Argentina
- Overview
- Obesity prevalence
- Trends over time
- Population breakdowns
- Drivers
- Comorbidities
- Health systems
- Policies
Obesity prevalence
Trends over time
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The report card collates all the most-recent graphics for this country. If you would like to produce a custom report based on selected graphics, just tap the Add to custom PDF button below the graphics you would like to use.Population breakdowns
Drivers
Insufficient activity
Soft drink consumption
Fruit consumption
Vegetable consumption
Fast food consumption
Processed meat consumption
Grains consumption
Depression
Anxiety
Roots of obesity »
Like all chronic diseases, the root causes/drivers of obesity are complex. Select here to view 'other' root causes/drivers.Breastfeeding
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Comorbidities
Health systems
Obesity prevalence
Adults, 2018
Survey type: | Measured |
Age: | 18+ |
Sample size: | 16577 |
Area covered: | National |
References: | 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) |
Notes: | Self report data also available for trends and other classifications |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2018-2019
Survey type: | Measured |
Age: | 18+ |
Sample size: | 7367 |
Area covered: | Regional (Urban) |
References: | 2° Encuesta Nacional de Nutrición 2018-2019. https://fagran.org.ar/wp-content/uploads/2020/01/Encuesta-nacional-de-nutricion-y-salud.pdf (Accessed 16.06.21) |
Notes: | Representative of 6 urban regions in Argentina. |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2018
Survey type: | Self-reported |
Age: | 18+ |
Sample size: | 5000 |
Area covered: | National |
References: | 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) |
Notes: | Trend also available by Region |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2013
Survey type: | Self-reported |
Age: | 18+ |
Sample size: | 32365 |
Area covered: | National |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2013
Survey type: | Self-reported |
Age: | 18+ |
References: | 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey |
Notes: | Subset of measured data available in 2018. 32.4% Obesity |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2012-2013
Survey type: | Measured |
Age: | 18-70 |
Sample size: | 1194 |
Area covered: | Regional |
References: | Zapata ME, Bibiloni MD and Tur JA. Prevalence of overweight, obesity, abdominal-obesity and short stature of adult population of Rosario, Argentina. (2016). 33(5):580. |
Notes: | Subnational (Rosario) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2009
Survey type: | Self-reported |
Age: | 18+ |
References: | 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey |
Notes: | Subset of measured data available in 2018. 32.4% Obesity |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2005
Survey type: | Self-reported |
Age: | 18+ |
References: | 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey |
Notes: | Subset of measured data available in 2018. 32.4% Obesity |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2003
Survey type: | Measured |
Age: | 18-65 |
Sample size: | 1100 |
Area covered: | Sub National |
References: | Virgolini M, & Ferrante D. Validación de la Herramienta de la OPS para vigilancia de ENT en Argentina 2003. Ministerio de Salud y Ambiente de la Nación. WHO Report. (unpublished work). |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 1997
Survey type: | Measured |
Age: | 20-79 |
Sample size: | Not specified |
References: | Filozof C, Gonzales C, Sereday M, Mazza C, Braguinsky J. Obesity prevalence and trends in Latin American countries. Obesity Reviews, 2001;2:99-196. Personal Communication by Prof. Rafael Figueredo Grijalba |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Children, 2018-2019
Survey type: | Measured |
Age: | 5-17 |
Sample size: | ~8000 |
Area covered: | Regional (Urban) |
References: | 2° Encuesta Nacional de Nutrición 2018-2019. https://fagran.org.ar/wp-content/uploads/2020/01/Encuesta-nacional-de-nutricion-y-salud.pdf (Accessed 16.06.21) |
