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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

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      , MD 1 , * , , , MD 1 , * , , , MPH 1 , 2 , , MSPH 1 , , MPH 1 , 3 , , MPH 1 , 4 , , MSPH 1 , , MD 1 , , MPH 5 , , MPH 6 , , MPH 6 , , MPH 7 , , PhD 7 , , MD 8 , 9 , 10 , , DrPH 9 , 10 , 11 , , MSW 12 , , MPH 13 , , MS 13 , , MPH 14 , , MPH 14 , , MPH 15 , , MD 15 , , MD 16 , , PhD 16 , , MPH 17 , , MD 18 , , MPH 19 , , MD 20 , , MPH 20 , , MD 21 , , MD 21 , 22 , , MPH 22 , , MPH 1 , , MPH 1 , , MD 1 , , DVM 1 , , MD 1
      Morbidity and Mortality Weekly Report
      Centers for Disease Control and Prevention

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          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. 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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          Clinical Characteristics of Coronavirus Disease 2019 in China

          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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            Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019 — United States, February 12–March 28, 2020

            On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic ( 1 ). As of March 28, 2020, a total of 571,678 confirmed COVID-19 cases and 26,494 deaths have been reported worldwide ( 2 ). Reports from China and Italy suggest that risk factors for severe disease include older age and the presence of at least one of several underlying health conditions ( 3 , 4 ). U.S. older adults, including those aged ≥65 years and particularly those aged ≥85 years, also appear to be at higher risk for severe COVID-19–associated outcomes; however, data describing underlying health conditions among U.S. COVID-19 patients have not yet been reported ( 5 ). As of March 28, 2020, U.S. states and territories have reported 122,653 U.S. COVID-19 cases to CDC, including 7,162 (5.8%) for whom data on underlying health conditions and other known risk factors for severe outcomes from respiratory infections were reported. Among these 7,162 cases, 2,692 (37.6%) patients had one or more underlying health condition or risk factor, and 4,470 (62.4%) had none of these conditions reported. The percentage of COVID-19 patients with at least one underlying health condition or risk factor was higher among those requiring intensive care unit (ICU) admission (358 of 457, 78%) and those requiring hospitalization without ICU admission (732 of 1,037, 71%) than that among those who were not hospitalized (1,388 of 5,143, 27%). The most commonly reported conditions were diabetes mellitus, chronic lung disease, and cardiovascular disease. These preliminary findings suggest that in the United States, persons with underlying health conditions or other recognized risk factors for severe outcomes from respiratory infections appear to be at a higher risk for severe disease from COVID-19 than are persons without these conditions. Data from laboratory-confirmed COVID-19 cases reported to CDC from 50 states, four U.S. territories and affiliated islands, the District of Columbia, and New York City with February 12–March 28, 2020 onset dates were analyzed. Cases among persons repatriated to the United States from Wuhan, China, and the Diamond Princess cruise ship were excluded. For cases with missing onset dates, date of onset was estimated by subtracting 4 days (median interval from symptom onset to specimen collection date among cases with known dates in these data) from the earliest specimen collection. Public health departments reported cases to CDC using a standardized case report form that captures information (yes, no, or unknown) on the following conditions and potential risk factors: chronic lung disease (inclusive of asthma, chronic obstructive pulmonary disease [COPD], and emphysema); diabetes mellitus; cardiovascular disease; chronic renal disease; chronic liver disease; immunocompromised condition; neurologic disorder, neurodevelopmental, or intellectual disability; pregnancy; current smoking status; former smoking status; or other chronic disease ( 6 ). Data reported to CDC are preliminary and can be updated by health departments over time; critical data elements might be missing at the time of initial report; thus, this analysis is descriptive, and no statistical comparisons could be made. The percentages of patients of all ages with underlying health conditions who were not hospitalized, hospitalized without ICU admission, and hospitalized with ICU admission were calculated. Percentages