This dataset is being provided under creative commons License (Attribution-Non-Commercial-Share Alike 4.0 International (CC BY-NC-SA 4.0)) https://creativecommons.org/licenses/by-nc-sa/4.0/

Context

This data was collected from patients admitted over a period of two years (1 April 2017 to 31 March 2019) at Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India. This is a tertiary care medical college and hospital. During the study period, the cardiology unit had 14,845 admissions corresponding to 12,238 patients. 1921 patients who had multiple admissions.

Specifically, data were related to patients ; date of admission; date of discharge; demographics, such as age, sex, locality (rural or urban); type of admission (emergency or outpatient); patient history, including smoking, alcohol, diabetes mellitus (DM), hypertension (HTN), prior coronary artery disease (CAD), prior cardiomyopathy (CMP), and chronic kidney disease (CKD); and lab parameters corresponding to hemoglobin (HB), total lymphocyte count (TLC), platelets, glucose, urea, creatinine, brain natriuretic peptide (BNP), raised cardiac enzymes (RCE) and ejection fraction (EF). Other comorbidities and features (28 features), including heart failure, STEMI, and pulmonary embolism, were recorded and analyzed.

Shock was defined as systolic blood pressure < 90 mmHg, and when the cause for shock was any reason other than cardiac. Patients in shock due to cardiac reasons were classified into cardiogenic shock. Patients in shock due to multifactorial pathophysiology (cardiac and non-cardiac) were considered for both categories. The outcomes indicating whether the patient was discharged or expired in the hospital were also recorded.

Further details about this dataset can be found here: https://doi.org/10.3390/diagnostics12020241

If you use this dataset in academic research all publications arising out of it must cite the following paper: Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

If you intend to use this data for commercial purpose explicit written permission is required from data providers.

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.

Inspiration

For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in

On 11/14/2025, we launched updated hospitalization reporting using data from the National Healthcare Safety Network (NHSN). The new dataset includes hospital admissions for respiratory viruses including COVID-19, flu, and RSV. You can access the new dataset here.

A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week.

B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center.

San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available.

D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week.

The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

E. CHANGE LOG

• 11/14/2025 COVID-19 hosipital admissions is tracked in a new dataset
• 7/18/2025 - Dataset update is paused to assess data quality and completeness.
• 9/12/2024 - We updated the data source for our COVID-19 hospitalization data to a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

This dataset simulates hospital inpatient admissions, modeling patient conditions, departmental assignments, care severity, and discharge outcomes. It follows a contextual generation flow, meaning later values like severity, length of stay, and readmission are dependent on earlier values such as condition type and age. Each row represents a unique patient encounter with story-driven logic embedded into the data.

The inspiration came from real-world Electronic Health Record (EHR) systems where every patient record is shaped by context: diagnosis, age, treatment environment, and outcomes. The goal was to create a synthetic dataset that:

• Feels realistic
• Honors cause-and-effect logic
• Can be used for ML modeling, dashboarding, and storytelling

In 2023, there were over **** million hospital admissions in the United States. The number of hospitals in the U.S. has decreased in recent years, although the country faces an increasing elder population. Predictably, the elderly account for the largest share of hospital admissions in the U.S. Hospital stays Stays in hospitals are more common among females than males, with around *** percent of females reporting one or more hospital stays in the past year, compared to *** percent of males. Furthermore, **** percent of those aged 65 years and older had a hospitalization in the past year, compared to just *** percent of those aged 18 to 44 years. The average length of a stay in a U.S. hospital is *** days. Hospital beds In 2022, there were ******* hospital beds in the U.S. In the past few years, there has been a decrease in the number of hospital beds available. This is unsurprising given the decrease in the number of overall hospitals. In 2021, the occupancy rate of hospitals in the U.S. was ** percent.

As of 9/12/2024, we have resumed reporting on COVID-19 hospitalization data using a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week.

B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center.

San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available.

D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week.

