Advertisement

Premature Deaths Attributable to the Consumption of Ultraprocessed Foods in Brazil

Published:November 07, 2022DOI:https://doi.org/10.1016/j.amepre.2022.08.013

      Introduction

      Ultraprocessed foods have been associated with an increased risk of noncommunicable diseases, such as diabetes, cardiovascular diseases, and cancer as well as all-cause mortality. The study aimed to estimate premature deaths attributable to the consumption of ultraprocessed food in Brazil.

      Methods

      A comparative risk assessment model was developed on the basis of RRs from a recent meta-analysis, national food consumption for 2017–2018, and demographic and mortality data for 2019. Population attributable fractions for all-cause mortality were then estimated within each sex and age stratum according to the distribution of the ultraprocessed food contribution to the total energy of the diet. Analysis was conducted in February 2022–April 2022.

      Results

      The contribution of ultraprocessed foods to the total energy intake of the diet across sex and age stratum of Brazilian adults ranged from 13% to 21% of the total energy intake. A total of 541,160 adults aged 30‒69 years died in 2019. The consumption of ultraprocessed foods was responsible for approximately 57,000 premature deaths (95% uncertainty interval=33,493, 82,570) or 10.5% of all premature deaths in adults aged 30‒69 years. Reducing the contribution of ultraprocessed foods to the total energy intake by 10%‒50% could potentially prevent 5,900 deaths (95% uncertainty interval=2,910, 10,613) to 29,300 deaths (95% uncertainty interval=16,514, 44,226), respectively.

      Conclusions

      The consumption of ultraprocessed foods represents a significant cause of premature death in Brazil. Reducing ultraprocessed food intake would promote substantial health gains for the population and should be a food policy priority to reduce premature mortality.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Monteiro CA
        • Cannon G
        • Levy RB
        • et al.
        Ultra-processed foods: what they are and how to identify them.
        Public Health Nutr. 2019; 22: 936-941https://doi.org/10.1017/S1368980018003762
        • Askari M
        • Heshmati J
        • Shahinfar H
        • Tripathi N
        • Daneshzad E.
        Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies.
        Int J Obes (Lond). 2020; 44: 2080-2091https://doi.org/10.1038/s41366-020-00650-z
        • Pagliai G
        • Dinu M
        • Madarena MP
        • Bonaccio M
        • Iacoviello L
        • Sofi F.
        Consumption of ultra-processed foods and health status: a systematic review and meta-analysis.
        Br J Nutr. 2021; 125: 308-318https://doi.org/10.1017/S0007114520002688
        • Lane MM
        • Davis JA
        • Beattie S
        • et al.
        Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies.
        Obes Rev. 2021; 22: e13146https://doi.org/10.1111/obr.13146
        • Fardet A
        • Méjean C
        • Labouré H
        • Andreeva VA
        • Feron G.
        The degree of processing of foods which are most widely consumed by the French elderly population is associated with satiety and glycemic potentials and nutrient profiles.
        Food Funct. 2017; 8: 651-658https://doi.org/10.1039/C6FO01495J
        • Zinöcker MK
        • Lindseth IA.
        The western diet-microbiome-host interaction and its role in metabolic disease.
        Nutrients. 2018; 10: 365https://doi.org/10.3390/nu10030365
        • Martínez Steele E
        • Khandpur N
        • da Costa Louzada ML
        • Monteiro CA
        Association between dietary contribution of ultra-processed foods and urinary concentrations of phthalates and bisphenol in a nationally representative sample of the U.S. population aged 6 years and older.
        PLoS One. 2020; 15e0236738https://doi.org/10.1371/journal.pone.0236738
        • Kim H
        • Rebholz CM
        • Wong E
        • Buckley JP.
        Urinary organophosphate ester concentrations in relation to ultra-processed food consumption in the general U.S. population.
        Environ Res. 2020; 182109070https://doi.org/10.1016/j.envres.2019.109070
        • Food and Agriculture Organization (FAO)
        Ultra-processed foods, diet quality, and health using the NOVA classification system.
        Food and Agriculture Organization of the United Nations, Rome, Italy2019 (Published 2019. Accessed April 4, 2022)
        • Monteiro CA
        • Moubarac JC
        • Cannon G
        • Ng SW
        • Popkin B.
        Ultra-processed products are becoming dominant in the global food system.
        Obes Rev. 2013; 14: 21-28https://doi.org/10.1111/obr.12107
      1. IBGE. Pesquisa de orçamentos familiares 2017–2018: avaliação nutricional da disponibilidade domiciliar de alimentos no Brasil. Rio de Janeiro, Brazil: IBGE. https://biblioteca.ibge.gov.br/visualizacao/livros/liv101704.pdf. Published 2020. Accessed April 4, 2022.

        • Stanaway JD
        • Afshin A
        • Gakidou E
        • et al.
        Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.
        Lancet. 2018; 392: 1923-1994https://doi.org/10.1016/S0140-6736(18)32225-6
      2. IBGE. Pesquisa de orçamentos familiares 2017–2018: análise do consumo alimentar pessoal no Brasil. Rio de Janeiro, Brazil: IBGE. https://biblioteca.ibge.gov.br/visualizacao/livros/liv101742.pdf. Published 2020. Accessed April 4, 2022.

        • Blanco-Rojo R
        • Sandoval-Insausti H
        • López-Garcia E
        • et al.
        Consumption of ultra-processed foods and mortality: a national prospective cohort in Spain.
        Mayo Clin Proc. 2019; 94: 2178-2188https://doi.org/10.1016/j.mayocp.2019.03.035
        • Kim H
        • Hu EA
        • Rebholz CM.
        Ultra-processed food intake and mortality in the USA: results from the Third National Health and Nutrition Examination Survey (NHANES III, 1988–1994).
        Public Health Nutr. 2019; 22: 1777-1785https://doi.org/10.1017/S1368980018003890
        • Rico-Campà A
        • Martínez-González MA
        • Alvarez-Alvarez I
        • et al.
        Association between consumption of ultra-processed foods and all cause mortality: SUN prospective cohort study.
        BMJ. 2019; 365: l1949https://doi.org/10.1136/bmj.l1949
        • Schnabel L
        • Kesse-Guyot E
        • Allès B
        • et al.
        Association between ultraprocessed food consumption and risk of mortality among middle-aged adults in France.
        JAMA Intern Med. 2019; 179: 490-498https://doi.org/10.1001/jamainternmed.2018.7289
        • Bonaccio M
        • Di Castelnuovo A
        • Costanzo S
        • et al.
        Ultra-processed food consumption is associated with increased risk of all-cause and cardiovascular mortality in the Moli-sani Study.
        Am J Clin Nutr. 2021; 113: 446-455https://doi.org/10.1093/ajcn/nqaa299
      3. SIM: mortality information system. Ministério da Saúde. http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt10uf.def. Updated February 7, 2022. Accessed June 23, 2022.

        • WHO
        Premature mortality from noncommunicable disease.
        WHO, Geneva, Switzerland2022 (Published 2022. Accessed June 23, 2022)
        • Pérez-Ríos M
        • Rey-Brandariz J
        • Galán I
        • et al.
        Methodological guidelines for the estimation of attributable mortality using a prevalence-based method: the STREAMS-P tool.
        J Clin Epidemiol. 2022; 147: 101-110https://doi.org/10.1016/j.jclinepi.2022.03.016
        • Groot Koerkamp B
        • Stijnen T
        • Weinstein MC
        • Hunink MGM
        The combined analysis of uncertainty and patient heterogeneity in medical decision models.
        Med Decis Making. 2011; 31: 650-661https://doi.org/10.1177/0272989X10381282
        • Barendregt JJ
        Ersatz user guide. Queensland.
        EpiGear International Pty Ltd, Australia2017 (Published 2017. Accessed June 23, 2022)
        • Moreira PV
        • Hyseni L
        • Moubarac JC
        • et al.
        Effects of reducing processed culinary ingredients and ultra-processed foods in the Brazilian diet: a cardiovascular modelling study.
        Public Health Nutr. 2018; 21: 181-188https://doi.org/10.1017/S1368980017002063
        • Rezende LF
        • Azeredo CM
        • Canella DS
        • Luiz Odo C
        • Levy RB
        • Eluf-Neto J
        Coronary heart disease mortality, cardiovascular disease mortality and all-cause mortality attributable to dietary intake over 20 years in Brazil.
        Int J Cardiol. 2016; 217: 64-68https://doi.org/10.1016/j.ijcard.2016.04.176
        • Seferidi P
        • Laverty AA
        • Pearson-Stuttard J
        • et al.
        Implications of Brexit for the effectiveness of the UK soft drinks industry levy on coronary heart disease in England: a modelling study.
        Lancet. 2017; 390: S9https://doi.org/10.1016/S0140-6736(17)32944-6
        • Sánchez-Romero LM
        • Penko J
        • Coxson PG
        • et al.
        Projected impact of Mexico's sugar-sweetened beverage tax policy on diabetes and cardiovascular disease: a modeling study.
        PLoS Med. 2016; 13e1002158https://doi.org/10.1371/journal.pmed.1002158
        • Salgado MV
        • Penko J
        • Fernandez A
        • 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; 17e1003224https://doi.org/10.1371/journal.pmed.1003224
        • Manyema M
        • Veerman LJ
        • Tugendhaft A
        • Labadarios D
        • Hofman KJ.
        Modelling the potential impact of a sugar-sweetened beverage tax on stroke mortality, costs and health-adjusted life years in South Africa.
        BMC Public Health. 2016; 16: 405https://doi.org/10.1186/s12889-016-3085-y
        • Pereda P
        • Christofoletti MA
        • Ng SW
        • Claro RM
        • Duran AC
        • Monteiro CA.
        Effects of a 20% price increase of sugar-sweetened beverages on consumption and welfare in Brazil.
        University of São Paulo, São Paulo, Brazil2019 (Published 2019. Accessed April 4, 2022)
        • Monteiro CA
        • Lawrence M
        • Millett C
        • et al.
        The need to reshape global food processing: a call to the United Nations Food Systems Summit.
        BMJ Glob Health. 2021; 6e006885https://doi.org/10.1136/bmjgh-2021-006885
        • Juul F
        • Vaidean G
        • Parekh N.
        Ultra-processed foods and cardiovascular diseases: potential mechanisms of action.
        Adv Nutr. 2021; 12: 1673-1680https://doi.org/10.1093/advances/nmab049
        • COVID-19 Excess Mortality Collaborators
        Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21.
        Lancet. 2022; 399: 1513-1536https://doi.org/10.1016/S0140-6736(21)02796-3
        • Malta DC
        • Andrade SSCA
        • Oliveira TP
        • Moura L
        • Prado RRD
        • Souza MFM.
        Probability of premature death for chronic noncommunicable diseases, Brazil and Regions, projections to 2025.
        Rev Bras Epidemiol. 2019; 22e190030https://doi.org/10.1590/1980-549720190030
        • Silver LD
        • Ng SW
        • Ryan-Ibarra S
        • et al.
        Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, U.S.: a before-and-after study.
        PLoS Med. 2017; 14e1002283https://doi.org/10.1371/journal.pmed.1002283
        • 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; 17e1003015https://doi.org/10.1371/journal.pmed.1003015
        • Rincon Gallardo Patino S
        • Carriedo Á
        • Tolentino-Mayo L
        • et al.
        Front-of-pack warning labels are preferred by parents with low education level in four Latin American countries.
        WN. 2019; 10: 11-26https://doi.org/10.26596/wn.201910411-26
        • WHO, World Economic Forum
        From burden to “best buys”: reducing the economic impact of noncommunicable diseases in low- and middle-income countries.
        WHO, World Economic Forum, Geneva, Switzerland2011 (Published 2011. Accessed June 23, 2022)
      4. Ministério da Saúde. Dietary Guidelines for the Brazilian Population. Brasília, Brazil: Ministry of Health of Brazil. http://bvsms.saude.gov.br/bvs/publicacoes/guia_alimentar_populacao_brasileira_2ed.pdf. Published 2014. Accessed April 4, 2022.

