Pooling of Confounders Did Not Induce Residual Confounding in Influenza Vaccination Studies

Rolf H. H. Groenwold*, Eelko Hak, Arno W. Hoes

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    2 Citations (Scopus)

    Abstract

    PURPOSE: In observational studies on influenza vaccine effectiveness, confounding variables such as individual chronic diseases often are pooled before inclusion into a multivariable regression model. It has been suggested, however, that the pooling of confounders induces residual confounding, although empirical evidence is scarce. We set out to study the effects of combining several confounders into classes of co-morbidity.

    METHODS: In a retrospective cohort study on the association between influenza vaccination and mortality, the effect of pooling of 20 individual diagnoses into three dichotomous co-morbidity variables indicating the presence of at least one of a range of diagnoses was studied. The sample size allowed for adjustments for 22 confounders (age, sex, and 20 individual cardiovascular, pulmonary, or oncologic diagnoses).

    RESULTS: After adjustment forage and sex, further adjustment for 20 separate confounders or the three pooled co-morbidity variables resulted in comparable estimates of influenza vaccine effectiveness: odds ratio 0.78 (95% confidence interval, 0.62-0.98) and odds ratio 0.74 (95% confidence interval, 0.59-0.93), respectively.

    CONCLUSION: We conclude that pooling of several (related) confounders in influenza vaccine effectiveness studies in health care databases is unlikely to induce important residual confounding. Ann Epidemiol 2009;19:432-436. (c) 2009 Elsevier Inc. All rights reserved.

    Original languageEnglish
    Pages (from-to)432-436
    Number of pages5
    JournalAnnals of Epidemiology
    Volume19
    Issue number6
    DOIs
    Publication statusPublished - Jun-2009

    Keywords

    • Bias
    • Confounding Factors
    • Confounding Variable
    • ELDERLY-PEOPLE
    • MORTALITY
    • REGRESSION
    • BENEFITS
    • SENIORS
    • EVENTS
    • PRISMA
    • RISK
    • BIAS

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