Environmentally sustainable opportunities for health systems: Metered-dose inhaler prescribing, dispensing, use and waste at a tertiary academic centre
Bibliographic record
Abstract
Background: The Canadian health sector's carbon footprint is among the highest in the world and is responsible for 4.6% of Canada's total greenhouse gas emissions, a quarter of which is linked to pharmaceuticals, with metered-dose inhalers (MDIs) contributing disproportionally high amounts. Objectives: To describe MDI prescribing, dispensing, use and waste patterns at a Canadian tertiary care academic hospital. Methods: In a retrospective point-prevalence cohort study, 100 consecutive patients discharged from medical and surgical services who were prescribed at least 1 MDI during their admission were included. Data were collected to describe patient demographics, MDI prescribing, dispensing, use and waste patterns. Use and waste data were applied to annual purchasing data to estimate annual usage and waste. Financial cost was computed using local purchasing estimates and carbon cost was calculated using published estimates. Results: In 100 consecutively discharged patients, 315 MDIs were dispensed in total, of which 96 were unused. This represents 61,440 actuations dispensed, with 56,773 (92%) of doses unused or wasted. Waste data were applied to annual estimates, with a calculated annual carbon footprint of 315.8 tons of carbon dioxide equivalent (tCO2e). We estimate that a 20% waste reduction would result in carbon savings of 68.5 tCO2e. If 20% of salbutamol prescriptions were switched to the dry powder inhaler alternative, terbutaline, a 14% reduction in waste would be required to offset the additional monetary cost. Conclusions: This study suggests that 92% of MDI doses are unused and wasted. Opportunities for waste reduction exist and would be associated with both financial and carbon savings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".