bluuebunny/crossref_metadata_2025 huggingface dataset:
384. SARS-CoV-2 Surveillance Testing Patterns among Hospitalized Pediatric Patients in a Single Academic Medical Center
Authors: Bukhari Areej , Seidelman Jessica , Smith Becky A , Lewis Sarah S , Smith Michael J , Moehring Rebekah W , Anderson Deverick J , Akinboyo Ibukunoluwa
Published: December 2021

Background

Children infected with SARS-CoV-2 often have mild or no symptoms, making symptom screening an ineffective tool for determining isolation precautions. As an infection control measure, universal pre-procedural and admission SARS-CoV-2 testing for pediatric patients was implemented in April and August 2020, respectively. Limited data exist on the utility screening programs in the pediatric population.

Methods

We performed a retrospective cohort study of pediatric patients (birth to 18 years) admitted to a tertiary care academic medical center from April 2020 to May 2021 that had one or more SARS-CoV-2 point-of-care or polymerase chain reaction tests performed. We describe demographic data, positivity rates and repeat testing trends observed in our cohort.

Results

A total of 2,579 SARS-CoV-2 tests were performed among 1,027 pediatric inpatients. Of these, 51 tests (2%) from 45 patients (4.3%) resulted positive. Community infection rates ranged from 4.5-60 cases/100,000 persons/day during the study period. Hispanic patients comprised 16% of the total children tested, but were disproportionately overrepresented (40%) among those testing positive (Figure1). Of 654 children with repeated tests, 7 (0.1%) converted to positive from a prior negative result. Median days between repeat tests was 12 (IQR 6-45), not necessarily performed during the same hospital stay. Five of these 7 patients had tests repeated < 3 days from a negative result, of which only 2 had no history of recent infection by testing performed at an outside facility. Pre-procedural tests accounted for 35% of repeat testing, of which 0.9% were positive. Repeated tests were most frequently ordered for patients in hematology/oncology (35%) and solid organ transplant/surgical (33%) wards, each with < 3% positive conversion rate. Notably, no hematopoietic stem cell transplant patients tested positive for SARS-CoV-2 during the study period.

Pediatric SARS-CoV-2 Testing Distributed by Race/Ethnicity

Conclusion

The positivity rate of universal pre-procedural and admission SARS-CoV-2 testing in pediatric patients was low in our inpatient cohort. Tests repeated < 3 days from a negative result were especially low yield, suggesting limited utility of this practice. Diagnostic testing stewardship in certain populations may be useful, especially as community infection rates decline.

Disclosures

Michael J. Smith, MD, M.S.C.E, Merck (Grant/Research Support)Pfizer (Grant/Research Support) Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)
385. SARS-CoV-2 Infections Among Military Personnel Deployed on the USNS COMFORT to New York City During the COVID-19 Pandemic and One-Year Follow-Up
Authors: Cooper Elizabeth , Lee Tida , Laing Eric , Ritter Andrew , Lee Melissa , Baker Matthew , Baldino Tyler , Mcadoo Terrance , Nguyen Huy , Broder Christopher C , Epsi Nusrat J , Richard Stephanie A , Warkentien Tyler , Millar Eugene , Burgess Timothy , Kronmann Karl , Lalani Tahaniyat
Published: December 2021

Background

The USNS COMFORT deployed to New York City to augment the inpatient health care capacity in March 2020. The aim of this study was to determine the prevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection among US Navy personnel upon return from deployment, and to identify incident cases of SARS-CoV-2 infection during 1 year of follow-up.

Methods

Crewmembers, the majority of whom were health care workers (HCW), were enrolled following deployment, in May 2020. PCR results from symptomatic crewmembers during deployment, and Day 0 and Day 14 post-deployment screening swabs conducted on all crewmembers, per military order, were abstracted. A questionnaire and serum were collected on Day 14 post-deployment. SARS-CoV-2 infection was defined as a positive SARS-CoV-2 spike glycoprotein immunoglobulin G antibody (IgG) or PCR. COVID-19 related medical encounters, PCR and antibody testing results within 1 year following deployment were abstracted from the Military Health System Data Repository (MDR). There was adequate provision of personal protective equipment (PPE) in the hospital and the COVID-19 vaccine roll-out for HCW began in December 2020.

Results

Of the 1200 crewmembers, 449 were enrolled and completed the questionnaire and screening swabs, and 432 (96.2%) completed the Day 14 blood draw (Table 1). The cumulative prevalence of SARS-CoV-2 infection was 3.01% (13/432; 95% CI, 1.61%–5.09%). One of 17 subjects did not complete the blood draw and was PCR positive on Day 14. 433/449 (96.4%) had a PCR performed during the follow-up period (i.e. after the Day 14 post-deployment visit until Feb 2021), for HCW screening or symptomatic illness (median number of tests: 2 [IQR: 1, 2; range: 1,6]). 25 of 433 (5.8%) were PCR positive (Fig 1). 19 (76.0%) occurred in corpsmen, 23 (92.0%) were symptomatic and none were hospitalized. One asymptomatic re-infection occurred in a crewmember who was PCR negative and IgG positive at Day 14 post-deployment.

Table 1. Characteristics of the overall cohort and by SARS-CoV-2 infection

Figure 1. Number of PCR tests (bar graph) and positivity rate (red line) by month in 449 USNS COMFORT crewmembers during 1-year follow-up after return from deployment

Conclusion

The post-deployment prevalence of SARS-CoV-2 infection was low. A high proportion of HCW underwent PCR testing during 1-year follow-up but a low incidence of infection was observed. This was likely from community transmission as nosocomial transmission was mitigated by adequate PPE and vaccine roll-out.

