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Background: It is estimated that vaccinating 50%-70% of school-aged children for influenza can produce population-wide indirect effects. We evaluated a city-wide school-located influenza vaccination (SLIV) intervention that aimed to increase influenza vaccination coverage. The intervention was implemented in ≥95 preschools and elementary schools in northern California from 2014 to 2018. Using a matched cohort design, we estimated intervention impacts on student influenza vaccination coverage, school absenteeism, and community-wide indirect effects on laboratory-confirmed influenza hospitalizations.Methods and Findings: We used a multivariate matching algorithm to identify a nearby comparison school district with pre-intervention characteristics similar to those of the intervention school district and matched schools in each district. To measure student influenza vaccination, we conducted cross-sectional surveys of student caregivers in 22 school pairs (2017 survey, N = 6,070; 2018 survey, N = 6,507). We estimated the incidence of laboratory-confirmed influenza hospitalization from 2011 to 2018 using surveillance data from school district zip codes. We analyzed student absenteeism data from 2011 to 2018 from each district (N = 42,487,816 student-days). To account for pre-intervention differences between districts, we estimated difference-in-differences (DID) in influenza hospitalization incidence and absenteeism rates using generalized linear and log-linear models with a population offset for incidence outcomes. Prior to the SLIV intervention, the median household income was $51,849 in the intervention site and $61,596 in the comparison site. The population in each site was predominately white (41% in the intervention site, 48% in the comparison site) and/or of Hispanic or Latino ethnicity (26% in the intervention site, 33% in the comparison site). The number of students vaccinated by the SLIV intervention ranged from 7,502 to 10,106 (22%-28% of eligible students) each year. During the intervention, influenza vaccination coverage among elementary students was 53%-66% in the comparison district. Coverage was similar between the intervention and comparison districts in influenza seasons 2014-2015 and 2015-2016 and was significantly higher in the intervention site in seasons 2016-2017 (7%; 95% CI 4, 11; p < 0.001) and 2017-2018 (11%; 95% CI 7, 15; p < 0.001). During seasons when vaccination coverage was higher among intervention schools and the vaccine was moderately effective, there was evidence of statistically significant indirect effects: The DID in the incidence of influenza hospitalization per 100,000 in the intervention versus comparison site was -17 (95% CI -30, -4; p = 0.008) in 2016-2017 and -37 (95% CI -54, -19; p < 0.001) in 2017-2018 among non-elementary-school-aged individuals and -73 (95% CI -147, 1; p = 0.054) in 2016-2017 and -160 (95% CI -267, -53; p = 0.004) in 2017-2018 among adults 65 years or older. The DID in illness-related school absences per 100 school days during the influenza season was -0.63 (95% CI -1.14, -0.13; p = 0.014) in 2016-2017 and -0.80 (95% CI -1.28, -0.31; p = 0.001) in 2017-2018. Limitations of this study include the use of an observational design, which may be subject to unmeasured confounding, and caregiver-reported vaccination status, which is subject to poor recall and low response rates.Conclusions: A city-wide SLIV intervention in a large, diverse urban population was associated with a decrease in the incidence of laboratory-confirmed influenza hospitalization in all age groups and a decrease in illness-specific school absence rate among students in 2016-2017 and 2017-2018, seasons when the vaccine was moderately effective, suggesting that the intervention produced indirect effects. Our findings suggest that in populations with moderately high background levels of influenza vaccination coverage, SLIV programs are associated with further increases in coverage and reduced influenza across the community. [ABSTRACT FROM AUTHOR]

