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
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Abstract |
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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 (9001000 p.m.p.), an average annual growth of 4.56%. 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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Introduction |
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This paper describes the use of a simulation model to estimate such demand. It is an update of an earlier, less powerful model [5] that used parameters from the late 1980s and early 1990s, and predicted demand over the subsequent 10 years. The model predicted a range of results, depending on the assumptions. Those using the highest acceptance rate (87 p.m.p.) and improved survival were consistent with the estimates of stock in England in 2000 [5]. The current model is a user-friendly Windows-based model, which is much faster than the previous model and incorporates more risk factors, live transplants and provides for the analysis transfers between the different modes of dialysis. It incorporates updated national information on transitions, survival, transplant supply and acceptance rates. It is designed to assess the impact on predicted patient numbers of a rising acceptance rate, potential increases in diabetic end-stage renal failure (ESRF), changing cadaveric and live donor organ supply, the provision and allocation of different treatments, and increasing patient survival.
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Subjects and methods |
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In order to take account of age, patients over the age of 16 were divided into 10-year age groups up to age 75, with those older grouped as 75+. Co-morbidity is also important but individual patient co-morbidity data were unavailable and so the presence of diabetes as a cause of ESRF was used as a proxy. A risk group was therefore defined as age group broken down by the presence or absence of diabetes. The parameters used in the model are posted on our website (http://www.som.soton.ac.uk/research/cbcs/hcru/pubs.htm).
Estimated acceptances in year 2000
The starting acceptance patterns were based on the incident data on those patients accepted for RRT in the 25 (40%) centres in England (out of a total number of 63) that were contributing to the UK Renal Registry (UKRR) between 1997 and 2000. To estimate the current acceptance rate in 2000 we used two methods to scale up the data to national levels. The first (Renal Registry based RR) used the estimated catchment populations of the participating units and increased the 2000 acceptances in proportion to the 2000 national population, giving an acceptance rate of 94 p.m.p. with 49% aged over 65 and 17% with diabetic ESRF. The second (Renal Survey based RS), which we believed to be more accurate, used the 1998 National Renal Survey [1], which had 100% response rates from all renal units. We obtained the survey data on number of acceptances, the proportions aged over 65 and the proportions with diabetic ESRF for those units contributing to the UKRR acceptance data in 2000. We then compared the data with figures for those units not contributing to the UKRR. This showed that the proportion of patients aged over 65 was higher for the UKRR than the non-UKRR units at 46 and 36%, respectively. Taking this into account gave a higher starting acceptance rate of 104 p.m.p., with 42% aged over 65 and 17% with diabetic ESRF. We used the RS figures for most estimates but investigated use of RR figures in sensitivity analysis.
Estimated acceptances in year 2010
In estimating the numbers of acceptances by risk group in England for year 2010 we assumed that there would be a linear increase or decrease in the acceptance rate by risk group from year 2000 to year 2010. The calculations were done in three parts: we first derived the estimated national population in 2010 for the ethnic minority (i.e. Indo-Asian and African-Caribbean) and non-ethnic minority groups separately, as there is a different age profile and risk of RRT in the different ethnic groups [2]. We then determined an acceptance rate for each age and ethnic minority group, in 2010 using rates from other countries as estimates of meeting unmet need in England, and giving a proportionately higher rate for ethnic minority groups [2]. Finally, we then multiplied the rates by the population, subdivided by the ethnic and non-ethnic populations and then totalled the acceptances by age group. The acceptances by age group were then subdivided into a diabetic and non-diabetic group, using the proportion of new patients listed as diabetic or non-diabetic by age and ethnicity from UK Renal Registry.
To derive the population projections, we used the Office for National Statistics (ONS) Labour Force Survey [7], which gives an estimate of the UK population by age and ethnic group. We predicted the two populations using all cause mortality data from ONS for 1999. Excluding migrants, we estimated the overall population of England at 2010 to be 50.1 million, compared with an ONS estimated population in 2011 of 51 million [8].
We took account of unmet need for RRT in England by assuming that the age sex acceptance rates in England in 2010 would have increased to the higher rates found in other UK countries, such as Scotland and Wales in 1998, and by applying these higher rates to the estimated 2010 English population. Due to the lower ethnic minority population in Scotland and Wales we did this for the non-ethnic minority population and adjusted the ethnic minority rate in England as above.
