The trend of direct medical costs and associated factors in patients with chronic hepatitis B in Guangzhou, China: an eight-year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Although the expenses of liver cirrhosis are covered by a critical illness fund under the current health insurance program in China, the medical costs associated with hepatitis B virus (HBV) related diseases is not well addressed. In order to provide evidence to address the problem, we investigated the trend of direct medical costs and associated factors in patients with chronic HBV infection. METHODS: A retrospective cohort study of 65,175 outpatients and 12,649 inpatients was conducted using a hospital information system database for the period from 2008 to 2015. Generalized estimating equations (GEE) were applied to explore associations between annual direct medical costs and corresponding factors, meanwhile quantile regression models were used to evaluate the effect of treatment modes on different quantiles of annual direct medical costs stratified by medical insurances. RESULTS: The direct medical costs increased with time, but the proportion of antiviral costs decreased with CHB progression. Antiviral costs accounted 54.61% of total direct medical costs for outpatients, but only 6.17% for inpatients. Non-antiviral medicine costs (46.06%) and lab tests costs (23.63%) accounted for the majority of the cost for inpatients. The direct medical costs were positively associated with CHB progression and hospitalization days in inpatients. The direct medical costs were the highest in outpatients with medical insurance and in inpatients with free medical service, and treatment modes had different effects on the direct medical costs in patients with and without medical insurance. CONCLUSIONS: CHB patients had a heavy economic burden in Guangzhou, China, which increased over time, which were influenced by payment mode and treatment mode.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".