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

Malnutrition is a condition of serious nutritional disorders that occurs when food intake does not match the amount of nutrients needed. This nutritional disorder is fatal to a toddler's health if not treated immediately. For this reason, the purposes of this study are to model and map malnutrition cases by taking into account regional aspects using the Bayes spatial analysis whose inference uses INLA (integrated nested Laplace approximation). The spatial Bayes model used is a generalized linear mixed model, by including random effects in the form of conditional autoregressive spatial structured components. The response variable is the number of cases of malnutrition in 22 city districts in Indonesia's East Nusa Tenggara province, which is assumed to have a Poisson distribution. In spatial modeling, the fixed effects as the explanatory variables are included, i.e. the number of children under five given complete immunization, the poverty depth index, the number of maternal and child health services, population density and the average duration of breastfeeding. The results of spatial modeling show that the poverty depth index is the main variable that has a significant effect on the number of malnutrition cases. From the results of spatial mapping, it can be seen that there are regional links that affect the number of malnutrition cases, including in Sumba Barat Daya, Sumba Barat and Sumba Utara which have a high probability of malnutrition risk rather than in Sumba Timur. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

This study aims to model and map malnutrition cases using spatial Bayes analysis, and finds that the poverty depth index is the main variable affecting the number of malnutrition cases. The spatial mapping results reveal regional links that influence the number of malnutrition cases.