Importance: Racial and ethnic minority groups, such as Black, Hispanic, American Indian or Alaska Native, and Asian or Pacific Islander persons, often experience higher rates of severe influenza disease. Objective: To describe rates of influenza-associated hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity over 10 influenza seasons. Design, Setting, and Participants: This cross-sectional study used data from the Influenza-Associated Hospitalization Surveillance Network (FluSurv-NET), which conducts population-based surveillance for laboratory-confirmed influenza-associated hospitalizations in selected counties, representing approximately 9% of the US population. Influenza hospitalizations from the 2009 to 2010 season to the 2018 to 2019 season were analyzed. Data were analyzed from October 2020 to July 2021. Main Outcomes and Measures: The main outcomes were age-adjusted and age-stratified rates of influenza-associated hospitalization, ICU admission, and in-hospital death by race and ethnicity overall and by influenza season. Results: Among 113352 persons with an influenza-associated hospitalization (34436 persons [32.0%] aged ≥75 years; 61009 [53.8%] women), 70225 persons (62.3%) were non-Hispanic White (White), 24850 persons (21.6%) were non-Hispanic Black (Black), 11903 persons (10.3%) were Hispanic, 5517 persons (5.1%) were non-Hispanic Asian or Pacific Islander, and 857 persons (0.7%) were non-Hispanic American Indian or Alaska Native. Among persons aged younger than 75 years and compared with White persons of the same ages, Black persons were more likely to be hospitalized (eg, age 50-64 years: rate ratio [RR], 2.50 95% CI, 2.43-2.57) and to be admitted to an ICU (eg, age 50-64 years: RR, 2.09; 95% CI, 1.96-2.23). Among persons aged younger than 50 years and compared with White persons of the same ages, American Indian or Alaska Native persons were more likely to be hospitalized (eg, age 18-49 years: RR, 1.72; 95% CI, 1.51-1.96) and to be admitted to an ICU (eg, age 18-49 years: RR, 1.84; 95% CI, 1.40-2.42). Among children aged 4 years or younger and compared with White children, hospitalization rates were higher in Black children (RR, 2.21; 95% CI, 2.10-2.33), Hispanic children (RR, 1.87; 95% CI, 1.77-1.97), American Indian or Alaska Native children (RR, 3.00; 95% CI, 2.55-3.53), and Asian or Pacific Islander children (RR, 1.26; 95% CI, 1.16-1.38), as were rates of ICU admission (Black children: RR, 2.74; 95% CI, 2.43-3.09; Hispanic children: RR, 1.96; 95% CI, 1.73-2.23; American Indian and Alaska Native children: RR, 3.51; 95% CI, 2.45-5.05). In this age group and compared with White children, in-hospital death rates were higher among Hispanic children (RR, 2.98; 95% CI, 1.23-7.19), Black children (RR, 3.39; 95% CI, 1.40-8.18), and Asian or Pacific Islander children (RR, 4.35; 95% CI, 1.55-12.22). Few differences were observed in rates of severe influenza-associated outcomes by race and ethnicity among adults aged 75 years or older. For example, in this age group, compared with White adults, hospitalization rates were slightly higher only among Black adults (RR, 1.05; 95% CI 1.02-1.09). Overall, Black persons had the highest age-adjusted hospitalization rate (68.8 [95% CI, 68.0-69.7] hospitalizations per 100000 population) and ICU admission rate (11.6 [95% CI, 11.2-11.9] admissions per 100000 population). Conclusions and Relevance: This cross-sectional study found racial and ethnic disparities in rates of severe influenza-associated disease. These data identified subgroups for whom improvements in influenza prevention efforts could be targeted..

