32
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Update: COVID-19 Among Workers in Meat and Poultry Processing Facilities ― United States, April–May 2020

      research-article
      , DVM 1 , 2 , , , MPH 1 , , PhD 1 , 3 , , MPH 4 , , PhD 4 , , DVM 5 , , MPH 5 , , PhD 1 , 2 , 6 , , DVM 7 , , PhD 8 , , MPH 8 , , MPH 9 , , MS 10 , , DVM 11 , , MPH 12 , , MS 12 , , MD 13 , , MPH 14 , , MD 1 , 2 , 14 , , MPH 15 , 15 , , MPH 16 , 17 , , MS 18 , , PhD 18 , , MD 19 , , DVM 19 , , PhD 20 , , MPH 20 , , MPH 21 , , MPH 22 , , DVM 23 , , DVM 23 , , MPH 24 , , MPH 24 , , PhD 1 , 2 , 25 , , MPH 26 , , MPH 1 , 1 , , MSPH 1 , , MPH 1 , , MPH 1 , , PhD 1 , , MPH 1 , , DVM 1 , , MPH 1 , 1 , , MD 1 , , MA 1 , , MPH 1 , , MD 1 , 2 , , PhD 1 , , MPH 1 , , MD 1 , , PhD 1 , , MD 1 , , MD 1 , , PhD 1 , , PhD 1 , COVID-19 Response Team COVID-19 Response Team COVID-19 Response Team , , , , , , , , , , , , , , ,
      Morbidity and Mortality Weekly Report
      Centers for Disease Control and Prevention

