The Indirect Costs of Arthritis Resulting From Unemployment, Reduced Performance, and Occupational Changes While at Work
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the cost attributable to lost productivity from arthritis and the association between the degree of loss and demographic, disease-related, occupational, and psychosocial variables for people. METHODS: In a prospective study, 383 employed individuals with arthritis were recruited from southwestern Ontario, Canada. Respondents completed structured questionnaires assessing demographic, disease-related, workplace, and psychosocial variables as well as employment-related transitions at 2 time points 18 months apart. Indirect costs resulting from arthritis-related absences, reduced performance, decreased work hours, job change, and work disability were estimated. A proportional odds model was used to assess the impact of the various variables on lost productivity. RESULTS: The average cost attributable to arthritis was CAN Dollars 11,553(Dollars CAN = 0.75 Dollars US) per person per year. The largest component of the loss was the result of reduced performance at work, which accounted for 41% (Dollars 4724) of the total loss. This was followed by wage loss resulting from stopping working or changing jobs, which comprised 37% (Dollars 4309) of the total. Another 12% (Dollars 1398) and 10% (Dollars 1121) of the loss were the result of the decrease in hours of work and absenteeism, respectively. Four variables were associated with the productivity loss: greater symptom severity (odds ratio [OR] = 1.11), low or medium control over the work schedule (OR = 0.55 and 0.60, respectively), greater workspace limitation (OR = 1.10), and higher depression (OR = 1.04). CONCLUSIONS: Indirect arthritis-related costs are substantial. Our results show that not only the disease itself, but also psychosocial and work-related factors affect the magnitude of the costs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".