Identification of Vaccine Effects When Exposure Status Is Unknown

Abstract

Results from randomized controlled trials (RCTs) help determine vaccination strategies and related public health policies. However, defining and identifying estimands that can guide policies in infectious disease settings is difficult, even in an RCT. The effects of vaccination critically depend on characteristics of the population of interest, such as the prevalence of infection, the number of vaccinated, and social behaviors. To mitigate the dependence on such characteristics, estimands, and study designs, that require conditioning or intervening on exposure to the infectious agent have been advocated. But a fundamental problem for both RCTs and observational studies is that exposure status is often unavailable or difficult to measure, which has made it impossible to apply existing methodology to study vaccine effects that account for exposure status. In this study, we present new results on this type of vaccine effects. Under plausible conditions, we show that point identification of certain relative effects is possible even when the exposure status is unknown. Furthermore, we derive sharp bounds on the corresponding absolute effects. We apply these results to estimate the effects of the ChAdOx1 nCoV-19 vaccine on SARS-CoV-2 disease (COVID-19) conditional on postvaccine exposure to the virus, using data from a large RCT.

The need for a clinical case definition in test-negative design studies estimating vaccine effectiveness

“…The application of a clinical case definition in test-negative studies provides some reassurance that the non-case group reflects the source population of the cases 12 . While this requirement increases the likelihood that the non-cases have a similar risk of exposure to the SARS-CoV-2 virus, it does not guarantee it.…”

Distinguishing Immunologic and Behavioral Effects of Vaccination

“…31 However, such information is often hard to obtain. 32 Yet Assumption 5 seems to be plausible whenever the vaccine does not induce noticeable (side) effects, for example, when blinding is successful. When both Assumptions 2 and 5 hold, we can interpret (3) and (4) as direct and indirect effects, quantifying immunologic and behavioral components, respectively.…”

“…31 However, such information is often hard to obtain. 32 Yet Assumption 5 seems to be plausible whenever the vaccine does not induce noticeable (side) effects, for example, when blinding is successful.…”

Revisiting assumptions in test-negative studies for estimating vaccine effectiveness: the need for a clinical case definition

“… 12 While this requirement increases the likelihood that the non-cases have a similar risk of exposure to the virus, it does not guarantee it; i.e., some non-cases may still fail to meet the exposure necessity assumption. 11 Moreover, the use of clinical criteria seeks to address internal validity; generalizability is limited to the healthcare seeking population. 12 In some special cases, it may be possible to estimate VE in the whole population; for example, when participants are recruited through point-prevalence surveys 13 or in studies that limit participants to close contacts of a case such as household transmission studies.…”