Efficacy and effectiveness of COVID-19 vaccines in Africa: A systematic review
Bibliographic record
Abstract
BACKGROUND: Data on COVID-19 vaccine effectiveness to support regional vaccine policy and practice are limited in Africa. Thus, this review aimed to evaluate the efficacy and effectiveness of COVID-19 vaccines administered in Africa. METHODS: We systematically searched peer-reviewed randomized controlled trials (RCTs), prospective and retrospective cohort studies, and case-control studies that reported on VE in Africa. We carried out a risk of bias assessment, and the findings of this review were synthesized and presented in a narrative form, including tables and figures. The synthesis was focused on COVID-19 VE against various levels of the disease condition and outcomes (infection, hospitalization or critical, and death), time points, and variants of concern. RESULTS: A total of 13 studies, with a total sample size of 913,285 participants, were included in this review. The majority (8/13) of studies were from South Africa and 38.5% (5/13) were randomized clinical trials. The studies reported that a full dose of Pfizer-BioNTech vaccine had a VE of 100% against COVID-19 infection by Beta (B.1.351) and Delta variants and 96.7% against hospitalization by Delta variant. The Johnson and Johnson vaccine had VE ranging from 38.1%-62.0% against hospitalization and 51.9%- 86% against critical disease by Beta (B 1.351) variant. The Oxford-AstraZeneca vaccine had a VE of 89.4% against hospitalization by the Omicron variant but was not effective against the B.1.351 variant (10.4%). The Sinopharm vaccine had a VE of 67% against infection and 46% against hospitalization by Delta variant. CONCLUSIONS: COVID-19 vaccines administered in Africa were effective in preventing infections, hospitalization, and death. These review findings underscore the need for concerted efforts of all stakeholders to enhance the access and availability of COVID-19 vaccines and reinforce public awareness to reach the high-risk, unvaccinated group of the African population.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".