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          On July 7, 2020, this report was posted online as an MMWR Early Release. Meat and poultry processing facilities face distinctive challenges in the control of infectious diseases, including coronavirus disease 2019 (COVID-19) ( 1 ). COVID-19 outbreaks among meat and poultry processing facility workers can rapidly affect large numbers of persons. Assessment of COVID-19 cases among workers in 115 meat and poultry processing facilities through April 27, 2020, documented 4,913 cases and 20 deaths reported by 19 states ( 1 ). This report provides updated aggregate data from states regarding the number of meat and poultry processing facilities affected by COVID-19, the number and demographic characteristics of affected workers, and the number of COVID-19–associated deaths among workers, as well as descriptions of interventions and prevention efforts at these facilities. Aggregate data on confirmed COVID-19 cases and deaths among workers identified and reported through May 31, 2020, were obtained from 239 affected facilities (those with a laboratory-confirmed COVID-19 case in one or more workers) in 23 states.* COVID-19 was confirmed in 16,233 workers, including 86 COVID-19–related deaths. Among 14 states reporting the total number of workers in affected meat and poultry processing facilities (112,616), COVID-19 was diagnosed in 9.1% of workers. Among 9,919 (61%) cases in 21 states with reported race/ethnicity, 87% occurred among racial and ethnic minority workers. Commonly reported interventions and prevention efforts at facilities included implementing worker temperature or symptom screening and COVID-19 education, mandating face coverings, adding hand hygiene stations, and adding physical barriers between workers. Targeted workplace interventions and prevention efforts that are appropriately tailored to the groups most affected by COVID-19 are critical to reducing both COVID-19–associated occupational risk and health disparities among vulnerable populations. Implementation of these interventions and prevention efforts † across meat and poultry processing facilities nationally could help protect workers in this critical infrastructure industry. Distinctive factors that increase meat and poultry processing workers’ risk for exposure to SARS-CoV-2, the virus that causes COVID-19, include prolonged close workplace contact with coworkers (within 6 feet for ≥15 minutes) for long time periods (8–12 hour shifts), shared work spaces, shared transportation to and from the workplace, congregate housing, and frequent community contact with fellow workers. Many of these factors might also contribute to ongoing community transmission ( 1 ). To better understand the effect of COVID-19 on workers in these facilities nationwide, on June 6, 2020, CDC requested that state health departments report aggregate surveillance data through May 31, 2020, for workers in all meat and poultry processing facilities affected by COVID-19, including 1) the number and type of such facilities that had reported at least one confirmed COVID-19 case among workers, 2) the total number of workers in affected facilities, 3) the number of workers with laboratory-confirmed COVID-19, and 4) the number of COVID-19–related worker deaths. States reported COVID-19 cases determined by the Council of State and Territorial Epidemiologists confirmed case definition. § States were asked to report demographic characteristics and symptom status of workers with COVID-19. Testing strategies and methods for collecting symptom data varied by workplace. Proportional distributions for demographic characteristics and symptom status were calculated for cases among workers in 21 states after excluding missing and unknown values; data were missing for sex in 25% of reports, age in 24%, race/ethnicity in 39%, and symptom status in 37%. States also provided information (from direct observation or from management at affected facilities) regarding specified interventions and prevention efforts that were implemented. A random-effects logistic regression model was used to obtain an estimate of the pooled proportion of asymptomatic (SARS-CoV-2 detected but symptoms never develop) or presymptomatic (SARS-CoV-2 detected before symptom onset) infections at the time of testing among workers who had positive SARS-CoV-2 test results. Five states provided prevalence data from facility-wide testing of 5,572 workers in seven facilities. Modeling was conducted and 95% confidence intervals (CIs) were calculated, with facilities treated as the random effect, using SAS software (version 9.4; SAS Institute). Twenty-eight (56%) of 50 states responded, including 23 (82%) that reported at least one confirmed COVID-19 case among meat and poultry processing workers. Overall, 239 facilities reported 16,233 COVID-19 cases and 86 COVID-19–related deaths among workers (Table 1). The median number of affected facilities per state was seven (interquartile range = 3–14). Among 14 states reporting the total number of workers in affected facilities, 9.1% of 112,616 workers received diagnoses of COVID-19. The percentage of workers with COVID-19 ranged from 3.1% to 24.5% per facility. TABLE 1 Laboratory-confirmed COVID-19 cases among workers in meat and poultry facilities — 23 states, April–May 2020* State Type of meat/poultry in affected facilities No. (%) Facilities affected Workers in affected facilities† Confirmed COVID-19 cases among workers COVID-19–related deaths§ Arizona Beef 1 1,750 162 (9.3) 0 (0) Colorado Beef, bison, lamb, poultry 7 7,711 422 (5.5) 9 (2.1) Georgia Poultry 14 16,500 509 (3.1) 1 (0.2) Idaho Beef 2 797 72 (9.0) 0 (0) Illinois Beef, pork, poultry 26 N/A 1,029 (―) 10 (1.0) Kansas Beef, pork, poultry 10 N/A 2,670 (―) 8 (0.3) Kentucky Pork, poultry 7 7,633 559 (7.3) 4 (0.7) Maine Poultry 1 411 50 (12.2) 1 (2.0) Maryland Poultry 2 2,036 208 (10.2) 5 (2.4) Massachusetts Poultry, other 33 N/A 263 (―) 0 (0) Missouri Beef, pork, poultry 9 8,469 745 (8.8) 2 (0.3) Nebraska Beef, pork, poultry 23 26,134 3,438 (13.2) 14 (0.4) New Mexico Beef, pork, poultry 2 550 24 (4.4) 0 (0) Pennsylvania Beef, pork, poultry, other 30 15,548 1,169 (7.5) 8 (0.7) Rhode Island Beef, pork, poultry, other 6 N/A 78 (―) 0 (0) South Carolina Beef, pork, poultry, other 16 N/A 97 (―) 0 (0) South Dakota