Shifting Demographics and Comorbidity Burden in Chinese Urban Patients With Chronic Hepatitis B, 2013 and 2016
Bibliographic record
Abstract
Abstract Background: The long-term safety of anti-hepatitis B virus (HBV) therapies is critical to assess, particularly in the context of aging chronic hepatitis B (CHB) populations and accumulating comorbidities. HBV is common and clinically consequential in China; however, the demographics and comorbidity burden of this population have not been fully characterized.Aim: To characterize changes in demographics and comorbidity burden of urban Chinese patients with CHB between 2013 and 2016. Methods: The China Health Insurance Research Association (CHIRA) annual urban health insurance claims database from 2013 and 2016 was used to identify adults with ≥1 ICD-10 code for CHB. Descriptive analyses were conducted to compare age and comorbidities distributions between 2013 and 2016.Results: Median age increased from 40 in 2013 (N=14,545) to 44 in 2016 (N=11,648) (P<0.001). The proportion of patients aged >45 years increased significantly from 40.3% in 2013 to 49% in 2016 (P<0.001). Significant increases in multiple comorbidities were observed, including hypertension (9.4% to 14.5%), hyperlipidemia (4.7% to 7.0%), and cardiovascular disease (5.7% to 10%) (P<0.001 for all comparisons). Increases were also observed in renal impairment (8.8% to 10.0%; P<0.001) and osteoporosis and/or pathologic nontraumatic bone fracture (3.8% to 7.3%; P<0.001). Conclusions: Even over the short interval assessed in this study, there was a significant shift in the characteristics of urban CHB patients in China. Aging of the population was accompanied by significant increases in the percentage of patients having potentially concerning comorbidities. Careful selection of treatment options and comorbidity monitoring should be considered when managing Chinese patients with CHB.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".