of hospitalizations with and without ICU admission were estimated for persons aged ≥19 years with and without underlying health conditions. This part of the analysis was limited to persons aged ≥19 years because of the small sample size of cases in children with reported underlying health conditions (N = 32). To account for missing data among these preliminary reports, ranges were estimated with a lower bound including cases with both known and unknown status for hospitalization with and without ICU admission as the denominator and an upper bound using only cases with known outcome status as the denominator. Because of small sample size and missing data on underlying health conditions among COVID-19 patients who died, case-fatality rates for persons with and without underlying conditions were not estimated. As of March 28, 2020, a total of 122,653 laboratory-confirmed COVID-19 cases (Figure) and 2,112 deaths were reported to CDC. Case report forms were submitted to CDC for 74,439 (60.7%) cases. Data on presence or absence of underlying health conditions and other recognized risk factors for severe outcomes from respiratory infections (i.e., smoking and pregnancy) were available for 7,162 (5.8%) patients (Table 1). Approximately one third of these patients (2,692, 37.6%), had at least one underlying condition or risk factor. Diabetes mellitus (784, 10.9%), chronic lung disease (656, 9.2%), and cardiovascular disease (647, 9.0%) were the most frequently reported conditions among all cases. Among 457 ICU admissions and 1,037 non-ICU hospitalizations, 358 (78%) and 732 (71%), respectively occurred among persons with one or more reported underlying health condition. In contrast, 1,388 of 5,143 (27%) COVID-19 patients who were not hospitalized were reported to have at least one underlying health condition. FIGURE Daily number of reported COVID-19 cases* — United States, February 12–March 28, 2020† * Cases among persons repatriated to the United States from Wuhan, China, and the Diamond Princess cruise ship are excluded. † Cumulative number of COVID-19 cases reported daily by jurisdictions to CDC using aggregate case count was 122,653 through March 28, 2020. The figure is a histogram, an epidemiologic curve showing the number of COVID-19 cases, by date of report, in the United States during February 12–March 28, 2020. TABLE 1 Reported outcomes among COVID-19 patients of all ages, by hospitalization status, underlying health condition, and risk factor for severe outcome from respiratory infection — United States, February 12–March 28, 2020 Underlying health condition/Risk factor for severe outcomes from respiratory infection (no., % with condition) No. (%) Not hospitalized Hospitalized, non-ICU ICU admission Hospitalization status unknown Total with case report form (N = 74,439) 12,217 5,285 1,069 55,868 Missing or unknown status for all conditions (67,277) 7,074 4,248 612 55,343 Total with completed information (7,162) 5,143 1,037 457 525 One or more conditions (2,692, 37.6%) 1,388 (27) 732 (71) 358 (78) 214 (41) Diabetes mellitus (784, 10.9%) 331 (6) 251 (24) 148 (32) 54 (10) Chronic lung disease* (656, 9.2%) 363 (7) 152 (15) 94 (21) 47 (9) Cardiovascular disease (647, 9.0%) 239 (5) 242 (23) 132 (29) 34 (6) Immunocompromised condition (264, 3.7%) 141 (3) 63 (6) 41 (9) 19 (4) Chronic renal disease (213, 3.0%) 51 (1) 95 (9) 56 (12) 11 (2) Pregnancy (143, 2.0%) 72 (1) 31 (3) 4 (1) 36 (7) Neurologic disorder, neurodevelopmental, intellectual disability (52, 0.7%)† 17 (0.3) 25 (2) 7 (2) 3 (1) Chronic liver disease (41, 0.6%) 24 (1) 9 (1) 7 (2) 1 (0.2) Other chronic disease (1,182, 16.5%)§ 583 (11) 359 (35) 170 (37) 70 (13) Former smoker (165, 2.3%) 80 (2) 45 (4) 33 (7) 7 (1) Current smoker (96, 1.3%) 61 (1) 22 (2) 5 (1) 8 (2) None of the above conditions¶ (4,470, 62.4%) 3,755 (73) 305 (29) 99 (22) 311 (59) Abbreviation: ICU = intensive care unit. * Includes any of the following: asthma, chronic obstructive pulmonary disease, and emphysema. † For neurologic disorder, neurodevelopmental, and intellectual disability, the following information was specified: dementia, memory loss, or Alzheimer’s disease (17); seizure disorder (5); Parkinson’s disease (4); migraine/headache (4); stroke (3); autism (2); aneurysm (2); multiple sclerosis (2); neuropathy (2); hereditary spastic paraplegia (1); myasthenia gravis (1); intracranial hemorrhage (1); and altered mental status (1). § For other chronic disease, the following information was specified: hypertension (113); thyroid disease (37); gastrointestinal disorder (32); hyperlipidemia (29); cancer or history of