The Effect of Initiation of Anti-TNF Therapy on the Subsequent Direct Health Care Costs of Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Anti-tumor necrosis factor (anti-TNF) drugs are highly effective in the treatment of moderate-to-severe Crohn's disease (CD) and ulcerative colitis (UC), but they are very costly. Due to their effectiveness, they could potentially reduce future health care spending on other medical therapies, hospitalization, and surgery. The impact of downstream costs has not previously been quantified in a real-world population-based setting. METHODS: We used the University of Manitoba IBD Database to identify all persons in a Canadian province with CD or UC who received anti-TNF therapy between 2004 and 2016. All inpatient, outpatient, and drug costs were enumerated both in the year before anti-TNF initiation and for up to 5 years after anti-TNF initiation. Costs before and after anti-TNF initiation were compared, and multivariate linear regression analyses were performed to look for predictors of higher costs after anti-TNF initiation. RESULTS: A total of 928 people with IBD (676 CD, 252 UC) were included for analyses. The median cost of health care in the year before anti-TNF therapy was $4698 for CD vs $6364 for UC. The median cost rose to $39,749 and $49,327, respectively, in the year after anti-TNF initiation, and to $210,956 and $245,260 in the 5 years after initiation for continuous anti-TNF users. Inpatient and outpatient costs decreased in the year after anti-TNF initiation by 12% and 7%, respectively, when excluding the cost of anti-TNFs. CONCLUSIONS: Direct health care expenditures markedly increase after anti-TNF initiation and continue to stay elevated over pre-initiation costs for up to 5 years, with only small reductions in the direct costs of non-drug-related health care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".