the beverage items included in each category.

We also categorized food items to include food prices as controls in the demand regressions. Other food groups included: (i) fruits; (ii) in natura food; (iii) in natura meat; (iv) snacks; (v) sugar; (vi) other processed food; and (vii) other food items. Prices were calculated as unit values, as total expenditure divided by total quantity consumed (implicit price). The economic theory posits that zero consumption represents a choice made by consumers who assess prices and opt not to make a purchase. If a household reported zero consumption of an item, we approximated its price by the median of the neighboring regions, under the assumption that those areas generally experience similar market prices. We estimated basic constant elasticity regressions using a log-log specification for all beverage categories, including income and squared income as covariates (inc+inc²):

l o g (q_{i, b}) = α_{0, b} + \sum_{f = 1}^{11} β_{f, b} \log (p_{f}) + η_{1} i n c + η_{2} i n c^{2} + X_{i} θ_{b} + ε_{i, b} .

In which q_i,b is the total quantity (in kg) of beverage b acquired by household i. We included the price logarithm for all 11 categories of food and beverages (indexed by f). We also included several sociodemographic variables as control variables (matrix X), such as (i) household head (gender, years of schooling, race, and marital status), and household characteristics (total members of the household, total children within the household, and logarithm of the income in levels and quadratic).

We considered different price elasticities by income level. Income levels were categorized using the tertiles (T) of the distribution of income per capita of the households (TertIncome_T) calculated using sampling weights. Thus, the basic regression was modified according to the following expression:

l o g (q_{i, b}) = α_{0, b} + \sum_{f = 1}^{11} \sum_{T = 1}^{3} β_{f, b} \log (p_{f}) \times T e r t I n c o m e_{i, T} + X_{i} θ_{b} + η_{1} i n c + η_{2} i n c^{2} + ε_{i, b} .

Baseline intake of beverages

Baseline intake of beverages and self-reported anthropometric data were collected using the POF 2017–2018. Food consumption was assessed using a single 24-h food recall, because two-day information was collected in a smaller subsample (31,762) for which sampling weights were not available. We selected individuals 20 years of age and older (37,689 adults), representing 147,852,423 individuals in the adult population in Brazil. Each subject reported the total number of beverages they consumed in standard servings, and then we transformed it to milliliters. We used the same beverages categorization as in the purchases data (Table A in S1 Appendix).

Reduction in energy intake

To estimate the expected impact of the tax on calories, we created 2 scenarios considering: (1) the own-price elasticity for SSB by income level; and (2) both own- and cross-price elasticities for alcoholic beverages, light/diet and low-calorie or artificially sweetened beverages, and other beverages by income level. We assumed that changes in household purchases derived from price elasticities translated directly into household consumption changes. Prior studies suggest the effects of soda taxes on consumption are linear; thus, we assumed linearity in the changes in consumption for the 20% and 30% tax scenarios [25].

Adjustment of self-reporting bias on weight and height

We adjusted self-reported height and weight from POF 2017–2018, linked to SSB consumption data, using measured data from the Brazilian National Health Survey (Pesquisa Nacional de Saúde–PNS 2019) [21]. The PNS 2019 is a population-based survey, nationally representative, aiming at estimating living and health conditions. This survey measured weight and height using scales, portable stadiometers, and anthropometric tapes with the appropriate training and supervisors. More information about the PNS 2019 can be found elsewhere [26]. Being both POF and PNS nationally representative surveys, we could adjust for self-reporting bias. We adjusted self-reported weight and height from POF to match the distribution of weight and height measured in PNS 2019, following previously published methods [23]. Briefly, we calculated the difference between weight and height data quantiles from both surveys and fitted a cubic spline to smoothly construct a distribution of bias for weight and height. Then, using the quantiles of self-reported weight and height, we estimated the bias using fitted cubic splines (Figs A and B in S1 Appendix). Finally, we added the predicted difference to the self-reported data by sex, as previous articles reported a larger underestimation of weight and overestimation of height in women compared to men [27]. Details are available in Section 2 from S1 Appendix.

Reduction in body weight, BMI, and obesity prevalence

To simulate weight changes attributable to the SSB tax on the Brazilian population, we used a microsimulation approach, a dynamic model developed by Hall and colleagues [22]. The model is implemented in the bw package in R (https://rdrr.io/github/INSP-RH/bw/). It considers the dynamic physiological adaptations to body weight that lead to changes in resting metabolic rate and physical activity [22] and has been previously implemented to estimate the impact of the sugar-sweetened beverages tax [9,28,29]. The model considers changes in extracellular fluid, glycogen, and fat and lean tissues caused by the change in caloric consumption, keeping the physical activity constant. Thus, the time lag that takes the caloric changes to be reflected in weight is considered by the model. Bodyweight is the result of the sum of fat mass and fat-free mass, and is determined using a system of ordinary differential equations. To initialize the model and obtain resting metabolic rate, we used sex, age, weight, and height from POF 2017–2018 assuming all adults were sedentary (physical activity level = 1.5). Table J and Section 3 in S1 Appendix presents more details of the simulation model and its parameters.

Uncertainty

Obesity prevalence considered the uncertainty of POF 2017–2018.

