Averaging attributable fractions in the multifactorial situation: assumptions and interpretation

Area of Science:

• Epidemiology
• Biostatistics
• Game Theory

Background:

• Quantifying population disease burden from multiple exposures presents methodological challenges.
• Existing epidemiologic parameters require careful application due to exposure interrelationships.
• New parameters have been proposed to address these complex multifactorial situations.

Purpose of the Study:

• To re-evaluate novel epidemiologic parameters for multifactorial disease burden assessment.
• To explore the application of game theory to understand these epidemiologic parameters.
• To provide new interpretations and motivation for using these parameters in practice.

Main Methods:

• Reconsideration of existing epidemiologic parameters from a game-theoretical perspective.
• Derivation of game-theoretical properties of these parameters.
• Illustration with real-world data from the Hordaland study on obstructive lung disease.

Main Results:

• Game-theoretical analysis provides a framework for understanding the interrelationships between exposure factors.
• The properties derived offer deeper insights into the interpretation of epidemiologic parameters.
• This approach enhances the methodological rigor for assessing population impact of exposures.

Conclusions:

• Game theory offers a valuable perspective for refining the calculation of attributable fractions in multifactorial settings.
• The study motivates the use of game-theoretical insights for more accurate epidemiologic assessments.
• Findings contribute to a better understanding of population disease load in the presence of interacting risk factors.