BACKGROUND. Circadian rhythms are evident in basic immune processes, but it is unclear if rhythms exist in clinical endpoints like vaccine protection. Here, we examined associations between COVID-19 vaccination timing and effectiveness. METHODS. We retrospectively analyzed a large Israeli cohort with timestamped COVID-19 vaccinations (n = 1,515,754 patients over 12 years old, 99.2% receiving BNT162b2). Endpoints included COVID-19 breakthrough infection and COVID-19-associated emergency department visits and hospitalizations. Our main comparison was among patients vaccinated during morning (800-1159 hours), afternoon (1200-1559 hours), or evening hours (1600-1959 hours). We employed Cox regression to adjust for differences in age, sex, and comorbidities. RESULTS. Breakthrough infections differed based on vaccination time, with lowest the rates associated with late morning to early afternoon and highest rates associated with evening vaccination. Vaccination timing remained significant after adjustment for patient age, sex, and comorbidities. Results were consistent in patients who received the basic 2-dose series and who received booster doses. The relationship between COVID-19 immunization time and breakthrough infections was sinusoidal, consistent with a biological rhythm that modifies vaccine effectiveness by 8.6%-25%. The benefits of daytime vaccination were concentrated in younger (<20 years old) and older patients (>50 years old). COVID-19-related hospitalizations varied significantly with the timing of the second booster dose, an intervention reserved for older and immunosuppressed patients (HR = 0.64, morning vs. evening; 95% CI, 0.43-0.97; P = 0.038). CONCLUSION. We report a significant association between the time of COVID-19 vaccination and its effectiveness. This has implications for mass vaccination programs.
CITATION STYLE
Hazan, G., Duek, O. A., Alapi, H., Mok, H., Ganninger, A., Ostendorf, E., … Haspel, J. A. (2023). Biological rhythms in COVID-19 vaccine effectiveness in an observational cohort study of 1.5 million patients. Journal of Clinical Investigation, 133(11). https://doi.org/10.1172/JCI167339
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