The Socio-Economic Situation of a Population as a Determinant of Childhood Obesity: Answers from Big Data

Abstract

Big data tools are currently a major tool for assessing population changes. There is a causal relationship between low economic levels and a higher prevalence of conditions associated with malnutrition and obesity. One of the causes of low income in different areas could be a higher unemployment rate and a confluence of people who are migrating for economic reasons. Objective: To assess in a child population the effect of the unemployment rate, average income and immigration rate as a possible effect of increasing the prevalence of malnutrition associated with childhood obesity. Material and methods: Data collected from computerized clinical history episodes, studying the variables of sex, age, weight, height, of a pediatric population (year 2022), comparing it with the average income of their residential district, unemployment rate and immigration rate. Use of big data methods for the study of 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 above 1.5 standard deviations from the average according to age. Results: 66,975 computerised episodes 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 analysed. There are significant differences, with an increase in the rate of overweight in those areas with lower economic income and higher unemployment and immigration rates. Big data technology allows for more efficient population studies, selecting populations most in need of health intervention, optimizing scarce health resources.