A body mass index-based mathematical model on not frequently collected data: the case of Chile

Abstract

The nutritional status is generally measured by body mass index (BMI) being obese, one of the most important non-communicable diseases, a risk factor whose estimation is especially relevant for the development of future health policies. In literature, several authors have addressed its estimation using mathematical/computational models considering frequently collected cross-sectional data to compute the required parameters and the so-called transition probabilities. In this work, we formulate and thereafter implement a non-linear programming (NLP) model to compute BMI transition probabilities, assuming one unit difference changes that remain steady over the time being influenced by the current BMI, sex and age of the person, to estimate a disaggregated characterization of the nutritional status of the over-15 population where, in contrast with other works, the data are not frequently collected.