Corruption risks in COVID-19 vaccine deployment: lessons learned for future pandemic preparedness
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic corruption risks were amplified in health systems globally, increasing health inequities within and between countries. During the pandemic, the deployment of COVID-19 vaccines, particularly concerning their procurement and distribution, had corruption risks given the large amounts of public and private funding allocated to them, the need for speed, the involvement of a high number of stakeholders, and often insufficient oversight. To explore this issue further, we conducted a descriptive, qualitative study of corruption risks in the COVID-19 vaccine deployment process. METHODS: We conducted a descriptive, qualitative study triangulating two data sources between May and August 2022: (1) published academic and grey literature and (2) key informant interviews with representatives from organizations involved with the COVAX Facility, representatives from COVAX donor and recipient countries, and individuals with expert knowledge of the COVID-19 vaccine deployment process (e.g., consultants for international organizations involved in COVID-19 vaccine deployment, members of non-governmental organizations, etc.). RESULTS: We identified 44 academic articles and policy documents and triangulated. Documentary data with 16 key informant interviews. A review of the literature identified several corruption risks in the international COVID-19 vaccine procurement and distribution process such as a lack of transparency in the vaccine procurement process; a lack of transparency in the operation of the COVAX Facility; a risk of bribery; and a risk of vaccine theft or the introduction of substandard and falsified vaccines at the point of distribution. Key informants further articulated concerns about a lack of transparency in vaccine pricing and contracts and the exclusion of civil society organizations from the vaccine deployment process. Reported anti-corruption, transparency, and accountability (ACTA) mechanisms implemented across the many levels of the vaccine procurement and distribution deployment included institutional oversight processes, blockchain-based supply-chain solutions, and civil society engagements. CONCLUSION: Public health emergencies require nimble and quick actions on the part of governments, international organizations and other actors Our study on the COVID-19 vaccine deployment process highlights the pressing need for more robust ACTA mechanisms to reduce corruption risks and ensure fair and equitable access to lifesaving vaccines for populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".