Methodological aspects in the food consumption assessment of pregnant women in the Multicenter Study of Iodine Deficiencys
Keywords:
Iodine, Methods, Nutrition surveys, Pregnant womenAbstract
Objective
To describe in detail the methodological aspects used in the assessment of food consumption of pregnant women within the Multicenter Study of Iodine Deficiency to streamline the reproducibility of this work and other studies.
Methods
The 24-hour dietary recall (24hR) was used to assess pregnant women’s dietary intake (n=2,247) in a nationwide sample. The method was adapted for paper application, followed by data entry in the GloboDiet software. Subsequently, the data were verified for inconsistencies and submitted to quality control (e.g., Goldberg analysis). Foods were also categorized under the NOVA classification and food groups.
Results
Several challenges were observed in the study: adaptation of the paper format-based data collection for the software data entry and the lack of iodine data composition. However, some potentialities of the collected data stood out, including the standardized, detailed collected data and the types of dietary indicators that can be generated from the created databases.
Conclusion
We expect the shared information to favor data harmonization and, therefore, enable the comparison of evidence generated among Brazilian studies.
References
Meltzer HM, Brantsaeter AL, Ydersbond TA, Alexander J, Haugen M. Methodological challenges when monitoring the diet of pregnant women in a large study: Experiences from the Norwegian Mother and Child Cohort Study (MoBa). Matern Child Nutr. 2008;4(1):14-27. https://doi.org/10.1111/j.1740-8709.2007.00104.x
Deitchler M, Arimond M, Carriquiry A, Hotz C, Tooze JA. Planning and Design Considerations for Quantitative 24-Hour Recall Dietary Surveys in Low-and Middle-Income Countries. INTAKE organization; 2020.
Micha R, Coates J, Leclercq C, Charrondiere UR, Mozaffarian D. Global dietary surveillance: data gaps and challenges. Food Nutr Bull. 2018;39(2):175-205. https://doi.org/10.1177/0379572117752986
de Quadros VP, Balcerzak A, Allemand P, de Sousa RF, Bevere T, Arsenault J, et al. Global Trends in the Availability of Dietary Data in Low and Middle-Income Countries. Nutrients. 2022;14(14):2987. https://doi.org/10.3390/nu14142987
Thompson FE, Kirkpatrick SI, Subar AF, Reedy J, Schap TE, Wilson MM, et al. The National Cancer Institute’s dietary assessment primer: A resource for diet research. J Acad Nutr Diet. 2015;115(12):1986-95. https://doi.org/10.1016/j.jand.2015.08.016
Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLoS Med. 2016;13(6):e1002036. https://doi.org/10.1371/journal.pmed.1002036
Faber M, Wenhold FA, Macintyre UE, Wentzel-Viljoen E, Steyn NP, Oldewage-Theron WH. Presentation and interpretation of food intake data: Factors affecting comparability across studies. Nutrition. 2013;29(11-12):1286-92. https://doi.org/10.1016/j.nut.2013.03.016
Patridge EF, Bardyn TP. Research electronic data capture (REDCap). J Med Libr Assoc. 2018;106(1):142-4. https://doi.org/10.5195/jmla.2018.319
Bel-Serrat S, Knaze V, Nicolas G, Marchioni DM, Steluti J, Mendes A, et al. Adapting the standardised computer- and interview-based 24 h dietary recall method (GloboDiet) for dietary monitoring in Latin America. Public Health Nutr. 2017;20(16):2847-58. https://doi.org/10.1017/S1368980017001872
Bel S, Van den Abeele S, Lebacq T, Ost C, Brocatus L, Stiévenart C, et al. Protocol of the Belgian food consumption survey 2014: Objectives, design and methods. Archives of Public Health. 2016;74(1):20. https://doi.org/10.1186/s13690-016-0131-2
Trolle E, Amiano P, Ege M, Bower E, Lioret S, Brants H, et al. Evaluation of 2 × 24-h dietary recalls combined with a food-recording booklet, against a 7-day food-record method among schoolchildren. Eur J Clin Nutr. 2011;65(1):S77-S83. https://doi.org/10.1038/ejcn.2011.90
Crispim SP, Maurício A, Almeida CCB, Garmus LM, Silva DLF, Ferreira GR, et al. Manual fotográfico de quantificação alimentar infantil. Curitiba: Universidade Federal do Paraná; 2018.
Fisberg RM, Marchioni DML, Previdelli AN, Carvalho AM, Mendes A, Timm AS, et al. Manual de avaliação do consumo alimentar em estudos populacionais: a experiência do inquérito de saúde em São Paulo (ISA). São Paulo: Faculdade de Saúde Pública da Universidade de São Paulo; 2012.
Instituto Brasileiro de Geografia e Estatística. Tabela de medidas caseiras: Rio de Janeiro: IBGE; 2009.
