Introduction: Argentinean quarantine during the COVID-19 pandemic is one of the most long-lasting worldwide. We focused on the first 80-days of this quarantine on Argentinean women. Our aims were to analyze differences in general mental health state (MHS) indicators, by the (1) sites of residence with different prevalence of COVID-19 cases, and (2) quarantine duration; (3) to assess multiple relationships between each general MHS indicator and potentially affecting factors.Methods: We used a cross-sectional design with convenience successive sampling (N = 5,013). The online survey included a socio-demographic questionnaire (elaborated ad hoc) with standardized and validated self-reported questionnaires (General Health Questionnaire, Kessler Psychological Distress Scale) measuring the MHS indicators: self-perceived health, psychological discomfort, social functioning and coping, and psychological distress.Results: Worse self-perceived health and higher psychological discomfort affected significantly more women residing in sites with high prevalence of COVID-19 cases, compared to those residing in sites with intermediate prevalence, but effect sizes were small. Mean scores of all general MHS indicators were significantly worse for longer quarantine sub-periods (up to 53, 68, and 80-day duration) than for shorter sub-periods (up to seven, 13, and 25-day duration). Being a younger age, having mental disorder history, and longer quarantine durations were associated to worsening MHS, while the lack of previous suicide attempt has a protective effect.Discussion: Our findings show that a worse MHS during quarantine may not be attributed to the objective risk of contagion (measured greater or less), and under quarantine, women MHS—as indicated by group central tendency measures—got worse as time went by. This strongly suggests that special attention needs to be paid to younger women and to women with history of mental disorder. Along with physical health, mental health must be a priority for the Government during and after quarantine and the COVID-19 pandemic.

Dataset

This dataset was created by Vu Nhu Duc

Released under MIT

Contents

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The outbreak of the novel coronavirus disease 2019 (COVID-19) has had an unprecedented impact worldwide, and it is of great significance to predict the prognosis of patients for guiding clinical management. This study aimed to construct a nomogram to predict the prognosis of COVID-19 patients. Clinical records and laboratory results were retrospectively reviewed for 331 patients with laboratory-confirmed COVID-19 from Huangshi Hospital of Traditional Chinese Medicine (TCM) (Infectious Disease Hospital) and Third Affiliated Hospital of Sun Yat-sen University. All COVID-19 patients were followed up for 80 days, and the primary outcome was defined as patient death. Cases were randomly divided into training (n=199) and validation (n=132) groups. Based on baseline data, we used statistically significant prognostic factors to construct a nomogram and assessed its performance. The patients were divided into Death (n=23) and Survival (n=308) groups. Analysis of clinical characteristics showed that these patients presented with fever (n=271, 81.9%), diarrhea (n=20, 6.0%) and had comorbidities (n=89, 26.9.0%). Multivariate Cox regression analysis showed that age, UREA and LDH were independent risk factors for predicting 80-day survival of COVID-19 patients. We constructed a qualitative nomogram with high C-indexes (0.933 and 0.894 in the training and validation groups, respectively). The calibration curve for 80-day survival showed optimal agreement between the predicted and actual outcomes. Decision curve analysis revealed the high clinical net benefit of the nomogram. Overall, our nomogram could effectively predict the 80-day survival of COVID-19 patients and hence assist in providing optimal treatment and decreasing mortality rates.

Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of positive COVID-19 cases among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Cases by Age Distribution data layer is a collection of positive COVID-19 test results that have been reported each day by the local health department via the ESSENCE system. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Colombia INS: COVID-19: Confirmed Cases: To Date: Age 80 to 89 data was reported at 138,454.000 Person in 26 Dec 2023. This records an increase from the previous number of 138,363.000 Person for 13 Dec 2023. Colombia INS: COVID-19: Confirmed Cases: To Date: Age 80 to 89 data is updated daily, averaging 92,188.500 Person from Mar 2020 (Median) to 26 Dec 2023, with 1176 observations. The data reached an all-time high of 138,454.000 Person in 26 Dec 2023 and a record low of 0.000 Person in 10 Mar 2020. Colombia INS: COVID-19: Confirmed Cases: To Date: Age 80 to 89 data remains active status in CEIC and is reported by National Institute of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table CO.D001: National Institute of Health: Coronavirus Disease 2019 (COVID-2019). Current day data is released daily between 4PM and 6PM Colombia Time. Weekend data are updated following Monday morning, Hong Kong Time.

