Objective: To harmonize food classification and food composition databases, allowing comparability of consumption at both food and nutrient levels in Europe.
Design: To establish the level of comparability at the food level, the EFCOSUM group benefited from the work already carried out within other European projects, which established a Euro Food Groups (EFG) classification system. Four food groups, ie bread, vegetables (excluding potatoes), fruits (excluding fruit juice) and fish and seafood, were judged on their applicability for making food consumption data comparable across countries at the food level.
Conclusions: It was concluded that the EFG system could be used but that still much work has to be done. For food consumption data to be collected in the future, the software that will be used should enable conversion of foods 'as consumed' to foods at the 'raw edible' level. With respect to comparability of nutrient intake estimations, EFCOSUM advises waiting for the European Nutrient Composition Database (ENDB) currently being prepared by the EPIC group. Until this is available, comparison of consumption data at the nutrient level cannot be carried out between countries.
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http://dx.doi.org/10.1038/sj.ejcn.1601427 | DOI Listing |
PLoS One
January 2025
Joint Global Change Research Institute, Pacific Northwest National Laboratory, Richland, WA, United States of America.
Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries' potential trade restrictions.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Clinical Sciences, Health Economics Unit, Lund University, Lund, Sweden.
Background: In the last three decades, the increasing trend in female employment in Bangladesh has been critically analyzed from a socioeconomic point of view; however, its impact on infant and young child feeding (IYCF) practices has yet to be systematically reviewed. The aim of this systematic review and meta-analysis is to investigate the association between these variables.
Methods: A systematic literature search was conducted in PubMed, Medline, Web of Science, Embase, CINAHL, and Google Scholar to retrieve relevant records with no restriction of publication period.
Sci Adv
January 2025
Doerr School of Sustainability, Stanford University, Stanford, CA, USA.
Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations.
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January 2025
Department of Entomology, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
Aquatic toxicology, as a result of industrial and agrieqcultural effluences, has become a global concern impacting not only the well-being of aquatic organisms but human health as well. The current study evaluated the impact of four toxic trace elements (TTEs) Cadmium (Cd), copper (Cu), lead (Pb), and nickel (Ni) in three organs (liver, gills, and muscles) of five fish species viz, Rita rita, Sperata sarwari, Wallago attu, Mastacembelus armatus, and Cirrhinus mrigala collected from right and left banks of Punjnad headworks during winter, spring, and summer. We investigated the accumulation (mg/kg) of these TTEs in fish in addition to the human health risk assessment.
View Article and Find Full Text PDFPLoS One
January 2025
Nova School of Business and Economics, Universidade Nova de Lisboa, Carcavelos, Portugal.
This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using only self-reported survey data. Such a model could enable scalable and cost-effective screening and targeted interventions, optimizing limited resources to improve oral health outcomes. To train and test the model, we used data from 2,133 students attending schools in a Portuguese municipality.
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