Using Food Records to Assess Excessive Meat Intake in Brazil and Its Environmental Impact

Brazil is the largest country in South America and the fifth largest nation in the world (CIA, 2019). Along with the United States, it is one of the world’s largest meat producers (FAO, 2017). In the second half of the 20^th century, countries that underwent a strong economic transition also increased per capita meat consumption (Vranken, et al., 2014). In Brazil, meat available for consumption has nearly quadrupled since 1961 (FAO, 2017). Excessive red and processed meat consumption can have adverse effects on human health, increasing the risk for many types of cancer (Bouvard et al., 2015 as cited in Carvalho et al., 2016). Besides the negative effects of excessive meat intake on human health, beef production also causes important environmental impacts (Willet et al., 2019).

The following case study, based on work by Carvalho et al. (2016), illustrates the utility of adding a nutrition module to the Brazilian Household Budget Survey, also commonly known as a Household Consumption and Expenditure Survey (HCES), to assess dietary intake of specific foods that may threaten environmental sustainability. The following paragraphs synthesize the Carvalho et al. (2016) study and review some strengths and weaknesses of using individual dietary intake data obtained through food records as part of an HCES to assess meat consumption in the Brazilian population.

The authors based their work on the Brazil HCES of 2008-2009, a nationally representative multi-part survey. One module of the survey was a food intake record, a quantitative diary of all food consumed that was maintained by individual respondents on two on-consecutive days. This National Dietary Survey was implemented among a representative subsample of households and individuals from the HCES (twenty-five percent of all households in the HCES were interviewed, consisting of 34,003 individuals ages 14-104). Respondents were given detailed guidelines in advance for assessing portion size and identifying and tracking foods. Enumerators reviewed each food record with participants through an in-person visit, checking for completeness and accuracy. This method had been previously validated through a doubly labeled water study (DLW is considered the gold standard for estimating energy expenditure, and to validate energy intake it assumes individuals are in energy balance) (Lopes et al., 2016). All data were transformed to gram equivalents and nutrient composition was calculated using a combination of the Nutrition Data System for Research (NDSR) (NCC, 2005) and the Brazilian Food Composition Table (FCT) (NEPA-UNICAMP, 2011). Usual intakes were calculated using a method called the multiple source method (see Harttig et al., 2011 as cited in Carvalho et al., 2016).

Dietary analyses were complemented with estimations of greenhouse gas emissions (CO2 equivalents) using beef consumption as a proxy for beef production to assess the environmental impact of meat consumption in Brazil. Finally, the authors estimated the reduction of CO2 emissions that would be feasible were the population to reduce current meat consumption to the maximum of 300g per week recommended by the World Cancer Research Fund (WCRF & AICR, 2007 as cited in Carvalho et al., 2016).

The authors found that, while meat intake differed significantly by population group, more than 75% of all respondents across age, sex, and geographic population groups in the sample consumed more red and processed meat than the recommendations. Beef consumption was estimated to emit 1,005 CO2 equivalents, which the authors illustrate as “the same quantity of CO2 produced if a car travelled a distance between the extreme north and south Brazil (5,370 km)” (p. 2013). Carvalho et al. (2016) estimated that if the Brazilian population were to follow the 2008 WCRF recommendations, emissions could be reduced by 31%. The results of this analysis have implications for public health policies concerning red and processed meat reduction. For instance, current Dietary Guidelines for Brazilians provide messages (“Eat less red meat”) to guide the Brazilian population toward healthier and sustainable food choices (Ministry of Health of Brazil, 2015). However, such messages should be complemented with policies that address the entire food supply chain, from production to consumption.

