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Implementation of a pooled surveillance testing program for asymptomatic SARS-CoV-2 infections in K-12 schools and universities
research-article
Author(s):
Rachelle P. Mendoza
a
,
b
,
1 ,
Chongfeng Bi
a
,
1 ,
Hui-Ting Cheng
a ,
Elmer Gabutan
a ,
Guillerre Jan Pagaspas
a ,
Nadia Khan
a ,
Helen Hoxie
a ,
Stephen Hanna
a ,
Kelly Holmes
a ,
Nicholas Gao
a ,
Raychel Lewis
a ,
Huaien Wang
a ,
Daniel Neumann
a ,
Angela Chan
a ,
Meril Takizawa
a ,
James Lowe
a ,
Xiao Chen
a ,
Brianna Kelly
a ,
Aneeza Asif
a ,
Keena Barnes
a ,
Nusrat Khan
a ,
Brandon May
a ,
Tasnim Chowdhury
a ,
Gabriella Pollonini
a ,
Nourelhoda Gouda
a ,
Chante Guy
a ,
Candice Gordon
a ,
Nana Ayoluwa
a ,
Elvin Colon
a ,
Noah Miller-Medzon
a ,
Shanique Jones
a ,
Rauful Hossain
a ,
Arabia Dodson
a ,
Meimei Weng
a ,
Miranda McGaskey
a ,
Ana Vasileva
a
,
c ,
Andrew E. Lincoln
d
,
e ,
Robby Sikka
f ,
Anne L. Wyllie
g ,
Ethan M. Berke
h ,
Jenny Libien
b ,
Matthew Pincus
a
,
b ,
Prem K. Premsrirut
a
,
c
,
*
Publication date (Electronic):
17 July 2021
Journal:
EClinicalMedicine
Publisher:
The Author(s). Published by Elsevier Ltd.
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Background
The negative impact of continued school closures during the height of the COVID-19 pandemic warrants the establishment of cost-effective strategies for surveillance and screening to safely reopen and monitor for potential in-school transmission. Here, we present a novel approach to increase the availability of repetitive and routine COVID-19 testing that may ultimately reduce the overall viral burden in the community.
Methods
We implemented a testing program using the SalivaClear࣪ pooled surveillance method that included students, faculty and staff from K-12 schools (student age range 5–18 years) and universities (student age range >18 years) across the country (Mirimus Clinical Labs, Brooklyn, NY). The data analysis was performed using descriptive statistics, kappa agreement, and outlier detection analysis.
Findings
From August 27, 2020 until January 13, 2021, 253,406 saliva specimens were self-collected from students, faculty and staff from 93 K-12 schools and 18 universities. Pool sizes of up to 24 samples were tested over a 20-week period. Pooled testing did not significantly alter the sensitivity of the molecular assay in terms of both qualitative (100% detection rate on both pooled and individual samples) and quantitative (comparable cycle threshold (Ct) values between pooled and individual samples) measures. The detection of SARS-CoV-2 in saliva was comparable to the nasopharyngeal swab. Pooling samples substantially reduced the costs associated with PCR testing and allowed schools to rapidly assess transmission and adjust prevention protocols as necessary. In one instance, in-school transmission of the virus was determined within the main office and led to review and revision of heating, ventilating and air-conditioning systems.
Interpretation
By establishing low-cost, weekly testing of students and faculty, pooled saliva analysis for the presence of SARS-CoV-2 enabled schools to determine whether transmission had occurred, make data-driven decisions, and adjust safety protocols. We provide strong evidence that pooled testing may be a fundamental component to the reopening of schools by minimizing the risk of in-school transmission among students and faculty.
Related collections
Most cited references42
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Virological assessment of hospitalized patients with COVID-2019
Roman Wölfel, Victor M Corman, Wolfgang Guggemos … (2020)
Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in late 20191,2. Initial outbreaks in China involved 13.8% of cases with severe courses, and 6.1% of cases with critical courses3. This severe presentation may result from the virus using a virus receptor that is expressed predominantly in the lung2,4; the same receptor tropism is thought to have determined the pathogenicity-but also aided in the control-of severe acute respiratory syndrome (SARS) in 20035. However, there are reports of cases of COVID-19 in which the patient shows mild upper respiratory tract symptoms, which suggests the potential for pre- or oligosymptomatic transmission6-8. There is an urgent need for information on virus replication, immunity and infectivity in specific sites of the body. Here we report a detailed virological analysis of nine cases of COVID-19 that provides proof of active virus replication in tissues of the upper respiratory tract. Pharyngeal virus shedding was very high during the first week of symptoms, with a peak at 7.11 × 108 RNA copies per throat swab on day 4. Infectious virus was readily isolated from samples derived from the throat or lung, but not from stool samples-in spite of high concentrations of virus RNA. Blood and urine samples never yielded virus. Active replication in the throat was confirmed by the presence of viral replicative RNA intermediates in the throat samples. We consistently detected sequence-distinct virus populations in throat and lung samples from one patient, proving independent replication. The shedding of viral RNA from sputum outlasted the end of symptoms. Seroconversion occurred after 7 days in 50% of patients (and by day 14 in all patients), but was not followed by a rapid decline in viral load. COVID-19 can present as a mild illness of the upper respiratory tract. The confirmation of active virus replication in the upper respiratory tract has implications for the containment of COVID-19.
