Costs of Healthcare Resource Utilization in the Management of Patients with Higher-Risk Myelodysplastic Syndrome in Canada
Bibliographic record
Abstract
Introduction: Treatment options for myelodysplastic syndrome (MDS) are dependent on individual disease characteristics and impact patient prognosis and healthcare resource utilization (HCRU). Specifically, higher-risk MDS (HR-MDS) treatment involves a range of interventions, which can be physically demanding, requiring frequent hospital visits, prolonged hospital stays and unpleasant side effects. Currently, no Canadian HCRU information and costing analysis is publicly available in the management of HR-MDS. This study aimed to estimate the costs of HCRU for HR-MDS treatments, to inform institutional decision makers, health technology assessment agencies, and healthcare professionals, from a Canadian perspective. Methods: A decision tree was developed to assess the costs of HCRU of HR-MDS, over a 2-year time horizon to capture the extent of costs for HR-MDS patients. Comparative treatments were divided into three comparator arms, aligned with different patient subgroups: transplant-ineligible (TI), transplant-eligible (TE) as well as those receiving best supportive care (BSC) only. TI patients were assumed to be treated with hypomethylating agents (HMA), while TE patients could either proceed directly to allogenic stem cell transplantation (allo-SCT) through a conditioning regimen or require cytoreductive/debulking therapy including HMAs or induction chemotherapy prior to evaluating allo-SCT eligibility. Both TI and TE patients could receive BSC as needed, which included a combination of blood transfusions and antimicrobial prophylaxis (antibiotics, antifungals, and antivirals). Patients receiving only BSC could also receive erythropoiesis-stimulating agents and granulocyte colony-stimulating factor. Cost estimates were provided from a healthcare system (HC) perspective, including costs related to pretreatment, acquisition, administration, allo-SCT, monitoring and adverse event (AE) management. Costs were also estimated from a societal perspective, including productivity loss for both patients and caregivers. Model inputs were retrieved from product monographs, treatment protocols and published literature. Subsequently, these model inputs were validated by Canadian clinical experts to reflect clinical practice. A scenario analysis was also developed to assess the real-world HCRU costs of HR-MDS in Canada, based on the estimated target HR-MDS incident Canadian population and the market utilization of the different comparators. Results: From a HC perspective, the total 2-year HCRU costs for HR-MDS were estimated at $117,887 for TI patients, $346,288 for TE patients and $59,942 for patients receiving BSC only. TE patients represent the subgroup with the most expensive 2-year HCRU costs, with the main driver for these costs being allo-SCT costs, estimated at $178,497 over 2 years. The costs of AEs were also higher for TE patients, while for TI patients, drug acquisition costs represent the most expensive cost category compared to TE and BSC patients. From a societal perspective, the total 2-year HCRU costs were estimated at $135,994 for TI patients, $414,861 for TE patients and $71,635 for patients receiving BSC only. Consistent with the HC perspective, HCRU costs for TE patients are the most expensive in terms of productivity loss, considering the time off work due to the long hospitalization for allo-SCT. In Canada, the total HR-MDS Canadian population is estimated at 496 patients, from which 329 (66.3%) are TI, 142 (28.7%) are TE and 25 (5.0%) received BSC only. From a real-world scenario reflecting the total HR-MDS Canadian population, the 2-year HCRU costs is estimated at $15.2M and $17.7M for the Canadian HC and societal perspectives, respectively. This scenario analysis demonstrates that the majority of the HCRU costs are from TI patients. Considering that these patients are treated with HMA, drug acquisition costs are the major cost driver, representing 53.8% and 46.2% of the total 2-year HCRU costs in Canada, from a HC and societal perspective, respectively. Conclusions: This study highlights the significant economic burden related to HR-MDS. Although TE patients have higher 2-year HCRU costs per patient, the majority of HR-MDS patients are TI and therefore reflect the highest cost expense for the Canadian HC. Optimizing treatment and disease management for TI patients could help reduce the overall economic burden of treating HR-MDS in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".