Defining and Measuring Financial Toxicity in Low- and Middle-Income Countries
Bibliographic record
Abstract
PURPOSE: Financial toxicity (FT) of cancer treatment likely affects more patients in low- and middle-income countries (LMICs); however, most of the research on FT comes from high-income countries, which may not apply to LMICs. The causes and consequences of FT in patients with cancer in LMICs remain understudied. METHODS: Following PRISMA guidelines, we searched MEDLINE, Web of Science, and CINAHL for FT literature in cancer originating from LMICs from inception until the end of 2023, and documented the different definitions used to define FT in LMICs, and the magnitude of FT documented using those definitions. LMIC was defined using the World Bank Country and Lending Group classification. RESULTS: Sixty-eight studies met the inclusion criteria. Studies on FT in cancer originating from LMICs have increased in recent years (>75% studies published 2020 onward) and used varying criteria to define FT, broadly categorized into five themes. Majority of the studies defined FT in terms of catastrophic health expenditure (45%) or household impoverishment (10%), while 26% of the studies used the Comprehensive Score for Financial Toxicity tool, developed and validated in US patients, to measure FT in LMIC settings. Twenty-six percent of the studies defined FT in terms of coping mechanisms and 10% in terms of subjective financial burden. The magnitude of FT in patients with cancer was substantial irrespective of the definitions used. CONCLUSION: This review synthesizes the different definitions of FT for LMICs that have been used in the literature so far. We conclude that the definitions that capture the coping mechanisms or hardships might reflect the magnitude of FT better than absolute dollar values or relative percentages of expenditures. Future studies can use our results to devise locally tailored definitions of FT.
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 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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".