Markov Chain Monte Carlo simulation for projection of end stage renal disease patients in Greece
- 作者：A. Rodina-Theocharaki ; arodina@upatras.gr ; K. Bliznakova ; N. Pallikarakis
- 关键词：End stage renal disease ; Markov Chain ; Monte Carlo simulation
- 刊名：Computer Methods and Programs in Biomedicine
- 出版年：2012
- 期刊代码：68_01692607
- 类别：med
- 出版时间：July, 2012
- 卷：107
- 期：1
- 页码：90-96
- 文件大小：739 K
摘要
End stage renal disease (ESRD) treatment methods are considered to be among the most expensive procedures for chronic conditions worldwide which also have severe impact on patients鈥?quality of life. During the last decade, Greece has been among the countries with the highest incidence and prevalence, while at the same time with the lowest kidney transplantation rates. Predicting future patients鈥?number on Renal Replacement Therapy (RRT) is essential for health care providers in order to achieve more effective resource management. In this study a Markov Chain Monte Carlo (MCMC) simulation is presented for predicting the future number of ESRD patients for the period 2009-2020 in Greece. The MCMC model comprises Monte Carlo sampling techniques applied on probability distributions of the constructed Markov Chain. The model predicts that there will be 15,147 prevalent patients on RRT in Greece by 2020. Additionally, a cost-effectiveness analysis was performed on a scenario of gradually reducing the hemodialysis patients in favor of increasing the transplantation number by 2020. The proposed scenario showed net savings of 86.54 million Euros for the period 2009-2020 compared to the base-case prediction.