ZERO-INFLATED NEGATIVE BINOMIAL MODELING IN INFANT DEATH CASE DUE TO PNEUMONIA IN EAST JAVA PROVINCE
- Pneumonia,
- ZINB,
- East Java,
- SDGs
Copyright (c) 2023 Cindy Cahyaning Astuti, Agnes Ona Bliti Puka, Akbar Wiguna

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract
Pneumonia is an acute infectious disease of the respiratory tract and an infection caused by a virus, bacteria or fungus that attacks the lung tissue. Several cases of pneumonia have resulted in deaths that occurred in toddlers aged 12-59 months. Based on official health in profile data, East Java's health in 2021 has a zero number of deaths under five aged 12-59 months due to pneumonia. Modeling data with many response variables is zero and there is overdispersion can be done using Zero Inflated Negative Binomial (ZINB) regression. This study aims to model the number of infant deaths aged 12-59 months due to pneumonia in East Java Province based on seven factors that are considered to influence the number of deaths in infants due to pneumonia. From this model, it can be seen that the factors that significantly influence the death of infants aged 12-59 months due to pneumonia in East Java Province using Zero Inflated Negative Binomial (ZINB) regression. The results of testing the parameters of the ZINB regression model show that the predictor variables that have a partial significant effect on the negative binomial model in East Java are the percentage of infants who received complete basic immunization, the percentage of coverage of under-five health services, the percentage of under-five children with malnutrition, the percentage of LBW (low birth weight babies). Selection of the best model is obtained by using the Bayesian Information Criterion (BIC) of 101,587.
Downloads
References
- J. Fox, Applied Regression Analysis and Generalized Linear Models, vol. 3rd Edition. Sage Publication, Inc, 2016.
- H. Permadi and S. D. Maulidah, “Zero Inflated Poisson Regression Analysis on IMB Ownership in Sidoarjo Regency 2019,” in Journal of Physics: Conference Series, IOP Publishing Ltd, May 2021. doi: 10.1088/1742-6596/1872/1/012030.
- H. Campbell, “The Consequences of Checking for Zero-Inflation and Overdispersion in The Analysis of Count Data,” Methods Ecol Evol, vol. 12, no. 4, pp. 665–680, Apr. 2021, doi: 10.1111/2041-210X.13559.
- F. Famoye and J. S. Preisser, “Marginalized zero-inflated generalized Poisson regression,” J Appl Stat, vol. 45, no. 7, pp. 1247–1259, May 2018, doi: 10.1080/02664763.2017.1364717.
- P. Saengthong, W. Bodhisuwan, and A. Thongteeraparp, “The Zero Inflated Negative Binomial Crack distribution: Some Properties and Parameter Estimation,” 2015. [Online]. Available: http://www.sjst.psu.ac.th
- C. C. Astuti and A. D. Mulyanto, “Estimation Parameters And Modelling Zero Inflated Negative Binomial,” CAUCHY: Jurnal Matematika Murni dan Aplikasi, vol. 4, no. 3, pp. 115–119, Nov. 2016, doi: 10.18860/ca.v4i3.3656.
- K. J. Hong and J. Kim, “Risk Factors Preventing Immediate Fall Detection: A Study Using Zero-Inflated Negative Binomial Regression,” Asian Nurs Res (Korean Soc Nurs Sci), vol. 15, no. 4, pp. 272–277, Oct. 2021, doi: 10.1016/j.anr.2021.09.001.
- N. Achmad, M. R. F. Payu, and Y. Rahim, “Pemodelan Pneumonia Berat Menggunakan Regresi Zero Inflated Negative Binomial di Gorontalo,” Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi, vol. 10, no. 1, pp. 45–53, May 2022, doi: 10.34312/euler.v10i1.13990.
- J. Kruppa and L. Hothorn, “A Comparison Study on Modeling of Clustered and Overdispersed Count Data for Multiple Comparisons,” Journal of Applied Statistics. Taylor and Francis Ltd., pp. 1–13, 2020. doi: 10.1080/02664763.2020.1788518.
- Y. Tiara, M. N. Aidi, E. Erfiani, and R. Rachmawati, “Overdispersion Handling In Poisson Regression Model By Applying Negative Binomial Regression,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, pp. 0417–0426, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0417-0426.
- J. Stachurski, A Primer in Econometric Theory. London, England: MIT Press, 2017.
- T. Kyriazos and M. Poga, “Dealing with Multicollinearity in Factor Analysis: The Problem, Detections, and Solutions,” Open J Stat, vol. 13, no. 03, pp. 404–424, 2023, doi: 10.4236/ojs.2023.133020.
- H. Zamani and N. Ismail, “Functional Form for the Zero Inflated Generalized Poisson Regression Model,” Commun Stat Theory Methods, vol. 43, no. 3, pp. 515–529, Feb. 2014, doi: 10.1080/03610926.2012.665553.
- Y. Gençtürk and A. Yiğiter, “Modelling Claim Number Using a New Mixture Model: Negative Binomial Gamma Distribution,” J Stat Comput Simul, vol. 86, no. 10, pp. 1829–1839, Jul. 2016, doi: 10.1080/00949655.2015.1085987.
- P. Roback and J. Legler, “Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R,” 2021.