To evaluate inhaler sustainability in asthma and Chronic Obstructive Pulmomary Disease (COPD) by analysing how inhaler design typology, prescribing and usage patterns, disposal and recycling practices influence human health and environmental outcomes, using a People-Process-Product (PPP) framework to identify actionable opportunities for improvement.
A systematic review was conducted in May 2024, with reporting structured around the PPP framework using narrative synthesis.
MEDLINE, Scopus, Cochrane Library and relevant grey literature were searched for publications over the period from April 2014 to April 2024.
Studies were included if published between 2014 and 2024, involved patients with asthma or COPD and healthcare professionals and specifically examined aspects of inhaler sustainability, including patient behaviours, healthcare provider prescribing practices and environmental impacts.
Two independent reviewers screened and extracted data from 63 studies. Due to diverse methodologies, quality assessment focused on research design robustness, completeness of outcome reporting and potential biases. Findings were synthesised narratively to address each research question using the PPP framework.
33% of included studies focused on two or more domains of the PPP framework as both primary and/or secondary outcomes. Studies mapped to the ‘People’ domain (n=34) showed limited awareness among patients and clinicians regarding the environmental impact of inhaler prescription patterns, use patterns and disposal methods, with over 75% of patients discarding inhalers in household waste. In the ‘Process’ domain (n=11), switching from pressurised metered-dose inhalers (pMDIs) to dry powder inhalers (DPIs) or soft mist inhalers (SMIs) was associated with improved inhaler adherence and asthma control, though uptake of new inhalers was influenced by patients’ prior experience, competence, proficiency and perceived usability. The ‘Product’ domain (n=41) showed that DPIs and SMIs consistently had lower carbon footprints than pMDIs, with short-acting beta-agonists (SABAs) pMDIs having the highest emissions due to prescription, use patterns and disposal.
Improving patient education on sustainable inhaler use and disposal and providing healthcare professionals with focused training on low-carbon prescribing are critical steps towards achieving significant clinical benefits and supporting environmental sustainability in asthma and COPD management.
CRD42024541927.
SARS-CoV-2 infection provides protection against reinfection and severe COVID-19 disease; however, this protective effect may diminish over time. We assessed waning of natural immunity conferred by previous infection against severe disease and symptomatic reinfection in Brazil and Scotland.
We undertook a test-negative design study and nested case–control analysis to estimate waning of natural immunity against severe COVID-19 outcomes and symptomatic reinfection using national linked datasets. We used logistic regression to estimate ORs with 95% CIs. A stratified analysis assessed immunity during the Omicron dominant period in Brazil.
We included data from the adult populations of Brazil and Scotland from 1 June 2020 to 30 April 2022.
Severe COVID-19 was defined as hospitalisation or death. Reinfection was defined as reverse-transcriptase PCR or rapid antigen test confirmed at least 120 days after primary infection.
From Brazil, we included 30 881 873 tests and 1 301 665 severe COVID-19 outcomes, and from Scotland, we included 1 520 201 tests and 7988 severe COVID-19 outcomes. Against severe outcomes, sustained protection was observed for at least 12 months after primary SARS-CoV-2 infection with little evidence of waning: 12 months postprimary infection: Brazil OR 0.12 (95% CI 0.10 to 0.14), Scotland OR 0.03 (95% CI 0.02 to 0.04). For symptomatic reinfection, Brazilian data demonstrated evidence of waning in the 12 months following primary infection, although some residual protection remained beyond 12 months: 12 months postprimary infection: OR 0.42 (95% CI 0.40 to 0.43). The greatest reduction in risk of SARS-CoV-2 infection was in individuals with hybrid immunity (history of previous infection and vaccination), with sustained protection against severe outcomes at 12 months postprimary infection. During the Omicron dominant period in Brazil, odds of symptomatic reinfection were higher and increased more quickly over time when compared with the overall study period, although protection against severe outcomes was sustained at 12 months postprimary infection (whole study: OR 0.12 (95% CI 0.10 to 0.14); Omicron phase: OR 0.15 (95% CI 0.12 to 0.19)).
Cross-national analyses demonstrate sustained protection against severe COVID-19 disease for at least 12 months following natural SARS-CoV-2 infection, with vaccination further enhancing protection. Protection against symptomatic reinfection was lower with evidence of waning, but there remained a protective effect beyond 12 months from primary infection.
by Mehdi Hosseinzadeh, Amir Haider, Mazhar Hussain Malik, Mohammad Adeli, Olfa Mzoughi, Entesar Gemeay, Mokhtar Mohammadi, Hamid Alinejad-Rokny, Parisa Khoshvaght, Thantrira Porntaveetus, Amir Masoud Rahmani
This paper seeks to enhance the performance of Mel Frequency Cepstral Coefficients (MFCCs) for detecting abnormal heart sounds. Heart sounds are first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs are used for heart sound classification. For that purpose, a single classifier and an innovative ensemble classifier strategy are presented and compared. In the single classifier strategy, the MFCCs from nine consecutive beats are averaged to classify heart sounds by a single classifier (either a support vector machine (SVM), the k nearest neighbors (kNN), or a decision tree (DT)). Conversely, the ensemble classifier strategy employs nine classifiers (either nine SVMs, nine kNN classifiers, or nine DTs) to individually assess beats as normal or abnormal, with the overall classification based on the majority vote. Both methods were tested on a publicly available phonocardiogram database. The heart sound classification accuracy was 91.95% for the SVM, 91.9% for the kNN, and 87.33% for the DT in the single classifier strategy. Also, the accuracy was 93.59% for the SVM, 91.84% for the kNN, and 92.22% for the DT in the ensemble classifier strategy. Overall, the results demonstrated that MFCCs were more effective than other features, including time, time-frequency, and statistical features, evaluated in similar studies. In addition, the ensemble classifier strategy improved the accuracies of the DT and the SVM by 4.89% and 1.64%, implying that the averaging of MFCCs across multiple phonocardiogram beats in the single classifier strategy degraded the important cues that are required for detecting the abnormal heart sounds, and therefore should be avoided.