      5. Sodium reduction in processed foods in Canada: an evaluation of progress toward voluntary targets from 2012 to 2016. Health Canada. https://www.canada.ca/en/health-canada/services/food-nutrition/legislation-guidelines/guidance-documents/guidance-food-industry-reducing-sodium-processed-foods-progress-report-2017.html. Updated January 12, 2018. Accessed June 23, 2022.

        • GAIN
        GAIN’s definition of nutritious and safe foods.
        GAIN, Geneva, Switzerland2021 (Accessed  June 23, 2022)
        • Lichtenstein AH
        • Appel LJ
        • Vadiveloo H
        • et al.
        Dietary guidance to improve cardiovascular health: a scientific statement from the American Heart Association.
        Circulation. 2021; 144: e472-e487https://doi.org/10.1161/CIR.0000000000001031
        • Smed S
        • Scarborough P
        • Rayner M
        • Jensen JD.
        The effects of the Danish saturated fat tax on food and nutrient intake and modelled health outcomes: an econometric and comparative risk assessment evaluation.
        Eur J Clin Nutr. 2016; 70: 681-686https://doi.org/10.1038/ejcn.2016.6
        • Kaur A
        • Scarborough P
        • Rayner M.
        Regulating health and nutrition claims in the UK using a nutrient profile model: an explorative modelled health impact assessment.
        Int J Behav Nutr Phys Act. 2019; 16: 18https://doi.org/10.1186/s12966-019-0778-5
        • Labonté ME
        • Emrich TE
        • Scarborough P
        • Rayner M
        • L'Abbé MR
        Traffic light labelling could prevent mortality from noncommunicable diseases in Canada: a scenario modelling study.
        PLoS One. 2019; 14e0226975https://doi.org/10.1371/journal.pone.0226975
        • Nilson EAF
        • Metlzer AB
        • Labonté ME
        • Jaime PC.
        Modelling the effect of compliance with WHO salt recommendations on cardiovascular disease mortality and costs in Brazil.
        PLoS One. 2020; 15e0235514https://doi.org/10.1371/journal.pone.0235514
        • Steenland K
        • Armstrong B.
        An overview of methods for calculating the burden of disease due to specific risk factors.
        Epidemiology. 2006; 17: 512-519https://doi.org/10.1097/01.ede.0000229155.05644.43
        • Briggs AH
        • Weinstein MC
        • Fenwick EAL
        • et al.
        Model parameter estimation and uncertainty: A report of the ISPOR-SMDM modeling good research practices task force-6.
        Value Health. 2012; 15: 835-842https://doi.org/10.1016/j.jval.2012.04.014