Universal screening for the SARS-CoV-2 virus on hospital admission in an area with low COVID-19 prevalence

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Abstract

Asymptomatic persons contribute to widespread transmission of the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) pandemic. 1 Published reports from areas of high COVID-19 incidence in the United States suggest that a significant percentage of asymptomatic persons are in healthcare systems. In 2 New York City (NYC) hospitals, 13.7% of asymptomatic pregnant women admitted for delivery tested positive for SARS-CoV-2 virus. 2 Similarly, the nursing facility in Washington state with the earliest death from COVID-19 infection and the first healthcare worker infected in the United States, reported >50% positivity of their asymptomatic residents for the virus. 3 Universal screening of healthcare populations may prevent in-hospital transmission of SARS-CoV-2 virus. However, testing resources and personal protective equipment (PPE) supplies to effectively isolate positive asymptomatic persons are currently limited, resulting in provider safety concerns. Upon developing real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) tests in-house with >98% sensitivity, as well as increasing the availability of PPE at our institution, we initiated universal screening of patients on hospital admission using nasopharyngeal swabs to identify and isolate asymptomatic positive patients to prevent in-hospital transmission of SARS-CoV-2. We report our experience with universal screening of asymptomatic hospitalized persons, including a comparison of demographics between symptomatic and asymptomatic populations. Methods On April 27, 2020, our 1,000-bed academic center instituted universal SARS-CoV-2 testing of patients on hospital admission. Clinicians performed COVID-19 symptom screening using clinical criteria reported in the literature. 4 They designated patients as symptomatic or asymptomatic when ordering the test. An infectious diseases physician conducted chart review of asymptomatic positive patients to confirm accuracy of classification. Asymptomatic patients were not isolated; test turnaround time was 6–24 hours. Statistical analyses were performed with the Fischer exact tests and paired t tests to compare asymptomatic and symptomatic positive patients using SAS version 9.4 software (SAS Institute, Cary, NC). Results Between April 27, 2020, and May 18, 2020, when the hospital averaged at 60%–70% capacity, we performed 1,811 SARS-CoV-2 tests on nasopharyngeal specimens: 1,335 (74%) were asymptomatic, 420 (23%) were symptomatic, 56 (3%) were incorrectly ordered. Of the 1,755 tests in this analysis, overall positivity for SAR-CoV-2 virus was 79 (4.5%). Of 79 patients, 12 were asymptomatic (15%) and 67 were symptomatic (85%). Of 1,335 asymptomatic patients, 12 tested positive, for a rate of ~ 1%. Of 420 symptomatic patients, 67 tested positive, for a rate of 16%. No test converted to positive among asymptomatic patients while hospitalized. A comparative analysis of patients with positive SARS-CoV2 tests is listed in Table 1. The mean age of asymptomatic patients was 37 years (SD, 19.71) versus a mean age of 59 years (SD, 13.08) among symptomatic patients (P = .0020). Hispanic patients were more likely to be asymptomatic (7 of 12) than symptomatic (9 of 67) at the time of testing (58% vs 13%; P = .0017). We observed no difference in positivity rate on admission of asymptomatic versus symptomatic patients (P = .21). In addition, 5 asymptomatic positive women were pregnant (5 of 12, 42%); no symptomatic patients were pregnant (P ≤ .0001). A baby born to an asymptomatic SARS-CoV-2–positive mother tested positive at 48 hours of life, and 1 asymptomatic, SARS-CoV-2–positive, immunocompromised patient was receiving chemotherapy for breast cancer. One asymptomatic patient developed a fever during hospitalization, and another was readmitted within 14 days of testing positive, both of these events were not considered to be related to COVID-19. Table 1. Comparative Analysis Between Asymptomatic and Symptomatic Patients With Positive SARS-CoV-2 Virus Tests Demographic Characteristics of Asymptomatic Patients,SARS-CoV-2–Positive Patients(N = 12, 0.62%) Demographic Characteristics of Symptomatic Patients,SARS-CoV-2–Positive Patients(N = 67, 3.5%) P Value Mean age, y (range) 37 (0–67)(SD, 19.71) Mean age, y (range) 59 (12–78)(SD, 13.08) .0020 No. (%) No. (%) Race/ethnicity Race/ethnicity, y (range) Hispanic 7 (58) Hispanic 9 (13) .0017 African American 4 (33) African American 36 (54) .22 Caucasian 1 (8) Caucasian 14 (21) .44 Other 0 (0) Other 8 (12) Gender Gender Male 5 (42) Male 42 (63) .21 Female 7 (58) Female 25 (37) Pregnant 5/7 (42) Pregnant 0/25 (0) ≤.0001 Note. SD, standard deviation. Discussion Universal screening for the detection of SARS-CoV-2 at our institution revealed that during the study period, the number of asymptomatic persons admitted to the hospital was relatively small. Our health system had a relatively low number of confirmed SARS-CoV-2–positive COVID-19 patients (n = 82) admitted during the observed 3-week interval, compared to 4,000 patients admitted to an NYC hospital reporting the use of convalescent serum for the treatment of COVID-19 in a similar time frame. 5 Although low prevalence of asymptomatic patients has limited generalizability to areas with higher rates of infection, it is valuable information for patients, healthcare workers, and epidemiology programs in similar areas of COVID-19 prevalence. During our study period, 7.6% of all admitted patients were Hispanic and 43.5% were African American, yet 11 of 12 (91.7%) asymptomatic patients who screened positive were African American or Hispanic. A similar trend was observed in other studies. 6,7 Furthermore, a higher proportion of pregnant women have asymptomatic infection, which supports screening of peripartum women. Consistent with the literature, asymptomatic patients were younger than those who presented to our healthcare system with COVID-19 symptoms. 8 The potential benefits of universal SARS-CoV-2 screening are many and are likely to increase with escalating COVID-19 incidence. In hospitalized patients, detection of asymptomatic infection can guide hospital isolation practices, bed assignments, and the use of PPE. 2 For healthcare workers, it might improve workforce depletion by unnecessary quarantine, reduce transmission in asymptomatic cases, contain the virus in healthcare settings, and protect hospital staff from infection. In the community, mass testing can identify asymptomatic cases and assist in eliminating the SARS-CoV-2 virus, as reported in a village near Venice, Italy. 9 However, there are barriers to universal screening. Current testing capacity and test turnaround time, staffing shortages, and availability of healthcare workers skilled to perform nasopharyngeal swabbing currently limit widespread feasibility. Patient discomfort from nasopharyngeal sample collection is another potential barrier to universal screening. This study has several limitations. The sample size was small, and the study was conducted at a single center. In an area with high prevalence of COVID-19 infection, asymptomatic screening would likely identify more asymptomatic cases. However, sensitivity of a test in asymptomatic persons cannot be precisely defined. We add to the body of literature on SARS-CoV-2 testing of asymptomatic patients at the time of hospital admission. More data on universal screening is necessary to evaluate the clinical impact on healthcare systems and to define optimal screening strategies of high-risk groups for asymptomatic COVID-19 infection.

