obÌåÓý

FluSurv-NET

About FluSurv-NET

The Influenza Hospitalization Surveillance Network (FluSurv-NET) is part of the Respiratory Virus Hospitalization Surveillance Network (RESP-NET), which is designed to conduct population-based surveillance for laboratory-confirmed influenza, COVID-19, and respiratory syncytial virus (RSV)-associated hospitalizations. RESP-NET also includes surveillance networks for COVID-19 and RSV (also known as COVID-NET and RSV-NET, respectively).

FluSurv-NET collects data on laboratory-confirmed influenza-associated hospitalizations among children and adults through a network of acute care hospitals in 14 states.

Why FluSurv-NET Data is Important

FluSurv-NET is obÌåÓý's source for important data on hospitalization rates associated with flu. FluSurv-NET also provides demographic and clinical information including age, sex, race and ethnicity, and underlying medical conditions among persons hospitalized with flu. Data gathered are used to estimate age-specific on a weekly basis and to describe . Additionally, FluSurv-NET data are used with other data sources to estimate annual and weekly influenza disease burden in the United States.

FluSurv-NET Case Definition

For FluSurv-NET, a case is defined as a person who is a resident in a defined FluSurv-NET catchment area and tests positive for influenza by a laboratory test ordered by a health care professional within 14 days prior to or during hospitalization. Laboratory confirmation is defined by a positive result of a viral culture, direct or indirect fluorescent antibody staining, rapid antigen test, or molecular assay.

How FluSurv-NET Calculates Hospitalization Rates

A minimum set of data are collected on all identified cases to produce weekly hospitalization rates:

• age
• sex
• race and ethnicity
• surveillance site
• date of hospital admission
• positive influenza test result/date

Hospitalization rates are calculated as the number of residents of a defined area who are hospitalized with a positive influenza laboratory test divided by the total population within the defined area. NCHS bridged-race population estimates are used as denominators prior to the 2020-2021 season. Beginning with the 2020-2021 season, unbridged census population estimates (U.S. Census Bureau, Population Division, Vintage 2020-2022 Special Tabulation) are used as denominators.

FluSurv-NET Coverage

FluSurv-NET coverage area includes more than 90 counties and county equivalents in 14 states that participate in the Emerging Infections Program (EIP) and the Influenza Hospitalization Surveillance Program (IHSP). Participating states include: California, Colorado, Connecticut, Georgia, Maryland, Michigan, Minnesota, New Mexico, New York, North Carolina, Ohio, Oregon, Tennessee and Utah.

FluSurv-NET covers more than 30 million people and includes an estimated 9 percent of the U.S. population. The counties covered are located in 9 . The designated FluSurv-NET surveillance area is generally similar to the U.S. population by demographics; however, the information might not be generalizable to the entire country.

*Since the 2023-2024 influenza season, Connecticut will use county equivalents instead of counties.

Accessing FluSurv-NET Data

Influenza-associated hospitalization rates are reported to obÌåÓý on a weekly basis each influenza season. During seasons with influenza activity outside of the typical influenza season of October 1 through April 30, sites will be given the option to extend influenza hospitalization surveillance beyond the established surveillance period. FluSurv-NET data, including hospitalization rates for different age groups and data on patient characteristics, are available on FluView and FluView Interactive.

FluSurv-NET hospitalization data are preliminary during each season; data presented may change as more reports are received. In particular, case counts for recent hospital admissions are subject to reporting lags. As data are received each week during the influenza season, prior case counts and rates may be updated.

FluSurv-NET Sampling Methodology for Collection of Clinical Data

Starting with the 2017-18 influenza season, FluSurv-NET implemented a sampling strategy for collection of clinical data. Clinical data were collected for a random sample of cases ≥50 years of age stratified by age and surveillance site; 100% of cases <50 years were sampled during this season and some sites elected to sample 100% of cases ≥50 years. Additionally, 100% of cases who died in-hospital or within 30 days after hospital discharge were also sampled. Random numbers were auto-generated and assigned to each case upon entry into the surveillance database. Trained surveillance staff conducted medical chart abstractions on sampled cases using a standardized case report form.

The following types of clinical data are collected for sampled cases:

• current season influenza vaccination status
• clinical (medical) history (underlying health conditions)
• clinical course (admission to intensive care unit)
• medical interventions (e.g., receipt of antiviral treatment, mechanical ventilation)
• outcomes (e.g., discharged from the hospital, in-hospital death)

Sampling occurred beyond the 2017-18 season using a similar methodology. The table below summarizes the sampling strategies offered during influenza seasons where any sampling occurred at rates <100%.

