The risk of sustaining an injury during a specific kind of crash can be calculated from real world accident data. We simply take the number of injured individuals following a crash and divide this by the number of individuals exposed to this kind of crash. The resulting number, expressed as a percentage, is the a priori risk of that injury for that kind of crash. Here, “a priori risk” simply means the risk of being injured before the crash occurs.
Once a crash occurs, however, the a priori risk of injury becomes less meaningful to the exposed individual, who is either injured following the crash or not injured following the crash. This diagnosis of an injury is made by a medical professional.
In our work, we are routinely asked to establish or refute the causal link between a diagnosed injury and an event alleged to have caused that injury. This question may be asked despite a medical diagnosis for various reasons, including: i) time has elapsed between the incident and when the injury was reported, ii) there is disagreement regarding the diagnosis, or iii) there may be other plausible explanations for the injury.
To assess this causal link, we routinely calculate the force applied to the injured tissue and then compare this force to that shown to cause the injury during controlled experiments. When the applied forces cannot be reliably calculated or the forces associated with the injury are not known, we sometimes turn to a priori risk data. These risk data, however, must be used carefully.
A priori risk data can tell us that a specific injury is possible in a specific kind of crash. This is useful because it confirms that other individuals exposed to a similar crash have been diagnosed with the injury, though these data alone fall short of demonstrating that the injury was probably caused by the crash.
A priori risk values are sometimes used to conclude incorrectly that a diagnosed injury is not causally linked to a specific crash. The argument goes like this: Since only 6% of individuals in a study sustained this specific injury in this specific kind of crash, it is unlikely that the injured party sustained his/her injury in this crash. This logic is flawed because it applies data from many individuals (the study population) to a single individual. Just as the a priori risk generated by the entire study population cannot be applied to a single subject within the study, the a priori risk cannot be applied to a single person outside the study.
In summary, estimates of the a priori risk of injury can help establish that an injury is possible in specific kinds of crashes; however, they cannot be used—in isolation—to conclude that an injury is probably or probably not related to a specific kind of crash.