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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Advance Sustainable Science, Engineering and Technology (ASSET)
Dewi Ratnasari Wijaya
Brawijaya University
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics

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Bayesian Geographically Weighted Generalized Poisson Regression Modeling on Maternal Mortality in NTT in 2022 Dewi Ratnasari Wijaya; Henny Pramoedyo; Ni Wayan Suryawardhani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.31626

Abstract

Maternal mortality is a crucial indicator of healthcare quality, particularly in East Nusa Tenggara (NTT) Province, which still records high mortality rates with significant spatial variation. This study aims to model maternal mortality in NTT in 2022 using the Bayesian Geographically Weighted Generalized Poisson Regression (BGWGPR) approach. This method integrates spatial weighting techniques with Bayesian parameter estimation through Gibbs Sampling to address spatial data characterized by overdispersion. Significant factors, including pregnant women's visits to healthcare facilities (K1), were found to influence the distribution of maternal deaths across districts in NTT. The model identifies that visits to healthcare facilities (K1) (X_1) are significant across all regions, while the variable for pregnant women receiving Tetanus Toxoid (X_3) is only significant in Alor and Timor Tengah Selatan. This model not only provides insights into determining factors but also helps identify priority areas for intervention. Therefore, this study contributes to evidence-based health policy-making aimed at reducing maternal mortality in NTT. The BGWGPR approach proves to be relevant for analyzing complex spatial data and can be applied to other epidemiological cases.
Bayesian Generalized Poisson Regression Modeling for Overdispersed Maternal Mortality Data Dewi Ratnasari Wijaya; Henny Pramoedyo; Ni Wayan Surya Wardhani
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1928

Abstract

Maternal mortality is a global health issue that reflects disparities in access to and the quality of healthcare services. This study applies the Bayesian Generalized Poisson Regression (BGPR) approach to address the problem of overdispersion in the data, which renders the standard Poisson regression model less appropriate. The Generalized Poisson model was chosen for its ability to handle overdispersion, while the Bayesian approach provides more stable parameter estimates, particularly when working with small sample sizes. The analysis results show that all independent variables have a statistically significant effect on maternal mortality. In addition, the BGPR model yields a lower Bayesian Information Criterion (BIC) value compared to the standard Poisson model, indicating better model performance. The BGPR model helps identify the key factors that truly contribute to maternal mortality, making the results useful for local governments or health institutions in setting priorities for intervention.
Co-Authors Henny Pramoedyo Henny Pramoedyo Ni Wayan Surya Wardhani Ni Wayan Suryawardhani