Background: In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19–attributable deaths. Methods: We estimated COVID-19 unrecognized attributable deaths, from March 2020—April 2021, using all-cause deaths reported to NVSS by week and six age groups (0–17, 18–49, 50–64, 65–74, 75–84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19–attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths. Findings: We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020—May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24–0•31 times lower among those 0–17 years relative to all other age groups. More COVID-19–attributable deaths were not captured during the early months of the pandemic (March–May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020—February 2021). Interpretation: Estimating COVID-19–attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic. Funding: None

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged ≥85 years who died. First, we used a spatial–temporal binomial regression model to estimate the percent of positive SARS-CoV-2 test results. A spatial–temporal negative-binomial model was then used to estimate unrecognized COVID-19 deaths by exploiting the spatial–temporal association between SARS-CoV-2 percent positive and all-cause mortality counts using an excess mortality approach. Computationally efficient Bayesian inference was accomplished via the Polya-Gamma representation of the binomial and negative-binomial models. Among those aged ≥85 years, we estimated 58,200 (95% CI: 51,300, 64,900) unrecognized COVID-19 deaths, which accounts for 26% (95% CI: 24%, 29%) of total COVID-19 deaths in this age group. Our modeling results suggest that COVID-19 mortality and the proportion of unrecognized deaths among deaths attributed to COVID-19 vary by time and across states.

Background: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. Objective: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. Conclusions: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

BACKGROUND: Influenza virus and SARS-CoV-2 are significant causes of respiratory illness in children. METHODS: Influenza and COVID-19-associated hospitalizations among children <18 years old were analyzed from FluSurv-NET and COVID-NET, two population-based surveillance systems with similar catchment areas and methodology. The annual COVID-19-associated hospitalization rate per 100 000 during the ongoing COVID-19 pandemic (October 1, 2020-September 30, 2021) was compared to influenza-associated hospitalization rates during the 2017-18 through 2019-20 influenza seasons. In-hospital outcomes, including intensive care unit (ICU) admission and death, were compared. RESULTS: Among children <18 years old, the COVID-19-associated hospitalization rate (48.2) was higher than influenza-associated hospitalization rates: 2017-18 (33.5), 2018-19 (33.8), and 2019-20 (41.7). The COVID-19-associated hospitalization rate was higher among adolescents 12-17 years old (COVID-19: 59.9; influenza range: 12.2-14.1), but similar or lower among children 5-11 (COVID-19: 25.0; influenza range: 24.3-31.7) and 0-4 (COVID-19: 66.8; influenza range: 70.9-91.5) years old. Among children <18 years old, a higher proportion with COVID-19 required ICU admission compared with influenza (26.4% vs 21.6%; p < 0.01). Pediatric deaths were uncommon during both COVID-19- and influenza-associated hospitalizations (0.7% vs 0.5%; p = 0.28). CONCLUSIONS: In the setting of extensive mitigation measures during the COVID-19 pandemic, the annual COVID-19-associated hospitalization rate during 2020-2021 was higher among adolescents and similar or lower among children <12 years old compared with influenza during the three seasons before the COVID-19 pandemic. COVID-19 adds substantially to the existing burden of pediatric hospitalizations and severe outcomes caused by influenza and other respiratory viruses.

Importance: Multisystem inflammatory syndrome in children (MIS-C) is associated with recent or current SARS-CoV-2 infection. Information on MIS-C incidence is limited. Objective: To estimate population-based MIS-C incidence per 1000000 person-months and to estimate MIS-C incidence per 1000000 SARS-CoV-2 infections in persons younger than 21 years. Design, Setting, and Participants: This cohort study used enhanced surveillance data to identify persons with MIS-C during April to June 2020, in 7 jurisdictions reporting to both the Centers for Disease Control and Prevention national surveillance and to Overcoming COVID-19, a multicenter MIS-C study. Denominators for population-based estimates were derived from census estimates; denominators for incidence per 1000000 SARS-CoV-2 infections were estimated by applying published age- and month-specific multipliers accounting for underdetection of reported COVID-19 case counts. Jurisdictions included Connecticut, Georgia, Massachusetts, Michigan, New Jersey, New York (excluding New York City), and Pennsylvania. Data analyses were conducted from August to December 2020. Exposures: Race/ethnicity, sex, and age group (ie, ≤5, 6-10, 11-15, and 16-20 years). Main Outcomes and Measures: Overall and stratum-specific adjusted estimated MIS-C incidence per 1000000 person-months and per 1000000 SARS-CoV-2 infections. Results: In the 7 jurisdictions examined, 248 persons with MIS-C were reported (median [interquartile range] age, 8 [4-13] years; 133 [53.6%] male; 96 persons [38.7%] were Hispanic or Latino; 75 persons [30.2%] were Black). The incidence of MIS-C per 1000000 person-months was 5.1 (95% CI, 4.5-5.8) persons. Compared with White persons, incidence per 1000000 person-months was higher among Black persons (adjusted incidence rate ratio [aIRR], 9.26 [95% CI, 6.15-13.93]), Hispanic or Latino persons (aIRR, 8.92 [95% CI, 6.00-13.26]), and Asian or Pacific Islander (aIRR, 2.94 [95% CI, 1.49-5.82]) persons. MIS-C incidence per 1000000 SARS-CoV-2 infections was 316 (95% CI, 278-357) persons and was higher among Black (aIRR, 5.62 [95% CI, 3.68-8.60]), Hispanic or Latino (aIRR, 4.26 [95% CI, 2.85-6.38]), and Asian or Pacific Islander persons (aIRR, 2.88 [95% CI, 1.42-5.83]) compared with White persons. For both analyses, incidence was highest among children aged 5 years or younger (4.9 [95% CI, 3.7-6.6] children per 1000000 person-months) and children aged 6 to 10 years (6.3 [95% CI, 4.8-8.3] children per 1000000 person-months). Conclusions and Relevance: In this cohort study, MIS-C was a rare complication associated with SARS-CoV-2 infection. Estimates for population-based incidence and incidence among persons with infection were higher among Black, Hispanic or Latino, and Asian or Pacific Islander persons. Further study is needed to understand variability by race/ethnicity and age group..

