MODEL GEOGRAPHICALLY WEIGHTED BIVARIATE GENERALIZED POISSON REGRESSION DENGAN ADAPTIVE TRICUBE KERNEL - Abdussamad, Trizaning Nursahbani; Rahmi, Emli (LPPM Universitas Bina Bangsa, 2025)
The Maternal Mortality Rate (MMR) and Neonatal Mortality Rate (NMR) are significant public health concerns in Indonesia. The elevated Maternal Mortality Rate (MMR) and Neonatal Mortality Rate (NMR) adversely affect the quality of life within the community, particularly for mothers and children. Mitigating maternal mortality rates (MMR) and neonatal mortality rates (NMR) has emerged as a priority to fulfil the Sustainable Development Goals and enhance the quality of healthcare services. Despite a reduction, the maternal mortality rate (MMR) and neonatal mortality rate (NMR) in Indonesia remain substantial, necessitating expedited efforts to achieve the 2024 objectives. Maternal and neonatal mortality are interconnected, as the mother's health status influences the infant's health status. This research used the Geographically Weighted Bivariate Generalised Poisson Regression (GWBGPR) model utilising Adaptive Bisquare and Adaptive Tricube Kernel weights to analyse maternal and neonatal mortality rates. The estimate of the GWBGPR model parameters use Maximum Likelihood estimate (MLE) with the Newton-Raphson iterative approach, alongside hypothesis testing via Maximum Likelihood Ratio Test (MLRT). This study's sample population comprises data on maternal and newborn death rates and their contributing factors throughout 34 provinces in Indonesia for the year 2022, sourced from the Profile of the Ministry of Health of the Republic of Indonesia. The analysis indicates that the GWBGPR model utilising the Adaptive Tricube Kernel is the most effective model, evidenced by the lowest AIC value. The primary independent variables influencing maternal and newborn mortality rates are the percentage of K4 prenatal care visits (X1) and the percentage of mothers getting Fe3 pills (X2).