Indicators of Suboptimal Treatment and Associated Healthcare Costs Among Patients With Crohn’s Disease Initiated on Biologic or Conventional Agents
Bibliographic record
Abstract
Background: As the treatment landscape for Crohn's disease (CD) evolves, an up-to-date understanding of the burden associated with indicators of suboptimal treatment is needed. The aim of this study was to describe suboptimal treatment indicators and associated healthcare costs among CD patients initiated on a biologic or conventional agent. Methods: Adults with CD were identified in a US healthcare claims database (Optum's Clinformatics Data Mart; 01/2004-03/2019). The first biologic or conventional agent claim within 12 months of a CD diagnosis was the index date/agent. Indicators of suboptimal treatment (nonadherence, dose escalation, chronic corticosteroid use, augmentation, ≥1 CD surgery, ≥2 CD emergency department visits, ≥1 CD inpatient (IP) stay, switch, cycling, restart, inadequate induction) were identified in the 12-month postindex landmark period. The mean per-patient-per-year (PPPY) healthcare costs (2019 USD) were evaluated in the year postlandmark. Results: There were 5107 patients (mean age ~44 years, 56% female) in the biologic and 6072 patients (~51 years; 59% female) in the conventional cohort. In the biologic cohort, 79.4% of patients had ≥1 suboptimal treatment indicator. Mean PPPY healthcare costs increased with the number of suboptimal treatment indicators, from $46 100 (no indicator) to $68 572 (≥4 indicators). The conventional cohort had similar patterns: 72.5% of patients presented ≥1 suboptimal treatment indicator, and mean PPPY healthcare costs increased from $17 329 (no indicator) to $67 568 (≥4 indicators). In both cohorts, IP and outpatient medical costs (excluding biologics) contributed a major portion of the increase. Conclusions: Among CD patients, suboptimal treatment indicators were common and were associated with an increased burden to the healthcare system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".