Population-level studies of the incremental economic burden of systemic autoimmune rheumatic diseases
Bibliographic record
Abstract
In systemic autoimmune rheumatic diseases (SARDs), immune dysregulation leads to systemic inflammation, organ damage, complications, and disability. I examined the longitudinal, incremental direct medical costs of newly-diagnosed SARDs, incremental productivity costs, and impact of socioeconomic status (SES) on costs, at the general population level. Methods: Nine population-based cohorts, one for each SARD, were identified from the administrative health databases of the province of British Columbia (BC), Canada. Nine non-SARD comparison cohorts were selected from the general population of BC, and matched to each SARD cohort on age, sex, and index-year. Direct Medical Costs: Administrative data captured provincially-funded outpatient encounters and hospitalisations, and all dispensed medications. From these data, I estimated direct medical costs of each SARD and non-SARD cohort for up to five years after diagnosis/index date. I used generalised linear models to determine incremental costs of each SARD overall, and by SES group, controlling for covariates, and incremental costs of systemic lupus erythematosus (SLE, the most common SARD) before diagnosis. Productivity Costs: Random sample of the population-based cohorts completed a survey on absenteeism and presenteeism (working at reduced levels/efficiency) from paid and unpaid work. Survey data were used to determine adjusted, incremental lost productivity costs of three SARDs: SLE, systemic sclerosis (SSc), and Sjogren’s (SjS). Results: Direct Medical Costs: I identified 8,858 incident adult SARD cases for the years 1996-2010 (79.8% female) and 32,727 non-SARDs (79.0% female). Adjusted mean per-person-year incremental costs (over-and-above non-SARDs’) ranged from $7,851 to $54,061 2013 CDN, mainly from hospitalisations. For nearly every SARD, incremental costs of the low-SES exceeded the high-SES, by ~$2,000-$3,000 per-person-year. In each of the five pre-index years, adjusted costs for SLE were significantly greater than non-SLE; male sex and low SES were associated with greater costs among SLE. Productivity Costs: 671 surveys were completed: SLE=167, SSc=42, SjS=90, and non-SARDs=375. Adjusted incremental productivity costs averaged $4,494, $3,582, and $4,357 annually for SLE, SSc, and SjS, respectively. Major contributors were unemployment, presenteeism from paid work, and impairments with unpaid work. Conclusion: These novel findings should inform health resource allocation, and ongoing research to improve outcomes and reduce costs in these chronic diseases.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".