Hospital spending and length of hospital stay for mental disorders in Hunan, China
Bibliographic record
Abstract
Objectives: To describe hospital spending and length of stay for mental disorders in Hunan, China. Methods: We extracted hospital care data for Hunan province from the Chinese National Health Statistics Network Reporting System. Patients with mental disorders (ICD-10 codes: F00 to F99) as the principal diagnosis and hospitalized between January 1, 2017 and December 31, 2019 were included. We retrieved information on age, sex, number of comorbidities, diagnosis, level of hospital, hospital costs, date of admission and discharge, length of stay (LOS), and method of payment of eligible participants. Spending at the provincial level, and spending and LOS at the individual level were described. Quantile regression and linear regression were conducted to investigate factors for hospital cost and LOS for major mental disorders. Results: The 2019 annual spending on mental disorders in Hunan province was 160 million US dollars, and 71.7% was paid by insurance. The annual spending on schizophrenia was 84 million dollars, contributing to a primary burden of mental disorders. The median spending for mental disorders was $1,085 per patient, and the median hospital stay was 22 days. The study identified several significant factors associated with hospital cost and LOS, including age, sex, comorbidity, and level of the hospital. In particular, a higher level of the hospital was associated with a higher hospital spending but a shorter LOS. Women with schizophrenia had a comparable hospital spending but a significantly shorter LOS than men with schizophrenia. Conclusion: Hospitalization spending for patients with mental disorders is substantial. Schizophrenia is the major burden of hospitalization for mental disorders. While patients treated at a higher level of hospital had higher spending, they stayed shorter in these hospitals.
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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 | 0.002 | 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".