Cost Drivers and Financial Burden for Cancer-Affected Families in China: A Systematic Review
Bibliographic record
Abstract
This systematic review examined cancer care costs, the financial burden for patients, and their economic coping strategies in mainland China. We included 38 quantitative studies that reported out-of-pocket payment for cancer care and patients' coping strategies in English or Chinese (PROSPERO: CRD42021273989). We searched PubMed, Embase, Ovid, Web of Science, Cochrane, CNKI, and Wanfang Data from 1 January 2009 to 10 August 2022. We referred to the standards for reporting observational studies to assess the methodological quality and transparent reporting of the included studies and reported the costs narratively. Annual mean medical costs (including inpatient and outpatient costs and fees for self-purchasing drugs) ranged from USD 7421 to USD 10,297 per patient. One study investigated medical costs for 5 years and indicated that inpatient costs accounted for 51.6% of the total medical costs, followed by self-purchasing drugs (43.9%). Annual medical costs as a percentage of annual household income ranged from 36.0% to 63.1% with a metaproportion of 51.0%. The common coping strategies included borrowing money and reduction of household expenses and expenses from basic health services. Costs of inpatient care and self-purchasing drugs are major drivers of medical costs for cancer care, and many affected households shoulder a very heavy financial burden.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".