Bayesian spatio-temporal conditional autoregressive localized modeling techniques for socioeconomic factors and stunting in Indonesia

Abstract

Stunting remains a persistent public health issue in Indonesia, exhibiting significant spatial and temporal variation. To address this, we employed a hierarchical Bayesian spatio-temporal localized Conditional Autoregressive (CAR) model that includes a clustering component to identify risk factors and estimate relative risk (RR) across 34 provinces from 2020 to 2022. A total of 480 models were evaluated, encompassing three variants of the Bayesian spatio-temporal localized CAR model, 32 covariate combinations, and five hyperprior settings. Assuming a Poisson likelihood for stunting counts, the optimal model was estimated using Markov Chain Monte Carlo methods and included two covariates, namely the poverty rate and the incidence of low birth weight, with up to five spatial clusters. Higher poverty levels and increased prevalence of low birth weight were significantly associated with elevated stunting risk among children under five. Spatio-temporal clustering patterns and the estimated relative risks of stunting varied across Indonesian provinces from 2020 to 2022. Nusa Tenggara Timur consistently ranked among the top three provinces with the highest risk (RR = 2.421 in 2020; 2.384 in 2021; 2.676 in 2022). The highest risk was observed in Sulawesi Barat in 2022 (RR = 2.768), while DKI Jakarta consistently showed the lowest (RR = 0.004).

Some key points of the article are:

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  Bayesian spatio-temporal models facilitate the classification of distinct area groups
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  The models were employed to analyze stunting patterns in Indonesia.
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  The inclusion of covariates influenced the number of groups identified.