Environmental sustainability in diabetes: improving the quality of diabetes management through HTA and system-level change?
Bibliographic record
Abstract
Diabetes affects over 500 million people worldwide and contributes substantially to the environmental impact of health care, including carbon emissions and plastic waste. As healthcare systems globally aim to reduce their environmental footprint, there is a need to embed environmental sustainability into decision making and foster innovation in health and life sciences.This commentary outlines the environmental sustainability challenges and opportunities across the diabetes care pathway, highlighting innovations that reduce the demand for healthcare resources and associated environmental impact. We discuss the current and potential role of health technology assessment (HTA) agencies in promoting more sustainable health systems, by incorporating environmental considerations into the value assessment of technologies. Several approaches, such as integrated and parallel evaluation, are emerging to support this aim, whereas HTA agencies increasingly consider parameters of environmental life cycle assessment (eLCA), a comprehensive framework for evaluating the environmental sustainability of technology. Although a framework is evolving, early implementation by HTA bodies, for example, in the United Kingdom, Thailand, Canada, and Italy, highlights growing momentum. Moreover, sustainability policies at government and health system levels are developing globally, signaling opportunities to incorporate environmental sustainability in HTA (ESHTA).Given the scale of health care's environmental footprint, large disease areas offer critical opportunities for sustainable action. Diabetes, with its growing global prevalence, presents a particularly suitable domain for piloting the integration of environmental sustainability into HTA.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".