P1-9 The health impact fund-meeting the challenge of health impact assessment
Bibliographic record
Abstract
Introduction The Health Impact Fund (HIF) is a publicly funded international agency proposed to enable pharmaceutical innovators to register a product for health impact rewards in exchange for selling it worldwide at cost. Supplementing the current patent regime, the HIF would improve incentives to research diseases concentrated among the poor. A workable HIF presupposes a consistent, predictable, and contractible method of health impact assessment. Methods We reviewed the literature using search terms,“health impact assessment tools” and an exploratory workshop for all stakeholders was held at the National Institute for Health and Clinical Excellence in April 2010. Results Although there are many challenges with the nature of current epidemiological data and their application to global health, there is scope for improvement and the HIF may help to trigger and sustain such enhancements. Moreover, the HIF would use much more information than the present system. The following steps in health impact assessment need to occur for each registered product: defining subgroups, establishing baseline treatments, defining incremental health impact by subgroup, measuring the numbers of patients treated in each subgroup, and a process of appeal. Conclusion Health impact assessment is a new science, and complexities involved in assessing new drugs in the global context are formidable. However, initial models suggest that the HIF could significantly change the focus of drug innovation. Only pilot studies will properly test the HIF's underlying principles and uncover the practical challenges which will determine its implementation and effectiveness. The creation of the HIF could bring great advances in epidemiological data collection and its application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.097 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".