Notes: | Representative of 6 urban regions in Argentina. |
Cutoffs: | WHO |
Children, 2018
Survey type: | Self-reported |
Age: | 13-15 |
Sample size: | 56981 |
Area covered: | National |
References: | Argentina Global School-Based Student Health Survey 2018. https://extranet.who.int/ncdsmicrodata/index.php/catalog/866/download/6099 (Accessed 13.07.21) |
Cutoffs: | WHO |
Children, 2012
Survey type: | Self-reported |
Age: | 13-15 |
Sample size: | 28368 |
Area covered: | National |
References: | Global School-based Student Health Survey (GSHS), available at https://www.who.int/ncds/surveillance/gshs/Argentina_GSHS_FS_2012_National.pdf?ua=1 (last accessed 25.11.20) |
Notes: | WHO cutoffs. |
Cutoffs: | WHO |
Children, 2007
Survey type: | Self-reported |
Age: | 13-15 |
Sample size: | 1980 |
Area covered: | National |
References: | Global School-based Student Health Survey, Argentina 2007 Fact Sheet. Available at https://www.who.int/ncds/surveillance/gshs/2007_Argentina_fact_sheet.pdf?ua=1 |
Cutoffs: | WHO |
Children, 2005
Survey type: | Measured |
Age: | 10 |
Sample size: | 1693 |
Area covered: | Urban |
References: | Kovalskys I, Rausch Herscovici C, De Gregorio MJ. Nutritional status of school-aged children of Buenos Aires, Argentina: data using three references.J Public Health (Oxf). 2011 Sep;33(3):403-11. doi: 10.1093/pubmed/fdq079. Epub 2010 Oct 12. |
Notes: | IOTF International Cut Off Points Children Aged 11 years also available in the paper |
Cutoffs: | IOTF |
% Adults living with obesity, 2005-2018
Men and women
Survey type: | Self-reported |
References: | 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². | |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
% Children living with obesity, 2007-2018
Girls
Survey type: | Self-reported |
References: | 2007: Global School-based Student Health Survey, Argentina 2007 Fact Sheet. Available at
https://www.who.int/ncds/surveillance/gshs/2007_Argentina_fact_sheet.pdf?ua=1 2012: Global School-based Student Health Survey (GSHS), available at https://www.who.int/ncds/surveillance/gshs/Argentina_GSHS_FS_2012_National.pdf?ua=1 (last accessed 25.11.20) 2018: Argentina Global School-Based Student Health Survey 2018. https://extranet.who.int/ncdsmicrodata/index.php/catalog/866/download/6099 (Accessed 13.07.21) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². | |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
Boys
Survey type: | Self-reported |
References: | 2007: Global School-based Student Health Survey, Argentina 2007 Fact Sheet. Available at
https://www.who.int/ncds/surveillance/gshs/2007_Argentina_fact_sheet.pdf?ua=1 2012: Global School-based Student Health Survey (GSHS), available at https://www.who.int/ncds/surveillance/gshs/Argentina_GSHS_FS_2012_National.pdf?ua=1 (last accessed 25.11.20) 2018: Argentina Global School-Based Student Health Survey 2018. https://extranet.who.int/ncdsmicrodata/index.php/catalog/866/download/6099 (Accessed 13.07.21) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². | |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
Boys and girls
Survey type: | Self-reported |
References: | 2007: Global School-based Student Health Survey, Argentina 2007 Fact Sheet. Available at
https://www.who.int/ncds/surveillance/gshs/2007_Argentina_fact_sheet.pdf?ua=1 2012: Global School-based Student Health Survey (GSHS), available at https://www.who.int/ncds/surveillance/gshs/Argentina_GSHS_FS_2012_National.pdf?ua=1 (last accessed 25.11.20) 2018: Argentina Global School-Based Student Health Survey 2018. https://extranet.who.int/ncdsmicrodata/index.php/catalog/866/download/6099 (Accessed 13.07.21) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². | |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
% Adults living with obesity, selected countries, 1960-2020
Men