Dataset

This dataset was created by Gaurav Srivastava

Released under Data files © Original Authors

Contents

Dataset Card for "COVID-QA-train-80-test-10-validation-10"

More Information needed

Pre-existing conditions of people who died due to COVID-19, broken down by country, broad age group, and place of death occurrence, usual residents of England and Wales.

BackgroundDuring the coronavirus disease-2019 (COVID-19) pandemic, there have been many studies on knowledge, attitudes, and practices (KAP) toward prevention of COVID-19 infection in China. Except for symptomatic treatment and vaccination, KAP toward COVID-19 plays an important role in the prevention of COVID-19. There is no systematic evaluation and meta-analysis of KAP toward COVID-19 in China. This study is the earliest meta-analysis of KAP toward COVID-19 in China’s general population. Hence, this systematic review aimed to summarize the knowledge, attitudes, and practices (KAP) of Chinese residents toward COVID-19 during the pandemic.MethodologyFollowing the PRISMA guidelines, articles relevant to COVID-19 KAP that were conducted among the Chinese population were found in databases such as Scopus, ProQuest, PubMed, EMbase, Web of Science, Cochrane Library, China Biology Medicine, China National Knowledge Infrastructure, CQVIP, Wanfang and Google Scholar. A random-effect meta-analysis is used to summarize studies on knowledge, attitudes, and practice levels toward COVID-19 infection in China’s general population.ResultsFifty-seven articles published between August 2020 and November 2022 were included in this review. Overall, 75% (95% CI: 72–79%) of Chinese residents had good knowledge about COVID-19, 80% (95% CI: 73–87%) of Chinese residents had a positive attitude toward COVID-19 pandemic control and prevention (they believe that Chinese people will win the battle against the epidemic), and the aggregated proportion of residents with a correct practice toward COVID-19 was 84% (95% CI: 82–87%, I2 = 99.7%).In the gender subgroup analysis, there is no significant difference between Chinese men and Chinese women in terms of their understanding of COVID-19. However, Chinese women tend to have slightly higher levels of knowledge and a more positive attitude toward the virus compared to Chinese men. When considering the urban and rural subgroup analysis, it was found that Chinese urban residents have a better understanding of COVID-19 compared to Chinese rural residents. Interestingly, the rural population displayed higher rates of correct behavior and positive attitudes toward COVID-19 compared to the urban population. Furthermore, in the subgroup analysis based on different regions in China, the eastern, central, and southwestern regions exhibited higher levels of knowledge awareness compared to other regions. It is worth noting that all regions in China demonstrated good rates of correct behavior and positive attitudes toward COVID-19.ConclusionThis study reviews the level of KAP toward COVID-19 during the pandemic period in China. The results show that the KAP toward COVID-19 in Chinese residents was above a favorable level, but the lack of translation of knowledge into practice should be further reflected on and improved. A subgroup analysis suggests that certain groups need more attention, such as males and people living in rural areas. Policy makers should pay attention to the results of this study and use them as a reference for the development of prevention and control strategies for major public health events that may occur in the future.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=348246, CRD42022348246.

