Background Studies on COVID-19 in people with HIV (PWH) had limitations. Further investigations on risk factors and outcomes of SARS-CoV-2 infection among PWH are needed. Methods This retrospective cohort study leveraged the national OPTUM COVID-19 dataset to investigate factors associated with SARS-CoV-2 positivity among PWH and risk factors for severe outcomes including hospitalization, Intensive Care Unit stays, and death. A subset analysis was conducted to examine HIV-specific variables. Multiple variable logistic regression was used to adjust for covariates. Results Of 43,173 PWH included in this study, 6,472 had a positive SARS-CoV-2 PCR or antigen test. For SARS-CoV-2 positivity, higher odds were among younger PWH (18-49 years), Hispanic Whites, African Americans, US South, uninsured, higher BMI, non-current smokers, and higher Charlson comorbidity index (CCI). When examining severe outcomes, higher odds were among SARS-CoV-2 positive; older PWH; US South; Medicaid, Medicare, or uninsured; current smokers; underweight; and higher CCI. In a subset analysis including PWH with HIV care variables (n = 5,098), those with unsuppressed HIV VL, low CD4 count, and not on ART had higher odds of severe outcomes. Conclusions This large US study found significannt ethnic, racial, and geographical differences in SARS-CoV-2 infection among PWH. Chronic comorbidities, older age, lower BMI, and smoking were associated with severe outcomes among PWH during the COVID-19 pandemic. SARS-CoV-2 infection was associated with severe outcomes, but once we adjusted for HIV-care variables, SARS-CoV-2 was no longer significant, while low CD4 count, high viral load, and lack of ART usage had higher odds of severe outcomes.

Background The percentage of children infected with COVID-19 has outpaced that of adults. As children >5 years are now eligible to receive vaccines, it is necessary to understand the effect of vaccination in the context of demographic characteristics, clinical factors, and variants on pediatric COVID-19 illness severity. Methods We conducted a descriptive study of patients ≤18 years from the Optum® COVID-19 electronic health record dataset. Patients were included if positive for COVID-19 by polymerase chain reaction or antigen testing for the first time from 3/12/2020 to 1/20/2022. We compare race and ethnicity, age, gender, US region of residence, vaccination status, body mass index (BMI), pediatric comorbidity index (PCI) (Sun, Am. J. Epidemiol. 2021), and predominant variant (by time and region) with 2-tailed t-test, multi-category chi-square test, and odds ratios (R version 4.1.2; α = 0.05). PCI is a validated comorbidity index predicting hospitalization in pediatric patients. Results Of all pediatric patients in our dataset, 165,468 (13.2%) tested positive for COVID-19. 3,087 (1.9%) were hospitalized, 1,417 (0.9%) were admitted to the ICU, 1545 (0.9%) received respiratory support, and 31 (0.02%) died, comparable to AAP-reported hospitalization and mortality rates in US children. Patients with severe outcomes were more likely to be younger, non-Caucasian, from the US South, unvaccinated, and have a higher PCI (Figure 1). Excluding non-severe outcomes, rates of death and ICU admission were higher in 0–4-year-olds compared to 5–11 or 12–18-year-olds (Figure 2). All patients receiving at least one dose of the vaccine survived. The odds ratio of a severe outcome is 0.11 (95% CI 0.07–0.16) in fully vaccinated patients compared to unvaccinated patients. The odds ratio of a severe outcome is 0.55 (95% CI 0.49–0.63) in partially vaccinated patients compared to unvaccinated patients. Demographic and clinical characteristics of pediatric patients with COVID-19 Relative proportion of clinically severe outcomes within age groups, excluding non-severe outcomes Conclusion In this large population, incidence rate of severe outcomes from COVID-19 in pediatric patients was higher among non-Caucasian patients, living in the South, with underlying comorbid illness, and those not yet eligible for vaccination. These findings reinforce the need for a vaccine for younger patients and targeted vaccine outreach to racial and ethnic minorities and children with chronic conditions. Disclosures Christoph U. Lehmann, MD, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria|UTSW: Employee.