FAO. FAO/WHO GIFT. Global Individual Food consumption data Tool. FAO; 2021.
Martinez-Steele E, Khandpur N, Batis C, Bes-Rastrollo M, Bonaccio M, Cediel G, et al. Best practices for applying the Nova food classification system. Nat Food. 2023;4(6):445-8. https://doi.org/10.1038/s43016-023-00779-w
Tabela Brasileira de Composição de Alimentos (TBCA). São Paulo: Universidade de São Paulo (USP), Food Research Center (FoRC). Versão 7.1.; 2020 [cited 2022 Jun 30]. Available from: http://www.fcf.usp.br/tbca.
Milagres RCRM, Souza ECG, Peluzio MCG, Franceschini SCC, Duarte MSL. Food Iodine Content Table compiled from international databases. Rev Nutr. 2020;33:e190222. https://doi.org/10.1590/1678-9865202033e190222
National Institute for Public Health and the Environment (RIVM). Nederlands Voedingsstoffenbestand (NEVO). Bilthoven: National Institute for Public Health and the Environment; 2019. Disponível em: https://nevo-online.rivm.nl/Home/En. Acesso em: 18 nov. 2024.
Nutrition & Food Science Association (NFSA). Norwegian Food Composition Database. Oslo: The Norwegian Directorate of Health and University of Oslo; 2019.
Ministry of Education, Culture, Sports, Science and Technology (Japan). Standard Tables of Food Composition in Japan. 7th ed. Japan: MEXT; 2015.
Food Standards Australia & New Zealand. Australian Food, Supplement and Nutrient Database. Canberra: FSANZ; 2013.
Federal Food Safety and Veterinary Office. The Swiss Food Composition Database. Bern: FSVO; 2019.
Willett W. Nutritional epidemiology. Oxford: Oxford University Press; 2012.
Goldberg G, Black A, Jebb S, Cole T, Murgatroyd P, Coward W, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cutoff limits to identify underrecording. Eur J Clin Nutr. 1991;45(12):569-81.
Black AE. Critical evaluation of energy intake using the Goldberg cutoff for energy intake: Basal metabolic rate. A practical guide to its calculation, use, and limitations. Int J Obes Relat Metab Disord. 2000;24(9):1119-30. https://doi.org/10.1038/sj.ijo.0801376
Prentice AM, Spaaij CJ, Goldberg GR, Poppitt SD, van Raaij JM, Totton M, et al. Energy requirements of pregnant and lactating women. Discussion. Eur J Clin Nutr. 1996;50:S82-S111.
Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39 Suppl 1:5-41.
Food and Agriculture Organization of the United Nations, World Health Organization, United Nations University. Human energy requirements: Report of a joint FAO/WHO/UNU expert consultation 2001. Rome: FAO/WHO/UNU; 2004.
Eldridge AL, Piernas C, Illner AK, Gibney MJ, Gurinović MA, de Vries JHM, et al. Evaluation of New Technology-Based Tools for Dietary Intake Assessment-An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients. 2018;11(1):55. https://doi.org/10.3390/nu11010055
Crispim SP, Nicolas G, Casagrande C, Knaze V, Illner AK, Huybrechts I, et al. Quality assurance of the international computerised 24 h dietary recall method (EPIC-Soft). Br J Nutr. 2014;111(3):506-15. https://doi.org/10.1017/S0007114513002766
Khatibzadeh S, Saheb Kashaf M, Micha R, Fahimi S, Shi P, Elmadfa I, et al. A global database of food and nutrient consumption. Bull World Health Organ. 2016;94(12):931-4. https://doi.org/10.2471/BLT.15.156323
Roe AJ, Sankavaram K, Baker S, Franck K, Puglisi M, Earnesty D, et al. 24-Hour Dietary Recall in the Expanded Food and Nutrition Education Program: Perspective of the Program Coordinator. Nutrients. 2023;15(19):4147. https://doi.org/10.3390/nu15194147
Charrondiere UR, Rittenschober D, Nowak V, Stadlmayr B, Wijesinha-Bettoni R, Haytowitz D. Improving food composition data quality: Three new FAO/INFOODS guidelines on conversions, data evaluation and food matching. Food Chemistry. 2016;193:75-81. https://doi.org/10.1016/j.foodchem.2014.11.055
Elias VCM. Presença de informações sobre o processamento dos alimentos em inquéritos alimentares: contribuição na classificação nova [dissertation]. Curitiba: Federal University of Paraná; 2020.
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Copyright (c) 2025 Sandra Patricia Crispim, Débora Letícia Frizzi Silva, Mariana de Souza Macedo, Claudia Choma Bettega Almeida, Vanessa Cardozo Mendes Elias, Sylvia do Carmo Castro Franceschini

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