14
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      COVID-19-Associated Hospitalizations Among Vaccinated and Unvaccinated Adults 18 Years or Older in 13 US States, January 2021 to April 2022

      research-article
      , MD, MHS 1 , 2 , , , MPH 1 , , PhD 1 , , MPH 1 , , MPH 1 , 3 , , MPH 1 , 3 , , MPH 1 , , MSPH 1 , , MStat 1 , 4 , , MD, MPH 1 , 2 , , MD, MPH 5 , 6 , , MPH 6 , , MPH 7 , , MD, MPH 7 , , MPH, CPH 8 , , MPH 8 , , DrPH 9 , 10 , , MD 10 , 11 , 12 , , MPH 13 , , MPH 13 , , MD 14 , , MPH 14 , , MA 15 , , MPH 15 , , MPH 16 , , MPH 16 , , MHS 17 , , MPH 17 , , MPH 18 , , MPH 18 , , MD, MPH 19 , , MD, MPH 19 , , MD, MPH 20 , , MD 20 , , MPH 21 , , MPH 21 , , DVM, MSPH 1 , , MD, MPH 1 , 2 , , MD 1 , 2 , , MD 1 , 2 , , MD, MPH 1 , 2
      JAMA Internal Medicine
      American Medical Association

      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.

          Key Points

          Question

          How do COVID-19–associated hospitalization rates compare among adults who are unvaccinated and vaccinated, and what are the risk factors for hospitalization for COVID-19 among vaccinated persons?

          Findings

          In this cross-sectional study of US adults hospitalized with COVID-19 during January 2022 to April 2022 (during Omicron variant predominance), COVID-19-associated hospitalization rates were 10.5 times higher in unvaccinated persons and 2.5 times higher in vaccinated persons with no booster dose, respectively, compared with those who had received a booster dose. Compared with unvaccinated hospitalized persons, vaccinated hospitalized persons were more likely to be older and have more underlying medical conditions.

          Meaning

          The study results suggest that COVID-19 vaccines are strongly associated with prevention of serious COVID-19 illness.

          Abstract

          Importance

          Understanding risk factors for hospitalization in vaccinated persons and the association of COVID-19 vaccines with hospitalization rates is critical for public health efforts to control COVID-19.

          Objective

          To determine characteristics of COVID-19–associated hospitalizations among vaccinated persons and comparative hospitalization rates in unvaccinated and vaccinated persons.

          Design, Setting, and Participants

          From January 1, 2021, to April 30, 2022, patients 18 years or older with laboratory-confirmed SARS-CoV-2 infection were identified from more than 250 hospitals in the population-based COVID-19–Associated Hospitalization Surveillance Network. State immunization information system data were linked to cases, and the vaccination coverage data of the defined catchment population were used to compare hospitalization rates in unvaccinated and vaccinated individuals. Vaccinated and unvaccinated patient characteristics were compared in a representative sample with detailed medical record review; unweighted case counts and weighted percentages were calculated.

          Exposures

          Laboratory-confirmed COVID-19–associated hospitalization, defined as a positive SARS-CoV-2 test result within 14 days before or during hospitalization.

          Main Outcomes and Measures

          COVID-19–associated hospitalization rates among vaccinated vs unvaccinated persons and factors associated with COVID-19–associated hospitalization in vaccinated persons were assessed.

          Results

          Using representative data from 192 509 hospitalizations (see Table 1 for demographic information), monthly COVID-19–associated hospitalization rates ranged from 3.5 times to 17.7 times higher in unvaccinated persons than vaccinated persons regardless of booster dose status. From January to April 2022, when the Omicron variant was predominant, hospitalization rates were 10.5 times higher in unvaccinated persons and 2.5 times higher in vaccinated persons with no booster dose, respectively, compared with those who had received a booster dose. Among sampled cases, vaccinated hospitalized patients with COVID-19 were older than those who were unvaccinated (median [IQR] age, 70 [58-80] years vs 58 [46-70] years, respectively; P < .001) and more likely to have 3 or more underlying medical conditions (1926 [77.8%] vs 4124 [51.6%], respectively; P < .001).

          Conclusions and Relevance

          In this cross-sectional study of US adults hospitalized with COVID-19, unvaccinated adults were more likely to be hospitalized compared with vaccinated adults; hospitalization rates were lowest in those who had received a booster dose. Hospitalized vaccinated persons were older and more likely to have 3 or more underlying medical conditions and be long-term care facility residents compared with hospitalized unvaccinated persons. The study results suggest that clinicians and public health practitioners should continue to promote vaccination with all recommended doses for eligible persons.

          Abstract

          This cross-sectional study examines characteristics of COVID-19–associated hospitalizations among vaccinated persons and comparative hospitalization rates in unvaccinated and vaccinated persons.

          Related collections

          Most cited references22

          • 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: not found

            Oral Nirmatrelvir for High-Risk, Nonhospitalized Adults with Covid-19

            Background Nirmatrelvir is an orally administered severe acute respiratory syndrome coronavirus 2 main protease (M pro ) inhibitor with potent pan–human-coronavirus activity in vitro. Methods We conducted a phase 2–3 double-blind, randomized, controlled trial in which symptomatic, unvaccinated, nonhospitalized adults at high risk for progression to severe coronavirus disease 2019 (Covid-19) were assigned in a 1:1 ratio to receive either 300 mg of nirmatrelvir plus 100 mg of ritonavir (a pharmacokinetic enhancer) or placebo every 12 hours for 5 days. Covid-19–related hospitalization or death from any cause through day 28, viral load, and safety were evaluated. Results A total of 2246 patients underwent randomization; 1120 patients received nirmatrelvir plus ritonavir (nirmatrelvir group) and 1126 received placebo (placebo group). In the planned interim analysis of patients treated within 3 days after symptom onset (modified intention-to treat population, comprising 774 of the 1361 patients in the full analysis population), the incidence of Covid-19–related hospitalization or death by day 28 was lower in the nirmatrelvir group than in the placebo group by 6.32 percentage points (95% confidence interval [CI], −9.04 to −3.59; P<0.001; relative risk reduction, 89.1%); the incidence was 0.77% (3 of 389 patients) in the nirmatrelvir group, with 0 deaths, as compared with 7.01% (27 of 385 patients) in the placebo group, with 7 deaths. Efficacy was maintained in the final analysis involving the 1379 patients in the modified intention-to-treat population, with a difference of −5.81 percentage points (95% CI, −7.78 to −3.84; P<0.001; relative risk reduction, 88.9%). All 13 deaths occurred in the placebo group. The viral load was lower with nirmaltrelvir plus ritonavir than with placebo at day 5 of treatment, with an adjusted mean difference of −0.868 log 10 copies per milliliter when treatment was initiated within 3 days after the onset of symptoms. The incidence of adverse events that emerged during the treatment period was similar in the two groups (any adverse event, 22.6% with nirmatrelvir plus ritonavir vs. 23.9% with placebo; serious adverse events, 1.6% vs. 6.6%; and adverse events leading to discontinuation of the drugs or placebo, 2.1% vs. 4.2%). Dysgeusia (5.6% vs. 0.3%) and diarrhea (3.1% vs. 1.6%) occurred more frequently with nirmatrelvir plus ritonavir than with placebo. Conclusions Treatment of symptomatic Covid-19 with nirmatrelvir plus ritonavir resulted in a risk of progression to severe Covid-19 that was 89% lower than the risk with placebo, without evident safety concerns. (Supported by Pfizer; ClinicalTrials.gov number, NCT04960202 .)
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Association Between mRNA Vaccination and COVID-19 Hospitalization and Disease Severity

                Bookmark

                Author and article information

                Journal
                JAMA Intern Med
                JAMA Intern Med
                JAMA Internal Medicine
                American Medical Association
                2168-6106
                2168-6114
                8 September 2022
                October 2022
                8 September 2022
                : 182
                : 10
                : 1071-1081
                Affiliations
                [1 ]US Centers for Disease Control and Prevention COVID-19 Response, Atlanta, Georgia
                [2 ]Public Health Service Commissioned Corps, Rockville, Maryland
                [3 ]General Dynamics Information Technology, Atlanta, Georgia
                [4 ]Stat-Epi Associates, Inc, Ponte Vedra Beach, Florida
                [5 ]Field Services Branch, Division of State and Local Readiness, Center for Preparedness and Response, US Centers for Disease Control and Prevention, Atlanta, Georgia
                [6 ]California Emerging Infections Program, Oakland
                [7 ]Colorado Department of Public Health and Environment, Denver
                [8 ]Connecticut Emerging Infections Program, Yale School of Public Health, New Haven
                [9 ]Division of Infectious Diseases, School of Medicine, Emory University, Atlanta, Georgia
                [10 ]Georgia Emerging Infections Program, Georgia Department of Public Health, Atlanta
                [11 ]Departments of Medicine and Pediatrics, Emory School of Medicine, Atlanta, Georgia
                [12 ]Atlanta Veterans Affairs Medical Center, Atlanta, Georgia
                [13 ]Michigan Department of Health and Human Services, Lansing
                [14 ]Minnesota Department of Health, St. Paul
                [15 ]New Mexico Department of Health, Santa Fe
                [16 ]New York State Department of Health, Albany
                [17 ]University of Rochester School of Medicine and Dentistry, Rochester, New York
                [18 ]Ohio Department of Health, Columbus
                [19 ]Public Health Division, Oregon Health Authority, Portland
                [20 ]Vanderbilt University Medical Center, Nashville, Tennessee
                [21 ]Salt Lake County Health Department, Salt Lake City, Utah
                Author notes
                Article Information
                Accepted for Publication: August 4, 2022.
                Published Online: September 8, 2022. doi:10.1001/jamainternmed.2022.4299
                Corresponding Author: Fiona Havers, MD, MHS, 1600 Clifton Rd, US Centers for Disease Control and PrevenMS H24-6, Atlanta, GA 30329 ( fhavers@ 123456cdc.gov ).
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Havers FP et al. JAMA Internal Medicine.
                Author Contributions: Dr Havers had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Havers, Pham, Milucky, Meek, Lynfield, Como-Sabetti, Talbot.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Havers, Pham, Billing, Murthy, McMorrow.
                Critical revision of the manuscript for important intellectual content: Pham, Taylor, Whitaker, Patel, Anglin, Kambhampati, Milucky, Zell, Moline, Chai, Daily Kirley, Alden, Armistead, Yousey-Hindes, Meek, Openo, Anderson, Reeg, Kohrman, Lynfield, Como-Sabetti, Davis, Cline, Muse, Barney, Bushey, Felsen, Shiltz, Sutton, Abdullah, Talbot, Schaffner, Hill, George, Hall, Bialek, Murthy, Patel Murthy, McMorrow.
                Statistical analysis: Pham, Whitaker, Patel, Milucky, Moline, Como-Sabetti.
                Obtained funding: Havers, Meek, Schaffner, McMorrow.
                Administrative, technical, or material support: Havers, Pham, Taylor, Anglin, Kambhampati, Milucky, Zell, Alden, Meek, Openo, Anderson, Lynfield, Como-Sabetti, Barney, Billing, Sutton, Schaffner, Hill, Hall, Murthy, Patel Murthy, McMorrow.
                Supervision: Havers, Meek, Anderson, Como-Sabetti, Sutton, Talbot, Schaffner, Hill, Hall, Bialek, McMorrow.
                Other - data collection and reporting: Bushey.
                Other - contribution of site specific data collection information: Daily Kirley.
                Other - reviewed the draft and provided comments: Abdullah.
                Conflict of Interest Disclosures: Drs Yousey-Hindes, Lynfield, Sutton, Talbot, and Meek reported grants from US Centers for Disease Control and Prevention (CDC) during the conduct of the study. Dr Anderson reported grants from Pfizer, Sanofi, GSK, Janssen, MedImmune, Regeneron, PaxVax, Micron, and Merck as well as personal fees from Moderna, Pfizer, Sanofi, GSK, Janssen, Medscape, Kentucky BioProcessing Inc, and WCG and ACI outside the submitted work; additionally, his institution has also received funding from the National Institutes of Health to conduct clinical trials of COVID-19 vaccines. Drs Reeg and Kohrman reported grants from the Michigan Department of Health and Human Services during the conduct of the study. Drs Billing and Shiltz reported grants from the Council of State and Territorial Epidemiologists (CTSTE) and the CDC Dr Schaffner reported grants from CDC during the conduct of the study as well as personal fees from VBI Vaccines outside the submitted work. Drs Hill and George reported grants from CSTE during the conduct of the study. No other disclosures were reported.
                Funding/Support: This work was supported by the CDC through an Emerging Infections Program cooperative agreement (grant CK17-1701) and through a CSTE cooperative agreement (grant NU38OT000297-02-00).
                Role of the Funder/Sponsor: CDC was involved with the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC.
                Additional Contributions: The following people were involved in the collection and management of study data and received funding from CDC for these activities: Roxanne Archer, MPH, Susan Brooks, MPH, Monica Napoles, BS, Jeremy Roland, MPH, Tiffany Tsukuda, MPH, California Emerging Infections Program; Arthur Reingold, MD, University of California, Berkeley; Rachel Herlihy, MD, MPH, Breanna Kawasaki, MPH, Sarah McLafferty, MPH, Millen Tsegaye, MHA Jordan Surgnier, MPH, Colorado Department of Public Health and Environment; Ann Basting, BS, Tessa Carter, MPH, Maria Correa, MPH, Daewi Kim, MBS, Carol Lyons, MPH, Amber Maslar, MPA, Julie Plano, MS, Hazhia Sorosindi, BS, Connecticut Emerging Infections Program, Yale School of Public Health; Katelyn Ward, MPH, Georgia Emerging Infections Program, Georgia Department of Public Health, Division of Infectious Diseases, Emory University School of Medicine; Marina Bruck, MPH, Rayna Ceasar, BS, Gracie Chambers, BS, Taylor Eisenstein, MPH, Emily Fawcett, MPH, Asmith Joseph, MPH, Grayson Kallas, MPH, Stephanie Lehman, BSN, RN, Jana Manning, MPH, Hope Wilson, MPH, Johanna Hernandez, MPH, Sabrina Hendrick, MPH, Annabel Patterson, MPH, Allison Roebling, DVM, MPH, Chandler Surell, MPH, Georgia Emerging Infections Program, Georgia Department of Public Health, Veterans Affairs Medical Center, Foundation for Atlanta Veterans Education and Research; Sue Kim, MPH, Val Tellez Nunez, MPH, Chloe Brown, MPH, Jim Collins, MPH, Justin Henderson, MPH, Shannon Johnson, MPH, Lauren Leegwater, MPH, Sierra Peguies-Khan, MPH, Michigan Department of Health and Human Services; Stephanie Meyer, MPH, Keeley Morris, MPH, Aaron Bieringer, BS, Erica Bye, MPH, Richard Danila, PhD, MPH, Corinne Holtzman, MPH, Sydney Kuramoto, MPH, Miriam Muscoplat, MPH, Kayla Bilski, MPH, Amanda Markelz, MPH, Margaret Roddy, MPH, Amy Saupe, MPH, Kristin Sweet, PhD, MPH, Minnesota Department of Health; Marianne Murphy, Murtada Khalifa, MBBS, Yassir Talha, MBBS, CDC Foundation, New Mexico Department of Health; Florent Nkouaga, MA, Melissa Judson, Sunshine Martinez, Jasmyn Sanchez, Susan L. Ropp, PhD, Chad Smelser, MD, Daniel M. Sosin, MD, MPH, New Mexico Department of Health; Dominic Rudin, BS, Kathy M. Angeles, MPH, Melissa Christian, MPH, Nancy Eisenberg, MPH, Emily B. Hancock, MS, Sarah A. Khanlian, MPH, Sarah Lathrop, DVM, PhD, Wickliffe Omondi, MPH, Mayvilynne Poblete, MA, MPH, Yadira Salazar-Sánchez, MPH, Sarah Shrum Davis, MPH, Chelsea McMullen, MSc-GH, New Mexico Emerging Infections Program; Nancy Spina, MPH, Kerianne Engesser, MPH, Suzanne McGuire, MPH, Adam Rowe, BA, New York State Department of Health; Nancy M. Bennett, MD, MS, Virginia Cafferky, BS, Maria Gaitán, BS, Christine Long, MPH, Thomas Peer, MPH, Kevin Popham, MPH, University of Rochester School of Medicine and Dentistry; Kylie Seeley, MD, MPH, Oregon Health & Science University School of Medicine; Sam Hawkins, MPH, Ama Owusu-Dommey, MPH, Emily Youngers, MPH, Breanna McArdle, MPH, Public Health Division; Oregon Health Authority; Tiffanie Markus, PhD, Kathy Billings, MPH, Katie Dyer, Anise Elie, MPH, BSN, RN, Karen Leib, BSN, RN, Terri McMinn, Danielle Ndi, MPH, Manideepthi Pemmaraju, MBBS, MPH, John Ujwok, MPH, Vanderbilt University Medical Center; Amanda Carter, BS, Ryan Chatelain, MPH, Andrew Haraghey, BS, Laine McCullough, MPH, Jacob Ortega, MPH, Andrea Price, LPN, Tyler Riedesel, MPH, Caitlin Shaw, BS, Melanie Crossland, MPH, Ashley Swain, Salt Lake County Health Department; Keegan McCaffrey, BS, Utah Department of Health; Kelly Oakeson, PhD, and Alessandro Rossi, PhD, Utah Public Health Laboratory. In addition, Li Deng, PhD, CDC, contributed statistical advice, and Hannah E. Fast, MPH, CDC, assisted with vaccination coverage data. Neither received additional funding for these activities.
                Article
                ioi220057
                10.1001/jamainternmed.2022.4299
                9459904
                36074486
                36c76ad7-8386-4d07-be0f-7aefa355a4ea
                Copyright 2022 Havers FP et al. JAMA Internal Medicine.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 23 May 2022
                : 4 August 2022
                Categories
                Research
                Research
                Original Investigation
                Online First

                Comments

                Comment on this article

                scite_

                Similar content258

                Cited by45

                Most referenced authors602