Trends in Hepatitis C and Hepatitis B Deaths Identify Successes and Disparities, Alameda County, CA, 2005 - 2022
Bibliographic record
Abstract
Background: Hepatitis B virus (HBV) and hepatitis C virus (HCV) mortality is a metric for viral hepatitis elimination. Assessments of HBV and HCV mortality at the local level can focus viral hepatitis prevention efforts. Methods: We conducted a cross-sectional and trend analysis of Alameda County residents with HBV or HCV who died in California, using California's Integrated Vital Records System, 2005 - 2022. We selected International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes specific to HBV, HCV, or both, as a cause of death. We used Joinpoint regression to investigate trend differences in age-adjusted HCV mortality rates by sex, race/ethnicity, and Healthy Places Index (HPI) quartiles. Results: A total of 2,165 HBV and HCV deaths were identified in Alameda County (313 HBV, 1,809 HCV, and 43 co-infected deaths). Most HBV decedents were Asian (73.2%) and born outside the United States (78.9%). Age-adjusted HCV mortality rates decreased for all groups from 2013 to 2022; HBV mortality did not decline. African American/Black and Hispanic/Latinx residents had smaller percent decreases in HCV mortality than Asian residents (average annual percent change (AAPC) difference: 6.6% (0.4%, 12.9%); P = 0.04 and 9.3% (3.5%, 15.1%); P = 0.002). The least advantaged HPI quartile 1 had a smaller percent decrease in HCV mortality than the most advantaged HPI quartile 4 (AAPC difference: 8.3% (3.6%, 12.9%); P = 0.01). Conclusions: We identified successes, challenges, and disparities in the burden and trends of HBV and HCV deaths in Alameda County. Focused efforts to expand viral hepatitis screening, vaccination, and treatment are needed to address these gaps and reach elimination targets.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".