Estimation of Factors Affecting Stunting Cases in West Java in 2021 Using Negative Binomial Spatial Regression

Anik Djuraidah; Mely Amelia; Rahma Anisa

doi:10.20956/j.v20i1.26984

Authors

Anik Djuraidah Department of Statistics, IPB University, Indonesia
Mely Amelia Department of Statistics, IPB University, Indonesia
Rahma Anisa Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.20956/j.v20i1.26984

Keywords:

negative binomial, overdispersion, stunting, spatial regression

Abstract

Stunting is a childhood growth and development disorder characterized by below-normal height. West Java, with its stunting rate of 24.5 percent, is one of the provinces included in the top 12 priority provinces in implementing the National Action Plan to Accelerate Stunting. Stunting cases are count data and their occurrence is rare. The analysis for the count data is Poisson regression with the assumption that equidispersion must be met. One way to overcome overdispersion is to use negative binomial regression. This study aimed to determine predictors/factors affecting stunting cases in West Java province in 2021 using negative binomial spatial regression. The data in this study comes from the publication of the West Java Health Service and the West Java Central Statistics Agency in 2021 with districts/cities as the object of observation. There is a spatial effect in the stunting data, so the spatial regression model is suitable. The results show that there is an overdispersion in the Poisson regression. The spatial effect test shows that there is a spatial dependence on the response variable and some predictors. The negative spatial autoregressive binomial is the best model with the lowest AIC value. The factors that have a significant effect are the percentage of infants aged less than six months who are breastfed, the percentage of food processing establishments that meet the requirements, and the percentage of infants with low birth weight.

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