This case study is an example of a country that embedded an ambitious individual-level dietary survey into a HCES, both of which were large scale and nationally representative. In this case, investing in individual-level data was appropriate and necessary to assess the dietary intake of different population groups—disaggregated by age, sex, urban and rural locations, and states—in order to determine group level usual intakes. While Weighed Food Records (WFR) would have been considered a “gold standard” for more accurate portion size measurement, the authors’ method used self-estimation relying on photos and other portion estimation aids. In cases where the population of interest is literate, a food diary or self-administered WFR can be used, in which the respondent weighs and/or records all of the food and beverages consumed over a specified period (e.g. 24 hours). Compared to a 24-hour Dietary Recall (24HR), the authors’ approach likely reduced recall bias as individuals were instructed to record their intake in real-time, as food was consumed. That said, the method may have suffered from other types of accuracy issues due to the fact that respondents were not as highly trained as a typical 24HR enumerator. Respondents may not have always been literate and numerate and may have had to rely on a proxy in the household for assistance. Having enumerators review the food records together with respondents after the fact may have helped to catch some types of potential errors and omissions.

This food record method can also be burdensome for respondents, who have to note everything consumed during the day; online food diary “apps”, appropriate for literate and numerate populations with access to smartphone technology, can facilitate this process, but these types of apps are generally not yet used for research among low- and middle-income country populations. Like most surveys, it is difficult to capture seasonal variation with data from only two recorded days of intake. Therefore, it is recommended that data collection span the entire year or be repeated in multiple seasons.

The authors took advantage of data from a nationwide individual dietary survey on a very large sample of individuals (>34,000) implemented on a subsample of households in the national HCES to assess meat consumption and its environmental impact in Brazil. Their analysis reaffirms the importance of dietary and nutritional policies that encourage consumption of healthy foods such as fruit, vegetables and whole grains, while at the same time encouraging a reduction in red and processed meat consumption in order to promote healthier and sustainable diets that are good for the population and for the planet.

Citations

1. Bouvard V., Loomis D., Guyton K.Z., et al. (2015) Carcinogenicity of consumption of red and processed meat. Lancet Oncol; 16:599-1600.
2. Carvalho, A.M, Selem, S.S., Miranda, A.M., Marchioni, D.M. (2016). Excessive red and processed meat intake: relations with health and environment in Brazil. British Journal of Nutrition; 115:2011-2016.
3. Central Intelligence Agency (CIA). (2019). The World Factbook: Brazil. Retrieved from https://www.cia.gov/library/publications/the-world-factbook/geos/br.html
4. Harttig U, Haubrock J, Knüppel S, et al. (2011). The MSM program: web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur J Clin Nutr; 65:S87–S91.
5. Instituto Brasileiro de Geografia e Estatística (IBGE) (2011). Pesquisa de Orçamentos Familiares 2008–2009: Tabela de Medidas Referidas Para os Alimentos Consumidos no Brasil (Household Budget Survey 2008–2009: Table of Reference Measures for Food Consumed in Brazil). Rio de Janeiro: IBGE.
6. Nutrition Coordinating Center (NCC). (2005). Nutrition Data System for Research. Minneapolis, MN: University of Minnesota.
7. Núcleo de Estudos e Pesquisas em Alimentação, Universidade Estadual de Campinas (NEPA-UNICAMP). (2011). Tabela Brasileira de Composição de Alimentos (Brazilian Table of Food Composition), 4th ed. Campinas: Núcleo de Estudos e pesquisas em Alimentação, Universidade Estadual de Campinas.
8. Lopes, T. S., Luiz, R. R., Hoffman, D. J., Ferriolli, E., Pfrimer, K., Moura, A. S., Sichieri, R., & Pereira, R. A. (2016). Misreport of energy intake assessed with food records and 24-h recalls compared with total energy expenditure estimated with DLW. European Journal of Clinical Nutrition, 70(11), 1259–1264. https://doi.org/10.1038/ejcn.2016.85
9. Ministry of Health of Brazil. (2015). Dietary Guidelines for the Brazilian Population. Retrieved from http://bvsms.saude.gov.br/bvs/publicacoes/dietary_guidelines_brazilian_population.pdf
10. Vranken, L., Avermaete, T., Petalios, D., Mathijs, E. (2014). Curbing global meat consumption: Emerging evidence of a second nutrition transition. Environmental Science and Policy; 39:95-106.
11. Willet, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, T., et al. (2019). Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet; 393:447-492.
12. World Cancer Research Fund & American Institute for Cancer Research (WCRF & AICR). (2007). Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective. Washington, DC: AICR.