ABSTRACT
Background. Inequities in healthcare among racial and ethnic minorities are globally recognized. The focus has centered on access to healthcare, equitable treatment, and optimizing outcomes. However, there has been relatively little investigation into potential racial and ethnic disparities in HAI. Methods. We performed a retrospective cohort analysis of select HAI prospectively-collected by a network of community hospitals in the southeastern US, including central line-associated bloodstream infection (CLABSI), catheterassociated urinary tract infection (CAUTI), and laboratory-identified Clostridioides difficile infection (CDI). Outcomes were stratified by race/ethnicity as captured in the electronic medical record. We defined the pre-pandemic period from 1/1/2019 to 2/29/2020 and the pandemic period from 3/1/2020 to 6/30/2021. Outcomes were reported by race/ethnicity as a proportion of the total events. Relative rates were compared using Poisson regression. Results. Overall, relatively few facilities consistently collect race/ethnicity information in surveillance databases within this hospital network (< 40%). Among 21 reporting hospitals, a greater proportion of CLABSI occurred in Black patients relative toWhite patients in both study periods (pre-pandemic, 49% vs 38%;during pandemic, 47% vs 31%;respectively, Figure 1a), while a higher proportion of CAUTI and CDI occurred in White patients (Figures 1b-c). Black patients had a 30% higher likelihood of CLABSI than White patients in the pre-COVID period (RR, 1.30;95% CI, 0.83-2.05), which was not statistically significant (Table 1). However, this risk significantly increased to 51% after the start of the pandemic (RR, 1.51;95% CI, 1.02-2.24). Similar trends were not observed in other HAI (Tables 2-3). Conclusion. We found differences in HAI rates by race/ethnicity in a network of community hospitals. Black patients had higher likelihood of CLABSI, and this likelihood increased during the pandemic. Patient safety events, including HAI, may differ across racial and ethnic groups and negatively impact health outcomes. (Figure Presented).
ABSTRACT
Background. Hospital-onset bloodstream infection (HOBSI) incidence has been proposed as a complementary quality metric to central line-associated bloodstream infection (CLABSI) surveillance. Several recent studies have detailed increases in median HOBSI and CLABSI rates during the COVID-19 pandemic. We sought to understand trends in HOBSI and CLABSI rates at a single health system in the context of COVID-19. Methods. We conducted a retrospective analysis of HOBSIs and CLABSIs at a three-hospital health system from 2017 to 2021 (Figure 1). We compared counts, denominators, and demographic data for HOBSIs and CLABSIs between the prepandemic (1/1/2017-3/30/2020) and pandemic period (4/1/2020-12/31/2021) (Table 1). We applied Poisson or negative binomial regression models to estimate the monthly change in incidence of HO-BSI and CLABSI rates over the study period. Figure 1: Definitions applied for hospital-onset bloodstream infections (HO-BSIs) and central line-associated bloodstream infections (CLABSIs). Potentially contaminated blood cultures were identified by microbiology laboratory technicians as any set of blood culture in which a single bottle was positive for organisms typically considered as skin contaminants. Uncertain cases undergo secondary review by senior lab technicians. Table 1: Count, denominator, and device utilization ratio data for hospital-onset bloodstream infections (HO-BSIs) and central line-associated bloodstream infections (CLABSIs) Note that central line utilization increased upon regression analysis (p<0.001). Results. The median monthly HOBSI rate per 1,000 patient days increased from 1.0 in the pre-pandemic to 1.3 (p< 0.01) in the pandemic period, whereas the median monthly CLABSI rate per central line days was stable (1.01 to 0.88;p=0.1;Table 2). Our regression analysis found that monthly rates of HO-BSIs increased throughout the study, but the increase was not associated with the onset of the COVID-19 pandemic based on comparisons of model fit (Figure 2;Table 3). Despite an increase in central line utilization, regression modelling found no changes in monthly CLABSIs rates with respect to time and the COVID-19 pandemic. Incidence of HOBSIs and CLABSIs by common nosocomial organisms generally increased over this time period, though time to infection onset remained unchanged in our studied population (Table 2). Conclusion. HOBSIs rates did not correlate with CLABSI incidence across a three-hospital health system from 2017 and 2021, as rates of HOBSI increased but CLABSI rates remained flat. Our observed increase in HOBSI rates did not correlate with the onset of the COVID-19 pandemic, and caution should be used in modeling the effects of COVID-19 without time-trended analysis. Further evaluation is needed to understand the etiology, epidemiology, and preventability of HO-BSI.
