Decarbonizing Respiratory Care: The Impact of a Low-carbon Salbutamol Metered-dose Inhaler
Bibliographic record
Abstract
Abstract Rationale: The World Health Organization considers climate change to be the biggest global health issue; patients with chronic respiratory diseases are particularly susceptible to variable weather conditions and extreme weather events.1 Short-acting β2-agonists (SABAs) are typically used as reliever medications for the short-term relief of asthma and chronic obstructive pulmonary disease symptoms. They are also responsible for 70% of total inhaler-related greenhouse gas (GHG) emissions.2 The development of metered-dose inhaler (MDI) devices that contain low global warming potential (GWP) propellants aims to reduce the carbon footprint of MDIs and balance reduced GHG emission goals with patient health and wellbeing. A new candidate propellant, hydrofluoroalkane (HFA)-152a, has a substantially reduced GWP100 (GWP over 100-year time horizon), expressed as carbon dioxide equivalence (CO2e), compared with the currently used HFA-134a propellant. The aim of this study was to assess the carbon footprints of salbutamol HFA-152a MDI, salbutamol HFA-134a MDI, and a salbutamol dry-powder inhaler (DPI). Methods: Three cradle-to-grave lifecycle analyses (LCA) were undertaken following ISO 14040, 14044 and 14067 standards along with PAS 2050 methodology, to compare the carbon footprints of salbutamol HFA-152a MDI, salbutamol HFA-134a MDI and a DPI (salbutamol). Over 600 individual emission factors were calculated from over 2000 data points and categorized into active pharmaceutical ingredients manufacture, micronization, device, formulation and packaging, use phase, distribution and end of life stages. Data were collected in 2023 from seven countries: Algeria, Australia, Canada, France, Poland, Romania and Saudi Arabia. The LCA were independently verified by the Carbon Trust. Results: Based on the data from all study countries together, the average carbon footprint values were 27.09, 2.24 and 0.76 kg CO2e per device for salbutamol HFA-134a MDI, salbutamol HFA-152a MDI and salbutamol DPI, respectively. This represents an approximate 92% reduction in carbon footprint for salbutamol HFA-152a MDI compared with salbutamol HFA-134a MDI. The difference was mainly driven by the patient use phase (Table). Conclusions: Substituting the currently available HFA-134a propellant with a new HFA-152a candidate propellant could substantially reduce the carbon footprint of a SABA reliever. This switch may be implemented in clinical practice, as patients are already familiar with this inhaler device. Funding: GSK (study ID: 223743). References: 1. Andersen ZJ, et al. Breathe (Sheff). 2023;19:220222. 2. Alzaabi A, et al. Adv Ther. 2023;40:4836-4856.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".