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 for age 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.