Lymphocyte Trajectories and Outcomes of Covid-19 Infection in People with Hiv

Background: Studies have shown that lymphopenia and a decreased CD4/ CD8 ratio are correlated with the severity of COVID-19 infections. As people with HIV (PWH) can have altered CD4/CD8 ratios at baseline, this study examined the relationship between lymphocyte and T-cell subsets with COVID-19 disease outcomes among PWH. Method(s): This retrospective study included adult PWH (identified by HIV ICD codes, HIV RNA or antibody results, or antiretroviral therapy use excluding preexposure prophylaxis) in the Optum COVID-19 EHR database with positive SARSCoV- 2 PCR or antigen tests from February 2020 to December 2021. Outcomes included 30-day hospitalization, ICU stay, mechanical ventilation, and death from COVID-19. Absolute lymphocyte counts and percent and CD4:CD8 ratios were collected prior to SARS-CoV-2 positivity (baseline) and then weekly for four weeks post-SARS-CoV-2 positivity. We examined lymphocyte trajectories in PWH who had available data at all time points, and we compared changes in counts and percentages at each week post-SARS-CoV-2 to baseline values, using Wilcoxon rank sum test. Result(s): Of a total of 4,525 PWH who tested positive for SARS-CoV-2, 102 PWH had available lymphocyte counts at all study time points. Compared to non-hospitalized PWH (n=38), hospitalized PWH (n=64) and PWH who were in the ICU (n=32) or ventilator dependent (n=27) experienced a larger drop in lymphocyte percentage in the first two weeks post-SARS-CoV-2 diagnosis with only a partial recovery in subsequent weeks. In patients who died (n=19), lymphocyte percentage recovered even more slowly. Hospitalized PWH, as compared to non-hospitalized PWH, had a significant decrease in lymphocyte percentage post-SARS-CoV-2 infection in the first week (-0.19 vs -0.05;< 0.001), second week (-0.23 vs -0.02;< 0.001), third week (-0.20 vs 0.00;< 0.001), and fourth week (-0.10 vs 0.00;0.001), a trend seen in the ICU, mechanically ventilated, and deceased groups as well (Table 1). By the first week, CD4/CD8 ratio in COVID-19 positive patients was lower in the deceased (-0.18 vs 0.00;p=0.4), ventilator dependent (-0.15 vs 0.00;p=0.2), and ICU (-0.15 vs 0.00;p=0.4) groups. Conclusion(s): Our study showed that not only is lymphopenia a marker of COVID-19 disease severity in PWH but also a failure of lymphocyte percentage recovery is associated with worse outcomes. There was also a trend towards worse outcomes associated with a lower CD4/CD8 ratio in the first week after COVID-19 infection. (Figure Presented).

Ten Year Trends of Typhus Fever in North Texas: Epidemiologic Characteristics and Clinical Manifestations

Background. Murine Typhus remains endemic in southern California and in southern Texas where it is transmitted by fleas, with opossums serving as the amplifying host. In Texas, the disease is increasingly recognized in municipalities outside its historic rural range and is spreading in a northward distribution. Since its expansion, we have observed increased cases in the Dallas-Fort Worth (DFW) area and aim to describe murine typhus in North Texas from 2011-2021. Methods. Leveraging the electronic health record, we retrospectively identified 482 individuals tested for murine typhus by Rickettsia typhi (R. typhi) serology in 2 Dallas hospitals. We subsequently collected epidemiologic characteristics, clinical features, and outcomes of 58 patients with positive R. typhi serologies ( >1:64). Results. Of the 58 patients with positive R. typhi serology, 39 (67%) were male, 45 (78%) were White, and 23 (40%) were Hispanic. Seventy-nine percent had symptom onset between May and November, and 36/58 (62%) were diagnosed in 2020 and 2021. Twenty-six (45%) had exposure to dogs, 18 (31%) to cats, and 13 (22%) to opossums. Twelve (21%) patients were immunocompromised. Fifty-two (90%) had fever, 35 (60%) headache, 26 (45%) nausea and vomiting, 26 (45%) rash, 25 (43%) myalgia, 20 (34%) cough, and 17 (29%) abdominal pain. In 2020 and 2021, 35/36 (97%) patients were additionally tested for COVID-19, and 29/35 (83%) patients had more than one negative SARS-CoV-2 test prior to R. typhi serologies being sent. Twenty-one out of fifty (42%) had an abnormal chest x-ray (CXR) and 28/30 (93%) had an abnormal chest computed tomography (CT). Nine (16%) had hypoxia, 9 (16%) required an intensive care unit, and 3 (5%) required mechanical ventilation. No patients died within 30 days of diagnosis. Conclusion. Our study highlights the expansion of murine typhus in North Texas (Figure 1) and demonstrates the heightened need for clinicians to be aware of this disease in the appropriate epidemiologic and clinical settings. We also describe increasing rates of respiratory findings, demonstrated through over half of patients having at least one respiratory symptom, and 93% having an abnormal chest CT (findings traditionally associated with severe disease).

