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The risk difference (RD), excess risk, or attributable risk [1] is the difference between the risk of an outcome in the exposed group and the unexposed group. It is computed as I e − I u {\displaystyle I_{e}-I_{u}} , where I e {\displaystyle I_{e}} is the incidence in the exposed group, and I u {\displaystyle I_{u}} is the incidence in the ...
In epidemiology, attributable fraction among the exposed (AF e) is the proportion of incidents in the exposed group that are attributable to the risk factor. The term attributable risk percent among the exposed is used if the fraction is expressed as a percentage. [ 1 ]
Attributable fraction for the population combines both the relative risk of an incident with respect to the factor, as well as the prevalence of the factor in the population. Values of AF p close to 1 indicate that both the relative risk is high, and that the risk factor is prevalent. In such case, removal of the risk factor will greatly reduce ...
Frequently used measures of risk and benefit identified by Jerkel, Katz and Elmore, [4] describe measures of risk difference (attributable risk), rate difference (often expressed as the odds ratio or relative risk), population attributable risk (PAR), and the relative risk reduction, which can be recalculated into a measure of absolute benefit ...
It is defined as the inverse of the absolute risk increase, and computed as / (), where is the incidence in the treated (exposed) group, and is the incidence in the control (unexposed) group. [1] Intuitively, the lower the number needed to harm, the worse the risk factor, with 1 meaning that every exposed person is harmed.
[1] [2] It is a synonym of the relative risk reduction. It is used when an exposure reduces the risk, as opposed to increasing it, in which case its symmetrical notion is attributable fraction among the exposed .
Example of risk increase Quantity Experimental group (E) Control group (C) ... Attributable fraction among the exposed: AF e (EER − CER) / EER: 0.2 Odds ratio: OR
An example of such a situation occurs when the numerator is a per event risk, and the denominator is a per-time risk (also known as a cumulative risk). An example of this type of analysis would be the investigation of a pulmonary embolism (PE) that occurred a week after a patient sustained a lower extremity fracture in a traffic crash.