Variation in cadaveric organ donor rates in the UK

Jeremy Wight1, Michael Jakubovic2, Stephen Walters1, Ravi Maheswaran3, Paul White3 and Veronica Lennon4

1School of Health and Related Research (ScHARR) and 3Public Health GIS Unit, School of Health and Related Research, University of Sheffield, 4Sheffield Kidney Institute, Northern General Hospital, Sheffield and 2Eastern Wakefield Primary Care Trust, Castleford, UK

Correspondence and offprint requests to: Dr Jeremy Wight, Section of Public Health Medicine, ScHARR, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK. Email: j.p.wight{at}sheffield.ac.uk



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Considerable variation exists in the organ donation rate between kidney retrieval areas (KRAs) within the UK. The causes for this are unknown. This study examines whether or not observed variations are correlated with various possible explanatory factors.

Methods. A geographical study involving Poisson regression analysis was carried out of all 21 KRAs in the UK in 1999 and 2000, with donor rate as dependent variable, and the following independent variables: road traffic accident, intracerebral haemorrhage and other trauma death rates; intensive care unit (ICU) bed numbers; co-location of transplant and neurosurgical units; population ethnicity; proportion of the population on the organ donor register; transplant coordinator numbers; and transplant unit numbers. Main outcome measures were: donor rate in each KRA; strength of association between independent and dependent variables; and magnitude of changes in the donor rate associated with changes in independent variables.

Results. The donor rate varied between eight and 27.4 donors per million population per year. There was an association between donor rate and general ICU bed numbers (more beds associated with a higher donor rate), but this was of borderline statistical significance (P = 0.065). However, the donor rate was negatively associated (P = 0.02) with neurosurgical ICU bed numbers (more beds, fewer donors) and the proportion of the population from minority ethnic communities. There was no statistically significant association with the other independent variables.

Conclusions. There is significant variation in the organ donor rate between different parts of the UK. More research is needed to explore the counter-intuitive association between neurosurgical ICU beds and donations, and to determine the remaining causes of the observed variation.

Keywords: organ donation; transplantation; variation



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The supply of organs for transplantation in the UK fails to match demand, so that the number of patients awaiting transplantation continues to increase [1]. Discussions about ways to increase organ donation [25] have overlooked the considerable variation in organ donor rates that exists between different areas within the UK. This variation implies a significant failure to maximize donation, in that if low donating areas increased their rates to the levels seen in higher donating areas, a significant increase in transplantation would follow [6].

The reasons for this variation are not known. Although different transplant units have different approaches to live and non-heart-beating donation, these only comprise a minority of donations overall, with the majority coming from heart-beating donors on intensive care units (ICUs). If any of the causes of the variation are amenable to intervention, it might be possible to increase the number of donations made. This would not preclude other interventions designed to achieve the same end.

Organ donation is a complex process and, as such, there are many factors which may influence it. Some of these may operate at the individual potential donor–clinician–ICU level, while others may operate at the population level. If variation in donation rates can be explained by variation in population level factors [such as road traffic accident (RTA) death rates], then studies of variation at the individual level would take place in a different context.

The objectives of this study were to describe the variation in cadaveric heart-beating organ donor rates between different kidney retrieval areas (KRAs) within the UK; identify associations which exist between donor rates and possible explanatory variables; estimate the magnitude of benefit which might result if changes in those explanatory variables led to changes in donor rates; and identify any areas for more detailed research.

Independent variables selected a priori as possibly being related to donor rate were: death rates from RTAs, intracerebral haemorrhage (ICH) and other trauma (OT) (the most common causes of death of organ donors, accounting for 82% of all donors in the years of the study); the number of general and neurosurgical ICU beds; co-location of transplant and neurosurgical units; proportion of the population from minority ethnic groups (because of their recognized high rates of end-stage renal failure but low organ donation rates); proportion of the population on the organ donor register (the national database of people who have indicated a willingness to be organ donors); the number of transplant coordinators working in organ retrieval (the people responsible for coordinating the organ donation process); and the number of transplant units. We did not include the number of whole time equivalent transplant surgeons because we were reassured that the availability of a transplant surgeon is never a determining factor as to whether or not a donation goes ahead (C. Newstead, personal communication).



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
All data (except ethnicity; see below) were for 1999 and 2000. The number of heart-beating cadaver donors in each of the 21 KRAs in the UK, broken down into 5-year age and sex bands, was obtained from United Kingdom Transplant (UKT). Over an 8-year period, the number of donors per year for each KRA has remained relatively constant, so that there is stability, year on year, in the numbers per KRA. Health Authorities (Health Boards in Scotland) contributing to each KRA were identified, and the population structure of KRAs determined using data from the Office of National Statistics (ONS), the Scottish Executive and the Northern Ireland Office. Five Health Authorities contributed to two KRAs. They were contacted, and provided estimates of the proportion of the population contributing to each KRA. This proportional split was then applied to all population age and sex bands.

