Although risk factors occupy a central place in medical and epidemiological research, it is also one of the most misunderstood concepts in all of medicine.
The World Health Organization (2009) defines a risk factor as: “A risk factor is any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. Some examples of the more important risk factors are underweight, unsafe sex, high blood pressure, tobacco and alcohol consumption, and unsafe water, sanitation and hygiene.” The CDC (2007) offers a similar definition: “an aspect of personal behavior or lifestyle, an environmental exposure, or a hereditary characteristic that is associated with an increase in the occurrence of a particular disease, injury, or other health condition.” However, the CDC also uses the term risk factor when it comes to sexual violence. For instance, they consider alcohol and drug use, antisocial tendencies, hostility towards women, and community-level tolerance to sexual violence.
Based on these sources, we can develop a simplified definition of a risk factor: if A is a risk factor for B, then the presence of A increases (but not necessarily in a causal sense) the probability of B occurring.
A is a risk factor for B does not necessarily mean that A causes B. It might be the case that A causes B only indirectly via some third factor, that B causes A, or that some third factor causes both A and B. In other words, correlation does not on its own imply causation. However, it is possible to disentangle these possibilities by measuring B at the start of the study. If physical punishment of children is a risk factor for aggressiveness, we can find out what comes first by measuring baseline child aggressiveness.
A is a risk factor for B does not mean that A will cause B in every instance of A. Smoking causes lung cancer, but some smokers can smoke all their lives without developing lung cancer. This does not mean that smoking is not a cause of lung cancer. It just means that there are other factors that also play a role. It is common for pseudoscientific cranks to bring up exceptions of this kind to argue against a correlational or causal association in an effort to spread uncertainty and doubt.
Similarly, it maybe the case that A is a moderate risk factor on its own, but becomes an enormous risk factor when modified by some other factor M. The classic example is asbestos-exposure and smoking, which together is very dangerous.
If a person X has risk factor A and gets B, we cannot automatically use the presence of A to create moral blame. Excessive alcohol use is a risk factor for becoming a victim of violent crime. However, this does not mean that we should blame the victim of a violent crime by saying that it is his or her fault for drinking alcohol. People should be able to drink alcohol without becoming a victimized. Similarly, if someone develops a chronic condition due to lifestyle choices, one cannot claim that they only have themselves to blame. This is because that assumes that people have equal access to health care, health education and support. This is clearly not the case.
We cannot excuse a person committing crime B by arguing that risk factor A existed. For instance, killing someone while driving drunk cannot be excused simply because the criminal was drunk. Issues related to crime and mitigating factors are complex, and some extreme cases like brain tumors in areas relevant for criminal responsibility may potentially be exceptions to this rule.
A is a risk factor for B does not mean that the risk increase influenced by A is going to be practically relevant. For instance, it may be the case that A only has a small increase in risk of B, or that A require some other factor C to cause an increase in risk of developing B. When thinking about risk factors, one cannot just focus on the direction. One has to seriously consider the size of the risk increase, the precision by which the risk increase was estimated and what it means in the scientific, medical or sociological context. Finally, one also has to take the baseline absolute risk into account. For instance, if the risk of a serious complication is 1 in 1 million, then a 5-fold risk increase results in an absolute risk of 5 in 1 million. A 5-fold increase in risk if the baseline risk is 10% is going to be much more relevant.
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