Sensitivity analysis: Measured weight and height from PNS 2019

We relied on self-reported weight and height to generate the expected impact of the SSB tax, adjusting the self-report bias expected in POF 2017/2018 using quantiles of weight and height data from PNS 2019. However, recent concerns have been raised about the potential limitations of this method [30]. To validate the results of our analysis, we followed an independent estimation procedure, using measured weight and height from PNS 2019 and SSB consumption from POF 2017/2018. Since these 2 surveys cannot be linked at the individual level, we estimated the average SSB consumption and BMI for 14 groups by age (20 to 39, 40 to 59, 60+) and socioeconomic status (low, middle, and high) for males and females. Then, we implemented Hall’s dynamic model using the averages, generating the expected weight reduction in kilograms for each group and compared against the estimated produced using the corrected self-reported data. Results and methods for this analysis are fully available in Section 6 in S1 Appendix.

Reduction in cases with obesity

We translated the reductions in the prevalence of obesity into obesity cases averted over 10 years using the baseline prevalence of obesity in Brazil (assuming a steady state) and the expected reductions in obesity, multiplied by the population projections from the Brazilian Institute of Geography and Statistics for the adult population from 2021 to 2030.

Reduction in healthcare costs

Obesity costs were obtained from a 2021 publication which estimated direct and indirect costs attributable to overweight and obesity for 8 countries, including Brazil [24]. The total cost of obesity in Brazil was estimated to be US$ 38.76 billion per year (16.19 direct and 22.57 indirect costs), using a societal perspective. Direct costs were estimated as the obesity-attributable fraction of healthcare expenditures and indirect costs included economic loss from premature mortality, missed days of work (absenteeism), and reduced productivity while at work (presenteeism). Using a systematic review that estimated that 87% of the overweight and obesity costs were attributed to obesity, we estimated the obesity costs in Brazil to be US$33.72 billion [31]. In 2018, we estimated a 25.2% obesity prevalence in the adult population in Brazil using POF, resulting in 37.2 million persons with obesity. Dividing the annual cost of obesity by the total number of obesity cases, we estimated an annual cost per obesity case of US$ 906.92. Using the Consumer Price Index Inflation rate, we converted costs from 2019 to costs in 2021 and obtained an annual cost of US$ 942.5. To account for the time lag in morbidity and mortality changes following obesity changes, no cost benefits were estimated during the first 3 years of intervention, based on average time lags for most obesity-related diseases in a dietary multistage life table [32]. The benefits of the full effect of the intervention on direct and indirect costs were modeled in years 4 to 10. To estimate the total cost savings, we multiplied the absolute yearly reduction in obesity prevalence by the direct and indirect obesity costs. We used a 5% discount rate to reduce overestimation [33]. A detailed description of cost estimations is included in Section 5 from S1 Appendix.

Results

Table 1 presents the estimated price elasticities for SSB by income level. Low-income households were, on average, more sensitive to changes in SSB prices, as they had higher price elasticities than higher-income households. SSB price elasticities ranged from −1.13 (high income) to −1.24 (low income). Substitution effects from increases in SSB prices were only observed for unsweetened beverages (which included milk, water, and 100% fruit juices) in low- and high-income households and for light/diet beverages/low-calorie or artificially sweetened beverages for high-income households. We observed a complementary behavior between the acquisition of SSB and alcoholic beverages (if one product decreased, the other also decreased).

Table 1. Price elasticities of beverages by income groups, 2018.

		Sugar sweetened beverages	Unsweetened beverages	Alcoholic beverages	Light/diet beverages†
Dependent variable: Log of Quantity Acquired (in Liters)
Low income	Elasticities	-1.241	0.046	-0.122	-0.174
	s.e.	(0.015)	(0.017)	(0.053)	(0.124)
	p-value	<0.001	0.006	0.022	0.160
Middle income	Elasticities	-1.186	0.030	-0.076	0.094
	s.e.	(0.019)	(0.021)	(0.047)	(0.110)
	p-value	<0.001	0.150	0.108	0.397
High income	Elasticities	-1.126	0.066	-0.141	0.201
	s.e.	(0.022)	(0.025)	(0.048)	(0.084)
	p-value	<0.001	0.007	0.003	0.018
Observations (n)		18,876	27,488	4,263	383

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^† This category includes low-calorie or artificially sweetened beverages.

All regressions used microdata from POF 2017–2018. Income levels were divided using the tertiles of the distribution of the income per capita of the households. Other controls include logarithm of prices of other products (11 categories included), of income (in levels and squared), characteristics of household head (schooling, age, gender, race), total number of household members, and total number of children living within the household (all ages). All regressions are weighted using sample survey weights. Standard errors (se) are clustered by socioeconomic status and presented in parentheses.

Table 2 presents the baseline intake of SSB and other beverages among Brazilian adults in 2018. We estimated a baseline intake of 72 kcal/person/day from SSB. The expected caloric change after the SSB tax was estimated to be −16.9 kcal/person/day for 20% and −25.3 kcal/day for 30% tax. If we included changes in other beverages to allow for substitution, expected caloric changes increased to −17.3 kcal/person/day for 20% (an extra reduction of 0.4 kcal) and −25.9 kcal/person/day for 30% tax (an extra reduction of 0.6 kcal). Expected caloric changes should be higher among younger than older adults and higher income levels. The caloric change was translated into −0.75 kg in females and −0.83 kg in males. In our sensitivity analysis using measured weight and height and Hall’s model at aggregated level, females were estimated to reduce 0.78 kg and males 0.84 kg (Table H in S1 appendix).

Table 2. Baseline caloric intake from SSB and other beverages (alcoholic, diet/light, and unsweetened beverages).

