AB026. Excess medical cost in patients with asthma and the role of comorbidity
Bibliographic record
Abstract
Background: Comorbid conditions are prevalent in asthma patients but its impact on the economic burden of asthma is not well understood. To estimate the excess direct medical costs in patients with asthma, accounting for both the costs attributable to asthma and to comorbidities. Methods: We created a propensity-score matched cohort of individuals aged 5 to 55 years between 1997 and 2012 with incident asthma and a comparison group of individuals without asthma from the health administrative data of British Columbia (BC), Canada. Sixteen major disease categories were identified using the International Classification of Diseases (ICD) codes. Excess costs [in 2013 Canadian dollars, ($)] were defined as the adjusted difference in total costs between the two groups. Results: There were 145,742 individuals in both asthma and comparison groups. Average excess costs were $1,186.5/person-year (95% CI: 1,130.4–1,242.6) overall, of which $145.2 (143.0–147.4) were attributable to asthma and $787.7 (95% CI: 743.7, 831.7) to major comorbidity classes. Psychological disorders were the largest component of excess comorbidity costs, followed by other respiratory diseases, digestive disorders and diseases of nervous system. Comorbidity-attributable excess costs greatly increased with age but did not increase over the 10-year course of asthma. Conclusions: In the asthma group, the excess costs attributable to comorbidity are five-times higher than costs attributable to asthma, which aggregated over age. In evaluating options for asthma management, consideration of asthma-related costs alone may result in sub-optimal policies and clinical decisions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".