Spatial Analysis of Pneumonia in Toddlers on Sumatra Island Using Geographically Weighted Poisson Regression

Authors

  • Ruth Natasya Sepbrina Br Lumban Gaol Politeknik Statistika STIS
  • Maura Bintang Potenza Politeknik Statistika STIS
  • Nur Faqih Ihsan Politeknik Statistika STIS
  • Galang Ali Fazral Pratama Politeknik Statistika STIS
  • Sarni Maniar Berliana

DOI:

https://doi.org/10.34123/icdsos.v2025i1.500

Keywords:

Geographically Weighted Poisson Regression, Pneumonia in Children, Spatial Analysis, Local Health Interventions

Abstract

Pneumonia remains a leading cause of mortality among toddlers (aged 1 to less than 5 years) in Indonesia, with notable spatial disparities across Sumatra Island. This study examines factors influencing pneumonia incidence in toddlers using a Geographically Weighted Poisson Regression (GWPR) model to capture local variations in the effects of community health centers, complete basic immunization coverage, exclusive breastfeeding rates, and low birth weight (LBW) prevalence. Analyzing 2022 cross-sectional data from 154 districts/cities on Sumatra, the global Poisson regression model confirmed all predictors as statistically significant at the 5% level. The GWPR model with a fixed Gaussian kernel outperformed the global model, revealing five regional clusters with distinct combinations of significant variables. The dominant cluster (140 locations) showed significant effects from all predictors, while smaller clusters (14 locations) highlighted localized patterns, such as reliance on immunization and breastfeeding in rural areas like Rejang Lebong. These findings underscore the need for tailored interventions to address regional disparities in toddler pneumonia.

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Published

2025-12-22

How to Cite

Lumban Gaol, R. N. S. B., Potenza, M. B., Ihsan, N. F., Pratama, G. A. F., & Berliana, S. M. (2025). Spatial Analysis of Pneumonia in Toddlers on Sumatra Island Using Geographically Weighted Poisson Regression. Proceedings of The International Conference on Data Science and Official Statistics, 2025(1), 746–759. https://doi.org/10.34123/icdsos.v2025i1.500