Economic Burden among Commercially Insured Patients with Systemic Sclerosis in the United States
Bibliographic record
Abstract
OBJECTIVE: To quantify healthcare resource utilization (HRU), work loss, and annual direct and indirect healthcare costs among patients with systemic sclerosis (SSc) compared to matched controls in the United States. METHODS: Data were obtained from a large US commercial claims database. Patients were ≥ 18 years old at the index date (first SSc diagnosis) and had ≥ 1 SSc diagnosis in the inpatient (IP) or emergency room (ER) setting, or ≥ 2 SSc diagnoses on 2 different dates in the outpatient (OP) setting between January 1, 2005, and March 31, 2015; continuous enrollment was required during the followup period (12 months after the index date). Individuals with no SSc diagnoses were matched 1:1 to patients with SSc. Wilcoxon signed-rank and McNemar tests were used for comparisons and regressions with generalized estimating equations for adjusted OR (aOR) and incidence rate ratios (IRR) between 2 cohorts. RESULTS: There were 2192 pairs of patients with SSc and matched controls included (mean age 57.6 yrs; 84.3% female); of these, 233 were eligible for work loss/indirect cost analyses. Compared to matched controls, patients with SSc had significantly higher HRU and costs during the 1-year followup period, IP admissions (adjusted IRR = 2.4), IP hospitalization days (adjusted IRR = 3.1), ER visits (adjusted IRR = 2.0), OP visits (adjusted IRR = 2.3), and days of work loss (adjusted IRR = 2.6). The adjusted difference in annual direct and indirect costs was US$12,820 and $3103, respectively (all p < 0.0001). CONCLUSION: Patients with SSc had a high direct and indirect economic burden postdiagnosis.
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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 | 0.000 |
| 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) | 0.001 | 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".