Toward including environmental sustainability in Health Technology Assessment
Bibliographic record
Abstract
INTRODUCTION: The life cycle of health technologies contribute to air pollution, ecotoxicity, and resource depletion, impacting the environment and human health. Increasing healthcare resource use globally increases emissions that accelerate climate change and negatively affect the health of current and future generations.Health Technology Assessment (HTA) should inform decision makers to prioritize the adoption of technologies demonstrating value in terms of health benefits, costs, and other relevant dimensions such as environmental sustainability.This paper reports on a multistakeholder approach to guiding an international working group for Environmental Sustainability in Health Technology Assessment (ESHTA) that has been formed by Health Technology Assessment international. METHODS: A multistakeholder online workshop was held with 32 participants in May 2024 to define the critical issues to be considered. The resulting report underwent consultation among the ESHTA members and in a broader group of 90 additional worldwide stakeholder representatives. RESULTS: The workshop participants recognized defining frameworks, mechanisms, and tools for embedding environmental sustainability into HTA as an opportunity to support sustainable development and quality improvement in healthcare. Achieving this requires (1) consensus on what environmental sustainability in healthcare means, (2) reconcilement with other healthcare and environmental policies, and (3) methods that are useful and applicable within HTA frameworks. CONCLUSION: This novel collaboration aims to align the global HTA community on the role of environmental sustainability in HTA. The report provides a path for the way forward for incorporating environmental sustainability into HTA based on broad perspectives from global multistakeholders.
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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 | 0.117 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".