The impact of symptom severity on the humanistic and economic burden of inflammatory bowel disease: a real-world data linkage study
Bibliographic record
Abstract
Few studies have examined the association between inflammatory bowel disease (IBD) severity, and humanistic, and economic burden. We addressed this gap using a unique real-world data source that links self-reported patient data from the US National Health and Wellness Survey (NHWS) to claims data. This cross-sectional study linked the 2015–2018 US NHWS data with medical, and pharmacy claims. Patients (≥18 years) who self-reported a physician diagnosis of IBD (ulcerative colitis [UC], or Crohn’s disease [CD]) in the NHWS, and had a medical or pharmacy claim indicating a possible diagnosis of IBD were included. Disease symptom severity was defined by a weighted symptom score and main outcomes include health-related quality of life (HRQoL), work productivity (WPAI), healthcare resource use (HRU), and associated costs. Overall, 687 patients with IBD were included, of which 347 were identified with UC and 340 with CD. Validation analysis showed that 94.7% of UC and 88.7% of patients with CD who self-reported diagnosis of CD or UC in NHWS had evidence of diagnosis and/or treatment patterns in claims. Patients with both UC and CD with moderate or severe symptoms had significantly lower HRQoL, increased work productivity loss, greater HRU, and associated costs compared with patients with mild symptoms. Patients with moderate/severe UC or CD experience substantial humanistic, and economic burden compared with patients with mild UC or CD. These factors should be considered within treatment goals for patients in order to provide holistic care beyond the treatment of objective markers or disease severity and symptoms alone.
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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 | 0.050 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".