Mathematical model for estimating nutritional status of the population with poor data quality in developing countries: The case of Chile

TY - GEN

T1 - Mathematical model for estimating nutritional status of the population with poor data quality in developing countries

T2 - 10th International Conference on Operations Research and Enterprise Systems, ICORES 2021

AU - Ávalos, Denisse

AU - Cuadrado, Cristóbal

AU - Dunstan, Jocelyn

AU - Moraga-Correa, Javier

AU - Rojo-González, Luis

AU - Troncoso, Nelson

AU - Vásquez, Óscar C.

N1 - Funding Information: The authors are grateful for partial support from the following sources: ANID Beca Magíster en el Extranjero, Becas Chile, Folio 73190041 (Javier Moraga-Correa) and Folio 73201112 (Luis Rojo-González), CONICYT-FONIS SA14ID0176 and RCUK-CONICYT Newton-Picarte MR /N026640/1 (Cristóbal Cuadrado), CMM-ANID AFB 170001 and CIMT-CORFO Cost Center 570111 (Jocelyn Dun-stan), Universidad de Santiago de Chile, Proyecto DI-CYT 061817VP (Óscar C. Vásquez). Funding Information: The authors are grateful for partial support from the following sources: ANID Beca Mag?ster en el Extranjero, Becas Chile, Folio 73190041 (Javier Moraga-Correa) and Folio 73201112 (Luis Rojo-Gonz?lez), CONICYT-FONIS SA14ID0176 and RCUK-CONICYT Newton-Picarte MR /N026640/1 (Crist?bal Cuadrado), CMM-ANID AFB 170001 and CIMT-CORFO Cost Center 570111 (Jocelyn Dunstan), Universidad de Santiago de Chile, Proyecto DICYT 061817VP (?scar C. V?squez). Publisher Copyright: Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/3/2

Y1 - 2021/3/2

N2 - Obesity is one of the most important risk factors for non-communicable diseases. Nutritional status is generally measured by the body mass index (BMI) and its estimation is especially relevant to analyse long-term trends of overweight and obesity at the population level. Nevertheless, in most context nationally representative data on BMI is scarce and the probability of individuals to progress to obese status is not observed longitudinally. In the literature, several authors have addressed the problem to obtain this estimation using mathematical/computational models under a scenario where the parameters and transition probabilities between nutritional states are possible to compute from regular official data. In contrast, the developing countries exhibit poor data quality and then, the approaches provided from the literature could not be extended to them. In this paper, we deal with the problem of estimating nutritional status transition probabilities in settings with scarce data such as most developing countries, formulating a non-linear programming (NLP) model for a disaggregated characterization of population assuming the transition probabilities depend on sex and age. In particular, we study the case of Chile, one of the countries with the highest prevalence of malnutrition in Latin America, using three available National Health Surveys between the years 2003 and 2017. The obtained results show a total absolute error equal to 5.11% and 10.27% for sex male and female, respectively. Finally, other model applications and extensions are discussed and future works are proposed.

AB - Obesity is one of the most important risk factors for non-communicable diseases. Nutritional status is generally measured by the body mass index (BMI) and its estimation is especially relevant to analyse long-term trends of overweight and obesity at the population level. Nevertheless, in most context nationally representative data on BMI is scarce and the probability of individuals to progress to obese status is not observed longitudinally. In the literature, several authors have addressed the problem to obtain this estimation using mathematical/computational models under a scenario where the parameters and transition probabilities between nutritional states are possible to compute from regular official data. In contrast, the developing countries exhibit poor data quality and then, the approaches provided from the literature could not be extended to them. In this paper, we deal with the problem of estimating nutritional status transition probabilities in settings with scarce data such as most developing countries, formulating a non-linear programming (NLP) model for a disaggregated characterization of population assuming the transition probabilities depend on sex and age. In particular, we study the case of Chile, one of the countries with the highest prevalence of malnutrition in Latin America, using three available National Health Surveys between the years 2003 and 2017. The obtained results show a total absolute error equal to 5.11% and 10.27% for sex male and female, respectively. Finally, other model applications and extensions are discussed and future works are proposed.

KW - Developing countries

KW - Non-linear programming

KW - Obesity

KW - Poor data quality

KW - Transition probabilities

UR - http://www.scopus.com/inward/record.url?scp=85103823135&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85103823135

T3 - ICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems

SP - 408

EP - 415

BT - ICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems

A2 - Parlier, Greg H.

A2 - Liberatore, Federico

A2 - Demange, Marc

PB - SciTePress

Y2 - 4 February 2021 through 6 February 2021

ER -