ABSTRACT
Background. The shift to more transmissible but less virulent strains of SARS-CoV-2 has altered the risk calculation for infection. Particularly among young adults, the economic burden of lost work due to isolation exceeds the economic burden of morbidity due to infection. Testing strategies must adapt to these changing circumstances. Methods. We modeled six testing strategies to estimate total societal costs for symptomatic people 18-49 years old: isolation of all individuals with no testing, rapid antigen test (RAg), RAg followed by a second RAg 48h later if first negative, RAg followed by a polymerase chain reaction (PCR) if negative, RAg followed by a PCR if positive, and PCR alone. We calculated costs for hypothetical cohorts of 100 symptomatic healthcare workers tested with each strategy;we included testing costs, lost wages, and hospitalization costs for the index, secondary, and tertiary cases. Key assumptions were 5% prevalence of infection, sensitivity of first/second RAg 40/80% with 97% specificity, PCR sensitivity/specificity 95/99%, all individuals isolate at symptom onset, are tested the same day, and isolate for 5 days if positive. RAg results were available the same day, PCR results were available the next day (Figure 1). One-way sensitivity analyses were performed for RAg sensitivity (20-80%) and positivity rate (1-80%). Results. The least expensive strategy was RAg alone (Figure 2). This was primarily driven by its low sensitivity, which reduced lost wages at the expense of missing cases. At a threshold for RAg sensitivity lower than 29%, PCR testing alone became the cheapest strategy. When the positivity rate was > 6% confirming a negative RAg with a PCR became the cheapest strategy, closely followed by PCR alone. At a positivity rate of > 29%, isolation without testing was cheapest followed by confirming a negative RAg with a PCR and by the serial RAg test strategies (Figure 3). Conclusion. In relatively young, healthy populations, a single rapid test was the least expensive strategy when the positivity rate was < 6%, testing that included PCR became cheapest at intermediate positivity, and empiric isolation was cheapest at positivity > 29%. Calibrating SARS-CoV-2 test strategies based on epidemiology may save societal costs.
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Background. We aimed to describe SARS-CoV-2 (COVID-19) infections among employees in a large, academic institution. Methods. We prospectively tracked and traced COVID-19 infections among employees across our health system and university. Each employee with a confirmed positive test and 3 presumed positive cases were interviewed with a standard contact tracing template that included descriptive variables such as high-risk behaviors and contacts, dates worked while infectious, and initial symptoms. Using this information, the most likely location of infection acquisition was adjudicated (Table 1). We compared behavior frequency between community and unknown, likely community and community and unknown cases using descriptive statistics. Results. From 3/2020 to 4/2021 we identified 3,140 COVID-19 infections in 3,119 employees out of a total of 34,562 employees (9.0%) (Figure 1). Of those 3,119 employees 1,685 (54.0%) were clinical employees working in the health system, 916 (29.4%) were non-clinical employees working in the health system, and 518 (16.6%) were university employees. Descriptive characteristics for the COVID-19 infections and adjudications are outlined in Table 2. Severe disease among employees was significantly less frequent compared to patients in the health system (15.3% vs 2.2%, p< 0.01). The frequency of travel within 14 days, masked gatherings and unmasked gatherings/ activities was not significantly different between the community and unknown, likely community groups or the community and unknown groups (Table 3). Conclusion. The majority of COVID-19 infections were linked to acquisition in the community, and few were attributed to workplace exposures. Employees with unknown sources of COVID-19 participated in higher-risk activities at approximately the same frequency as employees with community sources of COVID-19. The most frequently reported initial symptoms were mild and non-specific and rarely included fever. Despite a comprehensive testing and benefit program, a large proportion of COVID-positive employees worked with symptoms, highlighting ongoing challenges with presenteeism in healthcare.
ABSTRACT
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. Conclusion. The positivity rate of universal pre-procedural and admission SARSCoV-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.
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Background. Despite schools reopening across the United States in communities with low and high Coronavirus disease 2019 (COVID-19) prevalence, data remain scarce about the effect of classroom size on the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) within schools. This study estimates the effect of classroom size on the risk of COVID-19 infection in a closed classroom cohort for varying age groups locally in Durham, North Carolina. Total number of Coronavirus Disease 2019 (COVID-19) infections over a 28-day follow-up period for varying classroom reproduction number (R0) and varying classroom cohort sizes of 15 students, 30 students and 100 students in Durham County, North Carolina. Methods. Using publicly available population and COVID-19 case count data from Durham County, we calculated a weekly average number of new confirmed COVID-19 cases per week between May 3, 2020 and August 22, 2020 according to age categories: < 5 years, 5-9 years, 10-14 years, and 15-19 years. We collated average classroom cohort sizes and enrollment data for each age group by grade level of education for the first month of the 2019-2020 academic school year. Then, using a SEIR compartmental model, we calculated the number of susceptible (S), exposed (E), infectious (I) and recovered (R) students in a cohort size of 15, 30 and 100 students, modelling for classroom reproduction number (R0) of 0.5, 1.5 and 2.5 within a closed classroom cohort over a 14-day and 28-day follow-up period using age group-specific COVID-19 prevalence rates. Results. The SEIR model estimated that the increase in cohort size resulted in up to 5 new COVID-19 infections per 10,000 students whereas the classroom R0 had a stronger effect, with up to 88 new infections per 10,000 students in a closed classroom cohort over time. When comparing different follow-up periods in a closed cohort with R0 of 0.5, we estimated 12 more infected students per 10,000 students over 28 days as compared to 14 days irrespective of cohort size. With a R0 of 2.5, there were 49 more infected students per 10,000 students over 28 days as compared to 14 days. Conclusion. Classroom R0 had a stronger impact in reducing school-based COVID-19 transmission events as compared to cohort size. Additionally, earlier isolation of newly infected students in a closed cohort resulted in fewer new COVID-19 infections within that group. Mitigation strategies should target promoting safe practices within the school setting including early quarantine of newly identified contacts and minimizing COVID-19 community prevalence.
