Knowledge of the protection afforded by vaccines might, in some circumstances, modify a vaccinated individual's behaviour, potentially increasing exposure to pathogens and hindering effectiveness. Although vaccine studies typically do not explicitly account for this possibility in their analyses, we argue that natural direct effects might represent appropriate causal estimands when an objective is to quantify the effect of vaccination on disease while blocking its influence on behaviour. There are, however, complications of a practical nature for the estimation of natural direct effects in this context. Here, we discuss some of these issues, including exposure-outcome and mediator-outcome confounding by healthcare seeking behaviour, and possible approaches to facilitate estimates of these effects. This work highlights the importance of data collection on behaviour, of assessing whether vaccination induces riskier behaviour, and of understanding the potential effects of interventions on vaccination that could turn off vaccine's influence on behaviour.
Vaccination is a preventive intervention whose uptake may be correlated with other healthprotective behaviours. However, vaccination may also lead to risk compensation, resulting in additional exposures compared to unvaccinated individuals. Hence, the real-world impact of vaccines could be affected by behavioural changes that occur due to knowledge of the vaccine's protective effect. This risk compensation could result in a reduced level of protection from vaccination in real life compared to what might be expected in a blinded vaccine trial. Specifically, vaccinated individuals might engage in activities that increase their risk of infection, either by choice or because of societal or cultural norms, e.g., by increasing the number of social contacts that can lead to an infection. Thus, during the COVID-19 pandemic, several studies [1][2][3][4] were performed to assess the influence of vaccination on behaviour associated with infection risk. For example, in France, McColl and colleagues [5] analysed online surveys on protective behaviours and showed that vaccinated individuals were less likely to avoid social gatherings and to wear masks in some of the months when surveys were performed. In Canada [6], vaccinated individuals with comorbidities had more social contacts in the third wave of the COVID-19 pandemic compared to unvaccinated individuals with comorbidities. Consistent with this, knowledge about vaccination has been associated with high-risk behaviour [7].
Here, we argue that, although risk compensation might not be pervasive, effectiveness studies should consider this unintended consequence of vaccination both to quantify an effect that is independent of changes in behaviour and to understand potential benefits of interventions that could prevent risk compensation. In particular, we suggest that, along with estimands typically targeted in vaccine studies, a causal estimand that corresponds to the effect of vaccination in the presence of an intervention that blocks its influence on behaviour might be of value for policy makers, in addition to being of scientific interest in its own right. Such an intervention could, for example, involve encouraging individuals not to change behaviour after vaccination, which might be, in some settings, more realistic than interventions that aim to set behaviour to a particular level for all individuals in a population. In fact, this latter type of intervention could be conceived of as being related to controlled direct effects, whilst the former type of intervention (that does not fix behaviour of all individuals to the same level, but rather, by blocking the influence of vaccination, allows natural variation in behaviour) is relevant to natural direct effects.
Below, to facilitate analyses of direct (with respect to behavioural factors) effects of vaccines on clinical outcomes, we present relevant causal diagrams, formally define the proposed effect, which is, as implied above, a natural direct effect, discuss identification, and compare this effect with that estimated in blinded trials. Further, we discuss practical issues in the study of this estimand. For completeness, we also discuss the distinct question of assessing effects of vaccination on behaviour when information on the latter is not available and the potential impact of interference. Throughout, we focus on observational studies and consider confounding by healthcare seeking behaviour, which might affect many vaccine effectiveness analyses.
Consider a hypothetical study undertaken during an epidemic. Let 𝐴 denote vaccination (1 = vaccinated, 0 = not vaccinated), and 𝑌, the outcome of interest (1 = disease caused by the pathogen targeted by vaccination, 0 = no disease caused by the pathogen). In Panel I of Figure 1, we present the scenario described above; 𝐵 corresponds to infection-related behaviour (e.g., number of social contacts of a relevant type per time unit) and is assessed after vaccine assignment. We assume that 𝐵 is a mediator of the effect of 𝐴 on 𝑌. Although we consider a scenario where vaccination, on average, leads to “riskier” behaviour among the vaccinated, in some situations, the behavioural changes might be linked to absence of vaccination. For example, compliance with public health measures during the COVID-19 pandemic was reported, in some countries, to be lowest among unvaccinated individuals [8]. Another possibility is for absence of vaccination to require safer behaviours. For example, some jurisdictions or healthcare facilities have required healthcare workers who choose not to be vaccinated against influenza to wear face masks [9,10].
In observational studies, there might be factors that determine both vaccination and outcome; we denote common causes of 𝐴 and 𝑌 (that is, confounders) by 𝐿. One of these common causes is healthcare seeking behaviour; for instance, individuals who are more likely to be vaccinated might also be more likely to go to a clinic or hospital if they b
This content is AI-processed based on open access ArXiv data.