Nephrol Dial Transplant (2004) 19: 692-701
Nephrol Dial Transplant Vol. 19 No. 3 (c) ERA-EDTA 2004; all rights reserved
Original Article
Simulation model of renal replacement therapy: predicting future demand in England
1Health Care Research Unit, Southampton University, 2School of Management, Southampton University, 3Richard Bright Renal Unit, Southmead Hospital, Bristol, Birmingham Heartlands Hospital, Birmingham and 5North Herts Hospital Trust, Stevenage, UK
Correspondence and offprint requests to: Dr Paul Roderick, Health Care Research Unit, Level B (805), South Academic Block, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, UK. Email: pjr{at}soton.ac.uk
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.
Conclusion. 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.
Keywords: demand; renal replacement therapy; simulation model
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