Ethnic Minorities and Low Socioeconomic Status Patients With Chronic Liver Disease Are at Greatest Risk of Being Uninsured
Bibliographic record
Abstract
BACKGROUND: Chronic liver disease (CLD) predominantly affects ethnic minorities and socially vulnerable populations, who have high prevalence of risk factors (e.g., suboptimal insurance coverage) predisposing to healthcare disparities. We evaluate prevalence and predictors of uninsured status among CLD adults, and secondarily, how this affects documented immunity or vaccination for hepatitis A virus (HAV) and hepatitis B virus (HBV). METHODS: Using 2011 - 2018 National Health and Nutrition Examination Survey data, self-reported insurance status was determined among adults with CLD. Prevalence of uninsured status was stratified by patient characteristics and evaluated using multivariable logistic regression models. Prevalence of self-reported completion of vaccination as well as laboratory value-based documented immunity to HAV and HBV was stratified by insurance status. RESULTS: Overall, 19.0% of adults with CLD reported having no insurance, which was highest among individuals of Hispanic ethnicity (33.5%), less than high school education (33.7%), and below poverty status (35.3%). On multivariable analyses, significantly lower odds of having any insurance coverage was observed in men, Hispanics, and individuals with lower education and lower household income. Prevalence of documented immunity or vaccination for HAV was low across all insurance categories, ranging from 46.5% to 54.0%. Prevalence of documented immunity or vaccination for HBV was similarly low across all insurance categories, ranging from 24.3% to 40.8%. CONCLUSION: Prevalence of uninsured status among CLD was more than twice the US adult population, and lack of insurance particularly impacted Hispanics and individuals with low education and low household income. Low prevalence of documented immunity or vaccination for HAV and HBV across all insurance categories is concerning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".