Comparison of the Effectiveness and Safety of Heterologous Booster Doses with Homologous Booster Doses for SARS-CoV-2 Vaccines: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
As vaccine resources were distributed unevenly worldwide, sometimes there might have been shortages or delays in vaccine supply; therefore, considering the use of heterogeneous booster doses for Coronavirus disease 2019 (COVID-19) might be an alternative strategy. Therefore, we aimed to review the data available to evaluate and compare the effectiveness and safety of heterologous booster doses with homologous booster doses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. We searched relevant studies up to 27 April 2022. Random-effects inverse variance models were used to evaluate the vaccine effectiveness (VE) and its 95% confidence interval (CI) of COVID-19 outcomes and odds ratio (OR) and its CI of safety events. The Newcastle–Ottawa quality assessment scale and Cochrane Collaboration’s tool were used to assess the quality of the included cohort studies. A total of 23 studies involving 1,726,506 inoculation cases of homologous booster dose and 5,343,580 inoculation cases of heterologous booster dose was included. The VE of heterologous booster for the prevention of SARS-CoV-2 infection (VEheterologous = 96.10%, VEhomologous = 84.00%), symptomatic COVID-19 (VEheterologous = 56.80%, VEhomologous = 17.30%), and COVID-19-related hospital admissions (VEheterologous = 97.40%, VEhomologous = 93.20%) was higher than homologous booster. Compared with homologous booster group, there was a higher risk of fever (OR = 1.930, 95% CI, 1.199–3.107), myalgia (OR = 1.825, 95% CI, 1.079–3.089), and malaise or fatigue (OR = 1.745, 95% CI, 1.047–2.906) within 7 days after boosting, and a higher risk of malaise or fatigue (OR = 4.140, 95% CI, 1.729–9.916) within 28 days after boosting in heterologous booster group. Compared with homologous booster group, geometric mean neutralizing titers (GMTs) of neutralizing antibody for different SARS-CoV-2 variants and response rate of antibody and gama interferon were higher in heterologous booster group. Our findings suggested that both homologous and heterologous COVID-19 booster doses had great effectiveness, immunogenicity, and acceptable safety, and a heterologous booster dose was more effective, which would help make appropriate public health decisions and reduce public hesitancy in vaccination.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".