In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

As of 09/24/24, this dataset is being retired and will no longer be updated.

On 10/1/2021, VDH adjusted the Vaccine Age Group categories to better serve the response's needs. This resulted in a decrease in cases, hospitalizations, and deaths among the 16-17 Year age group and an addition of cases, hospitalizations, and deaths to the 18-24 Years age group.

This dataset includes the cumulative (total) number of COVID-19 cases, hospitalizations, and deaths for each health district in Virginia by report date and by age group. This dataset was first published on March 29, 2020. The data set increases in size daily and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set. The Virginia Department of Health’s Thomas Jefferson Health District (TJHD) will be renamed to Blue Ridge Health District (BRHD), effective January 2021. More information about this change can be found here: https://www.vdh.virginia.gov/blue-ridge/name-change/

It was estimated that around 34 percent of those aged 70 to 79 years who had COVID-19 in the United States from January 22 to May 30, 2020 were hospitalized. Hospitalizations due to COVID-19 are much higher among those with underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes. This statistic shows the percentage of people in the U.S. with COVID-19 from January 22 to May 30, 2020 who were hospitalized, by age.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

IntroductionAn excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered.MethodsAn inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies.ResultsThis study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations.DiscussionThis study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

As of September 29, 2020, the coronavirus (COVID-19) pandemic in the Netherlands resulted in over 12.7 thousand hospitalizations. However, the distribution of hospital admissions differed greatly by age. To this day, most hospitalizations occurred with older patients. In the Netherlands, roughly 70 percent of hospitalized patients were notably aged 60 years old and over. Children have also been admitted to Dutch hospitals due to the coronavirus, although to a much lesser extent.

The COVID-19 pandemic revealed the disproportionate risk of poor clinical outcomes among population subgroups. The study investigates length of stay (LOS), intensive care unit (ICU) admission, and in-hospital death across age, sex, and race among patients hospitalized with COVID-19. A pooled cross-sectional study analyzed hospital discharge data of state-licensed hospitals in Texas from April to December 2020. Of 98,879 patients, males accounted for 52.3%. The age distribution was 31.9% for the 65–79 age group, 29.6% for those aged 50–64, and 16.3% for those older than 79. Whites constituted the largest proportion (42.6%), followed by Hispanics (36.2%) and Blacks (13.1%). Higher in-hospital death rates were found among patients aged 80 and over (Adjusted Risk Ratio (aRR) 1.12, 95%CI 1.11–1.13) and patients aged 65–79 (aRR 1.08, 95%CI 1.07–1.09) compared to patients aged 19 and below. Hispanics (aRR 1.03, 95%CI 1.02–1.03) and other minorities (aRR 1.02, 95%CI 1.02–1.03) exhibited higher in-hospital death rates than whites, and these patients also had longer LOS and higher ICU admission rates. Patients aged 65–79, 50–64, and 80 and over all had longer hospital stays and higher ICU admission rates. Males experienced poor health outcomes in all assessed outcomes. Findings showed that disparities in clinical outcomes among population subgroups existed and remained throughout 2020. While the nation has to continue practicing public health measures to minimize the harm caused by the novel virus, serious consideration must be given to improving the health of marginalized populations during and beyond the pandemic.

ABSTRACT SARS-CoV-2 infection can cause severe acute respiratory syndrome (SARS), leading to hypoxemia. Physical capacity assessment can be performed before hospital discharge using submaximal exercise testing. This study sought to assess physical capacity and exercise tolerance with the six-minute step test (6MST) in hospitalized COVID-19 patients who required oxygen (O2) support during hospitalization. A prospective, interventional study was conducted with patients aged from 18 to 90 years who required oxygen therapy during hospitalization. Assessment was performed using Perme Score, followed by the 6MST tests, assessing the peripheral oxygen saturation (SpO2), heart rate (HR), blood pressure (BP), and subjective exertion perception by Borg Scale, before and immediately after the 6MST. A total of 31 patients, with a mean age of 51.9 years, were evaluated. Nasal cannula (NC) was the most used device (64.5% of patients). Regarding HR, BP, and Borg Scale, their mean value increased after 6MST. SpO2 showed a lower mean value after 6MST. Out of the 86.9% of patients who completed the test, 48.3% completed it with interruptions, and 12.9% had to suspend it. The 6MST was able to assess physical capacity and exercise tolerance, proving to be an effective tool for evaluating COVID-19 patients.