*All other age groups were sampled at 100%, if applicable, and 100% of in-hospital and 60 day-post-discharge deaths were sampled. For the 2024-25 influenza season, sampling rates will be determined mid-season.

In seasons where sampling occurred at rates <100%, clinical estimates are weighted to reflect the probability of selection. The FluSurv-NET sampling methodology has also been described in the below studies:

Chow EJ, Rolfes MA, O'Halloran A, et al. Respiratory and nonrespiratory diagnoses associated with influenza in hospitalized adults. JAMA Netw Open. 2020; 3(3):e201323. doi:

Chow EJ, Rolfes MA, O'Halloran A, et al. Acute cardiovascular events associated with influenza in hospitalized adults: a cross-sectional study. Ann Intern Med. 2020; 173(8): 605- 613. doi:

How FluSurv-NET contributes to obÌåÓý’s Influenza Burden Estimates

obÌåÓý uses FluSurv-NET data in combination with other data sources to estimate annual and weekly disease burden of influenza in the United States. Estimates are made of symptomatic illnesses, medically attended illnesses, hospitalizations and deaths. Reported rates are adjusted in an attempt to correct for the under-detection of influenza. This adjustment is done by using the percent of people hospitalized with respiratory illnesses who were tested for influenza and the average sensitivity of influenza tests used in the participating FluSurv-NET hospitals. Weekly estimates of influenza burden are available during each influenza season. Annual estimates of US influenza burden and influenza burden averted by influenza vaccination are available online.

Publications

2020 to Present

• Tenforde M, Noah K, O’Halloran A, et al. , Influenza Hospitalization Surveillance Network (FluSurv-NET), 2012–2019. Clinical Infectious Diseases. 2024.
• Famati E, Ujamaa D, O’Halloran A, et al. , FluSurv-NET, 2011-2019. Open Forum Infectious Diseases. 2023.
• Kojima N, Taylor C, Tenforde M, et al. , October 2021 – September 2022. Open Forum Infectious Diseases. 2023.
• White E, O'Halloran A, Sundaresan D, et al. High Influenza Incidence and Disease Severity Among Children and Adolescents Aged <18 Years ― United States, 2022–23 Season. Morbidity and Mortality Weekly Report (MMWR). 2023.
• Sumner K, Masalovich S, O'Halloran A, et al. . The Lancet Microbe. 2023.
• Tenforde M, Cummings C, O'Halloran A, et al. . Open Forum Infectious Diseases. 2022.
• Adams K, Tastad K, Huang S, et al. Prevalence of SARS-CoV-2 and Influenza Coinfections and Clinical Characteristics Among Children and Adolescents Aged <18 Years Who Were Hospitalized or Died with Influenza – Unite States, 2021-22 Influenza Season. Morbidity and Mortality Weekly Report (MMWR). 2022.
• Black C, O'Halloran A, Hung M, et al. Vital Signs: Influenza Hospitalizations and Vaccination Coverage by Race and Ethnicity – United States, 2009-10 Through 2021-22 Influenza Seasons. Morbidity and Mortality Weekly Report (MMWR). 2022.
• Parisi C, Yousey-Hindes K, Holstein R, et al. . Influenza and Other Respiratory Viruses. 2022.
• Delahoy M, Ujamaa D, Taylor C, et al. . Clinical Infectious Diseases. 2022.
• Kamidani S, Garg S, Rolfes M, et al. . Clinical Infectious Diseases. 2022.
• Holstein R, Dawood F, O'Halloran A, et al. Annals of Internal Medicine. 2021.
• Cummings C, O'Halloran A, Azenkot T, et al. Infection Control and Hospital Epidemiology. 2021.
• Campbell A, Tokars J, Reynolds S, et al. . Pediatrics. 2021.
• O'Halloran A, Holstein R, Cummings C, et al. . Journal of American Medical Association (JAMA) Network Open. 2021.
• Chow E, Rolfes M, O'Halloran A, et al. . Annals of Internal Medicine. 2020.
• Sloan C, Chandrasekhar R, Mitchel E, et al. . Epidemics. 2020.
• Collins J, Campbell A, Openo K, et al. . Clinical Infectious Diseases. 2020.
• Chow E, Rolfes M, O'Halloran A, et al. . Journal of American Medical Association (JAMA) Network Open. 2020.