cancer (29); rheumatologic disorder (19); hematologic disorder (17); obesity (17); arthritis, nonrheumatoid, including not otherwise specified (16); musculoskeletal disorder other than arthritis (10); mental health condition (9); urologic disorder (7); cerebrovascular disease (7); obstructive sleep apnea (7); fibromyalgia (7); gynecologic disorder (6); embolism, pulmonary or venous (5); ophthalmic disorder (2); hypertriglyceridemia (1); endocrine (1); substance abuse disorder (1); dermatologic disorder (1); genetic disorder (1). ¶ All listed chronic conditions, including other chronic disease, were marked as not present. Among patients aged ≥19 years, the percentage of non-ICU hospitalizations was higher among those with underlying health conditions (27.3%–29.8%) than among those without underlying health conditions (7.2%–7.8%); the percentage of cases that resulted in an ICU admission was also higher for those with underlying health conditions (13.3%–14.5%) than those without these conditions (2.2%–2.4%) (Table 2). Small numbers of COVID-19 patients aged <19 years were reported to be hospitalized (48) or admitted to an ICU (eight). In contrast, 335 patients aged <19 years were not hospitalized and 1,342 had missing data on hospitalization. Among all COVID-19 patients with complete information on underlying conditions or risk factors, 184 deaths occurred (all among patients aged ≥19 years); 173 deaths (94%) were reported among patients with at least one underlying condition. TABLE 2 Hospitalization with and without intensive care unit (ICU) admission, by age group among COVID-19 patients aged ≥19 years with and without reported underlying health conditions — United States, February 12–March 28, 2020* Age group (yrs) Hospitalized without ICU admission, No. (% range†) ICU admission, No. (% range†) Underlying condition present/reported§ Underlying condition present/reported§ Yes No Yes No 19–64 285 (18.1–19.9) 197 (6.2–6.7) 134 (8.5–9.4) 58 (1.8–2.0) ≥65 425 (41.7–44.5) 58 (16.8–18.3) 212 (20.8–22.2) 20 (5.8–6.3) Total ≥19 710 (27.3–29.8) 255 (7.2–7.8) 346 (13.3–14.5) 78 (2.2–2.4) * Includes COVID-19 patients aged ≥19 years with known status on underlying conditions. † Lower bound of range = number of persons hospitalized or admitted to an ICU among total in row stratum; upper bound of range = number of persons hospitalized or admitted to an ICU among total in row stratum with known outcome status: hospitalization or ICU admission status. § Includes any of following underlying health conditions or risk factors: chronic lung disease (including asthma, chronic obstructive pulmonary disease, and emphysema); diabetes mellitus; cardiovascular disease; chronic renal disease; chronic liver disease; immunocompromised condition; neurologic disorder, neurodevelopmental, or intellectual disability; pregnancy; current smoker; former smoker; or other chronic disease. Discussion Among 122,653 U.S. COVID-19 cases reported to CDC as of March 28, 2020, 7,162 (5.8%) patients had data available pertaining to underlying health conditions or potential risk factors; among these patients, higher percentages of patients with underlying conditions were admitted to the hospital and to an ICU than patients without reported underlying conditions. These results are consistent with findings from China and Italy, which suggest that patients with underlying health conditions and risk factors, including, but not limited to, diabetes mellitus, hypertension, COPD, coronary artery disease, cerebrovascular disease, chronic renal disease, and smoking, might be at higher risk for severe disease or death from COVID-19 ( 3 , 4 ). This analysis was limited by small numbers and missing data because of the burden placed on reporting health departments with rapidly rising case counts, and these findings might change as additional data become available. It is not yet known whether the severity or level of control of underlying health conditions affects the risk for severe disease associated with COVID-19. Many of these underlying health conditions are common in the United States: based on self-reported 2018 data, the prevalence of diagnosed diabetes among U.S. adults was 10.1% ( 7 ), and the U.S. age-adjusted prevalence of all types of heart disease (excluding hypertension without other heart disease) was 10.6% in 2017 ( 8 ). The age-adjusted prevalence of COPD among U.S. adults is 5.9% ( 9 ), and in 2018, the U.S. estimated prevalence of current asthma among persons of all ages was 7.9% ( 7 ). CDC continues to develop and update resources for persons with underlying health conditions to