The burden of disease and economic impact of sugar-sweetened beverages’ consumption in Argentina: A modeling study

Ariel Esteban Bardach; Natalia Espínola; Federico Rodríguez Cairoli; Lucas Perelli; Darío Balan; Alfredo Palacios; Federico Augustovski; Andrés Pichón-Riviere; Andrea Olga Alcaraz

doi:10.1371/journal.pone.0279978

. 2023 Feb 23;18(2):e0279978. doi: 10.1371/journal.pone.0279978

The burden of disease and economic impact of sugar-sweetened beverages’ consumption in Argentina: A modeling study

Ariel Esteban Bardach ^1,^2,^*, Natalia Espínola ¹, Federico Rodríguez Cairoli ¹, Lucas Perelli ¹, Darío Balan ¹, Alfredo Palacios ¹, Federico Augustovski ^1,², Andrés Pichón-Riviere ^1,², Andrea Olga Alcaraz ¹

Editor: Vijay S Gc³

PMCID: PMC9949658 PMID: 36821592

Abstract

Background

Approximately two-thirds of Argentine adults are overweight or obese, and 11% have diabetes. Over the last two decades, all population groups have increased their consumption of ultra-processed foods and sugar-sweetened beverages (SSB). We aimed to estimate the disease burden—deaths, events, and costs to the health system—attributed to SSB consumption in Argentina.

Methods

We used a comparative risk assessment framework to estimate the health and economic impacts that would be avoided in a scenario without sugar-sweetened beverage (SSB) consumption. We calculated the direct effects on diabetes, cardiovascular disease, and BMI, and then estimated the effects of BMI on disease incidence. Finally, we applied the population attributable factor to calculate the health and economic burden avoided in Argentina in 2020.

Results

Our model estimated that about 4,425 deaths, 110,000 healthy life years lost to premature death and disability, more than 520,000 cases of overweight and obesity in adults, and 774,000 in children and adolescents would be attributed to SSB Consumption in Argentina. This disease burden corresponds to 23% of type-2 diabetes cases and other significant proportions of cardiovascular disease and cancer. The overweight and obesity costs attributable to SSB totaled approximately $47 million in adults and $15 million in children and adolescents.

Conclusion

A significant number of disease cases, deaths, and health care costs could be attributed to SSB consumption in Argentina. Implementing measures to reduce the sugar content in beverages is a pending debt for the country and could lead to measurable improvements in population health, especially among children and adolescents.

Introduction

Approximately 70% of deaths worldwide are caused by non-communicable diseases (NCDs) [1]. The World Health Organization (WHO) estimates that 77% of premature deaths from NCDs occur in low- and middle-income countries [1]. In addition, NCDs are associated with high attributable costs to health systems and society, so these diseases and economic burdens restrict global development to a considerable degree [2]. In 2015 the United Nations defined the Sustainable Development Goals, which regarded prevention and control of non-communicable diseases as core priorities (SDG 3 or SDG 3.4) [3].

Worldwide, eight million deaths in 2019 were attributable to overweight or obesity—two of the most critical determinants of NCD morbidity and mortality [4]. In the past few years, obesity prevalence worldwide has tripled, with over 1.9 billion overweight adults and 650 million obese adults [5]. Childhood obesity and overweight are rising worldwide, particularly in Latin America and the Caribbean [6]. Studies have also shown that overweight and obesity have a high economic cost in developed and developing countries [7, 8]. For instance, Okunogbe et al. reported that by 2019, the economic impact of overweight and obesity ranged from 0.8% of India’s total gross domestic product (GDP) to 2.4% in Saudi Arabia [8].

Sugar-sweetened beverages (SSB) are high in calories, low in nutritional value, and are one of the primary sources of added sugars in the diet [9]. Consumption varies considerably by sociodemographic characteristics and is higher among young people, men, and the Americas than in other regions [10, 11]. SSB consumption promotes weight gain and increases the risk of other metabolic disorders, such as type 2 diabetes, in adults [12, 13]. Several studies have shown that reducing SSB consumption reduces the risk of overweight and obesity and thus reduces the risk of obesity-related diseases such as cardiovascular and cerebrovascular disease, cancer, musculoskeletal disease, asthma, depression, social isolation, and dental caries, to name a few [14, 15]. SSB consumption has also been linked to weight gain in children and adolescents, according to WHO [16]. People who are overweight or obese are at greater risk of developing severe health problems, such as type 2 diabetes, high blood pressure, asthma, sleep disorders, liver disease, low self-esteem, and social and emotional issues [17]. Several studies have reported an association between consuming sugar-sweetened beverages and increased incidence and mortality from type 2 diabetes mellitus and cardiovascular disease, regardless of BMI [12, 13, 18]. Sugar raises blood glucose levels and promotes insulin resistance, a risk factor for cardiovascular diseases and type 2 diabetes [12, 13, 18].

Treating all conditions associated with SSB consumption has a significant organizational and economic impact on the health system that needs to be measured.

In the last two decades in Argentina, energy consumption from ultra-processed foods has increased by 53%. It now accounts for more than one-third of the total kcal consumed per day, decreasing the consumption of unprocessed or minimally processed foods [19].

The consumption of SSB is high in Argentina, particularly in children. The results of the Second National Nutrition and Health Survey (ENNyS2), conducted in 2018, showed excess weight is the most severe malnutrition problem, with a prevalence of 13.6% in children under the age of 5 and 41.1% in the 5–17 age group [20] showing a high consumption of foods of low nutritional quality with high sugar, fat, and salt content, such as sugary drinks, snack products, candies, and bakery products. According to this survey, children and adolescents consume 40% more SSB, twice as much confectionery, and three times as much candy as adults [20].

Furthermore, around 66% of adults suffer from being overweight or obese, and 11% of Argentineans have been diagnosed with diabetes. Also, adults average 85 liters of SSBs per year [21]. Estimates of the health and economic burden of disease of SSB consumption are valuable information inputs for regional policy decision-making, given this high consumption and budgetary constraints of health systems. They facilitate the evaluation of the potential impact of policies such as taxes, front labeling, advertising restrictions, or changes in the school environment [22, 23]. The Pan American Health Organization (PAHO) prioritizes reducing the consumption of SSBs across the region in its regional action plan [24] by recognizing the importance of modifying the food environment, as it influences preferences, purchasing decisions, and eating behaviors. This manuscript presented the results for Argentina obtained through a collaborative project with researchers, policymakers, and scientists from universities, research centers, and public institutions in Argentina (Institute for Clinical Effectiveness and Health Policy), Brazil (ACT Promoção da Saúde and the University of Rio de Janeiro) El Salvador (Ministry of Health), and Trinidad and Tobago (University of the West Indies). This study aimed to estimate the disease burden in terms of deaths, events, Disability-Adjusted Life Years (DALYs), and costs to the health system that may be attributed to SSB consumption in Argentina for 2020.

Methods

Model structure

We used a comparative risk assessment framework to estimate the health and economic effects of sugar-sweetened beverage (SSB) consumption in one year. The causal framework of the model forecasts the health effects of SSB consumption through two main pathways. One is mediated by the impact of SSB consumption on body mass index (BMI). The other path reflects the independent effects of SSB consumption on diabetes mellitus type II and cardiovascular disease (see Fig 1). The design of the model and the selection of outcomes followed a systematic review of SSB models available in the international scientific literature [25] and a policy dialogue held in Buenos Aires in 2018, with the participation of 35 experts and decision-makers from seven countries of Latin America and the Caribbean (for more information, please visit the official report of this policy dialogue [26]). These activities aimed to shape the development of the model so that it can adequately meet the information needs of decision-makers and contribute to the design and implementation of effective public policies to control SSB in the region.

The model estimated only the impact of SSB consumption on the prevalence of overweight and obesity for children and adolescents without extrapolating the long-term effects on this population.

We used the Population Attributable Fraction (PAF) to calculate the impact of SSB intake on each health event. The PAF estimates the proportional reduction in population disease (incidence or prevalence) or mortality that would occur if exposure to a risk factor (SSB current consumption) were reduced to a minimal theoretical risk exposure scenario (zero consumption). The number of deaths, cases, and costs attributed to a risk factor was quantified by applying the population attributable factor to the total number of deaths, cases, or costs. The equation used to calculate the PAF is as follows (for more information please see S1 File):

P A F = 1 - \frac{1}{\sum_{i = 1}^{n} R R (X i; θ k)}

Where X_i represents the consumption of sugar-sweetened beverages (servings per day) by sex and simple age; θk corresponds to the relative risk value reported per one unit daily serving increase in sugar-sweetened beverage consumption for each simple age and sex, and finally, RR (Xi; θk) represents the specific relative risk for each disease by simple age and sex, according to consumption level.

We followed three consecutive steps to estimate the impact of SSB consumption on Argentina’s health and economic burden for 2020. As a first step, we calculated the direct effect of changes in SSB consumption on diabetes, CVD, and BMI. Secondly, we estimated the impact of BMI on disease incidence. Finally, we applied the population attributable factor to estimate the health and economic burden attributable to SSB.

The model calculated the number of cases, deaths, DALYs, and costs attributable to SSB. Disease burden was estimated by comparing Argentina’s disease events, deaths, and associated costs based on current SSB consumption (baseline) prevalence with a counterfactual scenario without SSB consumption. We used Monte Carlo simulation for the uncertainty analyses. We examined the impact of uncertainty related to SSB consumption and the RR linking SSB with BMI, diabetes, and CVD on the results. The results were generated by 1000 iterations using the distribution of SSB consumption (mean and standard error) and the relative risks for disease incidence (using normal distributions and standard deviations calculated from the 95% confidence intervals) per gender and age group. We reported the mean and 95% confidence interval. We performed our analysis using Stata, version 14.04, and Visual Basic Excel.

Epidemiological data

Concerning the epidemiological parameters, we prioritized the following sources of data:

1) Argentinean sources when available, 2) Latin American sources when local data was not available, 3) international sources, and 4) estimates from the research group when data was not considered "transferable" from other sources.

Sugar-sweetened beverage (SSB) consumption

We defined SSBs, based on Singh et al. [10], as the following ones: sports and energy drinks, sugar-sweetened sodas, sugar-sweetened fruit juices, and sugar-sweetened and flavored waters. Sugar-sweetened dairy products -like coffee, mate, or tea- and homemade SSBs were not included. One serving was defined as the intake of 240 ml of any SSB.

For SSB consumption, Argentinean official data obtained from national population-based surveys were preferred as sources for children, adolescents, and adults [20, 21, 27]. For children (0–11 years), SSB consumption data was obtained from the second National Nutrition and Health Survey (ENNyS2) [20], while for adolescents (11–18 years), this data was obtained from the Argentinean Global School-based Student Health Survey (2018) [27]. Finally, data on SSB consumption was obtained from the Fourth National Risk Factor Survey for adults [21].

SSB impact on BMI and other diseases

We defined overweight and obesity, from BMI values, according to the World Health Organization [28].

An increase of 0.10 (95% CI, 0.05–0.15) kg/m² in BMI in subjects with basal BMI <25, and an increase of 0.23 (95% CI, 0.14–0.32) kg/m² in BMI in subjects with basal BMI ≥25, was inputted per each serving of SSB consumed per day in adults [29]. Also, BMI reductions of ~0.57 kg/m² were calculated for a 1.7 servings reduction in SSB in children [30].

Overweight and obesity prevalence data were obtained from the Fourth National Risk Factor Survey (2018) [21]. Data were gathered on each disease’s demographic structure, incidence, prevalence, and mortality, stratified by gender and age. We used the DISMOD II software [31] to model missing data regarding incidence, prevalence, or case-fatality rate for each disease, sex, and age when not available. We used different relative risk (RR) values to quantify both the direct path (the association of SSB daily consumption on incidence/mortality of diabetes mellitus type 2 and cardiovascular diseases) and the indirect pathway (effects of SSB consumption on remaining conditions through BMI).

Concerning the direct path, and based on Imamura et al. [12], we assumed a RR value of 1.37 for diabetes type 2 incidence (also assuming that it consequently affects prevalent cases and mortality in the same way) per SSB serving per day. For cardiovascular disease, a RR of 1.08 (95% CI, 1.04 to 1.13) and 1.08 (95% CI, 1.02 to 1.14) per serving per day were considered for incidence/prevalence and mortality, respectively, based on Yin et al. [13].

On the other hand, we modeled the remaining diseases included in the model (esophageal, colon-rectum, uterine body, kidney and gallbladder, and biliary tract cancer, osteoarthritis, low back pain, asthma, dementia, chronic kidney disease, biliary tract, and gallbladder diseases,) through a mediated BMI increase (indirect pathway). Given that a direct RR was already being applied for the association between the consumption of SSBs and cardiovascular events or diabetes events, and these RRs were not adjusted for BMI, we did not include RRs for the association between BMI and cardiovascular events or diabetes to avoid double counting. The respective RR values of developing these diseases from different basal BMI measures were obtained from the Global Burden of Disease study (GBD, Institute for Health Metrics and Evaluation) [4].

Most of the model inputs are differentiated by age and gender: BMI, SSB consumption, population, epidemiological data (incidence, prevalence, mortality), and the RR of BMI on the events (included in the Supporting Information, S1 Table in S1 File). However, no data disaggregated by age and gender was found regarding the association in terms of RR between SSB consumption on BMI, CVD, and diabetes.