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Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility

Melissa M. Arons, Kelly Hatfield, Sujan Reddy … (2020)

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can spread rapidly within skilled nursing facilities. After identification of a case of Covid-19 in a skilled nursing facility, we assessed transmission and evaluated the adequacy of symptom-based screening to identify infections in residents. Methods We conducted two serial point-prevalence surveys, 1 week apart, in which assenting residents of the facility underwent nasopharyngeal and oropharyngeal testing for SARS-CoV-2, including real-time reverse-transcriptase polymerase chain reaction (rRT-PCR), viral culture, and sequencing. Symptoms that had been present during the preceding 14 days were recorded. Asymptomatic residents who tested positive were reassessed 7 days later. Residents with SARS-CoV-2 infection were categorized as symptomatic with typical symptoms (fever, cough, or shortness of breath), symptomatic with only atypical symptoms, presymptomatic, or asymptomatic. Results Twenty-three days after the first positive test result in a resident at this skilled nursing facility, 57 of 89 residents (64%) tested positive for SARS-CoV-2. Among 76 residents who participated in point-prevalence surveys, 48 (63%) tested positive. Of these 48 residents, 27 (56%) were asymptomatic at the time of testing; 24 subsequently developed symptoms (median time to onset, 4 days). Samples from these 24 presymptomatic residents had a median rRT-PCR cycle threshold value of 23.1, and viable virus was recovered from 17 residents. As of April 3, of the 57 residents with SARS-CoV-2 infection, 11 had been hospitalized (3 in the intensive care unit) and 15 had died (mortality, 26%). Of the 34 residents whose specimens were sequenced, 27 (79%) had sequences that fit into two clusters with a difference of one nucleotide. Conclusions Rapid and widespread transmission of SARS-CoV-2 was demonstrated in this skilled nursing facility. More than half of residents with positive test results were asymptomatic at the time of testing and most likely contributed to transmission. Infection-control strategies focused solely on symptomatic residents were not sufficient to prevent transmission after SARS-CoV-2 introduction into this facility.

