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Estimating the population attributable risk for multiple risk factors using case-control data.

P Bruzzi, S B Green, D P Byar

    American Journal of Epidemiology
    |November 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

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    This study presents a unified method for calculating population attributable risk percent (etiologic fraction) using case-control data. The approach simplifies risk estimation for multiple factors, even with confounding variables.

    Area of Science:

    • Epidemiology
    • Biostatistics

    Background:

    • Population attributable risk (PAR) calculation is crucial for public health interventions.
    • Existing methods for multivariate settings can be complex and data-intensive.

    Purpose of the Study:

    • To present a straightforward and unified approach for calculating PAR (etiologic fraction) in multivariate settings.
    • To facilitate the estimation of summary attributable risk for multiple factors using case-control studies.

    Main Methods:

    • Utilizes relative risk values for factor combinations and the distribution of factors among cases.
    • Employs logistic regression models to handle confounding and interactions efficiently.
    • Demonstrates the approach with a case-control study of breast cancer risk factors.

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    Main Results:

    • The proposed method allows for the estimation of summary attributable risk with or without adjustment for confounding factors.
    • Relative risk estimates from one population can be applied to calculate attributable risk in another.
    • The approach effectively incorporates important interactions between risk factors.

    Conclusions:

    • The unified approach simplifies PAR calculation from case-control data.
    • Logistic regression models enhance the efficiency and applicability of the method.
    • This facilitates better understanding of population-level impact of risk factors.