DOI: https://doi.org/10.14710/medstat.17.1.45-56

CONWAY-MAXWELL POISSON REGRESSION MODELING OF INFANT MORTALITY IN SOUTH SULAWESI

Oktaviana Oktaviana  -  Mathematics Study Program, Postgraduate Program, Universitas Negeri Makassar, Indonesia
*Wahidah Sanusi  -  Mathematics Department, Universitas Negeri Makassar, Indonesia
Aswi Aswi  -  Statistics Study Program, Universitas Negeri Makassar, Indonesia
Sukarna Sukarna  -  Mathematics Department, Universitas Negeri Makassar, Indonesia
Serifat Adedamola Folorunso  -  Department of Statistics, University of Ibadan Nigeria, Nigeria
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Overdispersion is a common problem in count data that can lead to inaccurate parameter estimates in Poisson regression models. Quasi-Poisson and negative binomial regressions are often used to address overdispersion but have limitations, especially with small samples. The Conway-Maxwell Poisson (CMP) regression model, an extension of the Poisson distribution, effectively addresses both overdispersion and underdispersion, even with limited data, due to additional parameters that better control data dispersion. The Infant Mortality Rate (IMR) is a critical public health indicator, reflecting healthcare quality and broader social, economic, and environmental factors. Accurate IMR estimation is essential for evaluating health policies. This study aims to (1) identify overdispersion in IMR data from South Sulawesi, (2) model IMR using CMP regression, and (3) identify factors influencing IMR. The dataset includes IMR, Low Birth Weight (LBW), diarrhea, asphyxia, pneumonia, and exclusive breastfeeding. Analysis showed significant overdispersion with a ratio of 4.639, making CMP the optimal model with an AIC of 186.845. Significant factors identified were LBW, asphyxia, pneumonia, and exclusive breastfeeding. These findings advance statistical methodologies for count data analysis and offer a more accurate approach to evaluating public health policies, supporting efforts to reduce infant mortality in South Sulawesi Province.
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Keywords: Poisson Regression; Overdispersion;Conway-Maxwell Poisson; Infant Mortality Rate

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  1. Afri, L. E. (2017). Perbandingan Regresi Binomial Negatif dan Regresi Conway-Maxwell-Poisson dalam Mengatasi Overdispersi pada Regresi Poisson. Jurnal Gantang, 2(1), 79–87. https://doi.org/10.31629/jg.v2i1.66
  2. Alfahmi, F. (2023). Hubungan Pola Makan dan Asupan Protein Ibu Hamil dengan Kejadian BBLR di Puskesmas Kadugede. Jurnal Ilmu Kesehatan, 2(2), 13–26. https://jurnal.unisa.ac.id/index.php/jfikes/article/view/363/350
  3. Ambarwati, P. C., Indahwati, & Aidi, M. N. (2020). Kajian Simulasi Overdispersi pada Regresi Poisson dan Binomial Negatif Terboboti Geografis untuk Data Balita Gizi Buruk. Journal of Statistics and Its Applications, 4(3), 484–497. https://doi.org/https://doi.org/10.29244/ijsa.v4i3.684
  4. Amin, M., Akram, M. N., & Amanullah, M. (2020). On the James-Stein Estimator for the Poisson Regression Model. Communications in Statistics: Simulation and Computation, 51(10), 5596–5608. https://doi.org/10.1080/03610918.2020.1775851
