Penerapan Regresi Conway Maxwell Poisson untuk Mengatasi Overdispersi pada Jumlah Kematian Bayi di Provinsi Jawa Barat

Vera Maya Santi, Adine Ihsan Kamil, Faroh Ladayya

Abstract


Infant Mortality Rate (IMR) is a key public health indicator reflecting the social, economic, environmental, and healthcare service quality conditions of a population. In 2023, West Java recorded the highest number of infant deaths in Indonesia. These data are count-type in nature and are commonly analyzed using Poisson regression. However, due to the frequent occurrence of overdispersion, the Poisson method becomes less appropriate. As an alternative, the Conway-Maxwell Poisson (CMP) regression is employed, offering greater flexibility in handling violations of the equidispersion assumption. This study aims to apply CMP regression to address overdispersion in the number of infant deaths in West Java Province using the Maximum Likelihood (ML) estimation method. The data used in this study comprise the total number of infant deaths in 2023 across 27 districts and cities in West Java Province. The ML parameter estimation analysis shows that the dispersion parameter values obtained from the CMP and Poisson models are 10.92 and 126.49, respectively. In terms of model evaluation criteria, the CMP model yields an AIC of 402.455 and BIC of 415.41, whereas the Poisson model shows an AIC of 4183.46 and BIC of 4195.12. These results indicate that the CMP model outperforms the Poisson model in handling infant mortality data. Furthermore, four variables are found to be statistically significant in explaining the number of infant deaths in West Java Province, namely the percentage of antenatal care coverage (K4), the number of health facilities by district/city, the percentage of households with clean and healthy living behavior (PHBS), and the percentage of neonatal asphyxia complications, with a significance level of alpha = 5%.

Keywords


Conway Maxwell Poisson; Infant Mortality; Overdispersion; Poisson Distribussion

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References


UNICEF, “Infant mortality rate.” [Online]. Available: https://data.unicef.org/resources/data_explorer/unicef_f/?ag=UNICEF&df=GLOBAL_DATAFLOW&ver=1.0&dq=.CME_MRY0..&startPeriod=2016&endPeriod=2023

L. Rocco, E. Fumagalli, A. J. Mirelman, and M. Suhrcke, “Mortality, morbidity and economic growth,” PLoS One, vol. 16, no. 5 May, pp. 1–22, 2021, doi: 10.1371/journal.pone.0251424.

Bappenas, “Goal 3: Kehidupan sehat dan sejahtera.” Accessed: Oct. 17, 2024. [Online]. Available: https://sdgs.bappenas.go.id/17-goals/goal-3/

UNICEF, “Goal 3: Good health and well-being.” Accessed: Oct. 17, 2024. [Online]. Available: https://data.unicef.org/sdgs/goal-3-good-health-wellbeing/

A. Prahutama, S. Sudarno, S. Suparti, and M. A. Mukid, “Analisis Faktor-faktor yang Mempengaruhi Angka Kematian Bayi di Jawa Tengah Menggunakan Regresi Generalized Poisson dan Binomial Negatif,” J. Stat. Univ. Muhammadiyah Semarang, vol. 5, no. 2, 2017.

A. B. Padatuan, S. Sifriyani, and S. Prangga, “Modeling Life Expectations and Infant Death Rates in Kalimantan with Nonparametric Regression Spline Birespon,” BAREKENG: J. Ilmu Mat. dan Terap., vol. 15, no. 2, pp. 283–296, 2021, doi: 10.30598/barekengvol15iss2pp283-296.

R. I. Puspita, R. Anisa, and L. O. A. Rahman, “Pemodelan Angka Kematian Bayi di Jawa Barat Menggunakan Pendekatan Analisis Regresi Spline dan Kernel,” Xplore: J. Stat., vol. 11, no. 3, pp. 203–214, 2022, doi: 10.29244/xplore.v11i3.1026.

D. D. Khofiyandi and Suliadi, “Pemodelam Regresi Conway-Maxwell-Poisson untuk Mengatasi Overdispersi pada Data Angka Kematian Ibu di Provinsi Jawa Timur,” Bandung Conf. Ser. Stat., vol. 3, no. 2, pp. 210–217, 2023, doi: 10.29313/bcss.v3i2.7865.

V. M. Santi and Y. Rahayuningsih, “Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia,” Pattimura Int. J. Math., vol. 2, no. 2, pp. 43–52, 2023, doi: 10.30598/pijmathvol2iss2pp43-52.

