Making difficult choices: community preferences for allocation of donor organs

Kirsten Howard¹, Stephen Jan², John M. Rose³, Michelle Irving^1,4, Germaine Wong^1,4, Allison Tong^1,4, Jonathan C. Craig^1,4, Steven Chadban^5,6, Richard D. Allen⁶, Alan Cass⁷

¹School of Public Health, University of Sydney, Sydney, Australia, ²The George Institute for International Health, Sydney, Australia, ³Institute for Transport and Logistics Studies, University of Sydney, Sydney, Australia, ⁴Centre for Kidney Research, The Children's Hospital Westmead, Westmead, Australia, ⁵Central Clinical School, Bosch Institute, University of Sydney, Sydney, Australia, ⁶Department of Renal Medicine, Royal Prince Alfred Hospital, Sydney, Australia, ⁷Menzies School of Health Research, Casuarina, Australia

Background: The demand for organs for transplant far exceeds supply.  Organs for transplant can be viewed as a community held resource, but little is known about community preferences for how they should be allocated.  We used discrete choice methods to assess community preferences for organ allocation.

Methods: Community respondents answered 30 scenarios each presenting two hypothetical patients, indicating which patient should receive a transplant if only one organ was available.  Patients were described by factors such as age, sex, number of previous transplants, time on waiting list, pre- and post- transplant life-expectancy, pre- and post- transplant quality of life (QOL).

Results: There were 2051 respondents with a mean age of 44 (range18-83); 51% were female.  Compared to a patient aged 40, younger patients were given higher preference, while older patients were less preferred.  Family members being registered as donors, the patient having caring responsibilities, and spending a longer time on waiting lists all increased priority.  Patients with lower pre-transplant life-expectancy and lower pre-transplant QOL were given a higher preference (need); as were patients who had a higher post-transplant life-expectancy and a higher post-transplant QOL (capacity to benefit or utility).  Lower pre-transplant life-expectancy was however, valued more highly than increased post-transplant life-expectancy. When LE and QOL were combined into quality adjusted life years (QALYs) there was a higher preference for giving organs to patients with lower pre-transplant QALYs, compared to higher post-transplant QALYs with one fewer pre-transplant QALY requiring an extra 2.12 post-transplant QALYs to compensate for the loss.

Conclusions: Many factors considered in current organ allocation algorithms are consistent with community preferences. Lower pre-transplant life expectancy and QALYs (which can be thought of as ‘need’) were more important than higher post-transplant life expectancy or QALYs. This suggests that whilst there is a current move toward allocation algorithms favouring high life expectancy after transplant (utility), emphasis also ought to be given to ‘need’ as measured by pre-transplant life expectancy and quality adjusted life expectancy.