Disclosures

All Authors: No reported disclosures
386. A Systematic Review of COVID-19 Transmission Dynamics and Clinical Response on Cruise Ships Globally Between January and October 2020
Authors: Willebrand Kathryn , Pischel Lauren , Malik Amyn A , Jenness Samuel , Omer Saad
Published: December 2021

Background

Cruise ships provide an ideal setting for efficient transmission of SARS-CoV- 2 given a socially dense exposure environment. No systematic review of transmission of COVID-19 on cruise ships to date has been completed.

Methods

MEDLINE was searched in accordance with PRIMSA guidelines for COVID-19 cases associated with cruise ships. A list of cruise ships with COVID-19 was crossed referenced with the Centers for Disease Controls’ list of cruise ships that had at least one COVID-19 case associated with them within 14 days of disembarkation. News articles were also searched for epidemiologic information. 43 full text articles from MEDLINE and 177 from news sources were included in the final analysis. Narratives of the outbreak in ships with over 100 cases are presented.

PRISMA Flow Diagram

PRISMA Flow diagram of articles screened, reviewed, and analyzed

Results

A total of 80 ships and 104 unique voyages on cruise ships were identified with at least one COVID-19 case before 30 October 2020. Nineteen ships had more than one voyage with a case of COVID-19. The median number of cases per ship was three (intraquartile range (IQR) 1–17.8), with two notable outliers the Diamond Princess and Ruby Princess which had 712 and 907 cases respectively. The median attack rate for COVID-19 was 0.2% (IQR 0.03% -1.5%), though this distribution was skewed to the right with a mean attack rate of 3.7%. 25.9% of voyages had at least one associated death. Outbreaks involving only crew were later than outbreaks with guests and crew.

Cases of COVID-19 on cruise ships in 2020

Number of cases of COVID-19 on cruise ships by date COVID-19 outbreak identified and if case was a guest or crew member. Percent of COVID-19 cases that were crew in 2020

Percent of COVID-19 cases that were in crew members by date outbreak identified in 2020

Percent of passengers on cruise ships that were crew

Percentage of passengers on cruise ships that were crew members in 2020 by date outbreak identified

Conclusion

COVID-19 can spread easily on cruise ships in a susceptible population when there is an absence of mitigation measures due to the confined space and high-density of contact networks. This can not only create super spreader events but also facilitate

international spread.

Disclosures

All Authors: No reported disclosures
387. Predictors of Seropositivity to SARS-CoV-2 Among Workforce Members at a Large Urban Medical Center
Authors: Flores Evelyn A , Kupferwasser Deborah , Merino Prudencio , Tran Donna Phan , Liu Honghu , Huang Yilan , Bolaris Michael , Nguyen Megan H , Gonzalez Mildred , Da Silva Wellington , Astorga-Cook Leslie , Abueg Angel , Mason Holli , Miller Loren G
Published: December 2021

Background

Prior to SARS-CoV-2 vaccination availability, medical centers workers were at significant COVID-19 (COVID) infection risk. As part of a program offering free SARS-CoV-2 serology tests to medical center employees, we examined risk factors for prior COVID infection.

Methods

From Sept. to Dec. 2020, we advertised free IgG antibody testing to all Los Angeles County-Univ. of Southern California Medical Center (LAC+USC) workforce members (clinical and non-clinical) via repeated email blasts. Antibody was determined using the Abbott SARS-Cov-2 IgG test against SARS-CoV-2 nucleocapsid protein. Program participants were asked to fill out a detailed epidemiologic questionnaire about work and non-work COVID risks on their cell phone or on paper at the time of phlebotomy. All testing was done prior to COVID vaccine availability.

Results

Among approximately 10,500 workforce members, 1327 had serologies done. Among those 1273 (96%) completed the questionnaire and were included in the analysis. SARS-CoV-2 IgG antibodies were found in 60 (4.7%). In bivariate analysis, we found associations between SARS-CoV-2 seropositivity and persons who previously tested positive for COVID (OR 175.8 [95% CI 77.6 – 398.6]), persons who thought they had prior COVID but tested negative (OR 3.9 [95% CI 1.3 – 11.4]), and persons who thought they had prior COVID but did not get a COVID test (OR 4.2 [95% CI 1.4 – 12.5]). In a multivariate model of SARS-CoV-2 seropositivity examining work- and non-work-related COVID exposures (Table), seropositivity was associated with work-related COVID exposure without adequate personal protective equipment (PPE) (OR 5.1 [95% CI 2.1 – 12.2]), work-related COVID exposure with adequate PPE (OR 3.5 [95% CI 1.5 – 8.0]), never wearing a mask outside of work (OR 7.1 [95% CI 1.3 – 38.4]), and Native Hawaiian/Pacific Islander race (OR 6.6 [95% CI 1.7 – 23.4]). Seropositivity was inversely associated with living at home with multiple age groups (OR 0.4 [95% CI 0.2 – 0.8]). Multivariate Model of Exposures Associated with Positive COVID Serology Among LAC+USC Workforce Members

Conclusion

Among workers in a large urban medical center prior to COVID vaccine availability, SARS-CoV-2 seropositivity was associated with work-related COVID exposures and low mask use outside of work, suggesting that COVID transmission in workforce members occurs both via occupational and non-occupational routes.

