Excess Productivity Costs of Systemic Lupus Erythematosus, Systemic Sclerosis, and Sjögren's Syndrome: A General Population–Based Study
Bibliographic record
Abstract
OBJECTIVE: To determine excess productivity losses and costs of systemic lupus erythematosus (SLE), systemic sclerosis (SSc), and Sjögren's syndrome (SS) at the population level. METHODS: Administrative databases from the province of British Columbia, Canada, were used to establish population-based cohorts of SLE, SSc, and SS, and matched comparison cohorts were selected from the general population. Random samples from these cohorts were surveyed about time absent from paid and unpaid work and working at reduced levels/efficiency (presenteeism), using validated labor questionnaires. We estimated excess productivity losses and costs of each diagnosis (over and above nonsystemic autoimmune rheumatic diseases [non-SARDs]), using 2-part models and work disability rates (not employed due to health). RESULTS: Surveys were completed by 167 SLE, 42 SSc, and 90 SS patients, and by 375 non-SARDs (comparison group) participants. Altogether, predicted excess hours of paid and unpaid work loss were 3.5, 3.2, and 3.4 hours per week for SLE, SSc, and SS patients, respectively. Excess costs were $86, $69, and $84 (calculated as 2015 Canadian dollars) per week, or $4,494, $3,582, and $4,357 per person annually, respectively. Costs for productivity losses from paid work stemmed mainly from presenteeism (SLE = 69% of costs, SSc = 67%, SS = 64%, and non-SARDs = 53%), not from absenteeism. However, many working-age patients were not employed at all, due to health (SLE = 36%, SSc = 32%, SS = 30%, and non-SARDs = 18%), and the majority of total productivity costs were from unpaid work loss (SLE = 73% of costs, SSc = 74%, SS = 60%, and non-SARDs = 47%). Adjusted excess costs from these unpaid production losses were $127, $100, and $82 per week, respectively, among SLE, SSc, and SS patients. CONCLUSION: In this population-based sample of prevalent SLE, SSc, and SS, lost productivity costs were substantial, mainly from presenteeism and unpaid work impairments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".