P-2054. Effectiveness of a Single COVID-19 mRNA Vaccine Dose in Individuals Previously Infected with SARS-CoV-2: A Systematic Review
Bibliographic record
Abstract
Abstract Background Based on the high levels of population immunity to SARS-CoV-2 from prior infection in many countries, in fall 2023 the US Food and Drug Administration, European Medicines Agency, and World Health Organization authorized/recommended a single mRNA XBB.1.5-adapted vaccine dose for unvaccinated individuals aged ≥ 5y. Methods This systematic literature review evaluated the vaccine effectiveness (VE) of a single COVID-19 mRNA dose (BNT162b2 or mRNA-1273) in individuals previously infected with SARS-CoV-2 compared to those with differing histories of prior infection and vaccination (PROSPERO ID: CRD42023453257). We searched MEDLINE and Embase for VE studies published from January 1, 2021–October 4, 2023. Data were synthesized following Synthesis Without Meta-Analysis guidelines. Bias was assessed using the Newcastle-Ottawa Scale. VE estimates were reported as a range of point estimates per clinical endpoint and meta-analyses were not performed due to heterogeneity. Results Of 1,661 initial results, 18 studies were eligible for inclusion. Among those previously infected, a single mRNA dose (compared to being unvaccinated) increased protection by 8–71% against infection (related to Omicron BA.1, BA.4/5, or XBB), 39–67% against symptomatic infection (BA.1, BA.2, or BA.4/5), and 25–60% against hospitalization or hospitalization or death (BA.1). The protection provided by a single dose among those previously infected was comparable to the VE provided by two doses, and was higher than VE following two doses without prior infection. No studies were identified that reported single-dose VE for adapted vaccines (BA.1 or BA.4/5 bivalent or XBB.1.5-adapted formulations), immunocompromised populations, or children aged < 5y. Conclusion Our results suggest that a single dose of COVID-19 vaccine provides similar protection to that conferred by a two-dose series for immunocompetent individuals aged ≥ 5y in the current setting of high pre-existing SARS-CoV-2 immunity. These findings support current recommendations for one dose of COVID-19 vaccines regardless of vaccination status to be given in advance of the viral respiratory season with considerations for additional doses for certain special populations including young children, older adults, and the immunocompromised. Disclosures Hannah R. Volkman, PhD, MPH, Pfizer Inc: Employment|Pfizer Inc: Stocks/Bonds (Public Company) Jennifer L Nguyen, ScD, MPH, Pfizer Inc: Employment|Pfizer Inc: Stocks/Bonds (Public Company) Mustapha M. Mustapha, MD, PhD, MPH, Pfizer Inc: Employment|Pfizer Inc: Stocks/Bonds (Public Company) Jingyan Yang, MHS, DrPH, Pfizer Inc: Employment|Pfizer Inc: Stocks/Bonds (Public Company) Luis Jodar, PhD, Pfizer Inc: Employment|Pfizer Inc: Stocks/Bonds (Public Company) John M. McLaughlin, PhD, Pfizer: Employee|Pfizer: Stocks/Bonds (Public Company)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".