Disclosures

Loren G. Miller, MD, MPH, Medline (Grant/Research Support, Other Financial or Material Support, Contributed product)Stryker (Other Financial or Material Support, Contributed product)Xttrium (Other Financial or Material Support, Contributed product)
388. Epidemiologic and Microbiologic Characteristics of Hospital-acquired Infections in Patients with COVID-19 at Intensive Care Unit, Mexico City
Authors: Castañeda-Méndez Paulo , Cabrera-Ruiz Maria Lorena , Barragán-Reyes Armando , Aguilar Esperanza Aleman , Sanchez Brenda Aceves , Salgado Maria Fernanda Ruiz , Viesca Maria Jose Ribe , Ortiz Brenda Alexia Velez , Mar Javier Reyes , Aguilar-Zapata Daniel , Hernandez Claudia Yanet Ruiz , Lopez Gloria Santillan , Hernandez Eva Juarez , Martinez Alejandra Jimena Garcia , Soto-Ramirez Luis E , Frias Lili
Published: December 2021

Background

Patients with severe SARS-CoV-2 infection are at high risk of complications due to the intensive care unit stay. Hospital-acquired infections (HAI) are one of the most common complication and cause of death in this group of patients, it is important to know the epidemiology and microbiology of this hospital-acquired infections in order to begin to the patients a proper empirical treatment. We describe the epidemiologic and microbiologic characteristics of HAI in patients with COVID-19 hospitalized at intensive care unit (ICU) in a tertiary level private hospital in Mexico City.

Methods

From April to December 2020, data from all HAIs in patients with severe pneumonia due to SARS-CoV-2 infection with mechanical ventilation at ICU were obtained. The type of infection, microorganisms and antimicrobial susceptibility patterns were determined.

Results

A total of 61 episodes of HAIs were obtained, the most common was ventilator associated pneumonia (VAP) in 52.4% (n=32) followed by urinary tract infection (UTI) 34.4%(n=21) and bloodstream infection (BSI) 9.84% (n=6). Only two episodes corresponded to C. difficile associated diarrhea. We identified 82 different microorganisms, the most frequent cause of VAP was P. aeruginosa 22% (10/45) followed by K. pneumoniae 20% (9/45); for UTI, E. coli 28.5% (6/21), and S. marcescens 19% (4/21); for BSI the most frequent microorganism was S. aureus 28.5 (2/7). Regarding the antimicrobial susceptibility patters the most common were Extended Spectrum Beta-Lactamase (ESBL) Gram-negative rods followed by Methicillin-resistant Staphylococcus aureus.

Conclusion

In patients with severe COVID-19 hospitalized in the ICU the most frequent HAIs were VAP and UTI caused by P. aeruginosa and E. coli respectively. ESBL enterobacteriaceae was the most common resistant pattern identifed in the bacterial isolations in our series.

Disclosures

All Authors: No reported disclosures
389. Demographic and Clinical Characteristics of Suspect SARS-CoV-2 Reinfection Cases in Los Angeles County from March 10 to June 1, 2021: A Cross-sectional Study of Case Interview Data
Authors: Thompson Joseph , Masai Laureen , Khalid Nageena , Chun Katie , Khuu Diana , Alkhoudairy Nouran Mahmoud , Griffin Jennifer B
Published: December 2021

Background

Probable and suspect SARS-CoV-2 reinfection has been reported globally, with implications for risk assessment and pandemic control. Genomic sequencing and supporting data are frequently unavailable to confirm SARS-CoV-2 reinfection.

Methods

In March 2021, Los Angeles County Department of Public Health began interviewing suspect reinfection cases, defined as individuals with SARS-CoV-2 RNA detected ≥ 90 days after the first detection of SARS-CoV-2 RNA via molecular testing. We conducted a cross-sectional study of case interview data from March 10 to June 1, 2021 to estimate the prevalence of suspect reinfection cases; describe the interval between repeat positives ≥ 90 days; and, estimate bivariate prevalence odds ratios (OR) with 95% confidence intervals (95% CI) for suspect reinfections and age, sex, race/ethnicity, reason for testing, symptomology, and comorbidities.

Results

From March 10 to June 1, 2021, we attempted 29,983 case interviews, including 1,901 (6.3%) suspect reinfection cases and 28,082 (93.7%) initial cases. Among suspect reinfection cases, the median interval between repeat positive tests was 117 days (interquartile range: 102, 141). Suspect reinfection cases had decreased odds of completing case interviews (n=738; 38.8%) compared to initial cases (n=13,263; 47.2%) (OR: 0.71; 95% CI: 0.65, 0.78). Among completed case interviews, suspect reinfection cases had increased odds of being older (50-64 years OR: 1.63 [95% CI: 1.32, 2.01]; ≥ 65 years OR: 3.77 [95% CI: 3.00, 4.74]; ref. 30-49 years); Hispanic/Latino (OR: 2.64 [95% CI: 2.10, 3.33]; ref. White); female (OR: 1.21 [95% CI: 1.04, 1.41]); reporting screening as their testing reason (OR: 10.39; [95% CI: 7.45, 14.48]; ref. known exposure); and reporting underlying health conditions (OR: 2.64; 95%CI: 2.24, 3.10). Suspect reinfection cases had decreased odds of being symptomatic (OR 0.15; 95% CI: 0.13, 0.18).

Conclusion

This analysis of case interview data indicates individuals who are older, Hispanic, female, and have underlying health conditions may be vulnerable populations for suspect reinfection. Limitations include unconfirmed reinfection and alternative explanations such as persistent positivity with decreased symptoms and infectivity over time.

