Likewise, this formulation might be applied to cases where a particular treatment had been applied (e.g. From this we can also see the difference between the risks if exposed versus if not exposed, which is p- q. You might (in theory) be 20 times more likely to suffer from blood clots if you take drug X rather than drug Y, but if the probabilities in both cases are close to zero, the relative risks are far less important than the absolute risks, which are negligible. However, the numbers involved matter also. If this ratio is 1, exposure is not likely to be an important issue, but if it was 20, say, then the risk from exposure would be considered very great. The relative risk of infection is the ratio of the risk if you are exposed to the risk if you are not exposed, so is simply p/ q. The example shown below was published by Richard Todd in an article for the Huffington Post in December 2017 (results are essentially 10 year averages, some being averages post 9/11):įrom this table we can see that the overall risk of becoming infected is (A+C)/(A+B+C+D), the risk of becoming infected if you are exposed is p=A/(A+B) and if you are not exposed, it is q=C/(C+D). Tabulating event data can provide an excellent way of highlighting the relative risks associated with different events. (D) Release of radiation from Nuclear station (A) Gastrointestinal affects of antibiotics (A) Transmission of HIV from mother to child (A) Transmission to susceptible household contacts of measles and chickenpox
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