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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 … (2020)
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. COVID-NET conducts population-based surveillance for laboratory-confirmed COVID-19–associated hospitalizations among persons of all ages in 99 counties in 14 states (California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan, Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah), distributed across all 10 U.S Department of Health and Human Services regions. § The catchment area represents approximately 10% of the U.S. population. Patients must be residents of a designated COVID-NET catchment area and hospitalized within 14 days of a positive SARS-CoV-2 test to meet the surveillance case definition. Testing is requested at the discretion of treating health care providers. Laboratory-confirmed SARS-CoV-2 is defined as a positive result by any test that has received Emergency Use Authorization for SARS-CoV-2 testing. ¶ COVID-NET surveillance officers in each state identify cases through active review of notifiable disease and laboratory databases and hospital admission and infection control practitioner logs. Weekly age-stratified hospitalization rates are estimated using the number of catchment area residents hospitalized with laboratory-confirmed COVID-19 as the numerator and National Center for Health Statistics vintage 2018 bridged-race postcensal population estimates for the denominator.** As of April 3, 2020, COVID-NET hospitalization rates are being published each week at https://gis.cdc.gov/grasp/covidnet/COVID19_3.html. For each case, trained surveillance officers conduct medical chart abstractions using a standard case report form to collect data on patient characteristics, underlying medical conditions, clinical course, and outcomes. Chart reviews are finalized once patients have a discharge disposition. COVID-NET surveillance was initiated on March 23, 2020, with retrospective case identification of patients admitted during March 1–22, 2020, and prospective case identification during March 23–30, 2020. Clinical data on underlying conditions and symptoms at admission are presented through March 30; hospitalization rates are updated weekly and, therefore, are presented through March 28 (epidemiologic week 13). The COVID-19–associated hospitalization rate among patients identified through COVID-NET for the 4-week period ending March 28, 2020, was 4.6 per 100,000 population (Figure 1). Hospitalization rates increased with age, with a rate of 0.3 in persons aged 0–4 years, 0.1 in those aged 5–17 years, 2.5 in those aged 18–49 years, 7.4 in those aged 50–64 years, and 13.8 in those aged ≥65 years. Rates were highest among persons aged ≥65 years, ranging from 12.2 in those aged 65–74 years to 17.2 in those aged ≥85 years. More than half (805; 54.4%) of hospitalizations occurred among men; COVID-19-associated hospitalization rates were higher among males than among females (5.1 versus 4.1 per 100,000 population). Among the 1,482 laboratory-confirmed COVID-19–associated hospitalizations reported through COVID-NET, six (0.4%) each were patients aged 0–4 years and 5–17 years, 366 (24.7%) were aged 18–49 years, 461 (31.1%) were aged 50–64 years, and 643 (43.4%) were aged ≥65 years. Among patients with race/ethnicity data (580), 261 (45.0%) were non-Hispanic white (white), 192 (33.1%) were non-Hispanic black (black), 47 (8.1%) were Hispanic, 32 (5.5%) were Asian, two (0.3%) were American Indian/Alaskan Native, and 46 (7.9%) were of other or unknown race. Rates varied widely by COVID-NET surveillance site (Figure 2). 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). The figure is a bar chart showing laboratory-confirmed COVID-19–associated hospitalization rates, by age group, in 14 states during March 1–28, 2020 according to the Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. 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). The figure is a bar chart showing laboratory-confirmed COVID-19–associated hospitalization rates, by surveillance site, in 14 states during March 1–28, 2020 according to the Coronavirus Disease 2019–Associated Hospitalization Surveillance Network. During March 1–30, underlying medical conditions and symptoms at admission were reported through COVID-NET for approximately 180 (12.1%) hospitalized adults (Table); 89.3% had one or more underlying conditions. The most commonly reported were hypertension (49.7%), obesity (48.3%), chronic lung disease (34.6%), diabetes mellitus (28.3%), and cardiovascular disease (27.8%). Among patients aged 18–49 years, obesity was the most prevalent underlying condition, followed by chronic lung disease (primarily asthma) and diabetes mellitus. Among patients aged 50–64 years, obesity was most prevalent, followed by hypertension and diabetes mellitus; and among those aged ≥65 years, hypertension was most prevalent, followed by cardiovascular disease and diabetes mellitus. Among 33 females aged 15–49 years hospitalized with COVID-19, three (9.1%) were pregnant. Among 167 patients with available data, the median interval from symptom onset to admission was 7 days (interquartile range [IQR] = 3–9 days). The most common signs and symptoms at admission included cough (86.1%), fever or chills (85.0%), and shortness of breath (80.0%). Gastrointestinal symptoms were also common; 26.7% had diarrhea, and 24.4% had nausea or