Background: Diabetes mellitus (DM) is common among older adults hospitalized with influenza, yet data are limited on the impact of DM on risk of severe influenza-associated outcomes. Methods: We included adults aged ≥65 years hospitalized with influenza during 2012-2013 through 2016-2017 from the Influenza Hospitalization Surveillance Network (FluSurv-NET), a population-based surveillance system for laboratory-confirmed influenza-associated hospitalizations conducted in defined counties within 13 states. We calculated population denominators using the Centers for Medicare and Medicaid Services county-specific DM prevalence estimates and National Center for Health Statistics population data. We present pooled rates and rate ratios (RRs) of intensive care unit (ICU) admission, pneumonia diagnosis, mechanical ventilation, and in-hospital death for persons with and without DM. We estimated RRs and 95% confidence intervals (CIs) using meta-analysis with site as a random effect in order to control for site differences in the estimates. Results: Of 31 934 hospitalized adults included in the analysis, 34% had DM. Compared to those without DM, adults with DM had higher rates of influenza-associated hospitalization (RR, 1.57 [95% CI, 1.43-1.72]), ICU admission (RR, 1.84 [95% CI, 1.67-2.04]), pneumonia (RR, 1.57 [95% CI, 1.42-1.73]), mechanical ventilation (RR, 1.95 [95% CI, 1.74-2.20]), and in-hospital death (RR, 1.48 [95% CI, 1.23-1.80]). Conclusions: Older adults with DM have higher rates of severe influenza-associated outcomes compared to those without DM. These findings reinforce the importance of preventing influenza virus infections through annual vaccination, and early treatment of influenza illness with antivirals in older adults with DM.

