Impact of influenza vaccination on mortality risk among the elderly

Abstract

Estimates of influenza vaccine effectiveness have mostly been derived from nonrandomised studies and therefore are potentially confounded. The aim of the current study was to estimate influenza vaccine effectiveness in preventing mortality among the elderly, taking both measured and unmeasured confounding into account.Information on patients aged o65 yrs from the computerised Utrecht General Practitioner database on eight influenza epidemic periods and summer periods was pooled to estimate influenza vaccine effectiveness in preventing mortality. Summer periods (during which no effect of vaccination was expected) were used as a reference to control for unmeasured confounding in epidemic periods.After adjustment for measured confounders using multivariable regression analysis, propensity score matching and propensity score regression analysis, influenza vaccination reduced mortality risk (odds ratios (ORs) 0.58 (95% confidence interval (CI) 0.46-0.72), 0.56 (95% CI 0.44-0.71) and 0.56 (95% CI 0.45-0.69), respectively). After additional adjustment for unmeasured confounding (as observed during summer periods), the association between influenza vaccination and mortality risk decreased (OR 0.69 (95% CI 0.52-0.92)).We conclude that after state-of-the-art adjustment for typical confounders such as age, sex and comorbidity status, unmeasured confounding still biased estimates of influenza vaccine effectiveness. After taking unmeasured confounding into account, influenza vaccination is still associated with substantial reduction in mortality risk.

Effectiveness of the influenza vaccine in preventing admission to hospital and death in people with type 2 diabetes

“…13,23,24 Some authors have suggested using the preinfluenza season as a control period. Many previous studies assessing influenza vaccine effectiveness in older adults described reduced mortality associated with vaccination during the entire year.…”

“…22,23 Therefore, patients who have and who have not received vaccine should have similar risks of outcomes during the summer period after adjustment for measured confounders, with an expected IRR of 1.0 for the summer period. We used effect estimates for the summer period to adjust for residual confounding that occurred during the influenza period using the following formula: 23,24 IRR adjusted = exp(β influenza season -β summer period ) where β is the regression coefficient obtained from Poisson regression models. To calculate 95% CIs for the effect estimates, we resampled 500 times from the distribution of the observed estimates for the influenza and summer periods.…”

“…After having taken the difference of each of the 500 sampled estimates, the 2.5th and 97.5th percentile of the distribution were taken to obtain 95% CIs for the adjusted IRRs. 23,24 In all analyses, a 2-sided p ≤ 0.05 was considered statistically significant. We performed statistical analyses using Stata version 11.0.…”

Adjustment for continuous confounders: an example of how to prevent residual confounding

“…14 We selected 20 000 participants aged 65-90 years. An important confounder (among others) of the association between influenza vaccination and death was use of health care, which is related to an increased risk of death as well as an increased probability of receiving a vaccination.…”

Sensitivity analysis for the effects of multiple unmeasured confounders

“…After including a number of confounders in the adjustment model, the exposure-outcome association indeed often appears to stabilize (little change in the estimate of the exposure-outcome association is observed when additional confounders are added). 22,23 This implies that all (important) confounders are included in the model, if confounders are highly correlated. This relative stabilization of the exposure-outcome association upon adjustment for an increasing number of confounders clearly depends on the joint association between confounders.…”