Prevalence and Intensity of Catastrophic Health Expenditure Among Residents in a Multiethnic Province in China: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hainan is a pilot free trade port in China and a multiethnic province. Catastrophic health expenditure (CHE) reflects health care inequity, particularly affecting vulnerable groups in rapidly developing multiethnic regions. OBJECTIVE: This study aims to analyze CHE prevalence and intensity and their influencing factors among residents in a Chinese multiethnic province. METHODS: Data from the 2023 Hainan Province Health Services Survey, conducted among 14,532 individuals aged 18 years or older, were used in a multistage stratified cluster sampling. CHE was defined as out-of-pocket health care payments exceeding 40% of the household's capacity to pay. The chi-square test and a logistic regression were used to identify influencing factors for CHE. Nonparametric tests and quantile regression were used to evaluate the influencing factors for CHE intensity. RESULTS: The prevalence of CHE in Hainan province was 8.37%, with a median intensity of 17.67% (IQR 8.31%-29.77%). Residents were more likely to experience CHE if they were older than 60 years (odds ratio [OR] 1.928, 95% CI 1.602-2.320; P<.001), unmarried (OR 1.241, 95% CI 1.075-1.433; P=.003), or had chronic illnesses (OR 2.214, 95% CI 1.930-2.540; P<.001). Ethnic minority groups (OR 0.774, 95% CI 0.679-0.883; P<.001) as well as middle-income (OR 0.722, 95% CI 0.600-0.869; P=.001), high-middle-income (OR 0.739; 95% CI 0.609-0.898; P=.002), and high-income (OR 0.591, 95% CI 0.474-0.738; P<.001) groups were less likely to experience CHE. At lower CHE intensity (20th percentile), individuals older than 60 years (β=1.935; P=.03) and middle-income (β=1.737; P=.04) and rural (β=2.202; P=.005) residents showed positive associations. At the 50th percentile, low-middle-income (β=-5.052; P=.005), high-middle-income (β=-4.203; P=.03), and high-income (β=-6.534; P=.004) groups showed negative associations. At the 80th percentile, high-middle-income (β=-7.143; P=.03) and rural (β=-6.241; P=.005) groups showed stronger financial protection. CONCLUSIONS: Addressing CHE risks remains a critical challenge in Hainan province, highlighting structural inequities rooted in socioeconomic disparities and health vulnerabilities. Therefore, policy should prioritize primary prevention in the lowest-income populations while implementing enhanced insurance coverage for rural populations facing extreme costs to alleviate the most severe financial burdens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".