Assessing drug deposition efficacies, environmental impact and affordability for inhalers among chronic respiratory diseases: A systematic review
Bibliographic record
Abstract
ABSTRACT Introduction Chronic respiratory diseases such as asthma, COPD, and other pulmonary conditions impose a substantial global health burden, affecting millions of individuals worldwide. These conditions are characterized by persistent respiratory symptoms and reduced airflow, significantly impacting quality of life, and increasing healthcare needs. Treatment typically involves the use of inhaler devices to administer medications directly to the lungs, which decreases symptoms and improves outcomes. However, the efficacy of inhaler devices is influenced by various factors, including the type of device, patient adherence, and the correct device usage by patients. In addition to clinical considerations, the environmental impact of inhaler devices, including their carbon footprint, as well as the cost implications for both healthcare systems and patients, are critical factors that require comprehensive evaluation. It is essential to develop sustainable and economically viable treatment strategies that address these considerations. Methods and Analysis We will conduct a systematic review aimed at providing a comprehensive understanding of the implications of inhaler use in treating asthma, COPD, and other chronic conditions requiring pulmonary drug delivery. Our focus will be on assessing efficiency, environmental sustainability, and cost-effectiveness. Studies lacking economic models or evaluations, without in-vivo deposition in the lungs, lacking assessment of the environmental impact of inhalers, not published in English, or falling into categories such as systematic reviews, letters, editorials, animal studies, or case studies will be excluded from this review. The primary outcome of interest in this systematic review is the efficacy of medication deposition in the lungs of individuals with respiratory diseases when using different types of inhalers. The secondary outcome is to determine their economic costs and the tertiary outcome is to evaluate the overall environmental footprints of inhalers. We will search for original research articles published until May 30, 2023, using databases such as MEDLINE (OVID), EMBASE (OVID), CENTRAL (OVID), the Canadian Drug and Health Technology Agency, and the US Food & Drug Administration. Our search method follows the PRISMA guidelines 2020. To determine study eligibility, a two-phase screening process will be conducted by three independent reviewers, with predefined outcomes extracted from eligible studies. The study will summarize findings through a narrative synthesis, using statistical analyses and sensitivity tests. In addition, funnel plots and Eggers test will be used for lung deposition analysis, while descriptive statistics will further compare costs and summarize environmental effects. We have ensured that a thorough risk of bias evaluation is part of this research approach, and it is carried out by three independent reviewers using a wide variety of tools according to the type of study. Ethics and Dissemination Ethics approval is not required for this study as it constitutes a protocol for a systematic review. The findings from this review will be disseminated via peer-reviewed publications and presented at conferences. Primary data will be available in the online repository on Open Science Framework, alongside a prior registration of this study. OSF Registration Number osf.io/xc5t6 https://doi.org/10.17605/OSF.IO/NT58B Strengths and limitations of this study The study will evaluate various aspects of inhaler use, including drug deposition efficiency, environmental sustainability, and cost-effectiveness. The findings aim to inform policy decisions and practice guidelines, focusing on the promotion of sustainable and economically viable healthcare solutions. The study seeks to enhance disease management, improve patient outcomes, and reduce healthcare costs. The study’s findings could be limited by the specific inhaler devices and patient populations examined, which may impact the conclusions’ generalizability. The fast pace of innovation in medical devices could mean that new inhaler technologies may emerge after the study’s completion that are not covered by the review but could have significant implications for patient care.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".