Direct medical costs

The direct medical costs (including diagnosis, treatment, and follow-up) were estimated for each condition considered in the model from a healthcare perspective. These direct medical costs represent a weighted average of the prices of Argentina’s three main healthcare subsectors (public, social security, and private). In turn, the costs were differentiated in the first year and subsequent years to distinguish and approximate the difference in the expenses of incident and prevalent cases. The research team estimated the healthcare costs through a micro-costing approach, depending on the availability and quality of the data. A Microsoft Excel spreadsheet was designed to estimate the direct medical cost of the following health conditions: type 2 diabetes (with and without complications), overweight, obesity type 1, 2, and 3, acute myocardial infarction and its follow-up, heart failure, kidney insufficiency (with and without dialysis) and stroke. These micro-costing spreadsheets were developed based on the clinical experience of local physicians and, when necessary, from local disease management guidelines [32, 33]. The direct medical costs were estimated in the year 2020, in local currency units (Argentinean pesos) and then converted to US dollars using the exchange rates of 2020, ARS 48.14 to 1 US dollar ($), published by the Central Bank of Argentina [34].

Main epidemiological and economic inputs

The key epidemiological and economic parameters included in the model are shown in Tables 1 and 2. The average SSB consumption in the Argentine population was one serving per day in adults and 1.5 servings per day in children and adolescents, with significant differences between sexes and age groups (see Table 1).

Table 1. Key epidemiological parameters.

			Total Population (millions)	Body Mass Index (Mean)	Mean daily SSB intake (8 oz servings/day)	Overweight (% total population)	Grade 1 Obesity (% total population)	Grade 2 Obesity (% total population)	Grade 3 Obesity (% total population)
Adults	Women	[18–44]	8.4	26.7	1.1	27.4%	14.9%	6.9%	3.7%
		[45–64]	4.3	29.4	0.8	29.6%	27.3%	9.0%	6.5%
		[65+)	2.8	29.8	0.5	33.9%	26.3%	10.8%	6.3%
		Overall	15.6	28.4	0.8	30.0%	22.0%	9.0%	7.0%
	Men	[18–44]	8.7	26.9	1.7	35.5%	17.1%	5.6%	2.0%
		[45–64]	4.0	29.5	1.2	42.7%	27.9%	9.6%	2.6%
		[65+)	2.0	29.2	0.6	43.0%	28.7%	9.0%	2.1%
		Overall	14.7	28.4	1.1	39.0%	24.0%	8.0%	3.0%
	Both	[18–44]	17.1	26.8	1.4	31.3%	15.9%	6.3%	2.9%
		[45–64]	8.4	29.4	1.0	35.8%	27.6%	9.3%	4.6%
		[65+)	4.7	29.5	0.6	38.0%	27.4%	10.0%	4.4%
		Overall	30.2	28.4	1.0	35.0%	23.0%	9.0%	5.0%
Children and adolescents	Girls	[0–4]	1.8	NA (^*)	0.7	11.0%	3.1%
		[5–17]	4.5		1.6	22.3%	17.6%
		Overall	6.3		1.4	17.0%	10.0%
	Boys	[0–4]	1.9		0.7	9.1%	4.0%
		[5–17]	4.7		1.9	19.3%	22.9%
		Overall	6.7		1.6	14.0%	13.0%
	Both	[0–4]	3.8		0.7	10.0%	3.6%
		[5–17]	9.2		1.8	20.7%	20.4%
		Overall	13.0		1.5	16.0%	11.5%
Sources			1	2	3	2;3	2;3	2;3	2;3

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Note

(*) This input was not used by the model since the impact of SSB consumption through BMI on other conditions was not modeled in children.

1. Instituto Nacional de Estadística y Censos de la República Argentina. [cited 19 Feb 2021]. Available at: https://www.indec.gob.ar/indec/web/Nivel4-Tema-2-24-84

2. Ministerio de Salud de Argentina. Cuarta Encuesta Nacional de Factores de Riesgo.

Available at: https://bancos.salud.gob.ar/sites/default/files/2020-01/4ta-encuesta-nacional-factores-riesgo_2019_principales-resultados.pdf

3. Local expert estimates, based on the First National Survey of Nutrition and Health, Argentina (ENNYS).

Available at: http://www.extensioncbc.com.ar/wp-content/uploads/ENNyS-2007.pdf)

Table 2. Other epidemiological inputs and key economic parameters.

Diseases	Incidence rate (per 100,000 population)	Prevalence rate (per 100,000 population)	Mortality rate (per 100,000 population)	Disability weights	Costs per incident event ($)	Costs per prevalent event ($)
Alzheimer disease and dementias	798	4311	483	0.15	$ 1,206
Asthma	251	4118	4.84	0.04	$ 869
Atrial fibrillation and flutter	139	2462	63.11	0.08	$ 573	$ 1,804
Ischemic heart disease	835	3409	476	0.03	$ 7,613	$ 1,209
Chronic kidney disease	152	3019	21.15	0.04	$ 869
Colon and rectum cancer	101	283	82.01	0.06	$ 8,780	$ 1,146
Diabetes Mellitus type 2	356	12363	71.91	0.07	$ 1,414
Esophageal cancer	16.39	14.20	16.16	0.20	$ 13,529	$ 9,163
Gallbladder and biliary diseases	245	1010	12.46	0	$ 162
Gallbladder and biliary tract cancer	20.04	12.13	19.62	0.28	$ 10,952	$ 7,497
Hypertensive heart disease	812	1296	121	0.08	$ 1,058
Stroke (ischemic and intracerebral hemorrhage)	184	1371	133	0.15	$ 1,718
Subarachnoid hemorrhage	36.33	234	17.28	0.15	$ 3,298
Kidney cancer	24.40	99.26	13.92	0.07	$ 11,597	$ 8,122
Low back pain	6282	15901	0	0.11	$ 10
Osteoarthritis	346	10592	0	0.03	$ 223
Uterine cancer	21.87	120	10.41	0.07	$ 5,492	$ 543
Overweight (< 18 years)	NA				$ 8
Obesity (< 18 years)					$ 24
Overweight (Adults)					$ 0
Grade 1 Obesity (Adults)					$ 10
Grade 2 Obesity (Adults)					$ 139
Grade 3 Obesity (Adults)					$ 536
Country Economic Data
Gross Domestic Product Per Capita (USD)	9,912
Percentage of GDP spending on health	9.1%
USD Exchange rate	48.14

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Source: Argentine Central Bank (2020), own elaboration through micro costing. USD Exchange rate. Argentina: 48.14; Source: http://www.bcra.gov.ar/

For events other than CVD and Cancer the cost represents an average of the annual costs of diagnosis and management for the condition

Results

Overweight and obesity

Our model estimated that more than 520,000 cases of overweight and obesity in adults and 774,000 in children and adolescents might be attributable to SSB consumption in Argentina. (Table 3). Fig 2 shows the total number of cases of overweight and obesity and the costs attributable to SSB consumption by sex and age group. Fig 2 panel A shows that children and adolescents diagnosed with obesity incurred costs of more than $ 15 million (2020). Boys were more affected than girls concerning the total number of obesity events by 25% (relative terms). Also, boys aged 5 to 11 showed the highest burden of obesity events: almost four times more than the lowest vulnerable group among children (girls aged 0 to 4 group). Men and women aged 18 to 44 years were most affected, accounting for 77% of the total cases among adults (Fig 2, panel B). Also, the overweight and obesity costs attributable to SSB totaled approximately $47 million in adults (2020).

Table 3. Health and economic burden attributed to SSBs consumption in adults: Mortality, disease events, DALYs, and direct medical costs, by cause and sex.

Condition	Women		Men		Both
	Total (%^*)	CI 95%	Total (%^*)	CI 95%	Total (%^*)	CI 95%
Obesity and Overweight (<18 y)
Events	318,855 / 2,053,392 (16%)	[153,592–503,791]	455,164 / 2,251,656 (20%)	[219,252–719,159]	774,018 / 4,305,048 (18%)	[372,839–1,223,565]
Direct medical cost, USD (million)	$ 5.77 / $29.4 (20%)	[$4.2-$8.2]	$ 9 / $36 (25%)	[$3.6-$15]	$ 14.8 / $ 65.4 (23%)	[$ 8 - $ 23]
Obesity and Overweight (adults)
Events	169,061 / 9,897,071 (2%)	[117,209–288,925]	351,702 / 10,212,675 (3%)	[243,835–601,058]	520,762 / 20,109,746 (3%)	[361,044–890,222]
Direct medical cost, USD (million)	$ 28.08 / 619.86 (5%)	[$27-$49]	$ 19.2 / $350 (5%)	[$18-$34]	$ 47.3 / $ 969.8 (5%)	[$ 45.5 - $ 83.2]
Diabetes Mellitus
Events	281,984 / 1,406,057 (20%)	[126,846–417,421]	356,887 / 1,349,033 (27%)	[169,944–505,523]	638,871 / 2,755,090 (23%)	[296,851–922,944]
Deaths	563 / 3,388 (17%)	[246–856]	754 / 3,633 (21%)	[341–1,114]	1,317 / 7,021 (19%)	[592–1,970]
DALYs	27,007 / 139,603 (19%)	[12,291–40,445]	34,925 / 138,834 (25%)	[16,904–50,282]	61,932 / 278,437 (22%)	[29,901–94,542]
Direct medical cost, USD (million)	$ 398.80 / $ 1,988.55 (20%)	[$181 - $597]	$ 504.74 / $1,908 (26%)	[$244-$727]	$ 903 / $ 3,896 (23%)	[$ 420 - $ 1,305]
Cardiac conditions ^**
Events	18,456 / 437,411 (4%)	[8,746–28,439]	35,119 / 588,503 (6%)	[16,866–53,402]	53,575 / 1,025,944 (5%)	[25,612–81,840]
Deaths	740 / 18,153 (4%)	[350–1,141]	1,061 / 18,844 (6%)	[508–1,618]	1,801 / 36,997 (5%)	[858–2,759]
DALYs	7,225 / 159,545 (5%)	[3,420–11,119]	15,664 / 208,848 (8%)	[7,528–23,834]	22,889 / 393,334 (6%)	[9,817–31,540]
Direct medical cost, USD (million)	$ 32.6 / $ 772 (4%)	[$15 - $50]	$ 65.1 / $ 1,074 (6%)	[$31-$99]	$ 97.7 / $ 1846 (5%)	[$ 47- $ 149]
Cerebrovascular conditions
Events	13,606 / 283,518 (5%)	[6,481–20,855]	15,425 / 231,778 (7%)	[7,453–23,316]	29,031 / 515,296 (6%)	[13,935–44,171]
Deaths	482 / 11,235 (4%)	[229–743]	574 / 9,920 (6%)	[275–873]	1,056 / 21,155 (5%)	[504–1,616]
DALYs	8,072 / 161,099 (5%)	[3,843–12,367]	10,652 / 153,406 (7%)	[5,155–16,125]	18,724 / 314,504 (6%)	[9,157–29,284]
Direct medical cost, USD (million)	$ 27.90 / $ 572.93 (5%)	[$13-$43]	$29.6 / $435 (7%)	[$15-$45]	$ 57.5 / $ 1,008 (6%)	[$ 28 - $ 87]
Chronic kidney conditions
Events	11,920 / 1,087,740 (1%)	[7,937–19,198]	9,508 / 581,952 (2%)	[6,254–15,250]	21,428 / 1,669,691 (1.3%)	[14,395–33,962]
Deaths	37 / 3,645 (1%)	[25–60]	53 / 3,536 (2%)	[35–87]	90 / 7,181 (1.3%)	[60–145]
DALYs	708 / 58,643 (1%)	[457–1,104]	963 / 52,808 (2%)	[604–1,473]	1,671 / 111,451 (1.5%)	[1,132–2,723]
Direct medical cost, USD (million)	$ 10.37 / $ 945.51 (1%)	[$7-$16]	$ 8.3 / $506 (2%)	[$5-$12]	$ 18.6 / $ 1451.4 (1.3%)	[$ 13 - $ 30]
Asthma
Events	7,878 / 968,062 (0.8%)	[5,223–12,395]	8,111 / 626,449 (1%)	[5,330–12,570]	15,989 / 1,594,510 (1%)	[10,581–25,097]
Deaths	2 / 303 (0.7%)	[2–4]	2 / 183 (1%)	[1–3]	4 / 486 (0.9%)	[3–7]
DALYs	334 / 40,245 (0.8%)	[216–513]	341 / 25,749 (1%)	[220–519]	675 / 65,994 (1%)	[472–1,116]
Direct medical cost, USD (million)	$ 6.86 / $ 841.44 (0.008%)	[$4-$11]	$ 7 / $545 (1%)	[$5-$11]	$ 13.9 / $ 1,386 (1%)	[$ 9- $ 22]
Musculoskeletal Conditions
Events	14,652 / 5,313,518 (0.3%)	[9,741–23,101]	19,125 / 4,143,750 (0.5%)	[12,556–29,894]	33,777 / 9,457,267 (0.4%)	[22,230–53,137]
Deaths	0/0	[0–0]	0/0	[0–0]	0/0	[0–0]
DALYs	1,011 / 361,979 (0.3%)	[655–1,554]	1,386 / 294,635 (0.5%)	[892–2,125]	2,397 / 656,615 (0.4%)	[2,111–5,045]
Direct medical cost, USD (million)	$ 0.85 / $ 325.86 (0.3%)	[$0.55-$1.31]	$ 0.8 / $195 (0.4%)	[$0,5-$1,2]	$ 1.6 / $ 520.3 (0.3%)	[$ 1 - $ 3]
Other conditions ^***
Events	6,311 / 581,030 (1%)	[4,195–9,877]	2,870 / 280,495 (1%)	[1,889–4,612]	9,181 / 861,525 (1%)	[6,159–14,421]
Deaths	81 / 19,838 (0.4%)	[53–131]	76 / 13,122 (0.6%)	[50–128]	157 / 32,061 (0.5%)	[106–253]
DALYs	910 / 191,118 (0.5%)	[590–1,389]	1,093 / 155,555 (0.7%)	[694–1,695]	2,003 / 346,674 (0.6%)	[1,382–3,290]
Direct medical cost, USD (million)	$ 3.26 / $ 553.22 (0.6%)	[$2-$5]	$ 3.6 / $489 (0.8%)	[$3-$6]	$ 6.9 / $ 1,042 (0.7%)	[$ 5 - $ 11]
Total
Events	842,722 / 22,027,829 (3%)	[439,970–1,324,2]	1,253,911 / 20,356,290 (6%)	[726,21–1,924,657]	2,096,631 / 42,294,119 (5%)	[1,361,844–3,3132,30]
Deaths	1,905 / 56,562 (3%)	[1,203–2,654]	2,520 / 49,238 (5%)	[1,589–3,500]	4,425 / 105,799 (4.2%)	[2,773–6,146]
DALYs	45,267 / 1,112,231 (4%)	[21,472–68,491]	65,024 / 1,054,778 (6%)	[31,997–96,053]	110,290 / 2,167,009 (5%)	[53,972–167,540]
Direct medical cost, USD (million)	$ 508.64 / $ 6,658 (8%)	[$252-$778]	$647 / $5,537 (12%)	[$326-$4,973]	$ 1,152.5 / $ 12,194.6 (10%)	[$ 687.2 - $ 1633.6]