The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

E. CHANGE LOG

A. SUMMARY This dataset includes weekly respiratory disease hospital admissions for Influenza, RSV, and COVID-19 into San Francisco hospitals. Columns in the dataset include a count and rate of hospital admissions per 100,000 people. The data are reported by week. B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) from the United States Center for Disease Control’s (CDC) National Healthcare Safety Network (NHSN) program. San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2019-2023 5-year American Community Survey (ACS). C. UPDATE PROCESS The dataset is updated every Friday and includes data from the previous Sunday through Saturday. For example, the update on Friday, October 17th will include data through Saturday, October 11th. Data may change as more current information becomes available. D. HOW TO USE THIS DATASET Weekly data represent a count of confirmed admissions of Influenza, RSV, and COVID-19 patients to San Francisco hospitals by week. The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

This dataset provides comprehensive hospital admission data from Riyadh, Saudi Arabia, for the period 2022 to 2024. The data was collected from public and private hospitals, including Riyadh General Hospital, King Saud Hospital, and Riyadh National Hospital. It aims to support research and policy development related to public health and healthcare system optimization. https://www.moh.gov.sa/en/Ministry/Pages/default.aspx

The dataset contains detailed information on cardiorespiratory hospital admissions, capturing hourly records to allow for in-depth temporal analyses. It integrates various features related to patient demographics, medical conditions, and hospital performance, providing a holistic view of healthcare demand and trends in Riyadh.

Dataset Overview Owner: General Directorate of Health Affairs, Ministry of Health, Saudi Arabia Location: Riyadh, Saudi Arabia Time Period: January 2022 – September 2024 Format: CSV Number of Records: ~23,000 hourly observations Periodicity: Hourly Features: admission_date: The precise date and time of hospital admission (YYYY-MM-DD HH:mm:ss). hospital_name: Name of the hospital where the admission occurred (e.g., Riyadh General Hospital). admission_count: The number of admissions during the specified hour. condition_type: Type of cardiorespiratory condition (e.g., Asthma, COPD, Heart Attack, Other Respiratory Issues). patient_age_group: The age group of admitted patients (e.g., 0–17, 18–45, 46–65, 66+). patient_gender: Gender of the patients (Male/Female). readmission_count: Count of patients readmitted within 30 days. severity_level: Severity level of the condition upon admission (Mild, Moderate, Severe). length_of_stay_avg: Average length of stay (in days) for admitted patients. seasonal_indicator: Seasonal classification for the date of admission (Winter, Spring, Summer, Fall). comorbid_conditions_count: Number of additional health conditions affecting admitted patients. primary_diagnosis_code: Diagnostic code for the primary condition (e.g., J45, J44, I21). daily_medication_dosage: Total daily dosage of medications prescribed for cardiorespiratory conditions (mg). emergency_visit_count: The number of emergency visits for cardiorespiratory issues during the hour. Key Applications: Healthcare Demand Analysis: Study patterns of hospital admissions and understand peak demand periods. Public Health Research: Investigate correlations between environmental factors and hospitalizations for respiratory and cardiovascular conditions. Policy and Decision-Making: Develop data-driven policies to optimize healthcare resource allocation and readiness. Epidemiological Studies: Analyze the impact of comorbidities and demographic factors on hospital admissions. Data Insights: The dataset highlights temporal trends in hospital admissions, enabling the identification of peak periods of healthcare demand. Features such as condition_type, severity_level, and seasonal_indicator offer valuable insights into the interplay between environmental factors and public health. It provides granular patient demographic data, supporting targeted healthcare strategies and policy development. Rich diagnostic and readmission data support advanced predictive modeling for patient outcomes. Licensing: Please refer to the terms and conditions of the General Directorate of Health Affairs and the Ministry of Health, Saudi Arabia, for the usage and redistribution of this dataset.