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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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Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19

Monica Gandhi, Deborah S. Yokoe, Diane Havlir (2020)

Traditional infection-control and public health strategies rely heavily on early detection of disease to contain spread. When Covid-19 burst onto the global scene, public health officials initially deployed interventions that were used to control severe acute respiratory syndrome (SARS) in 2003, including symptom-based case detection and subsequent testing to guide isolation and quarantine. This initial approach was justified by the many similarities between SARS-CoV-1 and SARS-CoV-2, including high genetic relatedness, transmission primarily through respiratory droplets, and the frequency of lower respiratory symptoms (fever, cough, and shortness of breath) with both infections developing a median of 5 days after exposure. However, despite the deployment of similar control interventions, the trajectories of the two epidemics have veered in dramatically different directions. Within 8 months, SARS was controlled after SARS-CoV-1 had infected approximately 8100 persons in limited geographic areas. Within 5 months, SARS-CoV-2 has infected more than 2.6 million people and continues to spread rapidly around the world. What explains these differences in transmission and spread? A key factor in the transmissibility of Covid-19 is the high level of SARS-CoV-2 shedding in the upper respiratory tract, 1 even among presymptomatic patients, which distinguishes it from SARS-CoV-1, where replication occurs mainly in the lower respiratory tract. 2 Viral loads with SARS-CoV-1, which are associated with symptom onset, peak a median of 5 days later than viral loads with SARS-CoV-2, which makes symptom-based detection of infection more effective in the case of SARS CoV-1. 3 With influenza, persons with asymptomatic disease generally have lower quantitative viral loads in secretions from the upper respiratory tract than from the lower respiratory tract and a shorter duration of viral shedding than persons with symptoms, 4 which decreases the risk of transmission from paucisymptomatic persons (i.e., those with few symptoms). Arons et al. now report in the Journal an outbreak of Covid-19 in a skilled nursing facility in Washington State where a health care provider who was working while symptomatic tested positive for infection with SARS-CoV-2 on March 1, 2020. 5 Residents of the facility were then offered two facility-wide point-prevalence screenings for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) of nasopharyngeal swabs on March 13 and March 19–20, along with collection of information on symptoms the residents recalled having had over the preceding 14 days. Symptoms were classified into typical (fever, cough, and shortness of breath), atypical, and none. Among 76 residents in the point-prevalence surveys, 48 (63%) had positive rRT-PCR results, with 27 (56%) essentially asymptomatic, although symptoms subsequently developed in 24 of these residents (within a median of 4 days) and they were reclassified as presymptomatic. Quantitative SARS-CoV-2 viral loads were similarly high in the four symptom groups (residents with typical symptoms, those with atypical symptoms, those who were presymptomatic, and those who remained asymptomatic). It is notable that 17 of 24 specimens (71%) from presymptomatic persons had viable virus by culture 1 to 6 days before the development of symptoms. Finally, the mortality from Covid-19 in this facility was high; of 57 residents who tested positive, 15 (26%) died. An important finding of this report is that more than half the residents of this skilled nursing facility (27 of 48) who had positive tests were asymptomatic at testing. Moreover, live coronavirus clearly sheds at high concentrations from the nasal cavity even before symptom development. Although the investigators were not able to retrospectively elucidate specific person-to-person transmission events and although symptom ascertainment may be unreliable in a group in which more than half the residents had cognitive impairment, these results indicate that asymptomatic persons are playing a major role in the transmission of SARS-CoV-2. Symptom-based screening alone failed to detect a high proportion of infectious cases and was not enough to control transmission in this setting. The high mortality (>25%) argues that we need to change our current approach for skilled nursing facilities in order to protect vulnerable, enclosed populations until other preventive measures, such as a vaccine or chemoprophylaxis, are available. A new approach that expands Covid-19 testing to include asymptomatic persons residing or working in skilled nursing facilities needs to be implemented now. Despite “lockdowns” in these facilities, coronavirus outbreaks continue to spread, with 1 in 10 nursing homes in the United States (>1300 skilled nursing facilities) now reporting cases, with the likelihood of thousands of deaths. 6 Mass testing of the residents in skilled nursing facilities will allow appropriate isolation of infected residents so that they can be cared for and quarantine of exposed residents to minimize the risk of spread. Mass testing in these facilities could also allow cohorting 7 and some resumption of group activities in a nonoutbreak setting. Routine rRT-PCR testing in addition to symptomatic screening of new residents before entry, conservative guidelines for discontinuation of isolation, 7 and periodic retesting of long-term residents, as well as both periodic rRT-PCR screening and surgical masking of all staff, are important concomitant measures. There are approximately 1.3 million Americans currently residing in nursing homes. 8 Although this recommendation for mass testing in skilled nursing facilities could be initially rolled out in geographic areas with high rates of community Covid-19 transmission, an argument can be made to extend this recommendation to all U.S.-based skilled nursing facilities now because case ascertainment is uneven and incomplete and because of the devastating consequences of outbreaks. Immediately enforceable alternatives to mass testing in skilled nursing facilities are few. The public health director of Los Angeles has recommended that families remove their loved ones from nursing homes, 9 a measure that is not feasible for many families. Asymptomatic transmission of SARS-CoV-2 is the Achilles’ heel of Covid-19 pandemic control through the public health strategies we have currently deployed. Symptom-based screening has utility, but epidemiologic evaluations of Covid-19 outbreaks within skilled nursing facilities such as the one described by Arons et al. strongly demonstrate that our current approaches are inadequate. This recommendation for SARS-CoV-2 testing of asymptomatic persons in skilled nursing facilities should most likely be expanded to other congregate living situations, such as prisons and jails (where outbreaks in the United States, whose incarceration rate is much higher than rates in other countries, are increasing), enclosed mental health facilities, and homeless shelters, and to hospitalized inpatients. Current U.S. testing capability must increase immediately for this strategy to be implemented. Ultimately, the rapid spread of Covid-19 across the United States and the globe, the clear evidence of SARS-CoV-2 transmission from asymptomatic persons, 5 and the eventual need to relax current social distancing practices argue for broadened SARS-CoV-2 testing to include asymptomatic persons in prioritized settings. These factors also support the case for the general public to use face masks 10 when in crowded outdoor or indoor spaces. This unprecedented pandemic calls for unprecedented measures to achieve its ultimate defeat.

