Time Series Analysis and Forecasting of Caesarian Section Births in Ghana
Biomedical Statistics and Informatics
Volume 4, Issue 1, March 2019, Pages: 1-9
Received: Jun. 24, 2019;
Accepted: Jul. 12, 2019;
Published: Jul. 30, 2019
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Bosson-Amedenu Senyefia, Department of Mathematics and ICT, Holy Child College of Education, Takoradi, Ghana
Otoo Joseph, Department of Statistics and Actuarial Science, University of Ghana, Legon, Greater Accra, Ghana
Eyiah-Bediako Francis, Department of Statistics, University of Cape Coast, Cape Coast, Ghana
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Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana’s situation (regional trend) and also to provide a two- year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each region and the model with the lowest AIC was selected as the “Best” model fit for respective regions of Ghana. The “best” Model fit for Greater Accra, Central and Eastern regions were respectively SARIMA (2, 0, 0) (0, 1, 1)12, SARIMA (2, 0, 0) (0, 1, 1)12 with a Drift and SARIMA (1, 1, 1) (0, 1, 1)12. Additionally, the best model fit for Northern and Volta regions were SARIMA (3,0,2) (0,1,1)12 with drift and SARIMA (0,1,1) (0,1,1)12. Ashanti, Upper East and Western regions failed the JB test or the normality test for the residuals. Upper West and Brong Ahafo Regions were not suitable for forecasting due failure to depict white noise and ARCH test failure, respectively. The best models fit were used to forecast for 2019 and 2020. The results showed that regional variations of CS exist in Ghana. The study recommended for future studies to apply methods that will allow for forecasting for regions which failed the test under the methods used in this study.
Forecasting, Unit Root Test, Time Series, Caesarian Section, Box Jenkins, Stationarity, Ghana
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Time Series Analysis and Forecasting of Caesarian Section Births in Ghana, Biomedical Statistics and Informatics.
Vol. 4, No. 1,
2019, pp. 1-9.
Copyright © 2019 Authors retain the copyright of this article.
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Heibrum J. Z, Park R. E (1995). Variations in use of Caesarian sections. Published by RAND1 Main Street, Santa Monica.
Declercq E., Cunningham D. K, Johnson C, and Sakala C., (2008). Mothers’ Reports of Postpartum Pain Associated with Vaginal and Cesarean Deliveries: Results of a National Survey. Accepted July 16, 2007, copyright the Authors Journal compilation. Blackwell Publishing, Inc.
Korb D., Goffinet F. (2019). Risk of severe maternal associated with caesarian delivery and the role of maternal age: A populatin based propensity score analysis. CMAJ. DOI: https://doi.org/10.1503/cmaj.181067
Lauer J. A, Betrán A. P, Mario M. and Wojdyla D. (2010). Determinants of caesarean section rates in developed countries: supply, demand and opportunities for control. World Health Report (2010). Background Paper, 29.
Keller E. B, Brodie M. (1993) Economic Incentives in the choice of Vaginal and cesarean Section. Vol. 71, N. 3 (1993), pp. 365-404. Published by Wiley. DOI: 10.2307/3350407.
Onwuka G. I., Babayemi A. W. (2013). Model for the Forecasting of Monthly Normal Child-Birth Delivery in Hospitals. International Journal of Engineering Sciences, 2 (4) April 2013, Pages: 100-109.
Essuman R, Nortey E. N. N, Aryee G., Osei-Asibey E. Owusu E., Djagbletey R (2017). Modeling births at a tertiary health-care facility in Ghana: Box-Jenkins time series approach. Journal of Biostatistics and Epidemiology. J Biostat Epidemiol. 2017; 3 (1): 13-9.
Shitan M. and Yung L. N (2015). Forecasting the Total Fertility Rate In Malaysia. Pak. J. Statist. 2015 Vol. 31 (5), 547-556.
Yanovitsky, I., & Van Lear, A. (2008). Time series analysis: Traditional and contemporary approaches. The Sage sourcebook of advanced data analysis methods for communication research, 89-194.
Dickey A. and Fuller W (1981). The likelihood Ratio for Autoregressive Times Serieswith a Unit Root. Econometrica 49 (4): 1057-22. DOI: 10.2307/1912517.
Hanke J. E and R Eitsch A. G (1990). Understanding Business Statistics. ISBN 0-256-06627-2. Hoffmann Press, Inc.
TuranKatircioglu, S., MeteFeridun, & Kilinc, C. (2014). Estimating tourism- induced energy consumption and CO2 emissions: The case of Cyprus. Renewable and Sustainable Energy Reviews, 634-640.
Wickham, H., & Bryan, J. (2019). readxl: Read Excel Files. R package version 1.3.1.
Jafari, Y., Othman, J., & Nor, A. H. (2012). Energy consumption, economic growth and environmental pollutants in Indonesia. Journal of Policy Modeling, 879-889.
Ryan, J. A., & Ulrich, J. M. (2018). xts: eXtensible Time Series. R package version 0.11-2.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Trapletti, A., & Hornik, K. (2018). Time Series Analysis and Computational Finance. R package version 20. 10-46.