We need proper explanations for apparent COVID-19 vaccine negative effectiveness
A striking phenomenon regarding COVID-19 vaccines, referred to as ‘immune imprinting’ or the more specific ‘negative effectiveness’, has been recently discussed here in The BMJ.1 Referring to Chemaitelly et al., which indicated that those with 3 doses of vaccine were more likely to be infected than those with 2,2 Monge et al. hypothesise that “the increased risk of reinfection in individuals vaccinated with a booster compared with no booster is the result” of a selection bias wherein those receiving the booster are those “more susceptible to reinfection”; a sort of counter to the hypothesised ‘healthy vaccinee bias’. Apart from the article’s inconclusive conclusion that this phenomenon “may be fully explained by selection bias”, this hypothesis would not apply to all such studies.
For example, while it could be reasonable to suppose that people opting for dose 3 and beyond would tend to be at higher risk of COVID-19, and thus more prone to reinfection, it is not obvious that this would apply to the recent study on healthcare workers presented by Shrestha et al.3 This study reveals an even greater problem. The phenomenon is not limited to boosters but is also found when comparing those receiving 2 doses to those receiving 0. In fact, Shrestha et al. indicates that each dose up to 3+ resulted in increased infections. And there are many other studies showing this phenomenon, also with regards to hospitalisations and deaths, in addition to the now widely accepted rapid waning of effectiveness, when comparing the double dosed to the unvaccinated, including another study with Chemaitelly as lead author.4 5 Several recently published papers also explain how counting window issues likely led to exaggerated effectiveness and safety estimates in both observational studies and clinical trials.6 7 8
The explanation offered up by Monge et al. fails. What we need is a proper explanation for perceived COVID-19 vaccine negative effectiveness, by the vaccine manufacturers or drug regulators. We need to know if this has always been the case or only since omicron, if the effect is dose-dependent, if certain groups are more at risk, etc. Otherwise, the notion that the benefits of the COVID-19 vaccines outweighs the risks is under threat. If the vaccines truly are negatively effective, it appears that the benefits do not outweigh the risks; there would be no benefits, and we simply add risks upon risks.
1. Monge S, Pastor-Barriuso R, Hernán MA. The imprinting effect of covid-19 vaccines: an expected selection bias in observational studies. BMJ. 2023;381:e074404. https://doi.org/10.1136/bmj-2022-074404.
2. Chemaitelly H, Ayoub HH, Tang P, et al. Long-term COVID-19 booster effectiveness by infection history and clinical vulnerability and immune imprinting: a retrospective population-based cohort study. The Lancet Infectious Diseases. 2023;23:816-27. https://doi.org/10.1016/S1473-3099(23)00058-0.
3. Shrestha NK, Burke PC, Nowacki AS, et al. Effectiveness of the Coronavirus Disease 2019 Bivalent Vaccine. Open Forum Infectious Diseases. 2023;10:ofad209. https://doi.org/10.1093/ofid/ofad209.
4. Goldberg Y, Mandel M, Bar-On YM, et al. Protection and Waning of Natural and Hybrid Immunity to SARS-CoV-2. New England Journal of Medicine. 2022;386:2201-12. https://www.nejm.org/doi/full/10.1056/NEJMoa2118946.
5. Chemaitelly H, Ayoub H, AlMukdad S, et al. Protection from previous natural infection compared with mRNA vaccination against SARS-CoV-2 infection and severe COVID-19 in Qatar: a retrospective cohort study. The Lancet Microbe. 2022;3:e944-55. https://doi.org/10.1016/S2666-5247(22)00287-7.
6. Fung K, Jones M, Doshi P. Sources of bias in observational studies of covid-19 vaccine effectiveness. Journal of Evaluation in Clinical Practice. 2023;1-7. https://doi.org/10.1111/jep.13839.
7. Lataster R. Reply to Fung et al. on COVID-19 vaccine case-counting window biases overstating vaccine effectiveness. Journal of Evaluation in Clinical Practice. 2023;1-4. https://doi.org/10.1111/jep.13892.
8. Doshi P, Fung K. How the case counting window affected vaccine efficacy calculations in randomized trials of COVID-19 vaccines. Journal of Evaluation in Clinical Practice. 2023;1-2. https://doi.org/10.1111/jep.13900.
Competing interests: No competing interests