1068. Large-Scale Migration and the Changing Viral Hepatitis Prevalences in North America: A modeling approach
Bibliographic record
Abstract
Abstract Background Infection by hepatitis B and C viruses causes inflammation of the liver and can lead to cirrhosis, liver failure, and hepatocellular carcinoma. The WHO’s ambition to eliminate viral hepatitis by 2030 requires strategies specific to the dynamic disease profiles each nation faces. Large-scale human movement from high-prevalence nations to the United States and Canada have altered the disease landscape, likely warranting adjustments to present elimination approaches. However, the nature and magnitude of the new disease burden remains unknown. This study aims to generate a modeled estimate of recent HBV and HCV prevalence changes to the United States and Canada due to migration. Methods Total migrant populations from 2010-2019 were obtained from United Nations Migrant Stock database. Country-of-origin HBV and HCV prevalences were obtained for the select 40 country-of-origin nations from the Polaris Observatory and systematic reviews. A standard pivot table was used to evaluate the disease contribution from and to each nation. Disease progression estimates were generated using the American Association for the Study of the Liver guidelines and outcome data. Results Between 2010 and 2019, 7,676,937 documented migrants arrived in US and Canada from the selected high-volume nations. Primary migrant source regions were East Asia and Latin America. Combined, an estimated 878,995 migrants were HBV positive, and 226,428 HCV positive. The majority of both migrants (6,477,506) and new viral hepatitis cases (HBV=840,315 and HCV=215,359) were found in the United States. The largest source of HBV cases stemmed from the Philippines, and HCV cases from El Salvador. Conclusion Massive human movement has significantly changed HBV and HCV disease burdens in both the US and Canada over the past decade and the long-term outcomes of cirrhosis and HCC are also expected to increase. These increases are likely to disproportionally impact individuals of the migrant and refugee communities and screening and treatment programs must be strategically adjusted in order to reduce morbidity, mortality, and healthcare expenses. Disclosures All Authors: No reported disclosures
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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 | 0.000 |
| 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".