Switching to the Dry Powder Inhaler: Disease Control with a Lower Carbon Footprint
Bibliographic record
Abstract
Dry powder inhalers (DPIs) have a 20–40-fold lower carbon footprint compared to pressurized metered-dose inhalers (pMDIs). Switching from pMDI to DPI is therefore beneficial from an environmental perspective, but many health care professionals are concerned that this may worsen treatment outcomes in asthma and chronic obstructive pulmonary disease (COPD). We analyzed patient outcomes and carbon footprints of switching inhaler treatment from pMDI to DPI. We performed a post hoc analysis on clinical outcomes data from a 12-week real-world, non-interventional study of adult patients with asthma or COPD who switched treatment from pMDI to the budesonide–formoterol Easyhaler DPI. Clinical end points included asthma control test (ACT), Mini-Asthma Quality of Life Questionnaire (Mini-AQLQ), lung function tests, and reliever use (asthma), and COPD assessment test (CAT), and modified Medical Research Council dyspnea scale (mMRC) (COPD). In the carbon footprint calculation, we used estimates from the Montreal Protocol for pMDI and for DPI the estimate as reported. Among all 237 patients (142 asthma, 95 COPD) by switching their treatment clinical improvements were observed in all the outcome measures ( p < 0.001). Furthermore, the need for reliever medication decreased among patients with asthma ( p < 0.001). The amount of estimated kg CO 2 e emissions per year for maintenance treatment was 97.0% lower for the DPI than for pMDI. For reliever medication among patients with asthma, it was 99.6% lower. Among them, the emission savings could amount to approximately 131 kg CO 2 e annually. This is of similar magnitude, as individual high-impact environmental actions such as eating a plant-based diet or purchasing green energy. Our results show that disease control was maintained among patients with asthma or COPD when they switched from pMDI to DPI, while the carbon footprint of inhaler treatment was reduced.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".