Background Few data on intracranial group A Streptococcus (GAS) infection in children are available. Here, we describe the demographic, clinical, and diagnostic characteristics of 91 children with intracranial GAS infection. Methods Cases of intracranial GAS infection in persons ≤18 years of age reported between 1997 and 2014 were identified by the Centers for Disease Control and Prevention's population- and laboratory-based Active Bacterial Core surveillance (ABCs) system. Medical charts were abstracted using a active, standardized case report form. All available isolates were emm typed. US census data were used to calculate rates. Results ABCs identified 2596 children with invasive GAS infection over an 18-year period; 91 (3.5%) had an intracranial infection. Intracranial infections were most frequent during the winter months and among children aged <1 year. The average annual incidence was 0.07 cases per 100000 children. For 83 patients for whom information for further classification was available, the principal clinical presentations included meningitis (35 [42%]), intracranial infection after otitis media, mastoiditis, or sinusitis (34 [41%]), and ventriculoperitoneal shunt infection (14 [17%]). Seven (8%) of these infections progressed to streptococcal toxic shock syndrome. The overall case fatality rate was 15%. GAS emm types 1 (31% of available isolates) and 12 (13% of available isolates) were most common. Conclusions Pediatric intracranial (GAS) infections are uncommon but often severe. Risk factors for intracranial GAS infection include the presence of a ventriculoperitoneal shunt and contiguous infections in the middle ear or sinuses. [ABSTRACT FROM AUTHOR]

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 <5% of persons living below poverty had an AOR of 1.31 (95% CI 1.16-1.47), those living in a tract with ≥5% vs <5% of persons living in crowded conditions had an AOR of 1.17 (95% CI 1.11-1.23), and those living in a tract with ≥40% vs <5% female heads of household had an AOR of 1.32 (95% CI 1.25-1.40). Conclusion Census tract-based determinants account for 11% of the variability in influenza hospitalization. [ABSTRACT FROM AUTHOR]

Introduction Previous FluSurv- NET studies found that adult females had a higher incidence of influenza-associated hospitalizations than males. To identify groups of women at higher risk than men, we analyzed data from 14 FluSurv- NET sites that conducted population-based surveillance for laboratory-confirmed influenza-associated hospitalizations among residents of 78 US counties. Methods We analyzed 6292 laboratory-confirmed, geocodable (96%) adult cases collected by FluSurv- NET during the 2010-12 influenza seasons. We used 2010 US Census and 2008-2012 American Community Survey data to calculate overall age-adjusted and age group-specific female:male incidence rate ratios ( IRR) by race/ethnicity and census tract-level poverty. We used national 2010 pregnancy rates to estimate denominators for pregnant women aged 18-49. We calculated male:female IRRs excluding them and IRRs for pregnant:non-pregnant women. Results Overall, 55% of laboratory-confirmed influenza cases were female. Female:male IRRs were highest for females aged 18-49 of high neighborhood poverty ( IRR 1.50, 95% CI 1.30-1.74) and of Hispanic ethnicity ( IRR 1.70, 95% CI 1.34-2.17). These differences disappeared after excluding pregnant women. Overall, 26% of 1083 hospitalized females aged 18-49 were pregnant. Pregnant adult females were more likely to have influenza-associated hospitalizations than their non-pregnant counterparts (relative risk [ RR] 5.86, 95% CI 5.12-6.71), but vaccination levels were similar (25.5% vs 27.8%). Conclusions Overall rates of influenza-associated hospitalization were not significantly different for men and women after excluding pregnant women. Among women aged 18-49, pregnancy increased the risk of influenza-associated hospitalization sixfold but did not increase the likelihood of vaccination. Improving vaccination rates in pregnant women should be an influenza vaccination priority. [ABSTRACT FROM AUTHOR]

We challenge the notion that influenza B is milder than influenza A by finding similar clinical characteristics between hospitalized adult influenza-cases. Among patients treated with oseltamivir, length of stay and mortality did not differ by type of virus infection. [ABSTRACT FROM AUTHOR]

