Unobserved confounding has been suggested to explain the effect of influenza vaccination on mortality reported in several observational studies. An instrumental variable (IV) is strongly related to the exposure under study, but not directly or indirectly (through other variables) with the outcome. Theoretically, analyses using IVs to control for both observed and unobserved confounding may provide unbiased estimates of influenza vaccine effects. We assessed the usefulness of IV analysis in influenza vaccination studies.
Information on patients aged 65 years and older from the computerized Utrecht General Practitioner (GP) research database over seven influenza epidemic periods was pooled to estimate the association between influenza vaccination and all-cause mortality among community-dwelling elderly. Potential IVs included in the analysis were a history of gout, a history of orthopaedic morbidity, a history of antacid medication use, and GP-specific vaccination rates.
Using linear regression analyses, all possible IVs were associated with vaccination status: risk difference (RD) 7.8% (95% confidence interval [CI] 3.6%; 12.0%), RD 2.8% (95% CI 1.7%; 3.9%), RD 8.1% (95% CI 6.1%; 10.1%), and RD 100.0% (95% CI 89.0%; 111.0%) for gout, orthopaedic morbidity, antacid medication use, and GP-specific vaccination rates, respectively. Each potential IV, however, also appeared to be related to mortality through other observed confounding variables (notably age, sex, and comorbidity).
The potential IVs studied did not meet the necessary criteria, because they were (indirectly) associated with the outcome. These variables may, therefore, not be suited to assess unconfounded influenza vaccine effects through IV analysis.