Economic burden of multimorbidity in patients with severe asthma: a 20-year population-based study
Bibliographic record
Abstract
BACKGROUND: The economic impact of multimorbidity in severe or difficult-to-treat asthma has not been comprehensively investigated. AIMS: To estimate the incremental healthcare costs of coexisting chronic conditions (comorbidities) in patients with severe asthma, compared with non-severe asthma and no asthma. METHODS: Using health administrative data in British Columbia, Canada (1996-2016), we identified, based on the intensity of drug use and occurrence of exacerbations, individuals who experienced severe asthma in an incident year. We also constructed matched cohorts of individuals without an asthma diagnosis and those who had mild/dormant or moderate asthma (non-severe asthma) throughout their follow-up. Health service use records during follow-up were categorised into 16 major disease categories based on the International Classification of Diseases. Incremental costs (in 2016 Canadian Dollars, CAD$1=US$0.75=₤0.56=€0.68) were estimated as the adjusted difference in healthcare costs between individuals with severe asthma compared with those with non-severe asthma and non-asthma. RESULTS: Relative to no asthma, incremental costs of severe asthma were $2779 per person-year (95% CI 2514 to 3045), with 54% ($1508) being attributed to comorbidities. Relative to non-severe asthma, severe asthma was associated with incremental costs of $1922 per person-year (95% CI 1670 to 2174), with 52% ($1003) being attributed to comorbidities. In both cases, the most costly comorbidity was respiratory conditions other than asthma ($468 (17%) and $451 (23%), respectively). CONCLUSIONS: Comorbidities accounted for more than half of the incremental medical costs in patients with severe asthma. This highlights the importance of considering the burden of multimorbidity in evidence-informed decision making for patients with severe asthma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".