vomiting. TABLE Underlying conditions and symptoms among adults aged ≥18 years with coronavirus disease 2019 (COVID-19)–associated hospitalizations — COVID-NET, 14 states,* March 1–30, 2020† Underlying condition Age group (yrs), no./total no. (%) Overall 18–49 50–64 ≥65 years Any underlying condition 159/178 (89.3) 41/48 (85.4) 51/59 (86.4) 67/71 (94.4) Hypertension 79/159 (49.7) 7/40 (17.5) 27/57 (47.4) 45/62 (72.6) Obesity§ 73/151 (48.3) 23/39 (59.0) 25/51 (49.0) 25/61 (41.0) Chronic metabolic disease¶ 60/166 (36.1) 10/46 (21.7) 21/56 (37.5) 29/64 (45.3) Diabetes mellitus 47/166 (28.3) 9/46 (19.6) 18/56 (32.1) 20/64 (31.3) Chronic lung disease 55/159 (34.6) 16/44 (36.4) 15/53 (28.3) 24/62 (38.7) Asthma 27/159 (17.0) 12/44 (27.3) 7/53 (13.2) 8/62 (12.9) Chronic obstructive pulmonary disease 17/159 (10.7) 0/44 (0.0) 3/53 (5.7) 14/62 (22.6) Cardiovascular disease** 45/162 (27.8) 2/43 (4.7) 11/56 (19.6) 32/63 (50.8) Coronary artery disease 23/162 (14.2) 0/43 (0.0) 7/56 (12.5) 16/63 (25.4) Congestive heart failure 11/162 (6.8) 2/43 (4.7) 3/56 (5.4) 6/63 (9.5) Neurologic disease 22/157 (14.0) 4/42 (9.5) 4/55 (7.3) 14/60 (23.3) Renal disease 20/153 (13.1) 3/41 (7.3) 2/53 (3.8) 15/59 (25.4) Immunosuppressive condition 15/156 (9.6) 5/43 (11.6) 4/54 (7.4) 6/59 (10.2) Gastrointestinal/Liver disease 10/152 (6.6) 4/42 (9.5) 0/54 (0.0) 6/56 (10.7) Blood disorder 9/156 (5.8) 1/43 (2.3) 1/55 (1.8) 7/58 (12.1) Rheumatologic/Autoimmune disease 3/154 (1.9) 1/42 (2.4) 0/54 (0.0) 2/58 (3.4) Pregnancy†† 3/33 (9.1) 3/33 (9.1) N/A N/A Symptom §§ Cough 155/180 (86.1) 43/47 (91.5) 54/60 (90.0) 58/73 (79.5) Fever/Chills 153/180 (85.0) 38/47 (80.9) 53/60 (88.3) 62/73 (84.9) Shortness of breath 144/180 (80.0) 40/47 (85.1) 50/60 (83.3) 54/73 (74.0) Myalgia 62/180 (34.4) 20/47 (42.6) 23/60 (38.3) 19/73 (26.0) Diarrhea 48/180 (26.7) 10/47 (21.3) 17/60 (28.3) 21/73 (28.8) Nausea/Vomiting 44/180 (24.4) 12/47 (25.5) 17/60 (28.3) 15/73 (20.5) Sore throat 32/180 (17.8) 8/47 (17.0) 13/60 (21.7) 11/73 (15.1) Headache 29/180 (16.1) 10/47 (21.3) 12/60 (20.0) 7/73 (9.6) Nasal congestion/Rhinorrhea 29/180 (16.1) 8/47 (17.0) 13/60 (21.7) 8/73 (11.0) Chest pain 27/180 (15.0) 9/47 (19.1) 13/60 (21.7) 5/73 (6.8) Abdominal pain 15/180 (8.3) 6/47 (12.8) 6/60 (10.0) 3/73 (4.1) Wheezing 12/180 (6.7) 3/47 (6.4) 2/60 (3.3) 7/73 (9.6) Altered mental status/Confusion 11/180 (6.1) 3/47 (6.4) 2/60 (3.3) 6/73 (8.2) Abbreviations: COVID-NET = Coronavirus Disease 2019–Associated Hospitalization Surveillance Network; N/A = not applicable. * 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). † COVID-NET included data for one child aged 5–17 years with underlying medical conditions and symptoms at admission; data for this child are not included in this table. This child was reported to have chronic lung disease (asthma). Symptoms included fever, cough, gastrointestinal symptoms, shortness of breath, chest pain, and a sore throat on admission. § Obesity is defined as calculated body mass index (BMI) ≥30 kg/m2, and if BMI is missing, by International Classification of Diseases discharge diagnosis codes. Among 73 patients with obesity, 51 (69.9%) had obesity defined as BMI 30–<40 kg/m2, and 22 (30.1%) had severe obesity defined as BMI ≥40 kg/m2. ¶ Among the 60 patients with chronic metabolic disease, 45 had diabetes mellitus only, 13 had thyroid dysfunction only, and two had diabetes mellitus and thyroid dysfunction. ** Cardiovascular disease excludes hypertension. †† Restricted to women aged 15–49 years. §§ Symptoms were collected through review of admission history and physical exam notes in the medical record and might be determined by subjective or objective findings. In addition to the symptoms in the table, the following less commonly reported symptoms were also noted for adults with information on symptoms (180): hemoptysis/bloody sputum (2.2%), rash (1.1%), conjunctivitis (0.6%), and seizure (0.6%). Discussion During March 1–28, 2020, the overall laboratory-confirmed COVID-19–associated hospitalization rate was 4.6 per 100,000 population; rates increased with age, with the highest rates among adults aged ≥65 years. Approximately 90% of hospitalized patients identified through COVID-NET had one or more underlying conditions, the most common being obesity, hypertension, chronic lung disease, diabetes mellitus, and cardiovascular disease. Using the existing infrastructure of two respiratory virus surveillance platforms, COVID-NET was implemented to produce robust, weekly, age-stratified hospitalization rates using standardized data collection methods. These data are being used, along with data from other surveillance platforms (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview.html), to monitor COVID-19 disease activity and severity in the United States. During the first month of surveillance, COVID-NET hospitalization rates ranged from 0.1 per 100,000 population in persons aged 5–17 years to 17.2 per 100,000 population in adults aged ≥85 years, whereas cumulative influenza hospitalization rates during the first 4 weeks of each influenza season (epidemiologic weeks 40–43) over the past 5 seasons have ranged from 0.1 in persons aged 5–17 years to 2.2–5.4 in adults aged ≥85 years ( 6 ). COVID-NET rates during this first 4-week period of surveillance are preliminary and should be interpreted with caution; given the rapidly evolving nature of the COVID-19 pandemic, rates are expected to increase as additional