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Geographically weighted bivariate zero inflated generalized Poisson regression model and its application.

Purhadi1, Dewi Novita Sari1,2, Qurotul Aini1,2

  • 1Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia.

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Summary

This study introduces the Geographically Weighted Bivariate Zero Inflated Generalized Poisson Regression (GWBZIGPR) model for analyzing maternal mortality data. The GWBZIGPR model offers improved accuracy over the Bivariate ZIGPR (BZIGPR) for understanding regional variations in mortality causes.

Keywords:
GWBZIGPRMLEMLRTMaternal mortality rate

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Area of Science:

  • Biostatistics
  • Spatial Epidemiology
  • Public Health Modeling

Background:

  • Maternal mortality remains a critical public health issue, necessitating advanced statistical methods for accurate analysis.
  • Existing models may not fully capture the spatial heterogeneity inherent in health outcome data, such as maternal mortality rates.
  • Zero-inflated count data, common in health studies, requires specialized regression techniques.

Purpose of the Study:

  • To develop and evaluate the Geographically Weighted Bivariate Zero Inflated Generalized Poisson Regression (GWBZIGPR) model.
  • To extend the Bivariate Zero Inflated Generalized Poisson Regression (BZIGPR) by incorporating spatial effects for localized parameter estimation.
  • To apply the developed model to analyze pregnant and postpartum maternal mortality data in Pekalongan Residency.

Main Methods:

  • Development of the Bivariate ZIGPR (BZIGPR) and its spatial extension, GWBZIGPR.
  • Parameter estimation using Maximum Likelihood Estimation (MLE) with Berndt Hall Hall Hausman (BHHH) numerical iteration due to non-closed-form equations.
  • Application to count data on pregnant and postpartum maternal mortality across 91 sub-districts.

Main Results:

  • The GWBZIGPR model demonstrated a smaller Akaike Information Criterion Corrected (AICc) value compared to the BZIGPR model.
  • This indicates that the GWBZIGPR model provides a superior fit for the maternal mortality data.
  • The GWBZIGPR model effectively captures local variations in the factors influencing maternal mortality.

Conclusions:

  • The GWBZIGPR model is a more effective statistical tool than BZIGPR for analyzing spatial count data in public health.
  • The findings provide valuable insights into the geographical distribution of maternal mortality risk factors.
  • This research will aid local governments in developing targeted interventions to reduce maternal mortality.