Geographically and Temporally Weighted Compound Correlated Bivariate Poisson Regression for Maternal and Post-Neonatal Mortality in East Java

DOI

10.2991/978-94-6239-762-0_8How to use a DOI?

Keywords

Bivariate Count Regression; CCBPR; GTWCCBPR; BHHH Algorithm; Maternal Mortality; Post-Neonatal Mortality; Health Policy

Abstract

Maternal and post-neonatal mortality remain persistent and pressing public health issues in East Java, Indonesia, reflecting ongoing inequalities in healthcare delivery and policy implementation. Traditional count regression models often struggle to address overdispersion and tend to ignore geographic and temporal variation in related health outcomes. To account for these complexities, this study applies and compares two bivariate count regression models. The Compound Correlated Bivariate Poisson Regression (CCBPR) is used as a global model, while the Geographically and Temporally Weighted Compound Correlated Bivariate Poisson Regression (GTWCCBPR) is applied as a local model to capture relationships influencing both maternal and post-neonatal mortality. By employing an adaptive Gaussian kernel and estimating parameters using the Berndt–Hall–Hall–Hausman (BHHH) algorithm, GTWCCBPR explicitly models dependency while addressing overdispersion and identifying localized effects across space and time. The models were applied to secondary data from East Java Province for the period 2021–2023. The results show that GTWCCBPR outperforms CCBPR in terms of predictive accuracy and its ability to uncover geographic clusters and temporal patterns of health-risk factors. These findings underscore the necessity of employing a bivariate spatiotemporal framework to better inform health policy decisions and effectively target interventions at the local level.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Priyanka Ratulangi Hargandi
AU  - Purhadi Purhadi
AU  - Achmad Choiruddin
PY  - 2026
DA  - 2026/09/04
TI  - Geographically and Temporally Weighted Compound Correlated Bivariate Poisson Regression for Maternal and Post-Neonatal Mortality in East Java
BT  - Proceedings of the 6th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BIOMIC 2025)
PB  - Atlantis Press
SP  - 120
EP  - 136
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6239-762-0_8
DO  - 10.2991/978-94-6239-762-0_8
ID  - Hargandi2026
ER  -