Open in a new tab

* Percentages indicate the proportion of all cases of X disease (i.e., diabetes) that could be attributed to SSBs consumption; for example: In Argentina, 513,958 cases of overweight + obesity in adults could be attributable to SSBs consumption, which represents 2.5% of all cases of overweight + obesity in Argentina. The same applied to direct cost results.

** Includes: Atrial fibrillation, Ischemic heart disease, Hypertensive heart disease

*** Includes: Cancer, Alzheimer’s, other dementias, Gallbladder, and biliary disease

USD Exchange rate. Argentina: 48.14. Source: https://data.worldbank.org/indicator/PA.NUS.FCRF

Fig 2 — Panel A: Children and Adolescents. Panel B: Adults. USD Exchange rate. Argentina: 48.14. Source: https://data.worldbank.org/indicator/PA.NUS.FCR.

Cardiovascular, diabetes type 2, and other diseases

Specifically, 23% of all cases of diabetes (639,000 patients in 2020) and other diseases, such as heart disease, cerebrovascular disease, and cancer, with 163,000 affected during this period, could be attributed to SSB consumption in Argentina. As shown in Table 3, type 2 diabetes mellitus would seem to be the disease with the highest burden among diseases attributable to SSB in Argentina, with 19% of the burden of attributable deaths, 22% of DALYs, and 23% of attributable events. Men were more affected than women by 35% concerning events and 32% concerning deaths (relative terms). Also, in terms of costs, approximately $903 million (direct cost of treating diabetes 2) could be saved in the case of non-SSB consumption, representing 23% of the total cost of the disease in Argentina.

Cardiac and cerebrovascular disease account for about 5–6% of attributable costs. Men showed to be more affected in both cases: 50% and 60% more events and deaths for cardiac disease and 40% more events and deaths for cerebrovascular illness than women. Chronic kidney disease, asthma, musculoskeletal disease, and other attributable diseases account for 0.5–1.5% of the burden.

In sum, under a theoretical scenario of zero SSB consumption in Argentina, 4,425 deaths and 110,000 healthy life-years lost to premature death and disability could be averted. The health system spends $1,153 million each year on the treatment of related diseases, which represents 2.8% of the country’s health expenditure. Fig 3 shows the disease burden that could be avoided in a case of non-SSB consumption in adults in Argentina. The most affected group in terms of events are men aged 45 to 64 years. Also, in terms of fatalities, men aged 65 years or more closely followed by women in the same age group.

Fig 3 — Sugar sweetened beverages (SSBs) attributable events (Panel A) and deaths (Panel B) in adults by sex and age.

Uncertainty analysis

The findings obtained from the uncertainty analysis are presented in Table 3 as 95% CI around the central value of the number of events, deaths, DALYs, and costs for each condition and gender.

Discussion

In this study, through a simulation model tailored for Latin America, we estimated the health and economic burden that could be attributed to SSB consumption in Argentina.

Our model estimated that approximately 4,000 deaths, two million events of illness, 520,000 cases of overweight or obesity in adults, 774,000 of overweight or obesity in children and adolescents, and 1.15 billion US dollars spent in direct medical costs could be attributed to SSB consumption in Argentina.

Our results are consistent with previous studies in both the direction and magnitude of the effects of SSB on health and economic outcomes. The study by Singh et al. [10], which considered only the BMI-mediated effects of SSB consumption, had already found a substantial absolute and proportional burden of SSB-related mortality and morbidity in LAC compared with other regions in 2010. These findings were interpreted in the context of the low cost of SSB in the area at the time, the suboptimal implementation of taxes and regulation of advertising, and limited access to safe drinking water. Ten years later, conditions are virtually unchanged in many Latin American and Caribbean countries. Also, in line with our findings, the B.A.S.T.A. project ("Bebidas Azucaradas, Salud y Tarifas en Argentina") estimated that over the period 2015–2024, a 10% reduction in SSB consumption would reduce diabetes cases by 13,300 to 27,700 and heart attacks by 2,500 to 5,100. Also, in this study, the most considerable expected reduction in incidence occurred in the youngest group modeled (35–44 years), particularly among men [35].

Countries in Latin America have a high consumption of unhealthy products. This fact is partly responsible for the epidemic of chronic disease that has afflicted the region in recent decades and has been rendered invisible. Latin America and the Caribbean are regions where the burden of illness and death attributable to SSB is disproportionately high. The excessive tendency of sugar consumption in Latin American countries—especially in Argentina—is a traditional fact that may be explained partly by specific population characteristics, such as a greater affinity for sweet taste [36]. There are several natural targets for policy intervention: low prices for SSBs, lack of regulation of advertising, lack of awareness among the population about the risks associated with SSBs, and lack of warning labels on food in many countries, among others.

Argentina recently passed Law 27642 [37] to promote healthy eating, which states that beverages and packaged foods in which the content of critical nutrients and their energy value exceed the established levels must include a black octagonal warning on the main page for each excess nutrient, as follows: "Excess Sugar Content"; "Excess Sodium Content"; "Excess Saturated Fat Content"; "Excess Total Fat Content"; "Excess Calorie Content." The law also prohibits all forms of advertising, promotion, and sponsorship of food and beverages specifically aimed at children and adolescents and containing at least one warning label. They may not be offered or marketed in educational institutions of the first and second levels of the national education system.

Although Argentina has recently approved the labeling law, it is essential to start implementing the policy of healthy taxes in Argentina and other countries in the region to decrease consumption [38]. There is evidence that increasing taxes can significantly benefit to the public, and Mexico was the first country to successfully promote this type of change in the region [39].

In Brazil, Claro et al. found that increases in the price of SSBs were associated with a reduction in consumption. As a result of a 1.0% increase in the SSB prices, 0.85% fewer calories were consumed [40].

Furthermore, reformulating SSBs to reduce sugar content is a debt for several countries and could result in measurable reductions in consumption in Latin America [39].

There is already evidence of the effectiveness of labeling, which would be a measure that could be applied to sugar-sweetened drinks. For example, research has been conducted for Mexico on the positive public health impact that warning labeling could have in effectively reducing obesity and its associated costs. This used data from national consumption surveys and the expected effects of labeling, based on experimental studies showing a reduced calorie consumption of up to 10.5% in the case of beverage labeling. Mexico, Chile, Peru, and Uruguay have already introduced labeling for highly processed foods and are starting to benefit from these changes [41].

These and other studies that address the burden of disease can contribute to drawing the attention of the public and policymakers to this crucial public health problem and serve as an argument to support the policies that many countries strive to implement. These burden of disease studies, in turn, are a necessary step for the developing studies that can measure the expected impact of various interventions.

A quasi-experimental study comparing the consumption of sugar-sweetened beverages with the trend before the passage of Chile’s pioneering law on front-end labeling and advertising restrictions on labeled products found a reduction of 22.8 ml/day and 11.9 calories/day in the consumption of beverages with excess sugars, with a more marked relative reduction at higher socioeconomic levels [42]. One of the most critical aspects of this research was the enormous impact on the decrease in consumption of beverages with at least one warning label (-23.7%) of this measure compared to the tax increases that had previously occurred in Chile (-3.4%) and Mexico (-7.6%) [42]. A similar law has recently been passed and regulated in Argentina, although its regulations have not yet come into force.

Our investigation had several strengths. We developed a model adapted to the availability and quality of epidemiological and cost data in Argentina and formally addressed the information needs of policymakers in the region. We included sound evidence on the direct effects of SSB on BMI, diabetes mellitus, cardiovascular disease, and BMI on overweight and obesity-related conditions. The inclusion of the direct impact of SSB on cardiovascular disease in our estimates is an innovation over the most previous burden of disease studies. It has been instrumental in producing results that more accurately reflect the epidemiological reality. One of the main limitations of our study is that the quality and availability of some epidemiological parameters for Argentina are not optimal. However, our results show that SSBs are responsible for a substantial disease burden, probably much higher than our estimates. For instance, in our analysis, we only considered the main health consequences of SSB. Still, other vital dimensions, such as the impact on oral cavities or the social implications for children and adults due to obesity, could play an important role. Moreover, we only consider the direct medical costs spent that could be avoided. These are only a part of the total financial burden when other dimensions such as lost productivity are considered. As another limitation, our study estimated the attributable burden of disease considering a hypothetical counterfactual scenario of zero SSB consumption. It could be possible that some of the burden of disease that we reported as attributed to SSB consumption still exists if people switch from SSB consumption to other harmful food or drink (due to a probable caloric compensation) in a scenario of no SSB consumption. However, this does not mean that the current disease burden attributable to the consumption of SSB is less than that reported in our analysis.

Research gaps include analyzing the direct/indirect costs of SSB exposure in Latin American countries, determining the expected impact on obesity of enforcing formal bans on SSB advertising in the media and social networks, assessing the impact of labeling in Argentina, and examining the combined effect of different food policies enacted together.

Conclusion

The present study results show that SSB consumption causes a significant number of diseases, deaths, and health care costs in Argentina. This study is the first to assess the financial burden of SSB consumption in the country, including children and a wide range of attributable conditions.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

Acknowledgments

The authors thank Mr. Daniel Comandé, librarian at IECS for their help with the search strategies, Dr Agustín Ciapponi, for his help in the initial phases of the study, BSc Natalia Elorriaga for her support with public health nutrition, and Bsc Mariana Comolli for her help with dissemination of findings.

Data Availability

All relevant data are within the paper.

