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[Trends and inequalities in food insecurity during the COVID-19 pandemic: results of four serial epidemiological surveys].

Leonardo Pozza Dos SantosAntônio Augusto SchäferFernanda de Oliveira MellerJenifer HarterBruno Pereira NunesInácio Crochemore Mohnsam da SilvaDébora da Cruz Payão Pellegrini
Published in: Cadernos de saude publica (2021)
The objective was to analyze trends and inequalities in the prevalence of food insecurity during the COVID-19 pandemic according to sociodemographic factors and social distancing measures. We analyzed data from four serial epidemiological surveys on COVID-19 in May and June 2020, with adults and elderly living in Bagé, Rio Grande do Sul State, Brazil. Food insecurity was assessed with the short version of the Brazilian Food Insecurity Scale (EBIA), with the recall period adapted to the beginning of the social distancing period in the city. Sociodemographic characteristics and the adoption of social distancing measures were analyzed, and their associations with food insecurity were assessed with chi-square test. The temporal trend in food insecurity according to these characteristics was assessed via linear regression. Inequalities in food insecurity were assessed with the angular inequality index and concentration index. Of the 1,550 individuals studied, 29.4% (95%CI: 25.0; 34.4) presented food insecurity. Analysis of inequality showed higher concentration of food insecurity among the younger and less educated and those living with five or more residents in the same household. Over the course of the four surveys, prevalence of food insecurity decreased most sharply among the younger, those living in households with up to two residents, and those with two or more workers. There was a strong association between food insecurity and sociodemographic factors, which may indicate the pandemic´s potential economic impact on households' food situation.
Keyphrases
  • coronavirus disease
  • sars cov
  • healthcare
  • mental health
  • cross sectional
  • risk factors
  • electronic health record
  • human health
  • middle aged
  • machine learning
  • artificial intelligence
  • risk assessment
  • deep learning