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Temporal trends of carbonated soft-drink consumption among adolescents aged 12-15 years from eighteen countries in Africa, Asia and the Americas.

Lee SmithGuillermo F López SánchezMark A TullyMasoud RahmatiHans OhKarel KostevLaurie T ButlerYvonne BarnettHelen KeyesJae Il ShinAi Koyanagi
Published in: The British journal of nutrition (2024)
Carbonated soft-drink consumption is detrimental to multiple facets of adolescent health. However, little is known about temporal trends in carbonated soft-drink consumption among adolescents, particularly in non-Western countries. Therefore, we aimed to examine this trend in representative samples of school-going adolescents from eighteen countries in Africa, Asia and the Americas. Cross-sectional data from the Global School-based Student Health Survey 2009-2017 were analysed. Carbonated soft-drink consumption referred to drinking carbonated soft-drinks at least once per day in the past 30 d. The prevalence of carbonated soft-drink consumption was calculated for each survey, and crude linear trends were assessed by linear regression models. Data on 74 055 students aged 12-15 years were analysed (mean age 13·9 (sd 1·0) years; 49·2 % boys). The overall mean prevalence of carbonated soft-drink consumption was 42·1 %. Of the eighteen countries included in the study, significant decreasing, increasing and stable trends of carbonated soft-drink consumption were observed in seven, two and nine countries, respectively. The most drastic decrease was observed in Kuwait between 2011 (74·4 %) and 2015 (51·7 %). Even in countries with significant decreasing trends, the decrease was rather modest, while some countries with stable trends had very high prevalence across time (e.g. Suriname 80·5 % in 2009 and 79·4 % in 2016). The prevalence of carbonated soft-drink consumption was high in all countries included in the present analysis, despite decreasing trends being observed in some. Public health initiatives to reduce the consumption of carbonated soft-drink consumption among adolescents are urgently required.
Keyphrases
  • public health
  • cross sectional
  • risk factors
  • young adults
  • mental health
  • machine learning
  • climate change
  • big data
  • deep learning
  • quality improvement
  • health information