Socioeconomic Status at Diagnosis Influences the Incremental Direct Medical Costs of Systemic Lupus Erythematosus : A Longitudinal Population-Based Study
Bibliographic record
Abstract
Objectives To assess the incremental direct medical costs of a population-based cohort of incident systemic lupus erythematosus (SLE) for the first five years after diagnosis, and impact of socioeconomic status (SES) on such incremental costs. Methods From the administrative health databases in British Columbia, Canada, we identified all adults with newly-diagnosed SLE from 1996-2010 and obtained a sample from the general non-SLE population matched on sex, age, and calendar-year. We captured costs for outpatient encounters, hospitalisations, and dispensed medications. Using two-part generalised linear models, we estimated per-person-year incremental costs of SLE (difference in costs between SLE and non-SLE, controlling for covariates) during the first five years after diagnosis, and assessed differences in incremental costs across SES groups. Results We included 4,679 newly-diagnosed SLE (86% identified from hospitalisations or rheumatologists) and 23,219 non-SLE individuals. Per-person direct costs for SLE in the first year after diagnosis averaged $13,038 (2013 Canadian), with 61% from hospitalisations, 23% from outpatient encounters, and 16% from medications; costs for non-SLE averaged $2,431. Following adjustment, incremental costs of SLE during the first five years after diagnosis averaged $10,078 per-person-year (95% CI=$2,062-$32,254). Predicted incremental hospitalisation, outpatient, and medication costs were all significantly-greater for the low-SES patients versus high-SES (additional $1,922 per-person-year in incremental costs for low-SES). Similar patterns were observed when restricting to those followed the full five-years after index date. Conclusion Even in a single-payer, publicly-funded healthcare setting, low SES at SLE diagnosis was associated with significantly-greater direct medical costs for the management of SLE and associated complications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".