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Statistical Modelling and Forecasting of Cervix Cancer Cases in Radiation Oncology Treatment: A Hospital Based Study from Western Nepal

  • Sathian, Brijesh (Department of Community Medicine, Manipal College of Medical Sciences) ;
  • Fazil, Abul (Department of General Medicine, Kasturba Medical College, Manipal University) ;
  • Sreedharan, Jayadevan (Research Division, Gulf Medical University) ;
  • Pant, Sadip (Department of Internal Medicine, University of Arkansas for Medical Sciences) ;
  • Kakria, Anjali (Department of Radiotherapy and Oncology, Kasturba Medical College, Manipal University) ;
  • Sharan, Krishna (Department of Radiotherapy and Oncology, Kasturba Medical College, Manipal University) ;
  • Rajesh, E. (School of Behavioural Sciences, Mahatma Gandhi University) ;
  • Vishrutha, K.V. (Department of Physiology, Srinivas Institute of Medical Sciences and Research Centre) ;
  • Shetty, Soumya B. (Department of Community Medicine, Srinivas Institute of Medical Sciences and Research Centre) ;
  • Shahnavaz, Shameema (Department of Physiology, Kasturba Medical College, Manipal University) ;
  • Rao, Jyothi H. (Department of Obstetrics and Gynaecology, Srinivas Institute of Medical Sciences and Research Centre) ;
  • Marakala, Vijaya (Department of Biochemistry, Srinivas Institute of Medical Sciences and Research Centre)
  • Published : 2013.03.30

Abstract

Background: To estimate the numbers and trends in cervix cancer cases visiting the Radiotherapy Department at Manipal Teaching Hospital, Pokhara, Nepal, statistical modelling from retrospective data was applied. Materials and Methods: A retrospective study was carried out on data for a total of 159 patients treated for cervix cancer at Manipal Teaching Hospital, Pokhara, Nepal, between $28^{th}$ September 2000 and $31^{st}$ December 2008. Theoretical statistics were used for statistical modelling and forecasting. Results: Using curve fitting method, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power and Exponential growth models were validated. Including the constant term, none of the models fit the data well. Excluding the constant term, the cubic model demonstrated the best fit, with $R^2$=0.871 (p=0.004). In 2008, the observed and estimated numbers of cases were same (12). According to our model, 273 patients with cervical cancer are expected to visit the hospital in 2015. Conclusions: Our data predict a significant increase in cervical cancer cases in this region in the near future. This observation suggests the need for more focus and resource allocation on cervical cancer screening and treatment.

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