How to Fairly Allocate Scarce Medical Resources: Justice Trade-Offs between an Individual and a Population Perspective

Wednesday, May 7, 2014

2.30 p.m.

ISI Red Room

Timo Smieszek


The establishment of allocation schemes for scarce medical goods and services is a serious matter as it may determine who lives and who dies. The decision concerning who is to receive an organ transplant primarily affects the patients on the waiting list.

However, the allocation of scarce treatment and prevention against infectious disease is more far reaching. Untreated individuals may infect additional people who could have been spared, had the untreated persons been treated.

An efficient allocation scheme that might avert many cases on a population level may be considered unjust when focusing on the individual level, and vice versa.

We tested, via a hypothetical infection transmission scenario, what kind of allocation scheme (‘lottery’, ‘youngest first’, ‘by behaviour’, etc.) is perceived to be the fairest by (A) medical lay-people and (B) general practitioners from Switzerland. The data were collected using an online survey tool. Participants belonging to one of the two groups were randomly distributed to one of four conditions, based on a 2x2 factorial design: Allocation purpose (a1: treatment for infected individuals vs. a2: prevention for uninfected ones) x Information (b1: information about the population-wide effects of each allocation scheme vs. b2: no information). We also asked participants to assess other scarcity situations.

We found, inter alia, that participants distinguished between treatment of infected and prevention for uninfected individuals: About one third of the lay-people chose the most efficient allocation scheme, even though it meant to prefer people whose behaviour was driving the infection spread. In the case of treating already infected people, only slightly more than one tenth chose the efficient scheme.

Here, the most popular scheme was prioritization by waiting time (approx. one third).

There is no universally preferred allocation scheme and fairness judgements are context-dependent. Furthermore, justice decisions made by lay-people and general practitioners differed partly from what ethicists recommend. BIO === Timo is a senior mathematical modeller at Public Health England and a honorary research associate at Imperial College working with Peter White. Before moving to England, he was a postdoctoral fellow with Marcel Salathé at the Pennsylvania State University.

Timo is Environmental Scientist by training and he received his PhD from ETH Zurich. His main research interests are in the field of Network Epidemiology.