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

Hospital Beds in the United States decreased to 2.75 per 1000 people in 2022 from 2.77 per 1000 people in 2021. This dataset includes a chart with historical data for the United States Hospital Beds.

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 facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). 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 hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities. For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020. Reported elements include an append of either “_coverage”, “_sum”, or “_avg”. A “_coverage” append denotes how many times the facility reported that element during that collection week. A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week. A “_avg” append is the average of the reports provided for that facility for that element during that collection week. The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”. A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020. Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect. For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied. For recent updates to the dataset, scroll to the bottom of the dataset description. On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_

Hospital Beds in Chile increased to 1.92 per 1000 people in 2024 from 1.91 per 1000 people in 2023. This dataset includes a chart with historical data for Chile Hospital Beds.

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

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.

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.

DISCLAIMER

Dataset consists of historical data of pre-pandemic period and doesn’t represent the current reality which may have changed due to the spikes in demand. This dataset has been generated in collaboration of efforts within CoronaWhy community.

Context

Last updated: April 26th 2020 Updates: April 14th 2020 - Added missing population data April 15th 2020 - Added Brazil statewise ICU hospital beds dataset April 21th 2020 - Added Italy, Spain statewise ICU hospital beds dataset, India statewise TOTAL hospital beds dataset April 26th 2020 - Added Sweden ICU(2019) and TOTAL(2018) beds datasets

Purpose of the dataset

I am trying to produce a dataset that will provide a foundation for policymakers to understand the realistic capacity of healthcare providers being able to deal with the spikes in demand for intensive care. As a way to help, I’ve prepared a dataset of beds across countries and states. Work in progress dataset that should and will be updated as more data becomes available and public on weekly basis.

Importance

This dataset is intended to be used as a baseline for understanding the typical bed capacity and coverage globally. This information is critical for understanding the impact of a high utilization event, like COVID-19.

Current challenges

Datasets are scattered across the web and are very hard to normalize, I did my best but help would be much appreciated.

Data sources / Acknowledgments

arcgis (USA) - https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Hospitals_1/FeatureServer/0 KHN (USA) - https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ datahub.io (World) - https://datahub.io/world-bank/sh.med.beds.zs eurostat - https://data.europa.eu/euodp/en/data/dataset/vswUL3c6yKoyahrvIRyew OECD - https://data.oecd.org/healtheqt/hospital-beds.htm WDI (World) - https://data.worldbank.org/indicator/SH.MED.BEDS.ZS NHP(India) - http://www.cbhidghs.nic.in/showfile.php?lid=1147 data.gov.sg (Singapore) - https://data.gov.sg/dataset/health-facilities?view_id=91b4feed-dcb9-4720-8cb0-ac2f04b7efd0&resource_id=dee5ccce-4dfb-467f-bcb4-dc025b56b977 dati.salute.gov.it (Italy)- http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=96 portal.icuregswe.org (Sweden) - https://portal.icuregswe.org/seiva/en/Rapport publications: Intensive Care Medicine Journal (Europe) - https://link.springer.com/article/10.1007/s00134-012-2627-8 Critical Care Medicine Journal (Asia) - https://www.researchgate.net/figure/Number-of-critical-care-beds-per-100-000-population_fig1_338520008 Medicina Intensiva (Spain) - https://www.medintensiva.org/en-pdf-S2173572713000878 news: https://lanuovaferrara.gelocal.it/italia-mondo/cronaca/2020/03/19/news/dietro-la-corsa-a-nuovi-posti-in-terapia-intensiva-gli-errori-del-passato-1.38611596 kaggle: germany - https://www.kaggle.com/manuelblechschmidt/icu-beds-in-germany brazil (IBGE) - https://www.kaggle.com/thiagobodruk/brazilianstates Manual population data search from wiki

