The economic impact of suboptimal treatment and treatment switch among patients with Crohn’s disease treated with a first-line biologic – A US retrospective claims database study
Bibliographic record
Abstract
Aims Suboptimal treatment indicators, including treatment switch, are common among patients with Crohn’s disease (CD), but little is known about their associated healthcare resource utilization (HRU) and costs. This study assessed the impact of suboptimal treatment indicators on HRU and costs among adults with CD newly treated with a first-line biologic.Methods Adult patients with CD were identified in the IBM MarketScan Commercial Subset (10/01/2015–03/31/2020). The index date was defined as initiation of the first-line biologic, and the study period was defined as the 12 months following the index date. Patients were classified into Suboptimal Treatment and Optimal Treatment cohorts based on observed indicators of suboptimal treatment during the study period. Patients in the Suboptimal Treatment Cohort with a treatment switch were classified into the Treatment Switch Cohort and compared to patients with no treatment switch. All-cause HRU and costs were measured during the study period and assessed for patients with suboptimal vs optimal treatment and patients with vs without a treatment switch.Results The study included 4,006 patients (Suboptimal Treatment: 2,091, Optimal Treatment: 1,915). Treatment switch was a common indicator of suboptimal treatment (Treatment Switch: 640, No Treatment Switch: 3,366). HRU and costs were significantly higher among patients with suboptimal treatment than those with optimal treatment (annual costs: $92,043 vs $73,764; p < 0.01), and among those with a treatment switch than those with no treatment switch (annual costs: $95,689 vs $81,027; p < 0.01). Increases in the number of suboptimal treatment indicators were associated with increased costs.Limitations Claims data were used to identify suboptimal treatment indicators based on observed treatment patterns; reasons for treatment decisions could not be assessed.Conclusion This study demonstrates that patients with suboptimal treatment indicators, including treatment switch, incur substantially higher HRU and costs compared to patients receiving optimal treatment and those that do not switch treatments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".