Importance: Seasonal influenza virus infection is a major cause of morbidity and mortality and may be associated with respiratory and nonrespiratory diagnoses. Objective: To examine the respiratory and nonrespiratory diagnoses reported for adults hospitalized with laboratory-confirmed influenza between 2010 and 2018 in the United States. Design, Setting, and Participants: This cross-sectional study used data from the US Influenza Hospitalization Surveillance Network (FluSurv-NET) from October 1 through April 30 of the 2010-2011 through 2017-2018 influenza seasons. FluSurv-NET is a population-based, multicenter surveillance network with a catchment area that represents approximately 9% of the US population. Patients are identified by practitioner-ordered influenza testing. Adults (aged ≥18 years) hospitalized with laboratory-confirmed influenza were included in the study. Exposures: FluSurv-NET defines laboratory-confirmed influenza as a positive influenza test result by rapid antigen assay, reverse transcription-polymerase chain reaction, direct or indirect fluorescent staining, or viral culture. Main Outcomes and Measures: Acute respiratory or nonrespiratory diagnoses were defined using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) discharge diagnosis codes. The analysis included calculation of the frequency of acute respiratory and nonrespiratory diagnoses with a descriptive analysis of patient demographic characteristics, underlying medical conditions, and in-hospital outcomes by respiratory and nonrespiratory diagnoses. Results: Of 89 999 adult patients hospitalized with laboratory-confirmed influenza, 76 649 (median age, 69 years; interquartile range, 55-82 years; 55% female) had full medical record abstraction and at least 1 ICD code for an acute diagnosis. In this study, 94.9% of patients had a respiratory diagnosis and 46.5% had a nonrespiratory diagnosis, including 5.1% with only nonrespiratory diagnoses. Pneumonia (36.3%), sepsis (23.3%), and acute kidney injury (20.2%) were the most common acute diagnoses. Fewer patients with only nonrespiratory diagnoses received antiviral therapy for influenza compared with those with respiratory diagnoses (81.4% vs 88.9%; P < .001). Conclusions and Relevance: Nonrespiratory diagnoses occurred frequently among adults hospitalized with influenza, further contributing to the burden of infection in the United States. The findings suggest that during the influenza season, practitioners should consider influenza in their differential diagnosis for patients who present to the hospital with less frequently recognized manifestations and initiate early antiviral treatment for patients with suspected or confirmed infection.

Background: Data collected by mobile devices can augment surveillance of epidemics in real time. However, methods and evidence for the integration of these data into modern surveillance systems are sparse. We linked call detail records (CDR) with an influenza-like illness (ILI) registry and evaluated the role that Icelandic international travellers played in the introduction and propagation of influenza A/H1N1pdm09 virus in Iceland through the course of the 2009 pandemic. Methods: This nested case-control study compared odds of exposure to Keflavik International Airport among cases and matched controls producing longitudinal two-week matched odds ratios (mORs) from August to December 2009. We further evaluated rates of ILI among 1st- and 2nd-degree phone connections of cases compared to their matched controls. Results: The mOR was elevated in the initial stages of the epidemic from 7 August until 21 August (mOR = 2.53; 95% confidence interval (CI) = 1.35, 4.78). During the two-week period from 17 August through 31 August, we calculated the two-week incidence density ratio of ILI among 1st-degree connections to be 2.96 (95% CI: 1.43, 5.84). Conclusions: Exposure to Keflavik International Airport increased the risk of incident ILI diagnoses during the initial stages of the epidemic. Using these methods for other regions of Iceland, we evaluated the geographic spread of ILI over the course of the epidemic. Our methods were validated through similar evaluation of a domestic airport. The techniques described in this study can be used for hypothesis-driven evaluations of locations and behaviours during an epidemic and their associations with health outcomes.

Background. Persons with influenza can develop complications that result in hospitalization and death. These are most commonly respiratory related, but cardiovascular or neurologic complications or exacerbations of underlying chronic medical conditions may also occur. Patterns of complications observed during pandemics may differ from typical influenza seasons, and characterizing variations in influenza-related complications can provide a better understanding of the impact of pandemics and guide appropriate clinical management and planning for the future. Methods. Using a population-based surveillance system, we compared clinical complications using International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnosis codes in adults hospitalized with seasonal influenza (n = 5270) or 2009 pandemic influenza A(H1N1) (H1N1pdm09; n = 4962). Results. Adults hospitalized with H1N1pdm09 were younger (median age, 47 years) than those with seasonal influenza (median age, 68 years; P <. 01), and differed in the frequency of certain underlying medical conditions. Whereas there was similar risk for many influenza-Associated complications, after controlling for age and type of underlying medical condition, adults hospitalized with H1N1pdm09 were more likely to have lower respiratory tract complications, shock/sepsis, and organ failure than those with seasonal influenza. They were also more likely to be admitted to the intensive care unit, require mechanical ventilation, or die. Young adults, in particular, had 2-4 times the risk of severe outcomes from H1N1pdm09 than persons of the same ages with seasonal influenza. Conclusions. Although H1N1pdm09 was thought of as a relatively mild pandemic, these data highlight the impact of the 2009 pandemic on the risk of severe influenza, especially among younger adults, and the impact this virus may continue to have.