Data columns

country,state,county,lat,lng,type,measure,beds,population,year,source,source_url - country - country of origin, if present - state - more granular location, if present - lat - latitude - lng - longtitude - type - [TOTAL, ICU, ACUTE(some data could include ICU beds too), PSYCHIATRIC, OTHER(merged ‘SPECIAL’, ‘CHRONIC DISEASE’, ‘CHILDREN’, ‘LONG TERM CARE’, ‘REHABILITATION’, ‘WOMEN’, ‘MILITARY’] - measure - type of measure (per 1000 inhabitants) - beds - number of beds per 1000 - population - population of location based on multiple sources and wikipedia - year - source year for beds and population data - source - source of data - source_url - URL of the original source

Files

for each of datasource: hospital_beds_per_source.csv

US only: US arcgis + khn (state/county granularity): hospital_beds_USA.csv

Global (state(region)/county granularity): hospital_beds_global_regional.csv

Global (country granularity): hospital_beds_global_v1.csv

Contributors

Igor Kiulian - extracting/normalizing/formatting/merging data Artur Kiulian - helped with Kaggle setup Augaly S. Kiedi - helped with country population data Kristoffer Jan Zieba - found Swedish data sources

Possible Improvements

Find and megre more detailed (state/county wise) or newer datasource

NOTE: This dataset is historical-only as of 5/10/2023. All data currently in the dataset will remain, but new data will not be added. The recommended alternative dataset for similar data beyond that date is  https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u. (This is not a City of Chicago site. Please direct any questions or comments through the contact information on the site.)

During the COVID-19 pandemic, the Chicago Department of Public Health (CDPH) required EMS Region XI (Chicago area) hospitals to report hospital capacity and patient impact metrics related to COVID-19 to CDPH through the statewide EMResource system. This requirement has been lifted as of May 9, 2023, in alignment with the expiration of the national and statewide COVID-19 public health emergency declarations on May 11, 2023. However, all hospitals will still be required by the U.S. Department of Health and Human Services (HHS) to report COVID-19 hospital capacity and utilization metrics into the HHS Protect system through the CDC’s National Healthcare Safety Network until April 30, 2024. Facility-level data from the HHS Protect system can be found at healthdata.gov.

Until May 9, 2023, all Chicago (EMS Region XI) hospitals (n=28) were required to report bed and ventilator capacity, availability, and occupancy to the Chicago Department of Public Health (CDPH) daily. A list of reporting hospitals is included below. All data represent hospital status as of 11:59 pm for that calendar day. Counts include Chicago residents and non-residents.

ICU bed counts include both adult and pediatric ICU beds. Neonatal ICU beds are not included. Capacity refers to all staffed adult and pediatric ICU beds. Availability refers to all available/vacant adult and pediatric ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in ICU on 03/19/2020. Hospitals began reporting ICU surge capacity as part of total capacity on 5/18/2020.

Acute non-ICU bed counts include burn unit, emergency department, medical/surgery (ward), other, pediatrics (pediatric ward) and psychiatry beds. Burn beds include those approved by the American Burn Association or self-designated. Capacity refers to all staffed acute non-ICU beds. An additional 500 acute/non-ICU beds were added at the McCormick Place Treatment Facility on 4/15/2020. These beds are not included in the total capacity count. The McCormick Place Treatment Facility closed on 05/08/2020. Availability refers to all available/vacant acute non-ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in acute non-ICU beds on 04/03/2020.

Ventilator counts prior to 04/24/2020 include all full-functioning mechanical ventilators, with ventilators with bilevel positive airway pressure (BiPAP), anesthesia machines, and portable/transport ventilators counted as surge. Beginning 04/24/2020, ventilator counts include all full-functioning mechanical ventilators, BiPAP, anesthesia machines and portable/transport ventilators. Ventilators are counted regardless of ability to staff. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases on ventilators on 03/19/2020. CDPH has access to additional ventilators from the EAMC (Emergency Asset Management Center) cache. These ventilators are included in the total capacity count.

