Modelling the climate impact of inhalers and mitigation strategies: a population-based study in British Columbia, Canada (2015–2032)
Bibliographic record
Abstract
BACKGROUND: Canada has one of the highest per capita greenhouse gas (GHG) emissions, with healthcare contributing ~5% of the total. Pressurised metered-dose inhalers (pMDIs) are significant contributors due to their use of hydrofluorocarbon propellants. While propellant-free dry powder inhalers (DPIs) and soft mist inhalers (SMIs) are available, their adoption remains limited. This population-based study evaluates inhaler dispensation trends in British Columbia (BC), Canada, projects future dispensation and emissions over the next decade, and explores mitigation strategies through pMDI substitution. METHODS: Historical inhaler dispensation data (2015-2022) from BC were analysed using negative binomial models to assess trends, project future usage and emissions (2023-2032) and evaluate four substitution scenarios replacing pMDIs with low-GHG alternatives or DPIs/SMIs. Emissions were estimated by inhaler type, sex, age and health region, with uncertainties addressed through Monte Carlo simulation for the projected values. RESULTS: An average of 2.1 million inhalers are dispensed annually in BC, with pMDIs comprising 64% of total inhaler use but contributing 98% of the ~30 000 tonnes of GHG emissions. There was regional variation and older populations contributed disproportionately, reflecting burden of disease. From 2015 to 2022 (excluding 2020 and 2021, the COVID-19 years), pMDI dispensations decreased by 1% annually while DPI/SMI dispensations increased by 5%. Projections show that, without intervention, emissions could rise to ~37 000 tonnes by 2032, varying by age group. All substitution scenarios, by replacing pMDIs with DPIs/SMIs, could reduce emissions by up to 42%. CONCLUSION: High quality, guideline-directed diagnosis and management of respiratory disease is known to improve health and reduce emissions. Building on these benefits, our analysis shows that substituting pMDIs with lower-emission inhalers, when guided by policy and clinical decisions that prioritise patient safety and preference, can significantly reduce healthcare-related GHG emissions.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".