Keywords: Healthcare, Public Health, Hospital Admissions, Riyadh, Cardiorespiratory Illness, Asthma, COPD, Emergency Visits, Saudi Arabia, Epidemiology, 2022–2024, Hourly Data

In 2023, there were around *** hospital admissions per 1,000 population in the state of West Virginia. In comparison, Alaska had just ** hospital admissions per 1,000 population in the same year. Hospital admission rates in the United States have been decreasing in the last decades before dropping at the start of the pandemic.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Note: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

This publication reports on Admitted Patient Care activity in England for the financial year 2024-25 This report includes but is not limited to analysis of hospital episodes by patient demographics, diagnoses, external causes/injuries, operations, bed days, admission method, time waited, specialty, provider level analysis and Adult Critical Care (ACC). It describes NHS Admitted Patient Care Activity, Adult Critical Care activity and performance in hospitals in England. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care and may also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. The data source for this publication is Hospital Episode Statistics (HES). It contains final data and replaces the provisional data that are released each month. HES contains records of all admissions, appointments and attendances at NHS-commissioned hospital services in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore, this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Please send queries or feedback via email to enquiries@nhsdigital.nhs.uk. Author: Secondary Care Open Data and Publications, NHS England. Lead Analyst: Karl Eichler

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 Sex Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU) COVID-19 diagnosis Number of times hospitalized by COVID-19 Hospitalization time in days Risk Classification Protocol Data 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.

The number of admissions has increased year-on-year from 2000 to 2020. Due to the COVID-19 pandemic, hospital admission dropped in 2020/21. In 2024/25 there were around **** million admissions* to NHS hospitals in England, showing that admission numbers have reached and exceeded pre-pandemic levels. Of these, *** million were emergency admissions.

🏥 Synthetic Healthcare Admissions Dataset

The Synthetic Healthcare Admissions dataset is a synthetically generated healthcare dataset that mimics patient hospital admission records. It is designed to provide researchers, data scientists, and machine learning practitioners with realistic healthcare data while preserving patient privacy and avoiding exposure of sensitive information.

📂 Dataset Overview

• Type: Tabular / Structured data
• Domain: Healthcare, Electronic Health Records (EHR)
• Content: Synthetic hospital admission records
• Use Cases:
  • Predictive modeling of patient outcomes
  • Length of stay estimation
  • Readmission prediction
  • Resource allocation & optimization in healthcare
  • Experimentation with ML models without privacy risks

⚙️ Features (common fields included in admissions data)

• Patient Demographics: Age, Gender, Ethnicity
• Admission Details: Admission type, Admission date, Discharge date
• Clinical Data: Diagnosis codes (ICD-like), Procedures, Comorbidities
• Hospital Metrics: Length of stay, Department/Unit info
• Synthetic Identifiers: Randomized patient IDs

✅ Why Synthetic?

Real healthcare data is heavily restricted due to HIPAA and GDPR compliance. This dataset provides a privacy-safe alternative, allowing open research while maintaining the structure and statistical properties of real hospital admissions data.

🔬 Applications

• Benchmarking healthcare ML models
• Developing explainable AI solutions in clinical settings
• Testing NLP/ML pipelines for structured EHR data
• Teaching and training purposes

Graph and download economic data for Rate of Preventable Hospital Admissions (5-year estimate) in New Castle County, DE (DISCONTINUED) (DMPCRATE010003) from 2008 to 2015 about New Castle County, DE; preventable; admissions; DE; hospitals; Philadelphia; 5-year; rate; and USA.

This dataset contains annual hospital inpatient summary data based upon the Patient’s County of Residence. The summary data includes discharge disposition, expected payer, sex, Medicare Severity-Diagnosis Related Group (MS-DRG), Major Diagnostic Categories (MDC), race group, admission source, and type of care.

Graph and download economic data for Rate of Preventable Hospital Admissions (5-year estimate) in New York County, NY (DISCONTINUED) (DMPCRATE036061) from 2008 to 2015 about preventable; New York County, NY; admissions; hospitals; New York; NY; 5-year; rate; and USA.

This dataset details the percentage of COVID-19 positive patients in hospitals and ICUs for COVID-19 related reasons, and for reasons other than COVID-19. Data includes: * reporting date * percentage of COVID-19 positive patients in hospital admitted for COVID-19 * percentage of COVID-19 positive patients in hospital admitted for other reasons * percentage of COVID-19 positive patients in ICU admitted for COVID-19 * percentage of COVID-19 positive patients in ICU admitted for other reasons **Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool ** Due to incomplete weekend and holiday reporting, data for hospital and ICU admissions are not updated on Sundays, Mondays and the day after holidays. This dataset is subject to change.

Hospital admissions - Residents: Male Total

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:

Hospital admissions - Residents: Female Total