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Submitted
Aug 26, 2021
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Sep 14, 2021
Published
Mar 30, 2022
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Abstract

Neonatalis is birth before 28 days of a baby. Factors that are considered to affect neonatal mortality include the number of visits in the 1st and 3 rd trimester, the number of pregnant women receiving Tetanus Diptheria Immunization, the estimated number of neonatal infants with complications, the number of infants receiving Hepatitis B Immunization for less than 24 hours, the number of infants receiving BCG Immunization and number of 1 and 3 neonatal visits. Neonatal mortality is still very rare so that the right analysis is used, namely Negative Binomial Regression. This research aim to investigate negative binomial regression in underdipersion on neonatal mortality at Jambi City. These two regression methods are specifically used for Poisson distributed data because they are rare. The stages of the research that will be carried out are the Poisson distribution test and the equidispersion assumption, parameter estimation, model feasibility test, and selection of the best model. The results obtained that the best model without the variable number of 3rd-trimester visits or without the variable number of infants who received BCG immunization with AIC was 36.3.

Keywords

Neonatal Mortality Negative Binomial Regression

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How to Cite
1.
Sormin C, Z G. Negative Binomial Regression in Underdispersion(Case Study: Neonatal Mortality in Jambi City). EKSAKTA [Internet]. 2022Mar.30 [cited 2024Nov.21];23(01):12-7. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/288

