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[Time trends in the consumption of fruits and vegetables among adults in Brazilian state capitals and the Federal District, 2008-2016].

Luiza Eunice Sá da SilvaRafael Moreira Claro
Published in: Cadernos de saude publica (2020)
The study aimed to analyze the time trend in the consumption of fruits and vegetables among adults in Brazilian state capitals and the Federal District from 2008 to 2016. This was a time series study with data from the VIGITEL survey from 2008 to 2016 (n = 463,817). The study analyzed the prevalence of regular consumption (≥ 5 days/week) and recommended consumption (≥ 5 times/day) of fruits and vegetables, for each of the years, for the complete set of the evaluated population and according to sex, age group, schooling, and location. Presence of linear trend (increase or decrease) in the indicators' variation was analyzed by Prais-Winsten regression. There was a significant increase (p < 0.05) in prevalence of regular consumption (from 33 to 35.2%, an increase of 1.86%/year) and recommended consumption of fruits and vegetables (from 20 to 24.4%, an increase of 3.32%/year). A similar trend was identified in the percentage of the population meeting the recommended consumption in most of the population strata, with the highest increase in men (4%/year vs. 3.05%/year for women), young adults (3.97%/year in the 18-24-year group vs. 2.30%/year in the 55-64-year group), those with low schooling (2.97%/year for 0 to 8 years of schooling vs. 2.76%/year for 12 years or more), and less developed regions of Brazil (5.02%/year in the North vs. 2.6%/year in the Southeast). There was an increase in the consumption of fruits and vegetables, especially among groups with lower levels of consumption at the start of the study period. However, three out of four individuals still consumed fewer fruits and vegetables than recommended.
Keyphrases
  • randomized controlled trial
  • type diabetes
  • risk factors
  • risk assessment
  • adipose tissue
  • insulin resistance
  • drinking water
  • tertiary care
  • neural network