Advertisement

Analysis of Evolutionary Trends of Incidence and Mortality by Cancers

  • Hajar Saoud
  • Abderrahim Ghadi
  • Mohamed Ghailani
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

Cancer has become the disease of the century, it knew a great evolution in recent years and it reaches several patients each year, it becomes necessary to find solutions to fight against this disease. Then our thesis comes in this direction, it will provide an analysis of the evolutionary trends of incidence and mortality by cancers in Morocco over a period of time.

This paper presents the state of art of the existing methods of analysis, projection and prediction of incidence and mortality by cancers.

At first, we will give a vision of the research carried out by the ministry of health that can be considered as the starting point of our subject. Then we will explain the three projection models and we will compare the existing prediction methods: Classical approach and Bayesian approach.

Also we will give a vision about the Material and methods that we will use.

Keywords

Cancer Incidence Mortality Projection Bayesian method Forecasting 

References

  1. 1.
  2. 2.
  3. 3.
    Maaroufi, Y.: Projections de la population totale par groupe d’âge et sexe (en milliers et au milieu de l’année): 1960-2050. http://www.hcp.ma/Projections-de-la-population-totale-par-groupe-d-age-et-sexe-en-milliers-et-au-milieu-de-l-annee-1960-2050_a676.html
  4. 4.
    Registre des cancers de la Région du Grand Casablanca: Année e (2004). www.contrelecancer.ma/fr/documents/registre-des-cancers-de-la-region-du-grand-casabla/
  5. 5.
    Registre des cancers de la Région du Grand Casablanca 2005–2006–2007. http://www.contrelecancer.ma/site_media/uploaded_files/RCRC_-_28_mai_2012.pdf
  6. 6.
    Registre des cancers de la Region du Grand Casablanca pour la période 2008–2012. http://www.contrelecancer.ma/site_media/uploaded_files/RCRGC.pdf
  7. 7.
    Registre des Cancers de Rabat (2005). http://biblio.medramo.ac.ma/bib/RECRAB_2005.pdf
  8. 8.
  9. 9.
    Clayton, D., Schifflers, E.: Models for temporal variation in cancer rates. I: age–period and age–cohort models. Stat. Med. 6(4), 449–467 (1987)CrossRefGoogle Scholar
  10. 10.
    Clayton, D., Schifflers, E.: Models for temporal variation in cancer rates. II: Age-period-cohort models. Stat. Med. 6(4), 469–481 (1987)CrossRefGoogle Scholar
  11. 11.
    Mouchart, M.L.: Inference bayesienne: principles generaux. In: Droesbecke, J., Fine, J., Saporta, G. (eds.) Methodes bayesiennes en statistique, pp. 101–102. Technip, Paris (2002)Google Scholar
  12. 12.
    Eilstein, D., Uhry, Z., Chérié-Challine, L., Isnard, H.: Mortalité par cancer du poumon chez les femmes françaises. Analyse de tendance et projection à l’aide d’un modèle âge-cohorte bayésien, de 1975 à 2014. Rev. Dépidémiologie Santé Publique 53(2), 167–181 (2005)CrossRefGoogle Scholar
  13. 13.
    Eilstein, D., Uhry, Z., Cherie-Challine, I., Isnard, H.: Mortalite par cancer du poumon chez les femmes en France, analyse de tendance et projection de 1975 a 2019Google Scholar
  14. 14.
    Bouée, S., Grosclaude, P., Alfonsi, A., Florentin, V., Clavel-Chapelon, F., Fagnani, F.: Projection de l’incidence du cancer du sein en 2018 en France. Bull. Cancer (Paris) 97(3), 293–299 (2010)Google Scholar
  15. 15.
    Eilstein, D., Uhry, Z., Chérié-Challine, L., Bloch, J.: Mortalité par cancer du poumon en France métropolitaine Analyse de tendance et projection de 1975 à 2014. In: Tavel, P. (ed.) Modeling and Simulation Design. AK Peters Ltd., Natick (2007). H. I. I. de veille sanitaireGoogle Scholar
  16. 16.
    Eilstein, D., Quoix, E., Hédelin, G.: Incidence du cancer du poumon dans le Bas-Rhin : tendance et projections en 2014. Rev. Mal. Respir. 23(2), 117–125 (2006)CrossRefGoogle Scholar
  17. 17.
    McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman and Hall, London (1989)CrossRefzbMATHGoogle Scholar
  18. 18.
    Osmond, C.: Using age, period and cohort models to estimate future mortality rates. Int. J. Epidemiol. 14(1), 124–129 (1985)CrossRefGoogle Scholar
  19. 19.
    Bray, I.: Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality. Appl. Stat. 51, 151–164 (2002)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Spiegelhalter, D., Thomas, A., Best, N., Gilks, W.: Bugs 0.5 bayesian inference using Gibbs sampling manual (version ii) (1996)Google Scholar
  21. 21.
    Negri, E., La Vecchia, C., Levi, F., Randriamharisoa, A., Boyle, P.: The application of age, period and cohort models to predict Swiss cancer mortality. J. Cancer Res. Clin. Oncol. 166, 207–214 (1990)CrossRefGoogle Scholar
  22. 22.
    Lejeune, M., Sprta, G., Droesbeke, J.: Modèles statiques pour données qualitatives (2005)Google Scholar
  23. 23.
    Berzuini, C., Clayton, D.: Bayesian analysis of survival on multiple time scals. Statist. Med. 13, 823–838 (1994)CrossRefGoogle Scholar
  24. 24.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Hajar Saoud
    • 1
  • Abderrahim Ghadi
    • 1
  • Mohamed Ghailani
    • 2
  1. 1.LIST LaboratoryUniversity of Abdelmalek Essaadi (UAE)TangierMorocco
  2. 2.LabTIC LaboratoryUniversity of Abdelmalek Essaadi (UAE)TangierMorocco

Personalised recommendations