COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022

Keywords: Generalized Poisson Regression, Infant mortality, Overdispersion, Poisson regression

Abstract

In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.

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References

Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, "Pilar Pembangunan Sosial Edisi II," Kedeputian Bidang Kemaritiman dan Sumber Daya Alam, Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, Jakarta, 2020.

Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, "Pilar Pembangunan Sosial Edisi II," Kedeputian Kemaritiman dan Sumber Daya Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, Jakarta, 2021.

Dinas Kesehatan Provinsi Jawa Barat, "Profil Kesehatan Provinsi Jawa Barat Tahun 2021," Dinas Kesehatan Provinsi Jawa Barat, Bandung, 2021.

World Health Organization, "Congenital Disorders," 27 February 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/birth-defects. [Accessed 21 November 2023].

V. A. Rahma, "Analisis Regresi Binomial Negatif Untuk Memodelkan Jumlah Kematian Bayi di Jawa Timur Tahun 2014," 2016.

A. Arisandi, E. T. Herdiani and S. Sahriman, "Aplikasi Generalized Poisson Regression dalam Mengatasi Overdispersi pada Data Jumlah Penderita Demam Berdarah Dengue," Statistika, vol. 18, no. 2, pp. 123-130, November 2018.

D. Y. Kurniawan, Studi Banding Metode Generalized Poisson Regression dan Regresi Binomial Negatif untuk Mengatasi Overdispersi pada Data Diskrit (Studi Kasus Jumlah Kasus Baru TB di Provinsi Jawa Timur Tahun 2013), Surabaya: ADLN Perpustakaan Universitas Airlangga, 2015.

A. D. Putri, D. Devianto and F. Yanuar, "PEMODELAN JUMLAH KEMATIAN BAYI DI KOTA BANDUNG DENGAN MENGGUNAKAN REGRESI ZERO-INFLATED POISSON," Jurnal Matematika UNAND, vol. Vol. 11 No. 1, pp. 12-24, Januari 2022.

N. Y. Irkan, R. A. Ahri and S. , "Analisis Faktor yang Berhubungan dengan Kejadian Kematian Bayi," JOURNAL OF MUSLIM COMMUNITY HEALTH (JMCH), vol. III, pp. 24-32, 2022.

Badan Pusat Statistik Provinsi Sulawesi Utara, "Jumlah Penduduk Menurut Provinsi di Indonesia (Ribu Jiwa), 2024," Badan Pusat Statistik Provinsi Sulawesi Utara, Manado, 2024.

B. Durmus and O. I. Guneri, "An Application of The Generalized Poisson Model for Over Dispersion Data on The Number of Strikes Between 1984 and 2017," Alphanumeric Journal, vol. 8, no. 1, pp. 249-260, 2020.

J. X. M.D, S. L. M. B. S, K. D, K. M. A and E. Arias, Ph.D., "Mortality in the United States, 2021," National Center For Health Statistics, Desember 2022.

A. P. Rachmadiani, M. A. Shodikin and C. Komariah, "Faktor-Faktor Risiko Kematian Bayi Usia 0-28 Hari di RSD dr. Soebandi Kabupaten Jember," Journal Of Agromedicine and Medical Sciences, vol. Vol. 4 No. 2, 2018.

L. M. N, D. Yuniarti and M. N. Hayati, "Penerapan Generalized Poisson Regression I Untuk Mengatasi Overdispersi Pada Regresi Poisson," Jurnal EKSPONENSIAL, Vols. Volume 7, Nomor 1, Mei 2016.

S. Ardifasalma and U. Azmi, "Pemodelan Kasus Covid-19 di Jawa Timur Menggunakan Metode Generalized Poisson Regression dan Negative Binomial Regression," JURNAL SAINS DAN SENI ITS, Vols. Vol. 11, No. 6, pp. 2337-3520, 2022.

P. R. Chaniago, D. Devianto and I. R. HG, "ANALISIS FAKTOR RISIKO ANGKA KEMATIAN IBU DENGAN PENDEKATAN REGRESI POISSON," Jurnal Matematika UNAND, vol. VII No. 2, pp. 126-131, 2018.

A. D. Chaniago and S. P. Wulandari, "Pemodelan Generalized Poisson Regression (GPR) dan Negative Binomial Regression (NBR) untuk Mengatasi Overdispersi pada Jumlah Kematian Bayi di Kabupaten Probolinggo," JURNAL SAINS DAN SENI ITS, vol. 11, p. 8, 27 07 2022.

R. P. Hendikawati and A. Agoestanto, "PEMODELAN GENERALIZED POISSON REGRESSION (GPR) UNTUK MENGATASI PELANGGARAN EQUIDISPERSI PADA REGRESI POISSON KASUS CAMPAK DI KOTA SEMARANG TAHUN 2013," UNNES Journal Of Matjematics, 2016.

I. and D. P. Sari, "PEMODELAN REGRESI POISSON, BINOMIAL NEGATIF, DAN PADA KASUS KECELAKAAN KENDARAAN BERMOTOR," in Seminar Nasional Matematika dan Pendidikan Matematika, 2013.

N. I. Islamiyah, "Pemodelan Generalized Poisson Regression (GPR) pada Faktor-Faktor yang Mempengaruhi Kasus Pneumonia pada Balita di Provinsi Sulawesi Selatan Tahun 2018," Jurusan Matematika, Fakultas Sains dan Teknologi, Universitas Islam Negeri Alauddin Makassar, Makassar, 2020.

M. Sriningsih, D. Hatidja and J. D. Prang, "Penanganan Multikolinieritas dengan Menggunakan Analisis Regresi Komponen Utama pada Kasus Impor Beras di Provinsi Sulut," Jurnal Ilmiah Sains , vol. 18, no. 1, pp. 18-24, 2018.

Y. Fahrizal and A. K. Mutaqin, "Pemodelan Distribusi Poisson-Amarendrapada Data Frekuensi Klaim Asuransi Kendaraan Bermotor di Indonesia," Bandung Conference Series: Statistics, vol. Vol. 3. No. 1, 2023.

T. Chai and R. R. Draxler, "Root mean square error (RMSE) or mean absolute error (MAE)? –Arguments against avoiding RMSE in the literature," Geosci. Model Dev, pp. 1247-1250, June 2014.

Published
2025-01-13
How to Cite
[1]
T. Saifudin, “COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022”, BAREKENG: J. Math. & App., vol. 19, no. 1, pp. 35-50, Jan. 2025.