Negative Binomial Regression Modeling to Analyze the Determinants of Infant Mortality in West Java Province
(Pemodelan Regresi Binomial Negatif untuk Menganalisis Faktor-faktor Penentu Kematian Bayi di Provinsi Jawa Barat)
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Firda Fadri
Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Jl. Kalimantan No. 37 Kampus Tegalboto, Jember, Jawa Timur, 68121, Indonesia
Ari Firmansyah
Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Jl. Kalimantan No. 37 Kampus Tegalboto, Jember, Jawa Timur, 68121, Indonesia
Victor Alesyus Erlanda
Jurusan Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Jember, Jl. Kalimantan No. 37 Kampus Tegalboto, Jember, Jawa Timur, 68121, Indonesia
DOI: https://doi.org/10.19184/bst.v13i1.53686
ABSTRACT
The Infant Mortality Rate (IMR) is an important indicator in assessing the quality of public health and the success of health programs in a region. Proper handling of factors that determine IMR is essential to reduce this number. The data used were 27 districts/cities in West Java in 2022 with predictor variables including the number of health workers, percentage of poor population, percentage of iron tablet consumption, percentage of clean and healthy living behavior, percentage of exclusive breastfeeding, and percentage of low birth weight babies. The results of the analysis with Poisson Regression showed overdispersion so that IMR modeling was carried out using Negative Binomial Regression. The AIC value for the Negative Binomial Regression model was 305.630 and the BIC value was 315.997. The deviance ratio and Pearson's Chi-square approached one, indicating effective handling of overdispersion. The only significant variable affecting IMR was the percentage of clean and healthy living behavior. This shows the importance of increasing clean and healthy living behavior as the main strategy for reducing IMR in West Java Province.
Keywords: Infant Mortality Rate, Poisson regression, Negative binomial regression, Overdispersion.
Published
27-03-2025
Issue
Vol. 13 No. 1 2025: BERKALA SAINSTEK
Pages
8-14
License
Copyright (c) 2025 BERKALA SAINSTEK
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