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

Matteo Di Maso1,2, Francesca Bravi1, Jerry Polesel3

  • 1Branch of Medical Statistics, Biometry and Epidemiology "G.A. Maccacaro," Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy.

Statistical Methods in Medical Research
|May 11, 2019
PubMed
Summary
This summary is machine-generated.

The attributable fraction quantifies risk factor contributions to disease burden. Sequential and average methods accurately sum to the joint attributable fraction, unlike crude or adjusted methods.

Keywords:
Attributable fractionadjusted methodscase–control studymultiple risk factorspartitioning methods

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • The attributable fraction is crucial for quantifying population disease burden attributable to specific risk factors.
  • Traditional methods for calculating attributable fractions often fail to account for multiple risk factors and confounders, leading to inaccurate sums.
  • Existing methods may result in sums exceeding one or not reflecting the joint impact of all risk factors.

Purpose of the Study:

  • To compare different methods of calculating attributable fractions, including crude, adjusted, sequential, and average approaches.
  • To evaluate the accuracy of these methods in summing to the joint attributable fraction, especially in the presence of multiple risk factors.
  • To provide guidance on the proper use and interpretation of attributable fractions in epidemiological research and public health.

Main Methods:

  • Utilized Italian case-control data for oral cavity cancer.
  • Calculated and compared crude, adjusted, sequential, and average attributable fractions for smoking and alcohol.
  • Reviewed available software for estimating these fractions and discussed their interpretations and limitations.

Main Results:

  • Crude and adjusted attributable fractions summed to over one, indicating overestimation or lack of joint effect accounting.
  • Sequential and average attributable fractions accurately summed to the joint attributable fraction (0.8112).
  • Average attributable fractions for smoking and alcohol were 0.4894 and 0.3218, respectively.

Conclusions:

  • Sequential and average attributable fractions provide a more accurate representation of disease burden attributable to multiple risk factors.
  • These methods correctly sum to the joint attributable fraction, offering a reliable measure for public health.
  • Emphasizes the importance of selecting appropriate methods for calculating attributable fractions to ensure accurate interpretation and application.