  5. Aswi, A., Astuti, S. A., & Sudarmin, S. (2022). Evaluating the Performance of Zero-Inflated and Hurdle Poisson Models for Modeling Overdispersion in Count Data. Inferensi, 5(1), 17. https://doi.org/10.12962/j27213862.v5i1.12422
  6. Dewanti, N. P. P., Susilawati, M., & Srinadi, I. G. M. (2016). PERBANDINGAN REGRESI ZERO INFLATED POISSON (ZIP) DAN REGRESI ZERO INFLATED NEGATIVE BINOMIAL (ZINB) PADA DATA OVERDISPERSION (Studi Kasus: Angka Kematian Ibu di Provinsi Bali). E-Jurnal Matematika, 5(4), 133. https://doi.org/10.24843/mtk.2016.v05.i04.p132
  7. Eminita, V., Kurnia, A., & Sadik, K. (2019). Penanganan Overdispersi Pada Pemodelan Data Cacah dengan Respon Nol Berlebih (Zero-Inflated). Fibonacci: Jurnal Pendidikan Matematika Dan Matematika, 5(1), 71. https://doi.org/10.24853/fbc.5.1.71-80
  8. Fitri, F., Sari, F. M., Gamayanti, N. F., & Utami, I. T. (2021). Infant Mortality Case: An Application of Negative Binomial Regression in order to Overcome Overdispersion in Poisson Regression. Eksata: Berkala Ilmiah Bidang MIPA, 22(03), 200–210. https://doi.org/https://doi.org/10.24036//eksakta/vol22-iss2/272 Eksakta BerkalaiIlmiah Bidang MIPA
  9. Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models (3rd ed.). SAGE Publications Ltd
  10. Hayati, M., Sadik, K., & Kurnia, A. (2018). Conwey-Maxwell Poisson Distribution: Approach for Over- and-Under-Dispersed Count Data Modelling. IOP Conference Series: Earth and Environmental Science, 187(1). https://doi.org/10.1088/1755-1315/187/1/012039
  11. Hida, A. A., Robby, R. R., Akbarita, R., Nur, M., Qomarudin, H., Nahdlatul, U., & Blitar, U. (2022). Penanganan Overdispersi pada Faktor-Faktor yang Mempengaruhi Stunting di Kabupaten Blitar Menggunakan Regresi Binomial Negatif. Prosiding SENKIM: Seminar Nasional Karya Ilmiah Multidisiplin, 2(1), 87–94. https://journal.unilak.ac.id/index.php/senkim/article/view/11311
  12. Jao, N., Islamiyati, A., & Sunusi, N. (2022a). Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan. Estimasi, 3(1), 14–23. https://doi.org/10.20956/ejsa.vi.11997
  13. Jao, N., Islamiyati, A., & Sunusi, N. (2022b). Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan. Estimasi: Journal of Statistics and Its Application, 3(1), 14–23. https://doi.org/10.20956/ejsa.vi.11997
  14. Kamalja, K. K., & Wagh, Y. S. (2018). Estimation in Zero-Inflated Generalized Poisson Distribution. Journal of Data Science, 16(1), 183–206. https://doi.org/10.6339/JDS.201801
  15. Koerniawan, V., Sunusi, N., & Raupong, R. (2020). Estimasi Parameter Model Poisson Hidden Markov Pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa. Estimasi: Journal of Statistics and Its Application, 1(2), 65–73. https://doi.org/10.20956/ejsa.v1i2.9302
  16. Maneking, F. D. G., Salaki, D. T., & Hatidja, D. (2020). Model Regresi Poisson Tergeneralisasi untuk Anak Gizi Buruk di Sulawesi Utara. Jurnal Ilmiah Sains, 20(2), 141. https://doi.org/10.35799/jis.20.2.2020.29133
  17. Nasution, A. R., Sadik, K., & Rizki, A. (2022). Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi. Xplore: Journal of Statistics, 11(3), 215–225. https://doi.org/https://doi.org/10.29244/xplore.v11i3.1018
  18. Putri, A. D., Devianto, D., & Yanuar, F. (2022). Pemodelan Jumlah Kematian Bayi di Kota Bandung dengan Menggunakan Regresi Zero-Inflated Poisson. Jurnal Matematika UNAND, 11(1), 12–24. https://doi.org/https://doi.org/10.25077/jmu.11.1.12-24.2022