	Population (million)	Baseline intake		Caloric changes from SSB				Caloric changes from other beverages
		Baseline intake		20% tax		30% tax		20% tax		30% tax
		kcal/ day	95% CI^*	kcal/ day	95% CI^*	kcal/ day	95% CI^*	kcal/ day	95% CI^*	kcal/ day	95% CI^*
Total	147.9	71.8	69.3, 74.4	−16.9	−17.5, −16.3	−25.3	−26.2, −24.4	−0.4	−0.4, −0.3	−0.6	−0.7, −0.5
Sex
Female	78.2	65.1	62.4, 67.7	−15.3	−15.9, −14.7	−22.9	−23.9, −22.0	−0.1	−0.1, −0.0	−0.1	−0.2, −0.0
Male	69.6	79.5	75.8, 83.1	−18.6	−19.5, −17.8	−27.9	−29.2, −26.7	−0.7	−0.8, −0.6	−1.0	−1.2, −0.9
Age groups
20–39	62.2	92.2	88.0, 96.5	−21.7	−22.7, −20.7	−32.6	−34.1, −31.1	−0.4	−0.5, −0.3	−0.7	−0.8, −0.5
40–59	53.9	62.9	59.4, 66.4	−14.7	−15.5, −13.9	−22.1	−23.3, −20.8	−0.5	−0.6, −0.3	−0.7	−0.8, −0.5
60+	31.7	46.9	43.3, 50.6	−11.0	−11.9, −10.2	−16.6	−17.8, −15.3	−0.1	−0.2, −0.0	−0.2	−0.3, −0.1
Income status
Low	47.4	53.5	49.9, 57.1	−13.3	−14.2, −12.4	−19.9	−21.3, −18.6	−0.2	−0.3, −0.2	−0.4	−0.5, −0.2
Middle	49.1	74.0	70.2, 77.8	−17.5	−18.5, −16.6	−26.3	−27.7, −25.0	−0.2	−0.3, −0.2	−0.3	−0.4, −0.2
High	51.4	86.7	81.7, 91.8	−19.5	−20.7, −18.4	−29.3	−31.0, −27.6	−0.6	−0.8, −0.5	−0.9	−1.2, −0.7

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* Confidence intervals were generated based on the uncertainty of POF 2017–2018.

Table 3 presents the expected impact of the SSB tax over the obesity prevalence, 10 years after implementation, using own- and cross-price elasticities. The obesity prevalence is expected to decrease from 25.2% to 23.6%, resulting in a 6.3% reduction with a 20% tax and an 8.7% obesity reduction with a 30% tax. This reduction is expected to be higher among younger (−8.3%) than older adults (−3.6%) and among higher (−7.8%) than lower-income levels (−5.4%). Allowing for substitution using cross-price elasticities, the expected decrease in the obesity prevalence becomes larger, being −6.7% for 20% and −9.1% for a 30% tax.

Table 3. Baseline obesity in Brazil and expected obesity reduction after the implementation of a tax on SSBs, considering caloric changes from sugar-sweetened beverages and from all beverages.

			Obesity changes from SSB						Obesity changes from all beverages
	Baseline obesity		20% tax			30% tax			20% tax			30% tax
			Absolute change		Relative change	Absolute change		Relative change	Absolute change		Relative change	Absolute change		Relative change
	%	95% CI^*	pp	95% CI^*	(%)	pp	95% CI^*	(%)	pp	95% CI^*	(%)	pp	95% CI^*	(%)
Total	25.2	24.4, 26.0	−1.6	−1.9, −1.4	−6.3%	−2.2	−2.5, −2.0	−8.7%	−1.7	−1.9, −1.4	−6.7%	−2.3	−2.5, 2.0	−9.1%
Sex
Female	28.8	27.8, 29.8	−1.4	−1.8, −1.2	−4.9%	−2.1	−2.5, −1.7	−7.3%	−1.5	−1.8, −1.2	−5.2%	−2.1	−2.5, 1.8	−7.3%
Male	21.2	20.2, 22.2	−1.9	−2.3, −1.5	−9.0%	−2.4	−2.8, −2.0	−11.3%	−1.9	−2.3, −1.6	−9.0%	−2.4	−2.9, −2.1	−11.3%
Age groups
20–39	21.8	20.7, 23.0	−1.8	−2.2, −1.5	−8.3%	−2.4	−2.9, 2.0	−11.0%	−1.8	−2.3, −1.5	−8.3%	−2.5	−3.0, −2.1	−11.5%
40–59	29.3	28.1, 30.5	−1.9	−2.4, −1.5	−6.5%	−2.4	−2.9, −2.0	−8.2%	−2.0	−2.5, −1.6	−6.8%	−2.4	−2.9, −2.0	−8.2%
60+	24.8	23.4, 26.4	−0.9	−1.4, −0.5	−3.6%	−1.5	−2.2, −1.0	−6.0%	−0.9	−1.4, −0.6	−3.6%	−1.5	−2.2, −1.0	−6.0%
Income status
Low	22.3	21.1, 23.4	−1.2	-1.6, −0.9	−5.4%	−1.7	−2.2, -1.3	−7.6%	−1.2	−1.7, −0.9	−5.4%	−1.7	−2.2, −1.3	−7.6%
Middle	26.2	25.0, 27.4	−1.6	−2.0, −1.3	−6.1%	−2.2	−2.7, −1.9	−8.4%	−1.7	−2.1, −1.3	−6.5%	−2.3	−2.8, −1.9	−8.8%
High	27.0	25.5, 28.5	−2.1	−2.7, −1.6	−7.8%	−2.7	−3.3, −2.2	−10.0%	−2.1	−2.7, −1.6	−7.8%	−2.7	−3.3, −2.2	−10.0%

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* Confidence intervals were generated based on the uncertainty of POF 2017–2018.

