International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Forecasting Maternal Deaths in Nigeria Using the Arima Model

Abstract

Nigeria is among the African countries with very high maternal mortality ratios and there is need to utilize early surveillance tools to understand the future trends of maternal deaths so as to put in place preventive and control measures. In this research article, the ARIMA technique was applied to analyze maternal deaths in Nigeria. The employed data covers the period 2000-2017 and the out-of-sample period ranges over the period 2018-2022. The residuals and forecast evaluation criteria (Error, MSE and MAE) of the applied model indicate that the model is stable in forecasting maternal deaths in Nigeria. The results of the study suggest that the Nigerian government is ought to allocate more human and financial resources towards maternal and child health to address gaps which exist in the rural and some of the urban healthcare facilities and continuous  health education among communities to promote institutional deliveries on the other hand discouraging home deliveries.

Country : Zimbabwe

1 Dr. Smartson. P. NYONI2 Thabani NYONI

  1. ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
  2. Department of Economics, University of Zimbabwe, Harare, Zimbabwe

IRJIET, Volume 5, Issue 3, March 2021 pp. 257-263

doi.org/10.47001/IRJIET/2021.503043

Full Paper
Download

References

  1. Adamu YM., Salihu H M., Sathiakumar N., & Alexander GR (2003). Maternal mortality in Northern Nigeria: A population-based study. Eur J Obstet Gynaecol Biol. 109:153-159. 5
  2. Dumont A., Gaye A., De Bernis L., Chaillet N., Landry A &  Delage J (2006). Facility-based maternal death reviews: effects on maternal mortality in a district hospital in Senegal. Bulletin of the World Health Organization | March 2006, (84):3.
  3. Federal Ministry of Health Abuja (2015). National Guidelines for Maternal Perinatal Death Surveillance and Response in Nigeria. 
  4. Fojnica, A., Osmanoviae & Badnjeviae A (2016). Dynamic model of tuberculosis-multiple strain prediction based on artificial neural network. In proceedings of the 2016 5th Mediterranean conference on embedded computing pp290-293.
  5. Hofman J & Mohammed H (2014). Experiences with facility-based maternal death reviews in northern Nigeria. Int. J. Gynecol. Obstet. (126):111–114.
  6. Hussein J., Hirose A., Oluwatoyin O., Mari I., Lovney K & Okonofua F (2016). Maternal death and obstetric care audits in Nigeria: a systematic review of barriers and enabling factors in the provision of emergency care. Reprod. Health. (13):47.
  7. Ibrahim DO (2016). Social-Economic Determinants of Maternal Mortality in Rural Communities of Oyo State, Nigeria. International Journal of Scientific and Research Publications. 6(9):280-5
  8. Josephine Aikpitanyi., Victor Ohenhen., Philip Ugbodaga., Best Ojemhen., Blessing I., Omo-Omorodion., Lorretta FC Ntoimo., Wilson Imongan., Joseph A. Balogun., Friday E.,  & Okonofua (2019) Maternal death review and surveillance: The case of Central Hospital, Benin City, Nigeria PLOS ONE.
  9. Kaushik AC & Sahi. S (2018). Artificial neural network-based model for orphan GPCRs.Neural.Comput.Appl. 29,985-992
  10. Knight H E., Self A., & Kennedy SH (2013). Why are women dying when they reach hospital on time? A systematic review of the ‘third delay’. PLoS One. 8 (5): e63846. ISSN 1932-6203 https://doi.org/10.1371/ journal.pone.0063846 PMID: 23704943.
  11. Lanre-Abass BA (2017). Poverty and maternal mortality in Nigeria: Towards a more viable ethics of modern medical practice. Available:http://www.equityhealth.com/cont ent/7/1/11  (Accessed on 20 May 2020)
  12. Mohammad B., Dwirani A., Stephanie S., Ary K., Mohammad HS & Benjamin E (2019). Maternal death reviews: A retrospective case series of 90 hospital-based maternal deaths in 11 hospitals in Indonesia. Int. J. Gynecol. Obstet.144 (Suppl. 1): 59–64 https://doi.org/10.1002/ijgo.12736 PMID: 30815870.
