Economic burden of patients with leading cancers in China: a cost-of-illness study
Bibliographic record
Abstract
BACKGROUND: China accounts for 24% of newly diagnosed cancer cases and 30% of cancer-related deaths worldwide. Comprehensive analyses of the economic burden on patients across different cancer treatment phases, based on empirical data, are lacking. This study aims to estimate the financial burden borne by patients and analyze the cost compositions of the leading cancers with the highest number of new cases in China. METHODS: This cross-sectional cost-of-illness study analyzed patients diagnosed with lung, breast, colorectal, esophageal, liver, or gastric cancer, identified through electronic health records (EHRs) from 84 hospitals across 17 provinces in China. Patients completed any one of the initial treatment phase, follow-up phase, and relapse/metastasis phase were recruited by trained attending physicians through a stratified sampling procedure to ensure enough cases for each cancer progression stage and cancer treatment phase. Direct and indirect costs by treatment phase were collected from the EHRs and self-reported surveys. We estimated per case cost for each type of cancer, and employed subgroup analyses and multiple linear regression models to explore cost drivers. RESULTS: We recruited a total of 13,745 cancer patients across three treatment phases. The relapse/metastasis phase incurred the highest per case costs, varying from $8,890 to $14,572, while the follow-up phase was the least costly, ranging from $1,840 to $4,431. Being in the relapse/metastasis phase and having an advanced clinical stage of cancer at diagnosis were associated with significantly higher cost, while patients with low socioeconomic status borne lower costs. CONCLUSIONS: There were substantial financial burden on patients with six leading cancers in China. Health policymakers should emphasize comprehensive healthcare coverage for marginalized populations such as the uninsured, less educated, and those living in underdeveloped regions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".