The potential for unmeasured confounding exists in any observational design. Exploring the nature of this uncertainty is important to the interpretation of observational studies to assess vaccine effectiveness [15
]. This study demonstrates a simple way to reveal the potential impact of unmeasured confounding that is related to temporary immunity on estimates of vaccine effectiveness using stratified tables. Overall we show that the presence of unmeasured confounder that is related to temporary immunity could possible bias the risk estimates away from the null and that this would result negatively for seasonal influenza (positive VE) and positively for pH1N1 (negative VE). The observed odds ratios are lower than what was observed in the studies by Skowronski et al
]; however, the direction of effect is consistent such that a protective effect for seasonal influenza and an increased risk for pandemic influenza may be observed. We have to acknowledge that this is an artificial, simplified and constructed scenario with a range of assumptions that are subject to uncertainty. Furthermore, it is possible that the baseline assumptions may differ from what was observed during the pandemic, but we believe them to be within a reasonable range of what is observed through a number of different data sources. This should be conceptualized as a thought experiment that illustrates the effect of confounding that is related to temporary immunity in a way that might be helpful for epidemiologists in the field.
Some unique circumstances during the 2009 pandemic in Canada may have affected the observations made, including the close timing of the first wave in relation to the end of the previous winter season of respiratory infections. Modelling has shown that this timing is critical [16
]. It is also worth noting that winter regularly brings a mixture of different influenza and other respiratory viruses. If it is true that the activation of temporary immunity by one respiratory virus might affect susceptibility to others, then this effect may operate to a greater or lesser extent during every influenza season, and would also be likely to vary during any given season. Given the relationship between respiratory infections and vaccination, there is significant potential for this to be an important contributor to unexplained variation in observed vaccine effectiveness. Such variation includes apparent lack of concordance between influenza strain match and vaccine effectiveness. No conclusions can be drawn about the biology of temporary immunity from this study, but these findings support further immunological research into potential mechanisms.
If unmeasured confounding distorts observations of vaccine effectiveness this creates a huge challenge for influenza epidemiologists. The influenza vaccine can be confounded more so than other vaccine exposures due to the fact that the uptake is not universal in all populations. Influenza vaccine uptake will vary according to health care access, indications, occupations and underlying medical conditions, all of which can also influence the likelihood of acquiring influenza.
Confounding is most effectively addressed either through design or through measurement so that we can adjust for its effects during the analysis. It is much harder to design out confounding in observational studies compared to randomized controlled trials. For observational studies on vaccine effectiveness the best solution is to measure confounders and adjust for them in the analysis. This requires greater sample size as well as the ability to accurately measure confounders using feasible and reproducible methods. Other methods to address unmeasured confounding include sensitivity analyses [4
] and simulation [12
], which require complex approaches as well as good baseline data into assumptions of potential confounders that exist in the population.
This analysis is subject to baseline assumptions and furthermore did not consider joint or multiplicative effects; thus, may not reflect the situation during the 2009 pandemic. Nevertheless it demonstrates that failure to control for confounders that may bias the associations with the seasonal influenza vaccine (or the pandemic influenza vaccine) will affect the measures of associations. Furthermore these measures of associations can influence different respiratory outcomes (i.e. seasonal influenza versus pandemic influenza) differently.