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Objective: To provide comprehensive population-level estimates of the burden of healthcare-associated influenza. Design: Retrospective cross-sectional study. Setting: US Influenza Hospitalization Surveillance Network (FluSurv-NET) during 2012–2013 through 2018–2019 influenza seasons. Patients: Laboratory-confirmed influenza-related hospitalizations in an 8-county catchment area in Tennessee. Methods: The incidence of healthcare-associated influenza was determined using the traditional definition (ie, positive influenza test after hospital day 3) in addition to often underrecognized cases associated with recent post-acute care facility admission or a recent acute care hospitalization for a noninfluenza illness in the preceding 7 days. Results: Among the 5,904 laboratory-confirmed influenza-related hospitalizations, 147 (2.5%) had traditionally defined healthcare-associated influenza. When we included patients with a positive influenza test obtained in the first 3 days of hospitalization and who were either transferred to the hospital directly from a post-acute care facility or who were recently discharged from an acute care facility for a noninfluenza illness in the preceding 7 days, we identified an additional 1,031 cases (17.5% of all influenza-related hospitalizations). Conclusions: Including influenza cases associated with preadmission healthcare exposures with traditionally defined cases resulted in an 8-fold higher incidence of healthcare-associated influenza. These results emphasize the importance of capturing other healthcare exposures that may serve as the initial site of viral transmission to provide more comprehensive estimates of the burden of healthcare-associated influenza and to inform improved infection prevention strategies.

Background Rhinovirus (RV) is one of the most common etiologic agents of acute respiratory infection (ARI), which is a leading cause of morbidity and mortality in young children. The clinical significance of RV co-detection with other respiratory viruses, including respiratory syncytial virus (RSV), remains unclear. We aimed to compare the clinical characteristics and outcomes of children with ARI-associated RV-only detection and those with RV co-detection—with an emphasis on RV/RSV co-detection. Methods We conducted a prospective viral surveillance study (11/2015–7/2016) in Nashville, Tennessee. Children χ2 test for categorical variables and the two-sample t-test with unequal variances for continuous variables. Results Of 1250 children, 904 (72.3%) were virus-positive. RV was the most common virus (n = 406; 44.9%), followed by RSV (n = 207; 19.3%). Of 406 children with RV, 289 (71.2%) had RV-only detection, and 117 (28.8%) had RV co-detection. The most common virus co-detected with RV was RSV (n = 43; 36.8%). Children with RV co-detection were less likely than those with RV-only detection to be diagnosed with asthma or reactive airway disease both in the ED and in-hospital. We did not identify differences in hospitalization, intensive care unit admission, supplemental oxygen use, or length of stay between children with RV-only detection and those with RV co-detection. Conclusion We found no evidence that RV co-detection was associated with poorer outcomes. However, the clinical significance of RV co-detection is heterogeneous and varies by virus pair and age group. Future studies of RV co-detection should incorporate analyses of RV/non-RV pairs and include age as a key covariate of RV contribution to clinical manifestations and infection outcomes.

BackgroundAcute myocardial infarction (AMI) events have been reported among patients with certain viral and bacterial infections. Whether invasive pneumococcal disease (IPD) increases the risk of AMI remains unclear. We examined whether laboratory-confirmed IPD was associated with the risk of AMI.MethodsWe conducted a self-controlled case series analysis among adult Tennessee residents with evidence of an AMI hospitalization (2003–2019). Patient follow-up started 1 year before the earliest AMI and continued through the date of death, 1 year after AMI, or study end (December 2019). Periods for AMI assessment included the 7 to 1 days before IPD specimen collection (pre-IPD detection), day 0 through day 7 after IPD specimen collection (current IPD), day 8 to 28 after IPD specimen collection (post-IPD), and a control period (all other follow-up). We used conditional Poisson regression to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for each risk period compared with control periods using within-person comparisons.ResultsWe studied 324 patients hospitalized for AMI with laboratory-confirmed IPD within 1 year before or after the AMI hospitalization. The incidence of AMI was significantly higher during the pre-IPD detection (IRR, 10.29; 95% CI: 6.33–16.73) and the current IPD (IRR, 92.95; 95% CI: 72.17–119.71) periods but nonsignificantly elevated in the post-IPD risk period (IRR, 1.83; 95% CI: .86–3.91) compared with control periods. The AMI incidence was higher in the post-IPD control period (29 to 365 days after IPD; IRR, 2.95; 95% CI: 2.01–4.32).ConclusionsHospitalizations with AMI were strongly associated with laboratory-confirmed IPD.

