Forecasting Maternal Mortality Ratio of India based on the Impact of Gross National Income
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Abstract
Background: Many women in reproductive age-span succumb to complications during and following pregnancy and childbirth or abortion. Gross National Income affects the MMR by directly relating to the social factors. Previous and projected reductions in maternal mortality of India and the impact of gross national income were not examined. The present study plans to compare maternal mortality of previous years and forecast the next 5 years of the same mortality based on the influence of Gross National Income by using Double Exponential Smoothing Model. Method: In this study, secondary data on the Maternal Mortality Ratio of India from 2001-03 to 2018-20 have been compiled from the Sample Registration System. Also, gross national income record from 1990 to 2020 data was mined from World Bank national accounts and OECD National Accounts data files. The time series approach Double Exponential Smoothing model was applied for forecasting. Minitab version 22 was used in the analysis. Results: The results revealed that MMR of India was statistically different in past years and there was a significant negative correlation between MMR and GNI (-0.95, p-value = 0.01). All Results are significant at p<0.05. A double exponential smoothing model (DESM) was fitted for the best forecast with MAPE (%) = 2.47. Conclusions: GNI had an impact on lowering maternal mortality, which caused the predicted maternal mortality to steadily decline. The Indian government should use GNI to reduce MMs at a high level, support pregnant women, and improve the health care system.
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Copyright (c) 2025 Divya Sharma, Dr. Pooja Soni, Dr. Utkarsh Khare, Dr. Ajit Singh Solanki

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[1] Hanke, J. E., & Wichern, D. (2015). Business Forecasting (9th ed., p. 363). Noida, India: Pearson Education, Inc.
[2] Kasapoglu, O.A. (2016). Selection of the Forecasting Model in Health Care. Journal of Hospital & Medical Management, 2(2:13), 1-13.
[3] Holt, C. C. (2004). Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages. International Journal of Forecasting, 20, 5-10
[4] Bulletin on maternal mortality (no date) SRS - Maternal Mortality Bulletin | Government of India. Available at: https://censusindia.gov.in/census.website/data/SRSMMB (Accessed: 17 February 2025).
[5] MMR of India declined from 384 in 2000 to 103 in 2020: UN Mmeig 2020 report (no date) Press Information Bureau. Available at: https://www.pib.gov.in/PressReleasePage.aspx?PRID=2003432 (Accessed: 17 February 2025).
[6] SAMPLE REGISTRATION SYSTEM (SRS)-BULLETIN 2020 VOLUME 55-I (2020) Office of the Registrar General & Census Commissioner, India (ORGI) Available at: https://censusindia.gov.in/nada/index.php/catalog/42687 (Accessed: 17 February 2025).
[7] Todaro, Michael P.; Smith, Stephen C. (2012). Economic development (11 ed.). Addison-Wesley. ISBN 978-0-13-801388-2.
[8] Meh C, Sharma A, Ram U, Fadel S, Correa N, Snelgrove JW, Shah P, Begum R, Shah M, Hana T, Fu SH, Raveendran L, Mishra B, Jha P. Trends in maternal mortality in India over two decades in nationally representative surveys. BJOG. 2022 Mar;129(4):550-561. doi: 10.1111/1471-0528.16888. Epub 2021 Sep 15. PMID: 34455679; PMCID: PMC9292773.
[9] Smartson. P. NYONI, Thabani NYONI, “Utilizing Holt’s Double Exponential Smoothing Technique to Draft Effective Adolescent Health Policies to Address High Teenage Pregnancy Rates and Child Births in Niger” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 2, pp 263-268, February 2023.
[10] Lewis, C. D. (1982). Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. London, UK: Butterworth Scientific.
[11] Pangestu, A. I., & Andayani, P. (2023). Implementation of Holt-Winter Exponential Smoothing Method to Forecast the Spread of Covid-19. Indonesian Journal of Mathematics and Applications, 1(2), 13-24.
[12] Pierre. J, & Mugabushaka (2020). Forecasting Maternal Complications Based on the Impact of Gross National Income Using Various Models for Rwanda Journal of Environmental and Public Health Volume 2020, Article ID 7692428, 8 pages.
[13] Sharma, D., Solanki, A.S., Soni, P., Khare, U. (2024). Inter-State Disparities in Maternal Mortality Ratio in India-Two Decade Analysis. In: Sharma, N., Goje, A.C., Chakrabarti, A., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2024. Lecture Notes in Networks and Systems, vol 998. Springer, Singapore. 443-458 page.
[14] M. DerSarkissian, C. A. ompson, and O. A. Arah, “Time series analysis of maternal mortality in Africa from 1990 to 2005,” Epidemiology Community Health, vol. 67, no.12, 2013.