2015-2019

• Collins J, Campbell A, Openo K, et al. Journal of the Pediatric Infectious Diseases Society. 2019.
• Budd A, Beacham L, Smith C, et al. . The Journal of Infectious Diseases. 2019.
• Czaja C, Miller L, Alden N, et al. . Open Forum Infectious Diseases. 2019.
• Biggerstaff M, Kniss K, Jernigan D, et al. American Journal of Epidemiology. 2018.
• Chandrasekhar R, Sloan C, Mitchel E, et al. . Influenza and Other Respiratory Viruses. 2017.
• Kline K, Hadler J, Yousey-Hindes K, et al. . Influenza and Other Respiratory Viruses. 2017.
• Arriola C, Garg S, Anderson E, et al. . Clinical Infectious Diseases. 2017.
• Appiah G, Chaves S, Kirley P, et al. . Clinical Infectious Diseases. 2017.
• Havers F, Sokolow L, Shay D, et al. . Clinical Infectious Diseases. 2016.
• Su S, Fry A, Kirley P, et al. . Influenza and Other Respiratory Viruses. 2016.
• Rolfes M, Yousey-Hindes K, Meek J, et al. . Open Forum Infectious Diseases. 2016.
• Oboho I, Reed C, Gargiullo P, et al. . The Journal of Infectious Diseases. 2016.
• Bramley A, Chaves S, Dawood F, et al. . Public Health Reports. 2016.
• Hadler J, Yousey-Hindes K, Perez A, et al. Influenza-Related Hospitalizations and Poverty Levels – United States, 2010-2012. Morbidity and Mortality Weekly Report (MMWR). 2016.
• Garg S, Jain S, Dawood F, et al. BMC Infectious Diseases. 2015.
• Arriola C, Anderson E, Baumbach J, et al. The Journal of Infectious Diseases. 2015.
• Laidler M, Thomas A, Baumbach J, et al. . Open Forum Infectious Diseases. 2015.
• Chaves S, Perez A, Miller L, et al. . Clinical Infectious Diseases. 2015.
• Millman A, Reed C, Kirley P, et al. Emerging Infectious Diseases. 2015.
• Chaves S, Lynfield R, Bresee J, et al. . Emerging Infectious Diseases. 2015.

2010-2014

• Jhung M, D'Mello T, Perez A, et al. . American Journal of Infection Control. 2014.
• Greenbaum A, Chaves S, Perez A, et al. . Infection. 2014.
• Dawood F, Chaves S, Perez A, et al. . The Journal of Infectious Diseases. 2014.
• Chaves S, Perez A, Farley M, et al. . The Pediatric Infectious Disease Journal. 2014.
• Dharan N, Sokolow L, Cheng P, et al. . The Pediatric Infectious Disease Journal. 2014.
• Su S, Chaves S, Perez A, et al. . Clinical Infectious Diseases. 2014.
• Reed C, Chaves S, Perez A, et al. Clinical Infectious Diseases. 2014.
• Fleming-Dutra K, Taylor T, Link-Gelles R, et al. . The Journal of Infectious Diseases. 2013.
• Chaves S, Aragon D, Bennett N, et al. . The Journal of Infectious Diseases. 2013.
• Thompson M, Sokolow L, Almendares O, et al. . Clinical Infectious Diseases. 2013.
• Vandermeer M, Thomas A, Kamimoto L, et al. . The Journal of Infectious Diseases. 2012.
• Garg S, Chaves S, Perez A, et al. . Clinical Infectious Diseases. 2012.
• Cox C, D'Mello T, Perez A, et al. . The Journal of Infectious Diseases. 2011.
• Fry A, Perez A, Finelli L. . Journal of American Medical Association (JAMA). 2011.
• Doshi S, Kamimoto L, Finelli L, et al. . The Journal of Infectious Diseases. 2011.
• Dawood F, Kamimoto L, D'Mello T, et al. . Pediatrics. 2011
• Creanga A, Kamimoto L, Newsome K, et al. . American Journal of Obstetrics and Gynecology. 2011.
• Dee D, Bensyl D, Gindler J, et al. . Annals of Epidemiology. 2011.
• Brammer L, Blanton L, Epperson S, et al. . Clinical Infectious Diseases. 2011.
• Dawood F, Fiore A, Kamimoto L, et al. The Pediatric Infectious Disease Journal. 2010.
• Dawood F, Fiore A, Kamimoto L, et al. The Journal of Pediatrics. 2010.
• Dao C, Kamimoto L, Nowell M, et al. . The Journal of Infectious Diseases. 2010.

2003-2009

• Schrag S, Shay D, Gershman K, et al. . The Pediatric and Infectious Disease Journal. 2006.

Resources