References

  1. Mahmud, A., Ekoriano, M., Titisari, A. S., Wijayanti, U. T., Sitorus, M. A., & Rahmadhony, A. (2021). Determinants Of Modern Contraceptives Use In Indonesia: A Spatial Analysis. Systematic Reviews in Pharmacy, 12(3), 769-777.
  2. Utari, D., & Eryando, T. (2018). Meeting the Unmet Need with a Fit Model for Contraception Mix. Indian Journal of Public Health Research and Development, 9(11), 538-543.
  3. Damayati, N., Heldayani, E., & Anggraini, W. (2020). Trends and Fertility Control in South Sumatera Province (Further Analysis of IDHS 2017). Sumatra Journal of Disaster, Geography and Geography Education, 4(1), 85-90.
  4. Utami, F. P. International Journal of Biomedicine and Public Health. The availability of family planning information enable the used of traditional contraceptive in Yogyakarta, 3(3).
  5. Ekawati, R., Rahayuwati, L., & Praptiwi, A. (2020). THE POTENTIAL VARIABLES OF FIRST CHILD’S ENVIRONMENTAL QUALITY: A RETROSPECTIVE ANALYSIS FROM 1994 TO 2012. Indonesian Journal Of Urban And Environmental Technology, 3(2), 209-219.
  6. Spencer, A., Pitt, M., Allen, M., & Mújica‐Mota, R. E. (2019). The heterogeneous effects of neonatal care: a model of endogenous demand for multiple treatment options based on geographical access to care.
  7. Dhrifi, A. (2019). Health-care expenditures, economic growth and infant mortality: Evidence from developed and developing countries. CEPAL Review No. 125, August 2018, 69.
  8. Patel, K. K., & Gouda, J. (2018). Infant mortality in northern and southern regions of India: differentials and determinants. Social Science Spectrum, 3(2), 81-92.
  9. Benshaul-Tolonen, A. (2019). Local industrial shocks and infant mortality. The Economic Journal, 129(620), 1561-1592.
  10. Karlsson, L. (2018). Indigenous infant mortality by age and season of birth, 1800–1899: did season of birth affect children’s chances for survival?. International journal of environmental research and public health, 15(1), 18.
  11. Blankenship, S. A., Brown, K. E., Simon, L. E., Stout, M. J., & Tuuli, M. G. (2020). Antenatal corticosteroids in preterm small-for-gestational age infants: A systematic review and meta-analysis. American Journal of Obstetrics & Gynecology MFM, 100215.
  12. Bois, A., Garcia-Roger, E. M., Hong, E., Hutzler, S., Irannezhad, A., Mannioui, A., ... & Tronche, S. (2019). Infant mortality across species. A global probe of congenital abnormalities. Physica A: Statistical Mechanics and its Applications, 535, 122308.
  13. Lehtonen, L., Gimeno, A., Parra-Llorca, A., & Vento, M. (2017, June). Early neonatal death: a challenge worldwide. In Seminars in Fetal and Neonatal Medicine (Vol. 22, No. 3, pp. 153-160). WB Saunders.
  14. Zhou, M., Wang, H., Zeng, X., Yin, P., Zhu, J., Chen, W., ... & Liang, X. (2019). Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 394(10204), 1145-1158.
  15. Jones, K., Robb, M., Murphy, S., & Davies, A. (2019). New understandings of fathers’ experiences of grief and loss following stillbirth and neonatal death: a scoping review. Midwifery, 79, 102531.
  16. Kokou-Kpolou, K., Megalakaki, O., & Nieuviarts, N. (2018). Persistent depressive and grief symptoms for up to 10 years following perinatal loss: Involvement of negative cognitions. Journal of affective disorders, 241, 360-366.
  17. Zeinalzadeh, A. H., Khodaei, R., & Heidarzadeh, M. (2017). Causes of neonatal mortality in the neonatal intensive care unit of Taleghani Hospital. Iranian Journal of Neonatology IJN, 8(3), 58-61.
  18. Karimi, P., Mahmudi, L., Azami, M., & Badfar, G. (2019). Mortality in neonatal intensive care units in Iran: a systematic review and meta-analysis. Iranian Journal of Neonatology IJN, 10(3), 70-80.
  19. Mangu, C. D., Rumisha, S. F., Lyimo, E. P., Mremi, I. R., Massawe, I. S., Bwana, V. M., ... & Mboera, L. E. (2021). Trends, patterns and cause-specific neonatal mortality in Tanzania: a hospital-based retrospective survey. International Health, 13(4), 334-343.
  20. Baldwin, H. J., Patterson, J. A., Nippita, T. A., Torvaldsen, S., Ibiebele, I., Simpson, J. M., & Ford, J. B. (2017). Maternal and neonatal outcomes following abnormally invasive placenta: a population‐based record linkage study. Acta obstetricia et gynecologica Scandinavica, 96(11), 1373-1381.
  21. Tang, Y. (2018). Sample size for comparing negative binomial rates in noninferiority and equivalence trials with unequal follow-up times. Journal of biopharmaceutical statistics, 28(3), 475-491.
  22. Zou, Y., Ash, J. E., Park, B. J., Lord, D., & Wu, L. (2018). Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. Journal of Applied Statistics, 45(9), 1652-1669.
  23. Ardiles, L. G., Tadano, Y. S., Costa, S., Urbina, V., Capucim, M. N., da Silva, I., ... & Martins, L. D. (2018). Negative Binomial regression model for analysis of the relationship between hospitalization and air pollution. Atmospheric Pollution Research, 9(2), 333-341.
  24. Yu, R., Wang, Y., Quddus, M., & Li, J. (2019). A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data. Analytic methods in accident research, 22, 100092.
  25. Caraka, R. E., Shohaimi, S., Kurniawan, I. D., Herliansyah, R., Budiarto, A., Sari, S. P., & Pardamean, B. (2018). Ecological show cave and wild cave: negative binomial gllvm’s arthropod community modelling. Procedia Computer Science, 135, 377-384.
  26. Yu, R., Wang, Y., Quddus, M., & Li, J. (2019). A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data. Analytic methods in accident research, 22, 100092.
  27. Rusli, R., Haque, M. M., King, M., & Voon, W. S. (2017). Single-vehicle crashes along rural mountainous highways in Malaysia: an application of random parameters negative binomial model. Accident Analysis & Prevention, 102, 153-164.
  28. Khodadadi, A., Tsapakis, I., Das, S., Lord, D., & Li, Y. (2021). Application of different negative binomial parameterizations to develop safety performance functions for non-federal aid system roads. Accident Analysis & Prevention, 156, 106103.
  29. Iqbal, W., Tang, Y. M., Chau, K. Y., Irfan, M., & Mohsin, M. (2021). Nexus between air pollution and NCOV-2019 in China: application of negative binomial regression analysis. Process Safety and Environmental Protection, 150, 557-565.
  30. Shaon, M. R. R., Qin, X., Shirazi, M., Lord, D., & Geedipally, S. R. (2018). Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data. Analytic methods in accident research, 18, 33-44.
  31. Khattak, M. W., Pirdavani, A., De Winne, P., Brijs, T., & De Backer, H. (2021). Estimation of safety performance functions for urban intersections using various functional forms of the negative binomial regression model and a generalized Poisson regression model. Accident Analysis & Prevention, 151, 105964.