Septian Rahardiantoro, Alfidhia Rahman Nasa Juhanda, Anang Kurnia, Aswi Aswi, Bagus Sartono, Dian Handayani, Agus Mohamad Soleh, Yusma Yanti, Susanna Cramb
This study investigates the factors associated with stunting prevalence in Indonesia, utilizing a generalized lasso framework with modified penalty matrices to accommodate spatio-temporal data structures. Novel approaches are introduced to construct the penalty matrices, with particular focus on defining neighborhood structures. The proposed method is applied to data from 34 Indonesian provinces, covering the years 2019 to 2023. The primary outcome is stunting prevalence, modeled against nine predictor variables: poverty, exclusive breastfeeding, low birth weight (LBW), high school completion, access to proper sanitation, unmet health service needs, Gross Domestic Product (GDP), calorie consumption, and protein consumption. A total of nine spatio-temporal models were compared, including a modified generalized lasso with three distinct penalty matrices for each two tuning selection methods and a generalized ridge regression with three penalty matrices. Results indicate that the generalized lasso model with a 3-nearest neighbor adjacency matrix outperformed the alternatives. Temporal variations were observed in the effects of exclusive breastfeeding, LBW, high school completion, and unmet health service needs. Positive associations with stunting prevalence were identified for poverty, exclusive breastfeeding, LBW, and unmet health service needs, while negative associations were found for high school completion rates, access to proper sanitation, GDP, calorie intake, and protein consumption. The strongest associations were observed in parts of Sumatra, Sulawesi, and Jakarta. These findings suggest that government interventions aimed at improving education, healthcare access, and poverty reduction may help alleviate stunting in Indonesia, particularly in regions with the greatest need. © 2024 Elsevier Ltd
Department of Statistics, IPB University, Indonesia; Department of Statistics, Universitas Negeri Makassar, Indonesia; Statistics Study Program, Universitas Negeri Jakarta, Indonesia; Computer Sciences Study Program, Universitas Pakuan, Indonesia; Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology, Australia