Fig 2 shows the expected obesity reduction in cases and potential cost savings 10 years after implementing the SSB tax, using own-price elasticities. A 20% tax is expected to reduce the number of people living with obesity by 2.8 million and by 3.7 million with a 30% tax; this would translate into cost savings of $US 13.1 billion with a 20% tax and $US 17.6 billion with a 30% tax. Using cross-price elasticities, the reduction in the number of people living in obesity is similar with −2.8 and −3.8 million for a 20% and 30% tax, respectively; cost savings reaches $US 13.3 and $US 17.9 billion with a 20% and 30% tax, respectively.

Discussion

We aimed to estimate the own and cross-price elasticities of SSB and translate them into the expected impact of a 20% and a 30% SSB tax on obesity over 10 years in Brazil. In Brazil, we found that SSB is elastic and weakly substituted for milk, water, 100% fruit juices, and low-calorie or artificially sweetened beverages, such as diet/light beverages. Low-income households were more sensitive to price increases and had a higher elasticity than other income levels. Considering own- and cross-price elasticities, we estimated that a 20% tax on SSB could translate into a 6.7% reduction in the obesity prevalence and US$ 13.3 billion savings in obesity costs over 10 years after implementation. A 30% tax was estimated to reduce 9.1% obesity prevalence and US$ 17.9 billion in the obesity costs over 10 years. The largest benefits of the tax are expected to be experienced by males, younger adults and by people in the high-income group.

Estimating the impact of price increases on demand for SSB and other beverages in Brazil is key to assess the potential impact of a SSB tax. Our own price-elasticities indicates that a 10% price increase to SSB in Brazil should result in a 12.4% reduction in purchases among households in lowest income group, 11.9% in households in the second income group, and 11.3% in the top income group. This is lower to what has been previously estimated in Brazil, where a 10% price increase was expected to produce a 13.6% reduction in carbonated SSB, but higher than a meta-analysis of observational studies in which a 10% tax was associated to a 10% reduction in SSB purchases and intake [5,34]. Our analysis suggests that SSB purchases in Brazil will decrease if prices increase, which indicate that an excise tax on SSB would contribute to lower the consumption of SSB among the Brazilian adult population.

We analyzed cross-price elasticities, finding only a weak substitution for milk, water, and 100% fruit juices. Substitution for these beverages was previously reported in a meta-analysis that included US, Mexico, France, and Brazil studies [35]. In contrast, our findings suggest that alcoholic beverage purchases are expected to decrease if SSB prices increase, while non-caloric beverages purchases will increase only among adults in the high-income group. Considering these substitutions, a 20% tax should lead to a direct caloric reduction of 16.9 kcal from SSB, which will be further increased by substitution or complementation for other liquids with a lower caloric density (−0.4 kcal after a 20% tax and −0.6 kcal after a 30% tax). This finding implies that if an SSB tax is implemented, no caloric substitution for liquids is to be expected; an observation that is in line with a prior observational study that found a weak caloric compensation for SSB in the Brazilian population [36] and with international studies that suggest that caloric compensation for liquids is incomplete [37].

While the SSB tax is being discussed in Brazil, information about the potential impact of a 20% and 30% SSB tax on obesity is limited. Based on our modeling exercise, a 20% tax could result in a 6.7% reduction in obesity, increasing to 9.1% under a 30% tax. This estimate is higher than what was previously estimated in Brazil, in which a 20% tax was predicted to increase obesity by 1.3% in men and by 2.4% in women [38]. Differences in estimates are likely due to differences in the method of estimating own- and cross-price elasticities and the caloric densities used. We predicted a 17.3 kcal/day/person reduction, considering own- and cross-price elasticities, yet the study by Enes and colleagues [38] predicted an increase of 8 kcal/day/person, also considering own- and cross-price elasticities. Both estimates were generated using the same dataset (POF, 2017–2018), yet, Enes and colleagues [38], used censored equations at the household level, while we estimated elasticities at the sample unit level, to avoid censored data. Differences in the categorization of food and beverage items and in the caloric content of products could have also contributed to the reported divergences. Given the observed difference, we searched for independent assessments of elasticities in Brazil and found a recent report by Venson and colleagues [39] using the same dataset (the 2017–2018 Brazilian Household Budget Survey), a similar model (QUAIDS model) together with censored demand and household data [39]. Using the elasticities by Venson and colleagues, we found similar results to our analysis, suggesting that this more sophisticated approach concurs with our analysis. Our approach allowed us to estimate elasticities and effects by income tertile, an important condition to assess the expected impacts by socioeconomic status. Also, this estimate is within the range of prior modeling studies using a comparable approach. For instance, in Mexico, a 20% tax was estimated to reduce 16.7 kcal using yearly estimate and 31.1 kcal using a monthly estimate [9].

We observed important differences in the expected impact on obesity rates by income. High-income groups are expected to have their obesity prevalence reduced by 7.8% compared with 5.4% in low-income groups. This could be explained by differences in baseline consumption of SSB, which were 86.7 kcal/day for high-, 74.0 kcal/day for middle-, and 53.5 kcal/day for low-income groups. Also, baseline obesity prevalence was 27.0% in the high-, 26.2% in the middle-, and 22.3% in the low-income groups. This, along with greater baseline SSB consumption, could explain the greater expected reductions in BMI among high-income individuals, despite having lower price-elasticities in comparison with low-income individuals. We found that a 20% and 30% excise tax on SSB will produce benefits for all income groups as it will reduce obesity levels in all SES groups; however, an investment of tax revenue could further benefit lower-income groups if used to fund redistributive social policies [40].

