Trends in the Prevalence of Hepatitis C Virus Infection based on the Insurance Status in the United States from 2013 to 2018
Bibliographic record
Abstract
BACKGROUND & AIMS: With the recent improvement in the treatment of hepatitis C virus (HCV) infection, a better understanding of the infection burden is needed. We aimed to (a) estimate the trends in the national prevalence of HCV infection based on the type of health insurance coverage and (b) identify at-risk populations for HCV infection in the United States (US) general population. METHODS: Population-based analyses using the National Health and Nutrition Examination Survey (2013-2018) were performed with a focus on HCV infection. We analysed the prevalence of HCV infection based on the health insurance status before the direct-acting antiviral (DAA) era (2013-2014) and during the DAA era (2015-2018). RESULTS: The age-adjusted prevalence of active HCV infection (HCV RNA [+]) was 0.92% (95% confidence interval, 0.71%-1.19%) in the US non-institutionalized civilian population. Although the prevalence of active HCV infection has remained stable, the prevalence of resolved HCV infection has increased after the introduction of DAA. In terms of health insurance coverage, the prevalence of active HCV infection decreased, and the prevalence of resolved HCV infection increased among individuals who had health insurance, especially private health insurance. The independent risk factors of active HCV infection were 40-69 years group, male, less than high school education, unmarried, below poverty status, being born in the US, history of blood transfusion and not having private health insurance. CONCLUSION: The burden of active HCV infection has decreased among individuals who had health insurance, especially private health insurance, during the DAA era.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".