The problem of estimation and forecasting of obesity prevalence using sparsely collected data

Simple item page
dc.contributor.authorRojo-Gonzalez, Luis
dc.contributor.authorDunstan, Jocelyn
dc.contributor.authorCuadrado, Cristobal
dc.contributor.authorAvalos, Denisse
dc.contributor.authorMoraga-Correa, Javier
dc.contributor.authorTroncoso, Nelson
dc.contributor.authorVasquez, oscar C.
dc.date.accessioned2025-01-20T17:08:33Z
dc.date.available2025-01-20T17:08:33Z
dc.date.issued2024
dc.description.abstractThe problem of estimation and forecasting of population nutritional status has been addressed in the literature, showing successful results when the data are available and frequently collected over time. However, most low and middle -income countries collect nutritional status data sparsely, and consequently, the uncertainty/absence of information may negatively affect decisions from policymakers. In this context, the problem of estimation and forecasting of obesity prevalence using sparsely collected cross-sectional data is formally stated and a novel sequential approach to address it is proposed. Specifically, this work describes the nutritional status dynamics using a system of nonlinear difference equations, where the set of transition probabilities are unknown parameters due to the sparsely collected cross-sectional data. Then, an artificial neural network alike model is proposed through its equivalent nonlinear programming model, considering the difference equations system as constraints as well as bounds for the transition probabilities based on literature data. In addition, comprehensive data collection and information analysis processes to compute demographic parameters are defined. As the model is non -convex, an optimal solution is characterized and coined as stable; and thereafter assessed in terms of its goodness -of -fit. Computational experiments and a resolution scheme using a rollinghorizon forecasting/back-casting approach and divergence metrics is proposed. To illustrate the usefulness of this novel approach, Chile is used as a case study. Results show an accuracy up to 90%, forecasting the men and women obese population (BMI >= 30.0 kg/m2) for 2024, reaching 30.6% (95% CI: 28.4-32.8%) and 32.6% (95% CI: 29.1-36.0%), respectively.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.engappai.2024.107860
dc.identifier.eissn1873-6769
dc.identifier.issn0952-1976
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2024.107860
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90961
dc.identifier.wosidWOS:001165139400001
dc.language.isoen
dc.revistaEngineering applications of artificial intelligence
dc.rightsacceso restringido
dc.subjectNonlinear pattern recognition
dc.subjectSystem dynamics modeling
dc.subjectSparsely collected data
dc.subjectCross-sectional data
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleThe problem of estimation and forecasting of obesity prevalence using sparsely collected data
dc.typeartículo
dc.volumen132
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas