COMPARATIVE COST ANALYSIS OF ASTHMA-COPD OVERLAP SYNDROME AND CHRONIC OBSTRUCTIVE PULMONARY DISEASE: SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Asthma-COPD (Chronic Obstructive Pulmonary Disease) Overlap Syndrome (ACOS) is a clinical condition characterized by features of asthma and COPD. This overlap leads to greater disease complexity, more frequent exacerbations, and increased healthcare utilization, potentially resulting in a higher economic burden than asthma or COPD. Objective: This systematic review aims to assess and compare the economic burden of ACOS with that of COPD and asthma individually, focusing on healthcare costs, resource utilization, and comorbidity profiles. Methods: A literature search was conducted in PubMed and Elsevier for studies published between January 2013 until September 2023. Keywords included "ACOS", "asthma-COPD overlap", "economic burden", "healthcare cost", "resource utilization". Inclusion criteria were observational studies that provided direct or indirect cost comparisons between ACOS and COPD/asthma populations. Seven eligible studies from South Korea, the United States, Canada, Taiwan, and Jordan were included. Results: All included studies used retrospective cohort designs and extracted data from national insurance databases, hospital claims, or medical records. ACOS patients consistently incurred higher direct medical costs, including hospitalization, emergency department visits, and pharmacotherapy expenses, compared to those with COPD or asthma alone. Commonly used medications included ICS, LABA, and LAMA. Most studies used short time horizons (1–3 years), with only two examining longer periods (8 and 15 years). ACOS populations also demonstrated higher rates of comorbidities and healthcare service utilization. Conclusions: ACOS is associated with a significantly higher economic burden than asthma or COPD. Findings highlight the need for targeted management strategies and longer-term economic evaluations to inform efficient healthcare resource allocation.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".