Spatial Bayes Analysis on Cases of Malnutrition in East Nusa Tenggara, Indonesia

Rachmawati, Ro’fah Nur; Pusponegoro, Novi Hidayat

doi:10.1016/j.procs.2021.01.014

Cited by 3 publications

(1 citation statement)

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“…Malnutrition can be caused by external and internal factors that, if not addressed early on, will have an impact on health conditions, the growth and development of toddlers, and productivity in adulthood [15]. Modeling related to the incidence of malnutrition has been carried out, including by using GLMM models [16], [17], Spatial Bayes approach [18], [19], Generalized Additive Mixed Models [20], and machine learning [21]. Modeling done in previous studies has not specifically examined cases of overdispersion and excess zero in malnutrition cases, so the modeling still contains bias.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Incidence of Malnutrition in Bogor Regency Using Zero-Inflated Negative Binomial Mixed Effect Model

Sirodj,

Sadik,

Kurnia

2024

BAREKENG: J. Math. & App.

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Modeling response variables in the form of count data generally uses a model based on the Poisson distribution. However, some conditions, such as the presence of excess zero, can be found in the data that result in overdispersion, which will have an impact on the resulting variance in the model. In this paper, three approaches, namely the Poisson Mixed Model, the Negative Binomial (NB) Mixed Model, and the Zero-Inflated Negative Binomial (ZINB) Mixed Model, are used to model the incidence of malnutrition in Bogor Regency. The data used in this study are secondary data sourced from the West Java open data website. Based on the results of data analysis, it appears that the ZINB Mixed Model method is a method capable of accommodating random effects, overdispersion, and excess zero in modeling malnutrition in Bogor Regency. Variables that significantly affect the occurrence of malnutrition cases in villages in Bogor Regency include the Number of Children Weighed Routinely Every Month, Number of Children Measured for Length and Height Twice a Year, Number of Children under 12 Months Old Who Received Complete Basic Immunization, Number of Posyandu (Integrated Health Post), and Number of Parents/Caregivers Participating in Monthly Parenting (PAUD).

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Section: Introductionmentioning

confidence: 99%