Burden of comorbidity in individuals with asthma
Bibliographic record
Abstract
BACKGROUND AND AIMS: Asthma comorbidity, such as depression and obesity, has been associated with greater healthcare use, decreased quality of life and poor asthma control. Treating this comorbidity has been shown to improve asthma outcomes as well as overall health. Despite this, asthma comorbidity remains relatively under-recognised and understudied-perhaps because most asthma occurs in young people who are believed to be healthy and relatively free of comorbidity. The aim of this study was to quantify empirically the amount of comorbidity associated with asthma. METHODS: A population-based cohort study was conducted using the health administrative data of the 12 million residents of Ontario, Canada in 2005. A validated health administrative algorithm was used to identify individuals with asthma. RESULTS: The amount of comorbidity among individuals with asthma, as reflected in rates of hospitalisations, emergency department visits and ambulatory care claims, was found to be substantial and much greater than that observed among individuals without asthma. Together, asthma and asthma comorbidity (the extra comorbidity found in individuals with asthma compared with those without asthma) were associated with 6% of all hospitalisations, 9% of all emergency room visits and 6% of all ambulatory care visits that occurred in Ontario. CONCLUSIONS: Asthma comorbidity places a significant burden on individuals and the healthcare system and should be considered in the management of asthma. Further research should focus on which types of asthma comorbidity are responsible for the greatest burden and how such comorbidity should be prevented and managed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".