Chicago (EMS Region 11) hospitals: Advocate Illinois Masonic Medical Center, Advocate Trinity Hospital, AMITA Resurrection Medical Center Chicago, AMITA Saint Joseph Hospital Chicago, AMITA Saints Mary & Elizabeth Medical Center, Ann & Robert H Lurie Children's Hospital, Comer Children's Hospital, Community First Medical Center, Holy Cross Hospital, Jackson Park Hospital & Medical Center, John H. Stroger Jr. Hospital of Cook County, Loretto Hospital, Mercy Hospital and Medical Center, , Mount Sinai Hospital, Northwestern Memorial Hospital, Norwegian American Hospital, Roseland Community Hospital, Rush University M

This dataset provides values for HOSPITAL BEDS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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.

The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). 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 hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities. For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 2020. Reported elements include an append of either “_coverage”, “_sum”, or “_avg”. A “_coverage” append denotes how many times the facility reported that element during that collection week. A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week. A “_avg” append is the average of the reports provided for that facility for that element during that collection week. The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”. This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020. Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect. For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied. On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_7_day_sum On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number report

The U.S. Census Bureau regularly collects information for many metropolitan areas in the United States, including data on number of physicians and number (and size) of hospitals. This dataset has such information for 83 different metropolitan areas.

This dataset is about countries per year in Bulgaria. It has 64 rows. It features 4 columns: country, hospital beds, and population.

Hospital Beds in Ireland increased to 2.89 per 1000 people in 2023 from 2.88 per 1000 people in 2022. This dataset includes a chart with historical data for Ireland Hospital Beds.

Hospital Beds in Belgium decreased to 5.38 per 1000 people in 2024 from 5.39 per 1000 people in 2023. This dataset includes a chart with historical data for Belgium Hospital Beds.

California Birth Report totals by Birth Characteristics to inform the public, stakeholders, and researchers. The DHCS Medi-Cal Birth Statistics tables present the descriptive statistics for California resident births that occurred in a hospital setting, including data on maternal characteristics, delivery methods, and select birth outcomes such as low birthweight and preterm delivery. Tables also include key comorbidities and health behaviors known to influence birth outcomes, such as hypertension, diabetes, substance use, pre-pregnancy weight, and smoking during pregnancy. DHCS additionally presents birth statistics for women participating in the Medi-Cal Fee-For-Service (FFS) and managed care delivery systems, as well as births financed by private insurance, births financed by other public funding sources, and births among uninsured mothers. Medi-Cal data reflect mothers that were deemed as Medi-Cal certified eligible. Note: Data for maternal comorbidities including hypertension, diabetes, and substance use have been provisionally omitted among calendar years 2020-2022 for the time being.

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

Context

This data-set is regarding the current pandemic COVID-19. It contains the total number of beds available in hospital in each and every state. The rows contains the state names and columns contains the "Number of hospital beds in public sector", "Number of hospital beds in private sector", "Total number of beds (private + public)". The data for Ladakh state is not available since it is not available on the govt. sites. This data-set is taken from the pdf available on the below link. This pdf is provided by the CDDEP (THE CENTRE FOR DISEASE DYNAMICS, ECONOMICS AND POLICY) and PRINCETON UNIVERSITY. They have done the study and research on the data of COVID-19 and publish their conclusions and findings namely in " INDIA : STATE-WISE ESTIMATES OF CURRENT HOSPITAL BEDS, ICU BEDS, AND VENTILATORS ".

Link: https://cddep.org/wp-content/uploads/2020/04/State-wise-estimates-of-current-beds-and-ventilators_5Apr2020-AN_gk.pdf

Acknowledgements

Thanks to these people for their valuable contribution. Geetanjali Kapoor, Aditi Sriram, Jyoti Joshi, Ramanan Laxminarayan

If anyone found anything wrong in this data-set, please feel free to let me know or any kind of feedback anyone wants to share I'm open to that also. Since, I'm a newbie in this field I would love to resolve any problem. Thanks.

Note: This dataset has been limited to show metrics for Ramsey County, Minnesota.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. 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

Calculation of county-level hospital metrics: County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level. Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

Metric details: Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Thursdays 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 hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week). New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data] New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week. New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data] COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 Inpatient Bed Occupancy percent 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 average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction. COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 ICU Bed Occupancy percent 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 week, compared with the prior week, in the in the entire jurisdiction. For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.