Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 - COVID-NET, 14 States, March 1-30, 2020

Shikha Garg, Lindsay Kim, Michael Whitaker, Alissa O'Halloran, Charisse Cummings, Rachel Holstein, Mila Prill, Shua J Chai, Pam D Kirley, Nisha B Alden, Breanna Kawasaki, Kimberly Yousey-Hindes, Linda Niccolai, Evan J Anderson, Kyle P Openo, Andrew Weigel, Maya L Monroe, Patricia Ryan, Justin Henderson, Sue Kim, Kathy Como-Sabetti, Ruth Lynfield, Daniel Sosin, Salina Torres, Alison Muse, Nancy M Bennett, Laurie Billing, Melissa Sutton, Nicole West, William Schaffner, H Keipp Talbot, Clarissa Aquino, Andrea George, Alicia Budd, Lynnette Brammer, Gayle Langley, Aron J Hall, Alicia Fry, Shikha Garg, Lindsay Kim, Michael Whitaker, Alissa O'Halloran, Charisse Cummings, Rachel Holstein, Mila Prill, Shua J Chai, Pam D Kirley, Nisha B Alden, Breanna Kawasaki, Kimberly Yousey-Hindes, Linda Niccolai, Evan J Anderson, Kyle P Openo, Andrew Weigel, Maya L Monroe, Patricia Ryan, Justin Henderson, Sue Kim, Kathy Como-Sabetti, Ruth Lynfield, Daniel Sosin, Salina Torres, Alison Muse, Nancy M Bennett, Laurie Billing, Melissa Sutton, Nicole West, William Schaffner, H Keipp Talbot, Clarissa Aquino, Andrea George, Alicia Budd, Lynnette Brammer, Gayle Langley, Aron J Hall, Alicia Fry

Abstract

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.

Conflict of interest statement

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.

Figures

FIGURE 1
FIGURE 1
Laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalization rates, by age group — COVID-NET, 14 states, March 1–28, 2020 Abbreviation: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. * Number of patients hospitalized with COVID-19 per 100,000 population. † Counties included in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Dona Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County).
FIGURE 2
FIGURE 2
Laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalization rates, by surveillance site— COVID-NET, 14 states, March 1–28, 2020 Abbreviation: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. * Number of patients hospitalized with COVID-19 per 100,000 population. † Counties included in COVID-NET surveillance: California (Alameda, Contra Costa, and San Francisco counties); Colorado (Adams, Arapahoe, Denver, Douglas, and Jefferson counties); Connecticut (New Haven and Middlesex counties); Georgia (Clayton, Cobb, DeKalb, Douglas, Fulton, Gwinnett, Newton, and Rockdale counties); Iowa (one county represented); Maryland (Allegany, Anne Arundel, Baltimore, Baltimore City, Calvert, Caroline, Carroll, Cecil, Charles, Dorchester, Frederick, Garrett, Harford, Howard, Kent, Montgomery, Prince George’s, Queen Anne’s, St. Mary’s, Somerset, Talbot, Washington, Wicomico, and Worcester counties); Michigan (Clinton, Eaton, Genesee, Ingham, and Washtenaw counties); Minnesota (Anoka, Carver, Dakota, Hennepin, Ramsey, Scott, and Washington counties); New Mexico (Bernalillo, Chaves, Dona Ana, Grant, Luna, San Juan, and Santa Fe counties); New York (Albany, Columbia, Genesee, Greene, Livingston, Monroe, Montgomery, Ontario, Orleans, Rensselaer, Saratoga, Schenectady, Schoharie, Wayne, and Yates counties); Ohio (Delaware, Fairfield, Franklin, Hocking, Licking, Madison, Morrow, Perry, Pickaway and Union counties); Oregon (Clackamas, Multnomah, and Washington counties); Tennessee (Cheatham, Davidson, Dickson, Robertson, Rutherford, Sumner, Williamson, and Wilson counties); and Utah (Salt Lake County).

References

    1. Guan WJ, Ni ZY, Hu Y, et al.; China Medical Treatment Expert Group for Covid-19. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;NEJMoa2002032. 10.1056/NEJMoa2002032
    1. Johns Hopkins University & Medicine. COVID-19 map. Baltimore, MD: Johns Hopkins University; 2020.
    1. CDC. Coronavirus disease 2019 (COVID-19): cases in U.S. Atlanta, GA: US Department of Health and Human Services, CDC; 2020.
    1. Chaves SS, Lynfield R, Lindegren ML, Bresee J, Finelli L. The US Influenza Hospitalization Surveillance Network. Emerg Infect Dis 2015;21:1543–50. 10.3201/eid2109.141912
    1. CDC. Coronavirus disease 2019 (COVID-19): people who need to take extra precautions. Atlanta, GA: US Department of Health and Human Services, CDC; 2020.
    1. CDC. FluView interactive: laboratory-confirmed influenza hospitalizations. Atlanta, GA: US Department of Health and Human Services, CDC; 2020.
    1. National Center for Health Statistics. Hypertension prevalence and control among adults: United States, 2015–2016. NCHS data brief, no. 289. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2017.
    1. National Center for Health Statistics. Prevalence of obesity and severe obesity among adults: United States, 2017–2018. NCHS data brief, no. 360. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2020.
    1. Chow N, Fleming-Dutra K, Gierke R, et al.; CDC COVID-19 Response Team. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 — United States, February 12–March 28, 2020. MMWR Morb Mortal Wkly Rep 2020;69:382–6. 10.15585/mmwr.mm6913e2
    1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One 2015;10:e0118369. 10.1371/journal.pone.0118369

Source: PubMed

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