Bayesian Spatio-Temporal Conditional Autoregressive Modeling of Stunting Risk Factors in East Java
DOI:
https://doi.org/10.52435/jaiit.v8i1.762Keywords:
Bayesian Spatio-Temporal CAR, East Java, INLA, Risk Factors, StuntingAbstract
This study analyzes stunting cases in East Java Province using district/city-level panel data covering the period 2022-2024. The data were obtained from the Indonesian Nutritional Status Survey (SSGI), the Indonesian Health Survey (SKI), and official statistical sources, consisting of stunting cases and several health, socioeconomic, and environmental indicators across 38 districts and municipalities. The study applies a Bayesian Spatio-Temporal Conditional Autoregressive (BST-CAR) model with the Integrated Nested Laplace Approximation (INLA) approach to account for spatial dependence among neighboring regions and temporal variation over time. The results show that stunting cases in East Java exhibit significant spatial and temporal dependence, supported by significant positive spatial autocorrelation across all observation years. Model evaluation yields a Deviance Information Criterion (DIC) value of 1477,267 and a Watanabe-Akaike Information Criterion (WAIC) value of 1442,479. The estimation results indicate that all examined covariates, including low birth weight, complete basic immunization, exclusive breastfeeding, proportion of poor population, access to improved drinking water, and access to improved sanitation, are statistically significant in explaining variations in stunting cases after controlling for spatial and temporal effects. Relative risk mapping reveals clear spatial heterogeneity, with higher-risk clusters concentrated in districts such as Jember, Lumajang, and Probolinggo, while lower-risk areas are mainly observed in urban regions such as Surabaya, Mojokerto, and Madiun. Overall, the findings suggest that stunting distribution in East Java is shaped by both spatial and temporal structures, highlighting the importance of geographically targeted intervention strategies at the district/city level.
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