Self-Reported Productivity Losses of People with Rheumatoid Arthritis in Alberta, Canada
Bibliographic record
Abstract
Objectives: To estimate the annual cost of productivity losses per person with RA by 0.5 increment in HAQscore, and the annual cost of productivity losses for Alberta province. Methods: Using data from the Alberta Biologics Registry - a prospective observational cohort of consecutive patients receiving DMARD or anti-TNF therapies created in 2004, we compared the mean and median costs of productivity losses per patient per year between HAQ-score categories using multiple linear and quantile regressions, respectively. We used a prevalence-based approach to estimate the cost (in 2010 CA$) of productivity losses of RA for Alberta. Results: In total there were 1222 patients with RA interviewed at the baseline. Of this, 358 were the “current employees” and 204 were the “previous employees” totalling 563 patients for analyses. For all HAQ-score categories, the mean (median) of the cost per patient per year was estimated at $18,242 ($3,840). The cost was increasing along with the HAQscore increase. The lowest cost ($6,295) was found in category HAQ<=0.5 and the highest ($31,095) in category HAQ>2.0. The significant differences were found between the worse categories (HAQ>1.5) and the better categories (HAQ<=1.5). The mean costs of productivity losses of RA for the province of Alberta were estimated at $270 million. Conservatively, if median was used for mean, the costs for province would be $57 million. Conclusion: The results suggest that an improvement in the controlling of RA could have a significant economic impact in Alberta and that preventing HAQ-score from the worse categories may be associated with substantial savings in terms of productivity losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".