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Population attributable fraction based on marginal sufficient component cause model for mediation settings.

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This study introduces a new method to calculate population attributable fraction (PAF) using a sufficient cause framework. This approach helps understand disease causes and guides public health interventions more effectively.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Population attributable fraction (PAF) is crucial for evaluating health policies by quantifying disease reduction from eliminating risk factors.
  • Existing methods may not fully capture complex disease pathways involving mediation and interaction.

Purpose of the Study:

  • To propose an alternative PAF definition based on the sufficient cause framework.
  • To develop a regression-based estimator for PAF within mediation settings.
  • To decompose disease occurrence into distinct causal pathways.

Main Methods:

  • Formal statistical definition and regression-based estimator for PAF.
  • Sufficient cause framework applied to mediation and mechanistic interaction.
  • Decomposition into nine PAFs based on exposure, mediator, interaction, and their combinations.

Main Results:

  • Application to Hepatitis C Virus (HCV)-induced hepatocellular carcinoma (HCC) with Alanine Aminotransferase (ALT) and Hepatitis B Virus (HBV).
  • 56.77% of HCC cases attributable to HCV or abnormal ALT when ALT is a mediator.
  • HCC primarily induced by high HBV viral load directly when HBV is a mediator.

Conclusions:

  • The proposed method identifies impacts of exposure and pathway effects.
  • Facilitates better resource allocation for intervention strategies.
  • Enhances understanding of complex disease etiology.