Patient Perceived Financial Burden in Haematological Malignancies: A Systematic Review
Bibliographic record
Abstract
Advances in scientific understanding have led to novel therapies and improved supportive care for many patients with haematological malignancies. However, these new drugs are often costly, only available at centralised health care facilities, require regular specialist reviews and lengthy treatment regimens. This leads to a significant financial burden. Understanding the impact of financial burden on haematological patients is important to appreciate the urgency of alleviating this systemic issue. METHOD: Eligible studies were identified by systematically searching Medline, PsycINFO, CINAHL and Embase. Self-reported data reported in both quantitative and qualitative studies that described the financial burden for patients with haematological malignancies were included. Quality appraisal of the included studies was undertaken using the Joanna Briggs Institute tools. A narrative synthesis was employed. For quantitative studies, outcomes were extracted, tabulated and categorised to find similarities and differences between the studies. For qualitative studies, quotations, codes and themes were extracted and then clustered. An inductive approach derived qualitative themes. RESULTS: Twenty studies were identified for inclusion. Of the quantitative studies most (83%) employed un-validated researcher-generated measures to assess financial burden. Between 15-59% of patients experienced a financial burden. Out-of-pocket expenditure was frequent for clinical appointments, prescription and non-prescription medication, and travel. Financial burden was associated with a worsening quality of life and living in metropolitan areas, but there was no evidence for impact on survival. Patient-centred experiences from the qualitative inquiry complemented the quantitative findings and five themes were determined: familial or household impact; reliance on others; barriers to care due to cost; and barriers to accessing financial assistance and sources of out-of-pocket expenses. CONCLUSION: The impacts of financial burden are yet to be fully appreciated in haematological malignancies, exacerbated by the heterogeneous methods employed by researchers. Future work should focus on identifying the long-term ramifications of financial burden for patients and should trial interventions to reduce its prevalence and patient impacts.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".