reduce the risk of acquiring COVID-19 ( 10 ). The estimated higher prevalence of these conditions among those in this early group of U.S. COVID-19 patients and the potentially higher risk for more severe disease from COVID-19 associated with the presence of underlying conditions highlight the importance of COVID-19 prevention in persons with underlying conditions. The findings in this report are subject to at least six limitations. First, these data are preliminary, and the analysis was limited by missing data related to the health department reporting burden associated with rapidly rising case counts and delays in completion of information requiring medical chart review; these findings might change as additional data become available. Information on underlying conditions was only available for 7,162 (5.8%) of 122,653 cases reported to CDC. It cannot be assumed that those with missing information are similar to those with data on either hospitalizations or underlying health conditions. Second, these data are subject to bias in outcome ascertainment because of short follow-up time. Some outcomes might be underestimated, and long-term outcomes cannot be assessed in this analysis. Third, because of the limited availability of testing in many jurisdictions during this period, this analysis is likely biased toward more severe cases, and findings might change as testing becomes more widespread. Fourth, because of the descriptive nature of these data, attack rates among persons with and without underlying health conditions could not be compared, and thus the risk difference of severe disease with COVID-19 between these groups could not be estimated. Fifth, no conclusions could be drawn about underlying conditions that were not included in the case report form or about different conditions that were reported in a single, umbrella category. For example, asthma and COPD were included in a chronic lung disease category. Finally, for some underlying health conditions and risk factors, including neurologic disorders, chronic liver disease, being a current smoker, and pregnancy, few severe outcomes were reported; therefore, conclusions cannot be drawn about the risk for severe COVID-19 among persons in these groups. Persons in the United States with underlying health conditions appear to be at higher risk for more severe COVID-19, consistent with findings from other countries. Persons with underlying health conditions who have symptoms of COVID-19, including fever, cough, or shortness of breath, should immediately contact their health care provider. These persons should take steps to protect themselves from COVID-19, through washing their hands; cleaning and disinfecting high-touch surfaces; and social distancing, including staying at home, avoiding crowds, gatherings, and travel, and avoiding contact with persons who are ill. Maintaining at least a 30-day supply of medication, a 2-week supply of food and other necessities, and knowledge of COVID-19 symptoms are recommended for those with underlying health conditions ( 10 ). All persons should take steps to protect themselves from COVID-19 and to protect others. All persons who are ill should stay home, except to get medical care; should not go to work; and should stay away from others. This is especially important for those who work with persons with underlying conditions or who otherwise are at high risk for severe outcomes from COVID-19. Community mitigation strategies, which aim to slow the spread of COVID-19, are important to protect all persons from COVID-19, especially persons with underlying health conditions and other persons at risk for severe COVID-19–associated disease (https://www.cdc.gov/coronavirus/2019-ncov/downloads/community-mitigation-strategy.pdf). Summary What is already known about this topic? Published reports from China and Italy suggest that risk factors for severe COVID-19 disease include underlying health conditions, but data describing underlying health conditions among U.S. COVID-19 patients have not yet been reported. What is added by this report? Based on preliminary U.S. data, persons with underlying health conditions such as diabetes mellitus, chronic lung disease, and cardiovascular disease, appear to be at higher risk for severe COVID-19–associated disease than persons without these conditions. What are the implications for public health practice? Strategies to protect all persons and especially those with underlying health conditions, including social distancing and handwashing, should be implemented by all communities and all persons to help slow the spread of COVID-19.
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              Estimating Influenza Disease Burden from Population-Based Surveillance Data in the United States

              Annual estimates of the influenza disease burden provide information to evaluate programs and allocate resources. We used a multiplier method with routine population-based surveillance data on influenza hospitalization in the United States to correct for under-reporting and estimate the burden of influenza for seasons after the 2009 pandemic. Five sites of the Influenza Hospitalization Surveillance Network (FluSurv-NET) collected data on the frequency and sensitivity of influenza testing during two seasons to estimate under-detection. Population-based rates of influenza-associated hospitalization and Intensive Care Unit admission from 2010–2013 were extrapolated to the U.S. population from FluSurv-NET and corrected for under-detection. Influenza deaths were calculated using a ratio of deaths to hospitalizations. We estimated that influenza-related hospitalizations were under-detected during 2010-11 by a factor of 2.1 (95%CI 1.7–2.9) for age < 18 years, 3.1 (2.4–4.5) for ages 18-64 years, and 5.2 (95%CI 3.8–8.3) for age 65+. Results were similar in 2011-12. Extrapolated estimates for 3 seasons from 2010–2013 included: 114,192–624,435 hospitalizations, 18,491–95,390 ICU admissions, and 4,915–27,174 deaths per year; 54–70% of hospitalizations and 71–85% of deaths occurred among adults aged 65+. Influenza causes a substantial disease burden in the U.S. that varies by age and season. Periodic estimation of multipliers across multiple sites and age groups improves our understanding of influenza detection in sentinel surveillance systems. Adjusting surveillance data using a multiplier method is a relatively simple means to estimate the impact of influenza and the subsequent value of interventions to prevent influenza.
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                Author and article information

                Journal
                MMWR Morb Mortal Wkly Rep
                MMWR Morb Mortal Wkly Rep
                WR
                Morbidity and Mortality Weekly Report
                Centers for Disease Control and Prevention
                0149-2195
                1545-861X
                17 April 2020
                17 April 2020
                : 69
                : 15
                : 458-464
                Affiliations
                CDC COVID-NET Team; Eagle Global Scientific, Atlanta, Georgia; Chickasaw Nation Industries, Norman, Oklahoma; Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee; California Emerging Infections Program, Oakland, California; Communicable Disease Branch, Colorado Department of Public Health and Environment, Denver, Colorado; Connecticut Emerging Infections Program, Yale School of Public Health, New Haven, Connecticut; Departments of Medicine and Pediatrics, Emory University School of Medicine, Atlanta, Georgia; Emerging Infections Program, Georgia Department of Health, Atlanta, Georgia; Veterans Affairs Medical Center, Atlanta, Georgia; Foundation for Atlanta Veterans Education and Research, Decatur, Georgia; Iowa Department of Public Health; Maryland Department of Health; Communicable Disease Division, Michigan Department of Health and Human Services, Lansing, Michigan; Minnesota Department of Health; New Mexico Department of Health; New York State Department of Health; University of Rochester School of Medicine and Dentistry, Rochester, New York; Bureau of Infectious Diseases, Ohio Department of Health, Columbus, Ohio; Oregon Public Health Division; Division of Infectious Disease, Vanderbilt University School of Medicine, Nashville, Tennessee; Salt Lake County Health Department, Salt Lake City, Utah.
                Author notes
                Corresponding authors: Shikha Garg, izj7@ 123456cdc.gov , 404-639-6142; Lindsay Kim, iyn2@ 123456cdc.gov , 404-639-5218.
                Article
                mm6915e3
                10.15585/mmwr.mm6915e3
                7755063
                32298251
                6b36464d-8c89-4b7a-a106-e203d2a62894

                All material in the MMWR Series is in the public domain and may be used and reprinted without permission; citation as to source, however, is appreciated.

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