All-cause Healthcare Costs and Mortality in Patients with Systemic Sclerosis with Lung Involvement
Bibliographic record
Abstract
OBJECTIVE: Patients with systemic sclerosis (SSc) often develop interstitial lung disease (ILD) and/or pulmonary arterial hypertension (PAH). The effect of ILD and PAH on healthcare costs among patients with SSc is not well described. The objective of this analysis was to describe healthcare costs in patients with newly diagnosed SSc and SSc patients newly diagnosed with ILD and/or PAH in the United States. METHODS: This retrospective cohort analysis was conducted in the Truven Health MarketScan Commercial and Medicare Supplemental healthcare claims databases from 2003 to 2014. Based on International Classification of Diseases-9-Clinical Modification diagnosis codes on medical claims, patients were classified into 3 groups: incident SSc, SSc with incident ILD (SSc-ILD), and SSc with incident PAH (SSc-PAH). Patients were required to have continuous enrollment for 5 years to measure all-cause healthcare costs. Costs (adjusted to US$) were reported overall and by service type and year following diagnosis. Because of the overlap between groups, statistical comparisons were not conducted. RESULTS: There were 1957 patients with incident SSc, 219 with incident SSc-ILD, and 108 patients with incident SSc-PAH. Average (mean ± SD) all-cause healthcare costs over followup were higher for patients with incident SSc-ILD ($191,107 ± $322,193) or patients with incident SSc-PAH ($254,425 ± $240,497), compared to patients with incident SSc ($101,839 ± $167,155). Average annual costs over the 5-year period ranged from $18,513 to $23,268 for patients with incident SSc, from $31,285 to $55,446 for patients with incident SSc-ILD, and from $44,454 to $63,320 for patients with incident SSc-PAH. Costs tended to be the highest in the fifth year of followup. CONCLUSION: Among patients with SSc, ILD and PAH can result in substantial increases in healthcare costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".