Funding Statement

This study received financial support from the International Development Research Centre (IDRC), Canada, Project Number: 108646-001. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Noncommunicable diseases. [cited 20 Apr 2022]. Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [Google Scholar]
2.Murphy A, Palafox B, Walli-Attaei M, Powell-Jackson T, Rangarajan S, Alhabib KF, et al. The household economic burden of non-communicable diseases in 18 countries. BMJ Glob Health. 2020;5: e002040. doi: 10.1136/bmjgh-2019-002040 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.United Nations. Partnership for the Goals. The Sustainable Development Goals Report. United Nations; 2020. pp. 58–59. [Google Scholar]
4.GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396: 1223–1249. doi: 10.1016/S0140-6736(20)30752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377: 13–27. doi: 10.1056/NEJMoa1614362 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Rivera JÁ, de Cossío TG, Pedraza LS, Aburto TC, Sánchez TG, Martorell R. Childhood and adolescent overweight and obesity in Latin America: a systematic review. Lancet Diabetes Endocrinol. 2014;2: 321–332. doi: 10.1016/S2213-8587(13)70173-6 [DOI] [PubMed] [Google Scholar]
7.Keller A, Bucher Della Torre S. Sugar-Sweetened Beverages and Obesity among Children and Adolescents: A Review of Systematic Literature Reviews. Child Obes. 2015;11: 338–346. doi: 10.1089/chi.2014.0117 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Okunogbe A, Nugent R, Spencer G, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for eight countries. BMJ Glob Health. 2021;6: e006351. doi: 10.1136/bmjgh-2021-006351 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Malik VS, Hu FB. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nat Rev Endocrinol. 2022;18: 205–218. doi: 10.1038/s41574-021-00627-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Singh GM, Micha R, Khatibzadeh S, Lim S, Ezzati M, Mozaffarian D, Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Estimated Global, Regional, and National Disease Burdens Related to Sugar-Sweetened Beverage Consumption in 2010. Circulation. 2015;132: 639–666. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.CDC. Get the facts: Sugar-sweetened beverages and consumption. In: Centers for Disease Control and Prevention [Internet]. 11 Apr 2022. [cited 20 Apr 2022]. Available: https://www.cdc.gov/nutrition/data-statistics/sugar-sweetened-beverages-intake.html [Google Scholar]
12.Imamura F, O’Connor L, Ye Z, Mursu J, Hayashino Y, Bhupathiraju SN, et al. Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. Br J Sports Med. 2016;50: 496–504. doi: 10.1136/bjsports-2016-h3576rep [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Yin J, Zhu Y, Malik V, Li X, Peng X, Zhang FF, et al. Intake of sugar-sweetened and low-calorie sweetened beverages and risk of cardiovascular disease: A meta-analysis and systematic review. Adv Nutr. 2021;12: 89–101. doi: 10.1093/advances/nmaa084 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Choi SE, Wright DR, Bleich SN. Impact of restricting sugar-sweetened beverages from the Supplemental Nutrition Assistance Program on children’s health. Am J Prev Med. 2021;60: 276–284. doi: 10.1016/j.amepre.2020.08.023 [DOI] [PubMed] [Google Scholar]
15.Chen L, Appel LJ, Loria C, Lin P-H, Champagne CM, Elmer PJ, et al. Reduction in consumption of sugar-sweetened beverages is associated with weight loss: the PREMIER trial. Am J Clin Nutr. 2009;89: 1299–1306. doi: 10.3945/ajcn.2008.27240 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.World Health Organization. WHO | Reducing consumption of sugar-sweetened beverages to reduce the risk of childhood overweight and obesity. 2018. [cited 22 Aug 2021]. Available: https://www.who.int/elena/titles/bbc/ssbs_childhood_obesity/en/ [Google Scholar]
17.Health risks of overweight & obesity. In: National Institute of Diabetes and Digestive and Kidney Diseases [Internet]. NIDDK | National Institute of Diabetes and Digestive and Kidney Diseases; 29 Aug 2022. [cited 15 Sep 2022]. Available: https://www.niddk.nih.gov/health-information/weight-management/adult-overweight-obesity/health-risks [Google Scholar]
18.Pacheco LS, Lacey JV Jr, Martinez ME, Lemus H, Araneta MRG, Sears DD, et al. Sugar-sweetened beverage intake and cardiovascular disease risk in the California Teachers Study. J Am Heart Assoc. 2020;9: e014883. doi: 10.1161/JAHA.119.014883 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.de Estudios sobre Nutrición Infantil (CESNI). C. La mesa argentina en las últimas dos décadas. Available: Disponible en: http://www.unsam.edu.ar/tss/wp-content/uploads/2017/06/CESNI-La-mesa-argentina-en-las-ultimas-dos-d%C3%83%C2%A9cadas-2016.pdf [Google Scholar]
20.Base de datos de la 2° Encuesta Nacional de Nutrición y Salud (ENNyS2) 2018–2019. [cited 20 Apr 2022]. Available: http://datos.salud.gob.ar/dataset/ennys2 [Google Scholar]
21.INDEC, Ciudad Autónoma de Buenos Aires: Secretaría de Gobierno de Salud de la Nación 2019. 4° Encuesta Nacional de Factores de Riesgo. Resultados definitivos. - 1a ed.-Ciudad Autónoma de Buenos Aires: Instituto Nacional de Estadística y Censos; -, editor. [Google Scholar]
22.Barrientos-Gutierrez T, Zepeda-Tello R, Rodrigues ER, Colchero MA, Rojas-Martínez R, Lazcano-Ponce E, et al. Expected population weight and diabetes impact of the 1-peso-per-litre tax to sugar sweetened beverages in Mexico. PLoS One. 2017;12: e0176336. doi: 10.1371/journal.pone.0176336 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Basto-Abreu A, Torres-Alvarez R, Reyes-Sánchez F, González-Morales R, Canto-Osorio F, Colchero MA, et al. Predicting obesity reduction after implementing warning labels in Mexico: A modeling study. PLoS Med. 2020;17: e1003221. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Pan American Health Organizatoin. STRATEGIC PLAN OF THE PAN AMERICAN HEALTH ORGANIZATION 2014–2019. [cited 22 Nov 2020]. Available: https://iris.paho.org/handle/10665.2/7654 [Google Scholar]
25.Alcaraz A, Pichon-Riviere A, Palacios A, Bardach A, Balan DJ, Perelli L, et al. Sugar sweetened beverages attributable disease burden and the potential impact of policy interventions: a systematic review of epidemiological and decision models. BMC Public Health. 2021;21: 1460. doi: 10.1186/s12889-021-11046-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Documento T, N^o XX. POLÍTICAS FISCALES PARA REDUCIR EL CONSUMO DE BEBIDAS AZUCARADAS Evidencia para la implementación de políticas de reducción del consumo de bebidas azucaradas. [cited 20 Apr 2022]. Available: https://www.iecs.org.ar/wp-content/uploads/Informe-Policy-dialogue-Azucar.pdf [Google Scholar]
27.Encuesta Mundial de Salud Escolar 2018. In: Fagran [Internet]. 27 Jan 2020. [cited 20 Apr 2022]. Available: https://fagran.org.ar/documentos/seccion/organismos-nacionales/2020/01/encuesta-mundial-de-salud-escolar-2018/ [Google Scholar]
28.Obesity. [cited 20 Apr 2022]. Available: https://www.who.int/health-topics/obesity
29.Ebbeling CB, Feldman HA, Chomitz VR, Antonelli TA, Gortmaker SL, Osganian SK, et al. A randomized trial of sugar-sweetened beverages and adolescent body weight. N Engl J Med. 2012;367: 1407–1416. doi: 10.1056/NEJMoa1203388 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ruyter JC de, de Ruyter JC, Olthof MR, Seidell JC, Katan MB. A Trial of Sugar-free or Sugar-Sweetened Beverages and Body Weight in Children. New England Journal of Medicine. 2012. pp. 1397–1406. doi: 10.1056/nejmoa1203034 [DOI] [PubMed] [Google Scholar]
31.Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJL. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr. 2003;1: 4. doi: 10.1186/1478-7954-1-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.[cited 26 Apr 2022]. Available: http://iah.salud.gob.ar/doc/Documento3.pdf
33.[cited 26 Apr 2022]. Available: https://bancos.salud.gob.ar/sites/default/files/2021-01/guia-nacional-practica-clinica-diabetes-mellitius-tipo2_version-abreviada.pdf
34.Banco Central de la República Argentina. [cited 15 Sep 2022]. Available: http://www.bcra.gov.ar/
35.Salgado MV, Penko J, Fernandez A, Konfino J, Coxson PG, Bibbins-Domingo K, et al. Projected impact of a reduction in sugar-sweetened beverage consumption on diabetes and cardiovascular disease in Argentina: A modeling study. PLoS Med. 2020;17: e1003224. doi: 10.1371/journal.pmed.1003224 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Fisberg M, Kovalskys I, Gómez G, Rigotti A, Sanabria LYC, García MCY, et al. Total and added sugar intake: Assessment in eight Latin American countries. Nutrients. 2018;10. doi: 10.3390/nu10040389 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.BOLETIN OFICIAL REPUBLICA ARGENTINA—MINISTERIO DE SALUD Y DESARROLLO SOCIAL SECRETARÍA DE GOBIERNO DE SALUD—Resolución 2004/2019. [cited 30 Sep 2021]. Available: https://www.boletinoficial.gob.ar/detalleAviso/primera/217123/20190923
38.Allen LN, Pullar J, Wickramasinghe KK, Williams J, Roberts N, Mikkelsen B, et al. Evaluation of research on interventions aligned to WHO ‘Best Buys’ for NCDs in low-income and lower-middle-income countries: a systematic review from 1990 to 2015. BMJ Glob Health. 2018;3: e000535. doi: 10.1136/bmjgh-2017-000535 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Basto-Abreu A, Braverman-Bronstein A, Camacho-García-Formentí D, Zepeda-Tello R, Popkin BM, Rivera-Dommarco J, et al. Expected changes in obesity after reformulation to reduce added sugars in beverages: A modeling study. PLoS Med. 2018;15: e1002664. doi: 10.1371/journal.pmed.1002664 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Claro RM, Levy RB, Popkin BM, Monteiro CA. Sugar-sweetened beverage taxes in Brazil. Am J Public Health. 2012;102: 178–183. doi: 10.2105/AJPH.2011.300313 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Taillie LS, Reyes M, Colchero MA, Popkin B, Corvalán C. An evaluation of Chile’s Law of Food Labeling and Advertising on sugar-sweetened beverage purchases from 2015 to 2017: A before-and-after study. PLoS Med. 2020;17: e1003015. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Caro JC, Corvalán C, Reyes M, Silva A, Popkin B, Taillie LS. Chile’s 2014 sugar-sweetened beverage tax and changes in prices and purchases of sugar-sweetened beverages: An observational study in an urban environment. PLoS Med. 2018;15: e1002597. doi: 10.1371/journal.pmed.1002597 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0279978.r001

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5 Apr 2022

PONE-D-22-04375The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages’ Consumption in Argentina: a Modeling StudyPLOS ONE

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Reviewer #1: This paper is a modelled study of the health and economic impact of SSB consumption in Argentina. There has clearly been much work into the development and execution of the model and the graphs and tables are great. With significant revision (minor and some major) and refinement of the use of the English language throughout, this paper has the potential to provide a good addition to the literature.

It is difficult to provide all details to such a complex modelling exercise however, a robust scientific paper is one that other research teams can obtain the same results through the same data sources, software, stakeholders, literature, reports etc. It is always useful to keep this in mind when doing scientific writing. Some specific considerations:

• Abstract:

Methods: The two last sentences should be in past tense. Please revise.

Results: It might be useful to say in the first instance of the results that ‘our model estimated that because of SSB consumption…..’.

• Introduction:

First paragraph, first sentence: needs citing.

First paragraph, last sentence: would be good to include which SDG regarded prevention and control of NCDs (i.e. SDG 3 or 3.4).

Second paragraph, first sentence, change the tense. So….’Worldwide, eight million deaths were attributed to overweight or obesity in 2019…… The sentence reads poorly. Please revise. Eg: Worldwide, eight million deaths in 2019 were attributable to overweight or obesity – two of the most critical determinants of NCD morbidity and mortality.’

Second paragraph, second sentence: what are the gender differences? This is too general. Either give values or delete. Similarly, what are some of the economic costs you speak of? Some stats would be useful for the reader.

Third paragraph, second sentence needs citing. There appears to be a lot of duplication in information in this paragraph. Suggest revising and citing correctly throughout.

Fourth paragraph: Results from studies done in the past should be written in past tense. Please revise.

Fourth, fifth and sixth paragraphs need revising. For instance, the authors begin paragraph seven by saying children and adolescents consume 40% more SSBs, confectionery and then go on to give stats for adult obesity. Would be much better to give stats at this point for child and adolescent obesity instead of talking about it in the fourth paragraph.

Seventh paragraph: You need to spell out PAHO in full the first time you use it and then use acronyms. Also, how have PAHO prioritized the need to reduce SSB consumption? It’s a very general statement and doesn’t really tell the reader anything, as it is currently is. Suggest further work.

• Methods

The authors describe in this section the two causal pathways in the model (ie. BMI and SSB consumption). However, there is no mention of how (or what) determines change in SSB consumption or how it directly effects overweight and obesity independently of BMI and this is an important and relatively new concept to understand. Some (cited) biochemical/pathological background from the global literature on this concept would be useful in the introduction to highlight just how damaging SSBs are. It would help build the argument of why the modelling is important. What determined the change in consumption? Literature? It is unclear what the difference is between the baseline and counterfactual and why that has been chosen for the model. Some explanation of this would make the results much clearer for the reader.

Figure 1 is a good figure. Describing the other diseases is useful for the reader.

First paragraph, fifth sentence needs more detail on the conceptualisation of the model. Did the authors have the policy diaglogue? Attend the dialogue? Or is the policy dialogue a report? Who hosted the dialogue? What was the dialogue on? How did these ‘activities’ help shape the model’s development? In what way? Did you follow a protocol? Did you have aims and objectives? What were the needs of the decision-makers? What policies are the ones you speak of to control SSB consumption?

For the PIFs, does it estimate the proportional reduction in incidence or prevalence of disease or mortality? The second sentence in this paragraph needs revising.

Throughout the methods section: your study was done in the past so you need to write it in the past tense (e.g., ….secondly, we estimated the effect of BMI on disease incidence…..Finally we applied….. The model calculated…

Fourth paragraph: should read ‘the estimation of the impact of SSB consumption….. please revise.

What other sensitivity analyses did you estimate? Is the STATA code available for your analyses?

The authors indicate results were generated by 1000 iterations using the distribution of SSB consumption and RRs for each country. Is the model estimating outputs for more than one country or is this an error?

• Epidemiological data

Please provide details of the data sources. What were the surveys? What were the international sources? Where did the authors obtain SSB consumption data from? What is the ‘latest nation risk factor survey’ actually called? Did the authors do any ‘fiddling’ within DISMOD? Could an independent person repeat this method and get the same results? How did you ‘identify’ the RR values? Why did you choose the diseases you did and not others?