The numbers of open, staffed, general and neurosurgical ICU beds in hospitals within each KRA in England were obtained from the Department of Health's twice yearly census for each of the 2 years. Similar data for Scotland and Northern Ireland were provided by their respective Intensive Care Societies. The numbers in Wales were determined by telephoning each hospital. Paediatric ICU bed numbers were obtained from routine Department of Health bed availability and occupancy data. The validity of the data was confirmed by contacting one ICU in each KRA. In every case, the data were found to be accurate.

The number and sessional commitments of transplant coordinators was obtained from the UKT Coordinators’ Association's survey in December 2000. The number of whole time equivalents working in an organ procurement role was calculated for each KRA.

The number of solid organ transplant units in each KRA was obtained from UKT. Co-location of transplant unit and neurosurgical unit was determined by cross-referencing lists of hospitals with a neurological ICU and transplant units.

The proportion of people on the organ donor register within each KRA was obtained from UKT.

The number of deaths, within 5-year age and sex bands, by Health Authority (Health Board in Scotland) from RTAs, ICH and OT was obtained from the ONS, Scottish Executive and Northern Ireland Office. The proportion of the population from ethnic minorities (i.e. ‘non-white’) for each KRA was obtained by aggregating the figures for each Health Authority (or Health Board) from the 1991 census (the latest data which are comprehensively available across the UK). Data for Northern Ireland were provided by the Multicultural Resource Centre Northern Ireland.

Paediatric (aged <15 years) donor rates were analysed separately. All data were truncated at age 69, because of the very low numbers of donors above this age. Each KRA's donor rate, ICH, RTA and OT death rate were indirectly age and sex standardized to the overall UK rate.

The sample size for the study was fixed at the 21 KRAs and 2 years of organ donation data (1999 and 2000). If we treat the organ donation rate for each KRA and the potential explanatory factors (such as ethnicity, the proportion of KRA population non-white) as continuous variables, then with 21 KRAs we have >80% power to detect a correlation of 0.60 between the KRA organ donation rate and the various explanatory variables (e.g. ethnicity) as statistically significantly different (at the 5% two-sided level) from a zero correlation between the variables.

We modelled the number of organ donors using Poisson regression in STATA [7]. The outcome for a Poisson model is a count of events in a group, usually over a period of time, e.g. the number of organ donations in a calendar year in a particular KRA. When the observed data vary from the predicted values by more than would be expected by a Poisson distribution, we have what is known as extra-Poisson variation. It means that the standard error given by the STATA output may not be valid. It may arise because an important covariate is omitted. The expected number of donors was fitted as an offset. A goodness of fit test was performed for the models. We examined the relationship between each covariate and donor rates in a series of univariate analyses. We used a cut-off of P<0.10 to identify covariates to take forward to the multivariate analysis.

Using only these variables, we then performed a multiple Poisson regression analysis. A forward stepwise procedure using the likelihood ratio test with a P-value of 0.05 for entry into the model, and 0.10 for removal, was used. In the final model, we then used the ‘robust’ option in STATA to calculate the standard errors, P-values and confidence intervals for the estimates in order to allow for possible overdispersion.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The variation in paediatric donor rates between KRAs was no greater than could be accounted for by chance. They were therefore not analysed further.

Over the 2 years combined, there were a total of 1330 donors aged 15–69 years. The crude overall UK donor rate was 16.1 donors per million population (p.m.p.) per year. This varied significantly (P = 0.0001) from 8.0 p.m.p. in Leicester KRA to 27.4 p.m.p. in Coventry KRA. Donor rates for each KRA are shown in Table 1.


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Table 1. Number of donors and donation rates by KRAs for 1999 and 2000 combined

 
The indirectly standardized donor rate ratios varied from 48.8 (Leicester) to 167.8 (Coventry) (against a UK figure of 100). Overall UK figures for each variable, together with the KRA maxima and minima, are given in Table 2.


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Table 2. Descriptive statistics for covariates and results of univariate Poisson analysis

 
In the univariate analyses, the following independent variables were significantly associated (P<0.10) with the number of organ donors.

These variables were therefore carried forwards to the multivariate analysis.

The following were not significantly associated with the number of organ donors: (i) proportion of the population on the organ donor register; (ii) transplant coordinators p.m.p.; and (iii) number of transplant units. These were therefore not considered further.