Disclosures

All Authors: No reported disclosures
391. Small Towns, Big Cities: Rural and Urban Disparities Among Hospitalized Patients with COVID-19
Authors: Hamilton Caroline , Ayyala Deepak Nag , Walsh David , Bramwell Christian-Jevon , Walker Christopher , Dib Rita Wilson , Gosse Jessica , Ladak Amber F , Morissette Patricia , Rao Arni S R Srinivasa , Chao Andrew , Vazquez Jose A
Published: December 2021

Background

More than half of all hospitals in the U.S. are rural hospitals. Frequently understaffed and resource limited, community hospitals serve a population that tends to be older and have less access to care with increased poverty and medical co-morbidities. There is a lack of data surrounding the impact of COVID-19 among rural minority communities. This study seeks to determine rural and urban disparities among hospitalized individuals with COVID-19.

Methods

This is a descriptive, retrospective analysis of the first 155 adult patients admitted to a tertiary hospital with a positive COVID-19 nasopharyngeal PCR test. Augusta University Medical Center serves the surrounding rural and urban counties of the Central Savannah River Area. Rural and urban categories were determined using patient address and county census data. Demographics, comorbidities, admission data and 30-day outcomes were evaluated.

Results

Of the patients studied, 62 (40%) were from a rural county and 93 (60%) were from an urban county. No difference was found when comparing the number of comorbidities of rural vs urban individuals; however, African Americans had significantly more comorbidities compared to other races (p-value 0.02). In a three-way comparison, race was not found to be significantly different among admission levels of care. Rural patients were more likely to require an escalation in the level of care within 24 hours of admission (p-value 0.02). Of the patients that were discharged or expired at day 30, there were no differences in total hospital length of stay or ICU length of stay between the rural and urban populations.

Baseline Characteristics of Hospitalized Patients with COVID-19

Day 30 Outcomes and Characteristics

Level of Care at Time of Admission

Conclusion

This study suggests that patients in rural communities may be more critically ill or are at a higher risk of early decompensation at time of hospitalization compared to patients from urban communities. Nevertheless, both populations had similar lengths of stay and outcomes. Considering this data is from an academic medical center with a large referral area and standardized inpatient COVID-19 management, these findings may prompt further investigations into other disparate outcomes.

Disclosures

All Authors: No reported disclosures
392. 3D-DOSS – Using Digital Twins and Spatiotemporal Data Mapping for Infectious Disease Surveillance and Outbreak Investigations
Authors: Philip Edwin , Sim Jean Xiang Ying , Whiteley Sean , Fang Andrew Hao Sen , Chow Weien , Lee Esther , Lee Jiunn Kee , Kee Low Seng , Cheong Joseph Kin Meng , Arora Shalvi , venkatachalam Indumathi
Published: December 2021

Background

The COVID-19 pandemic has brought to light the importance of contact tracing in outbreak management. Digital technologies have been leveraged to enhance contact tracing in community settings. However, within complex hospital environments, where patient and staff movement and interpersonal interactions are central to care delivery, tools for contact tracing and cluster detection remain limited. We aimed to develop a system to promptly, identify contacts in infectious disease exposures and detect infectious disease clusters.

Methods

We prototyped a 3D mapping tool 3-Dimensional Disease Outbreak Surveillance System (3D-DOSS), to have a spatial representation of patients in the hospital inpatient locations. Based on the AutoCAD drawings, the hospital physical spaces are built within a game-development software to obtain accurate digital replicas. This concept borrows from the way gamers interact with the virtual world/space, to mimic the interactions in physical space, like the SIMS franchise. Clinical, laboratory and patient movement data is then integrated into the virtual map to develop syndromic and disease surveillance systems. Risk assignment to individuals exposed is through mathematical modeling based on distance coordinates, room type and ventilation parameters and whether the disease is transmitted via contact, droplet or airborne route.

Results

We have mapped acute respiratory illness (ARI) data for the period September to December 2018. We identified an influenza cluster of 10 patients in November 2018. In a COVID-19 exposure involving a healthcare worker (HCW), we identified 44 primary and 162 secondary contacts who were then managed as per our standard exposure management protocols. MDRO outbreaks could also be mapped.

Conclusion

Through early identification of at-risk contacts and detection of infectious disease clusters, the system can potentially facilitate interventions to prevent onward transmission. The system can also support security, environmental cleaning, bed assignment and other operational processes. Simulations of novel diseases outbreaks can enhance preparedness planning as health systems that had been better prepared have been more resilient in this current pandemic.

Disclosures

All Authors: No reported disclosures
393. Characteristics of SARS-CoV-2 RNA Viral Loads among Nursing Home Residents and Staff with Repeat Positive Tests ≥ 90 Days After Initial Infection: 5 US Jurisdictions, July 2020–March 2021
Authors: Wyatt Wilson W , Hatfield Kelly M , Tressler Stacy , Kinsey Cara Bicking , Zell Renee , Williams Channyn , Spicer Kevin , Kamal-Ahmed Ishrat , Abdalhamid Baha , Gemechu Mahlet , Folster Jennifer , Thornburg Natalie J , Tamin Azaibi , Harcourt Jennifer L , Queen Krista , Tong Suxiang , Parra Gemma , Jernigan John A , Jernigan John A , Crist Matthew B , Perkins Kiran , Reddy Sujan
Published: December 2021

Background

Background. Understanding the viral load and potential infectivity of individuals in nursing homes (NH) with repeat positive SARS-CoV-2 tests ≥ 90 days after initial infection has important implications for safety related to transmission in this high-risk setting.

Methods

Methods. We collected epidemiologic data by reviewing records of a convenience sample of NH residents and staff with respiratory specimens who had positive SARS-CoV-2 rRT-PCR test results from July 2020 through March 2021 and had a SARS-CoV-2 infection diagnosed ≥ 90 days prior. No fully vaccinated individuals were included. Each contributed one repeat positive specimen ≥ 90 days after initial, which was sent to CDC and retested using rRT-PCR. Specimens were assessed for replication-competent virus in cell culture if Cycle threshold (Ct) < 34 and sequenced if Ct < 30. Using Ct values as a proxy for viral RNA load, specimens were categorized as high (Ct < 30) or low (if Ct ≥ 30 or rRT-PCR negative at retesting). Continuous variables were compared using Wilcoxon signed-rank tests. Proportions were compared using Chi-squared or Fisher’s exact tests.