The section could be written in a much clearer way. Suggest revising. For instance, the last sentence about the RRs from GBD (which needs to be spelt out in full the first time it’s used) should go before you begin talking about ‘identifying RRs’, the readers then know where the authors obtained the RRs from and makes it clear that is how they ‘identified’ them (if indeed it was where the values for T2DM and CVD were obtained from, it’s really not clear).

• Direct medical costs

The authors speak of macro- and micro-costing approaches but only describe the micro-costing approach. What is the macro-costing approach? Where do these concepts originate? Are they backed by theory? Previous modelling studies? Please provide more detail.

Fifth sentence: please either name the previous cost estimation projects, clinical guidelines and local experts or provide a reference for each for the reader.

• Results

Overall, the authors need to describe their findings in much more detail. It is clear they have gone to the trouble to make detailed tables, but the text descriptions to highlight the significant findings for each would improve this section a lot. Compare and contrast. For instance, the authors indicate boys were more affected than girls. By how much? How much higher was the 5-11 age group’s burden compared to the lowest? How much more affected were adults 18-44 compared to older groups? 47 million. Is that USD?

Table 1

Please provide a definition for the reader of the different grades of obesity, either in the text or in the table or as a table footnote. Different countries have different thresholds for obesity and it is important to provide explicit detail for all tables and their content. Nowhere in the paper have the authors given any definition of the different BMI groups and what constitutes underweight, overweight or anything about the different ‘grades’ of obesity. Please revise for those readers not familiar with BMI ranges. Similarly, the authors may like to explain the reason for not having BMI means for children? Doing so will give this table a lot more meaning and relevance.

Table 2.

Please check the rounding of values for incidence, prevalence and mortality. It may be preferable to have rates per 100,000 as whole numbers when above 100. Similarly, for very low numbers (e.g., 0.0) it may be better to have increased decimal points.

Please ensure units for numbers are included in tables. Overweight and obesity have <18 in the ‘diseases’ column, but what is that? Age? BMI? It is unknown without units.

Are the costs First Year (Acute Event) per capita? If so, please make clear.

Table 3.

Please check your units. Some dollars have no $ signs. Also, what do the two values for the direct medical cost, USD (million) represent? Is one Argentinian dollars and one USD? If so, please make this very clear for the reader within the table or as a footnote.

Or is it per capita/ for the whole population? As it is, no one knows.

Could you add up T2DM and CVD costs etc to show even more significant results and the significance of multiple morbidities?

Figures 2 & 3

These are nice graphs, particularly Figure 3. Where did you source the data from? This should be included in your paper.

• Discussion

Overall, the discussion needs revising to improve the flow of information. There are some interesting points the authors make, but it’s adhoc and disjointed.

Paragraph 1, use past tense for studies already done. So, ‘In this study, we estimated….’

Paragraph 3: What is BASTA? Please spell out acronyms if only being used once or twice.

Paragraphs 4 & 5: Why are the authors discussing the strengths and limitations in the middle of your discussion? These go at the end of your discussion.

Paragraph 6: Why do countries in Latin America have a high consumption of unhealthy products?

Paragraphs 7 & 8: These would be better in the introduction.

The paper has some interesting, significant findings. With revision, it will be much improved and useful for fellow modellers. All the best with the revision.

Reviewer #2: Thank you for the opportunity to review this paper, entitled ‘The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages Consumption in Argentina: a Modeling Study’. The authors estimate the burden of disease and costs imparted by SSB consumption in Argentina. The authors have used a strong modelling approach to estimate this, and the both the rationale and modelling techniques appear to be sound. However, much of the manuscript should be restructured to increase comprehension and readability. Further, the authors may wish to consider their interpretation of the model, as rather than estimating the exact burden of SSBs, it instead estimates the reduce burden if consumption were reduced to a predetermined level. Finally, should this paper be accepted for publication, I suggest further editing for English language and readability.

Abstract

- As with much of the results and the discussion, please reframe to highlight that the model does not specifically estimate the cases of disease and deaths caused by SSB consumption (for example, of the 4,425 deaths mentioned, in the real world they are not only caused by SSB consumption but reducing SSB consumption may prevent them).

- Reformulating SSBs is not a good policy option to highlight in the abstract. I suggest discussing SSB taxation, marketing restrictions, or food and nutrition policies broadly.

Introduction

- The introduction is too long, and the structure makes it difficult to identify the issue being discussed. I suggest restructuring the introduction into three paragraphs:

1) Overweight, obesity and NCDs are a serious health concern globally.

2) An unhealthy diet, and consumption of SSBs is a key driver of overweight, obesity and NCDs. These are increasing worldwide.

3) In Argentina, consumption of SSBs is both high and increasing, and this is leading to an increased burden of disease.

4) Study rationale and aims

- Discussion of current policies to address unhealthy diets in Argentina should be left for the discussion.

Methods

- I feel that adding additional subheadings to the methods might clarify the approach for an audience with less economic modelling experience. For example, different sections for each of the three stages of estimating health and economic outcomes.

- Instead of a sperate section on data sources, please describe each data source as its use is first mentioned in the text.

- Please define DALYs when first used

Results

- The main epidemiological and economic parameters included in the model should be presented in the methods, not the results (assuming that these are derived from pre-existing data sets).

- Please edit the results to use people-first language. For example, instead of “Overweight and obesity burden children and adolescents the most”, use “Children and adolescents face the greatest burden of overweight and obesity”.

- Similarly, cases of obesity are not attributed to children/adolescents – children/adolescents are living with overweight and obesity.

- In table 3, please present ‘events’ before ‘deaths’

- It is difficult to say that SSBs cause 23% of all cases on diabetes, as so many factors contribute to disease. Instead, what the model estimates is that, if no SSBs were consumed, 23% of diabetes cases would be prevented. Please edit the results throughout to reflect this.

- I suggest not comparing the burden of disease across different NCDs as this implies that some are more important than others. Simply say how many cases of each would be avoided if SSB consumption was reduced to 0.

- Please reference the results of the included sensitivity analyses

Discussion

- As with the results, please be careful about directly attributing costs/deaths to SSB consumption – the model simply estimates that if SSB consumption were reduced, a certain number of costs and deaths would be prevented.

- Please mode the strengths and limitations to later in the discussion (just before the conclusion).

- The authors state (in the limitations section) that their estimates are likely an underestimation, but it is not clear why this is the case – is this because oral health was not included? Please explain further.

- Overall, the discussion would be improved by additional sections on drivers of SSB consumption and further consideration of policies. The authors discuss many policy options for addressing SSB consumption in Argentina, they may want to condense this a little and instead include more discussion on a) why SSB consumption is high (there is some discussion of advertising and prices, but there should be more on this) and b) how to implement best-practice policies (notably how to limit SSB industry influence).

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 Feb 23;18(2):e0279978. doi: 10.1371/journal.pone.0279978.r002

Author response to Decision Letter 0

17 Jun 2022

Thank you for these comments. We agree on the importance of the transparency and reproducibility of findings in research. We have edited the manuscript and refined the English language use, following your suggestions.

• Abstract:

Methods: The two last sentences should be in past tense. Please revise.

Thank you. We have already corrected these typos.

Results: It might be useful to say in the first instance of the results that ‘our model estimated that because of SSB consumption…..’.

Thank you. We totally agree with this suggestion and we have already corrected it.

• Introduction:

First paragraph, first sentence: needs citing.

Thanks. It is done now.

First paragraph, last sentence: would be good to include which SDG regarded prevention and control of NCDs (i.e. SDG 3 or 3.4).

Thanks. It is done now.

Thanks. We have edited the sentence after your suggestion.

Thanks. To avoid excess in the number of words, we decided to omit the statement related to gender differences in the introduction. Also, we are now providing some statistics related to economic costs.

Third paragraph, second sentence needs citing. There appears to be a lot of duplication in information in this paragraph. Suggest revising and citing correctly throughout.

Thanks. We have cited these statements correctly now.

Fourth paragraph: Results from studies done in the past should be written in past tense. Please revise.

Thanks. We have edited it.

Thanks. We relocated ENNyS2 data

Seventh paragraph: You need to spell out PAHO in full the first time you use it and then use acronyms.

Thanks. Done

Also, how have PAHO prioritized the need to reduce SSB consumption? It’s a very general statement and doesn’t really tell the reader anything, as it currently is. Suggest further work.

Thanks. We added some strategies highlighted in the PAHO document

• Methods

The authors describe in this section the two causal pathways in the model (ie. BMI and SSB consumption). However, there is no mention of how (or what) determines change in SSB consumption or how it directly effects overweight and obesity independently of BMI and this is an important and relatively new concept to understand. Some (cited) biochemical/pathological background from the global literature on this concept would be useful in the introduction to highlight just how damaging SSBs are. It would help build the argument of why modeling is important. What determined the change in consumption? Literature? It is unclear what the difference is between the baseline and counterfactual and why that has been chosen for the model. Some explanation of this would make the results much clearer for the reader.

Thanks for your comments.

- We now added a paragraph in the introduction explaining the effects of consumption of SSB on BMI and SSB on diabetes II and CVD independent of BMI.

- The model estimates a hypothetical change in the consumption of SSB. The baseline is the current consumption of SSB in Argentina, and the counterfactual is the zero consumption. This statement is now explained better in the estimation of the PIF: “The PIF estimates the proportional reduction in population disease (incidence or prevalence) or mortality would occur if exposure to a risk factor (SSB current consumption) were reduced to a minimal theoretical risk exposure scenario (zero consumption).”

Figure 1 is a good figure. Describing the other diseases is useful for the reader.

Thanks. We appreciate your comment.

Thank you for your comment. We have now cited the report of the policy dialogue carried out in the framework of this project where the answers to these questions can be found. (available on: https://www.iecs.org.ar/wp-content/uploads/Informe-Policy-dialogue-Azucar.pdf)

For the PIFs, does it estimate the proportional reduction in incidence or prevalence of disease or mortality? The second sentence in this paragraph needs revising.

Thanks for your comment. We are explaining better now this statement:

“The PIF estimates the proportional reduction in population disease (incidence or prevalence) or mortality would occur if exposure to a risk factor (SSB current consumption) were reduced to a minimal theoretical risk exposure scenario (zero consumption).”

Thanks. We have corrected it.

Fourth paragraph: should read ‘the estimation of the impact of SSB consumption….. please revise.

Thanks. We have corrected it.

What other sensitivity analyses did you estimate? Is the STATA code available for your analyses?

Thanks. We did not carry out multiple sensitivity analyses. Now we are stating: “We used Monte Carlo simulations for the sensitivity analysis” (instead of reporting “sensitivity analyses”).

As mentioned in the manuscript, the sensitivity analysis was programmed in STATA and Visual Basic. Programming codes are available upon request by the reader.

Yes, this was an error. We have now corrected it. Thanks for your comment.

• Epidemiological data

Thanks. We have edited this paragraph according to your suggestions.

Did the authors do any ‘fiddling’ within DISMOD?

No. DISMOD was mainly used to calculate case fatality rate values from incidence and mortality data of the included diseases.

Could an independent person repeat this method and get the same results? How did you ‘identify’ the RR values? Why did you choose the diseases you did and not others?

Thanks for this comment. We have edited this paragraph according to your suggestions.

• Direct medical costs

Thank you for your comment. This project was carried out not only in Argentina but also in three other Latin American countries. For the Argentina case (purpose of this manuscript), we only used a micro-costing approach. We made a mistake when writing this paragraph, but now we only mention the micro-costing method.

Fifth sentence: please either name the previous cost estimation projects, clinical guidelines and local experts or provide a reference for each for the reader.

We appreciate your comment. In line with our response to the previous comment, we agree we can be more specific about what was done specifically for the case of Argentina. All micro-costings were carried out between a research economist and at least one local medical expert for each disease. Local medical experts suggested the annual resources that these patients usually need. This information was based on their daily experience and, in some cases, on local practice guidelines (diabetes and obesity). Therefore we have edited this paragraph and added the citations for these two local guidelines.

• Results

Thank you for your contribution. We have already added this information.

Table 1

Thanks. We have now explained that the division by BMI values is the same as the World Health Organization. Regarding the mean point BMI in children, this input was not used by the model since the impact of SSB consumption through BMI on other diseases was not modeled in children.

Table 2.

Please ensure units for numbers are included in tables. Overweight and obesity have <18 in the ‘diseases’ column, but what is that? Age? BMI? It is unknown without units.

Are the costs First Year (Acute Event) per capita? If so, please make clear.

Thanks. We have edited this table according to your suggestions.

Table 3.

Or is it per capita/ for the whole population? As it is, no one knows.

Thanks. We now explain in the footer of the table that we first present the value attributed to the consumption of sugar-sweetened beverages for both health outcomes and direct costs. We also offer the percentage that this value represents of the total number of events or deaths or direct costs that this disease brings to Argentina (not only what is attributed to sugary drinks).

In this case, the total direct cost in the denominator represents all the expenditures in Argentina for this disease.

Be careful with the text. First sentence after Table 3 shows – 639,000 which makes it look like a negative number but it’s just punctuation. Could you use brackets instead or use the dash properly. Could you add up T2DM and CVD costs etc to show even more significant results and the significance of multiple morbidities?

Thanks. It was edited as you suggest.

Figures 2 & 3

These are nice graphs, particularly Figure 3. Where did you source the data from? This should be included in your paper.

Thanks. We appreciate your comment. Figure 2 and 3 show findings estimated through the model.