Background As the risk for concomitant COVID-19 infection in people living with HIV (PLHIV) remains largely unknown, we explored a large national database to identify risk factors for COVID-19 infection among PLHIV. Methods Using the COVID-19 OPTUM de-identified national multicenter database, we identified 29,393 PLHIV with either a positive HIV test or documented HIV ICD9/10 codes. Using a multiple logistic regression model, we compared risk factors among PLHIV, who tested positive for COVID-19 (5,134) and those who tested negative (24,259) from January 20, 2020, to January 20, 2022. We then compared secondary outcomes including hospitalization, Intensive Care Unit (ICU) stay, and death within 30 days of test among the 2 cohorts, adjusting for COVID-19 positivity and covariates. We adjusted all models for the following covariates: age, gender, race, ethnicity, U.S. region, insurance type, adjusted Charlson Comorbidity Index (CCI), Body Mass Index (BMI), and smoking status. Results Among PLHIV, factors associated with higher odds for acquiring COVID-19 (Figure 1) included lower age (compared to age group 18–49, age groups 50–64 and >65 were associated with odds ratios (OR) of 0.8 and 0.75, P= 0.001), female gender (compared to males, OR 1.06, P= 0.07), Hispanic White ethnicity/race (OR 2.75, P= 0.001), Asian (OR 1.35, P= 0.04), and African American (OR 1.23, P= 0.001) [compared to non-Hispanic White], living in the U.S. South (compared to the Northeast, OR 2.18, P= 0.001), being uninsured (compared to commercial insurance, OR 1.46, P= 0.001), higher CCI (OR 1.025, P= 0.001), higher BMI category (compared to having BMI< 30, Obesity category 1 or 2, OR 1.2 and obesity category 3, OR 1.34, P= 0.001), and noncurrent smoking status (compared to current smoker, OR 1.46, P= 0.001). Compared to PLHIV who tested negative for COVID-19, PLHIV who tested positive, had an OR 1.01 for hospitalization (P = 0.79), 1.03 for ICU stay (P=0.73), and 1.47 for death (P=0.001). Conclusion Our study found that among PLHIV, being Hispanic, living in the South, lacking insurance, having higher BMI, and higher CCI scores were associated with increased odds of testing positive for COVID-19. PLHIV who tested positive for COVID-19 had higher odds of death. Disclosures Christoph U. Lehmann, MD, Celanese: Stocks/Bonds|Markel: Stocks/Bonds|Springer: Honoraria|UTSW: Employee Jeremy Y. Chow, M.D., M.S., Gilead Sciences: Grant/Research Support.

Background: Pseudo-randomized testing can be applied to perform rigorous yet practical evaluations of clinical decision support tools. We apply this methodology to an interruptive alert aimed at reducing free-text prescriptions. Using free-text instead of structured computerized provider order entry elements can cause medication errors and inequity in care by bypassing medication-based clinical decision support tools and hindering automated translation of prescription instructions. Objective: Evaluate the effectiveness of an interruptive alert at reducing free-text prescriptions via pseudo-randomized testing using native electronic health records (EHR) functionality. Methods: Two versions of an EHR alert triggered when a provider attempted to sign a discharge free-text prescription. The visible version displayed an interruptive alert to the user, and a silent version triggered in the background, serving as a control. Providers were assigned to the visible and silent arms based on even/odd EHR provider IDs. The proportion of encounters with a free-text prescription was calculated across the groups. Alert trigger rates were compared in process control charts. Free-text prescriptions were analyzed to identify prescribing patterns. Results: Over the 28 week study period, 143 providers triggered 695 alerts (345 visible and 350 silent). The proportions of encounters with free-text prescriptions were 83% (266/320) and 90% (273/303) in the intervention and control groups respectively (p-value = 0.01). For the active alert, median time to action was 31 seconds. Alert trigger rates between groups were similar over time. Ibuprofen, oxycodone, steroid tapers, and oncology-related prescriptions accounted for most free-text prescriptions. A majority of these prescriptions originated from user preference lists. Discussion: An interruptive alert was associated with a modest reduction in free-text prescriptions. Furthermore, the majority of these prescriptions could have been reproduced using structured order entry fields. Targeting user preference lists shows promise for future intervention.

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