  13. Mpumelelo M., Tsitsi P., Juru N., Mungati M., Gombe N., & Peter N (2019). Evaluation of the Maternal Death Surveillance and response system in Hwange District, Zimbabwe. BMC Pregnancy and Childbirth.  (19):103.
  14. Naizhuo Zhao., Katia Charland., Mabel Carabali., Elaine O., Nsoesie., Mathieu MaheuGiroux., Erin Rees., Mengru Yuan., Cesar Garcia Balaguera., Gloria Jaramillo Ramirez., & Kate Zinszer (2020). Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLOS Neglected Tropical Diseases.
  15. National Population Commission (NPC) [Nigeria] and ICF Macro. 2009. Nigeria Demographic and Health Survey, 2008. Abuja, Nigeria. National Population Commission and ICTF Macro.
  16. Ntiomo L., Okonofua F E., Gana M., Ogu R., Abdus-salam A & Galadanci H (2018). A. Prevalence and risk factors for maternal mortality in Nigerian referral hospitals. Int J Women’s Health (10): 69–76. https://doi.org/10.2147/IFWH.S151784 PMID: 29440934.
  17. Nyoni & Nyoni (2020). USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING MATERNAL DEATHS AT CHITUNGWIZA CENTRAL HOSPITAL IN ZIMBABWE   INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]   7 (7): 2394-3696.
  18. Nyoni T (2019). Maternal deaths in Zimbabwe: Is it a crime to be a woman in Zimbabwe? Munich Personal RePEc Archive. Online at https://mpra.ub.uni-muenchen.de/96789/ MPRA Paper No. 96789, posted 03 Nov 2019 09:59 UTC.
  19. Nyoni, T (2018b). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40.
  20. Nyoni, T. (2018a). Modeling and Forecasting Naira/USD Exchange Rate in Nigeria: A Box-Jenkins ARIMA Approach, MPRA Paper No. 88622, University Library of Munich, Munich, Germany.
  21. Nyoni, T. (2018c). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, MPRA Paper No. 87737, University Library of Munich, Munich, Germany.
  22. Okonofua F., Imosemi D., Igboin B., Adeyemi A., Chibuko C., Idowu A (2017). Maternal death review and outcomes: An assessment in Lagos State, Nigeria. PLoS ONE 12 (12): e0188392. https://doi.org/ 10.1371/journal.pone.0188392 PMID: 29240754.
  23. Omo-aghoja L O., Aisien O A., Akuse J T., Bergstrom S., Okonofua FE (2010). Maternal mortality and emergency obstetric care in Benin City, South-South Nigeria. J Clin Med Res 2(4):055-060.
  24. Sageer R., Kongnyuy E., Olalekan WA., Omosehin O., Elijah AO, Bola S. Causes and contributory factors of maternal and perinatal deaths surveillance and response in Ogun State, Southwest Nigeria (2019). BMC Pregnancy and Childbirth. (19):63 https://doi.org/10.1186/s12884-019-2202-1 PMID: 30744576. 
  25. Singh K., Puri S & Chopra G (2018). Maternal Mortality in India: An Overview of Social Causes. IJSR. (8):3, ISSN 2250-3153.
  26. Smartson. P. Nyoni, Thabani Nyoni, Tatenda. A. Chihoho (2020) PREDICTION OF DAILY NEW COVID-19 CASES IN GHANA USING ARTIFICIAL NEURAL NETWORKS IJARIIE Vol-6 Issue-6, 2395-4396
  27. Smartson. P. Nyoni., Thabani Nyoni., Tatenda. A. Chihoho (2020).  PREDICTION OF DAILY NEW COVID-19 CASES IN EGYPT USING ARTIFICIAL NEURAL NETWORKS IJARIIE- Vol-6 Issue-6         2395-4396.
  28. Teka H & Yibrah BZ (2018). A 3 years review of maternal death and associated factors at Ayder Comprehensive Specialized Hospital, Northern Ethiopia. Ethiop J Reprod Health. (10):3.
  29. World Bank (2021). World Bank Indicators, World Bank, Washington DC.
  30. World Health Organization (2004). Beyond the numbers: Reviewing maternal deaths and complications to make pregnancy safer. www.who.int/documents (accessed 12th August, 2020).
  31. Yan C Q., Wang R B., Liu C H., Jiang Y (2019). Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018-2019. Zhonghua 40(6):633-637.
  32. Zhang G P, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing 50: 159–175.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies