Trend Analysis Forecast of Life Expectancy at Birth in Nigeria

I.M. Ogolo¹

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.4 , pp.33-40, Aug-2022

Online published on Aug 31, 2022

Copyright © I.M. Ogolo . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The current study aims to examine trends in Nigeria`s birth life expectancy. Four different trend models of time series, including linear, quadratic, exponential, and s-curve trend models, were fitted to examples of life expectancy at birth in Nigeria (annual data set from 1950 to 2021). The best trend model was then chosen using model accuracy metrics like Mean Absolute Percentage (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD). The results showed that in all three model accuracy approaches examined, the quadratic and s-curve trend models compete favorably. On the other hand, the s-curve trend model had the lowest MAD and MAPE and best fit the data. A six-year projection of cases of life expectancy at birth in Nigeria was created using the s-curve trend model, and it showed that cases of life expectancy at birth in Nigeria will rise (grow) over the following six years, from 2022 to 2027. We urge the government to take charge of enhancing the delivery of products and services, including the establishment of a health care infrastructure, the training of health professionals, immunization programs, and other preventative health care initiatives.

Key-Words / Index Term :
Trend Models, Forecast, Life Expectancy, Birth, Time Series Analysis.

References :
[1] A. Galor, H.S. Moay, “Infant and child mortality: Levels trend and data deficiencies,”. Disease and Mortality in Sub-Shraran, Africa, pp. 31-42, 2005.
[2] B. Chen, Y. Hong, “Testing for smooth structural changes in time series models via nonparametric regression,”. Econoometric, Vol. 80, Issue. 3, pp.1157-1183, 2001.
[3] T.O. Hasegama, “Average and healthy life expectancies and self-rated health in the European Country”. China Prefectural University of Health Science, Vol.51, Issue.2, pp.144-150, 2014.
[4] L.V. Hoi, H.O. Phuc, T.V. Dung, N.T. Chuc, L. Lindholm, “Remaining life expectancy among older people in rural area socio-economic inequalities during a Period of Multiple Transitions,”. BMC Public Health. Vol.9, p.471, 2009.
[5] L. Kyte, C. Wells, “Variation in life expectancy between rural and urban areas of England,”. Health Stat Q. Vol.46, pp.25-50, 2010.
[6] R.M. Chetty, S.A. Abraham, S. Lin, B. Scuderi, N. Turner, A. Bergeron, D. Cutler, “The association between income and life expectancy in the United State, 2001-2014” JAMA. Vol.315, Issue.16, 1pp.750-766, 2016.
[7] H. Waldron, “Trends in mortality differentials and life expectancy for male and social security-covered workers by soci-economic status. Soc Secur Bull. Vol.67, Issue.3, pp.1-28, 2007.
[8] D.J. Strauss, P.J. Achon, R.M. Shavelle, “Estimation of future mortality rates and life expectancy in chronic medical conditions,”. J Insur Med. Vol.37, Issue.1, pp.20-34, 2005.
[9] D. Lai, R.J. Hardy, S.P. Tsai, “Statistical analysis of the standardized mortality ratio and life expectancy,”. School of Public Health, University of Texas. Am j Epidemiol. Vol.143, Issue.8, pp.832-840, 1996.
[10] L. Nkpordee, N. Wonu, “Application of time series analysis on the forecasting of the outbreak of malaria epidemic in Nigeria”. International Journal of Statistics and Applied Mathematics; Vol.3, Issue.6, pp.98-105, 2018.