An SSB tax in Brazil is expected to produce important savings related to obesity prevention. We estimated a reduction of US$ 13.3 billion in obesity costs 10 years after the implementation of a 20% SSB tax (US$ 5.6 billion including only direct costs, available in appendix). The US $5.6 billion reduction is smaller than what has been estimated in the US (US$ 23.6 billion) but higher than for Mexico (US$ 159.5 million) for a similar tax increase (20%) and a similar period (10 years) [12,41]. We compared obesity direct costs savings only, because these 2 previous studies only measured direct obesity costs. The differences across countries could be due to differential obesity costs per case, which vary by how healthcare is financed and organized and by differences in obesity prevalence and estimated reductions. Finally, our cost-analysis does not consider the potential benefits derived from the social investment of the tax revenue, particularly if revenue is directed at strengthening obesity prevention measures, which can further increase the health benefits from the tax.

Some limitations of our study should be mentioned. Baseline weight and height were self-reported. We relied on a quantile distribution method to calibrate it using objectively measured weight and height available at the Brazilian National Health Survey (Section 2 in S1 Appendix). Yet, this method is imperfect and could be insufficient to correct reporting bias [30]. For that reason, we performed a sensitivity analysis using the same dynamic model, but at an aggregated level. In this analysis, we used objectively measured weight and height from 14 groups (aggregated data) and linked it to SSB consumption. In the 14 groups, the combined estimation fell within the confidence intervals for the individual model (Table H in S1 Appendix). This sensitivity analysis increases our confidence in the correction we implemented on the self-reported measures to estimate weight reductions in kilograms. A similar approach could not be used to assess the effect of the correction on the BMI classification. However, we estimated a 16.9% obesity prevalence using self-reported weight and height, compared to 25.2% using adjusted self-reported data, and to 26.8% obesity prevalence using objective measures of weight and height (Table B in S1 Appendix). The potential error of adjusting self-reported data on the BMI classification (obesity versus non-obesity) in comparison with using measured data is in the downward direction, suggesting that adjusted data could underestimate the SSB impact on obesity, but in a lower magnitude if we would have used self-reported data without adjustments.

Our model could overestimate cost savings. Prevalent costs of obesity were obtained from Okunogbe and colleagues [24], yet, the obesity changes estimated in our model will take years to fully materialize after the intervention. Some indicators will improve faster after weight loss, such as disease incidence, but others will take years to decades to change, such as mortality. To fully model these dynamics, we would have needed to use a life-table model with an open population to capture these changes, discount the health gains over time, and go beyond the 10-year modeling timeframe used in our analysis. We attempted to reduce overestimation by introducing a 3-year lag of no cost savings and including a 5% discount rate, but this could still fall short. Future studies interested in modeling obesity cost savings should rely on life-table models to capture the net present value of health gains. Also, the literature on obesity costs in Brazil provides heterogeneous figures. A systematic review found that obesity costs in Brazil ranged from USD 133.8 million to USD 6.3 billion per year (47 times the lowest estimation) [42]. We relied on an international estimation that includes 8 countries, uses a societal perspective, and found a higher obesity cost compared to the highest estimation included in the systematic review. We are not considering that over the 10 years simulated, the Brazilian population will age, which could affect the average cost of obesity; our cost per person could underestimate the cost at the end of the simulation period. Our estimates depend on a steady-state assumption for body weight, implying a constant obesity prevalence over time; however, the prevalence of obesity in Brazil has increased over time (11.8% to 20.3% between 2009 and 2019). The impact of this assumption depends on the drivers of the obesity trend in Brazil, with the worst-case scenario being an overestimation of the effect that could occur if obesity increases are driven by products not included in the SSB tax. However, SSB are recognized as important drivers of the obesity epidemic in the world. Despite this, future studies should aim at including obesity trends into models. Finally, we simulated this intervention in the adult population, so further reductions in obesity prevalence are to be expected in children and adolescents who may change dietary habits and reduce weight as they transition into adulthood.

SSB taxes have been implemented in more than 73 countries worldwide [7]. Studies have shown that SSB are elastic and respond to increases in price; yet no negative economic effects have been observed in countries where SSB taxes have been implemented [40], and analyses in Brazil suggest an economic net benefit from implementing an SSB tax [43]. Our findings suggest that implementing an SSB tax in Brazil will lead to an important reduction in the caloric intake of beverages with little to no nutritional added value; this reduction could help reduce the obesity prevalence across all socioeconomic strata. From an economic perspective, an SSB tax could reduce costs while generating revenue that could be in turn invested in other obesity prevention interventions. In 2016, the WHO recommended that all countries should implement an SSB tax of at least 20% [44]. Our analysis suggests that a 30% tax in Brazil could produce even greater population health benefits for all income strata.

Supporting information

S1 Appendix. Additional information on the simulation model, parameters, assumptions, and supplementary results.

(PDF)

pmed.1004399.s001.pdf^{(1MB, pdf)}

Abbreviations

BMI: body mass index
SSB: sugar-sweetened beverages
WHO: World Health Organization

Data Availability

The script and database for this analysis can be found in the following repository: https://github.com/INSP-RH/Brazil_SSB_Tax.

Funding Statement

This work was funded by a Bloomberg Philanthropies grant (5104695 to ACD; 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.1004399.r001

Decision Letter 0

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10 Oct 2023

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PLoS Med. doi: 10.1371/journal.pmed.1004399.r002

Decision Letter 1

Katrien G Janin

6 Nov 2023

Dear Dr. Barrientos-Gutierrez,

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COMMENTS FROM THE ACADEMIC EDITOR

I'm supportive of the paper, but have a few comments.

1. I don't see any particular rationale for essentially replicating similar studies that have been conducted elsewhere beyond no-one's done it in Brazil yet. It would be helpful to understand whether the authors expect price elasticity of SSBs in Brazil to be different from elsewhere and why. I guess really my underlying question is why are modelling studies from elsewhere not generalisable to Brazil? I think there are probably some good reasons, but it would be good to be explicit.

2. I don't understand how NOVA would be used to classify beverages into the four categories listed - NOVA categorises based on degree of industrial processing.

3. Consumption was based on individual data for participants aged 20y+. In many countries SSB consumption peaks in late childhood. So I expect that their estimates of population consumption are underestimates. This could be considered further.

COMMENTS FROM THE EDITORS

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For in-text reference, citations are placed within square parentheses and should precede punctuation. (e.g see line 83: ‘… metabolic syndrome, and cancer.[1–3] Governments … ‘ should be changed to ‘ ‘…metabolic syndrome, and cancer [1–3].’‘

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In line 338: Please avoid assertions of primacy. We suggest you add ‘ to our knowledge’ or something alike.

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COMMENTS FROM THE REVIEWERS:

Reviewer #1: Dear authors, this is an important study, which aimed to estimate the price elasticity of sweetened drinks and other drinks and evaluate the impact of taxation on sweetened drinks on the prevalence of obesity in Brazil. There are few studies that have evaluated the impact of fiscal measures to reduce SSB consumption on health outcomes, especially in low- and middle-income countries. The study makes important contributions to the scientific community in the area of impact assessment of tax policies.

Title: I suggest that the aspect of impact on cost be incorporated into the title as this was an important objective of the study.

Page 3 line 86: I suggest removing the word recent, as this is a study published in 2019.

Introduction:

In 2022, Brazil approved a bill that is still under analysis by the economic affairs committee that provides for a 20% tax on sugary drinks. I think it is important to include this information in the introduction to inform the reader where the country is in this fiscal policy.

The authors should make it clearer in the introduction about the importance of the analysis carried out in this study, that is, why it is relevant to estimate the potential impact of SSB taxation on both obesity and costs in the health system. Other studies in this regard have already been published in Brazil. It is important to include. Enes, C.C.; Rinaldi, A.E.M.; Nucci, L.B.; Itria, A. The Potential Impact of Different Taxation Scenarios towards Sugar-Sweetened Beverages on Overweight and Obesity in Brazil: A Modeling Study. Nutrients 2022, 14, 5163. https://doi.org/10.3390/nu14235163

Methods

Why were weight and height data not used directly from PNS 2019, since POF 2017-2018, from which consumption data were obtained, was carried out in a period close to PNS 2019? Although it is not possible to combine the databases at the individual level, when establishing the study population >= 20 years old, it is possible to cross-reference consumption data with obesity prevalence data, as the research was carried out at similar times. This way there would be no need to calculate adjustments for weight and height.

The dynamic model proposed by Hall et al predicts that 50% of the expected weight loss will occur in the first year after taxation and 95% of the reduction by the end of the third year of taxation. How was this considered in the model to calculate weight reduction over a 10-year period?

Results

Page 7 line 272: remove the indication of % -25.3% as it is kcal

Discussion

In line 338, the authors state that this is the first study in Brazil that estimated the impact of SSB taxation on body weight. There is a mistake in this statement because a study was published in 2022 that also proposed to evaluate the impact of SSB taxation. Enes, C.C.; Rinaldi, A.E.M.; Nucci, L.B.; Itria, A. The Potential Impact of Different Taxation Scenarios towards Sugar-Sweetened Beverages on Overweight and Obesity in Brazil: A Modeling Study. Nutrients 2022, 14, 5163. https://doi.org/10.3390/nu14235163

Even the results found in this study were different from those presented in this article and the possible reasons for these differences should be discussed and explored

Reviewer #2: This is a well-written paper on a topic that is of interest to a wide health readership. The methods are sound and the findings are novel. The conclusions are supported by the results. The results are immediately relevant to Brazil, and also of value to similar countries in the region and around the world.

The derivation of price elasticities, by income tertile, using Brazilian data, is a clear strength of this work. So are the use of a microsimulation model with Hall et al's energy balance, and the correction applied to self-reported height and weight, the latter with an alternative approach in the sensitivity analysis for validation purposes.

I have a few comments, but none detract from the great work you have done for this paper.

The approach to estimating health care cost is suitable for ballpark estimates but is likely to underestimate the true health care cost savings by a considerable margin, as the authors duly indicate in their discussion. Not mentioned is that the cost per case is the same across ages, whereas in reality it would rise with age as the prevalence of obesity-related conditions rises. Given population aging, not factoring this in would lead for further underestimation of the cost savings.

Line 249 / 394 / S1 appendix section 5: I believe that usually, the health system perspective does include out-of-pocket expenses. The perspective used is perhaps better described as a 'payer perspective', as per an ISPOR Special Task Force Report (Garrison et al, ViH 2018; https://doi.org/10.1016/j.jval.2017.12.006) (although in that guidance the term 'health care sector perspective' is used).