Vaccination During Acute COVID-19 Infection Protects Against 30 Day Adverse Outcomes

Background. Therapeutic vaccination following SARS-CoV-2 infection might stimulate anti-viral immunity and improve patient outcomes. We investigated, amongst previously unvaccinated patients, whether vaccination with the Pfizer, Moderna, or Johnson & Johnson vaccines within 14 days of a positive SARS-CoV-2 test affected 30-day patient outcomes. Methods. Using a deidentified national electronic health record dataset (Optum, Inc.), we identified previously unvaccinated patients who tested positive forCOVID-19 between 12/11/2020 and 12/19/2021. Among this cohort, 1,909 patients received a first vaccine dose within 14 days (vaccinated) while 446,309 did not receive a first dose of vaccine within 30 days of their first positive test (unvaccinated). We performed 1:1 propensity score matching based on age, gender, race, ethnicity, region, BMI, insurance, and comorbidities from the Charlson Comorbidity Index. Next, we compared odds of severe outcomes within 30 days between vaccinated and unvaccinated groups using a partial proportional odds model with the following ordinal severity outcome: no hospitalization, hospitalization, ICU stay, or death (Stata version 17.0, alpha = 0.05). Results. 1,909 vaccinated patients were propensity score-matched to 1,909 unvaccinated patients. The final matched cohort was statistically indistinguishable (p > 0.05) for all metrics used in propensity score calculation. This matched cohort (n = 3,818) was 58.6% female, 67.7% white, 12.6% Hispanic, and 56.4% commercially insured, with a mean age of 50.6 years and a similar comorbidity profile. A partial proportional odds model showed that unvaccinated patients were at increased risk for hospitalization and higher ordered outcomes (OR = 1.19, 95% CI: 1.02-1.39), ICU stay and higher ordered outcomes (OR 1.63, 95% CI: 1.21-2.20), and death (OR 4.57, 95% CI: 2.50-8.37). Conclusion. Among previously unvaccinated patients, those who received a first dose vaccine within 14 days of a positive COVID-19 test were less likely to experience hospitalization, ICU stay, or death compared to matched peers who did not receive a first dose in the acute phase of infection. The sample size of patients vaccinated during the acute phase is limited, so further studies are indicated to evaluate the safety and efficacy of this practice.

Risk Factors for COVID-19 Infection and Outcomes in People Living with HIV

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 AfricanAmerican (OR1.23, 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,OR1.34, 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. (Figure Presented).

Clinical and Demographic Characteristics of COVID-19 in Pediatric Patients in the United States

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. Weconducted a descriptive study of patients<=18 years fromthe 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;alpha = 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 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. (Figure Presented).

NetworkSIR and EnvironmentalSIR: Effective, Open-Source Epidemic Modeling in the Absence of Data

The rapidly changing situation characterized by the COVID-19 pandemic highlighted a need for new epidemic modeling strategies. Due to an absence of computationally efficient models robust to paucity of reliable data, we developed NetworkSIR, a model capable of making predictions when only the approximate population density is known. We then extend NetworkSIR to capture the effect of indirect disease spread on the progression of an epidemic (EnvironmentalSIR).

