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Abstract

Abstract

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.
1.1.Main objective: To assess the possible changes in the prevalence of malnutrition in a child population after having suffered the confinement of COVID-19

1.2.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)

1.3.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.

1.4.Results: 66,975 computerized episodes of minors 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. Due to the COVID effect, an increase of 60 cases/10 5 inhabitants is recorded, in a heterogeneous way, being more in men than women, in rural areas in girls and in urban areas in the case of boys.

1.5.Conclusions: Big data technology allows for more efficient population studies, selecting populations most in need of health intervention, optimizing scarce health resources. In this case, the active search for cases in certain neighborhoods of the city should focus on boys and in certain municipalities on girls, since they may appear as hidden cases.

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