cases are identified and as SARS-CoV-2 testing capacity in the United States increases. In the COVID-NET catchment population, approximately 49% of residents are male and 51% of residents are female, whereas 54% of COVID-19-associated hospitalizations occurred in males and 46% occurred in females. These data suggest that males may be disproportionately affected by COVID-19 compared with females. Similarly, in the COVID-NET catchment population, approximately 59% of residents are white, 18% are black, and 14% are Hispanic; however, among 580 hospitalized COVID-19 patients with race/ethnicity data, approximately 45% were white, 33% were black, and 8% were Hispanic, suggesting that black populations might be disproportionately affected by COVID-19. These findings, including the potential impact of both sex and race on COVID-19-associated hospitalization rates, need to be confirmed with additional data. Most of the hospitalized patients had underlying conditions, some of which are recognized to be associated with severe COVID-19 disease, including chronic lung disease, cardiovascular disease, diabetes mellitus ( 5 ). COVID-NET does not collect data on nonhospitalized patients; thus, it was not possible to compare the prevalence of underlying conditions in hospitalized versus nonhospitalized patients. Many of the documented underlying conditions among hospitalized COVID-19 patients are highly prevalent in the United States. According to data from the National Health and Nutrition Examination Survey, hypertension prevalence among U.S. adults is 29% overall, ranging from 7.5%–63% across age groups ( 7 ), and age-adjusted obesity prevalence is 42% (range across age groups = 40%–43%) ( 8 ). Among hospitalized COVID-19 patients, hypertension prevalence was 50% (range across age groups = 18%–73%), and obesity prevalence was 48% (range across age groups = 41%–59%). In addition, the prevalences of several underlying conditions identified through COVID-NET were similar to those for hospitalized influenza patients identified through FluSurv-NET during influenza seasons 2014–15 through 2018–19: 41%–51% of patients had cardiovascular disease (excluding hypertension), 39%–45% had chronic metabolic disease, 33%–40% had obesity, and 29%–31% had chronic lung disease ( 6 ). Data on hypertension are not collected by FluSurv-NET. Among women aged 15–49 years hospitalized with COVID-19 and identified through COVID-NET, 9% were pregnant, which is similar to an estimated 9.9% of the general population of women aged 15–44 years who are pregnant at any given time based on 2010 data. †† Similar to other reports from the United States ( 9 ) and China ( 1 ), these findings indicate that a high proportion of U.S. patients hospitalized with COVID-19 are older and have underlying medical conditions. The findings in this report are subject to at least three limitations. First, hospitalization rates by age and COVID-NET site are preliminary and might change as additional cases are identified from this surveillance period. Second, whereas minimum case data to produce weekly age-stratified hospitalization rates are usually available within 7 days of case identification, availability of detailed clinical data are delayed because of the need for medical chart abstractions. As of March 30, chart abstractions had been conducted for approximately 200 COVID-19 patients; the frequency and distribution of underlying conditions during this time might change as additional data become available. Clinical course and outcomes will be presented once the number of cases with complete medical chart abstractions are sufficient; many patients are still hospitalized at the time of this report. Finally, testing for SARS-CoV-2 among patients identified through COVID-NET is performed at the discretion of treating health care providers, and testing practices and capabilities might vary widely across providers and facilities. As a result, underascertainment of cases in COVID-NET is likely. Additional data on testing practices related to SARS-CoV-2 will be collected in the future to account for underascertainment using described methods ( 10 ). Early data from COVID-NET suggest that COVID-19–associated hospitalizations in the United States are highest among older adults, and nearly 90% of persons hospitalized have one or more 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. 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. Summary What is already known about this topic? Population-based rates of laboratory-confirmed coronavirus disease 2019 (COVID-19)–associated hospitalizations are lacking in the United States. What is added by this report? COVID-NET was implemented to produce robust, weekly, age-stratified COVID-19–associated hospitalization rates. Hospitalization rates increase with age and are highest among older adults; the majority of hospitalized patients have underlying conditions. What are the implications for public health practice? Strategies to prevent COVID-19, including social distancing, respiratory hygiene, and face coverings in public settings where social distancing measures are difficult to maintain, are particularly important to protect older adults and those with underlying conditions. Ongoing monitoring of hospitalization rates is critical to understanding the evolving epidemiology of COVID-19 in the United States and to guide planning and prioritization of health care resources.
