46. Racial and Ethnic Disparities in COVID-19 Incidence among Persons with HIV in a Multisite-Cohort
Notice bibliographique
Résumé
Abstract Background Little is known about how race and ethnicity, imperfect (albeit accessible) proxies for structural racism, impact COVID-19 incidence among people with HIV (PWH). We report the cumulative incidence and incidence rate ratios (IRR) for COVID-19 in a long-term multi-site cohort of PWH across the US Figure 1. Cumulative incidence of COVID-19 in the CNICS cohort Methods We examined COVID-19 cumulative incidence and IRR among PWH in care between 3/1/2020 and 12/31/2020 at seven sites in the CFAR Network of Integrated Clinical Systems (CNICS) cohort. We define COVID-19 incident case as having a laboratory-confirmed (RT-PCR/Ag) SARS-CoV-2 positive result or diagnosis verified by chart review. Reinfections were excluded. Results are presented as monthly and quarterly cumulative incidence and IRR with 95% CI stratified by CD4 count, self-reported race/ethnicity, and site. Follow-up was censored on the earliest of diagnosis of COVID-19 disease, loss to follow up, or 12/31/2020 Results Among 15,780 PWH in care in the CNICS cohort during the study period, 62% were non-white, with a median (IQR) age of 52 (IQR 40-59), 95% were on antiretroviral therapy, 17% had a CD4 count less than 350, and 6% less than 200. Overall, 651 PWH tested positive for COVID-19 for a cumulative incidence of 4.13%. COVID-19 cumulative incidence increased from 0.77% at the end of the first quarter to 4.12% by the end of December 2020. At the peak of the pandemic in December 2020, the cumulative incidence in Black PWH was 1.68 fold higher than in white PWH (p=.033) and 2.35 fold higher in Hispanics than in whites (P< .0001), figure 1. Similarly, the IRR for COVID-19 was 1.71 (95% CI 1.42-2.07) for Black and 2.40 (95% CI 1.91-3.01) for Hispanic PWH relative to white. Although there was variation across sites, reflecting geographic differences in pandemic waves and access to COVID-19 testing, overall individual trends remained the same. COVID-19 cumulative incidence was similar across CD4 cell count strata Conclusion Our results suggest effects of structural racial disparities on COVID-19 incidence in this diverse population of PWH across the US, with higher and disproportionate rates of COVID-19 in Black and Hispanic PWH. Incidence estimates are conservative because testing was not uniform, and no systematic testing was conducted Disclosures Edward R. Cachay, MD, MAS, Gilead Science (Grant/Research Support, Advisor or Review Panel member)Merck Sharp & Dohme Corp (Grant/Research Support) Adrienne Shapiro, MD, PhD, Vir Biotechnology (Scientific Research Study Investigator) Darcy Wooten, MD, MS, Nothing to disclose Rachel A. Bender Ignacio, MD MPH, Abbvie (Individual(s) Involved: Self): Consultant; SeaGen (Individual(s) Involved: Self): Consultant Greer A. Burkholder, MD, MSPH, Eli Lilly (Grant/Research Support) Heidi Crane, MD, MPH, ViiV (Grant/Research Support)
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».