References: | 1960, 1971, 1973, 1976, 1991: Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960-1994. International Journal of Obesity (1998);22:39-47 1975: Monteiro CA, Conde WL, Popking BM. Is obesity replacing or adding to undernutrition? Evidence from different social classes in Brazil. 2002. Public Health Nutrition:51(1A), 105-112 1988: Berrios X, Jadue I, Zenteno J, Ross MI, Rodriguez H. Prevalencia de factores de riesgo para enfermedades cronicas. Estudio de la poblacion general de la region Metropolitana, 1986-1987. Rev. Med. Chile. 1990;118:597-604 1992, 1994, 1995: Martorell R, Khan LK, Hughes ML, Grummer Strawn LM. Obesity in women from developing countries. EJCN (2000) 54;247-252 1997: Filozof C, Gonzales C, Sereday M, Mazza C, Braguinsky J. Obesity prevalence and trends in Latin American countries. Obesity Reviews, 2001;2:99-196 1998: http://www.unscn.org/layout/modules/resources/files/rwns5.pdf; MEAN BMI Data DHS Survey 1999: Centres for Disease Control and Prevention. http://www.cdc.gov/ 2000: Demographic Health Survey, Peru 2000 2001: Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of Overweight and Obesity in the United States, 1999-2004. JAMA 2006;295(13):1549-1555 2002: Monteiro CA, Conde WL and Popkin BA. (2007). Income-specific trends in obesity in Brazil: 1975 - 2003. American Journal of Public Health, 97 (10): 1808 - 1812. 2003: 2003 ENS Report. Final results on the National Health Survey. Http://epi.minsal.cl/epi/html/invest/ENS/informeFinalENS.pdf. 2005: Demographic Health Survey 2006. 2006: Olaiz-Fernández G, Rivera-Dommarco J, Shamah-Levy T, Rojas R, Villalpando-Hernández S, Hernández-Avila M, Sepúlveda-Amor J. Encuesta Nacional de Salud y Nutrición 2006. Cuernavaca, México: Instituto Nacional de Salud Pública, 2006. (National Health and Nutrition Survey 2006). 2007: DHS 2007 - 2008 2008: Ramirez-Zea M, Kroker-Lobos MF, Close-Fernandez R, Kanter R. The double burden of malnutrition in indigenous and nonindigenous Guatemalan populations. Am J Clin Nutr. 2014 Dec;100(6):1644S-51S. doi: 10.3945/ajcn.114.083857 2009: 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey 2010: Demographic Health Survey 2010 2011: Ruopeng An, “Prevalence and Trends of Adult Obesity in the US, 1999–2012”, ISRN Obesity, vol. 2014, Article ID 185132, 6 pages, 2014. doi:10.1155/2014/185132 2012: Demographic Health Survey Haiti 2012 2013: DHS Peru 2013 2014: Demographic Health Survey, Guatemala 2014-15 2015: NHANES 2015/16. Analysis conducted by the World Obesity Federation, Caroline Litts, Fiona Montague & R Jackson-Leach 2017 2016: Encuesta Nacional de Salud. Chile. 2016-2017 https://www.minsal.cl/wp-content/uploads/2017/11/ENS-2016-17_PRIMEROS-RESULTADOS.pdf (Last accessed 04.08.20) 2017: Pickens, C. M., Flores-Ayala, R., Addo, O. Y., Whitehead, R. D., Jr, Palmieri, M., Ramirez-Zea, M., Hong, Y., & Jefferds, M. E. (2020). Prevalence and Predictors of High Blood Pressure Among Women of Reproductive Age and Children Aged 10 to 14 Years in Guatemala. Preventing chronic disease, 17, E66. https://doi.org/10.5888/pcd17.190403 2018: 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) 2019: Bahamas STEPS Survey (Preliminary results) https://www.bahamas.gov.bs/wps/wcm/connect/891fac39-ad7d-4aa8-ac54-39912a1afcea/Preliminary+Factsheet+v7+%28med+resl%27n%29.pdf?MOD=AJPERES (Accessed 03.11.2020) 2020: Shamah-Levy T, Romero-Martínez M, Barrientos-Gutiérrez T, Cuevas-Nasu L, Bautista-Arredondo S, Colchero MA, GaonaPineda EB, Lazcano-Ponce E, Martínez-Barnetche J, Alpuche-Arana C, Rivera-Dommarco J. Encuesta Nacional de Salud y Nutrición 2020 sobre Covid-19. Resultados nacionales. Cuernavaca, México: Instituto Nacional de Salud Pública, 2021. https://ensanut.insp.mx/encuestas/ensanutcontinua2020/doctos/informes/ensanutCovid19ResultadosNacionales.pdf (Accessed 11.01.2022) |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
Women
References: | 1960, 1971, 1973, 1976, 1991: Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960-1994. International Journal of Obesity (1998);22:39-47 1975: Monteiro CA, Conde WL, Popking BM. Is obesity replacing or adding to undernutrition? Evidence from different social classes in Brazil. 2002. Public Health Nutrition:51(1A), 105-112 1988: Berrios X, Jadue I, Zenteno J, Ross MI, Rodriguez H. Prevalencia de factores de riesgo para enfermedades cronicas. Estudio de la poblacion general de la region Metropolitana, 1986-1987. Rev. Med. Chile. 1990;118:597-604 1992, 1994, 1995: Martorell R, Khan LK, Hughes ML, Grummer Strawn LM. Obesity in women from developing countries. EJCN (2000) 54;247-252 1997: Filozof C, Gonzales C, Sereday M, Mazza C, Braguinsky J. Obesity prevalence and trends in Latin American countries. Obesity Reviews, 2001;2:99-196 1998: http://www.unscn.org/layout/modules/resources/files/rwns5.pdf; MEAN BMI Data DHS Survey 1999: Centres for Disease Control and Prevention. http://www.cdc.gov/ 2000: Demographic Health Survey, Peru 2000 2001: Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of Overweight and Obesity in the United States, 1999-2004. JAMA 2006;295(13):1549-1555 2002: Monteiro CA, Conde WL and Popkin BA. (2007). Income-specific trends in obesity in Brazil: 1975 - 2003. American Journal of Public Health, 97 (10): 1808 - 1812. 2003: 2003 ENS Report. Final results on the National Health Survey. Http://epi.minsal.cl/epi/html/invest/ENS/informeFinalENS.pdf. 2005: Demographic Health Survey 2006. 2006: Olaiz-Fernández G, Rivera-Dommarco J, Shamah-Levy T, Rojas R, Villalpando-Hernández S, Hernández-Avila M, Sepúlveda-Amor J. Encuesta Nacional de Salud y Nutrición 2006. Cuernavaca, México: Instituto Nacional de Salud Pública, 2006. (National Health and Nutrition Survey 2006). 2007: DHS 2007 - 2008 2008: Ramirez-Zea M, Kroker-Lobos MF, Close-Fernandez R, Kanter R. The double burden of malnutrition in indigenous and nonindigenous Guatemalan populations. Am J Clin Nutr. 2014 Dec;100(6):1644S-51S. doi: 10.3945/ajcn.114.083857 2009: 1-4th Argentinian National Survey of Risk Factors (Encuesta Nacional de Factores de Riesgo). 4th (2018) Survey 2010: Demographic Health Survey 2010 2011: Ruopeng An, “Prevalence and Trends of Adult Obesity in the US, 1999–2012”, ISRN Obesity, vol. 2014, Article ID 185132, 6 pages, 2014. doi:10.1155/2014/185132 2012: Demographic Health Survey Haiti 2012 2013: DHS Peru 2013 2014: Demographic Health Survey, Guatemala 2014-15 2015: NHANES 2015/16. Analysis conducted by the World Obesity Federation, Caroline Litts, Fiona Montague & R Jackson-Leach 2017 2016: Encuesta Nacional de Salud. Chile. 2016-2017 https://www.minsal.cl/wp-content/uploads/2017/11/ENS-2016-17_PRIMEROS-RESULTADOS.pdf (Last accessed 04.08.20) 2017: Pickens, C. M., Flores-Ayala, R., Addo, O. Y., Whitehead, R. D., Jr, Palmieri, M., Ramirez-Zea, M., Hong, Y., & Jefferds, M. E. (2020). Prevalence and Predictors of High Blood Pressure Among Women of Reproductive Age and Children Aged 10 to 14 Years in Guatemala. Preventing chronic disease, 17, E66. https://doi.org/10.5888/pcd17.190403 2018: 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) 2019: Bahamas STEPS Survey (Preliminary results) https://www.bahamas.gov.bs/wps/wcm/connect/891fac39-ad7d-4aa8-ac54-39912a1afcea/Preliminary+Factsheet+v7+%28med+resl%27n%29.pdf?MOD=AJPERES (Accessed 03.11.2020) 2020: Shamah-Levy T, Romero-Martínez M, Barrientos-Gutiérrez T, Cuevas-Nasu L, Bautista-Arredondo S, Colchero MA, GaonaPineda EB, Lazcano-Ponce E, Martínez-Barnetche J, Alpuche-Arana C, Rivera-Dommarco J. Encuesta Nacional de Salud y Nutrición 2020 sobre Covid-19. Resultados nacionales. Cuernavaca, México: Instituto Nacional de Salud Pública, 2021. https://ensanut.insp.mx/encuestas/ensanutcontinua2020/doctos/informes/ensanutCovid19ResultadosNacionales.pdf (Accessed 11.01.2022) |
Different methodologies may have been used to collect this data and so data from different surveys may not be strictly comparable. Please check with original data sources for methodologies used. |
Overweight/obesity by education
Adults, 2018
Survey type: | Measured |
Age: | 18+ |
Sample size: | 16577 |
Area covered: | National |
References: | 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Men, 2012-2013
Survey type: | Measured |
Sample size: | 1194 |
Area covered: | Subnational (Rosario) |
