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Attributable fractions for partitioning risk and evaluating disease prevention: a practical guide
1Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway. Geir.Egil.Eide@Haukeland.No
The Clinical Respiratory Journal
|March 20, 2010
Summary
The attributable fraction (AF) quantifies disease risk from exposures. New methods improve risk apportionment, graphical descriptions, and survival data analysis for better epidemiological insights.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- The attributable fraction (AF) is crucial for quantifying disease proportion linked to specific exposures.
- Methodologies and software for AF estimation have significantly advanced in recent decades.
Purpose of the Study:
- To present methods for apportioning excess risk among multiple exposures, exposure groups, and subpopulations.
- To introduce graphical methods for visualizing risk attribution.
- To incorporate survival data into AF calculations.
Main Methods:
- Utilized adjusted, sequential, and average attributable fractions (AFs).
- Employed scaled sample space cubes for ordered, preventive strategies.
- Applied pie charts for visualizing risk portions.
- Incorporated time-to-disease and intervention data for survival analysis.
Main Results:
- Average AFs offer ordering-independent risk apportionment.
- Sequential and average AFs sum to the combined AF for multiple exposures.
- Graphical tools like cubes and pie charts effectively illustrate risk reduction strategies.
- Time-dependent AF measures (attributable hazard fraction, AF before time t, AF within study) account for disease onset and interventions.
Conclusions:
- Crude AF calculations in epidemiology are outdated and should be discontinued.
- Further research into AF methods for survival data and causal modeling is recommended.
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