Uptake of immunotherapy in patients with advanced cancer: A population-based study using health administrative data from Ontario, Canada.
Bibliographic record
Abstract
6529 Background: The introduction of immunotherapy (IO) in the treatment of patients with cancer has significantly improved clinical outcomes. Herein we report on IO uptake in Ontario, Canada, a publicly funded healthcare system. Methods: We conducted a retrospective cohort study using provincial health administrative data to: 1) assess IO uptake in adult patients with advanced melanoma, bladder, lung, head and neck (HN) and kidney cancers; and 2) identify predictors of IO usage between 2011 (pre-IO funding) and 2019. The datasets were linked using unique encoded identifiers and analyzed at ICES. IO uptake was captured between cancer diagnosis and last follow up and reported as a proportion of the entire cohort and by tumor site and drug type. A competing risk Fine and Gray regression model with death as competing risk was used to identify factors associated with IO use. Results: Among 59,510 patients with one of the five advanced cancers of interest, 7,660 (12.9%) received IO. Details of IO uptake are summarized in Table. IO uptake increased yearly from 2011 (2.7%) to 2019 (34.0%). Uptake was highest in melanoma (48.2%) and lowest in HN cancer (5.8%). The most commonly used drugs used were pembrolizumab (41.1%) and nivolumab (40.5%). In adjusted analysis, predictors of lower IO uptake included older age (hazard ratio (HR) 0.953, 95%CI 0.934-0.972 with every additional 10 years), female sex (HR 0.859, 95%CI 0.819-0.9), lower income quintile (HR 0.893, 95%CI 0.83-0.96), history of hospital admission (HR 0.768, 95%CI 0.734-0.805), female oncologist (HR 0.942, 95%CI 0.892-0.995), and de novo stage 4 cancer (HR 0.918, 95%CI 0.873-0.966). Predictors of higher IO uptake were low Charlson score (HR 1.118, 95%CI 1.01-1.236) and previous radiation therapy (HR 1.438, 95%CI 1.367-1.512). IO uptake was heterogeneous across cancer centres levels (1 to 4) and regions. Conclusions: While the use of IO for advanced cancer has steadily increased over time, uptake is associated with patient and physician characteristics, as well as system level factors. This variation suggests potential inequity in access to these potentially life-prolonging drugs and should be further investigated and addressed.[Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".