Background The 2018–19 influenza season was characterized by prolonged co-circulation of Influenza A H3N2 (H3) and H1N1pdm09 (H1) viruses. We used data from the Influenza Hospitalization Surveillance Network (FluSurv-NET) to describe age-related differences in the distribution of influenza A subtypes. Methods We included all cases residing within a FluSurv-NET catchment area and hospitalized with laboratory-confirmed influenza during October 1, 2018–April 30, 2019. We multiply imputed influenza A subtype for 63% of cases with unknown subtype and based imputation on factors that could be associated with missing subtype including surveillance site, 10-year age groups and month of hospital admission. We calculated influenza hospitalization rates and 95% confidence intervals (95% CI) by type and subtype per 100,000 population. We compared the proportion of cases with H1 by year of age in FluSurv-NET to the distribution obtained from US public health laboratories participating in virologic surveillance and providing specimen-level influenza Results. Results Based on available data, 18,669 hospitalizations were reported; 41% received influenza vaccination ≥2 weeks prior to hospitalization and 90% received antivirals. Cumulative hospitalization rates per 100,000 population were as follows: H1 32.5 (95% CI 31.7–33.3), H3 29.3 (95% CI 28.5–30.1) and B 2.5 (95% CI 2.3–2.7). Based on weekly rates, H1 hospitalizations peaked during February (week 8) and H3 hospitalizations during March (week 11) (Figure A). FluSurv-NET data showed distinct patterns of subtype distribution by age, with H1 predominating among cases 0–9 and 24–70 years, and H3 predominating among cases 10–23 and ≥71 years. Data on the proportion of H1 results by age correlated well between FluSurv-NET and US virologic surveillance (Figure B). Conclusion Influenza A H1 and H3 virus circulation patterns varied by age group during the 2018–2019 season. The proportion of cases with H1 relative to H3 was low among those born between 1996 and 2009 and those born before 1948. These findings may indicate protection against H1 viruses in age groups with exposure to H1N1pdm09 during the 2009 pandemic or to older antigenically similar H1N1 viruses as young children. Disclosures Evan J. Anderson, MD, AbbVie (Consultant), GSK (Grant/Research Support), Merck (Grant/Research Support), Micron (Grant/Research Support), PaxVax (Grant/Research Support), Pfizer (Consultant, Grant/Research Support), sanofi pasteur (Grant/Research Support), Keipp Talbot, MD, MPH, Sequirus (Other Financial or Material Support, On Data Safety Monitoring Board). [ABSTRACT FROM AUTHOR]

• We used a matched cohort design to study the differential impacts of SLIV by race and ethnicity. • Racial disparities in vaccine coverage and hospitalizations persisted in the presence of SLIV. • Vaccine hesitance remained a common reason for non-vaccination in school-aged children. Increasing influenza vaccination coverage in school-aged children may substantially reduce community transmission. School-located influenza vaccinations (SLIV) aim to promote vaccinations by increasing accessibility, which may be especially beneficial to race/ethnicity groups that face high barriers to preventative care. Here, we evaluate the effectiveness of a city-wide SLIV program by race/ethnicity from 2014 to 2018. We used multivariate matching to pair schools in the intervention district in Oakland, CA with schools in a comparison district in West Contra Costa County, CA. We distributed cross-sectional surveys to measure caregiver-reported student vaccination status and estimated differences in vaccination coverage levels and reasons for non-vaccination between districts stratifying by race/ethnicity. We estimated difference-in-differences (DID) of laboratory confirmed influenza hospitalization incidence between districts stratified by race/ethnicity using surveillance data. Differences in influenza vaccination coverage in the intervention vs. comparison district were larger among White (2017–18: 21.0% difference [95% CI: 9.7%, 32.3%]) and Hispanic/Latino (13.4% [8.8%, 18.0%]) students than Asian/Pacific Islander (API) (8.9% [1.3%, 16.5%]), Black (5.9% [−2.2%, 14.0%]), and multiracial (6.3% [−1.8%, 14.3%)) students. Concerns about vaccine effectiveness or safety were more common among Black and multiracial caregivers. Logistical barriers were less common in the intervention vs. comparison district, with the largest difference among White students. In both districts, hospitalizations in 2017–18 were higher in Blacks (Intervention: 111.5 hospitalizations per 100,00; Comparison: 134.1 per 100,000) vs. other races/ethnicities. All-age influenza hospitalization incidence was lower in the intervention site vs. comparison site among White/API individuals in 2016–17 (DID −25.14 per 100,000 [95% CI: −40.14, −10.14]) and 2017–18 (−36.6 per 100,000 [−52.7, −20.5]) and Black older adults in 2017–18 (−282.2 per 100,000 (−508.4, −56.1]), but not in other groups. SLIV was associated with higher vaccination coverage and lower influenza hospitalization, but associations varied by race/ethnicity. SLIV alone may be insufficient to ensure equitable influenza outcomes. [ABSTRACT FROM AUTHOR]