In the multivariate analysis, three variables were significantly associated with the number of donors. These were the number of neuro-ICU beds p.m.p. (P<0.001), the number of ICU beds p.m.p. (P = 0.007) and population ethnicity (P<0.001). However, the Poisson goodness of fit test ({chi}2 = 52.37 on 17 df, P = 0.0001) suggested that the Poisson model may not be ideal, and that there may be overdispersion. In this case, the overdispersion is probably due to the fact that there is substantial variability in each KRA's organ donation rate that cannot be fully explained by the variables we have included in the model, i.e. we have omitted covariates. The pseudo R2 value of 0.17 suggests that the model with three covariates is ~17% better than the model with no covariates (the ‘constant-only’ model), but is ~83% worse than the theoretical perfect fitting or predicting model (with a pseudo R2 value of 1.0).

In the final model, using the ‘robust’ option, only these three independent variables were included. Using this option, the standard errors, and hence the confidence limits, are increased, so that the statistical significance of the association between general ICU bed numbers and donation rates became marginal (P = 0.065). Details of the final model parameters are given in Table 3.


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Table 3. Model parameters

 
If the links between independent variables and donor rate are indeed causal, the magnitude of changes in the donor rate associated with changes in independent variables can be calculated from the incident rate ratio (IRR), the factor by which the donor rate varies with each unit change in the independent variable. Thus an increase of one ICU bed p.m.p. would be associated with the donor rate increasing by a factor of 1.02 (or 2%). Specifically, an increase from 24.06 beds p.m.p. (the national average) to 25.06 would be associated with an increase in donor rate from 16.57 to 16.90, a net increase of 0.33 donors p.m.p. per year. In order to achieve a 5% increase in donor rate, there would need to be an increase of 2.5 beds p.m.p., i.e. from 24.06 to 26.56 beds p.m.p. Similarly, an increase in neuro-ICU beds from 1.32 p.m.p. (the national average) to 2.32 would be associated with a decrease in donors of 1.33 p.m.p. per year. An increase in the non-white population from 4.05 to 5.05% would be associated with a decrease in the donor rate of 0.33 donors p.m.p. per year. The difference in population ethnicity between the KRAs with the highest and lowest figures (14.6%) would account for a difference in donor rates of 4.83 p.m.p. per year.

There was no evidence of interaction between ICU bed numbers and the relationship between RTA and ICH death rates and donor numbers.



   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
In the multivariate analysis of adult donation rates, only three variables were correlated with donor rate: the number of general ICU beds (positively correlated), the number of neuro-ICU beds and population ethnicity (both negatively correlated). When the model was corrected for possible overdispersion, the association between general ICU beds and donor rates became of only marginal significance.

Although there have been studies of variations in transplantation rates between countries (presumably reflecting differences in donation rates) [8], we were able to identify few studies looking at geographical variations in donor rates within a country [911]. A comparison between European countries’ transplantation rates in the early 1990s showed that they correlated with RTA death rates [2]. A geographic analysis of potential organ donors (identified as those drivers who had a donor sticker on their licence) in Ohio, USA, identified a 2-fold variation in potential donors between different counties, which correlated with income, education, political affiliation and race [9]. By focusing on potential donor numbers, this study did not explore any variation in actual donor numbers that might be due to infrastructural or procedural differences between geographical areas. An audit of intensive care deaths in England and Wales in 1989 and 1990 [12] revealed a 2-fold variation between (then) NHS regions in the number of deaths on ICUs, and a wide variation in medical contra-indication to transplantation and relative refusal rates.

The association between population ethnicity and donor rates is consistent with previous observations and clinical experience of the difficulty in recruiting donors from the ethnic minority population [1315], and efforts must continue to increase donations from ethnic minorities.

Although there is a correlation between general ICU bed numbers and donor rates, it is of only marginal statistical significance, so may be a chance observation. It is also possible that a study with more power would have revealed a correlation with a greater degree of statistical significance. Nevertheless, the cost savings associated with transferring patients from a dialysis programme to transplant maintenance, as well as the enormous clinical benefits from both renal and other solid organ transplantation, should be recognized as possible additional benefits from investment in intensive care.

Some international comparisons have suggested that ICU bed availability may be a factor in determining donation rates, so our failure to observe similar links (in the final analysis) is perhaps surprising. We can be confident that this is not due to insufficient variation in ICU bed numbers (there is a 2-fold difference between those areas with the highest and lowest numbers). Further, the fact that there is no evidence of interaction between ICU bed numbers and the relationship between RTA and ICH death rates and donor numbers suggests that there is no ‘threshold’ effect of ICU bed numbers within the range that exists across the KRAs.