• Discussion

Overall, the discussion needs revising to improve the flow of information. There are some interesting points the authors make, but it’s adhoc and disjointed.

Paragraph 1, use past tense for studies already done. So, ‘In this study, we estimated….’

Thanks. It is done.

Paragraph 3: What is BASTA? Please spell out acronyms if only being used once or twice.

Thanks. It is the acronyms of a previous model published in the field in Argentina (B.A.S.T.A., for “Bebidas Azucaradas, Salud y Tarifas en Argentina”. We have now corrected it.

Paragraphs 4 & 5: Why are the authors discussing the strengths and limitations in the middle of your discussion? These go at the end of your discussion.

Thanks. It is done.

Paragraph 6: Why do countries in Latin America have a high consumption of unhealthy products?

Thanks. It is done.

Paragraphs 7 & 8: These would be better in the introduction.

We consider that what is related to the policies in this field in Argentina and the region can be better addressed in the discussion section. Also, the second reviewer suggested not addressing the issue of implemented or unimplemented policies in the introduction section.

The paper has some interesting, significant findings. With revision, it will be much improved and useful for fellow modelers. All the best with the revision.

Thanks. We appreciate your comments. We totally agree that your suggestions help a lot in improving this manuscript.

Reviewer #2: Thank you for the opportunity to review this paper, entitled ‘The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages Consumption in Argentina: a Modeling Study’. The authors estimate the burden of disease and costs imparted by SSB consumption in Argentina. The authors have used a strong modelling approach to estimate this, and both the rationale and modelling techniques appear to be sound. However, much of the manuscript should be restructured to increase comprehension and readability. Further, the authors may wish to consider their interpretation of the model, as rather than estimating the exact burden of SSBs, it instead estimates the reduced burden if consumption were reduced to a predetermined level. Finally, should this paper be accepted for publication, I suggest further editing for English language and readability.

Thanks. We appreciate your comments. We have worked on your revisions and also in editing English language and readability.

Abstract

Thanks. We agree. We have now corrected it.

- Reformulating SSBs is not a good policy option to highlight in the abstract. I suggest discussing SSB taxation, marketing restrictions, or food and nutrition policies broadly.

Thanks. We agree. We have now corrected it.

Introduction

- The introduction is too long, and the structure makes it difficult to identify the issue being discussed. I suggest restructuring the introduction into three paragraphs:

1) Overweight, obesity and NCDs are a serious health concern globally.

2) An unhealthy diet, and consumption of SSBs is a key driver of overweight, obesity and NCDs. These are increasing worldwide.

3) In Argentina, consumption of SSBs is both high and increasing, and this is leading to an increased burden of disease.

4) Study rationale and aims

- Discussion of current policies to address unhealthy diets in Argentina should be left for the discussion.

Thanks. We agree. We have edited it following your suggestions.

Methods

Thanks. We agree. We have edited it following your suggestions.

- Instead of a separate section on data sources, please describe each data source as its use is first mentioned in the text.

Thanks. We agree. We have edited it following your suggestions.

- Please define DALYs when first used

Thanks. We have now corrected it.

Results

- The main epidemiological and economic parameters included in the model should be presented in the methods, not the results (assuming that these are derived from pre-existing data sets).

Thanks. We have now corrected it following your suggestion.

Thanks. We have now edited it.

- Similarly, cases of obesity are not attributed to children/adolescents – children/adolescents are living with overweight and obesity.

Thanks. We have now edited it.

- In table 3, please present ‘events’ before ‘deaths’

Thanks. We have now edited it.

Thank you for your comment. We have now corrected it following your suggestion.

Thanks. We have now edited it following your suggestion.

- Please reference the results of the included sensitivity analyses

Thanks. We are now referring to sensitivity analysis findings.

Discussion

Thanks. We agree, and we have corrected it in this and other sections.

- Please mode the strengths and limitations to later in the discussion (just before the conclusion).

Thanks. We have now edited it following your suggestion.

Thanks. We have now added information concerning those points.

Attachment

Submitted filename: SSBS Argentina - Responses to reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0279978.r003

Decision Letter 1

Vijay S Gc

10 Aug 2022

PONE-D-22-04375R1The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages’ Consumption in Argentina: a Modeling StudyPLOS ONE

Dear Dr. Bardach,

Please submit your revised manuscript by Sep 24 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vijay S. Gc, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: No

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have done an excellent job of revising the manuscript in light of the suggestions from both reviewers. It flows well, it is clear and great to see the amended version. Congratulations.

A few further minor corrections are required for accuracy, consistency and transparency:

• It looks like there’s a few instances where writing in the present tense have slipped through and it should be past tense. Suggest doing a final check.

• I question your definition of a PIF. It looks like the definition of a PAF.

See:

Barendregt & Veerman (2009) Categorical versus continuous risk factors and the calculation of potential impact fractions. J Epidemiol Community Health. 64:209-212. doi:10.1136/jech.2009.090274

• Your sources of epidemiological parameters require references. What might be useful is including a technical appendix where all the data sources are provided in a table.

• Where did you get the definition of SSBs from? It needs a reference.

• You need to reference the exchange rates published by the Central Bank of the country. You have included it at the bottom of Table 2, it will be consistent to include it also in your text.

• In your text regarding economic values, clarify the values are US dollars (e.g., US$ 40 million, etc.) Otherwise, it is open to interpretation, even though you indicate in the tables it is USD. You could also just highlight at the beginning of your results all monetary values are in USD. Just make it clear.

• Something has gone wrong with ‘Deaths’ in table 3. Looks like an overlap or duplication.

• Discussion. Last sentence, third paragraph

What is the age group in numbers of the ‘youngest group’? The current definition tells the reader nothing. The reader is looking for detail (e.g.,” …..’incidence occurred in the youngest group (12-18 years), particularly among men……”

All the best for the final hurdle. It will be a great paper and will attract much interest from public health researchers and policy makers alike.

Reviewer #3: The consumption of sugar-sweetened beverages is a growing public health problem. In this study, the authors analyze SSB-associated burden in Argentina, one of the countries with the highest consumption of SSB in the world.

The authors have tried to carefully model the health burden and costs of SSB use. However, given the complexity of modeling itself, the article contains little information on the process of obtaining the results. I believe that the article needs a major revision of the methodological aspects before considering its publication in Plos One.

Here is a detail of suggested changes:

Major aspects

- reviewer #1 already mentioned the importance of providing accurate and explicit information about how the modeling process was conducted, and the lack of information in this regard. I do not believe you have addressed this concern. Although I understand that space constraints precludes authors from providing extensive and detailed data, these data are still needed, and you should consider adding an appendix that contains details about your assumptions.l. This appendix should at a minimum address/include:

- the list of all outcomes evaluated and their attached RRs (used in the PIF equation)

- a clarification of how the modeling pathway worked. For example:

- How exactly does BMI affect diabetes and CVD outcomes? (the link between direct and indirect pathways). Is not clear to me looking at Figure 1.

- How did you avoid duplicity of events? For example:

- you applied a risk of mortality attached to diabetes, but then another risk for CVD outcomes. Did you exclude the population of diabetics for the latest? If not, wouldn´t there be a duplicity of CVD events among diabetics?

- In figure 1, it seems you applied an effect of BMI on CVD (then reflects events through the direct pathway), but also a BMI effect directly on outcomes, which also includes CVD events. Did you differentiate between incident and established CVD?

- Are age and gender differences in results based solely on differences on SSB consumption? How do you account for the inherent differential risk of events among different age and gender groups?

- Cost analysis: although is not explicitly mentioned, it seems the analysis only covers one year of avoided events and related costs. Nevertheless, in table 2, you include a column describing costs since 2nd year/follow up. How were they modeled? Was there any assumption regarding economic burden in previous years?

- population size assumptions, by age and gender. Source of information for population inputs (like census)

- The paper models a scenario of not only a zero SSB consumption (as if they were withdrawn from market) but also no caloric compensation at all (an aspect that should be discussed at some point in the article, and it's currently not). Numbers are therefore strikingly high regarding SSB burden. Nevertheless, the discussion barely highlights the utopic nature of the modeled scenario, and I think it should be talked through in the discussion and highlighted in limitations.

Minor changes:

Introduction:

- 3rd paragraph, first sentence: it needs a reference.

- 3rd paragraph. The sentence “People who are overweight or obese are at greater risk of developing severe health problems, such as type 2 diabetes, high blood pressure, asthma, sleep disorders, liver disease, low self-esteem, and social and emotional issues” needs a reference

- Last paragraph reads: “This manuscript presents the results for Argentina obtained through a collaborative, more comprehensive project….”. More than what?

Methods:

- For what period of time is the intervention being conducted? It seems to be 2020 (for what it says in Results) but this should be clearly stated in Methods (and in the abstract)

- You used Monte Carlo simulations to get uncertainty intervals. I wouldn't call this a sensitivity analysis since you don't compare your results under a different assumption but rather estimate the uncertainty around the result you already got. In the same way, I wouldn´t call having reported UI a strength (as you do in discussion), since uncertainty around the main results is expected to be reported in almost any study.

Conclusion

- The last sentence reads: “This study is the first to assess the health and financial burden of SSB consumption in the country, including children and a wide range of attributable conditions.”. Nevertheless, and as you mentioned previously, the BASTA study already evaluated the health burden of SSB consumption among adults in Argentina. I believe this sentence is misleading and should be rephrased.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #3: No

**********

Attachment

Submitted filename: SSB IECS - Plos One submitted.docx

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

PLoS One. 2023 Feb 23;18(2):e0279978. doi: 10.1371/journal.pone.0279978.r004

Author response to Decision Letter 1

15 Sep 2022

A few further minor corrections are required for accuracy, consistency and transparency:

• It looks like there’s a few instances where writing in the present tense have slipped through and it should be past tense. Suggest doing a final check.

Thanks. We have corrected these typos.

• I question your definition of a PIF. It looks like the definition of a PAF.

See:

Barendregt & Veerman (2009) Categorical versus continuous risk factors and the calculation of potential impact fractions. J Epidemiol Community Health. 64:209-212. doi:10.1136/jech.2009.090274

Thanks for your comment. We analyzed both terms,1,2 and we agree that the best definition of our analysis is the PAF, so we modified it accordingly.

• Your sources of epidemiological parameters require references. What might be useful is including a technical appendix where all the data sources are provided in a table.

Thanks. We have already included all the epidemiological references in the manuscript.

• Where did you get the definition of SSBs from? It needs a reference.

Thanks. We defined SSBs as the following ones: sports and energy drinks, sugar-sweetened sodas, sugar-sweetened fruit juices, and sugar-sweetened and flavored waters. This decision was based on a previous paper that assessed the disease burden attributed to SSBs (Singh, G. M., Micha, R., Khatibzadeh, S., Shi, P., Lim, S., Andrews, K. G., ... & Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). (2015). Global, regional, and national consumption of sugar-sweetened beverages, fruit juices, and milk: a systematic assessment of beverage intake in 187 countries. PloS one, 10(8), e0124845.)

• You need to reference the exchange rates published by the Central Bank of the country. You have included it at the bottom of Table 2, it will be consistent to include it also in your text.

Thanks. We used the exchange rate published by the Central Argentinian Bank. We have corrected it within the manuscript. Also, now we are referencing it correctly in the manuscript.

Thanks. Now we are just using one symbol, the $ symbol referring to USD.

• Something has gone wrong with ‘Deaths’ in table 3. Looks like an overlap or duplication.

Thanks. We do not see that duplication in our document. Maybe it occurs when converting from word to pdf.

• Discussion. Last sentence, third paragraph

Thanks. We have edited it according to your suggestions.

All the best for the final hurdle. It will be a great paper and will attract much interest from public health researchers and policy makers alike.

Many thanks. We really appreciate your suggestions.

Here is a detail of suggested changes:

Major aspects

- reviewer #1 already mentioned the importance of providing accurate and explicit information about how the modeling process was conducted, and the lack of information in this regard. I do not believe you have addressed this concern. Although I understand that space constraints preclude authors from providing extensive and detailed data, these data are still needed, and you should consider adding an appendix that contains details about your assumptions.l. This appendix should at a minimum address/include:

- the list of all outcomes evaluated and their attached RRs (used in the PIF equation)

Thanks. We are now including a statement and respective reference in the supplementary material with links to the specific GBD RRs used.

- a clarification of how the modeling pathway worked. For example:

- How exactly does BMI affect diabetes and CVD outcomes? (the link between direct and indirect pathways). Is not clear to me looking at Figure 1.

Thanks for your comment. To avoid double counting for cardiovascular events, we just applied one RR value, which is the RR of the association between SSBs consumption and cardiovascular events (the same for diabetes). Since the RR of the association between SSBs consumption and cardiovascular events is not adjusted for BMI, somehow, the indirect association (through BMI) between BMI and cardiovascular (the same for diabetes) is also being considered. For that reason, we show in figure 1 two different arrows regarding the direct and indirect (through BMI) association between SSBs and cardiovascular events (the same for diabetes). We are explaining it better in the manuscript, and we modified Figure 1 to avoid a misunderstanding.

- How did you avoid duplicity of events? For example:

Thanks. Your comment is really interesting. No, we are not double counting cardiovascular events. On the one hand, we assumed a RR value of 1.37 for diabetes type 2 incidence (assuming that it consequently affects prevalent cases and also mortality in the same way) per SSB serving day.