This dataset has been retired as of February 17, 2023. This dataset will be kept for historical purposes, but will no longer be updated. Similar data are available on the state’s open data portal: https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.

Provides the proportion of COVID-19 Cases, Hospitalizations, and Deaths by Age, Gender, and Race/Ethnicity categories.

Note: Between 1/1/2022 and 3/4/2022 hospitalization counts did not include in-patient hospitalizations with a COVID-19 positive test when the patient was in the hospital for a reason other than COVID-19. This included in-patient stays due to labor/delivery, trauma, or emergency surgery. Hospitalization reporting was modified to represent the disease severity of the Omicron variant accurately. As of 3/5/2022, we have resumed publishing the CDPH daily hospitalized patient census, which includes all in-patient hospitalizations with a COVID-19 positive test.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Data Repository

Dataset name: covid19_rn-br.csv

Version: 1.0

Data collection period: 04/2020 - 08/2021

Dataset Characteristics: Multivalued

Number of Instances: 12,635

Number of Attributes: 16

Missing Values: Yes

Area(s): Health

Sources:

- Primary:

• RegulaRN (https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/).

- Secondary:

• RN Mais Vacina (https://rnmaisvacina.lais.ufrn.br/cidadao/).

Description: The covid19_rn-br.csv dataset is composed of data from individuals who were hospitalized due to the Sars-CoV-2 virus. The data comes from the ecosystem of services that includes the regulatory system for clinical and critical beds related to Covid-19 (RegulaRN) and the vaccination system against Covid-19 that records the data of the general population (RN Mais Vacina) from Rio Grande do Norte state, Brazil. This dataset provides elementary data to analyze the impact of vaccination on patients hospitalized in the state. Table 1 presents the dictionary used during the data analysis.

Table 1: Description of Dataset Features.

Attributes	Description	datatype	Value
usp	Unified Score for Prioritization scale, which combines the parameters described in the quick Sequential Organ Failure Assessment (qSOFA), the Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS) and The Karnofsky Performance Status scores	Numerical	2.0. 3.0, 4.0, 5.0, 6.0+
age	Informs the patient's age	Numerical.	integer value for age
outcome	Informs the outcome of the hospitalized patient after leaving the hospital	Categorical	“Discharge” or “Death"
comorbidities	Informs if the patient has comorbidities	Categorical.	“Yes” or “No”
vaccine	Informs which type of vaccine was applied to the patient	Categorical	“Vaccine #1”, “Vaccine #2” or NaN
bed_date_admission	Informs the date the patient was hospitalized	Date	Date
bed_date_outcome	Informs the date that the patient left the hospital bed	Date	Date
length_hospitalization	Informs the number of days that the patient was hospitalized	Numerical	An integer value for days
interval_d1_hospitalization	Informs the interval (in days) that the patient had between the first dose and admission	Numerical	An integer value for days or NaN
interval_d2_hospitalization	Informs the interval (in days) that the patient had between the second dose and admission	Numerical	An integer value for days or NaN
dt_d1	Informs the date of application of the patient's first dose	Date	Date or NaN
dt_d2	Informs the patient's second dose application date	Date	Date or NaN
comorbidities_txt	Informs patients' comorbidities	Categorical	Free text or NaN
immunization	It informs the patient's immunization level according to the number of doses received and the interval (in days) of application of these doses	Categorical	“Partially”, “Fully” or “Not vaccinated”
health_professionals	Informs if the patient is a health professional	Boolean	0 or 1
age_group	Informs the age group of the hospitalized patient according to their age	Categorical	0-19, 20-49, 50-59, 60-69, 70-79, 80-89, 90+

Article: Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil

Authors: Ana Isabela L. Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva, Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Gustavo F. Souza, Agnaldo S. Cruz, Ion G. M. Andrade, Lúcio Gama, Ricardo A. M. Valentim

