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Coronavirus disease 2019 case surveillance—United States, January 22–May 30, 2020.
Open accessAbstract
was similar among males (401.1) and females (406.0) and highest among persons aged ≥80 years (902.0). Among 599,636 (45%) cases with known information, 33% of persons were Hispanic or Latino of any race (Hispanic), 22% were non-Hispanic black (black), and 1.3% were non-Hispanic American Indian or Alaska Native (AI/AN). Among 287,320 (22%) cases with sufficient data on underlying health conditions, the most common were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Overall, 184,673 (14%) patients were hospitalized, 29,837 (2%) were admitted to an intensive care unit (ICU), and 71,116 (5%) died. Hospitalizations were six times higher among patients with a reported underlying condition (45.4%) than those without reported underlying conditions (7.6%). Deaths were 12 times higher among patients with reported underlying conditions (19.5%) compared with those without reported underlying conditions (1.6%). The COVID-19 pandemic continues to be severe, particularly in certain population groups. These preliminary findings underscore the need to build on current efforts to collect and analyze case data, especially among those with underlying health conditions. These data are used to monitor trends in COVID-19 illness, identify and respond to localized incidence increase, and inform policies and practices designed to reduce transmission in the United States.
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“Persons with underlying medical conditions that increase their risk for severe COVID-19 (6) are more likely to require care to monitor and treat these conditions, potentially contributing to their more frequent report of avoidance.”
From Delay or avoidance of medical care because of COVID-19–related concerns—United States, June 2020. (2020) · cites this paper as [6]
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Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic
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Metadata
- Authors
- Stokes, E. K., Zambrano, L. D., Anderson, K. N., Marder, E. P., Raz, K. M., El Burai Felix, S., Tie, Y., & Fullerton, K. E.
- Affiliations
- CDC COVID-19 Emergency Response.
- Corresponding author
- Stokes, Erin K.
- Venue
- MMWR Morbidity and Mortality Weekly Report
- Published
- 2020-06-19
- Volume
- 69
- Issue
- 24
- Pages
- 759-765
- ISSN
- 0149-2195 (ISSN-L)
- 1545-861X
- PMC
- PMC7302472
- OpenAlex
- W3034312608
- Cited by
- 1,538
- References
- 5
- Citations by year
- 2026: 20 · 2025: 54 · 2024: 108 · 2023: 227 · 2022: 372 · 2021: 520 · 2020: 235
- FWCI
- 53.79
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- MMWR Morbidity and Mortality Weekly Report (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Centers for Disease Control and Prevention
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, SARS-CoV-2, case surveillance, hospitalization, intensive care unit admission, mortality, underlying disease, cardiovascular disease, diabetes mellitus, chronic lung disease, racial and ethnic disparities, age differences, sex differences, United States
- MeSH terms
- COVID-19, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Pneumonia, Viral, Population Surveillance, United States, Incidence, Coronavirus Infections, Young Adult, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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