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Controlling for socioeconomic confounding using regression methods

J F Bithell, S J Dutton, N M Neary

    Journal of Epidemiology and Community Health
    |December 1, 1995
    PubMed
    Summary
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    Poisson regression offers superior control for confounding factors in disease occurrence compared to standardisation. This method reveals significant variations in childhood leukaemia incidence, which become homogeneous after accounting for region and socioeconomic factors.

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Cancer Research

    Background:

    • Standardisation methods are commonly used for computing expected disease occurrences.
    • Controlling for confounding factors is crucial in epidemiological studies.
    • Assessing model fit, especially with small expected values, presents challenges.

    Purpose of the Study:

    • To highlight the advantages of Poisson regression over standardisation for disease occurrence analysis.
    • To address model fit assessment challenges in Poisson regression with small expectations.
    • To illustrate the application of Poisson regression using childhood tumour data.

    Main Methods:

    • Utilisation of Poisson regression for calculating expected disease occurrences.
    • Application of analytical calculations and simulations to assess model adequacy.

    Related Experiment Videos

  • Analysis of childhood leukaemia and non-Hodgkin lymphoma data from a national register (1966-1987).

    • Main Results:

    • Identified significant variations in leukaemia incidence by standard region.
    • Observed a negative association between leukaemia incidence and socioeconomic deprivation (Townsend index).
    • Demonstrated that incidence becomes homogeneous after adjusting for identified confounding factors.

    Conclusions:

    • Poisson regression provides greater flexibility in factor selection and assessment compared to standardisation.
    • The method effectively controls for confounding variables in disease incidence studies.
    • Poisson regression allows for the assessment of residual variability in incidence post-adjustment.