ABSTRACT
Background. Data on occupational acquisition of COVID-19 in healthcare settings are limited. Contact tracing efforts are high resource investments. Methods. Duke Health developed robust COVID-19 contact tracing methods as part of a comprehensive prevention program. We prospectively collected data on HCW exposures and monitored for development of symptomatic (SYX) and asymptomatic (ASYX) COVID-19 infection after documented high-, medium, and low-risk exposures. HCWs were required to self-report exposures or were identified through contact tracing as potentially exposed to COVID-19 positive HCWs, patients or visitors. Contact tracers interviewed exposed HCWs and assessed the risk of exposure as high-, medium-, or low-risk based on CDC guidance (Table 1). Testing was recommended at 6 days after high- or medium-risk exposures and was provided upon HCW request following low-risk exposures. Our vaccination campaign began in 12/2020. Results. 12,916 HCWs registered in the contact tracing database. From March 2020-May 2021, we identified 6,606 occupational exposures (0.51 exposures/HCW). The highest incidence of workplace exposures per number of HCWs in each job category was among respiratory therapists (RT) (0.95 exposures/RT), nursing assistants (NA) (0.79 exposures/NA), and physicians (0.64 exposures/physician). The most common exposure risk level was medium (51.4%), followed by low (35.5%), and then high (13.1%). A total of 260 (2%) HCW had positive tests/conversions;28 (10.8%) were ASYX at the time of testing. High-risk exposures had a significantly greater number of post-exposure infections compared to medium- and low-risk exposures (12.5% vs. 4.2%, vs. 0.4%;p < 0.001). The rate of SYX infection following exposure to a fellow HCW (179/3,198;5.6%) was higher than that following exposure to a patient (81/3,408;2.4%;p< 0.001). Conclusion. Conversion following exposure to COVID-19 in the healthcare setting with appropriate protective equipment was low. Incomplete testing of all exposed individuals was a limitation and our data may under-estimate the true conversion rate. Our findings support our local practice of not quarantining HCWs following non-household exposures. Limiting contact tracing to only high or medium risk exposures may best utilize limited personnel resources.
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Background. The correlation between SARS-CoV-2 RNA and infectious viral contamination of the hospital environment is poorly understood. Methods. housed in a dedicated COVID-19 unit at an academic medical center. Environmental samples were taken within 24 hours of the first positive SARS-CoV-2 test (day 1) and again on days 3, 6, 10 and 14. Patients were excluded if samples were not obtained on days 1 and 3. Surface samples were obtained with flocked swabs pre-moistened with viral transport media from seven locations inside (bedrail, sink, medical prep area, room computer, exit door handle) and outside the room (nursing station computer). RNA extractions and RT-PCR were completed on all samples. RT-PCR positive samples were used to inoculate Vero E6 cells for 7 days and monitored for cytopathic effect (CPE). If CPE was observed, RT-PCR was used to confirm the presence of SARS-CoV-2. Results. We enrolled 14 patients (Table 1, Patient Characteristics) between October 2020 and May 2021. A total of 243 individual samples were obtained - 97 on day 1, 98 on day 3, 34 on day 6, and 14 on day 10. Overall, 18 (7.4%) samples were positive via RT-PCR - 9 from bedrails (12.9%), 4 from sinks (11.4%), 4 from room computers (11.4%) and 1 from the exit door handle (2.9%). Notably, all medical prep and nursing station computer samples were negative (Figure 1). Of the 18 positive samples, 5 were from day 1, 10 on day 3, 1 on day 6 and 2 on day 10. Only one sample, obtained from the bedrails of a symptomatic patient with diarrhea and a fever on day 3, was culture-positive (Figure 2). Conclusion. Overall, the amount of environmental contamination of viable SARS-CoV-2 virus in rooms housing COVID-19 infected patients was low. As expected, more samples were considered contaminated via RT-PCR compared to cell culture, supporting the conclusion that the discovery of genetic material in the environment is not an indicator of contamination with live infectious virus. More studies including RT-PCR and viral cell culture assays are needed to determine the significance of discovering SARS-CoV-2 RNA versus infectious virus in the clinical environment.