V. M. Santi, D. Ambarwati, and B. Sumargo, “Zero Inflated Poisson Regression Analysis in Maternal Death Cases on Java Island,” Pattimura Int. J. Math., vol. 1, no. 2, pp. 59–68, 2022, doi: 10.30598/pijmathvol1iss2pp59-68.

D. P. Prami Meitriani, K. G. Sukarsa, and I. P. E. N. Kencana, “Penerapan Regresi Quasi-Likelihood Pada Data Cacah (Count Data) Yang Mengalami Overdispersi Dalam Regresi Poisson,” E-Jurnal Mat., vol. 2, no. 2, p. 37, 2013, doi: 10.24843/mtk.2013.v02.i02.p036.

K. F. Sellers and G. Shmueli, “A flexible regression model for count data,” Ann. Appl. Stat., vol. 4, no. 2, 2010, doi: 10.1214/09-AOAS306.

K. F. Sellers, The Conway-Maxwell-Poisson Distribution, 1st ed. Cambridge: Cambridge University Press, 2023, doi: 10.1017/9781108646437.

L. E. Afri, “Perbandingan Regresi Binomial Negatif dan Regresi Conway-Maxwell-Poisson dalam Mengatasi Overdispersi pada Regresi Poisson,” J. Gantang, vol. 2, no. 1, pp. 79–87, 2017, doi: 10.31629/jg.v2i1.66.

A. R. Nasution, K. Sadik, and A. Rizki, “Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi,” Xplore J. Stat., vol. 11, no. 3, pp. 215–225, 2022, doi: 10.29244/xplore.v11i3.1018.

W. G. Cochran and G. W. Snedecor, Statistical Methods, 8th ed. Ames, 1989, doi: 10.1016/B978-0-12-823043-5.00015-1.

R. Shier, “Statistics: 1.4 Chi-squared goodness of fit test Example: The Poisson Distribution,” vol. 75, pp. 1–2, 2004. [Online]. Available: https://www.statstutor.ac.uk/resources/uploaded/chi-square-goodness-of-fit.pdf

National Institute of Standards and Technology, “e-Handbook of Statistical Methods,” U.S. Department of Commerce. Accessed: Nov. 09, 2024. [Online]. Available: https://www.itl.nist.gov/div898/handbook/

A. Agresti, An Introduction to Categorical Data Analysis, 3rd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2019.

J. M. Hilbe, Negative Binomial Regression, 2nd ed. New York: Cambridge University Press, 2011. [Online]. Available: www.cambridge.org/9780521198158

D. C. Montgomery and G. C. Runger, Applied Statistics and Probability for Engineers, 7th ed. New York: John Wiley & Sons, Inc., 2018.

A. Agresti, Categorical Data Analysis, 3rd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2013.

P. McCullagh and J. A. Nelder, Generalized Linear Models, 2nd ed. New York: Chapman and Hall, 1998.

D. W. Hosmer and S. Lemeshow, Applied Logistic Regression, 2nd ed. Danvers, MA: John Wiley & Sons, Inc., 2000.

B. West, K. Welch, and A. Gałecki, Linear Mixed Models: A Practical Guide Using Statistical Software, 2nd ed. Ann Arbor, MI: CRC Press, 2014, doi: 10.1201/b17198-2.

K. A. Palinoan, “Pemodelan Regresi Binomial Negatif Menggunakan Estimator Jackknife Negative Binomial Ridge Regression Pada Data Angka Kematian Bayi Provinsi Sulawesi Selatan,” BASIS J. Ilm. Mat., vol. 3, no. April, pp. 1–8, 2023.

E. Frankenberg, “The effects of access to health care on infant mortality in Indonesia,” Health Transit. Rev., vol. 5, no. 2, pp. 143–163, 1995.

A. I. Yasril et al., “Penerapan Analisis Jalur (Path Analysis) Pada Faktor Yang Mempengaruhi Angka Kematian Bayi Di Sumatera Barat,” J. Endur., vol. 6, no. 2, pp. 236–249, 2022, doi: 10.22216/jen.v6i2.189.

P. Rukmono, A. Anggunan, A. Pinili, and K. D. P. Madienda, “Hubungan Antara Asfiksia dengan Kematian Neonatal di RSUD Dr. H. Abdoel Moeloek Bandar Lampung,” MAHESA Malahayati Health Student J., vol. 2, no. 3, pp. 428–437, 2022, doi: 10.33024/mahesa.v2i3.4059.




DOI: https://doi.org/10.37905/euler.v13i2.31356

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