ECEESPE2025 ePoster Presentations Environmental Endocrinology (23 abstracts)
Possible effects of the COVID-19 pandemic on the prevalence of malnutrition-anorexia cases in a pediatric population. using BIG DATA tools
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
1Pediatrics. BIOARABA 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, Bilbao, Spain; 4Psychiatry. BIOARABA Health Research Institute. OSI Araba. University Hospital. CIBERSAM, UPV/EHU, Vitoria, Spain., Vitoria, Spain
JOINT132
Summary: The coincidence of COVID-19 and confinement on children’s health has been studied. One possible cause of malnutrition is eating disorders. Big data tools are currently a first-rate tool for assessing population changes and possible causes.
Main objective: To assess the possible changes in the prevalence of malnutrition in a child population after having suffered the confinement of COVID-19
Material and Methods: Data collected from episodes of computerized medical records, studying the variables sex, age, weight, height, of a pediatric population comparing the situation just before COVID (2020) and after the social isolation measures were completely finished (2022) Using big data methods to study variables. 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: 66,975 computerized cases of children under 16 years of age and a total of 1,205,000 variables studied. The data and comparative graphs between districts of the population studied are represented with respect to the variables analyzed. There is an increase in cases of malnutrition, especially in districts with specific characteristics.
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
Article tools
My recent searches
My recently viewed abstracts
Authors
Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
© Bioscientifica 2025 |
Privacy policy |
Cookie settings
BiosciAbstracts
Bioscientifica Abstracts is the gateway to a series of products that provide a permanent, citable record of abstracts for biomedical and life science conferences.
Find out more