Population-Based Health Care Cost Estimates Related to Non-Hodgkin Lymphoma (NHL)
Bibliographic record
Abstract
Abstract Abstract 2068 Background: There is limited information on the cost of non-Hodgkin lymphoma (NHL) management. This study provides population-based estimates of the costs for individuals with NHL from a Canadian perspective using provincial administrative databases. Methods: All individuals residing in Ontario with a diagnosis of first incident NHL from the Ontario Cancer Registry (2005–2009) were matched to non-NHL controls. Matching was based on age (same birth year), geography, income quintile, and resource utilization bands (2 years prior to cancer diagnosis). Each NHL case was matched to a maximum 5 controls. NHL cases from the Ontario Cancer registry were linked with their unique and encrypted health card number to provincial health claims databases (Ontario Ministry of Health and Long-Term Care and Cancer Care Ontario). Resources for this analysis included physician visits, hospitalizations, emergency room visits, medications, home care and same day surgeries. Unit costs (in 2009 Canadian dollars) were applied to resources. All costs were inflated to 2009. Costs were presented as the mean annual cost for all patients and by clinical stage. Results: There were 13,336 NHL cases matched to 65,668 controls. The NHL and control groups were demographically similar. For both groups, the median (25th and 75th percentile) age was 68 (56 to 77) years and 55% were male. Geographically, 86% of both groups were from urban areas. Total and disaggregated mean annual costs for NHL and controls are presented in the table. The mean cost difference between the NHL and control groups represents the mean cost attributable to NHL. Inpatient hospitalization and medication costs attributable to NHL represented 51% and 30% respectively, of the total mean annual cost attributable to NHL. Clinical stage at NHL diagnosis was available for a subset of the NHL cases (37%). Total mean annual cost by stage is also presented in the table. For the entire cohort, mean annual cost attributable to NHL was $16,778. In comparison, mean annual cost attributable to NHL by stage at diagnosis ranged from $9,575 (stage I) to $26,099 (stage IV). Conclusion: This study provides total and stage-specific cost estimates for NHL where attributable costs were 3 to 7 fold higher than those for non-NHL controls and increased by stage. Cost drivers included medications and inpatient care. Further work will focus on costing other resources including radiation, other chemotherapies and chronic care. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".