Just a relative risk is not enough to fully understand the implications of your findings. Sure, if you are an expert in a field, the context of that field will help you to assess the RR. But if ou are not, the context of the numerator and denominator is often lost. There are several ways to work towards that. If you have a question that revolves around group discrimination (i.e. questions of diagnosis or prediction) the RR needs to be understood in relation to other predictors or diagnostic variables. That combination is best assessed through the added discriminatory value such as the AUC improvement or even more fancy methods like reclassification tables and net benefit indices. But if you are interested in are interested in a single factor (e.g. in questions of causality or treatment) a number needed to treat (NNT) or the Population Attributable Fraction can be used.

The PAF has been subject of my publications before, for example in these papers where we use the PAF to provide the context for the different OR of markers of hypercoagulability in the RATIO study / in a systematic review. This paper is a more general text, as it is meant to provide in insight for non epidemiologist what epidemiology can bring to the field of law. Here, the PAF is an interesting measure, as it has relation to the etiological fraction – a number that can be very interesting in tort law. Some of my slides from a law symposium that I attended addresses these questions and that particular Dutch case of tort law.

But the PAF is and remains an epidemiological measure and tells us what fraction of the cases in the population can be attributed to the exposure of interest. You can combine the PAF to a single number (given some assumptions which basically boil down to the idea that the combined factors work on an exact multiplicative scale, both statistically as well as biologically). A 2016 Lancet paper, which made huge impact and increased interest in the concept of the PAF, was the INTERSTROKE paper. It showed that up to 90% of all stroke cases can be attributed to only 10 factors, and all of them modifiable.

We had the question whether this was the same for young stroke patients. After all, the longstanding idea is that young stroke is a different disease from old stroke, where traditional CVD risk factors play a less prominent role. The idea is that more exotic causal mechanisms (e.g. hypercoagulability) play a more prominent role in this age group. Boy, where we wrong. In a dataset which combines data from the SIFAP and GEDA studies, we noticed that the bulk of the cases can be attributed to modifiable risk factors (80% to 4 risk factors). There are some elements with the paper (age effect even within the young study population, subtype effects, definition effects) that i wont go into here. For that you need the read the paper -published in stroke- here, or via my mendeley account. The main work of the work was done by AA and UG. Great job!