Results

Results. Of 64 unvaccinated individuals with specimens from 61 unique NHs, 14 (22%) were sent for culture and sequencing. Ten of 64 (16%) had a high viral RNA load, of which four (6%) were culture positive and none were known variants of interest or concern (Figure 1). Median days to repeat positive test result were 122 (Interquartile range (IQR): 103–229) and 201 (IQR: 139–254), respectively, for high versus low viral load specimens (p=0.13). More individuals with high viral loads (5/10, 50%) reported COVID-19 symptoms than with a low viral load (1/27, 4%, p=0.003). Most individuals (46/58, 79%) were tested following known or suspected exposures, with no significant differences between high and low viral load (p=0.18).

Conclusion

In this study, nearly 1 in 6 NH residents and staff with repeat positive tests after 90 days demonstrated high viral RNA loads and viable virus, indicating possible infectivity. While individuals with high RNA viral load may be more likely to be symptomatic, distinguishing asymptomatic individuals who have high viral loads may be difficult with timing since initial infection, other test results, or exposure history alone.

Disclosures

John A. Jernigan, MD, MS, Nothing to disclose.
394. Descriptive Evaluation of Epidemiology and Microbiology of Patients with COVID-19 Pre/post Implementation of Corticosteroids as Standard of Care
Authors: Flajc Goran , Zaki Ahmed
Published: December 2021

Background

The coronavirus disease 2019 (COVID-19) pandemic continues to present a significant global public health concern. As of June 2021, nearly 174 million cases of SARS-CoV-2 infection worldwide have been reported to the World Health Organization. Rigorous data on the efficacy of corticosteroids have now established its role as standard of care (SOC). Less recognition has been given to corticosteroid therapy and its association with risk of infection especially in those who are critically ill and prone to nosocomial pathogens.

Methods

This is a retrospective study of mechanically ventilated patients with COVID - 19 from March 2020 to September 2020 at a single center. The primary endpoint for this study was description of microbiology and epidemiology of secondary infections and co -infections, defined as any infection following treatment for COVID - 19. Secondary endpoints included the duration of corticosteroid use, length of hospital stay, ICU length of stay, and mortality.

Results

Of the 104 patients, 73% had co-infections or secondary infections. Pre-SOC patients were more likely to receive >10 days of corticosteroids (71% vs 30%). Co-infections were present in 12% of patients (13% in pre-SOC vs 11% in post-SOC), secondary infections occurred in 61% of patients (74% in pre-SOC vs 53% in post-SOC). The most common causative organism of co-infections and secondary infections were Staphylococcus aureus in the pre-SOC group and Escherichia coli and Pseudomonas aeruginosa in the post-SOC group. The mean hospital length of stay was 43 days pre-SOC vs 33 days post-SOC with a mean ICU length of stay of 33 vs 29 days, respectively. Mortality rate was similar between the two groups (55% vs 58%).

Conclusion

Differences in epidemiology and microbiology was seen pre and post implementation of dexamethasone in June, 2020. Higher rates of co-infections were seen with this prolonged use of corticosteroids pre-SOC but it is unclear whether patients developed more co-infections as result of extended corticosteroid use, a longer hospital stay, or other factors. Further studies are needed to assess the optimal duration of corticosteroid use in this patient population with consideration to weigh benefit vs risk.

Disclosures

All Authors: No reported disclosures
395. Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar
Authors: Abuyousef Safae , Alnaimi Shaikha , Omar Nabil E , Elajez Reem , Elmekaty Eman Zeyad Ibrahim , Abdelfattah-Arafa Eiman , Barazi Raja , Ghasoub Rola , Rahhal Ala , Hamou Fatima , Al-Amri Maha K , Karawia Ahmed , Ajaj Fatima , Alkhawaja Raja , Kardousha Ahmed , Awaisu Ahmed , Abou-Ali Adel , Khatib Mohamad , AbouKamar Mohamed , Al-Hail Moza
Published: December 2021

Background

Coronavirus disease (COVID-19) is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p=0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), obesity (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphopenia (aOR=2.69, 95% CI: 1.80-4.02, p< 0.001), high aspartate aminotransferase (AST) (aOR= 2.59, 95% CI: 1.53-4.36, p< 0.001), high ferritin (aOR=1.96, 95% CI: 1.40-2.74, p< 0.001), high C-reactive protein (CRP) (aOR=4.09, 95% CI: 2.81-5.96, p< 0.001), and dyspnea (aOR=2.50, 95% CI: 1.77-3.54, p< 0.001). Similarly, significant predictors of mortality included CVD (aOR=2.16, 95% CI: 1.32- 3.53, p=0.002), diabetes (aOR=1.77, 95% CI: 1.07-2.90, p=0.025), cancer (aOR=4.65, 95% CI: 1.50-14.42, p= 0.008), lymphopenia (aOR=2.34, 95% CI: 1.45-3.78, p= 0.001), and high AST (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036).

Risk Factors for ICU admission among patients with COVID-19 (N=1560)

Conclusion

Having CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.

