Forecasting mortality patterns of thalassaemia major patients in Iraq by using VAR model and reasons for this mortality
DOI:
https://doi.org/10.24297/jam.v12i11.18Keywords:
Forecasting, mortality for thalassemia, model (VAR).Abstract
The vector autoregression model (VAR) is a natural extension of the univariate autoregressive model dynamic multivariable time series. It is one of the most successful, flexible, and easy to use models for the analysis of multivariable time series. The VAR model has proved to be particularly useful describing the dynamic behaviour of economic and financial time series and forecasting. Often it provides superior forecasts to those of time-series models and univariate and detailed forecasts, based on the theory of simultaneous equation models. Expectations of VAR models are very flexible because they can be conditioned on possible paths for the future in the form of specific variables. In addition to describing the data and forecasting, the VAR model is used to deduce structural and policy analysis. This study used the VAR model for forecasting the number of deaths in patients with thalassemia in Maysan province in southern Iraq, and also addressed the causes of these deaths. There was a strong relationship between mortality in thalassemia patients and an increase in the proportion of iron and the highest number of deaths was recorded for patients who had a very high proportion of iron. It was „the most important cause of mortality (Cardiac disease, infections, the liver, the spleen).Downloads
Download data is not yet available.
References
1. Aessopos A, Farmakis D, Hatziliami A, Fragodimitri C, Karabatsos F, Joussef J, et al: “Cardiac status in welltreated
patients with thalassemia majorâ€, Eur J Haematol. 2004; 73: 359-366.
2. Bera A K, and Jarque C M: “An efficient large Sample test for normality of observations and regression
residualsâ€,Working paper in Econometrics No 40,Australion National University, Canberra; 1982.
3. Cromwell J B Hannan M J, Labys W C and Terraza M: Multivariate Tests for Time Series Models. SAGE
Publications, Inc California; 1994. pp. 73-75.
4. Dickey D. and Fuller W: “The likelihood ratio statistics for autoregressive time series with a unit rootâ€,
Econometrica, 1981; 1057-1072
5. Dickey D. and Fuller W “Distribution of the estimators for autoregressive time series with a unit rootâ€, Journal of
the American Statistical Association, 1979; 74: 427- 431.
6. Dickey and Fuller W A “Distribution of the estimators for autoregressive time series with a Unit Rootâ€, Journal of the American Statistical Association, 1979; 74 (366): 427–431.doi:10.2307/2286348. JSTOR 2286348.
7. Galanello R Origa R: “Beta-thalassemiaâ€, Orphanet Journal of Rare Disease 2010; 5.(11): 1750-1172 (electronic).
8. Hacker R S and Hatemi-J A: “Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCHâ€, Journal of Applied Statistics 2008; 35(6): 601– 615.
9. Henin P Y: Bilans et essais sur la non-stationnarite des series macroeconomiques revue d' economie politique; 1989; 5: 661-691.
10. Kirchgassner G and Wolters J: Introduction to Modern Time Series Analysis. springer-Verlag, Berlin Heidelberg;2007; pp. 13-14.
11. Lal A, Porter J, Sweeters N, Ng V, Evans P, Neumayr L, et al: “Combined chelation therapy with deferasirox and deferoxamine in thalassemiaâ€,. Blood Cells Mol Dis. 2013; 50; 99-104.
12. Lardic S et Mignon V: Econometrie des Series Temporelles Macroeconomiques et Financieres. Ed. Economica. Paris; 2002 pp. 97.
13. Ljung G M, and Box G E P: “on a measure of the lack of fit in time Series modelsâ€, Biometrika, 1978; 65: 297-303.
14. Prati D: “Benefits and complications of regular blood transfusion in patients with beta thalassaemia majorâ€, Vox Sang. 2000; 79; 129-137.
15. Quenouille MH: The Analysis of Multiple Time-Series. Griffin, London, 1957.
16. Rund D and Rachmilewitz E: “Beta-thalassemiaâ€, N Engl J Med. 2005; 353: 1135-1146.
17. Shander A, Cappellini MD and Goodnough LT: “Iron overload and toxicity: the hidden risk of multiple blood transfusionsâ€, Vox Sang 2009; 97; 185-197.
18. Shumway RH and Stoffer D S: Time Series Analysis and its Applications Springer, New York; 2006; pp. 303-304.
19. Sims C A: “Macroeconomics and Realityâ€, Econometrica, 1981; 48: 1- 48.
20. Toumba M, Sergis A, kanaris C and Skordis N: “Endocrine complications in patients with thalassemia majorâ€, Pediatr Endocrinol rev, 2007;5: 642-648.
