Changes in Economic Status of Households Associated with Catastrophic Health Expenditures for Cancer in South Korea
Bibliographic record
Abstract
BACKGROUND: Cancer imposes significant economic challenges for individuals, families, and society. Households of cancer patients often experience income loss due to change in job status and/or excessive medical expenses. Thus, we examined whether changes in economic status for such households is affected by catastrophic health expenditures. MATERIALS AND METHODS: We used the Korea Health Panel Survey (KHPS) Panel 1st-4th (2008- 2011 subjects) data and extracted records from 211 out of 5,332 households in the database for this study. To identify factors associated with catastrophic health expenditures and, in particular, to examine the relationship between change in economic status and catastrophic health expenditures, we conducted a generalized linear model analysis. RESULTS: Among 211 households with cancer patients, 84 (39.8%) experienced catastrophic health expenditures, while 127 (40.2%) did not show evidence of catastrophic medical costs. If a change in economic status results from a change in job status for head of household (job loss), these households are more likely to incur catastrophic health expenditure than households who have not experienced a change in job status (odds ratios (ORs)=2.17, 2.63, respectively). A comparison between households with a newly-diagnosed patient versus households with patients having lived with cancer for one or two years, showed the longer patients had cancer, the more likely their households incurred catastrophic medical costs (OR=1.78, 1.36, respectively). CONCLUSIONS: Change in economic status of households in which the cancer patient was the head of household was associated with a greater likelihood that the household would incur catastrophic health costs. It is imperative that the Korean government connect health and labor policies in order to develop economic programs to assist households with cancer patients.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".