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Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas.

Lee SmithGuillermo F López SánchezMark A TullyLouis JacobKarel KostevHans Y OhLaurie ButlerYvonne BarnettYoun Ho ShinAi Koyanagi
Published in: Nutrients (2023)
(1) Background: Temporal trends of food insecurity among adolescents are largely unknown. Therefore, we aimed to examine this trend among school-going adolescents aged 12-15 years from 31 countries in Africa, Asia, and the Americas. (2) Methods: Data from the Global School-based Student Health Survey 2003-2017 were analyzed in 193,388 students [mean (SD) age: 13.7 (1.0) years; 49.0% boys]. The prevalence and 95%CI of moderate (rarely/sometimes hungry), severe (most of the time/always hungry), and any (moderate or severe) food insecurity (past 30-day) was calculated for each survey. Crude linear trends in food insecurity were assessed by linear regression models. (3) Results: The mean prevalence of any food insecurity was 52.2% (moderate 46.5%; severe 5.7%). Significant increasing and decreasing trends of any food insecurity were found in seven countries each. A sizeable decrease and increase were observed in Benin (71.2% in 2009 to 49.2% in 2016) and Mauritius (25.0% in 2011 to 43.6% in 2017), respectively. Severe food insecurity increased in countries such as Vanuatu (4.9% in 2011 to 8.4% in 2016) and Mauritius (3.5% in 2011 to 8.2% in 2017). The rate of decrease was modest in most countries with a significant decreasing trend, while many countries with stable trends showed consistently high prevalence of food insecurity. (4) Conclusion: Global action is urgently required to address food insecurity among adolescents, as our data show that achieving the United Nations Sustainable Development Goal 2 to end hunger and all forms of malnutrition by 2030 would be difficult without strong global commitment.
Keyphrases
  • physical activity
  • early onset
  • young adults
  • high intensity
  • electronic health record
  • drug induced
  • big data
  • cross sectional
  • artificial intelligence