Hepatitis B virus screening in Asian immigrants: Community‐based campaign to increase screening and linkage to care: A cross‐sectional study
Bibliographic record
Abstract
Background and Aims: Despite established screening guidelines, many Asian immigrants remain unscreened. Furthermore, those with chronic hepatitis B (CHB) are not linked to care citing multiple barriers. The objective of this study was to determine the role of our community-based hepatitis B virus (HBV) campaign on HBV screening and the success of linkage to care (LTC) efforts. Methods: Asian immigrants from the New Jersey and New York metropolitan areas were screened for HBV from 2009 to 2019. We started to collect LTC data starting in 2015, and those found to be positive were followed up. In 2017, because of low LTC rates, nurse navigators were hired to aid in the LTC process. Those excluded from the LTC process included those who were already linked to care, declined, and/or had moved or passed away. Results: Total of 13,566 participants were screened from 2009 to 2019, of which, the results for 13,466 were available. Of these, 372 (2.7%) were found to have positive HBV status. Approximately 49.3% were female and 50.1% were male, and the rest were of unknown gender. A total of 1191 (10.0%) participants were found to be HBV negative but required vaccination. When we started to track LTC, we found 195 participants that were eligible for LTC between 2015 and 2017 after the exclusion criteria were applied. It was found that only 33.8% were successfully linked to care in that time period. After hiring nurse navigators, we saw LTC rates increase to 85.7% in 2018 and to 89.7% in 2019. Conclusion: HBV community screening initiatives are imperative to increase screening rates in the Asian immigrant population. We were also able to demonstrate that nurse navigators can successfully help increase LTC rates. Our HBV community screening model can address issues with barriers to care including lack of access in comparable populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".