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).

Risk Prediction for AKI in Patients Hospitalized With COVID-19: Withstanding Variants Over Time

Background: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI and assess performance over time. Method(s): Patients with positive SARS CoV-2 PCR hospitalized between 3/1/2020 to 1/14/2022 at 19 Texas hospitals were included. Those with AKI present on admission were excluded. Comorbidities, demographics, baseline laboratory data, and inflammatory biomarkers were obtained from the EHR and used to build nested models for AKI in an inception cohort. Models were validated in four out-of-time cohorts. Model discrimination and calibration measures were compared to assess performance. Result(s): Of 13,468 patients, 5,676 were in the Inception Cohort and 7,792 in subsequent validation cohorts grouped based on predominance of COVID variants, with cohorts 1 and 3 containing a mix of variants, cohort 2 corresponding to Delta predominance, and cohort 4 to Omicron. Prevalence of AKI was 13.7% in inception and 12.6%, 12.4%, 13.3%, and 14.4% in the validation cohorts. Proportion of AKI stages 2 or 3 vs. 1 was lower in the Omicron-dominant cohort 4 compared to the inception cohort (28/139 vs. 257/776, P=0.008), but was no different for cohorts 1-3. The final model containing demographics, comorbidities and baseline WBC, hemoglobin, hsCRP, ferritin, and D-dimer, had an AUC=0.781 (95% CI, 0.763, 0.799). Compared to the inception cohort, discrimination by AUC (validation 1: 0.785 [0.760, 0.810], P=0.14, validation 2: 0.754 [0.716, 0.795], P=0.14, validation 3: 0.778 [0.751, 0.806], P=0.14, and validation 4: 0.743 [0.695, 0.789], P=0.14) and calibration by ECI (validation 1: 0.116 [0.041, 0.281], P=1.0, validation 2: 0.081 [0.045, 0.295], P=0.64, validation 3: 0.055 [0.030, 0.162], P=1.0, and validation 4: 0.120 [0.043, 0.472], P=0.50) showed stable performance over time. Conclusion(s): Using demographics, comorbidities, admission laboratory values, and inflammatory biomarkers, we developed and externally validated a model to accurately predict AKI in hospitalized patients with COVID-19. A lower proportion of patients hospitalized during the Omicron-dominant period of the pandemic experienced severe AKI, but our predictive model withstood changes in practice patterns and virus variants.

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).