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A Cross-Sectional Study of the Street Foods Purchased by Customers in Urban Areas of Central Asia.

Sofia SousaInês Lança de MoraisGabriela AlbuquerqueMarcello GelorminiSusana I P CasalOlívia PinhoCarla MottaAlbertino DamascenoPedro MoreiraJoao J BredaNuno LunetPatrícia Padrão
Published in: Nutrients (2021)
This study aimed to describe street food purchases in cities from Central Asia, considering customers' characteristics and the nutritional composition of the foods and beverages. Cross-sectional studies were conducted in 2016/2017 in Dushanbe (Tajikistan), Bishkek (Kyrgyzstan), Ashgabat (Turkmenistan) and Almaty (Kazakhstan). Direct observation was used to collect data on the purchases made by street food customers, selected by random and systematic sampling. Nutritional composition was estimated using data from chemical analyses, food composition tables or food labels. A total of 714 customers (56.6% females, 55.5% aged ≥35 years, 23.3% overweight/obese) were observed, who bought 852 foods and beverages, the most frequent being savoury pastries/snacks (23.2%), main dishes (19.0%), sweet pastries/confectionery (17.9%), tea/coffee (11.3%) and soft drinks/juices (9.8%). Fruit was the least purchased food (1.1%). Nearly one-third of customers purchased industrial food items (31.9%). The median energy content of a street food purchase was 529 kcal/serving. Saturated and trans-fat median contents were 4.7 g/serving and 0.36 g/serving (21.4% and 16.5% of maximum daily intake recommendations, respectively). Median sodium and potassium contents were 745 mg/serving (37.3% of maximum recommendation) and 304 mg/serving (8.7% of minimum recommendation), respectively. In general, the purchases observed presented high contents of energy, saturated-fat, trans-fat and sodium, and low levels of potassium. Policies towards the improvement of these urban food environments should be encouraged.
Keyphrases
  • adipose tissue
  • human health
  • cross sectional
  • type diabetes
  • risk assessment
  • electronic health record
  • machine learning
  • clinical practice
  • fatty acid
  • wastewater treatment
  • case control