Contribution of causal factors to disease burden: how to interpret attributable fractions

Abstract

What proportion of the risk in a given population is attributable to a risk factor? The population attributable fraction (PAF) answers this question. “Attributable to” is understood as “due to”, which makes PAFs closely related to the concept of potential impact or potential benefits of reducing the exposure. The PAF is a tool at the border between science and decision making. PAFs are estimated based on strong assumptions and the calculations are data intensive, making them vulnerable to gaps in knowledge and data. Current misconceptions include summing up PAFs to 100% or subtracting a PAF for a factor from 100% to deduce what proportion is left to be explained or prevented by other factors. This error is related to unrecognised multicausality or shared causal responsibility in disease aetiology. Attributable cases only capture cases in excess and should be regarded as a lower bound for aetiological cases, which cannot be estimated based on epidemiological data alone (exposure-induced cases). The population level might not be relevant to discuss prevention priorities based on PAFs, for instance when exposures concentrate in a subgroup of the population, as for occupational lung carcinogens and other workplace hazards. Alternative approaches have been proposed based on absolute rather than relative metrics, such as estimating potential gains in life expectancy that can be expected from a specific policy (prevention) or years of life lost due to a specific exposure that already happened (compensation).