Understanding public perception of COVID-19 social distancing on twitter

Background: Managing and changing public opinion and behavior are vital for social distancing to successfully slow transmission of COVID-19, preserve hospital resources, and prevent overwhelming the healthcare system's resources. We sought to leveraging organic, large-scale discussion on Twitter about social distancing to understand public's beliefs and opinions on this policy. Methods: Between March 27 and April 10, 2020, we sampled 574,903 English tweets that matched the two most trending social distancing hashtags at the time, #socialdistancing and #stayathome. We used natural language processing techniques to conduct a sentiment analysis that identifies tweet polarity and emotions. We also evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and compared the sentiment by topic. Results: There was net positive sentiment toward both #socialdistancing and #stayathome with mean sentiment scores of 0.150 (standard deviation [SD], 0.292) and 0.144 (SD, 0.287) respectively. Tweets were also more likely to be objective (median, 0.40;IQR, 0 to 0.6) with approximately 30% of all tweets labeled as completely objective. Approximately half (50.4%) of all tweets primarily expressed joy and one-fifth expressed fear and surprise each (Figure 1). These trends correlated well with topic clusters identified by frequency including leisure activities and community support (i.e., joy), concerns about food insecurity and effects of the quarantine (i.e., fear), and unpredictability of COVID and its unforeseen implications (i.e., surprise) (Table 1). Conclusion: The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing measures in the early stages of their implementation. (Table Presented).

The utility of infectious diseases e-consults in the era of COVID-19

Background: During the COVID-19 pandemic, rapid Infectious Diseases (ID) consultation has been required to answer novel questions regarding SARS-CoV-2 testing and infection prevention. We sought to evaluate the utility of e-consults to triage and provide rapid ID recommendations to providers. Methods: We performed a retrospective study reviewing ID e-consults in three institutions in the North Texas region: Clements University Hospital (CUH), Parkland Hospital and Health System (PHHS), and the VA North Texas Health Care System (VA) from March 1, 2020 to May 15, 2020. Variables collected include age, sex, ethnicity, comorbidities, time to completion, reason for consult and outcome of consult (initiation or removal of personal protective equipment (PPE) and recommendation to test or retest for COVID-19). Results: We performed all analysis using R studio (Version 1.3.959). Characteristics of 198 patients included: 112(57%) male, 86(43%) female, 86(43%) Caucasian, 71(36%) Hispanic, 42(21%) African American, 6(3%) Asian and mean(sd) age of 55.1(15.9). Patient comorbidities included: 89(45%) with a heart condition, 77(39%) diabetes, 30(15%) asthma and 14(7%) liver disease. Median time to completion for all hospitals was 4 hours(h);((CUH (4h) vs PHHS (2h), p< 0.05;VA (5.5h) vs PHHS (2h) p< 0.05)). Most common reasons for e-consult included: (63)32% regarding re-testing ((CUH 14(21%) vs PHHS 43(50%), p< 0.05;CUH vs VA 14(27%), p< 0.05;PHHS vs VA, p< 0.05)), (61)31% testing ((CUH 25(37%) vs PHHS 39(45%), p< 0.05;CUH vs VA 7(16%), p< 0.05;PHHS vs VA, p< 0.05)) and 61(31%) infection prevention (IP). Based on the e-consult recommendation, 53(27%) of patients were tested ((CUH 31(45%) vs PHHS 11(13%), p< 0.05, CUH vs VA 11(25%), PHHS vs VA, p< 0.05)), 45(23%) were re-tested, 44(22%) of patients had PPE started on and 19% had PPE removed ((CUH 0(0%) vs PHHS 16(19%), p< 0.05;CUH vs VA 21(48%), p< 0.05;PHHS vs VA, p< 0.05)). Conclusion: E-consult services can provide prompt ID input during the COVID-19 pandemic, minimizing the risk of infection to the patient and health care workers while preserving PPE and testing supplies. (Figure Presented).