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Journal

Journal ID (nlm-ta): Infect Control Hosp Epidemiol

Journal ID (iso-abbrev): Infect Control Hosp Epidemiol

Journal ID (publisher-id): ICE

Title: Infection Control and Hospital Epidemiology

Publisher: Cambridge University Press (New York, USA )

ISSN (Print): 0899-823X

ISSN (Electronic): 1559-6834

Publication date (Electronic, pub): 23 July 2020

Pages: 1-2

Affiliations

Division of Infectious Diseases, Virginia Commonwealth University , Richmond, Virginia

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Author for correspondence: Sangeeta R. Sastry, E-mail: Sangeeta.Sastry@ 123456vcuhealth.org

Article

Publisher ID: S0899823X2000358X

DOI: 10.1017/ice.2020.358

PMC ID: 7411438

PubMed ID: 32698924

SO-VID: f706d75e-ba4b-4aee-99e5-cd96cea9c49e

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 25 May 2020

Date revision received : 08 July 2020

Date accepted : 18 July 2020

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Tables: 1, References: 9, Pages: 2

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Universal screening for the SARS-CoV-2 virus on hospital admission in an area with low COVID-19 prevalence

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Novel Coronavirus Disease COVID-19

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Presymptomatic SARS-CoV-2 Infections and Transmission in a Skilled Nursing Facility

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

Asymptomatic Transmission, the Achilles’ Heel of Current Strategies to Control Covid-19

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