Environmental sustainability in asthma: reducing carbon footprint and medication wastage
Bibliographic record
Abstract
INTRODUCTION: Asthma inhalers are significant contributors of greenhouse gas emissions. However, less is known about the potentially avoidable carbon footprint i.e. medication wastage and oversupply. We aimed to analyse dispensing patterns and carbon footprints of asthma inhalers, quantify medication wastage, and identify determinants of medication oversupply. METHODS: We reviewed the asthma-related dispensation records from 2015 to 2019, in an anonymised, cluster-wide repository linking electronic medical, pharmacy and administrative records, containing patient and visit details on demographics, comorbidities, GINA step, and site of care. Medication wastage, a visit-level measure, was defined as the number of inhalers dispensed in excess of the quantity required during each refill interval. Medication oversupply, a patient-level aggregated measure defined by medication possession ratio (MPR) > 1.2, where MPR equals total dispensed days (summed across all maintenance inhalers) divided by the follow-up period. All analyses were performed using R Studio. RESULTS: 205,337 inhaler units were dispensed over the study period, contributing an estimated 1,541,591 kgCO2e. The most frequently prescribed inhalers were SABA MDIs (79,007 units; 38.5%), followed by ICS-LABA MDIs (46,335 units; 22.6%), ICS MDIs (36,635 units; 17.8%), ICS-LABA DPIs (33,730 units; 16.4%), and ICS DPIs (9,630 units; 4.7%). ICS-LABA MDIs remained the greatest contributor of carbon footprint, with annual carbon emissions nearly doubling from 114,476 kgCO2e in 2015 to 214,575 kgCO2e in 2019. A total of 6,427 canisters were dispensed in excess of refill intervals, accounting for 46,798 kgCO2e. Beclomethasone MDIs accounted for the majority of wasted inhalers. In a multinomial regression analysis, patients receiving care in primary care settings were significantly more likely to be oversupplied medications compared to those in specialist care (OR 1.93, 95% CI 1.49-2.51). CONCLUSION: ICS-LABA MDIs are the predominant source of inhaler-related carbon footprint, with additional contribution from excessive dispensation of inhalers.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".