ECEESPE2025 ePoster Presentations Environmental Endocrinology (23 abstracts)
Detection cases of childhood malnutrition using BIG DATA techniques: social and economic determinants
Ignacio Diez-Lopez 1 , Sandra Maeso Mendez 2 , Ioar Casado Tellechea 3 , Jose A. Lozano 3 , Aritz Pérez 3 , Iñaki Zorrilla 4 & Ana Gonzalez-Pinto 4
1BIOARABA Health Research Institute. OSI Araba. University Hospital. UPV/EHU, Vitoria, Spain., Vitoria, Spain; 2Pediatrics. BIOARABA Health Research Institute. OSI Araba. University Hospital. UPV/EHU, Vitoria, Spain, Vitoria, Spain; 3Basque Center for Applied Mathematics BCAM, Bilbao, Spain; 4Psychiatry. BIOARABA Health Research Institute. OSI Araba. University Hospital. CIBERSAM, UPV/EHU, Vitoria, Spain., Vitoria, Spain
JOINT131
Big data tools are currently a major tool for assessing population changes. There is a possible causal relationship between a family’s economic capacity and social dystocias and malnutrition. It is also known that a possible cause of malnutrition is eating disorders, which are more frequent in populations with different levels of education.
Main objective: To assess the possible relationship between family income and prevalence of malnutrition in a child population.
Material and Methods: Using the Cole-Green LMS algorithm with penalized likelihood, implemented in the RefCurv 0.4.2 software (2020), which allows managing large amounts of data. The hyperparameters have been selected using the BIC (Bayesian information criterion). To calculate population deviations from the reference, the reference was taken as being below 1.5 standard deviations from the average according to age.
Results: The data and comparative graphs between districts of the population studied are represented with respect to the variables analyzed.
Conclusions: Big data technology allows for more efficient population studies, selecting populations most in need of health intervention, optimizing scarce health resources.NOTE: CEIC OSI ARABA Approval Expte 2022-058
Volume 110
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Endocrine Abstracts
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