For English, see below This file contains: - the number of COVID-19 hospital and IC admissions per age group in the Netherlands, per week of hospital or IC admission and per week on which the data were reported to the NICE registry (https: //www.stichting-nice.nl). The numbers concern COVID-19 hospital and IC admissions since the first report in the Netherlands (27/02/2020) up to and including the most recent complete week of admission. The registration of the number of COVID-19 hospital and IC admissions may be lagging behind. This may result in the date of recording and the date of the report falling in a different calendar week. Hospital or ICU admissions from the most recent complete week of admission may have been reported in the current incomplete week and are therefore shown in this file. Hospital and ICU admissions from the most recent incomplete week are not included in this file but are censored with the value “NaN” (Not a number). The file is structured as follows: - One record per week of statistics for the Netherlands, even if there are no recordings or reports for the week in question. The numbers are then 0 (zero). -The stated date for statistics may relate to a hospital or IC admission date or the date on which the hospital reported a hospital or IC admission to the NICE registry. Description of the variables: Version: version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (Https://data.rivm.nl) . Version 2 update (August 9, 2022): - From August 9, 2022, new admissions of persons with a SARS-CoV-2 infection who were also admitted during a previous COVID-19 episode have been added to this open data file. For this reason, the number of withdrawals with retroactive effect is higher than in our previous files. The underestimation of admissions since the start of the pandemic to August 9, 2022 is less than 1%. A recording is counted as a new recording when a person with a SARS-CoV-2 infection has a recording date that is more than 90 days after the previous recording. Version 3 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every Wednesday, but on Tuesdays. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday. Version 4 update (November 24, 2022): - From November 24, 2022, the age group 0-14 years will be split into age groups 0-4, 5-9 and 10-14 years. This will be retroactively updated for the entire pandemic. Version 5 update (April 4, 2023): - From April 4, 2023, this file will be updated weekly on Tuesdays. The data is retroactively updated for the other days. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistics_week_start: The date of the Monday - first day of that week - for which the numbers per week are presented. Week of hospital admission (variable Hospital_admission), week of IC admission (variable IC_admission), the week on which the hospital admission (variable Hospital_admission_notification) or IC admission was reported (variable IC_admission_notification) to the NICE registry. Age_group: Age group in years of the admitted or reported patients. Intervals every five years are used with the exception of 90 years and above (90+). Patients with an unknown age are added to 'Unknown'. Hospital_admission_notification: The number of new COVID-19 patients admitted to the NICE registry per age group [Age_group] per week on which the hospital admission was reported [Date_of_statistics_week_start]. Hospital_admission: The number of new COVID-19 patients admitted to hospital per age group [Age_group] per hospital admission week [Date_of_statistics_week_start] reported to the NICE registry. IC_admission_notification: The number of new COVID-19 patients reported to the NICE registry who were admitted to the ICU per age group [Age_group] per week on which the ICU admission was reported [Date_of_statistics_week_start]. IC_admission: The number of new COVID-19 patients reported to the NICE registry who have been admitted to the ICU per age group [Age_group] per ICU admission week [Date_of_statistics_week_start]. A patient can be admitted to hospital or ICU multiple times (see version 2 update). RIVM and the NICE registry have aligned the method for determining the most relevant admission date in such cases as much as possible, but the numbers may differ slightly from the data as presented by the NICE registry. A patient admitted to the ICU also counts in the hospital admission figures. Despite the fact that hospitals are asked to register COVID-19 patients several times a day, the registration of the number of patients may lag. As a result, the numbers for the past calendar week may still be incomplete (https://www.stichting-nice.nl). Corrections made in reports in the source system of the NICE registration by employees of hospitals can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. At the time of creation and publication, this file therefore always contains the most up-to-date data according to the source system of the NICE registration after processing by RIVM. -------------------------------------------------- --------------------------------------------- Covid-19 hospital and intensive care unit (ICU) admissions in the Netherlands by age group by hospital and ICU admission week and reporting week (according to NICE registration) This file contains: - the number of COVID-19 hospital and ICU admissions by age group in the Netherlands, per week of hospitalization or ICU admission and per week on which the data were reported to the NICE registry (https://www.stichting-nice.nl). The numbers concern COVID-19 hospital and ICU admissions since the first report in the Netherlands (27/02/2020) up to and including the most recent complete week of admission. The registration of the number of COVID-19 hospital and ICU admissions may be lagging behind. This may result in the date of recording and the date of the report falling in a different calendar week. Hospital or ICU admissions from the most recent complete week of admission may have been reported in the current incomplete week and are therefore shown in this file. Hospital and ICU admissions from the most recent incomplete week are not included in this file but are censored with the value “NaN” (Not a Number). The file is structured as follows: - A record per week of statistics for the Netherlands, even if there are no recordings or reports on the week in question. The numbers are then 0 (zero). -The stated date for statistics may relate to a hospital or ICU admission date or the date on which the hospital reported a hospital or ICU admission to the NICE registry. Description of the variables: Version: version number of the dataset. When the content of the dataset is structurally changed (so not the daily update or a correction at record level), the version number will be adjusted (+1) and also the corresponding metadata in RIVMdata (Https://data.rivm.nl ). Version 2 update (August 9, 2022): - From August 9, 2022, new admissions of persons with a SARS-CoV-2 infection who were also admitted during a previous COVID-19 episode have been added to this open data file. For this reason, the number of withdrawals with retroactive effect is higher than in our previous files. The underestimation of admissions since the start of the pandemic to August 9, 2022 is less than 1%. A recording is counted as a new recording when a person with a SARS-CoV-2 infection has a recording date that is more than 90 days after the previous recording. Version 3 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every Wednesday, but on Tuesdays. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic till October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday. Version 4 update (November 24, 2022): - From November 24, 2022, the age group 0-14 years will be split into age groups 0-4, 5-9 and 10-14 years. This will be retroactively updated for the entire pandemic. Version 5 update (April 4, 2023): - From April 4, 2023, this file will be updated weekly on Tuesdays. The data has been retroactively updated for the other days. Date_of_report: Date and time on which the data file was created by the RIVM. Date_of_statistics_week_start: The date of the Monday - first day of that week - for which the numbers per week are presented. Week of hospital admission (variable Hospital_admission), week of ICU admission (variable IC_admission), the week on which the hospital admission (variable Hospital_admission_notification) or ICU admission was reported (variable IC_admission_notification) to the NICE registry. Age_group: Age group in years of the admitted or reported patients. Five-year intervals are used with the exception of 90 years and above (90+). Patients with an unknown age are added to 'Unknown'. Hospital_admission_notification: The number of new COVID-19 patients admitted to the NICE registry per age group [Age_group] per week on which the hospital admission was reported [Date_of_statistics_week_start]. Hospital_admission: The number of new COVID-19 patients admitted to hospital per age group [Age_group] per hospital admission week [Date_of_statistics_week_start] reported to the NICE registry. IC_admission_notification:

The dataset contains information from a cohort of 799 patients admitted in the hospital for COVID-19, characterized with sociodemographic and clinical data. Retrospectively, from November 2020 to January 2021, data was collected from the medical records of all hospital admissions that occurred from March 1st, 2020, to December 31st, 2020. The analysis of these data can contribute to the definition of the clinical and sociodemographic profile of patients with COVID-19. Understanding these data can contribute to elucidating the sociodemographic profile, clinical variables and health conditions of patients hospitalized by COVID-19. To this end, this database contains a wide range of variables, such as: Month of hospitalization Gender Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU)COVID-19 diagnosisNumber of times hospitalized by COVID-19Hospitalization time in daysRisk Classification ProtocolData is presented as a single Excel XLSX file: dataset.xlsx of clinical and sociodemographic characteristics of hospital admissions by COVID-19: retrospective cohort of patients in two hospitals in the Southern of Brazil. Researchers interested in studying the data related to patients affected by COVID-19 can extensively explore the variables described here. Approved by the Research Ethics Committee (No. 4.323.917/2020) of the Federal University of Santa Catarina.

BackgroundCOVID-19 infection is less severe among children than among adults; however, some patients require hospitalization and even critical care. Using data from the French national medico-administrative database, we estimated the risk factors for critical care unit (CCU) admissions among pediatric COVID-19 hospitalizations, the number and characteristics of the cases during the successive waves from January 2020 to August 2021 and described death cases.MethodsWe included all children (age < 18) hospitalized with COVID-19 between January 1st, 2020, and August 31st, 2021. Follow-up was until September 30th, 2021 (discharge or death). Contiguous hospital stays were gathered in “care sequences.” Four epidemic waves were considered (cut off dates: August 11th 2020, January 1st 2021, and July 4th 2021). We excluded asymptomatic COVID-19 cases, post-COVID-19 diseases, and 1-day-long sequences (except death cases). Risk factors for CCU admission were assessed with a univariable and a multivariable logistic regression model in the entire sample and stratified by age, whether younger than 2.ResultsWe included 7,485 patients, of whom 1988 (26.6%) were admitted to the CCU. Risk factors for admission to the CCU were being younger than 7 days [OR: 3.71 95% CI (2.56–5.39)], being between 2 and 9 years old [1.19 (1.00–1.41)], pediatric multisystem inflammatory syndrome (PIMS) [7.17 (5.97–8.6)] and respiratory forms [1.26 (1.12–1.41)], and having at least one underlying condition [2.66 (2.36–3.01)]. Among hospitalized children younger than 2 years old, prematurity was a risk factor for CCU admission [1.89 (1.47–2.43)]. The CCU admission rate gradually decreased over the waves (from 31.0 to 17.8%). There were 32 (0.4%) deaths, of which the median age was 6 years (IQR: 177 days–15.5 years).ConclusionSome children need to be more particularly protected from a severe evolution: newborns younger than 7 days old, children aged from 2 to 13 years who are more at risk of PIMS forms and patients with at least one underlying medical condition.

