Reductions in inhaler greenhouse gas emissions by addressing care gaps in asthma and chronic obstructive pulmonary disease: an analysis
Bibliographic record
Abstract
INTRODUCTION: Climate change from greenhouse gas (GHG) emissions represents one of the greatest public health threats of our time. Inhalers (and particularly metred-dose inhalers (MDIs)) used for asthma and chronic obstructive pulmonary disease (COPD), constitute an important source of GHGs. In this analysis, we aimed to estimate the carbon footprint impact of improving three distinct aspects of respiratory care that drive avoidable inhaler use in Canada. METHODS: We used published data to estimate the prevalence of misdiagnosed disease, existing inhaler use patterns, medication class distributions, inhaler type distributions and GHGs associated with inhaler actuations, to quantify annual GHG emissions in Canada: (1) attributable to asthma and COPD misdiagnosis; (2) attributable to overuse of rescue inhalers due to suboptimally controlled symptoms; and (3) avoidable by switching 25% of patients with existing asthma and COPD to an otherwise comparable therapeutic option with a lower GHG footprint. RESULTS: We identified the following avoidable annual GHG emissions: (1) ~49 100 GHG metric tons (MTs) due to misdiagnosed disease; (2) ~143 000 GHG MTs due to suboptimal symptom control; and (3) ~262 100 GHG MTs due to preferential prescription of strategies featuring MDIs over lower-GHG-emitting options (when 25% of patients are switched to lower GHG alternatives). Combined, the GHG emission reductions from bridging these gaps would be the equivalent to taking ~101 100 vehicles off the roads each year. CONCLUSIONS: Our analysis shows that the carbon savings from addressing misdiagnosis and suboptimal disease control are comparable to those achievable by switching one in four patients to lower GHG-emitting therapeutic strategies. Behaviour change strategies required to achieve and sustain delivery of evidence-based real-world care are complex, but the added identified incentive of carbon footprint reduction may in itself prove to be a powerful motivator for change among providers and patients. This additional benefit can be leveraged in future behaviour change interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".