Listing of Washoe County COVID-19 case data, by day posted to public dashboard. This table is based on best available information from the Washoe County Health District. Not all fields are populated for all dates.Name FieldName FieldType Comment

OBJECTID OBJECTID ObjectID System generated unique ID

Date Reported reportdt Date Effective date of this row of data

Confirmed confirmed Integer Total number of confirmed cases to date

Recovered recovered Integer Number of recoveries to date

Deaths deaths Integer Number of deaths to date

Active active Integer Current number of active cases

Male Male Small Integer Total confirmed cases to date: Male

Female Female Small Integer Total confirmed cases to date: Female

OtherGender GenderOther Small Integer Total confirmed cases to date: OtherGender

Total Cases 0-9 Age0to9 Small Integer Total confirmed cases to date: Total Cases 0-9

Total Cases 10-19 Age10to19 Small Integer Total confirmed cases to date: Total Cases 10-19

Total Cases 20-29 Age20to29 Small Integer Total confirmed cases to date: Total Cases 20-29

Total Cases 30-39 Age30to39 Small Integer Total confirmed cases to date: Total Cases 30-39

Total Cases 40-49 Age40to49 Small Integer Total confirmed cases to date: Total Cases 40-49

Total Cases 50-59 Age50to59 Small Integer Total confirmed cases to date: Total Cases 50-59

Total Cases 60-69 Age60to69 Small Integer Total confirmed cases to date: Total Cases 60-69

Total Cases 70-79 Age70to79 Small Integer Total confirmed cases to date: Total Cases 70-79

Total Cases 80-89 Age80to89 Small Integer Total confirmed cases to date: Total Cases 80-89

Total Cases 90-99 Age90to99 Small Integer Total confirmed cases to date: Total Cases 90-99

Total Cases 100+ Age100plus Small Integer Total confirmed cases to date: Total Cases 100+

UnknownAge AgeNA Small Integer Total confirmed cases to date: UnknownAge

Native American E_NativeAmerican Integer Total Cases to date: Native American

Asian E_Asian Integer Total Cases to date: Asian

African American E_Black Integer Total Cases to date: African American

Hispanic E_Hispanic Integer Total Cases to date: Hispanic

Hawaiian or Pacific Islander E_HawaiianPacific Integer Total Cases to date: Hawaiian or Pacific Islander

Caucasian E_White Integer Total Cases to date: Caucasian

Multiple E_Multiple Integer Total Cases to date: Multiple

OtherEthnicity E_Other Integer Total Cases to date: OtherEthnicity

EthnicityUnknown E_Unknown Integer Total Cases to date: EthnicityUnknown

New Cases 7 Day Moving Average NewCases7DMA Double Average New Cases over last 7 days

NewCases NewCases Integer New Cases in last day

ActiveCasesAge0to9per100K Age0to9_100K Double Active Cases per 100,000: Age0to9

ActiveCasesAge10to19per100K Age10to19_100K Double Active Cases per 100,000: Age10to19

ActiveCasesAge20to29per100K Age20to29_100K Double Active Cases per 100,000: Age20to29

ActiveCasesAge30to39per100K Age30to39_100K Double Active Cases per 100,000: Age30to39

ActiveCasesAge40to49per100K Age40to49_100K Double Active Cases per 100,000: Age40to49

ActiveCasesAge50to59per100K Age50to59_100K Double Active Cases per 100,000: Age50to59

ActiveCasesAge60to69per100K Age60to69_100K Double Active Cases per 100,000: Age60to69

ActiveCasesAge70to79per100K Age70to79_100K Double Active Cases per 100,000: Age70to79

ActiveCasesAge80to89per100K Age80to89_100K Double Active Cases per 100,000: Age80to89

ActiveCasesAge90to99per100K Age90to99_100K Double Active Cases per 100,000: Age90to99

ActiveCasesAge100plusper100K Age100plus_100K Double Active Cases per 100,000: Age100plus

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, case mortality ratios, country, latitude, and longitude.

Disclaimer: Data will be more accurate as more data comes in. Deaths/Infections will be a better measure of mortality rate after a pandemic is over, when the estimates of the number of infections start to get closer to the true number of infected individuals. Note discussion of case mortality ratio (numbers as they are reported) vs infection mortality ratio (estimates of the actual numbers). This dataset discusses case mortality ratios.