Disclosures

Adel Abou-Ali, PharmD, PhD, Astellas Pharma Global Development, Inc. (Employee)
397. Impact of School Opening Model on Cases of SARS-CoV-2 in Surrounding Communities: A Nationwide, Retrospective Cohort Study
Authors: Branch-Elliman Westyn , Ertem Zeynep , Perkins Elissa , van den Berg Polly , Epshtein Isabella , Chaiyakunapruk Nathorn , Wilson Fernando , Oster Emily , Nelson Richard
Published: December 2021

Background

Early in the COVID-19 pandemic, elementary and secondary schools were closed. There was variation in school opening mode (traditional, hybrid, remote) in fall 2020.The aim of this national, retrospective cohort study is to evaluate the impact of in-person learning on community incidence of SARS-CoV-2 and COVID-19-related deaths.

Methods

Data were extracted from several data sources. School opening mode was collected from the Burbio school tracker, which tracks school openings in a sample of school districts across the US. Incidence of SARS-CoV-2 and COVID-19 related deaths were obtained from the CDC. Data on community-level SARS-CoV-2 mitigation measures were obtained from the Oxford University COVID-19 Government Response Tracker. The effect of school mode on SARS-CoV-2 cases and deaths/100,000 during the 12-weeks following the start of school was estimated using a log-linear model with state, week, and state-week fixed effects. Models were stratified by 9 US Census divisions and adjusted for variables determined a priori to be potentially associated with the outcome.

Results

519 US counties were included (Figure 1); mean cases of COVID-19 were increasing across all regions during the weeks following the start of school, regardless of school mode. Adjusted absolute differences in COVID-19 cases in counties with hybrid and traditional school opening modes relative to fully remote learning models are presented in Figure 2. In the Northeast and Midwest regions of the country, COVID-19 case rates were not statistically different between different school modes. However, in the South and West regions, there was a statistically significant increase in cases per week among counties that opened in an in-person relative to remote learning model, ranging from 17.1 (95% CI: 0.3-33.8) to 24.4 (95% CI: 7.3-41.5) in the South and from 19.0 (95% CI: 8.8-29.3) to 109.2 (95% CI: 50.4-168.0) in the West. There was no impact of school opening mode on COVID-19-related deaths.

Figure 1. Map with distribution of counties and school opening mode across the United States

Figure 2. Impact of school opening mode on subsequent cases of SARS-CoV-2, stratified by region.

Conclusion

Impact of school mode on community case-rates of SARS-CoV-2 varied across the US. In some areas of the country, traditional school mode was associated with increases in case rates relative to virtual while there were no differences in other regions.

Disclosures

All Authors: No reported disclosures
399. Epidemiology of Laboratory-identified Late-onset SARS-CoV-2 Positivity in Two Large, Urban, Acute-Care Hospitals: Implications for Surveillance of Hospital-Acquired COVID-19
Authors: Trick William , Lin Michael Y , Welbel Sharon F , Donceras Onfofre T , Zhang Huiyuan , Tseng Marion , Santos Carlos
Published: December 2021

Background

Laboratory identification (Lab-ID) of late-onset SARS-CoV-2 positive tests during a hospital stay is a potential public health surveillance approach for hospital-acquired COVID-19. However, prolonged RNA fragment shedding and intermittent detection of SARS-CoV-2 virus via PCR testing among infected patients may hamper interpretation of laboratory-identified events. We aimed to describe the epidemiology of late-onset SARS-CoV-2 laboratory events using clinical criteria, to evaluate the feasibility of a Lab-ID approach to detection of nosocomial SARS-COV-2 infection.

Methods

We evaluated all SARS-CoV-2 RT-PCR positive results recovered from patients at two acute-care hospitals in Chicago, IL, during March 1 — November 30, 2020. Each hospital maintained stringent infection control policies through-out the study period. Through chart review (WT & CS), we categorized all initial SARS-CoV-2 positive tests collected > Hospital Day 5 (defined as ‘late-onset’ based on the 5-day mean incubation period for COVID-19) into the following clinical categories: Community Acquired; Unlikely Hospital Acquired; Possible Hospital Acquired; and Probable Hospital Acquired. Categorizations were made using hospital day, symptoms, alternative diagnoses, and clinical notes (Figure 1).

Results

Of 2,671 SARS-CoV-2-positive patients, most positive tests (n=2,551; 96%) were recovered pre-admit or by Hospital Day 2; first positive tests were uncommon during Hospital Days 6 to 14 (n=40; 1.5%); and rare after Hospital Day 14 (n=15; 0.6%). By chart review, of the 55 late-onset records reviewed, categorizations in descending order were: Prior positive at outside facility (n=23); Possible Hospital Acquired (n=16); Community Acquired (n=12); Probable Hospital Acquired (n=4). Less than half of the late-onset cases were categorized as a possible or probable hospital acquisition (Figure 2).

Conclusion

Hospital-acquired SARS-CoV-2 infection was uncommon. Most late-onset episodes of SARS-CoV-2 were explained by detection at an outside healthcare facility or by delayed diagnosis of patients with symptoms at time of presentation. A Lab-ID approach to nosocomial COVID-19 surveillance would potentially misclassify a substantial number of patients.

Disclosures

All Authors: No reported disclosures
400. Impact of SARS-CoV-2 Test-based Strategy to Reduce Quarantine Days Among Asymptomatic Healthcare Workers After Household Exposure
Authors: Navalkele Bhagyashri D , Lucar Jose , Brock James B , Parham Jason
Published: December 2021

Background

Appropriate staffing is essential to provide safe patient care. During the COVID-19 pandemic, healthcare workers (HCWs) are missing work days due to illness or high-risk exposure (HRE) to an infected person. To avoid staffing shortages, we implemented a SARS-CoV-2 test-based strategy among asymptomatic HCWs after HRE to facilitate early return to work.

