Expediting approval for medical countermeasures to address high burden disease: an ethical justification to move beyond emergency use authorisation
Bibliographic record
Abstract
Addressing global health crises requires a receptive and expedient policy environment to minimise delays in making available potentially life-saving technologies. Over time, the policy environment has adapted to ensure that communities have expedited access to promising technologies, such as vaccines, that can mitigate morbidity and mortality. Emergency authorisations are one such policy mechanism. While these have been employed successfully for several diseases, such as influenza, Ebola and COVID-19, the policy mechanism is tied to contexts where key bodies have designated the disease an 'emergency', whereas no equivalent mechanism exists for those failing to acquire the designation (eg, malaria and tuberculosis). In this paper, we examine ethical issues associated with emergency authorisations. We argue that there is no moral difference between those diseases considered emergencies and many that fail to be designated as such with respect to impact on affected communities. Thus, tying access to an expedient policy mechanism for approval to an emergency designation is ethically unjustified-it should be based on considerations of risks and benefits, the disease burden and the values of the communities that carry those risks and not contingent on if the disease is designated an emergency. We suggest the need to further enhance the policy environment to ensure access to similar expedited approval programmes irrespective of if a disease is an emergency. Levelling the field for access to expedited approval programmes across diseases can help in moving towards achieving global health equity but is not a panacea.
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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 | 0.136 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.061 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.052 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".