On the other hand, we assumed a RR of 1.08 (95% CI, 1.04 to 1.13) and 1.08 (95% CI, 1.02 to 1.14) per serving per day for incidence and mortality of cardiovascular diseases. This value came from a meta-analysis in which the primary studies made adjustments for main potential confounders:

“All studies adjusted for age, smoking, and physical activity (8–11, 13, 14, 16, 21, 23, 25, 48, 49). Most studies controlled for other RR factors, including BMI (8, 9, 11, 13, 14, 21, 23, 48, 49) (N = 9), education (8, 13, 14, 16, 23, 49) (N = 7), alcohol consumption (8, 9, 11, 13, 14, 16, 21, 23, 25, 48) (N = 10), total energy intake (8–11, 13, 14, 21, 23, 48, 49) (N = 10), and dietary quality (8–11, 14, 16, 21, 25, 48, 49) (N = 10). SSBs and LCSBs were mutually adjusted in 4 studies.” (Yin et al: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7850046/pdf/nmaa084.pdf)

As far as we know, these studies adjusted for main lifestyle potential confounders (many of which are also associated with diabetes developing) but did not adjust for diabetes particularly. However, our model did not count cardiovascular events attributed to diabetes, so we are not double-counting cardiovascular events.

Thank you for your comment. Yes, the model estimates incidents of CVD events attributed to BMI and how many established CVD events are also attributable to BMI.

- Are age and gender differences in results based solely on differences on SSB consumption? How do you account for the inherent differential risk of events among different age and gender groups?

Thank you for your comment. Most of the model inputs are differentiated by age and gender: BMI, SSB consumption, population, epidemiological data (incidence, prevalence, mortality), and the RR of BMI on the events (included in the supplementary material). Regarding the parameters of RR of SSB on BMI, CVD, or Diabetes, no information disaggregated by age and gender was found. We are adding this statement in the methods section now.

Thank you for your comment. As you have correctly mentioned, the model presents the diagnostic analysis of one year. The cost values that refer to consecutive years in Table 2 have been added to differentiate the costs in the incident and prevalent cases in those health conditions known to have essential differential costs between the first and consecutive years. So, we applied the healthcare costs only to incident cases and the consecutive year healthcare costs to the prevalent cases.

We added a explanation in the cost section:

“In turn, the costs were differentiated in the first year and subsequent years to distinguish and approximate the difference in costs of incident cases and prevalent cases.”

And we added a clarification in the model structure section:

“We used a comparative risk assessment framework to estimate the health and economic effects of sugar-sweetened beverage (SSB) consumption in one year.”

- population size assumptions, by age and gender. Source of information for population inputs (like census)

Thanks. We have included this information in Table 1 (we use the last National Argentinian demographic source, as referenced)

Thanks. We totally agree with you. Now we are including a statement in the discussion section.

Minor changes:

Introduction:

- 3rd paragraph, first sentence: it needs a reference.

Thanks. We have added a reference now.

- Last paragraph reads: “This manuscript presents the results for Argentina obtained through a collaborative, more comprehensive project….”. More than what?

Thanks. We have edited this sentence now.

Methods:

- For what period of time is the intervention being conducted? It seems to be 2020 (for what it says in Results) but this should be clearly stated in Methods (and in the abstract)

Thanks. We are mentioning this statement now. However, we would like to emphasize that there is no intervention here, since we are just estimating the disease and economic burden attributed to SSBs consumption. To do that, we are comparing the context in Argentina in 2020 with a hypothetical scenario of no SSB consumption.

Thanks. We have edited these sentences now.

Conclusion

Thanks. We have edited this sentence now.

Attachment

Submitted filename: Response to reviewers SSBs ARGENTINA.docx

Click here for additional data file.^{(13KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0279978.r005

Decision Letter 2

Vijay S Gc

18 Oct 2022

PONE-D-22-04375R2The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages’ Consumption in Argentina: a Modeling StudyPLOS ONE

Dear Dr. Bardach,

Please submit your revised manuscript by Dec 02 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vijay S. Gc, PhD

Academic Editor

PLOS ONE

Journal Requirements:

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. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: Well done to the authors for improving this paper. There are only minor comments that require addressing:

Abstract

• If you have changed to describe a PAF, then you need to correct the wording in the abstract (it still says you’ve used the population impact factor).

Second page of introduction, fourth paragraph:

• The text indicates adults consume 85 litres of SSB per year. In the paragraph above that, it says children and adolescents consume 40% more SSBs that adults. Does that mean children and adolescents are consuming nearly 120litres per year?

Fourth page, first line:

• The authors need to correct the first line on this page to say…..’or mortality that would occur…….’

• Fourth line: the authors need to correctly say PAF, not population impact factor, and again, in the third paragraph on the same page.

Fifth page:

• Second paragraph: A reference needs to be included for the defining of SSBs

• Second paragraph from bottom: is it possible to superscript the BMI, like so: kg/m2?

Sixth page:

• Second paragraph, third line: it should read ….’per SSB serving per day

• Direct medical costs: the authors have not included in the methods section if the costs were calculated from a societal perspective or a health perspective. Is this relevant for this micro-costing approach (not knowing what a micro-costing approach is).

Table 3

• Although the authors have indicted there’s no problem with the ‘deaths’ row in Table 3, there still seems to be a doubling up of values over each other.

Sensitivity analysis

• In the second round of reviewing, reviewer 3 mentioned uncertainty analysis using bootstrapping is not sensitivity analysis. It appears this has not been corrected?

Figures – great graphs!

A general comment: The paragraphs spacing is inconsistent throughout. Please check.

Also, the authors use the word ‘information’ to describe data. Would it be more academic to describe it as data? Finally, it may be useful to get a final proofread - there are still typos.

Reviewer #3: Thank you for the opportunity to review this revised version of the manuscript.

We appreciate the changes the authors have made with regards to our previous comments, and appreciate the inclusion of the supplementary material.

Nevertheless, we've noticed that the list of all outcomes evaluated and their attached RRs (used in the PAF equation) is still incomplete (for example: there is still no information regarding how outcomes such as asthma, musculoskeletal conditions or other conditions were modeled. If you this this is to detailed, even for the appendix, please amend the answers “Yes - all data are fully available without restriction” and “All relevant data are within the manuscript”, and specify how interested people can contact you to request the missing information.

Regarding figure 1, if we are understanding it correctly, wouldn't something like this be more accurate, with CVD and other conditions as intermediate outcomes?

If we understood it incorrectly, please rearrange it so that it is clearer.

Finally, you say “Regarding the parameters of RR of SSB on BMI, CVD, or Diabetes, no

information disaggregated by age and gender was found”. We understand that RR may not be disaggregated by age and gender. We may have not been clear in our previous comment, but what we are wondering is: How did you model the risk of CVD and other diseases inherently associated with both age and gender, independent of the added risk of diabetes, BMI, or other variables under study? (For example, CVD risk is different for a 70-year-old man in comparison with a 35-year-old woman, even if both have no diabetes and a BMI below 25). Please clarify that in the methods.

On a minor note, you are still referring to Monte Carlo simulations as “Sensitivity analysis” (results section).

I think the manuscript has improved since the previous version but still needs these relatively minor clarifications. We look forward to your answers to our comments.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

**********

PLoS One. 2023 Feb 23;18(2):e0279978. doi: 10.1371/journal.pone.0279978.r006

Author response to Decision Letter 2

3 Nov 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

Deadline: Dec 02 2022 11:59P

Reviewer #1: Well done to the authors for improving this paper. There are only minor comments that require addressing:

Abstract

• If you have changed to describe a PAF, then you need to correct the wording in the abstract (it still says you’ve used the population impact factor).

Response: Thanks, we have edited it now.

Second page of introduction, fourth paragraph:

Response: Yes. It can also be seen in Table 1.

Fourth page, first line:

• The authors need to correct the first line on this page to say…..’or mortality that would occur…….’

Response: Thanks, we have edited it now.

• Fourth line: the authors need to correctly say PAF, not population impact factor, and again, in the third paragraph on the same page.

Response: Thanks, we have edited it now.

Fifth page:

• Second paragraph: A reference needs to be included for the defining of SSBs

Response: Thanks, we have added it now.

• Second paragraph from bottom: is it possible to superscript the BMI, like so: kg/m2?

Response: Thanks, we have edited it now.

Sixth page:

• Second paragraph, third line: it should read ….’per SSB serving per day

Response: Thanks, we have edited it now.

Response: Thanks for this comment. We have clarified it now.

Table 3

• Although the authors have indicated there’s no problem with the ‘deaths’ row in Table 3, there still seems to be a doubling up of values over each other.

Response: Thanks. However, we do not see any overlap or duplication in Table 3 of our document. Maybe the Journal staff could help us with it.

Sensitivity analysis

• In the second round of reviewing, reviewer 3 mentioned uncertainty analysis using bootstrapping is not sensitivity analysis. It appears this has not been corrected?

Response: Thanks, we have edited it now.

Figures – great graphs!

A general comment: The paragraph's spacing is inconsistent throughout. Please check.

Also, the authors use the word ‘information’ to describe data. Would it be more academic to describe it as data? Finally, it may be useful to get a final proofread - there are still typos.

Response: Thanks, we appreciate those suggestions. We have edited it accordingly.

Reviewer #3: Thank you for the opportunity to review this revised version of the manuscript.

We appreciate the changes the authors have made with regards to our previous comments, and appreciate the inclusion of the supplementary material.

Nevertheless, we've noticed that the list of all outcomes evaluated and their attached RRs (used in the PAF equation) is still incomplete (for example: there is still no information regarding how outcomes such as asthma, musculoskeletal conditions or other conditions were modeled. If this is too detailed, even for the appendix, please amend the answers “Yes - all data are fully available without restriction” and “All relevant data are within the manuscript”, and specify how interested people can contact you to request the missing information.

Response: Many thanks. It is in our best interest that the methodology used in this analysis is well understood. Therefore, consequently to your suggestion, we have added detailed information in the supplementary appendix. Now all the formulas used can be seen, and you can understand better how we have modeled the indirect way (through BMI). Likewise, although the last PAF formula that we shared is mathematically correct and coincides with our analysis, we considered that it might be clearer for the reader to see it expressed in another way. That's why you'll see that we've also made this edit to the manuscript.

Regarding figure 1, if we are understanding it correctly, wouldn't something like this be more accurate, with CVD and other conditions as intermediate outcomes?

If we understood it incorrectly, please rearrange it so that it is clearer.

Response: Thanks for this comment. We are wondering if you tried to share with us some graphics since you mentioned, "wouldn't something like this be more accurate".

We will explain this more clearly so that it can be understood more adequately. Figure 1 is a general conceptual scheme of the model. It tries to summarize and simplifies what, in complex terms, the model estimates. The RR values of the direct effect between SSB consumption and incidence, prevalence, and mortality of Diabetes/CVD were Not reported as adjusted for BMI. For this reason, and to avoid double counting, we did not add RR values between BMI and incidence, prevalence and mortality of Diabetes/CVD. However, as the RR of the direct effect between SSB consumption and these two diseases was Not adjusted for BMI, our model somehow considers the effect between BMI and Diabetes/CVD. That is why Figure 1 schematically shows these two effect arrows reaching both Diabetes and CVD. Since the last figure was not fully understood, we are sending a new figure that, in our consideration, better represents what has been explained.

Finally, you say “Regarding the parameters of RR of SSB on BMI, CVD, or Diabetes, no

Response: Thanks for this comment. This is correct. But our model does not need CVD risk information for every age and gender. Our model uses data on mortality and events by disease cause for all ages for the year 2020 in Argentina. Then, as the PAF formula explains, the model estimates the proportion of these deaths and events attributable to SSBs. Although the RR values of the consumption of SSB and CDV were the same for all genders and ages, the number of events and deaths from CDV was definitely different for the different ages and gender in Argentina in 2020. Also, the average consumption of SSB differed between those groups.

On a minor note, you are still referring to Monte Carlo simulations as “Sensitivity analysis” (results section).

Response: Thanks, we have edited it now.

I think the manuscript has improved since the previous version but still needs these relatively minor clarifications. We look forward to your answers to our comments.

Response: Many thanks. We really appreciate your suggestions.

Attachment

Submitted filename: SSB Arg R3 responses to reviewers.docx

Click here for additional data file.^{(10.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0279978.r007

Decision Letter 3

Vijay S Gc

20 Dec 2022

The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages’ Consumption in Argentina: a Modeling Study

PONE-D-22-04375R3

Dear Dr. Bardach,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Vijay S. Gc, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Please correct the first sentence under the Results section in the abstract. 110,000 healthy life-years (DALYs) to 110,000 healthy life years lost to premature death and disability.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0279978.r008

Acceptance letter

Vijay S Gc

26 Dec 2022

PONE-D-22-04375R3

The Burden of Disease and Economic Impact of Sugar-Sweetened Beverages' Consumption in Argentina: A Modeling Study.

Dear Dr. Bardach:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Vijay S. Gc

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 File

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

Attachment

Submitted filename: SSBS Argentina - Responses to reviewers.docx

Click here for additional data file.^{(294.6KB, docx)}

Attachment

Submitted filename: SSB IECS - Plos One submitted.docx

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

Attachment

Submitted filename: Response to reviewers SSBs ARGENTINA.docx

Click here for additional data file.^{(13KB, docx)}

Attachment

Submitted filename: SSB Arg R3 responses to reviewers.docx

Click here for additional data file.^{(10.4KB, docx)}

Data Availability Statement

All relevant data are within the paper.