Methods

In July 2020, our institution implemented a SARS-CoV-2 RT-PCR test-based strategy among HCWs within 7 days of HRE. HCWs were defined as any paid or unpaid persons directly or indirectly involved in patient care. HRE was defined as close contact < 6 feet with an infected household member without use of mask and lasting for ≥ 15 minutes. Contact with a patient or coworker was not considered high-risk due to universal masking and eye protection use. HCWs underwent SARS-CoV-2 RT PCR testing of a nasopharyngeal swab at least once (1-2 days post-exposure) or twice (5-7 days post-exposure). HCWs with symptoms at baseline were excluded. HCWs who were asymptomatic during evaluation were considered as truly asymptomatic (TA). Saved work-days (SWD) were calculated based on number of days saved due to testing strategy compared to the Centers for Disease Control and Prevention’s recommended 14-day quarantine. HCWs were allowed to return to work within 7 days of HRE if they tested negative, or after completing 10-day isolation period ± improvement in symptoms from symptom onset if they tested positive.

Results

Between 07/01/2020 to 12/31/2020, 450 unique asymptomatic HCWs underwent SARS-CoV-2 testing. Of those, 84% were women and median age was 36 years, 347 tested negative and 103 tested positive. Of those positives, 33% of HCWs tested positive on day 2 after HRE with 141 SWDs (average 2 days/person). Only 37% were TA positives. Of those negatives, 94% were TA SARS-CoV-2 negative with 2620 SWDs (average 7.5 days/person). There were no healthcare outbreaks related to HCWs allowed to return to work following this strategy.

Asymptomatic healthcare workers commonly tested positive for SARS-CoV-2 on day 2 from household exposure compared to other days

Conclusion

Test-based strategy among asymptomatic HCWs with HRE reduced loss of workdays and helped limit staffing shortages. Majority of positive HCWs developed symptoms after positive SARS-CoV-2 testing, which may support allowing most fully vaccinated HCWs with no COVID-like symptoms to continue to work unless symptomatic.

Disclosures

All Authors: No reported disclosures
401. Natural History of Shedding and Household Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Using Intensive High-Resolution Sampling
Authors: Altamirano Jonathan , Govindarajan Prasanthi , Blomkalns Andra , Leary Sean , Robinson India , Chun Leanne , Shaikh Nuzhat , Tam Grace , Lopez Marcela , Robinson Makeda , Carrington Yuan J , De Araujo Monique , Walter Katharine , Andrews Jason , Hogan Catherine , Pinksy Benjamin A , Maldonado Yvonne A
Published: December 2021

Background

In order to mitigate the spread of SARS-CoV-2 and the COVID-19 pandemic, public health officials have recommended self-isolation, self-quarantine of exposed household contacts (HHC), and mask use to limit viral spread within households and communities. While household transmission of SARS-CoV-2 is common, risk factors for HHC transmission are poorly understood.

Methods

In this prospective cohort study, we enrolled 37 households with at least one reverse transcription polymerase chain reaction-confirmed (RT-PCR) COVID-19 index case from March 2020 - March 2021, in order to calculate secondary attack rates (SAR) and define risk factors for secondary infections. Participants were tested daily for SARS-CoV-2 via RT-PCR, using self-collected lower nasal samples. Households were followed until all members tested negative for seven consecutive days. We collected demographics, medical conditions, relationship to index case, and socioeconomic indicators. Subgroup data analysis was conducted and stratified by positivity status.

Results

Of 99 enrolled participants, 37 were index cases and 62 were household contacts (HHC), of whom 25 HHC were infected (40.3%). Secondary attack rate (SAR) was highest among adults caring for a parent (n=4/4, 100%) and parents of index cases (5/10, 50%). Households whose income came from service work had greater risk of transmission compared to households whose primary income was technology (n=5/7; 71.4% vs 3/8; 37.5% respectively). Pediatric contacts were at lower risk of infection when compared to adult contacts (n=5/18, 27.8% vs n=20/44, 45.5% respectively).

Conclusion

This study suggests that household transmission represents a key source of community-based infection of SARS-CoV-2. Allocating resources for education/training regarding prevention among infected individuals and their close contacts will be critical for control of future outbreaks of SARS-CoV-2.

Disclosures

All Authors: No reported disclosures
402. COVID-19 Infection in Nepal: Epidemiological Analysis from April 2020 to March 2021
Authors: Le Hai V
Published: December 2021

Background

In December 2019, SARS-CoV-2 or coronavirus disease 2019 (COVID-19) emerged from Wuhan, China. A global pandemic quickly unfolded, infecting >137 million people and causing >2.9 million deaths globally as of April 13, 2021. Before April 1, 2020, there were only five confirmed COVID-19 cases in Nepal. Like many countries around the world, the COVID-19 situation quickly escalated in Nepal. The purpose of this study was to determine the trends in COVID-19 cases and deaths in Nepal from April 2020 to March 2021.

Methods

We utilized epidemiological data from daily Situation Reports published by the Ministry of Health and Population (MOHP) of Nepal. Data were extracted or calculated from April 1, 2020 to March 31, 2021. Primary variables of interest were national and provincial daily cases, total cases, daily deaths, and total deaths.

Results

Between April 1, 2020 to March 31, 2021, there were 277,304 cases. October 2020 had the highest monthly cases with 92,926 cases. During the one-year study period, the infection rate was 915 cases per 100,000 people. The largest single-day new cases was October 21, 2020 with 5,743 cases, which is calculated to 19 cases per 100,000 people. There were a total of 3,030 deaths. The largest daily new deaths was November 4, 2020 with 43 cases. June 10, 2020 had the highest number of people in quarantine with 172,266 people. October 23, 2020 had the highest number of active cases with 46,329 cases. By March 31, 2021, the percent of mortality was 1.1%, active infection was 0.5%, and recovery was 98.4%.

