Environmental sustainability in health technology assessment: an analysis of the activities of Canada’s Drug Agency and the England’s NICE
Bibliographic record
Abstract
OBJECTIVES: Medicines and devices have significant negative impacts on the environment. Increasingly, Health Technology Assessment (HTA) agencies, which inform healthcare decision making, are expected to integrate environmental issues into their assessments. This study assessed how HTA agencies have responded to these calls, with a focus on two national agencies that have committed to this agenda. METHODS: This descriptive study was conducted using document review. All relevant documents from both agencies were systematically collected and analyzed using descriptive statistics and content analysis. RESULTS: Thirty documents (2015-2024), from Canada's Drug Agency (CDA) (17) and England's National Institute for Health and Care Excellence (NICE) (13) that included environmental considerations were analyzed. Although no HTAs have assessed environmental data, primarily due to a lack of data and methods, documents demonstrate that CDA and NICE are employing varied strategies to incorporate environmental considerations through technology guidance. The agencies demonstrate both differences and similarities in approach: NICE focused on carbon and the use phase, whereas CDA focused on multiple environmental impacts across the lifecycle; both agencies are beginning to address the environmental impacts of devices, but there is a notable absence of medicines-related work. CONCLUSIONS: This study demonstrates that the agencies are exploring alternative strategies to elevate attention to the environmental impacts of health technologies. Differences in focus (e.g., whether to prioritize carbon emissions) and shared inattention to medicines point to deeper tensions. Thus, although both agencies have taken important steps forward, much work remains to fully address the environmental harms of health technologies.
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 | 0.036 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".