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Correlation of Sugar-Sweetened Beverage Consumption and School Free and Reduced Lunch Eligibility as a Measure of Socioeconomic Status.

Eileen C GajoJacob OberwetterMerin MathewMoumita DamTimothy SanbornLynn G Chehab
Published in: Journal of community health (2020)
To determine whether a correlation exists between sugar-sweetened beverage consumption (SSB) and school free and reduced lunch (FRL) eligibility as a measure of socioeconomic status (SES). In January 2016, a modified version of the Bev 15 survey was anonymously administered to 5th and 6th grade students in 14 Chicago suburban public elementary schools. Students were asked to recall and record their beverage intake over the last 24 h for five predefined beverage groups [SSB, real fruit juice (RFJ), diet or sugar free beverages, milk, and water]. Concurrently, data regarding FRL eligibility for each of the 14 schools was obtained from the Illinois State Board of Education website. Mean student consumption of the five beverage categories in each school was correlated with the school's respective FRL status. A total of 1389 student surveys were used for analysis. FRL eligibility ranged from 16 to 64% in the 14 schools. There was a significant correlation between school FRL eligibility and consumption of SSB (p = 0.001), RFJ (p = 0.004) and diet or sugar-free beverage (p = 0.04). There was no significant correlation between FRL eligibility and consumption of water (p = 0.5), and milk (p = 0.2). This study shows that consumption of SSB highly correlates with school FRL eligibility, which can be a measure of SES. These findings reinforce the idea that there is a link between lower SES and unhealthy behaviors pertaining to dietary choices. Knowing this relationship between SSB consumption and FRL eligibility, specific schools can be targeted to reduce SSB consumption and its negative health consequences.
Keyphrases
  • physical activity
  • high school
  • mental health
  • healthcare
  • emergency department
  • body mass index
  • machine learning
  • weight loss
  • artificial intelligence
  • drug delivery
  • weight gain
  • psychometric properties
  • data analysis