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Deprecated as of 4/21/2023On 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. For more information, visit https://imap.maryland.gov/pages/covid-dataSummaryThe cumulative number of COVID-19 vaccinations among Maryland residents by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; unknownDescriptionMD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Abstract The paper examines the implications of Covid-19 pandemic for the city of Belo Horizonte (BH), during the first 80 days of the disease. We use a descriptive-analytical approach to estimate the growth of Covid-19 cases over time, the excess of deaths, the virus’ rate of transmissibility, and the consequent burden on the municipal the health system, measured by the rate of occupancy of public hospital beds. Also, we identify the main containment policies adopted by local authorities, and the implications of reopening measures and the following reduction of social distancing. Our findings reveal that a well-managed Unified Health System (SUS) is paramount to effectively tackle the pandemic and its consequences for the population. The reopening process has imposed new challenges that will require close monitoring by the authorities and by the society.

WARNING: This asset has been deprecated and will no longer be updated (Last Updated April 14, 2022). Summary The cumulative number of COVID-19 vaccinations by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description MD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

Dataset of COVID-19 HSE Weekly Vaccination data. Time series available from Week 53 2020 to Week 18 2024. CategoryField labelField NameExplanation ExtractDateExtract DateDate the data is Extracted LatitudeLatitude LongitudeLongitude VaccinationDateVaccination DateDate the Vaccination occurred WeekWeekDetails of epidemiological weeks available here https://www.hpsc.ie/notifiablediseases/resources/epidemiologicalweeks/ TotalDailyVaccinesTotal Daily Vaccines GenderMale Female NA Dose NumberDose1Dose 1 Dose2Dose 2 SingleDoseSingle Dose Vaccine BrandModerna Pfizer Janssen AstraZeneca Age GroupPartial_Age0to9At Least One Dose Age 0 to 11Dose 1 of Astrazenenca, MRNA or Single Dose VaccinePartial_Age10to19At Least One Dose Age 12 to 19Partial_Age20to29At Least One Dose Age 20 to 29Partial_Age30to39At Least One Dose Age 30 to 39Partial_Age40to49At Least One Dose Age 40 to 49Partial_Age50to59At Least One Dose Age 50 to 59Partial_Age60to69At Least One Dose Age 60 to 69Partial_Age70to79At Least One Dose Age 70 to 79Partial_Age80+At Least One Dose Age80+Partial_NAAt Least One Dose Not AssignedAge Group CumulativeParCum_Age0to9Cumulative Age 0 to 11Cumulative At least One Dose Age 0 to 11ParCum_Age10to19Cumulative Age 12 to 19Cumulative At least One Dose Age 12 to 19ParCum_Age20to29Cumulative Age 20 to 29Cumulative At least One Dose Age 20 to 29ParCum_Age30to39Cumulative Age 30 to 39Cumulative At least One Dose Age 30 to 39ParCum_Age40to49Cumulative Age 40 to 49Cumulative At least One Dose Age 40 to 49ParCum_Age50to59Cumulative Age50 to 59Cumulative At least One Dose Age 50 to 59ParCum_Age60to69Cumulative Age 60 to 69Cumulative At least One Dose Age 60 to 69ParCum_Age70to79Cumulative Age 70 to 79Cumulative At least One Dose Age 70 to 79ParCum_80+Cumulative Age 80+Cumulative At least One Dose Age 80+Age Group Cumulative PercentParCum_NACumulative Age Not AssignedCumulative At least One Dose Age Not AssignedParPer_Age0to9At Least One Dose Percent Age 0 to 11Cumulative At least One Dose Age cohort/ Age cohort populationParPer_Age10to19At Least One Dose Percent Age 12 to 19ParPer_Age20to29At Least One Dose Percent Age 20 to 29ParPer_Age30to39At Least One Dose Percent Age 30 to 39ParPer_Age40to49At Least One Dose Percent Age 40 to 49ParPer_Age50to59At Least One Dose Percent Age 50 to 59ParPer_Age60to69At Least One Dose Percent Age 60 to 69ParPer_Age70to79At Least One