Poisson Regression Model for Fertility Count Data and Its Applications
Main Article Content
Abstract
Introduction: Count data represents the number of occurrences of an event within a fixed period. For example the number of caesarean-section delivery in the lifetime of women. Count data is encountered in almost all research areas including economics, medicine, management, industrial organizations, and many more. Count data is very common in various fields such as biomedical science, public health, and marketing.
Obj Introduction: Count data represents the number of occurrences of an event within a fixed period. For example the number of caesarean-section delivery in the lifetime of women. Count data is encountered in almost all research areas including economics, medicine, management, industrial organizations, and many more. Count data is very common in various fields such as biomedical science, public health, and marketing.
Objectives: The main aim of this study is to estimate the parameters of interest and compare the number of caesarean-section deliveries (NCSD) among women aged 15-49, in the state of Andhra Pradesh, India, using the Poisson regression model (PRM) and negative binomial regression model (NBRM). The fertility counts data set, the real-world data of the National Family Health Survey (NFHS-5), 2019-2021, from the Demography and Health Survey (DHS), 2019-2021 phase VII data is used for the analysis.
Methods: Investigating the delivery patterns among pregnant women. This study develops an algorithm based on Integrated Nested Laplace Approximation (INLA) for fitting the model NCSD in PRM and NBRM. The analysis is carried out using the INLA package in R.
Results: By use of the Deviance Information Criterion (DIC) and Watanabe-Akaike information criterion (WAIC), the result shows that the NBRM; DIC (7079.61) and WAIC (7079.61) present a comparatively better fit in modelling the NCSD than the PRM; DIC (7096.79) and WAIC (7097.89).
Conclusions: The INLA provides an efficient algorithm to model in PRM and NBRM. For further research, comparing the PRM with other models that estimate over-dispersion in count data is recommended.ectives: The main aim of this study is to estimate the parameters of interest and compare the number of caesarean-section deliveries (NCSD) among women aged 15-49, in the state of Andhra Pradesh, India, using the Poisson regression model (PRM) and negative binomial regression model (NBRM). The fertility counts data set, the real-world data of the National Family Health Survey (NFHS-5), 2019-2021, from the Demography and Health Survey (DHS), 2019-2021 phase VII data is used for the analysis.
Methods: Investigating the delivery patterns among pregnant women. This study develops an algorithm based on Integrated Nested Laplace Approximation (INLA) for fitting the model NCSD in PRM and NBRM. The analysis is carried out using the INLA package in R.
Results: By use of the Deviance Information Criterion (DIC) and Watanabe-Akaike information criterion (WAIC), the result shows that the NBRM; DIC (7079.61) and WAIC (7079.61) present a comparatively better fit in modelling the NCSD than the PRM; DIC (7096.79) and WAIC (7097.89).
Conclusions: The INLA provides an efficient algorithm to model in PRM and NBRM. For further research, comparing the PRM with other models that estimate over-dispersion in count data is recommended.