Starting stock and the initial mode of therapy by age group and diabetic kidney disease status
A timeline of treatment modes for each patient from the UKRR, including their cause of renal failure, starting mode, changes of mode and their duration were obtained. These were used to ascertain the initial mode of RRT by age and diabetes ESRF for the model population. Age- and diabetes-specific RRT prevalence data from the proportion of units in England in the UKRR at end of 2000 were used to ascertain the characteristics of the starting stock of prevalent patients. Again, two different methods were used to scale up the numbers to represent the whole of England, denoted RR and RS, as for acceptances. The RS starting stock is 33 307 (660 p.m.p.) with 24% over 65 years. The RR starting stock is 29 312 (582 p.m.p.) with 28% over 65 years. Table 1 shows the breakdown of stock by age, diabetic ESRD and mode of treatment.
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Transplant supply and organ allocation
UKT provided data on the numbers of live and cadaver transplants performed in the UK in 2000 by age group. We then used estimates from the UK Transplant Business Case, which detailed the steps being taken to increase the number of organs available over the 5-year period 20002005 [3], to test the effect of three scenarios of the change in transplant supply to 2005. Low transplant assumed no change in the number of transplants (22 p.m.p. cadaver, 5 p.m.p. live), High transplant assumed all of the UKT target average annual increase of 11% over 5 years is achieved (30 p.m.p. cadaver, 13 p.m.p. live), Pragmatic assumed only 90% of the live and 65% of the cadaver target increase would be achieved in this 5-year period.
Some prevalent patients had to be put on the transplant waiting list at the start, all patients in the simulation had to be labelled as being suitable for transplantation or not and those on the transplant waiting list were given one of three priority classifications. The allocations were based on data provided by UKT by age and diabetes status (personal communication Rachel Johnson 2002), including:
Modelling uncertainty
The simulation was run with data for the whole of England for up to 30 years with five replications.
The confidence limits of the estimated numbers on treatment for England in 10 years time, given the expected variability in the acceptance and transplant rate were within plus or minus 2.5% of the total for each mode of treatment (HD, PD and transplanted patients) and within plus or minus 1% for the predicted total number on treatment.
Scenarios
In the Base scenario we assumed that 2010 age and ethnic acceptance rates were based on year 2000 for Wales, taking account of demographic population changes in England. It used RS as baseline incidence and prevalence rates for year 2000 and assumed a pragmatic increase in transplants.
The other scenarios analysed for this paper are:
Table 2 shows the age and diabetic ESRD distributions for the scenarios, which vary the acceptance rate.
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Results |
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Dialysis choice
Increasing the proportion of incident patients starting HD and reducing those starting PD (Choice scenario) increased the overall number on HD by approximately 700, a 4% increase over the standard Base scenario (Figure 4).
Starting position
Changing the initial starting stock from 33 307 (RS) to 29 312 (RR) (RR scenario) and acceptance rates from 104 (RS) to 94 p.m.p. (RR) (Figure 4) reduces the total number on RRT at 2010 by nearly 4000 patients (from 48 170 to 44 500). This significant effect may be partly due to the higher age distribution in the RR scenario.
Steady state
Figure 5 shows the impact of running the model for longer than 10 years using the Base scenario, on the assumption that all parameters stay the same after 2010 (and it therefore does not take account of changes in population, acceptance rates or survival after 2010). RRT growth continues, though at a declining rate, so that by 25 years the total number of RRT is nearly 60 000 patients and beyond that time a steady state appears to be reached. At 25 years the proportion aged >65 years increases to 35%. Even in projections based on current acceptance rates in England the steady state approaches 50 000 patients.
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Discussion |
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It is difficult to be certain about the future acceptance rate or its rate of change. The age specific acceptance rates assumed for 2010 in the Base scenario were similar to rates from Austria (129 p.m.p.), Belgium (144 p.m.p.), Greece (154 p.m.p.), Canada (143 p.m.p.) and Spain (132 p.m.p.) found in 2000 [13]. In view of the apparent levelling off of acceptance rates discussed in the introduction, the UK trends in acceptance rate need to be reviewed over the next few years [14]. Our analysis indicates that it is likely that there is unmet need amongst the current population, and this, along with population factors, which will increase the underlying incidence of ESRD such as the ageing of the ethnic minority population, will give rise to a continuing increase in the acceptance rate. Indeed, most other developed countries, including those with higher acceptance rates than the UK, have seen continuing increases in acceptance rates [15,16]. Although rates in the USA, where acceptance rates are highest, may be levelling off [17]. It is even possible that we have underestimated future growth in acceptance rates. The forthcoming National Service Framework for Renal Services in England will provide an impetus for expansion of RRT [18] to meet the expected future demand.