Beef, pork, poultry 4 6,500 1,593 (24.5) 3 (0.2) Tennessee Pork, poultry, other 7 N/A 640 (―) 2 (0.3) Utah Beef, pork, poultry 4 N/A 67 (―) 1 (1.5) Virginia Pork, poultry, other 14 N/A 1,109 (―) 10 (0.9) Washington Beef, poultry 7 4,452 468 (10.5) 4 (0.9) Wisconsin Beef, pork, veal 14 14,125 860 (6.1) 4 (0.5) Wyoming Beef 0 N/A 1 (―) 0 (0) Total¶ Beef, bison, lamb, pork, poultry, veal, other 239 112,616 16,233 86 Combined total** ― 264 ― 17,358 91 Abbreviations: COVID-19 = coronavirus disease 2019; N/A = not available. * Data reported through May 31, 2020. Five states that responded to the data request did not report any laboratory-confirmed COVID-19 cases among workers in the animal slaughtering and processing industry; 22 states with animal slaughtering and processing facilities did not respond to the data request. The 13 states that contributed to both an earlier assessment and this update provided any updates to previously reported data, in addition to reporting new cases and facilities, through May 31, 2020. † Among 14 of 23 states reporting the number of workers in affected workplaces, 9.1% of workers received diagnoses of COVID-19. § Percentage of deaths among cases. ¶ Data on workers with COVID-19 from 23 states that submitted data to this report. ** Combining data on workers with COVID-19 (1,125), COVID-19–related deaths (five), and COVID-19–affected facilities (25) through April 27 from six states that contributed to an earlier assessment of COVID-19 among meat and poultry processing workers that did not submit updated data to this report (https://www.cdc.gov/mmwr/volumes/69/wr/mm6918e3.htm?s_cid=mm6918e3_w). Twenty-one states provided information on demographic characteristics and symptom status of workers with COVID-19. Among the 12,100 (75%) and 12,365 (76%) patients with information on sex and age, 7,288 (60%) cases occurred among males, and 5,741 (46%) were aged 40–59 years, respectively (Figure). Among the 9,919 (61%) cases with race/ethnicity reported, 5,584 (56%) were in Hispanics, 1,842 (19%) in non-Hispanic blacks (blacks), 1,332 (13%) in non-Hispanic whites (whites), and 1,161 (12%) in Asians. Symptom status was reported for 10,284 (63%) cases; among these, 9,072 (88%) workers were symptomatic, and 1,212 (12%) were asymptomatic or presymptomatic. FIGURE Characteristics * , † of reported laboratory-confirmed COVID-19 cases among workers in meat and poultry processing facilities — 21 states, April–May 2020 § Abbreviation: COVID-19 = coronavirus disease 2019. * The analytic dataset excludes cases reported by states that were missing information on sex (4,133), age (3,868), race/ethnicity (6,314), and symptom status (5,949). White, black, and Asian workers were non-Hispanic; Hispanic workers could be of any race. † Testing strategies and methods for collecting symptom data varied by workplace. Symptom status was available for a single timepoint, at the time of testing or at the time of interview. § Data reported through May 31, 2020. The figure is a bar chart showing characteristics of reported laboratory-confirmed COVID-19 cases among workers in meat and poultry processing facilities, by sex, age group, race/ethnicity, and symptom status, in 21 states during April–May 2020. Among 239 facilities reporting cases, information on interventions and prevention efforts was available for 111 (46%) facilities from 14 states. Overall, 89 (80%) facilities reported screening workers on entry, 86 (77%) required all workers to wear face coverings, 72 (65%) increased the availability of hand hygiene stations, 70 (63%) educated workers on community spread, and 69 (62%) installed physical barriers between workers (Table 2). Forty-one (37%) of 111 facilities offered testing for SARS-CoV-2 to workers; 24 (22%) reported closing temporarily as an intervention measure. TABLE 2 Interventions and prevention efforts implemented by facilities in response to COVID-19 among workers in 111 meat and poultry processing facilities* —14 states, April–May 2020 † Intervention/Prevention effort COVID-19–affected facilities, no. (%§) Implemented intervention Did not implement intervention Intervention status unknown Worker screening on entry 89 (80) 5 (5) 17 (15) Required universal face covering 86 (77) 5 (5) 20 (18) Added hand hygiene stations 72 (65) 8 (7) 31 (28) Educated employees on community spread 70 (63) 13 (12) 28 (25) Installed physical barriers between workers 69 (62) 17 (15) 25 (23) Staggered shifts 57 (51) 17 (15) 37 (33) Offered SARS-CoV-2 testing to employees¶ 41 (37) 35 (32) 35 (32) Removed financial incentives (e.g., attendance bonuses) 33 (30) 20 (18) 58 (52) Closed facility temporarily 24 (22) 69 (62) 18 (16) Reduced rate of animal processing 23 (21) 14 (12) 74 (67) Decreased crowding of transportation to worksite 17 (15) 10 (9) 84 (76) Abbreviation: COVID-19 = coronavirus disease 2019. * Affected facilities defined as those having one or more laboratory-confirmed COVID-19 cases among workers. † Based on data collected through May 31, 2020. § Because of rounding, row percentages might not equal 100%. ¶ Testing strategies varied by facility. Among seven facilities that implemented facility-wide testing, the crude prevalence of asymptomatic or presymptomatic infections among 5,572 workers who had positive SARS-CoV-2 test results was 14.4%. The pooled prevalence estimated from the model for the proportion of asymptomatic or presymptomatic infections among workers in meat and poultry processing facilities was 11.2% (95% CI = 0.9%–23.1%). Discussion The animal slaughtering and processing industry employs an estimated 525,000 workers in approximately 3,500 facilities nationwide ( 2 , 3 ). Combining data on workers with COVID-19 and COVID-19–related deaths identified and reported through May 31 from 23 states (16,233 cases; 86 deaths) with data from an earlier assessment through April 27 (1,125 cases; five deaths) ( 1 ) that included data from six states that did not contribute updated data to this report, ¶ at least 17,358 cases and 91 COVID-19–related deaths have occurred among U.S. meat and poultry processing workers. The effects of COVID-19 on racial and ethnic minority groups are not yet fully understood; however, current data indicate a disproportionate burden of illness and death among these populations ( 4 , 5 ). Among animal slaughtering and processing workers from the 21 states included in this report whose race/ethnicity were known, approximately 39% were white, 30% were Hispanic, 25% were black, and 6% were Asian.** However, among 9,919 workers with COVID-19 with race/ethnicity reported, approximately 56% were Hispanic, 19% were black, 13% were white, and 12% were Asian, suggesting that Hispanic and Asian workers might be disproportionately affected by COVID-19 in this workplace setting. Ongoing efforts to reduce incidence and better understand the effects of COVID-19 on the health of racial and ethnic minorities are important to ensure that workplace-specific prevention strategies and intervention messages are tailored to those groups most affected by COVID-19. The proportion of asymptomatic or presymptomatic SARS-CoV-2 infections identified in investigations of COVID-19 outbreaks in other high-density settings has ranged from 19% to 88% ( 6 , 7 ). Among cases in workers with known symptom status in this report, 12% of patients were asymptomatic or presymptomatic; however, not all facilities performed facility-wide testing, during which these infections are more likely to be identified. Consequently, many asymptomatic and presymptomatic infections in the overall workforce might have gone unrecognized, and the approximations for disease prevalence in this report might underestimate SARS-CoV-2 infections. Recently derived estimates of the total proportion of asymptomatic and presymptomatic infections from data on COVID-19 investigations among cruise ship passengers and evacuees from Wuhan, China, ranged from 17.9% to 30.8%, respectively ( 8 , 9 ). The estimated proportion of asymptomatic and presymptomatic infections among meat and poultry processing workers (11.2%) is lower than are previously reported estimates and should be reevaluated as more comprehensive facility-wide testing data are reported. In coordination with state and local health agencies, many meat and poultry processing facilities have implemented interventions to reduce transmission or prevent ongoing exposure within the workplace, including offering testing to workers. †† Expanding interventions across these facilities nationwide might help protect workers in this industry. Recognizing the interaction of workplace and community, many facilities have also educated workers about strategies for reducing transmission of COVID-19 outside the workplace. §§ The findings in this report are subject to at least seven limitations. First, only 28 of 50 states responded; 23 states with COVID-19 cases among meat and poultry processing facility workers submitted data for this report. In addition, only facilities with at least one laboratory-confirmed case of COVID-19 among workers were included. Thus, these results might not be representative of all U.S. meat and poultry processing facilities and workers. Second, delays in identifying workplace outbreaks and linking cases or deaths to outbreaks might have resulted in an underestimation of the number of affected facilities and cases among workers. Third, data were not reported on variations in testing availability and practices, which might influence the number of cases reported. Fourth, industry data were used for race/ethnicity comparisons; demographic characteristics of total worker populations in affected facilities were not available, limiting the ability to quantify the degree to which some racial and ethnic minority groups might be disproportionately affected by COVID-19 in this industry. Reported frequencies of demographic and symptom data likely underestimate the actual prevalence because of missing data, which limits the conclusions that can be drawn from descriptive analyses. Fifth, information on interventions and prevention efforts was available for a subset of affected facilities and therefore might not be generalizable to all facilities. Information was subject to self-report by facility management, and all available intervention efforts might not have been captured. Further evaluation of the extent of control measures and timing of implementations is needed to assess effectiveness of control measures. Sixth, symptom data collected at facility-wide testing was self-reported and might have been influenced by the presence of employers. Finally, workers in this industry are members of their local communities, and their source of exposure and infection could not be determined; for those living in communities experiencing widespread transmission, exposure might have occurred within the surrounding community as well as at the worksite. High population-density workplace settings such as meat and poultry processing facilities present ongoing challenges to preventing and reducing the risk for SARS-CoV-2 transmission. Collaborative implementation of interventions and prevention efforts, which might include comprehensive testing strategies, could help reduce COVID-19–associated occupational risk. Targeted, workplace-specific prevention strategies are critical to reducing COVID-19–associated health disparities among vulnerable populations Lessons learned from investigating outbreaks of COVID-19 in meat and poultry processing facilities could inform investigations in other food production and agriculture workplaces to help prevent and reduce COVID-19 transmission among all workers in these essential industries. Summary What is already known about this topic? COVID-19 outbreaks among meat and poultry processing facility workers can rapidly affect large numbers of persons. What is added by this report? Among 23 states reporting COVID-19 outbreaks in meat and poultry processing facilities, 16,233 cases in 239 facilities occurred, including 86 (0.5%) COVID-19–related deaths. Among cases with race/ethnicity reported, 87% occurred among racial or ethnic minorities. Commonly implemented interventions included worker screening, source control measures (universal face coverings), engineering controls (physical barriers), and infection prevention measures (additional hand hygiene stations). What are the implications for public health practice? Targeted workplace interventions and prevention efforts that are appropriately tailored to the groups most affected by COVID-19 are critical to reducing both COVID-19–associated occupational risk and health disparities among vulnerable populations.

          Related collections

          Most cited references7

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 — COVID-NET, 14 States, March 1–30, 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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

            On 5 February 2020, in Yokohama, Japan, a cruise ship hosting 3,711 people underwent a 2-week quarantine after a former passenger was found with COVID-19 post-disembarking. As at 20 February, 634 persons on board tested positive for the causative virus. We conducted statistical modelling to derive the delay-adjusted asymptomatic proportion of infections, along with the infections’ timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5–20.2%). Most infections occurred before the quarantine start.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              COVID-19 and Racial/Ethnic Disparities

                Bookmark

                Author and article information

                Contributors
                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
                10 July 2020
                10 July 2020
                : 69
                : 27
                : 887-892
                Affiliations
                CDC COVID-19 Emergency Response; Epidemic Intelligence Service, CDC; Arizona Department of Health Services; Colorado Department of Public Health and Environment; Georgia Department of Public Health; Idaho Department of Health and Welfare; Illinois Department of Public Health; Kansas Department of Health and Environment; Kentucky Department for Public Health; Maine Center for Disease Control and Prevention; Maryland Department of Health; Massachusetts Department of Public Health; Missouri Department of Health and Senior Services; Nebraska Department of Health and Human Services; New Mexico Department of Health; Pennslyvania Department of Health; Pennsylvania Department of Agriculture; Rhode Island Department of Health; South Carolina Department of Health and Environmental Control; South Dakota Department of Health; Tennessee Department of Health; Utah Department of Health; Virginia Department of Health; Washington State Department of Health; Wisconsin Department of Health Services; Wyoming Department of Health.
                Rhode Island Department of Health
                CDC
                CDC
                CDC
                CDC
                Emory University, Atlanta, Georgia
                Global Center for Health Security
                University of Nebraska Medical Center
                CDC
                Global Center for Health Security
                University of Nebraska Medical Center
                Global Center for Health Security
                University of Nebraska Medical Center
                Rhode Island Department of Health
                Global Center for Health Security
                South Dakota Department of Health
                CDC
                CDC.
                Author notes
                Corresponding author: Michelle A. Waltenburg, mwaltenburg@ 123456cdc.gov .
                Article
                mm6927e2
                10.15585/mmwr.mm6927e2
                7732361
                32644986
                61936171-7670-442d-9a15-e6747b2a58bc

                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.

                History
                Categories
                Full Report

                Comments

                Comment on this article

                scite_

                Similar content29

                Cited by111

                Most referenced authors550