Abstract
Obesity is a non-communicable disease that has a major impact on people’s health, increasing the risk of other chronic diseases such as diabetes, hypertension, and cardiovascular problems. Usually, the nutritional status of the population is determined by the body mass index (BMI) applied on a population sample via a national health survey (NHS), whose results are extrapolated. Except for highlighted cases such as the United States of America, these NHSs are infrequently carried out with different sampling methodologies. The outcomes are sparse and low-quality data, which complicate the estimation and forecasting of the population’s BMI distribution. In this work, this problem is addressed by considering the case of Chile, one of the countries with the highest prevalence of obesity, with an NHS every 7 years. Our approach proposes a maximum entropy optimization model to estimate the probability transition between different nutritional states, considering age and sex, which is based on the analogy with the determination of the origin-destination trip matrix used in the transport setting. The obtained results show that for the year 2024, there will be an increase of 798,898 (35%) and 758,124 (30%) men and women respectively, with overweight and obesity.
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References
Apovian, C.M.: Obesity: definition, comorbidities, causes, and burden. Am. J. Manag. Care 22, S176–S185 (2016). https://www.ajmc.com/view/obesity-definition-comorbidities-causes-burden
Ávalos, D., et al.: Mathematical model for estimating nutritional status of the population with poor data quality in developing countries: the case of Chile. In: Parlier, G., Liberatore, F., Demange, M. (eds.) Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, ICORES 2021, pp. 408–415. SciTePress (2021)
CDC, HW: Assessing your weight (2021). Accessed 26 Jan 2023
NRF, Collaboration et al.: Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19 \(\cdot \) 2 million participants. Lancet 387(10026), 1377–1396 (2016)
Cuadrado, C.: The health and economic burden of obesity in Chile–an epidemiological and economic simulation model. Value Health 19(7), A584 (2016)
Centers for Disease Control and Prevention et al.: Adult obesity facts. Centers for disease control and prevention (2021). https://www.cdc.gov/obesity/data/adult.html. Accessed 26 Jan 2023
Elgart, J.F., Prestes, M., Gonzalez, L., Rucci, E., Gagliardino, J.J., QUALIDIAB Net Study Group: Relation between cost of drug treatment and body mass index in people with type 2 diabetes in Latin America. PLoS ONE 12(12), e0189755 (2017)
Instituto Nacional de Estadísticas: Proyecciones de población. https://www.ine.gob.cl/estadisticas/sociales/demografia-y-vitales/proyecciones-de-poblacion. Accessed 26 Jan 2023
Kelly, I.R., Doytch, N., Dave, D.: How does body mass index affect economic growth? A comparative analysis of countries by levels of economic development. Econ. Hum. Biol. 34, 58–73 (2019)
Liu, S., Xue, H., Li, Y., Xu, J., Wang, Y.: Investigating the diffusion of agent-based modelling and system dynamics modelling in population health and healthcare research. Syst. Res. Behav. Sci. 35(2), 203–215 (2018)
López-Ospina, H., Cortés, C.E., Pérez, J., Peña, R., Figueroa-García, J.C., Urrutia-Mosquera, J.: A maximum entropy optimization model for origin-destination trip matrix estimation with fuzzy entropic parameters. Transportmetrica A: Transp. Sci. 18(3), 963–1000 (2022)
World Health Organization: Obesidad y sobrepeso (2021). https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. Accessed 26 Jan 2023
Reátegui, R., Ratté, S., Bautista-Valarezo, E., Duque, V.: Cluster analysis of obesity disease based on comorbidities extracted from clinical notes. J. Med. Syst. 43, 1–9 (2019)
Saraswati, C.M., et al.: Estimating childhood stunting and overweight trends in the European region from sparse longitudinal data. J. Nutr. 152(7), 1773–1782 (2022)
Thomas, D.M., et al.: Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends. Obesity 22(2), 590–597 (2014)
UNICEF, et al.: The state of food security and nutrition in the world 2021 (2021)
Xue, H., Slivka, L., Igusa, T., Huang, T., Wang, Y.: Applications of systems modelling in obesity research. Obes. Rev. 19(9), 1293–1308 (2018)
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The authors are grateful for partial support from ANID, FONDECYT No1211640.
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Suazo-Morales, F., Vásquez, Ó.C. (2023). Estimation of the Distribution of Body Mass Index (BMI) with Sparse and Low-Quality Data. The Case of the Chilean Adult Population. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_31
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DOI: https://doi.org/10.1007/978-3-031-34020-8_31
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