Background Candidemia is a common healthcare-associated infection with high mortality. Estimates of recurrence range from 1% to 17%. Few studies have focused on those with recurrent candidemia, who often experience more severe illness and greater treatment failure. We describe recurrent candidemia trends and risk factors. Methods We analyzed population-based candidemia surveillance data collected during 2011–2018. Persons with >1 episode (defined as the 30-day period after a positive Candida species) were classified as having recurrent candidemia. We compared factors during the initial episode between those who developed recurrent candidemia and those who did not. Results Of the 5428 persons identified with candidemia, 326 (6%) had recurrent infection. Recurrent episodes occurred 1.0 month to 7.6 years after any previous episode. In multivariable logistic regression controlling for surveillance site and year, recurrent candidemia was associated with being 19–44 years old (vs ≥65 years; adjusted odds ratio [aOR], 3.05 [95% confidence interval {CI}, 2.10–4.44]), being discharged to a private residence (vs medical facility; aOR, 1.53 [95% CI, 1.12–2.08]), hospitalization in the 90 days prior to initial episode (aOR, 1.66 [95% CI, 1.27–2.18]), receipt of total parenteral nutrition (aOR, 2.08 [95% CI, 1.58–2.73]), and hepatitis C infection (aOR, 1.65 [95% CI, 1.12–2.43]). Conclusions Candidemia recurrence >30 days after initial infection occurred in >1 in 20 persons with candidemia. Associations with younger age and hepatitis C suggest injection drug use may play a modifiable role. Prevention efforts targeting central line care and total parenteral nutrition use may help reduce the risk of recurrent candidemia.

Background The COVID-19 pandemic has resulted in unprecedented healthcare challenges, and COVID-19 has been linked to secondary infections. Candidemia, a fungal healthcare-associated infection, has been described in patients hospitalized with severe COVID-19. However, studies of candidemia and COVID-19 coinfection have been limited in sample size and geographic scope. We assessed differences in patients with candidemia with and without a COVID-19 diagnosis. Methods We conducted a case-level analysis using population-based candidemia surveillance data collected through the Centers for Disease Control and Prevention’s Emerging Infections Program during April–August 2020 to compare characteristics of candidemia patients with and without a positive test for COVID-19 in the 30 days before their Candida culture using chi-square or Fisher’s exact tests. Results Of the 251 candidemia patients included, 64 (25.5%) were positive for SARS-CoV-2. Liver disease, solid-organ malignancies, and prior surgeries were each >3 times more common in patients without COVID-19 coinfection, whereas intensive care unit–level care, mechanical ventilation, having a central venous catheter, and receipt of corticosteroids and immunosuppressants were each >1.3 times more common in patients with COVID-19. All-cause in-hospital fatality was 2 times higher among those with COVID-19 (62.5%) than without (32.1%). Conclusions One-quarter of candidemia patients had COVID-19. These patients were less likely to have certain underlying conditions and recent surgery commonly associated with candidemia and more likely to have acute risk factors linked to COVID-19 care, including immunosuppressive medications. Given the high mortality, it is important for clinicians to remain vigilant and take proactive measures to prevent candidemia in patients with COVID-19.

We characterized common exposures reported by a convenience sample of 202 US patients with coronavirus disease during January–April 2020 and identified factors associated with presumed household transmission. The most commonly reported settings of known exposure were households and healthcare facilities; among case-patients who had known contact with a confirmed case-patient compared with those who did not, healthcare occupations were more common. Among case-patients without known contact, use of public transportation was more common. Within the household, presumed transmission was highest from older (>65 years) index case-patients and from children to parents, independent of index case-patient age. These findings may inform guidance for limiting transmission and emphasize the value of testing to identify community-acquired infections.

