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Related Experiment Video

Updated: Jun 7, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Confounding and bias in the attributable fraction.

Lyndsey A Darrow¹, N Kyle Steenland

¹Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. ldarrow@sph.emory.edu

Epidemiology (Cambridge, Mass.)

|October 27, 2010

Summary

Incorrectly calculating the population attributable fraction (AF) leads to biased results. Bias is influenced by confounding, exposure prevalence, and the strength of the exposure-disease association, impacting epidemiological studies.

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Last Updated: Jun 7, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Population attributable fraction (AF) calculations are crucial for understanding disease burden from exposures.
Inappropriate methods are often used to estimate AF, particularly when using secondary data sources without original data access.
Confounding is a significant factor that can introduce bias into these estimates.

Purpose of the Study:

To examine the relationship between confounding and bias in population attributable fraction (AF) calculations.
To assess how the direction and magnitude of confounding affect AF bias when using adjusted relative risks (RRs) from literature.
To investigate the influence of exposure prevalence and exposure-disease association strength on AF bias.

Main Methods:

Utilized generated data to simulate various scenarios of exposure prevalence and association strength.
Assessed confounding using the confounding risk ratio (crude RR / adjusted RR).
Quantified bias in AF by comparing incorrectly calculated AF to correctly calculated AF.

Main Results:

Underestimation of AF occurred when confounding risk ratios were >1.0 (crude RR > adjusted RR).
Overestimation of AF occurred when confounding risk ratios were <1.0 (crude RR < adjusted RR).
Bias magnitude increased with confounding magnitude, was greatest at low exposure prevalence, and was higher for weaker exposure-disease associations.

Conclusions:

Inappropriate AF calculations using adjusted RRs without original data can lead to substantial bias.
Understanding the interplay of confounding, exposure prevalence, and association strength is vital for accurate AF estimation.
These findings aid in interpreting potentially biased AF estimates frequently found in epidemiological literature.