Controlling for Socioeconomic Confounding Using Regression Methods

Study Objective: To describe the advantages of using Poisson regression methods as an alternative to standardisation when computing expected numbers of disease occurrences adjusted for possible confounding factors. The problem of assessing the adequacy of model fit when the expectations are small is addressed by analytical calculations and by simulation. The method is illustrated with data from the national register of childhood tumours.

Design: The tumour data are recorded in a national register.

Setting: England, Scotland, and Wales.

Subjects: The cases considered are all children registered with leukaemia or non-Hodgkin lymphoma under the age of 15 years between 1966-87.

Main Results: The methods show a significant variation of leukaemia incidence in relation to the Register General's standard region and a negative association with socioeconomic deprivation, as measured by the Townsend index. After allowing for these variables, the incidence seems to be reasonably homogeneous throughout the population, in the sense that the residual deviance does not seem to be much larger than would be expected by chance.

Conclusions: The methods described have major advantages over standardisation in controlling for confounding, both in terms of flexibility of factor selection and assessment and also in the ability to determine whether there is residual variability of incidence after allowing for these factors.