Measurement and determinants of catastrophic health expenditure among elderly households in China using longitudinal data from the CHARLS
Bibliographic record
Abstract
BACKGROUND: Catastrophic health expenditure (CHE) among the Chinese elderly warrants attention. However, the incidence, intensity and determinants of CHE have not been fully investigated. This study explores the incidence, intensity and determinants of CHE among elderly Chinese citizens, i.e., those aged 60 years or older. METHODS: Data were obtained from three waves of the China Health and Retirement Longitudinal Study (CHARLS): 2011, 2013 and 2015. The cut-off points used in this study for CHE were 10% of the total expenditures and 40% of non-food expenditure. Under the guidance of Andersen's model of health services utilization, this study used logistic regression analysis to explore the determinants of CHE. RESULTS: The incidence of CHE defined as more than 40% of non-food expenditure rose over the study period, 2011-2015, from 20.86% (95% CI: 19.35 to 22.37%) to 31.00% (95% CI: 29.28 to 32.72%). The intensity of CHE also increased. The overshoot (O) based on non-food expenditure rose from 3.12% (95% CI: 2.71 to 3.53%) to 8.75% (95% CI: 8.14 to 9.36%), while the mean positive overshoot (MPO) rose from 14.96% (95% CI: 12.99 to 16.92%) to 28.23% (95% CI: 26.26 to 30.19%). Thus, the problem of CEH was even more serious in 2015 than in 2011. Logistic regression revealed that households were more likely to face CHE if they had a spouse as a household member, reported an inpatient event in the last year, reported an outpatient visit in the last month, were disabled, were members of a poor expenditure quartile, lived in the middle and western zones or resided in an urban area. In contrast, CEH was not significantly affected by respondents being older than 75 years or having a chronic health condition, by household size or by insurance type. CONCLUSIONS: Key policy recommendations include the gradual improvement of medical assistance and the expansion of the use of health insurance to reduce household liability for health expenditures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".