[pone.0279978.ref001] 1.Noncommunicable diseases. [cited 20 Apr 2022]. Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [Google Scholar]

[pone.0279978.ref002] 2.Murphy A, Palafox B, Walli-Attaei M, Powell-Jackson T, Rangarajan S, Alhabib KF, et al. The household economic burden of non-communicable diseases in 18 countries. BMJ Glob Health. 2020;5: e002040. doi: 10.1136/bmjgh-2019-002040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref003] 3.United Nations. Partnership for the Goals. The Sustainable Development Goals Report. United Nations; 2020. pp. 58–59. [Google Scholar]

[pone.0279978.ref004] 4.GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396: 1223–1249. doi: 10.1016/S0140-6736(20)30752-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref005] 5.GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377: 13–27. doi: 10.1056/NEJMoa1614362 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref006] 6.Rivera JÁ, de Cossío TG, Pedraza LS, Aburto TC, Sánchez TG, Martorell R. Childhood and adolescent overweight and obesity in Latin America: a systematic review. Lancet Diabetes Endocrinol. 2014;2: 321–332. doi: 10.1016/S2213-8587(13)70173-6 [DOI] [PubMed] [Google Scholar]

[pone.0279978.ref007] 7.Keller A, Bucher Della Torre S. Sugar-Sweetened Beverages and Obesity among Children and Adolescents: A Review of Systematic Literature Reviews. Child Obes. 2015;11: 338–346. doi: 10.1089/chi.2014.0117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref008] 8.Okunogbe A, Nugent R, Spencer G, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for eight countries. BMJ Glob Health. 2021;6: e006351. doi: 10.1136/bmjgh-2021-006351 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref009] 9.Malik VS, Hu FB. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nat Rev Endocrinol. 2022;18: 205–218. doi: 10.1038/s41574-021-00627-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref010] 10.Singh GM, Micha R, Khatibzadeh S, Lim S, Ezzati M, Mozaffarian D, Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Estimated Global, Regional, and National Disease Burdens Related to Sugar-Sweetened Beverage Consumption in 2010. Circulation. 2015;132: 639–666. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref011] 11.CDC. Get the facts: Sugar-sweetened beverages and consumption. In: Centers for Disease Control and Prevention [Internet]. 11 Apr 2022. [cited 20 Apr 2022]. Available: https://www.cdc.gov/nutrition/data-statistics/sugar-sweetened-beverages-intake.html [Google Scholar]

[pone.0279978.ref012] 12.Imamura F, O’Connor L, Ye Z, Mursu J, Hayashino Y, Bhupathiraju SN, et al. Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. Br J Sports Med. 2016;50: 496–504. doi: 10.1136/bjsports-2016-h3576rep [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref013] 13.Yin J, Zhu Y, Malik V, Li X, Peng X, Zhang FF, et al. Intake of sugar-sweetened and low-calorie sweetened beverages and risk of cardiovascular disease: A meta-analysis and systematic review. Adv Nutr. 2021;12: 89–101. doi: 10.1093/advances/nmaa084 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref014] 14.Choi SE, Wright DR, Bleich SN. Impact of restricting sugar-sweetened beverages from the Supplemental Nutrition Assistance Program on children’s health. Am J Prev Med. 2021;60: 276–284. doi: 10.1016/j.amepre.2020.08.023 [DOI] [PubMed] [Google Scholar]

[pone.0279978.ref015] 15.Chen L, Appel LJ, Loria C, Lin P-H, Champagne CM, Elmer PJ, et al. Reduction in consumption of sugar-sweetened beverages is associated with weight loss: the PREMIER trial. Am J Clin Nutr. 2009;89: 1299–1306. doi: 10.3945/ajcn.2008.27240 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref016] 16.World Health Organization. WHO | Reducing consumption of sugar-sweetened beverages to reduce the risk of childhood overweight and obesity. 2018. [cited 22 Aug 2021]. Available: https://www.who.int/elena/titles/bbc/ssbs_childhood_obesity/en/ [Google Scholar]

[pone.0279978.ref017] 17.Health risks of overweight & obesity. In: National Institute of Diabetes and Digestive and Kidney Diseases [Internet]. NIDDK | National Institute of Diabetes and Digestive and Kidney Diseases; 29 Aug 2022. [cited 15 Sep 2022]. Available: https://www.niddk.nih.gov/health-information/weight-management/adult-overweight-obesity/health-risks [Google Scholar]

[pone.0279978.ref018] 18.Pacheco LS, Lacey JV Jr, Martinez ME, Lemus H, Araneta MRG, Sears DD, et al. Sugar-sweetened beverage intake and cardiovascular disease risk in the California Teachers Study. J Am Heart Assoc. 2020;9: e014883. doi: 10.1161/JAHA.119.014883 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref019] 19.de Estudios sobre Nutrición Infantil (CESNI). C. La mesa argentina en las últimas dos décadas. Available: Disponible en: http://www.unsam.edu.ar/tss/wp-content/uploads/2017/06/CESNI-La-mesa-argentina-en-las-ultimas-dos-d%C3%83%C2%A9cadas-2016.pdf [Google Scholar]

[pone.0279978.ref020] 20.Base de datos de la 2° Encuesta Nacional de Nutrición y Salud (ENNyS2) 2018–2019. [cited 20 Apr 2022]. Available: http://datos.salud.gob.ar/dataset/ennys2 [Google Scholar]

[pone.0279978.ref021] 21.INDEC, Ciudad Autónoma de Buenos Aires: Secretaría de Gobierno de Salud de la Nación 2019. 4° Encuesta Nacional de Factores de Riesgo. Resultados definitivos. - 1a ed.-Ciudad Autónoma de Buenos Aires: Instituto Nacional de Estadística y Censos; -, editor. [Google Scholar]

[pone.0279978.ref022] 22.Barrientos-Gutierrez T, Zepeda-Tello R, Rodrigues ER, Colchero MA, Rojas-Martínez R, Lazcano-Ponce E, et al. Expected population weight and diabetes impact of the 1-peso-per-litre tax to sugar sweetened beverages in Mexico. PLoS One. 2017;12: e0176336. doi: 10.1371/journal.pone.0176336 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref023] 23.Basto-Abreu A, Torres-Alvarez R, Reyes-Sánchez F, González-Morales R, Canto-Osorio F, Colchero MA, et al. Predicting obesity reduction after implementing warning labels in Mexico: A modeling study. PLoS Med. 2020;17: e1003221. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref024] 24.Pan American Health Organizatoin. STRATEGIC PLAN OF THE PAN AMERICAN HEALTH ORGANIZATION 2014–2019. [cited 22 Nov 2020]. Available: https://iris.paho.org/handle/10665.2/7654 [Google Scholar]

[pone.0279978.ref025] 25.Alcaraz A, Pichon-Riviere A, Palacios A, Bardach A, Balan DJ, Perelli L, et al. Sugar sweetened beverages attributable disease burden and the potential impact of policy interventions: a systematic review of epidemiological and decision models. BMC Public Health. 2021;21: 1460. doi: 10.1186/s12889-021-11046-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref026] 26.Documento T, N^o XX. POLÍTICAS FISCALES PARA REDUCIR EL CONSUMO DE BEBIDAS AZUCARADAS Evidencia para la implementación de políticas de reducción del consumo de bebidas azucaradas. [cited 20 Apr 2022]. Available: https://www.iecs.org.ar/wp-content/uploads/Informe-Policy-dialogue-Azucar.pdf [Google Scholar]

[pone.0279978.ref027] 27.Encuesta Mundial de Salud Escolar 2018. In: Fagran [Internet]. 27 Jan 2020. [cited 20 Apr 2022]. Available: https://fagran.org.ar/documentos/seccion/organismos-nacionales/2020/01/encuesta-mundial-de-salud-escolar-2018/ [Google Scholar]

[pone.0279978.ref028] 28.Obesity. [cited 20 Apr 2022]. Available: https://www.who.int/health-topics/obesity

[pone.0279978.ref029] 29.Ebbeling CB, Feldman HA, Chomitz VR, Antonelli TA, Gortmaker SL, Osganian SK, et al. A randomized trial of sugar-sweetened beverages and adolescent body weight. N Engl J Med. 2012;367: 1407–1416. doi: 10.1056/NEJMoa1203388 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref030] 30.Ruyter JC de, de Ruyter JC, Olthof MR, Seidell JC, Katan MB. A Trial of Sugar-free or Sugar-Sweetened Beverages and Body Weight in Children. New England Journal of Medicine. 2012. pp. 1397–1406. doi: 10.1056/nejmoa1203034 [DOI] [PubMed] [Google Scholar]

[pone.0279978.ref031] 31.Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJL. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr. 2003;1: 4. doi: 10.1186/1478-7954-1-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref032] 32.[cited 26 Apr 2022]. Available: http://iah.salud.gob.ar/doc/Documento3.pdf

[pone.0279978.ref033] 33.[cited 26 Apr 2022]. Available: https://bancos.salud.gob.ar/sites/default/files/2021-01/guia-nacional-practica-clinica-diabetes-mellitius-tipo2_version-abreviada.pdf

[pone.0279978.ref034] 34.Banco Central de la República Argentina. [cited 15 Sep 2022]. Available: http://www.bcra.gov.ar/

[pone.0279978.ref035] 35.Salgado MV, Penko J, Fernandez A, Konfino J, Coxson PG, Bibbins-Domingo K, et al. Projected impact of a reduction in sugar-sweetened beverage consumption on diabetes and cardiovascular disease in Argentina: A modeling study. PLoS Med. 2020;17: e1003224. doi: 10.1371/journal.pmed.1003224 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref036] 36.Fisberg M, Kovalskys I, Gómez G, Rigotti A, Sanabria LYC, García MCY, et al. Total and added sugar intake: Assessment in eight Latin American countries. Nutrients. 2018;10. doi: 10.3390/nu10040389 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref037] 37.BOLETIN OFICIAL REPUBLICA ARGENTINA—MINISTERIO DE SALUD Y DESARROLLO SOCIAL SECRETARÍA DE GOBIERNO DE SALUD—Resolución 2004/2019. [cited 30 Sep 2021]. Available: https://www.boletinoficial.gob.ar/detalleAviso/primera/217123/20190923

[pone.0279978.ref038] 38.Allen LN, Pullar J, Wickramasinghe KK, Williams J, Roberts N, Mikkelsen B, et al. Evaluation of research on interventions aligned to WHO ‘Best Buys’ for NCDs in low-income and lower-middle-income countries: a systematic review from 1990 to 2015. BMJ Glob Health. 2018;3: e000535. doi: 10.1136/bmjgh-2017-000535 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref039] 39.Basto-Abreu A, Braverman-Bronstein A, Camacho-García-Formentí D, Zepeda-Tello R, Popkin BM, Rivera-Dommarco J, et al. Expected changes in obesity after reformulation to reduce added sugars in beverages: A modeling study. PLoS Med. 2018;15: e1002664. doi: 10.1371/journal.pmed.1002664 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref040] 40.Claro RM, Levy RB, Popkin BM, Monteiro CA. Sugar-sweetened beverage taxes in Brazil. Am J Public Health. 2012;102: 178–183. doi: 10.2105/AJPH.2011.300313 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref041] 41.Taillie LS, Reyes M, Colchero MA, Popkin B, Corvalán C. An evaluation of Chile’s Law of Food Labeling and Advertising on sugar-sweetened beverage purchases from 2015 to 2017: A before-and-after study. PLoS Med. 2020;17: e1003015. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279978.ref042] 42.Caro JC, Corvalán C, Reyes M, Silva A, Popkin B, Taillie LS. Chile’s 2014 sugar-sweetened beverage tax and changes in prices and purchases of sugar-sweetened beverages: An observational study in an urban environment. PLoS Med. 2018;15: e1002597. doi: 10.1371/journal.pmed.1002597 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The burden of disease and economic impact of sugar-sweetened beverages’ consumption in Argentina: A modeling study

Ariel Esteban Bardach

Natalia Espínola

Federico Rodríguez Cairoli

Lucas Perelli

Darío Balan

Alfredo Palacios

Federico Augustovski

Andrés Pichón-Riviere

Andrea Olga Alcaraz

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Model structure

Fig 1. Model causal framework and main outcomes.

Epidemiological data

Sugar-sweetened beverage (SSB) consumption

SSB impact on BMI and other diseases

Direct medical costs

Main epidemiological and economic inputs

Table 1. Key epidemiological parameters.

Table 2. Other epidemiological inputs and key economic parameters.

Results

Overweight and obesity

Table 3. Health and economic burden attributed to SSBs consumption in adults: Mortality, disease events, DALYs, and direct medical costs, by cause and sex.

Fig 2. Overweight and obesity total cases and costs attributable to SSBs consumption by sex and age group.

Cardiovascular, diabetes type 2, and other diseases

Fig 3.

Uncertainty analysis

Discussion

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Vijay S Gc

Roles

Author response to Decision Letter 0

Decision Letter 1

Vijay S Gc

Roles

Author response to Decision Letter 1

Decision Letter 2

Vijay S Gc

Roles

Author response to Decision Letter 2

Decision Letter 3

Vijay S Gc

Roles

Acceptance letter

Vijay S Gc

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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