Attributable risk is calculated by multiplying the proportion of the population exposed to the risk factor in question (often based on population surveys), by the RR associated with that risk factor (often based on meta-analyses).[1]

**Calculating attributable risk/Population attributable fraction**

Usually the calculation takes into account a delay (lag) between exposure to the risk factor and cancer diagnosis, e.g. using exposure 10 years ago to calculate PAF for current cancer cases; this is often based on the lag between exposure and cancer diagnosis seen in the study from which the RR is taken.

‘Exposure’ may be defined as any exposure (versus none), or as exposure above/below an optimum level (that level is sometimes defined using Government guidelines).

If a risk factor is known to account for almost all cases of a particular cancer, but prevalence of exposure to that risk factor in the population is not known, then a ‘notional prevalence’ can be calculated. This is done by comparing observed cancer incidence rates in the population overall, with expected cancer incidence rates in an unexposed population.[2]

Each cancer type may have multiple risk factors, but summing the PAFs for all those risk factors would overestimate the total attributable proportion for that cancer type, because there is overlap between exposure to different factors. PAFs for a cancer type can be combined by applying the ‘risk factor B’ PAF only to the proportion of cases not attributable to ‘risk factor A’, and then applying the ‘risk factor C’ PAF only to the proportion of cases not attributable to ‘risk factor A’ or ‘risk factor B’, and so on until all the risk factors have been combined (risk factors can be added in any order).

**Combining Population attributable fractions**

1. |
Calculate ‘% not attributable to RFA’ (‘not RFA’) |
100% - 10% = 90% |

2. |
Apply RFB to ‘not RFA’, to get % of ‘not RFA’ which is attributable to RFB (‘not RFA but RFB’) |
5% * 90% = 4.5% |

3. |
Subtract this from ‘not RFA’ to get % not attributable to RFA or RFB (‘not RFA or RFB’) |
90% – 4.5% = 85.5% |

4. |
Apply RFC to ‘not RFA or RFB’, to get % of ‘not RFA or RFB’ which is attributable to RFC (‘not RFA or RFB but RFC’) |
3% * 85.5% = 2.565% |

5. |
Subtract this from ‘not RFA or RFB’ to get % not attributable to RFA or RFB or RFC (‘not RFA or RFB or RFC’) |
85.5% – 2.565% = 82.935% |

6. |
Subtract ‘not RFA or RFB or RFC’ from 100% to get % attributable to RFA or RFB or RFC (‘RFA or RFB or RFC’) |
100% – 82.935% = 17.065% |

Risk factor A PAF (RFA) = 10%, Risk factor B PAF (RFB) = 5%, Risk factor C PAF (RFC)= 3%

Simply summing would give 18%.

Theoretically all cancer cases attributable to a risk factor could be prevented by removing exposure to that risk factor. However we acknowledge that it is very difficult to completely remove a risk factor at population level, and so the total number of ‘preventable cancer cases’ based on PAFs is a very ambitious target.

PAFs can be expressed as a percentage, a proportion, or an absolute number of cases or deaths.

**Last reviewed:**