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Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020
Erin K. Stokes, Laura Zambrano, Kayla N. Anderson … (2020)
The coronavirus disease 2019 (COVID-19) pandemic resulted in 5,817,385 reported cases and 362,705 deaths worldwide through May, 30, 2020, † including 1,761,503 aggregated reported cases and 103,700 deaths in the United States. § Previous analyses during February–early April 2020 indicated that age ≥65 years and underlying health conditions were associated with a higher risk for severe outcomes, which were less common among children aged 10% of persons in this age group. TABLE 2 Reported underlying health conditions* and symptoms † among persons with laboratory-confirmed COVID-19, by sex and age group — United States, January 22–May 30, 2020 Characteristic No. (%) Total Sex Age group (yrs) Male Female ≤9 10–19 20–29 30–39 40–49 50–59 60–69 70–79 ≥80 Total population 1,320,488 646,358 674,130 20,458 49,245 182,469 214,849 219,139 235,774 179,007 105,252 114,295 Underlying health condition§ Known underlying medical condition status* 287,320 (21.8) 138,887 (21.5) 148,433 (22.0) 2,896 (14.2) 7,123 (14.5) 27,436 (15.0) 33,483 (15.6) 40,572 (18.5) 54,717 (23.2) 50,125 (28.0) 34,400 (32.7) 36,568 (32.0) Any cardiovascular disease¶ 92,546 (32.2) 47,567 (34.2) 44,979 (30.3) 78 (2.7) 164 (2.3) 1,177 (4.3) 3,588 (10.7) 8,198 (20.2) 16,954 (31.0) 21,466 (42.8) 18,763 (54.5) 22,158 (60.6) Any chronic lung disease 50,148 (17.5) 20,930 (15.1) 29,218 (19.7) 363 (12.5) 1,285 (18) 4,537 (16.5) 5,110 (15.3) 6,127 (15.1) 8,722 (15.9) 9,200 (18.4) 7,436 (21.6) 7,368 (20.1) Renal disease 21,908 (7.6) 12,144 (8.7) 9,764 (6.6) 21 (0.7) 34 (0.5) 204 (0.7) 587 (1.8) 1,273 (3.1) 2,789 (5.1) 4,764 (9.5) 5,401 (15.7) 6,835 (18.7) Diabetes 86,737 (30.2) 45,089 (32.5) 41,648 (28.1) 12 (0.4) 225 (3.2) 1,409 (5.1) 4,106 (12.3) 9,636 (23.8) 19,589 (35.8) 22,314 (44.5) 16,594 (48.2) 12,852 (35.1) Liver disease 3,953 (1.4) 2,439 (1.8) 1,514 (1.0) 5 (0.2) 19 (0.3) 132 (0.5) 390 (1.2) 573 (1.4) 878 (1.6) 1,074 (2.1) 583 (1.7) 299 (0.8) Immunocompromised 15,265 (5.3) 7,345 (5.3) 7,920 (5.3) 61 (2.1) 146 (2.0) 646 (2.4) 1,253 (3.7) 2,005 (4.9) 3,190 (5.8) 3,421 (6.8) 2,486 (7.2) 2,057 (5.6) Neurologic/Neurodevelopmental disability 13,665 (4.8) 6,193 (4.5) 7,472 (5.0) 41 (1.4) 113 (1.6) 395 (1.4) 533 (1.6) 734 (1.8) 1,338 (2.4) 2,006 (4.0) 2,759 (8.0) 5,746 (15.7) Symptom§ Known symptom status† 373,883 (28.3) 178,223 (27.6) 195,660 (29.0) 5,188 (25.4) 12,689 (25.8) 51,464 (28.2) 59,951 (27.9) 62,643 (28.6) 70,040 (29.7) 52,178 (29.1) 28,583 (27.2) 31,147 (27.3) Fever, cough, or shortness of breath 260,706 (69.7) 125,768 (70.6) 134,938 (69.0) 3,278 (63.2) 7,584 (59.8) 35,072 (68.1) 42,016 (70.1) 45,361 (72.4) 51,283 (73.2) 37,701 (72.3) 19,583 (68.5) 18,828 (60.4) Fever †† 161,071 (43.1) 80,578 (45.2) 80,493 (41.1) 2,404 (46.3) 4,443 (35.0) 20,381 (39.6) 25,887 (43.2) 28,407 (45.3) 32,375 (46.2) 23,591 (45.2) 12,190 (42.6) 11,393 (36.6) Cough 187,953 (50.3) 89,178 (50.0) 98,775 (50.5) 1,912 (36.9) 5,257 (41.4) 26,284 (51.1) 31,313 (52.2) 34,031 (54.3) 38,305 (54.7) 27,150 (52.0) 12,837 (44.9) 10,864 (34.9) Shortness of breath 106,387 (28.5) 49,834 (28.0) 56,553 (28.9) 339 (6.5) 2,070 (16.3) 13,649 (26.5) 16,851 (28.1) 18,978 (30.3) 21,327 (30.4) 16,018 (30.7) 8,971 (31.4) 8,184 (26.3) Myalgia 135,026 (36.1) 61,922 (34.7) 73,104 (37.4) 537 (10.4) 3,737 (29.5) 21,153 (41.1) 26,464 (44.1) 28,064 (44.8) 28,594 (40.8) 17,360 (33.3) 6,015 (21.0) 3,102 (10.0) Runny nose 22,710 (6.1) 9,900 (5.6) 12,810 (6.5) 354 (6.8) 1,025 (8.1) 4,591 (8.9) 4,406 (7.3) 4,141 (6.6) 4,100 (5.9) 2,671 (5.1) 923 (3.2) 499 (1.6) Sore throat 74,840 (20.0) 31,244 (17.5) 43,596 (22.3) 664 (12.8) 3,628 (28.6) 14,493 (28.2) 14,855 (24.8) 14,490 (23.1) 13,930 (19.9) 8,192 (15.7) 2,867 (10.0) 1,721 (5.5) Headache 128,560 (34.4) 54,721 (30.7) 73,839 (37.7) 785 (15.1) 5,315 (41.9) 23,723 (46.1) 26,142 (43.6) 26,245 (41.9) 26,057 (37.2) 14,735 (28.2) 4,163 (14.6) 1,395 (4.5) Nausea/Vomiting 42,813 (11.5) 16,549 (9.3) 26,264 (13.4) 506 (9.8) 1,314 (10.4) 6,648 (12.9) 7,661 (12.8) 8,091 (12.9) 8,737 (12.5) 5,953 (11.4) 2,380 (8.3) 1,523 (4.9) Abdominal pain 28,443 (7.6) 11,553 (6.5) 16,890 (8.6) 349 (6.7) 978 (7.7) 4,211 (8.2) 5,150 (8.6) 5,531 (8.8) 6,134 (8.8) 3,809 (7.3) 1,449 (5.1) 832 (2.7) Diarrhea 72,039 (19.3) 32,093 (18.0) 39,946 (20.4) 704 (13.6) 1,712 (13.5) 9,867 (19.2) 12,769 (21.3) 13,958 (22.3) 15,536 (22.2) 10,349 (19.8) 4,402 (15.4) 2,742 (8.8) Loss of smell or taste 31,191 (8.3) 12,717 (7.1) 18,474 (9.4) 67 (1.3) 1,257 (9.9) 6,828 (13.3) 6,907 (11.5) 6,361 (10.2) 5,828 (8.3) 2,930 (5.6) 775 (2.7) 238 (0.8) Abbreviation: COVID-19 = coronavirus disease 2019. * Status of underlying health conditions known for 287,320 persons. Status was classified as “known” if any of the following conditions were reported as present or absent: diabetes mellitus, cardiovascular disease (including hypertension), severe obesity (body mass index ≥40 kg/m2), chronic renal disease, chronic liver disease, chronic lung disease, immunocompromising condition, autoimmune condition, neurologic condition (including neurodevelopmental, intellectual, physical, visual, or hearing impairment), psychologic/psychiatric condition, and other underlying medical condition not otherwise specified. † Symptom status