Public versus Private Drug Insurance and Outcomes of Patients Requiring Biologic Therapies for Inflammatory Bowel Disease
Bibliographic record
Abstract
Background. Antitumor necrosis factor (anti-TNF) therapy is a highly effective but costly treatment for inflammatory bowel disease (IBD).Methods. We conducted a retrospective cohort study of IBD patients who were prescribed anti-TNF therapy (2007–2014) in Ontario. We assessed if the insurance type was a predictor of timely access to anti-TNF therapy and nonroutine health utilization (emergency department visits and hospitalizations).Results. There were 268 patients with IBD who were prescribed anti-TNF therapy. Public drug coverage was associated with longer median wait times to first dose than private one (56 versus 35 days, P=0.002 ). After adjusting for confounders, publicly insured patients were less likely to receive timely access to anti-TNF therapy compared with those privately insured (adjusted hazard ratio, 0.66; 95% CI: 0.45–0.95). After adjustment for demographic and clinical characteristics, publicly funded subjects were more than 2-fold more likely to require hospitalization (incidence rate ratio [IRR], 2.30; 95% CI: 1.19–4.43) and ED visits (IRR 2.42; 95% CI: 1.44–4.08) related to IBD.Conclusions. IBD patients in Ontario with public drug coverage experienced greater delays in access to anti-TNF therapy than privately insured patients and have a higher rate of hospitalizations and ED visits related to IBD.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".