46. Racial and Ethnic Disparities in COVID-19 Incidence among Persons with HIV in a Multisite-Cohort
Bibliographic record
Abstract
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".