It is predicted that there will be a substantial rise in the incidence and prevalence of Type 2 diabetes over the coming decades. We have made an attempt at estimating the impact of this epidemic. The diabetic ESRF acceptance rates predicted in 2010 with the 50% increase are similar to the current rates in countries (e.g. New Zealand, Austria) with the highest rates outside of the US. The key question is whether the transition to diabetic ESRF can be prevented or reduced by more effective management. There is good evidence that progression can be reduced by effective control of glycaemia and hypertension and by the use of ACE inhibitors. However, most countries are seeing rises in the diabetic ESRF rate and this coupled with the likely unmet need in diabetics, may on balance prevail and increase the demand for RRT. Despite this, the impact on overall stock was not substantial because those with diabetic ESRF have poorer survival. Furthermore, earlier referral and better pre-ESRD management may delay or even prevent progression to RRT for all types of chronic kidney disease and hence may reduce demand. However, as patients would reach end-stage renal failure in a better clinical and psychological state, the countervailing effect of reducing the risk of death from competing causes and improved survival on RRT may occur. The future trends in acceptance rates and patient survival clearly need to be kept under review to assess these potential effects.
Increasing the transplant supply, as targeted by UKT, has a small effect on overall numbers as there is slightly better patient survival but the main effect is to change the proportion of patients with a transplant, with the proportion on dialysis falling from 58 to 50% if the target is reached. However, the predominant growth will be in HD even with increasing transplant supply.
Despite the increasingly elderly and co-morbid patients being accepted for RRT, there is the possibility that patient survival will continue to improve, partly due to the dissemination of national standards [4] and feedback of audit data to renal units by the UK Renal Registry. However, although there is evidence of improvement over time in some measures of dialysis performance, which might impact on survival, published data from national and international renal registries suggest that survival on dialysis has remained largely stable for some time [19]. We showed that if survival were to improve, it would have a significant effect on overall stock, with the number increasing by approximately 3000 (5%) over our Base scenario. Trends in dialysis survival will be produced by the UKRR and can be taken into account in future modelling. Likewise further work is needed to evaluate the effect of possible changes in transplant survival.
There is a relative lack of HD facilities in certain areas in England at present, possibly causing an artificially high number of patients on PD. An increase in patient choice is expected to increase the take up of HD with respect to PD [12]. If HD facilities expand and we assume a higher HD to PD ratio at the start then we can expect a small change in the long-term balance between HD and PD.
The model used here is an update of an earlier model, which uses newer data on treatment patterns and transitions from national databases UKT and UK Renal Registry, and is able to explore different patterns of dialysis and make more realistic assessments of future acceptance rates. We have had to make several assumptions to simplify the model. To adjust for the increased demand from ethnic minorities we used the age and ethnicity specific relative risks from the 1991/2 National Renal Review [2]. Because the demand estimates are uncertain, we have provided a range of estimates based on different assumptions about unmet need. As co-morbidity data are incomplete in the UK Registry, diabetic ESRF has been used as a proxy for predicting patient survival. The independent effect of diabetes is, however, much weaker in older age groups, the groups in which the most growth is expected. The exact current acceptance and stock rates in England are unknown because the UK Renal Registry only has partial coverage. The UK Renal Registry will be able to provide robust demographic and outcome data as more units are recruited and as follow-up times increase. Such data can be incorporated on a regular basis into the model. We have not separated home and hospital HD, home HD currently being <3% of dialysis pool, and have assumed no change in the timing of initiation of RRT.
There have been a variety of other modelling approaches, such as extrapolation of trends and Markov models, used to predict future RRT demand in various countries [2025]. Our findings are consistent with these, with all predicting continued growth, particularly in the elderly [22]. These include countries with both significantly higher acceptance rates than England (Canada [24], USA [25]) and similar acceptance rates (Denmark [23], Australia [22]). An Australian model [22] showed the beneficial effect of increasing transplant supply on the balance of modes. Growth in RRT was shown to be sensitive to future acceptance rates [22,23]. The advantages of the simulation model, particularly over trend analysis, are that it is possible to take account of risk factors and to independently vary prevalent patient numbers, acceptances, treatment availability and survival probabilities. It also provides an independent source of transplants that can be allocated to prioritized patient groups.
In summary, we have developed a model, which predicts a continued and substantial growth in the national RRT population to over 45 000 patients by 2010, with the steady state position not being reached for over 25 years. This is due to inbuilt growth even at current acceptance rates, increases in acceptance rates from health care policies aimed at better meeting need for RRT, and changes in the epidemiology of ESRD from the rising incidence of Type 2 diabetes and demographic shifts. Transplant supply does have an impact on the dialysis to transplant ratio but, even with optimistic projections of the future supply, still there is likely to be substantial increase in the need for HD delivered to all patient age groups but especially to an elderly co-morbid group of patients. Funding for RRT provision will have to grow to meet demand.
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Acknowledgments |
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Conflict of interest statement. None declared.
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References |
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