Cultural models in food: population analysis of two Brazilian regions and their implication with health

• Grant number: 17/20390-9
• Support Opportunities: Scholarships abroad - Research
• Start date: December 18, 2018
• End date: March 18, 2019
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Rosane Pilot Pessa
• Grantee: Rosane Pilot Pessa
• Host Investigator: William W. Dressler
• Host Institution: Escola de Enfermagem de Ribeirão Preto (EERP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: University of Alabama at Birmingham (UAB), United States

Abstract

The study of human nutrition requires a multidisciplinary approach and anthropology, through the analysis of cultural models, consensus and consonance, evaluates the degree to which different individuals share the knowledge about a particular domain and its association with health risk. Studies have shown that cultural changes are disease-causing, especially hypertension and altered body composition. The objective of this study is to investigate and compare cultural models in the diet of two Brazilian populations from distinct regions and areas, seeking to identify the presence of cultural consensus in this domain. This is a cross-sectional, descriptive and comparative study to be carried out with data already collected in two studies developed in Brazil by American researchers in the Southeast region (Ribeirão Preto-SP), with adults living in the urban area and, in the north El Dorado dos Carajás-PA), with families from a land settlement. In both studies, sociodemographic and information data were collected through cognitive techniques for the definition of the cultural model in the diet, through free lists for later free grouping. The sociodemographic data will be analyzed in a descriptive way and for the analysis of the clusters and definition of the cultural model in the feeding, will be used the Multidimensional Scheduling and the Grouping Analysis belonging to the Multivariate Analysis.