title: Simulation model of renal replacement therapy: predicting future demand in England.
abstract: BACKGROUND
  The demand for renal replacement therapy (RRT) in England has risen steadily, although from a lower base than many other developed countries. Predicting the future demand for RRT and the impact of factors such as the acceptance rate, transplant supply and patient survival, is required in order to inform the planning of such services.
  
  
  METHODS
  A discrete event simulation model estimates the future demand for RRT in England in 2010 for a range of scenarios. The model uses current prevalence and current and projected future acceptance rates, survival rates and the transitions between modalities to predict future patient numbers. National population and mortality data, published literature and data from the UK Renal Registry and UK Transplant, are used to estimate unmet need for RRT, the impact of changing demography and incidence of Type 2 diabetes, patient haemodialysis (HD) survival and transplant supply.
  
  
  RESULTS
  By 2010 the predicted prevalence will have increased from about 30,000 in 2000 to between 42 and 51,000 (900-1000 p.m.p.), an average annual growth of 4.5-6%. Changing transplant supply has a small effect on overall numbers but changes the proportion of patients with functioning graft by up to 8%. Even with an optimistic increase in transplant supply (11% p.a. for 5 years), numbers on HD will continue to rise substantially, especially in the elderly. The factors most influencing future patient numbers are the acceptance rate and dialysis survival.
  
  
  CONCLUSIONS
  This model predicts a substantial growth in the RRT population to 2010 to a rate approaching 1000 p.m.p., particularly in the elderly and those on HD, with a steady state not being reached for at least 25 years.
authors.affiliation: Health Care Research Unit, Southampton University, UK. pjr@soton.ac.uk
authors.forename: Paul
authors.initials: P
authors.lastname: Roderick
authors.forename: Ruth
authors.initials: R
authors.lastname: Davies
authors.forename: Chris
authors.initials: C
authors.lastname: Jones
authors.forename: Terry
authors.initials: T
authors.lastname: Feest
authors.forename: Steve
authors.initials: S
authors.lastname: Smith
authors.forename: Ken
authors.initials: K
authors.lastname: Farrington
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003198:Computer Simulation; D004739:England / Q000453:epidemiology; D006301:Health Services Needs and Demand / Q000639:trends*; D006801:Humans; D007676:Kidney Failure, Chronic / Q000453:epidemiology / Q000209:etiology / Q000628:therapy; D008875:Middle Aged; D008954:Models, Biological; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data; D017582:Renal Replacement Therapy / Q000706:statistics & numerical data*; D012307:Risk Factors; D015996:Survival Rate / Q000639:trends; D014019:Tissue Donors / Q000600:supply & distribution