Background Influenza hospitalizations result in substantial morbidity and mortality each year. Little is known about the association between influenza hospitalization and census tract‐based socioeconomic determinants beyond the effect of individual factors. Objective To evaluate whether census tract‐based determinants such as poverty and household crowding would contribute significantly to the risk of influenza hospitalization above and beyond individual‐level determinants. Methods We analyzed 33 515 laboratory‐confirmed influenza‐associated hospitalizations that occurred during the 2009‐2010 through 2013‐2014 influenza seasons using a population‐based surveillance system at 14 sites across the United States. Results Using a multilevel regression model, we found that individual factors were associated with influenza hospitalization with the highest adjusted odds ratio (AOR) of 9.20 (95% CI 8.72‐9.70) for those ≥65 vs 5‐17 years old. African Americans had an AOR of 1.67 (95% CI 1.60‐1.73) compared to Whites, and Hispanics had an AOR of 1.21 (95% CI 1.16‐1.26) compared to non‐Hispanics. Among census tract‐based determinants, those living in a tract with ≥20% vs

Background Further understanding of hospital readmissions after influenza illness could reduce readmissions. The aim of our study was to characterize the morbidity associated with laboratory confirmed influenza hospitalizations. Methods This was a retrospective study using data from 2006 to 2016 from the Tennessee (TN) Emerging Infections Program Influenza Surveillance Network, which prospectively identifies laboratory-confirmed influenza hospitalizations in Nashville, TN and surrounding counties. Using the TN Hospital Discharge Data System, which collects information on all hospitalizations and discharges in TN, cases were linked to subsequent hospitalizations up to 1 year. The International Classification of Diseases was used to define the primary diagnosis associated with each hospitalization. Demographic characteristics and outcomes were compared by using χ2 tests for categorical variables. Multivariable logistic regression was used to compare study outcomes. Results Of the 2,897 patients with a laboratory-confirmed influenza hospitalization, 1,364 (47%) had a hospital readmission during the subsequent year (figure). Multiple readmissions occurred in 740 patients (54%). The readmission group was older, female predominant, and had more comorbidities than patients not re-hospitalized. Acute COPD/asthma exacerbation, pneumonia, septicemia, and acute renal failure were the most common causes for readmission. Underlying cardiovascular disease (OR 1.6), lung disease (OR 1.6), kidney disease (OR 1.7), diabetes (OR 1.3), immunosuppression (OR 1.6), and liver disease (OR 2.1) were associated with increased risk of readmission (table). Conclusion An influenza hospitalization is associated with increased hospital readmissions. Approximately 47% of patients hospitalized with influenza are readmitted within 1 year. Patient comorbidities could be an important link to influenza readmissions. Disclosures W. Schaffner, Merck: Member, Data Safety Monitoring Board, Consulting fee. Pfizer: Member, Data Safety Monitoring Board, Consulting fee. Dynavax: Consultant, Consulting fee. Seqirus: Consultant, Consulting fee. SutroVax: Consultant, Consulting fee. Shionogi: Consultant, Consulting fee. H. K. Talbot, sanofi pasteur: Investigator, Research grant. Gilead: Investigator, Research grant. MedImmune: Investigator, Research grant. Vaxinnate: Safety Board, none. Seqirus: Safety Board, none.

Background Molecular testing for respiratory viruses in clinical practice is common, often with multiple viruses detected. Viral load has been correlated with illness severity, but correlation of co-detection of viruses and viral load is less clear. We sought to compare cycle threshold (Ct) values, a marker inversely related to viral load, between single vs. co-detection of common respiratory viruses. Methods Children Results From 11/15/15-7/15/16, 1255 children were enrolled, with median age of 26.5 months, 53.4% male, 54.3% White, 38.7% Black, 6.4% other, and 23.5% Hispanic. The median days of illness were 3 days. Of the total cohort, 904 (72%) tested positive for at least one viral pathogen. Table 1compares Ct values of single vs. co-detection for each individual virus. Conclusion Single detection with RSV, HRV, AdV, and PIV had lower Ct values, indicating higher viral loads, compared with co-detection with other viruses. Additional research is needed to understand the reason for lower viral loads for co-detection vs. single detection in select respiratory viruses. Disclosures W. Schaffner, Pfizer: Scientific Advisor, Consulting fee. Merck: Scientific Advisor, Consulting fee. Novavax: Consultant, Consulting fee. Dynavax: Consultant, Consulting fee. Sanofi-pasteur: Consultant, Consulting fee. GSK: Consultant, Consulting fee. Seqirus: Consultant, Consulting fee. N. B. Halasa, sanofi pasteur: Research Contractor, Research support. Astra Zeneca: Research Contractor, Grant recipient.