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Statistical "biases" in respiratory disability determinations.

P Harber, R Schnur, J Emery

    The American Review of Respiratory Disease
    |September 1, 1983
    PubMed
    Summary
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    Statistical choices in pulmonary function tests significantly impact respiratory disability assessments. Adjustments for sex and race, and the comparison method, greatly influence disability declarations and their distribution among applicants.

    Area of Science:

    • Pulmonary Medicine
    • Medical Statistics
    • Disability Assessment

    Background:

    • Pulmonary function tests (PFTs) are crucial for assessing respiratory disability.
    • The interpretation of PFT results involves several statistical decisions.
    • These decisions can influence disability determination outcomes.

    Purpose of the Study:

    • To evaluate the impact of four statistical choices on respiratory disability assessments.
    • To determine how these choices affect the number of disability declarations.
    • To analyze the influence of personal characteristics on disability likelihood.

    Main Methods:

    • Analysis of records from 900 respiratory disability applicants.
    • Estimation of the effects of prediction equations, adjustment factors (sex, race), criterion values, and comparison methods.

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  • Assessment of impact on overall disability declarations and subgroup distributions.

    • Main Results:

    • Choice of prediction equation had minor effects on disability outcomes.
    • Adjustments for race and sex significantly influenced disability declarations.
    • Criterion values affected the total number of disability declarations.
    • Method of comparison significantly altered the distribution of disability declarations across subgroups.

    Conclusions:

    • Statistical choices in PFT interpretation have a substantial impact on respiratory disability assessments.
    • Adjustments for demographic factors and the chosen comparison method are particularly influential.
    • Careful manipulation of these statistical choices is essential for designing effective disability systems.