Question

1. Attributable risk (AR) is useful when selecting risk factors to target in order to have the greatest health impact. Assume that you have several planned interventions that are all equally effective but resources are limited. You can only select one intervention on one risk factor. Why would the AR provide the best information on which risk factor to focus on?

Answer 1

Attributable risk helps to determine how much of an outcome may be attributable to a particular risk factor (i.e. an estimate of the excess risk ) in a population exposed to that factor.This is called the population attributable risk and when expressed as a percent, the population attributable risk percent.

Attributable risk (AR) helps measure the excess risk associated with the risk factor. Population attributable risk (PAR) gives the added risk in relation to the total population. Population attributable risk percent (PAR%), gives the percent of cases in the total population that can be attributed to the risk factor.
The PAR% is an especially useful, and underutilized tool in program planning. It can be used to predict the impact of public health interventions on adverse outcomes, since it considers both the excess risk associated with the exposure and the proportion of the population that is exposed. A risk factor with a large excess risk and widespread exposure poses the most severe public health risk. One with a relatively small excess risk and relatively rare exposure poses the lowest public health risk. Factors with small excess risk and wide exposure, or large excess risk and relatively rare exposure form an intermediate group of public health risks.
The PAR% quantifies the contribution of the risk factor to the outcome and can thus help direct interventions. The higher the PAR%, the greater the proportion of the outcome that is
attributable to the risk factor. One can compare the values of population attributable risk percents for selected risk factors to identify those risk factors that are most important for planning interventions.
Most of the time when we examine risk factors, we look at behaviors, medical conditions, and
environmental factors. It makes sense to talk of "eliminating" these kinds of risk factors. There
are other risk factors, such as race or age that cannot be changed. Instead, identifying people at risk for an adverse outcome by race or age groups provides populations for targeting
interventions.
It is important to note, however, that population attributable risk percents calculated from a 2 x
2 table are crude measures of attributable risk. Because the outcome is compared to only one risk factor at a time, there is no way to know if other risk factors may underlie or explain the associations found in a 2 x 2 table. More advanced statistical methods, such as multivariate analysis, can be used to calculate attributable risks for individual risk factors that adjust for the influence of other potential risk factors. Another option is to review the literature for studies on the outcomes of interest that use multivariate analyses to assess the impact of the risk factors of interest.