Conclusion

Nepal had lower COVID-19 infection and case-fatality rates compared to other countries most affected by the pandemic. This was due to several factors, most notably early implementation of strict lockdown measures and closing of international borders on March 24, 2020 after the second confirmed COVID-19 case. As lockdown restrictions were lifted on July 7, 2020, COVID-19 cases and deaths in Nepal rose rapidly. As vaccination begun on January 27, 2021, cases started to slow down until the most recent outbreak coinciding with the second wave in its neighboring country, India. Now, infection and case-fatality rates in Nepal are at an all-time high, prompting further lockdowns on April 29, 2021.

Disclosures

All Authors: No reported disclosures
403. Clinical Characteristics and Outcomes of Patients with COVID-19 Admitted to the Intensive Care Unit in the Dominican Republic
Authors: Rojas-Fermin Rita Alexandra , Sanchez Ann , Guzman Anel E , Germosen Edwin , Matos Cesar , Mena Lora Alfredo J
Published: December 2021

Background

The disease caused by SARS-CoV-2, COVID-19, has caused a global public health crisis. Reported mortality rates across the world vary by region, local population characteristics and healthcare systems. There is a paucity of data on COVID-19 in low and middle income countries (LMICs). Our objective is to describe the clinical characteristics of critically ill patients with COVID-19 in the Dominican Republic (DR)

Methods

We performed a retrospective review of patients admitted to the intensive care unit (ICU) with severe COVID-19 from March to December 31, 2020, at a 295-bed tertiary teaching hospital in the DR. Clinical characteristics, demographics, comorbidities, management and outcomes were tabulated. Survival was categorized by age and comorbidities.

Results

A total of 382 patients were admitted to the ICU. The median age was 64 (range 14-97) and 64.3% (246) were male. Hypertension, diabetes, and obesity were the most common risk factors (Table 1). Corticosteroids were used in 91.6% (350), tocilizumab in 63% (82), and remdesivir in 31.6% (31). Antibacterials were used in 99.2% (379) of patients in the ICU. All-cause mortality in the ICU was 35.3% (135). Mortality was higher in older age groups (Figure 1) and in patients with multiple coexisting comorbidities (Figure 2).

Table 1. Comorbidities of patients with COVID-19 admitted to the ICU

Conclusion

Hypertension, obesity and diabetes were common in critically ill patients with COVID-19 in the DR. Corticosteroids and tocilizumab were commonly used. Antibacterials were used in >99% of patients admitted to the ICU and may signal a target for future antimicrobial stewardship. Higher mortality rates were present in older age groups and those with multiple comorbidities. Risk of death increased drastically after age 40 and was comparative to those in advanced age groups. In patients with 4 comorbidities and above, mortality was more than three times higher.

Disclosures

All Authors: No reported disclosures
404. COVID-19 Normality Rate: Criteria for Optimal Time to Return to In-person Learning
Authors: Starling Carlos , Machado-Pinto Jackson , Tupinambás Unaí , Silva Estevão Urbano , Couto Bráulio R G M
Published: December 2021

Background

The COVID-19 pandemic created the most severe global education disruption in history. According to UNESCO, at the peak of the crisis over 1.6 billion learners in more than 190 countries were out of school. After one year, half of the world’s student population is still affected by full or partial school closures. Here we investigated whether or not it is possible to build a multivariate score for dynamic school decision-making specially in scenarios without population-scale RT-PCR tests.

Methods

Normality rate is based on a COVID-19 risk matrix (Table 1). Total score (TS) is obtained by summing the risk scores for COVID-19, considering the six parameters of the pandemic in a city. The COVID-19 Normality Rate (CNR) is obtained by linear interpolation in such a way that a total score of 30 points is equivalent to a 100% possibility of normality and, in a city with only six total points would have zero percent chance of returning to normality: CNR = (TS – 6)/24 (%). The criteria for opening and closing schools can be defined based on the percentages of return to normality (Table 2).

Table 1. Limits for each parameter of the risk matrix and "normality" scores in relation to COVID-19: the lower the risk, higher is the “normality” score.

Table 2. Criteria for opening and closing schools in a city according to the COVID-19 Normality Rate.

Results

at June 3rd, 2021, we evaluated all 5,570 Brazilian cities (Figure 1): 2,708 cities (49%) with COVID-19 normality rate less than 50% (full schools closure), 2,223 cities (40%) with normality rate between 50% and 70% (in-person learning only for 5 years and 8 months-old children), 583 with normality rate between 71% and 80% (in-person learning extended to children age 12 years and less), 583 cities (1%) with normality rate between 81% to 90% (in-person learning extended to the student population age 18 years), and just one city with 92% COVID-19 normality rate (in-person learning extended to all the student population). We calculated the COVID-19 normality rate between January and May, 2021, in four countries: Brazil, USA, UK, and Italy (Figure 2). At Jun, 3rd, 2021, percentage of people fully vaccinated in Brazil varied from 0% to 69%, an average of 11%.

Figure 1. COVID-19 Normality Rate in 5,570 cities in Brazil, Jun/03/2021.

Figure 2. COVID-19 Normality Rate between January and May, 2021: comparison among Brazil, USA, UK, and Italy.

Conclusion

COVID-19 vaccination programs take several months to implement. Besides fully vaccination of the population, it is important to check if people became really safe from the virus. The COVID-19 Normality Rate is a double check multivariate score that can be used as a criteria for optimal time to return to in-person learning safely.

Disclosures

All Authors: No reported disclosures