Global Vegetable Intake and Supply Compared to Recommendations: A Systematic Review.
Aliki KalmpourtzidouAns EilanderElise Francina TalsmaPublished in: Nutrients (2020)
Low vegetable intake is associated with higher incidence of noncommunicable diseases. Data on global vegetable intake excluding legumes and potatoes is currently lacking. A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted to assess vegetable consumption and supply in adult populations and to compare these data to the existing recommendations (≥240 g/day according to World Health Organization). For vegetable intake data online, websites of government institutions and health authorities, European Food Safety Authority (EFSA) Comprehensive European Food Consumption Database, STEPwise approach to surveillance (STEPS) and Pubmed/Medline databases were searched from March 2018 to June 2019. Vegetable supply data was extracted from Food Balance Sheets, Food and Agriculture Organization Corporate Statistical Database (FAOSTAT), 2013. Vegetable intake was expressed as means and 95% confidence intervals. Data were summarized for each region by calculating weighted means. Vegetable intake and supply data were available for 162 and 136 countries, respectively. Weighted mean vegetable intake was 186 g/day (56-349 g/day). Weighted mean vegetable supply was 431 g/day (71-882 g/day). For 88% of the countries vegetable intake was below the recommendations. Public health campaigns are required to encourage vegetable consumption worldwide. In the 61% of the countries where vegetable supply is currently insufficient to meet the recommendations, innovative food system approaches to improve yields and decrease post-harvest losses are imperative.
Keyphrases
- public health
- electronic health record
- big data
- weight gain
- magnetic resonance
- healthcare
- emergency department
- meta analyses
- randomized controlled trial
- human health
- clinical practice
- risk factors
- mental health
- body mass index
- risk assessment
- machine learning
- magnetic resonance imaging
- young adults
- adverse drug
- deep learning