Consider reporting on the results of sensitivity analysis in results section, even if it's in one sentence.

Line 411: Add a reference for the statement that "In 2017, the World Health Organization recommended that all countries should implement an SSB tax of at least 20%."

S1, Fig. B: The adjustments made to height seem to have exacerbated the clustering of values around round numbers, no doubt due to the splines. If there were a way to smooth the adjusted curves, that wo

PLoS Med. 2024 Jul 17;21(7):e1004399. doi: 10.1371/journal.pmed.1004399.r003

Author response to Decision Letter 1

15 Jan 2024

Attachment

Submitted filename: Response to reviewers SSB tax Brazil.docx

pmed.1004399.s002.docx^{(522.6KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004399.r004

Decision Letter 2

Katrien G Janin

16 Feb 2024

Dear Dr. Barrientos-Gutierrez,

Thank you very much for re-submitting your manuscript "Expected reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to Sugar-Sweetened-Beverages in Brazil: a modeling study" (PMEDICINE-D-23-02904R2) 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 comments of reviewer #2 are dealt with we are planning to accept the paper for publication in the journal.

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

[LINK]

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

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

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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.

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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 (plosmedicine@plos.org).

We look forward to receiving the revised manuscript by Feb 23 2024 11:59PM.

Sincerely,

Katrien Janin, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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

Requests from Editors:

Comments from Reviewers:

Reviewer #1: Dear editor, I thank you for the opportunity to evaluate the second version of the article. Analyzing the document sent by the authors, I realized that the notes I presented were responded to appropriately, and justifications were also presented. Furthermore, considering the notes from the other reviewers, I see that the new version of the article provides more complete information. I have no further notes on the present.

Reviewer #2: The paper has improved, particularly, the perspective and costs are clearer. It did occur to me that there is an issue with the timing of the health care cost and productivity savings that I missed in my previous reading of the paper.

The source study, Okunogbe et al, estimated those costs using a cost-of-illness approach, which aims to take a snapshot of costs in a particular year. Some of these costs are the result of exposures in prior years. The most extreme example is the mortality component of productivity losses, which are calculated by attributing a proportion of deaths to obesity, then multiplying with (productive) life expectancy at the age of death, and with wages. In the model in the present paper, all costs are related to obesity in the year of analysis.

In reality, a change in SSB consumption would translate to a change in body mass (this is covered very well in the model), which would reduce the number of new cases of obesity-related disease (diabetes, heart disease, etc.). Over time, the pool of prevalent cases prevented would rise. Impacts on mortality would follow, and mortality-related productivity gains (and health care costs in added years of life) would trail those. The model used here omits several of these steps, so part of the cost savings are brought forward, potentially by up to decades.

This could be incorporated in the analysis if the approximate timing of the cost savings and productivity gains can be estimated, and the model can be refined to deal with those lagged effects. Alternatively, this could be addressed in the discussion section.

(That does not solve the issue around 'health care costs in added years of life' - see e.g. Van Baal et al, Health Econ. 2007 Apr;16(4):421-33 - which is ignored in cost-of-illness studies too, and best left to future studies that use lifetable modelling.)

Reviewer #3: Thank you for your close attention to the comments given by the editors and all four reviewers. I have no additional suggestions for revision at this time.

Reviewer #4: In my opinion, the authors have adequately addressed questions and concerns raised by me and other reviewers. I think the paper is acceptable in its current form. Congratulations to the authors!

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

[LINK]

PLoS Med. 2024 Jul 17;21(7):e1004399. doi: 10.1371/journal.pmed.1004399.r005

Author response to Decision Letter 2

27 Feb 2024

Attachment

Submitted filename: Response to reviewers.docx

pmed.1004399.s003.docx^{(4MB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004399.r006

Decision Letter 3

Katrien G Janin

14 Mar 2024

Dear Dr. Barrientos-Gutierrez,

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

[LINK]

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

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 Mar 21 2024 11:59PM.

Sincerely,

Katrien Janin, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

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

Comments from Reviewers:

Reviewer #2: The work now contains a lag time between the start of the intervention, and the costs. And a 5% discount rate has been applied. Both are improvements, but the chosen solutions still falls a bit short, I'm afraid.

In reference 30, the BODE3 team wrote, in Table 1: "Time lags for intervention effect: It takes time for a change in body mass index (BMI) to impact on disease incidence. As there are no precise data on just how long these are, we have used wide windows of time lags. For cancers, the time lag is assumed to range between 10 and 30 years. For CHD, stroke, diabetes, and osteoarthritis (the noncancers), the time lag is assumed to be shorter and ranges between 0 and 5 years. Wide uncertainty is included around these estimates."

That is, the lag time accounts for the impact of a risk factor change on incidence. Cleghorn et al then use a proportional multi-state life table model that accounts for the subsequent lags between incidence and prevalence, and prevalence and mortality, and for the fact that some lost life years are further into the future than others.

To illustrate, I had hoped to add a few graphs. The system will not allow that, but I'll see if the editorial team can help. The graphs show the results of a study in which we modelled the potential impact of an SSB tax (compared to BAU) using a model similar to the one used by BODE3. The graphs show the time patterns in IHD, where impact is quick for incidence (we modelled no lag time between exposure change and incidence), slower for prevalence (this builds up over time), and mortality (which lags more as it is a function of prevalence), and HALYs (not visible but this lags even more as it is partly a function of deaths in previous years).