References: | Zapata ME, Bibiloni MD and Tur JA. Prevalence of overweight, obesity, abdominal-obesity and short stature of adult population of Rosario, Argentina. (2016). 33(5):580. |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Women, 2012-2013
Survey type: | Measured |
Sample size: | 1194 |
Area covered: | Subnational (Rosario) |
References: | Zapata ME, Bibiloni MD and Tur JA. Prevalence of overweight, obesity, abdominal-obesity and short stature of adult population of Rosario, Argentina. (2016). 33(5):580. |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2005
Survey type: | Self-reported |
Age: | 18+ |
Sample size: | 41392 |
Area covered: | National |
References: | "De Maio FG, Linetzky B, Virgolini M. An average/deprivation/inequality (ADI) analysis of chronic disease outcomes and risk factors in Argentina. Popul Health Metr. 2009 Jun 8;7:8. doi: 10.1186/1478-7954-7-8." |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Overweight/obesity by age
Adults, 2018
Survey type: | Measured |
Sample size: | 16577 |
Area covered: | National |
References: | 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Overweight/obesity by socio-economic group
Adults, 2018
Survey type: | Measured |
Age: | 18+ |
Sample size: | 16577 |
Area covered: | National |
References: | 4th National Survey, Full report available at http://www.msal.gob.ar/images/stories/bes/graficos/0000001622cnt-2019-10_4ta-encuesta-nacional-factores-riesgo.pdf (last accessed 29.04.20) |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Adults, 2005-2012
Survey type: | Measured |
Age: | 18+ |
Sample size: | 4328 |
Area covered: | Córdoba (the capital city of the state of Córdoba in the centre of Argentina) |
References: | Aballay LR, Osella AR, De La Quintana AG, and Diaz MP. Nutritional profile and obesity: results from a random sample population based study in Córdoba, Argentina. Eur J Nutr DOI 10.1007/s00394-015-0887-0 |
Notes: | Socio-economic status (SES) was built as a composite indicator following the Argentine Marketing Association’s guidelines, which include four domains: educational level, job, housing, and amenities. |
Unless otherwise noted, overweight refers to a BMI between 25kg and 29.9kg/m², obesity refers to a BMI greater than 30kg/m². |
Insufficient physical activity
Adults, 2016
References: | Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet 2018 http://dx.doi.org/10.1016/S2214-109X(18)30357-7 |
Men, 2016
References: | Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet 2018 http://dx.doi.org/10.1016/S2214-109X(18)30357-7 |
Women, 2016
References: | Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet 2018 http://dx.doi.org/10.1016/S2214-109X(18)30357-7 |
Children, 2016
Survey type: | Self-reported |
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, https://apps.who.int/gho/data/node.main.A893ADO?lang=en (last accessed 16.03.21) |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Boys, 2016
Survey type: | Self-reported |
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, https://apps.who.int/gho/data/node.main.A893ADO?lang=en (last accessed 16.03.21) |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Girls, 2016
Survey type: | Self-reported |
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, https://apps.who.int/gho/data/node.main.A893ADO?lang=en (last accessed 16.03.21) |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Children, 2010
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A893?lang=en |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Boys, 2010
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A893?lang=en |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Girls, 2010
Age: | 11-17 |
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A893?lang=en |
Notes: | % of school going adolescents not meeting WHO recommendations on Physical Activity for Health, i.e. doing less than 60 minutes of moderate- to vigorous-intensity physical activity daily. |
Definitions: | % Adolescents insufficiently active (age standardised estimate) |
Average daily frequency of carbonated soft drink consumption
Children, 2009-2015
Survey type: | Measured |
Age: | 12-17 |
References: | Beal et al. (2019). Global Patterns of Adolescent Fruit, Vegetable, Carbonated Soft Drink, and Fast-food consumption: A meta-analysis of global school-based student health surveys. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119848287 sourced from Food Systems Dashboard http://www.foodsystemsdashboard.org/food-system |
Estimated per capita fruit intake
Adults, 2017
Survey type: | Measured |
Age: | 25+ |
References: | Global Burden of Disease, the Institute for Health Metrics and Evaluation http://ghdx.healthdata.org/ |
Definitions: | Estimated per-capita fruit intake (g/day) |
Prevalence of less than daily fruit consumption
Children, 2009-2015
Survey type: | Measured |
Age: | 12-17 |
References: | Global School-based Student Health Surveys. Beal et al (2019). Global Patterns of Adolescent Fruit, Vegetable, Carbonated Soft Drink, and Fast-food consumption: A meta-analysis of global school-based student health surveys. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119848287. Sourced from Food Systems Dashboard http://www.foodsystemsdashboard.org/food-system |
Definitions: | Prevalence of less-than-daily fruit consumption (% less-than-daily fruit consumption) |
Prevalence of less than daily vegetable consumption
Children, 2009-2015
Survey type: | Measured |
Age: | 12-17 |
References: | Beal et al. (2019). Global Patterns of Adolescent Fruit, Vegetable, Carbonated Soft Drink, and Fast-food consumption: A meta-analysis of global school-based student health surveys. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119848287 sourced from Food Systems Dashboard http://www.foodsystemsdashboard.org/food-system |
Definitions: | Prevalence of less-than-daily vegetable consumption (% less-than-daily vegetable consumption) |
Average weekly frequency of fast food consumption
Children, 2009-2015
Age: | 12-17 |
References: | Beal et al. (2019). Global Patterns of Adolescent Fruit, Vegetable, Carbonated Soft Drink, and Fast-food consumption: A meta-analysis of global school-based student health surveys. Food and Nutrition Bulletin. https://doi.org/10.1177/0379572119848287 sourced from Food Systems Dashboard http://www.foodsystemsdashboard.org/food-system |
Estimated per-capita processed meat intake
Adults, 2017
Survey type: | Measured |
Age: | 25+ |
References: | Global Burden of Disease, the Institute for Health Metrics and Evaluation http://ghdx.healthdata.org/ |
Definitions: | Estimated per-capita processed meat intake (g per day) |
Estimated per capita whole grains intake
Adults, 2017
Survey type: | Measured |
Age: | 25+ |
References: | Global Burden of Disease, the Institute for Health Metrics and Evaluation http://ghdx.healthdata.org/ |
Definitions: | Estimated per-capita whole grains intake (g/day) |
Mental health - depression disorders
Adults, 2015
References: | Prevalence data from Global Burden of Disease study 2015 (http://ghdx.healthdata.org) published in: Depression and Other Common Mental Disorders: Global Health Estimates. Geneva:World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO. |
Definitions: | % of population with depression disorders |
Mental health - anxiety disorders
Adults, 2015
References: | Prevalence data from Global Burden of Disease study 2015 (http://ghdx.healthdata.org) published in: Depression and Other Common Mental Disorders: Global Health Estimates. Geneva:World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGO. |
Definitions: | % of population with anxiety disorders |
% Infants exclusively breastfed 0-5 months
Children, 1998-2019
Area covered: | National |
References: | Encuesta de Indicadores Múltiples por Conglomerados 2011/2012, Informe Final. Buenos Aires, Argentina |
Notes: | See UNICEF website for further survey information. Available at : https://data.unicef.org/resources/dataset/infant-young-child-feeding/ (last accessed 28.9.21) Citation: United Nations Children’s Fund, Division of Data, Analysis, Planning and Monitoring (2021). Global UNICEF Global Databases: Infant and Young Child Feeding: Exclusive breastfeeding, New York, September 2021. |
Definitions: | % exclusively breastfed 0-5 months |
Oesophageal cancer
Men, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, oesophagus, adults ages 20+. ASR (World) per 100,000 |
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, oesophagus, adults ages 20+. ASR (World) per 100,000 |
Breast cancer
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, breast, females, ages 20+. ASR (World) per 100,000 |
Colorectal cancer
Men, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, colorectum, adults, ages 20+. ASR (World) per 100,000 |
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, colorectum, adults, ages 20+. ASR (World) per 100,000 |
Pancreatic cancer
Men, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, pancreas, adults, ages 20+. ASR (World) per 100,000 |
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, pancreas, adults, ages 20+. ASR (World) per 100,000 |
Gallbladder cancer
Men, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, gallbladder, adults, ages 20+. ASR (World) per 100,000 |
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, gallbladder, adults, ages 20+. ASR (World) per 100,000 |
Kidney cancer
Men, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, kidney, adults, ages 20+. ASR (World) per 100,000 |
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, kidney, adults, ages 20+. ASR (World) per 100,000 |
Cancer of the uterus
Women, 2018
Age: | 20+ |
References: | Global Cancer Observatory, Cancer incidence rates http://gco.iarc.fr/ (last accessed 30th June 2020) |
Definitions: | Estimated age-standardized incidence rates (World) in 2018, cervix uteri, females, ages 20+. ASR (World) per 100,000 |
Raised blood pressure
Adults, 2015
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A875?lang=en |
Definitions: | Age Standardised estimated % Raised blood pressure 2015 (SBP>=140 OR DBP>=90). |
Men, 2015
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A875?lang=en |
Definitions: | Age Standardised estimated % Raised blood pressure 2015 (SBP>=140 OR DBP>=90). |
Women, 2015
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A875?lang=en |
Definitions: | Age Standardised estimated % Raised blood pressure 2015 (SBP>=140 OR DBP>=90). |
Raised cholesterol
Adults, 2008
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A885 |
Definitions: | % Raised total cholesterol (>= 5.0 mmol/L) (age-standardized estimate). |
Men, 2008
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A885 |
Definitions: | % Raised total cholesterol (>= 5.0 mmol/L) (age-standardized estimate). |
Women, 2008
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A885 |
Definitions: | % Raised total cholesterol (>= 5.0 mmol/L) (age-standardized estimate). |
Raised fasting blood glucose
Men, 2014
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A869?lang=en |
Definitions: | Age Standardised % raised fasting blood glucose (>= 7.0 mmol/L or on medication). |
Women, 2014
References: | Global Health Observatory data repository, World Health Organisation, http://apps.who.int/gho/data/node.main.A869?lang=en |
Definitions: | Age Standardised % raised fasting blood glucose (>= 7.0 mmol/L or on medication). |
Diabetes prevalence
Adults, 2021
Age: | 20-79 |
Area covered: | National |
References: | Reproduced with kind permission International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels, Belgium:International Diabetes Federation, 2021. http://www.diabetesatlas.org |
Definitions: | Age-adjusted comparative prevalence of diabetes, % |
Adults, 2019
Age: | 20-79 |
References: | Reproduced with kind permission International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels,Belgium: 2019. Available at: https://www.diabetesatlas.org |
Definitions: | Diabetes age-adjusted comparative prevalence (%). |
Adults, 2017
References: | Reproduced with kind permission of IDF, International Diabetes Federation. IDF Diabetes Atlas, 8th edition. Brussels, Belgium: International Diabetes Federation, 2017. http://www.diabetesatlas.org |