The negative correlation between neuro-ICU bed numbers and donor rates demands further investigation. One possibility is that where there is a high level of neurosurgical expertise, patients undergo definitive assessment sooner, and fewer of those who will not survive are admitted to intensive care (a necessary pre-requisite for becoming a heart-beating donor). In the absence of neurosurgical expertise, such patients may be admitted to general ICU beds whilst a neurosurgical opinion is sought, and then become available as donors. This is consistent with observations from 1993 that mixed general and neurosurgical ICUs produced more donors than pure neurosurgical ICUs, that units with lower acceptance thresholds have higher in-unit mortality, and that doctors in hospitals without neurosurgical facilities would more often resuscitate deeply comatose patients with extensive subarachnoid haemorrhage than would those in hospitals with neurosurgical facilities [16].

There is thus potentially a paradox in that increasing the availability of neurosurgical expertise, which must be expected to improve outcomes for patients in need of neurosurgical intervention, may be associated with a reduction in the number of donors. The ventilation of persons who are expected to die soon with the primary purpose of securing organs for transplantation is illegal in the UK [17,18]. Pressure to review this position might increase if it became clear that with increasing neurosurgical input, and earlier radiological assessment, fewer seriously ill patients were being actively treated for their own benefit, and that this was having a negative effect on organ donor rates.

The lack of association between the numbers of transplant coordinators and the donor rates is surprising, and at variance with observations from 1993 [16]. It is likely that the number of transplant coordinators employed has increased significantly since then, but the number of coordinators per million population still varies substantially (from 1 to 6.6 p.m.p.), so that if there were a genuine link between the two, one would have expected it to be manifest.

It is surprising that RTA, ICH and OT death rates are not associated with donor rates, as these are the most common causes of death in donors, and do vary substantially between KRAs. We searched for any evidence (in the form of statistical interaction) that increases in deaths do not translate into more donors because ICU bed numbers act as a rate-limiting factor, but found none.

There are a number of limitations to this study. The accuracy of the ICU bed data may be questioned, but they were found to be accurate when validated in a subsample. Unfortunately, it was necessary to use 1991 census data for ethnicity. However, there is likely to be a close correlation between those figures and the true figures for 1999–2000. In any event, a significant correlation between ethnicity and donor numbers remained, as was expected. One possible explanation for the lack of correlation between some variables and donor rates is misclassification. For example, with regard to transplant coordinators, it may be that the number of procurement sessions per se is not crucial, but there are other important considerations such as training and recruitment.

We only had a fixed sample size of 21 KRAs for this study. As a result, the study had low power to detect anything other than major associations or correlations between the organ donation rates and the various potential explanatory factors. However, the observed associations for many of the potential explanatory factors were small (IRRs close to 1) and, although low power can explain the lack of significance, it cannot explain the size of the observed effect or association. Overall, it is unlikely that the study failed to detect an important association between the potential explanatory factors and the organ donation rate, although smaller associations cannot be ruled out.

We analysed only data from the UK. Given the differences that exist between countries in terms of organization of health services in general, and organ donation and transplantation services in particular [5], it would be inappropriate to generalize the results outside the UK. Moreover, we do not know whether the variation in donation rates within other countries is as great as that observed within the UK.

This was an ecological study, so it would be incorrect to assume causation in the associations observed. A substantial amount of variation in donor rates between KRAs remains unexplained, but it is perhaps encouraging that the variation in donation rates observed between KRAs does not appear to be due to variations in RTA, ICH or OT death rates. This implies that it is due to other factors, albeit as yet not fully determined, which are likely to be more amenable to intervention.

Clearly, there is a need to gain a better understanding of the variations in the pathway between hospital admission, ICU and organ donation, which will most probably come from qualitative studies. To be most fruitful, these would have to include detailed interviews of the personnel involved in the different steps in the pathway, covering, for example, how possible donors are treated, how medical contra-indications are evaluated and how donation is discussed with relatives. In this way, light may be thrown on the decisions which lead to the observed variation in donation rates, and the much lower donation rates in certain areas.



   Acknowledgments
 
We thank Professor Stewart Cameron, Professor David Kerr and Dr Chas Newstead for their advice. We thank United Kingdom Transplant, the Office of National Statistics, the Scottish Executive, Northern Ireland Office, Multicultural Resource Centre, Northern Ireland, Scottish and Northern Irish Intensive Care Societies, UK Transplant Coordinators’ Association, and various Health Authorities and Hospital Trusts for supplying data. This research was funded by the National Kidney Research Fund.

Conflict of interest statement. None declared.



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

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Received for publication: 27. 9.02
Accepted in revised form: 22.10.03





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