  19. Rahayu, A. (2020). Model-model Regresi untuk Mengatasi Masalah Overdispersi pada Regresi Poisson. Journal Peqguruan: Conference Series, 1(April), 1–5. https://doi.org/http://dx.doi.org/10.35329/jp.v2i1.1866
  20. Rahayu, A. (2021). Model-Model Regresi untuk Mengatasi Masalah Overdipersi pada Regresi Poisson. Journal Peqguruang: Conference Series. https://doi.org/10.35329/jp.v2i1.1866
  21. Rahmadeni, & Sari, N. (2018). Solusi Overdispersi Menggunakan Generalized Poisson Regression (Studi Kasus : Penderita HIV di Provinsi Riau). Jurnal Sains Matematika Dan Statistika, 4(2), 28–36
  22. Sanusi, W., Sukarna, S., & Darwis, E. S. (2020). Analisis Jalur dan Aplikasinya dalam Menentukan Faktor yang Mempengaruhi Derajat Kesehatan Balita di Sulawesi Selatan. Journal of Mathematics Computations and Statistics, 3(1), 64. https://doi.org/10.35580/jmathcos.v3i1.19903
  23. Sanusi, W., Syam, R., & Adawiyah, R. (2020). Model Regresi Nonparametrik dengan Pendekatan Spline (Studi Kasus: Berat Badan Lahir Rendah di Rumah Sakit Ibu dan Anak Siti Fatimah Makassar). Journal of Mathematics, Computations, and Statistics, 2(1). https://doi.org/10.35580/jmathcos.v2i1.12460
  24. Sellers, K. F., & Premeaux, B. (2020). Conway–Maxwell–Poisson Regression Models for Dispersed Count Data. Wiley Interdisciplinary Reviews: Computational Statistics, 13(6), 1–13. https://doi.org/10.1002/wics.1533
  25. Sellers, K. F., & Shmueli, G. (2010). A flexible regression model for count data. Annals of Applied Statistics, 4(2), 943–961. https://doi.org/10.1214/09-AOAS306
  26. Side, S., Sanusi, W., & Bohari, N. A. (2021). Pemodelan Matematika SEIR Penyebaran Penyakit Pneumonia pada Balita dengan Pengaruh Vaksinasi di Kota Makassar. Journal of Mathematics Computations and Statistics, 4(1), 1. https://doi.org/10.35580/jmathcos.v4i1.20444
  27. Sundari, M., & Sihombing, P. R. (2021). Penanaganan Overdispersi Pada Regresi Poisson (Studi Kasus : Pengaruh Faktor Iklim Terhadap Jumlah Penderita Penyakit Demam Berdarah di Kota Bogor). Lebesgue: Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 2(1), 1–9. https://doi.org/10.46306/lb.v2i1
  28. Ulfa, Y. A., Soleh, A. M., & Sartono, B. (2021). Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java. Indonesian Journal of Statistics and Its Applications, 5(1), 1–13. https://doi.org/10.29244/ijsa.v5i1p1-13
  29. Winata, H. M. (2023). Mengatasi Overdispersi dengan Regresi Binomial Negatif pada Angka Kematian Ibu di Kota Bandung. Jurnal Gaussian, 11(4), 616–622. https://doi.org/10.14710/j.gauss.11.4.616-622
  30. Yasril, A. I., Yuhelmi, & Safitri, Y. (2021). Penerapan Analisis Jalur (Path Analysis) Pada Faktor yang Mempengaruhi Angka Kematian Bayi di Sumatera Barat. Jurnal Endurance : Kajian Ilmiah Problema Kesehatan, 6(2), 236–249. https://doi.org/10.22216/jen.v6i2.189
  31. Yasril, A. I., Yuhelmi, & Safitri, Y. (2022). Penerapan Analisis Jalur (Path Analysis) Pada Faktor yang Mempengaruhi Angka Kematian Bayi di Sumatera Barat. Jurnal Endurance : Kajian Ilmiah Problema Kesehatan, 6(2), 236–249. https://doi.org/10.22216/jen.v6i2.189

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