Since SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in December 2019 (1), approximately 1.3 million cases have been reported worldwide (2), including approximately 330,000 in the United States (3). To conduct population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations in the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was created using the existing infrastructure of the Influenza Hospitalization Surveillance Network (FluSurv-NET) (4) and the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET). This report presents age-stratified COVID-19-associated hospitalization rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020, the first month of U.S. surveillance. Among 1,482 patients hospitalized with COVID-19, 74.5% were aged ≥50 years, and 54.4% were male. The hospitalization rate among patients identified through COVID-NET during this 4-week period was 4.6 per 100,000 population. Rates were highest (13.8) among adults aged ≥65 years. Among 178 (12%) adult patients with data on underlying conditions as of March 30, 2020, 89.3% had one or more underlying conditions; the most common were hypertension (49.7%), obesity (48.3%), chronic lung disease (34.6%), diabetes mellitus (28.3%), and cardiovascular disease (27.8%). These findings suggest that older adults have elevated rates of COVID-19-associated hospitalization and the majority of persons hospitalized with COVID-19 have underlying medical conditions. These findings underscore the importance of preventive measures (e.g., social distancing, respiratory hygiene, and wearing face coverings in public settings where social distancing measures are difficult to maintain) † to protect older adults and persons with underlying medical conditions, as well as the general public. In addition, older adults and persons with serious underlying medical conditions should avoid contact with persons who are ill and immediately contact their health care provider(s) if they have symptoms consistent with COVID-19 (https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html) (5). Ongoing monitoring of hospitalization rates, clinical characteristics, and outcomes of hospitalized patients will be important to better understand the evolving epidemiology of COVID-19 in the United States and the clinical spectrum of disease, and to help guide planning and prioritization of health care system resources., Competing Interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Linda Niccolai reports personal fees from Merck outside the submitted work; Evan Anderson reports personal fees from AbbVie and Pfizer, and grants from MedImmune, Regeneron, PaxVax, Pfizer, GSK, Merck, Novavax, Sanofi-Pasteur, and Micron, outside the submitted work; Andrew Weigel reports grants from the Council of State and Territorial Epidemiologists during the conduct of the study; Ruth Lynfield reports that she is the coeditor for a book on public health and an associate editor for American Academy of Pediatrics Report of the Committee of Infectious Diseases (Red Book); Laurie Billing reports a grant from the Council of State and Territorial Epidemiologists during the conduct of the study; William Schaffner reports personal fees from Pfizer, Roche Diagnostics, and Pepsico outside the submitted work; H. Keipp Talbot reports compensation from Seqiris outside the submitted work; Andrea George reports a grant from the Council of State and Territorial Epidemiologists during the conduct of the study; Sue Kim reports a grant from the Council of State and Territorial Epidemiologists during the conduct of the study; Justin Henderson reports a grant from the Council of State and Territorial Epidemiologists during the conduct of the study; and Clarissa Aquino reports a grant from the Council of State and Territorial Epidemiologists during the conduct of the study. No other potential conflicts of interest were disclosed.