was known for 373,883 persons. Status was classified as “known” if any of the following symptoms were reported as present or absent: fever (measured >100.4°F [38°C] or subjective), cough, shortness of breath, wheezing, difficulty breathing, chills, rigors, myalgia, rhinorrhea, sore throat, chest pain, nausea or vomiting, abdominal pain, headache, fatigue, diarrhea (≥3 loose stools in a 24-hour period), or other symptom not otherwise specified on the form. § Responses include data from standardized fields supplemented with data from free-text fields. Information for persons with loss of smell or taste was exclusively extracted from a free-text field; therefore, persons exhibiting this symptom were likely underreported. ¶ Includes persons with reported hypertension. ** Includes all persons with at least one of these symptoms reported. †† Persons were considered to have a fever if information on either measured or subjective fever variables if “yes” was reported for either variable. Among 287,320 (22%) cases with data on individual underlying health conditions, those most frequently reported were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%) (Table 2); the reported proportions were similar among males and females. The frequency of conditions reported varied by age group: cardiovascular disease was uncommon among those aged ≤39 years but was reported in approximately half of the cases among persons aged ≥70 years. Among 63,896 females aged 15–44 years with known pregnancy status, 6,708 (11%) were reported to be pregnant. Among the 1,320,488 cases, outcomes for hospitalization, ICU admission, and death were available for 46%, 14%, and 36%, respectively. Overall, 184,673 (14%) patients were hospitalized, including 29,837 (2%) admitted to the ICU; 71,116 (5%) patients died (Table 3). Severe outcomes were more commonly reported for patients with reported underlying conditions. Hospitalizations were six times higher among patients with a reported underlying condition than those without reported underlying conditions (45.4% versus 7.6%). Deaths were 12 times higher among patients with reported underlying conditions compared with those without reported underlying conditions (19.5% versus 1.6%). The percentages of males who were hospitalized (16%), admitted to the ICU (3%), and who died (6%) were higher than were those for females (12%, 2%, and 5%, respectively). The percentage of ICU admissions was highest among persons with reported underlying conditions aged 60–69 years (11%) and 70–79 years (12%). Death was most commonly reported among persons aged ≥80 years regardless of the presence of underlying conditions (with underlying conditions 50%; without 30%). TABLE 3 Reported hospitalizations,* , † intensive care unit (ICU) admissions, § and deaths ¶ among laboratory-confirmed COVID-19 patients with and without reported underlying health conditions, ** by sex and age — United States, January 22–May 30, 2020 Characteristic (no.) Outcome, no./total no. (%)†† Reported hospitalizations*,† (including ICU) Reported ICU admission§ Reported deaths¶ Among all patients Among patients with reported underlying health conditions Among patients with no reported underlying health conditions Among all patients Among patients with reported underlying health conditions Among patients with no reported underlying health conditions Among all patients Among patients with reported underlying health conditions Among patients with no reported underlying health conditions Sex Male (646,358) 101,133/646,358 (15.6) 49,503/96,839 (51.1) 3,596/42,048 (8.6) 18,394/646,358 (2.8) 10,302/96,839 (10.6) 864/42,048 (2.1) 38,773/646,358 (6.0) 21,667/96,839 (22.4) 724/42,048 (1.7) Female (674,130) 83,540/674,130 (12.4) 40,698/102,040 (39.9) 3,087/46,393 (6.7) 11,443/674,130 (1.7) 6,672/102,040 (6.5) 479/46,393 (1.0) 32,343/674,130 (4.8) 17,145/102,040 (16.8) 707/46,393 (1.5) Age group (yrs) ≤9 (20,458) 848/20,458 (4.1) 138/619 (22.3) 84/2,277 (3.7) 141/20,458 (0.7) 31/619 (5.0) 16/2,277 (0.7) 13/20,458 (0.1) 4/619 (0.6) 2/2,277 (0.1) 10–19 (49,245) 1,234/49,245 (2.5) 309/2,076 (14.9) 115/5,047 (2.3) 216/49,245 (0.4) 72/2,076 (3.5) 17/5,047 (0.3) 33/49,245 (0.1) 16/2,076 (0.8) 4/5,047 (0.1) 20–29 (182,469) 6,704/182,469 (3.7) 1,559/8,906 (17.5) 498/18,530 (2.7) 864/182,469 (0.5) 300/8,906 (3.4) 56/18,530 (0.3) 273/182,469 (0.1) 122/8,906 (1.4) 24/18,530 (0.1) 30–39 (214,849) 12,570/214,849 (5.9) 3,596/14,854 (24.2) 828/18,629 (4.4) 1,879/214,849 (0.9) 787/14,854 (5.3) 135/18,629 (0.7) 852/214,849 (0.4) 411/14,854 (2.8) 21/18,629 (0.1) 40–49 (219,139) 19,318/219,139 (8.8) 7,151/24,161 (29.6) 1,057/16,411 (6.4) 3,316/219,139 (1.5) 1,540/24,161 (6.4) 208/16,411 (1.3) 2,083/219,139 (1.0) 1,077/24,161 (4.5) 58/16,411 (0.4) 50–59 (235,774) 31,588/235,774 (13.4) 14,639/40,297 (36.3) 1,380/14,420 (9.6) 5,986/235,774 (2.5) 3,335/40,297 (8.3) 296/14,420 (2.1) 5,639/235,774 (2.4) 3,158/40,297 (7.8) 131/14,420 (0.9) 60–69 (179,007) 39,422/179,007 (22.0) 21,064/42,206 (49.9) 1,216/7,919 (15.4) 7,403/179,007 (4.1) 4,588/42,206 (10.9) 291/7,919 (3.7) 11,947/179,007 (6.7) 7,050/42,206 (16.7) 187/7,919 (2.4) 70–79 (105,252) 35,844/105,252 (34.1) 20,451/31,601 (64.7) 780/2,799 (27.9) 5,939/105,252 (5.6) 3,771/31,601 (11.9) 199/2,799 (7.1) 17,510/105,252 (16.6) 10,008/31,601 (31.7) 286/2,799 (10.2) ≥80 (114,295) 37,145/114,295 (32.5) 21,294/34,159 (62.3) 725/2,409 (30.1) 4,093/114,295 (3.6) 2,550/34,159 (7.5) 125/2,409 (5.2) 32,766/114,295 (28.7) 16,966/34,159 (49.7) 718/2,409 (29.8) Total (1,320,488) 184,673/1,320,488 (14.0) 90,201/198,879 (45.4) 6,683/88,441 (7.6) 29,837/1,320,488 (2.3) 16,974/198,879 (8.5) 1,343/88,441 (1.5) 71,116/1,320,488 (5.4) 38,812/198,879 (19.5) 1,431/88,441 (1.6) Abbreviation: COVID-19 = coronavirus disease 2019. * Hospitalization status was known for 600,860 (46%). Among 184,673 hospitalized patients, the presence of underlying health conditions was known for 96,884 (53%). † Includes reported ICU admissions. § ICU admission status was known for 186,563 (14%) patients among the total case population, representing 34% of hospitalized patients. Among 29,837 patients admitted to the ICU, the status of underlying health conditions was known for 18,317 (61%). ¶ Death outcomes were known for 480,565 (36%) patients. Among 71,116 reported deaths through case surveillance, the status of underlying health conditions was known for 40,243 (57%) patients. ** Status of underlying health conditions was known for 287,320 (22%) patients. Status was classified as “known” if any of the following conditions were noted as present or absent: diabetes mellitus, cardiovascular disease including hypertension, severe obesity body mass index ≥40 kg/m2, chronic renal disease, chronic liver disease, chronic lung disease, any immunocompromising condition, any autoimmune condition, any neurologic condition including neurodevelopmental, intellectual, physical, visual, or hearing impairment, any psychologic/psychiatric condition, and any other underlying medical condition not otherwise specified. †† Outcomes were calculated as the proportion of persons reported to be hospitalized, admitted to an ICU, or who died among total in the demographic group. Outcome underreporting could result from outcomes that occurred but were not reported through national case surveillance or through clinical progression to severe outcomes that occurred after time of report. Discussion As of May 30, a total of 1,761,503 aggregate U.S. cases of COVID-19 and 103,700 associated deaths were reported to CDC. Although average daily reported cases and deaths are declining, 7-day moving averages of daily incidence of COVID-19 cases indicate ongoing community transmission. ¶¶¶¶ The COVID-19 case data summarized here are essential statistics for the pandemic response and rely on information systems developed at the local, state, and federal level over decades for communicable disease surveillance that were rapidly adapted to meet an enormous, new public health threat. CDC aggregate counts are consistent with those presented through the Johns Hopkins University (JHU) Coronavirus Resource Center, which reported a cumulative total of 1,770,165 U.S. cases and 103,776 U.S. deaths on May 30, 2020.***** Differences in aggregate counts between CDC and JHU might be attributable to differences in reporting practices to CDC and jurisdictional websites accessed by JHU. Reported cumulative incidence in the case surveillance population among persons aged ≥20 years is notably higher than that among younger persons. The lower incidence in persons aged ≤19 years could be attributable to undiagnosed milder or asymptomatic illnesses among this age group that were not reported. Incidence in persons aged ≥80 years was nearly double that in persons aged 70–79 years. Among cases with known race and ethnicity, 33% of persons were Hispanic, 22% were black, and 1.3% were AI/AN. These findings suggest that persons in these groups, who account for 18%, 13%, and 0.7% of the U.S. population, respectively, are disproportionately affected by the COVID-19 pandemic. The proportion of missing race and ethnicity data limits the conclusions that can be drawn from descriptive analyses; however, these findings are consistent with an analysis of COVID-19–Associated Hospitalization Surveillance Network (COVID-NET) ††††† data that found higher proportions of black and Hispanic persons among hospitalized COVID-19 patients than were in the overall population ( 4 ). The completeness of race and ethnicity variables in case surveillance has increased from 20% to >40% from April 2 to June 2. Although reporting of race and ethnicity continues to improve, more complete data might be available in aggregate on jurisdictional websites or through sources like the COVID Tracking Project’s COVID Racial Data Tracker. §§§§§ The data in this report show that the prevalence of reported symptoms varied by age group but was similar among males and females. Fewer than 5% of persons were reported to be asymptomatic when symptom data were submitted. Persons without symptoms might be less likely to be tested for COVID-19 because initial guidance recommended testing of only symptomatic persons and was hospital-based. Guidance on testing has evolved throughout the response. ¶¶¶¶¶ Whereas incidence among males and females was similar overall, severe outcomes were more commonly reported among males. Prevalence of reported severe outcomes increased with age; the percentages of hospitalizations, ICU admissions, and deaths were highest among persons aged ≥70 years, regardless of underlying conditions, and lowest among those aged ≤19 years. Hospitalizations were six times higher and deaths 12 times higher