Browsing by Author "Dickie S."

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    Choosing an effective food classification system for promoting healthy diets in Thailand: a comparative evaluation of three nutrient profiling-based food classification systems (government, WHO, and Healthier Choice Logo) and a food-processing-based food classification system (NOVA)
    (2023-01-01) Phulkerd S.; Dickie S.; Thongcharoenchupong N.; Thapsuwan S.; Machado P.; Woods J.; Mo-Suwan L.; Prasertsom P.; Ungchusak C.; Khitdee C.; Lawrence M.; Mahidol University
    Introduction: This study aimed to assess the nutritional quality of food and beverage products in Thailand by comparing four different food classification systems: the nutrient profiling-based food classification systems by the Department of Health (DOH), the WHO South-East Asia Region (WHO SEA), the Healthier Choice Logo (HCL), and the food-processing-based food classification system, NOVA. Methods: This study used secondary data from the Mintel Global New Products Database (N = 17,414). Food subgroups were classified differently based on these four systems. The DOH classified food products into three groups: Group A—healthy pass or meeting standard, Group B—not meeting the standard, and Group C—far below standard. The WHO SEA classified food products into two groups: marketing prohibited products and marketing permitted products. The HCL classified food products into two groups: eligible products for the logo; and ineligible products for the logo. The NOVA classified food products into four groups: unprocessed or minimally processed foods (MP), processed culinary ingredients (PCI), processed foods (P), and ultra-processed foods (UPF). Descriptive statistics (percentage and frequency) were used for analysis. Agreement analysis was conducted using Cohen’s kappa statistic between each pair of food classification systems. Results: Of the total sample that could be classified by any of the four classification systems (n = 10,486), the DOH, the WHO SEA and the HCL systems classified products as healthy (Group A, marketing permitted or eligible for HCL logo) at 10.4, 11.1, and 10.9%, respectively. Only 5.6% were classified as minimally processed foods using NOVA and 83.1% were ultra-processed foods (UPFs). Over 50% of products classified as healthy by the nutrient profiling systems were classified as UPF according to the NOVA system. Products that were eligible for the HCL had the highest proportion of UPF products (84.4%), followed by the Group A products (69.2%) and the WHO marketing-permitted products (65.0%). Conclusion: A hybrid food classification approach taking both nutrients and food processing into account is needed to comprehensively assess the nutritional quality of food and beverage products in Thailand.
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    Profiling ultra-processed foods in Thailand: sales trend, consumer expenditure and nutritional quality
    (2023-12-01) Phulkerd S.; Thongcharoenchupong N.; Dickie S.; Machado P.; Woods J.; Mo-Suwan L.; Prasertsom P.; Ungchusak C.; Khitdee C.; Lawrence M.; Mahidol University
    Background: Ultra-processed foods (UPF) are associated with adverse health outcomes. This study aimed to analyse the national trends in retail sales, consumer expenditure and nutritional quality of UPFs in Thailand. Methods: The study used data from the Euromonitor Passport database for analysis of retail sales and consumer expenditure, and from the Mintel Global New Products Database for nutritional analysis using the WHO Southeast Asian Region nutrient profile model. Results: The study found the highest per capita sales volume and value of UPFs in 2021 were sauces, dressings & condiments (8.4 kg/capita) and carbonated soft drinks (27.1 L/capita), respectively. However, functional & flavoured water, ready-made meals and baked goods had the highest observed (2012–2021) and expected (2021–2026) sales growth. Supermarkets were responsible for most of the UPF sales since 2012, but convenience stores had larger growth in retail values. Growth in consumer expenditure per capita on UPFs from 2012 to 2020, ranged between 12.7% and 34%, and till 2026 is forecast to grow between 26% and 30%. More than half of UPFs exceeded at least one nutrient cutoff, 59.3% for total fats, 24.8% for saturated fats, 68.2% for total sugars and 94.3% for sodium. Conclusions: The findings suggest a need for regulatory and non-regulatory measures such as UPF taxation and marketing restrictions, and market incentives for producing non-UPFs. A system for regularly monitoring and evaluating healthiness (both nutritional and processing aspects) of food products, especially UPFs, is required.