Insight into “Calculated Risk”: An Application to the Prioritization of Emerging Infectious Diseases for Blood Transfusion Safety

Abstract

Increasing identification of transmissions of emerging infectious diseases (EIDs) by blood transfusion raised the question which of these EIDs poses the highest risk to blood safety. For a number of the EIDs that are perceived to be a threat to blood safety, evidence on actual disease or transmission characteristics is lacking, which might render measures against such EIDs disputable. On the other hand, the fact that we call them "emerging" implies almost by definition that we are uncertain about at least some of their characteristics. So what is the relative importance of various disease and transmission characteristics, and how are these influenced by the degree of uncertainty associated with their actual values? We identified the likelihood of transmission by blood transfusion, the presence of an asymptomatic phase of infection, prevalence of infection, and the disease impact as the main characteristics of the perceived risk of disease transmission by blood transfusion. A group of experts in the field of infectious diseases and blood transfusion ranked sets of (hypothetical) diseases with varying degrees of uncertainty associated with their disease characteristics, and used probabilistic inversion to obtain probability distributions for the weight of each of these risk characteristics. These distribution weights can be used to rank both existing and newly emerging infectious diseases with (partially) known characteristics. Analyses show that in case there is a lack of data concerning disease characteristics, it is the uncertainty concerning the asymptomatic phase and the disease impact that are the most important drivers of the perceived risk. On the other hand, if disease characteristics are well established, it is the prevalence of infection and the transmissibility of the disease by blood transfusion that will drive the perceived risk. The risk prioritization model derived provides an easy to obtain and rational expert assessment of the relative importance of an (emerging) infectious disease, requiring only a limited amount of information. Such a model might be used to justify a rational and proportional response to an emerging infectious disease, especially in situations where little or no specific information is available.

A consensus‐based tool for ranking the risk of blood‐transmissible infections

“…The out-of-sample validation of the model, in which the ranking of a set of existing diseases obtained by the model was compared with the ranking obtained directly from the experts, showed strong agreement (R 2 value for linear fit on the risk scores 5 73%; results not shown here but elaborated in detail in the technical report on this project). 12 The model performed particularly well in identifying the diseases with the highest risks.…”

“…The out of sample validation of the model showed that there was a fair agreement between the ranking of a set of existing diseases obtained from the model and the ranking obtained by the experts. However, the model performed particularly well in identifying the diseases with the highest risks, which should be its most important feature …”

“…However, the model performed particularly well in identifying the diseases with the highest risks, which should be its most important feature. 12 In the illustration of the model that ranked previously identified EIDs, some unexpected diseases were identified as having the highest risk for blood safety. This is because most of their disease characteristics were assigned either a high level of expression (possible, likely, very likely) or the largest uncertainty level (unknown).…”

“…With PI, a sample of the probability distribution of the weights for various characteristics is obtained that includes their respective interactions (the effect of selecting particular combinations of expression levels). A more detailed description of the analysis and results is provided elsewhere . On the basis of the probability distributions obtained, the risk for any EID given its specific disease characteristics can be estimated.…”

“…A more detailed description of the analysis and results is provided elsewhere. 12 On the basis of the probability distributions obtained, the risk for any EID given its specific disease characteristics can be estimated. Because this risk is obtained in terms of a probability distribution, various characteristics, like the mean risk or the dispersion of this estimate, are also available.…”