among those with reported underlying conditions compared with those with none reported. These findings are consistent with previous reports that found that severe outcomes increased with age and underlying condition, and males were hospitalized at a higher rate than were females ( 2 , 4 , 5 ). The findings in this report are subject to at least three limitations. First, case surveillance data represent a subset of the total cases of COVID-19 in the United States; not every case in the community is captured through testing and information collected might be limited if persons are unavailable or unwilling to participate in case investigations or if medical records are unavailable for data extraction. Reported cumulative incidence, although comparable across age and sex groups within the case surveillance population, are underestimates of the U.S. cumulative incidence of COVID-19. Second, reported frequencies of individual symptoms and underlying health conditions presented from case surveillance likely underestimate the true prevalence because of missing data. Finally, asymptomatic cases are not captured well in case surveillance. Asymptomatic persons are unlikely to seek testing unless they are identified through active screening (e.g., contact tracing), and, because of limitations in testing capacity and in accordance with guidance, investigation of symptomatic persons is prioritized. Increased identification and reporting of asymptomatic cases could affect patterns described in this report. Similar to earlier reports on COVID-19 case surveillance, severe outcomes were more commonly reported among persons who were older and those with underlying health conditions ( 1 ). Findings in this report align with demographic and severe outcome trends identified through COVID-NET ( 4 ). Findings from case surveillance are evaluated along with enhanced surveillance data and serologic survey results to provide a comprehensive picture of COVID-19 trends, and differences in proportion of cases by racial and ethnic groups should continue to be examined in enhanced surveillance to better understand populations at highest risk. Since the U.S. COVID-19 response began in January, CDC has built on existing surveillance capacity to monitor the impact of illness nationally. Collection of detailed case data is a resource-intensive public health activity, regardless of disease incidence. The high incidence of COVID-19 has highlighted limitations of traditional public health case surveillance approaches to provide real-time intelligence and supports the need for continued innovation and modernization. Despite limitations, national case surveillance of COVID-19 serves a critical role in the U.S. COVID-19 response: these data demonstrate that the COVID-19 pandemic is an ongoing public health crisis in the United States that continues to affect all populations and result in severe outcomes including death. National case surveillance findings provide important information for targeted enhanced surveillance efforts and development of interventions critical to the U.S. COVID-19 response. Summary What is already known about this topic? Surveillance data reported to CDC through April 2020 indicated that COVID-19 leads to severe outcomes in older adults and those with underlying health conditions. What is added by this report? As of May 30, 2020, among COVID-19 cases, the most common underlying health conditions were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Hospitalizations were six times higher and deaths 12 times higher among those with reported underlying conditions compared with those with none reported. What are the implications for public health practice? Surveillance at all levels of government, and its continued modernization, is critical for monitoring COVID-19 trends and identifying groups at risk for infection and severe outcomes. These findings highlight the continued need for community mitigation strategies, especially for vulnerable populations, to slow COVID-19 transmission.
Author and article information
Journal
Journal ID (nlm-ta): EClinicalMedicine
Journal ID (iso-abbrev): EClinicalMedicine
Title:
EClinicalMedicine
Publisher:
The Author(s). Published by Elsevier Ltd.
ISSN
(Electronic):
2589-5370
Publication date PMC-release: 17
July
2021
Publication date
(Print):
August
2021
Publication date
(Electronic):
17
July
2021
Volume: 38
Page: 101028
Affiliations
[b
]Department of Pathology, SUNY Downstate Health Sciences University, 450 Clarkson Ave.,
Brooklyn, NY 11226, USA
[c
]Department of Cell Biology, SUNY Downstate Health Sciences University, 450 Clarkson
Ave., Brooklyn, NY 11226, USA
[d
]MedStar Sports Medicine Research Center, MedStar Health Research Institute, 2900 S
Hanover St., Baltimore, MD 21225, USA
[e
]Department of Rehabilitation Medicine, Georgetown University Medical Center, 3800
Reservoir Rd NW, Washington, DC 20007, USA
Author notes
Article
Publisher Item ID: S2589-5370(21)00308-4 Publisher ID: 101028DOI: 10.1016/j.eclinm.2021.101028
PMC ID: 8286123
PubMed ID: 34308321
SO-VID: 328f6236-de2a-4208-a06b-f30c2356a3a2
Copyright © © 2021 The Author(s)
License:
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
History
Date
received
: 22
April
2021
Date
revision received
: 21
June
2021
Date
accepted
: 29
June
2021
Categories
Subject:
Research Paper
Data availability: