Environmentally sustainable opportunities for health systems: metered-dose inhaler prescribing, dispensing, usage, and waste at The Ottawa Hospital
Bibliographic record
Abstract
BACKGROUND: The carbon footprint of Canada's health sector is among the worst in the world, responsible for 4·6% of Canada's total greenhouse gas emissions. A quarter of emissions from Canada's health sector are linked to pharmaceuticals, including metered dose inhalers (MDIs). MDIs use propellants, such as hydrofluorocarbons, which act as greenhouse gas emissions and contribute to the health-care sector's overall carbon footprint. The objective of this study was to describe MDI prescribing, dispensing, usage, and waste patterns at The Ottawa Hospital (Ottawa, ON, Canada). Secondary objectives included estimating the monetary and carbon cost of current practice and the potential benefits and costs of switching to the more environmentally friendly dry powder inhalers. METHODS: In this retrospective point-prevalence cohort study, we identified 100 consecutive patients from medical and surgical services at both campuses of The Ottawa Hospital from health records discharged from medical and surgical services and who were prescribed at least one MDI during their admission. Medical records were reviewed and data related to demographics, MDI prescribing, dispensing, usage, and wastage were collected using a pre-piloted electronic case report form. Financial cost was calculated using local costing estimates and carbon cost was calculated using published estimates. FINDINGS: Between Jan 1, 2023, and June 1, 2023, we collected data for 100 eligible patients, of whom 60 (60%) were female and 90 (90%) were admitted to hospital medicine wards (10% from surgical wards). The median length of stay was 7 (range 1-47) days. The most common inpatient diagnoses were respiratory tract infections in 43 (43%) of 100 patients and chronic obstructive pulmonary disease exacerbations in 28 (28%) of 100 patients. The median number of MDIs prescribed during a patients stay was two (range one to 15) and the median number dispensed was one (range one to seven). For formulary options of MDIs, of the 200 (range 30-1400) actuations dispensed per patient, 8% were used, representing 92% wastage. During the audit, 315 MDIs were dispensed in total, of which 97 were not used at all. INTERPRETATION: MDIs are significant contributors to the carbon footprint attributed to pharmaceutical use in hospitals. This study suggests that 90% of MDI doses are wasted, showing that there is substantial room for improvement. FUNDING: None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".