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Replacing ultra-processed foods with fresh foods to meet the dietary recomendations: a matter of cost?

Emanuella Gomes MaiaCamila Mendes Dos PassosFernanda Serra GranadoRenata Bertazzi LevyRafael Moreira Claro
Published in: Cadernos de saude publica (2022)
The study aimed to analyze the economic impact of the adoption of optimized and nutritionally balanced diets to Brazilian families, considering the Brazilian dietary guidelines and the economic disparities of the population. Data from the Brazilian Household Budget Survey from 2008-2009 (550 strata; 55,970 households) were used. About 1,700 foods and beverages purchased by the Brazilians were classified into 4 groups according to NOVA system. Linear programming models estimated isoenergetic diets preserving the current diet as baseline and optimizing healthier diets gradually based on the "golden rule" of the Brazilian dietary guidelines, respecting nutritional restrictions for macronutrients and micronutrients (based on international recommendations) and food acceptance limits (10th and 90th percentiles of the per capita calorie distribution from the population). The diet cost was defined based on the sum of the average cost of each food group, both in the current and optimized diets (BRL per 2,000Kcal/person/day). The economic impact of the Brazilian dietary guidelines to Brazilian household budget was analyzed by comparison the cost of the optimized diets to the cost of the current diet, calculated for the total population and by income level. Three healthier diets were optimized. Current diet cost was BRL 3.37, differed among low- and high-income strata (BRL 2.62 and BRL 4.17, respectively). Regardless of income, diet cost decreased when approaching the guidelines. However, low-income strata compromised their household budget more than two times the high-income strata (20.2% and 7.96%, respectively). Thus, the adoption of healthier eating practices can be performed with the same or lower budget.
Keyphrases
  • weight loss
  • physical activity
  • clinical practice
  • mental health
  • healthcare
  • electronic health record
  • primary care
  • machine learning
  • high resolution
  • mass spectrometry
  • big data