ESPE Abstracts

To date, knowledge of population dynamics and its repercussions on health required complex, long and expensive field studies. Big data tools are nowadays postulated as a tool of first magnitude for weighted population changes observed in real time if reliable sources of collection and adequate mathematical and computer tools for their assessment are available.

Main Objective: Carry out a methodological approach to the use of big data applications to prepare auxological growth tables in our population with high statistical power, as a first step to infer the measurement for the entire CCAA. Assess how our population is in the auxological variables with respect to the current Orbegozo 2011 standards and Spanish growth studies 2010.

Material and Methods: Data collected from episodes of computerized medical records, studying the variables sex, age, weight, height, place of residence (PC, health center, neighborhood) of our population between 01/01/2020-03/31/2020 (avoiding effect pandemic). To calculate the curves and percentile tables we have used the Cole-Green LMS algorithm with penalized likelihood, implemented in the RefCurv 0.4.2 (2020) software, 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, being above 1.5 standard deviations from the mean according to age has been taken as a reference.

Results: 66,975 computerized episodes of children under 16 years of age and a total of 1,205,000 variables studied are collected. Although data is available, individuals >16ª are excluded due to low N. The graphs of our population are represented with respect to the standards, observing that there are differences with Orbegozo 2011 and Spain 2010. We present the data and percentages of overweight/obesity by age and sex. There are significant differences of more overweight in the entire sample of men and women in our population than the usual standards. Big data technology surpasses classic population studies in power and is an innovative tool compared to auxological studies (limited in N) carried out to date. The development of these new strategies in auxology will allow us to know almost in real time the epidemiological situation of the population in different variables, being able to infer health actions in a more effective way.