21. Wetherill DJ and Clegg JB: The Thalassaemia Syndromes. Blackwell Science 2001.
22. Wood JC, Origa R, Agus A, Matta G, Coates TD and Galanello R: “Onset of cardiac iron loading in pediatric patients with thalassemia majorâ€, Haematologica, 2008; 93(6) 917 – 920. doi:10.3324/haematol.12513
patients with thalassemia majorâ€, Eur J Haematol. 2004; 73: 359-366.
2. Bera A K, and Jarque C M: “An efficient large Sample test for normality of observations and regression
residualsâ€,Working paper in Econometrics No 40,Australion National University, Canberra; 1982.
3. Cromwell J B Hannan M J, Labys W C and Terraza M: Multivariate Tests for Time Series Models. SAGE
Publications, Inc California; 1994. pp. 73-75.
4. Dickey D. and Fuller W: “The likelihood ratio statistics for autoregressive time series with a unit rootâ€,
Econometrica, 1981; 1057-1072
5. Dickey D. and Fuller W “Distribution of the estimators for autoregressive time series with a unit rootâ€, Journal of
the American Statistical Association, 1979; 74: 427- 431.
6. Dickey and Fuller W A “Distribution of the estimators for autoregressive time series with a Unit Rootâ€, Journal of the American Statistical Association, 1979; 74 (366): 427–431.doi:10.2307/2286348. JSTOR 2286348.
7. Galanello R Origa R: “Beta-thalassemiaâ€, Orphanet Journal of Rare Disease 2010; 5.(11): 1750-1172 (electronic).
8. Hacker R S and Hatemi-J A: “Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCHâ€, Journal of Applied Statistics 2008; 35(6): 601– 615.
9. Henin P Y: Bilans et essais sur la non-stationnarite des series macroeconomiques revue d' economie politique; 1989; 5: 661-691.
10. Kirchgassner G and Wolters J: Introduction to Modern Time Series Analysis. springer-Verlag, Berlin Heidelberg;2007; pp. 13-14.
11. Lal A, Porter J, Sweeters N, Ng V, Evans P, Neumayr L, et al: “Combined chelation therapy with deferasirox and deferoxamine in thalassemiaâ€,. Blood Cells Mol Dis. 2013; 50; 99-104.
12. Lardic S et Mignon V: Econometrie des Series Temporelles Macroeconomiques et Financieres. Ed. Economica. Paris; 2002 pp. 97.
13. Ljung G M, and Box G E P: “on a measure of the lack of fit in time Series modelsâ€, Biometrika, 1978; 65: 297-303.
14. Prati D: “Benefits and complications of regular blood transfusion in patients with beta thalassaemia majorâ€, Vox Sang. 2000; 79; 129-137.
15. Quenouille MH: The Analysis of Multiple Time-Series. Griffin, London, 1957.
16. Rund D and Rachmilewitz E: “Beta-thalassemiaâ€, N Engl J Med. 2005; 353: 1135-1146.
17. Shander A, Cappellini MD and Goodnough LT: “Iron overload and toxicity: the hidden risk of multiple blood transfusionsâ€, Vox Sang 2009; 97; 185-197.
18. Shumway RH and Stoffer D S: Time Series Analysis and its Applications Springer, New York; 2006; pp. 303-304.
19. Sims C A: “Macroeconomics and Realityâ€, Econometrica, 1981; 48: 1- 48.
20. Toumba M, Sergis A, kanaris C and Skordis N: “Endocrine complications in patients with thalassemia majorâ€, Pediatr Endocrinol rev, 2007;5: 642-648.
21. Wetherill DJ and Clegg JB: The Thalassaemia Syndromes. Blackwell Science 2001.
22. Wood JC, Origa R, Agus A, Matta G, Coates TD and Galanello R: “Onset of cardiac iron loading in pediatric patients with thalassemia majorâ€, Haematologica, 2008; 93(6) 917 – 920. doi:10.3324/haematol.12513
Downloads
Published
2016-12-30
How to Cite
Alsudani, R. S. A., Liu, J., & Salman, Z. I. (2016). Forecasting mortality patterns of thalassaemia major patients in Iraq by using VAR model and reasons for this mortality. JOURNAL OF ADVANCES IN MATHEMATICS, 12(11), 6785–6798. https://doi.org/10.24297/jam.v12i11.18
Issue
Section
Articles
License
All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.