Dose Percent Age 70 to 79ParPer_80+At Least One Dose Percent 80+ParPer_NAAt Least One Dose Percent Not AssignedAge GroupFully_Age0to9Fully vaccinated Age 0 to 11Dose 2 of An MRNA or AztraZeneca Vaccine or a single dose vaccine of a JanssenFully_Age10to19Fully vaccinated Age 12 to 19Fully_Age20to29Fully vaccinated Age 20 to 29Fully_Age30to39Fully vaccinated Age 30 to 39Fully_Age40to49Fully vaccinated Age 40 to 49Fully_Age50to59Fully vaccinated Age 50 to 59Fully_Age60to69Fully vaccinated Age 60 to 69Fully_Age70to79Fully vaccinated Age 70 to 79Fully_Age80+Fully vaccinated Age 80+Fully_NAFully vaccinated Age Not Available Age Group CumulativeFullyCum_Age0to9Cumulative Fully vaccinated Age 0 to 11 FullyCum_Age10to19Cumulative Fully vaccinated Age 12 to 19 FullyCum_Age20to29Cumulative Fully vaccinated Age 20 to 29 FullyCum_Age30to39Cumulative Fully vaccinated Age 30 to 39 FullyCum_Age40to49Cumulative Fully vaccinated Age 40 to 49 FullyCum_Age50to59Cumulative Fully vaccinated Age 50 to 59 FullyCum_Age60to69Cumulative Fully vaccinated Age 60 to 69 FullyCum_Age70to79Cumulative Fully vaccinated Age 70 to 79 FullyCum_80+Cumulative Fully vaccinated Age 80+ Age Group Cumulative PercentFullyCum_NACumulative Fully vaccinated Age Not Available FullyPer_Age0to9Cumulative Percent Fully vaccinated Age 0 to 11Cumulative Fully Vaccinated Age cohort/ Age cohort populationFullyPer_Age10to19Cumulative Percent Fully vaccinated Age 12 to 19FullyPer_Age20to29Cumulative Percent Fully vaccinated Age 20 to 29FullyPer_Age30to39Cumulative Percent Fully vaccinated Age 30 to 39FullyPer_Age40to49Cumulative Percent Fully vaccinated Age 40 to 49FullyPer_Age50to59Cumulative Percent Fully vaccinated Age 50 to 59FullyPer_Age60to69Cumulative Percent Fully vaccinated Age 60 to 69FullyPer_Age70to79Cumulative Percent Fully vaccinated Age 70 to 79FullyPer_80+Cumulative Percent Fully vaccinated Age 80+FullyPer_NACumulative Percent Fully vaccinated Age Not Available

Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19).Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications.Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057–1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582–1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355–1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055–1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067–1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118–1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507–2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427–2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323–2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40–80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged <40 years, only cancer (OR = 17.112 [95% CI 6.264–39.73], p < 0.001) and ICH (OR = 31.538 [95% CI 5.213–158.787], p < 0.001) were significantly associated with higher odds of death. For patients aged >80 years, only age (OR = 1.033 [95% CI 1.008–1.059], p = 0.01) and male gender (OR = 1.585 [95% CI 1.301–1.933], p < 0.001) were associated with higher odds of death. The incidence of most complications increases with age, but arrhythmias, gastrointestinal bleeding, and sepsis were more common in younger deceased patients with COVID-19, with only arrhythmia reaching statistical difference (p = 0.039). We found a relatively poor correlation between preexisting risk factors and complications.Conclusions: Coronavirus disease 2019 are disproportionally affected by age for its clinical manifestations, risk factors, complications, and outcomes. Prior complications have little effect on the incidence of extrapulmonary complications.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides state-aggregated data for hospital utilization. These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

On June 26, 2023 the field "reporting_cutoff_start" was replaced by the field "date".

On April 27, 2022 the following pediatric fields were added: