Hepatitis B virus screening in Asian immigrants: Community‐based campaign to increase screening and linkage to care: A cross‐sectional study
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Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle