Attributable fraction for multiple risk factors: Methods, interpretations, and examples

Matteo Di Maso, Francesca Bravi, Jerry Polesel, Eva Negri, Adriano Decarli, Diego Serraino, Carlo La Vecchia, Monica Ferraroni

Research output: Contribution to journalArticle

Abstract

The attributable fraction is the candidate tool to quantify individual shares of each risk factor on the disease burden in a population, expressing the proportion of cases ascribable to the risk factors. The original formula ignored the presence of other factors (i.e. multiple risk factors and/or confounders), and several adjusting methods for potential confounders have been proposed. However, crude and adjusted attributable fractions do not sum up to their joint attributable fraction (i.e. the number of cases attributable to all risk factors together) and their sum may exceed one. A different approach consists of partitioning the joint attributable fraction into exposure-specific shares leading to sequential and average attributable fractions. We provide an example using Italian case–control data on oral cavity cancer comparing crude, adjusted, sequential, and average attributable fractions for smoking and alcohol and provide an overview of the available software routines for their estimation. For each method, we give interpretation and discuss shortcomings. Crude and adjusted attributable fractions added up over than one, whereas sequential and average methods added up to the joint attributable fraction = 0.8112 (average attributable fractions for smoking and alcohol were 0.4894 and 0.3218, respectively). The attributable fraction is a well-known epidemiological measure that translates risk factors prevalence and disease occurrence in useful figures for a public health perspective. This work endorses their proper use and interpretation.

Original languageEnglish
Number of pages12
JournalStatistical Methods in Medical Research
DOIs
Publication statusAccepted/In press - Jan 1 2019

Keywords

  • adjusted methods
  • Attributable fraction
  • case–control study
  • multiple risk factors
  • partitioning methods

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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