The COVID-19–associated hospitalization rate among patients aged 85 years and older identified through COVID-NET for the 4-week period ending March 28, 2020, was 17.2 per 100,000 population. This statistic shows laboratory-confirmed COVID-19 associated hospitalization rates per 100,000 population from March 1 to 28, in the 14 U.S. states under surveillance by COVID-NET.

Additional file 2. Distribution of DNAm age acceleration in six epigenetic clocks (a–f) in the peripheral blood from 14 COVID-19 patients at inclusion versus end of follow-up. The y-axis shows the epigenetic age acceleration. The p value is shown above the corresponding line. In the box plots, the lower and upper hinges indicate the 25th and 75th percentiles and the black line within the box marks the median. ns: non-significant.

Abstract INTRODUCTION: Coronavirus disease 2019 (COVID-19), a potentially fatal disease, is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The number of cases has increased rapidly, but information on the clinical characteristics remains limited. METHODS: Cohort study. We collected and analyzed epidemiological, demographic, and clinical data of critically and noncritically ill patients and compared the outcomes. RESULTS: The mean age of hospitalized patients with COVID-19 was 54 years (standard deviation 16.9; 53.8% men), 29% required ICU admission, and 18.6% died. CONCLUSIONS: The main risk factors for ICU admission were age over 60 years, obesity, and preexisting chronic lung diseases.

Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

Socioeconomic disparities play an important role in the development of severe clinical outcomes including deaths from COVID-19. However, the current scientific evidence in regard the association between measures of poverty and COVID-19 mortality in hospitalized patients is scant. The objective of this study was to investigate whether there is an association between the Colombian Multidimensional Poverty Index (CMPI) and mortality from COVID-19 in hospitalized patients in Colombia from May 1, 2020 to August 15, 2021. This was an ecological study using individual data on hospitalized patients from the National Institute of Health of Colombia (INS), and municipal level data from the High-Cost Account and the National Administrative Department of Statistics. The main outcome variable was mortality due to COVID-19. The main exposure variable was the CMPI that ranges from 0 to 100% and was categorized into five levels: (i) level I (0%−20%), (ii) level II (20%−40%), (iii) level III (40%−60%), (iv) level IV (60%−80%); and (v) level V (80%−100%). The higher the level, the higher the level of multidimensional poverty. A Bayesian multilevel logistic regression model was applied to estimate Odds Ratio (OR) and their corresponding 95% credible intervals (CI). In addition, a subgroup analysis was performed according to the epidemiological COVID-19 waves using the same model. The odds for dying from COVID-19 was 1.46 (95% CI 1.4–1.53) for level II, 1.41 (95% CI 1.33–1.49) for level III and 1.70 (95% CI 1.54–1.89) for level IV hospitalized COVID-19 patients compared with the least poor patients (CMPI level I). In addition, age and male sex also increased mortality in COVID-19 hospitalized patients. Patients between 26 and 50 years-of-age had 4.17-fold increased odds (95% CI 4.07–4.3) of death compared with younger than 26-years-old patients. The corresponding for 51–75 years-old patients and those above the age of 75 years were 9.17 (95% CI 8.93–9.41) and 17.1 (95% CI 16.63–17.56), respectively. Finally, the odds of death from COVID-19 in hospitalized patients gradually decreased as the pandemic evolved. In conclusion, socioeconomic disparities were a major risk factor for mortality in patients hospitalized for COVID-19 in Colombia.

Additional file 1. Correlation of chronological age with DNA methylation age using Horvath, Hannum, SkinandBlood, PhenoAge, and GrimAge clocks and the DNA methylation-based telomere length (TL) estimator.

NOTE: This dataset has been retired and marked as historical-only.

Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age.

Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine.

Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS).

Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death.

Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test.

CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset.

Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000.

Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people.

Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population.

Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

For all datasets related to COVID-19, see https://data.cityofchic