Bibliographic record
Abstract
Rationale Health-related productivity loss and the impact of comorbidities on the economic burden of asthma are important, yet overlooked, components. I aimed at revising recent estimates of the costs of asthma worldwide. The empirical research involved evaluating the effect of comorbidities on productivity loss among adult asthma patients. Methods A literature review was conducted on studies regarding the costs of asthma (January 2008 to January 2015) and subsequently on the effects of comorbidities on productivity. In parallel data from a prospectively evaluated random sample of employed adults with asthma was used and the prevalence of comorbidities measured using a validated self-administered comorbidity questionnaire (SCQ), range 0 – 39, (the higher the score, the higher the level of comorbidity). Productivity loss, including absenteeism and presenteeism, were also measured using validated instruments in 2010 Canadian dollars ($). I used a two-part regression model to estimate the adjusted difference of productivity loss across levels of comorbidity, controlling for potential confounding variables. Results The review demonstrated that asthma imposes a major economic burden, however there are large discrepancies in the reported estimates. There is also uncertainty about the indirect costs of asthma and the effects of comorbidities on these costs. A random sample of 284 adults with the mean age of 47.8 (SD 11.8) was included (68% women). The mean SCQ score was 2.47 (SD 2.97, range 0-15) and the average productivity loss was $317.5 per week (SD $858.8). Comorbidity was significantly associated with productivity loss. One-unit increase in the SCQ score was associated with a 14% (OR=1.14, 95% CI 1.02-1.28) increase in the odds of reporting productivity loss, and 9.0% (OR=1.09, 95% CI 1.01-1.18) increase in productivity loss among those who reported any loss of productivity. A person with a SCQ score of 15 had $1,685 per week more productivity loss than a patient with a SCQ of zero. Conclusion This study demonstrates that comorbidities substantially decrease productivity in working asthma patients. Asthma management strategies must be cognizant of the role of comorbidities and should properly incorporate the effect of comorbidity and productivity loss in estimating the benefit of disease management strategies.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".