Definitions: | Diabetes age-adjusted comparative prevalence (%). |
Health systems
Health systems summary
The Argentinian health care system is set up to provide affordable health care regardless of an individual’s personal circumstances, but it is considered to be very fragmented. It is composed of three strands: the public sector available to all and paid for through taxes, Obras Sociales which is compulsory for all workers of the formal economy and the private sector for those with private health insurance. The Argentinian health system is therefore financially sustained by a combination of taxes, payroll contributions, and out-of-pocket contributions. The private sector accounts for 30% of total health expenditure, of which nearly 60% is from out-of-pocket expenditure.
It is thought that the different schemes in Argentina generally cover the same treatments but the difference lies in the quality of care. For the public sector, individuals must meet very strict criteria to be eligible for free care, but then you are still subject to long waiting lists. One stakeholder noted that the economic crisis had presented a challenge to the health system.
Indicators
Where is the country’s government in the journey towards defining ‘Obesity as a disease’? | Defined as disease |
Where is the country’s healthcare provider in the journey towards defining ‘Obesity as a disease’? | Some progress |
Is there specialist training available dedicated to the training of health professionals to prevent, diagnose, treat and manage obesity? | No |
Have any taxes or subsidies been put in place to protect/assist/inform the population around obesity? | No |
Are there adequate numbers of trained health professionals in specialties relevant to obesity in urban areas? | Yes |
Are there adequate numbers of trained health professionals in specialties relevant to obesity in rural areas? | No |
Are there any obesity-specific recommendations or guidelines published for adults? | Yes |
Are there any obesity-specific recommendations or guidelines published for children? | Yes |
In practice, how is obesity treatment largely funded? | Insurance |
Summary of stakeholder feedback
The Argentinian health system was described as “fragmented”, made up of different subsystems that worked in different ways. Stakeholders felt that despite there being an “obesity law” in place, neither the government nor the health providers wholly recognised obesity as a disease. Government investment into obesity was considered to be low, and there was noted to be more resources dedicated to obesity treatment in the private sector.
There was a lack of consensus on the BMI level that people tended to be picked up by the system - perhaps suggesting inconsistency across the country and different health systems. There was, however, agreement that those that lived in rural areas struggled to access care. For those who could access care, they seemed to enter the system through primary or hospital care (and sometimes private institutes). Different reasons were given for people leaving the system, including cost, treatment ‘failure’, lack of follow-up or motivation and lack of referrals.
There is considered to be little to no specialist obesity training, but there seems to be limited training available for specific professionals such as nutritionists.
Stakeholders noted that there was a non-communicable disease strategy that mentioned obesity. The effectiveness of the strategy and the extent of implementation was, however, questioned. It was also noted that there are obesity-specific recommendations and guidelines e.g. clinical practice guidelines for diagnosis and treatment.
Overall, stakeholders felt that the obesity agenda in Argentina needed better leadership at a national level along with more financial support. Stakeholders did recognise, however, that there was several national programmes and initiatives attempting to address obesity.
Based on interviews/survey returns from 7 stakeholders
Last updated: June 2020