Utilization of Immunotherapy in Patients with Cancer Treated in Routine Care Settings: A Population-Based Study Using Health Administrative Data
Bibliographic record
Abstract
INTRODUCTION: The introduction of immunotherapy (IO) in the treatment of patients with cancer has significantly improved clinical outcomes. Population level information on actual IO utilization is limited. METHODS: We conducted a retrospective cohort study using provincial health administrative data from Ontario, Canada to: (1) assess the extent of IO use from 2011 (pre-IO funding) to 2019; and (2) identify factors associated with IO use in patients with advanced cancers for which IO is reimbursed including melanoma, bladder, lung, head and neck, and kidney tumors. The datasets were linked using a unique encoded identifier. A Fine and Gray regression model with death as a competing risk was used to identify factors associated with IO use. RESULTS: Among 59 510 patients assessed, 8771 (14.7%) received IO between 2011 and 2019. Use of IO increased annually from 2011 (3.3%) to 2019 (39.2%) and was highest in melanoma (52%) and lowest in head and neck cancer (6.6%). In adjusted analysis, factors associated with lower IO use included older age (hazard ratio (HR) 0.91 (95% CI, 0.89-0.93)), female sex (HR 0.85 (95% CI, 0.81-0.89)), lower-income quintile, hospital admission (HR 0.78 (95% CI, 0.75-0.82)), high Charlson score and de novo stage 4 cancer. IO use was heterogeneous across cancer centers and regions. CONCLUSION: IO utilization for advanced cancers rose substantially since initial approval albeit use is associated with patient characteristics and system-level factors even in a universal healthcare setting. To optimize IO utilization in routine practice, survival estimates and potential inequity in access should be further investigated and addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".