In this paper, you now apply the delay factors intended for incidence, to all those sequelae. (As explained in my previous review, the Okunogbe paper used a cost-of-illness approach, which takes a snapshot of costs in a particular year.) In years 4-10, you use the full impact, which still underestimates the lag. Most of the impact would materialise after that 10-year mark. It would nevertheless be fair to attribute those cost savings in later years to the exposure changes within those first 10 years - it's just that they would properly need to be discounted in a lifetable or similar structure. Since the benefits would stretch over decades, the impact of discounting would be much greater than in your current calculations, which go up to year 10. In another graph below, you can see that with a 5% discount rate, the 'net present value' for an event in year 10 is 61%, but a life year in year 25 would count only for 0.3 life years (NPV).

This would affect mainly the cost of premature mortality, which in Okunogbe's study "constitute[d] a substantial proportion of indirect costs (about 56%-92%) across all countries". Although Okunogbe et al ignore this, it would apply also, to a lesser extent, to the direct obesity costs. Longstanding obesity is more costly than recent obesity due to the time it takes to develop disease (see above).

Modelling this properly is clearly not an option here - you would need a sophisticated life table model. The current solution is not elegant and still overestimates the 10-year gains, and this is not explained in the paper; not mentioned in the limitations.

The best solution would be to omit the estimates of the cost savings altogether, and instead focus on the reductions in obesity (by income), which is where the strength of this paper lies. The discussion could then mention the likely impact on obesity-related costs, referring to the Okunogbe paper.

Alternatively, you could proceed as you did now, but with more discussion in the limitations section. You would have to point out the cost savings could be attributed to changes in obesity prevalence in those 10 years, but would in reality materialise largely in the decades following, that discounting would reduce the net present value of the gains (which you accounted for only partially), and that future life table-based modelling could produce more realistic estimates of these impacts over time.

Minor comment: 'dietary multistate life table' rather than 'Diet multistage life table'.

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

Requests from Editors:

After a long discussion with my editorial colleagues, and taken fully on board the reviewer comments, we like to suggest the following solution to move forward:

We prefer to go down the route to ask you to expanded the limitations in the discussion and ask to use very appropriate language when talking about the projected cost savings in your manuscript. The reason we prefer to go down that route is that the current calculation do give an indication of the cost involved but we agree with the reviewer's points about time lag and the resulting likelihood that their cost savings estimates are inflated MUST be discussed carefully in the discussion/limitations. We therefore ask you to implement the alternative option as presented by the reviewer.

We like to thank the reviewer for such a terrific and informative discussion

Please do not hesitate to contact me directly at kjanin@plos.org if you have any question about the above.

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

[LINK]

Attachment

Submitted filename: Comments by Reviewer #2 with graphs.docx

pmed.1004399.s004.docx^{(83.4KB, docx)}

PLoS Med. 2024 Jul 17;21(7):e1004399. doi: 10.1371/journal.pmed.1004399.r007

Author response to Decision Letter 3

22 Mar 2024

Attachment

Submitted filename: Brazil SSB tax plos med 3rd review.docx

pmed.1004399.s005.docx^{(21.7KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004399.r008

Decision Letter 4

Katrien G Janin

5 Apr 2024

Dear Dr Barrientos-Gutierrez,

On behalf of my colleagues and the Academic Editor, [AE Name], I am pleased to inform you that we have agreed to publish your manuscript "Expected reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to Sugar-Sweetened-Beverages in Brazil: a modeling study" (PMEDICINE-D-23-02904R4) 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.

I do have one editorial request: please change your title to "Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to Sugar-Sweetened-Beverages in Brazil: a modeling study".

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

PRESS

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

Sincerely,

Katrien G. Janin, PhD

Senior Editor

PLOS Medicine

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, parameters, assumptions, and supplementary results.

(PDF)

pmed.1004399.s001.pdf^{(1MB, pdf)}

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pmed.1004399.s004.docx^{(83.4KB, docx)}

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Data Availability Statement

The script and database for this analysis can be found in the following repository: https://github.com/INSP-RH/Brazil_SSB_Tax.

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PERMALINK

Estimated reduction in obesity prevalence and costs of a 20% and 30% ad valorem excise tax to sugar-sweetened beverages in Brazil: A modeling study

Ana Basto-Abreu

Rossana Torres-Alvarez

Tonatiuh Barrientos-Gutierrez

Paula Pereda

Ana Clara Duran

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

Model overview

Fig 1. Representation of the simulation strategy [20–24].

Price elasticities by income level

Baseline intake of beverages

Reduction in energy intake

Adjustment of self-reporting bias on weight and height

Reduction in body weight, BMI, and obesity prevalence

Uncertainty

Sensitivity analysis: Measured weight and height from PNS 2019

Reduction in cases with obesity

Reduction in healthcare costs

Results

Table 1. Price elasticities of beverages by income groups, 2018.

Table 2. Baseline caloric intake from SSB and other beverages (alcoholic, diet/light, and unsweetened beverages).

Table 3. Baseline obesity in Brazil and expected obesity reduction after the implementation of a tax on SSBs, considering caloric changes from sugar-sweetened beverages and from all beverages.

Fig 2. Reduction in cases and cumulative cost savings expressed in 2021 US$ 10 years after implementing an SSB tax, using own-price elasticities considering the effect on sugar-sweetened beverages and using cross-price elasticities considering all beverages.

Discussion

Supporting information

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

Author response to Decision Letter 3

Decision Letter 4

Katrien G Janin

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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