Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province

Authors

  • Siswanto Department of Statistics, Hasanuddin University, Indonesia
  • Edy Saputra R Department of Mathematics, Hasanuddin University, Indonesia
  • Nurtiti Sunusi Department of Statistics, Hasanuddin University, Indonesia
  • Nirwan Ilyas Department of Statistics, Hasanuddin University, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i2p170-179

Keywords:

geographically weighted poisson regression, mortality, negative binomial regression, spatial

Abstract

The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.

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References

Agresti, A. (2018). An Introduction to Categorical Data Analysis. New York: John Wiley & Sons.

Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (Vol. 53). New York: Cambridge university press.

Caraka, R. E., & Yasin, H. (2017). Geographically Weighted Regression (GWR) Sebuah Pendekatan Regresi Geografis. Yogyakarta: Mobius.

[Dinkes] Dinas Kesehatan Provinsi Sulawesi Selatan. (2018). Profil Kesehatan Provinsi Sulawesi Selatan Tahun 2017. Makassar: Pusat Data dan Informasi.

[Dinkes] Dinas Kesehatan Provinsi Sulawesi Selatan. (2019). Laporan Kinerja (LKJ) Organisasi Perangkat Daerah Tahun 2019. Makassar: Dinkes Sulsel.

Elyna, M. A. K., Srinadi, I. G. A. M., & Susilawati, M. (2012). Pemodelan Angka Kematian Bayi dengan Pendekatan Geographically Weighted Poisson Regression di Provinsi Bali. E-Jurnal Matematika, 1(1): 94-98.

Irawati, B. (2012). Perbandingan Analisis Generalized Poisson Regression (GPR) dan Regresi Binomial Negatif untuk Mengatasi Overdispersi Studi Kasus: Pemodelan Jumlah Kasus Kanker Serviks di Jawa Timur. Jurnal Matematika, 2(2): 13-24.

[Kemenkes RI] Kementerian Kesehatan Republik Indonesia. (2019). Profil Kesehatan Indonesia Tahun 2018. Jakarta: Kementerian Kesehatan RI.

Lengkong, G. T., Langi, F. L., & Posangi, J. (2020). Faktor-Faktor yang Berhubungan dengan Kematian Bayi di Indonesia. Kesmas, 9(4): 41-47.

Ulfa, Y. A., Soleh, A. M., & Sartono, B. (2020). Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java: Penanganan Overdispersi pada Model Regresi Poisson dengan Binomial Negatif untuk Jumlah Kasus Baru Kusta di Jawa. Indonesian Journal of Statistics and Its Applications, 5(1): 1-13.

Nakaya, T., Fotheringham, A. S., Brudson, C., & Charlton, M. (2005). Geographically weighted Poisson regression for disease association mapping. Statistics in medicine, 24(17): 2695-2717.

Wang, W., & Famoye, F. (1997). Modeling household fertility decisions with generalized Poisson regression. Journal of population economics, 10(3), 273-283.

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Published

2022-08-31

How to Cite

Siswanto, S., Saputra R, E., Sunusi, N., & Ilyas, N. (2022). Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province. Indonesian Journal of Statistics and Its Applications, 6(2), 170–179. https://doi.org/10.29244/ijsa.v6i2p170-179

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