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

ICU bed per 100,000 population in high, low- and middle-income countries.

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.

ICU Beds in Sweden decreased to 1.90 per 1000 people in 2019 from 1.96 per 1000 people in 2018. This dataset includes a chart with historical data for Sweden ICU Beds.

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.

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

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, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents daily 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.

Notes: 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.

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”.

ICU Beds in Uzbekistan increased to 333.87 per 100.000 people in 2014 from 333.64 per 100.000 people in 2013. This dataset includes a chart with historical data for Uzbekistan ICU Beds.

There are several objectives faced by the operation on Structural Indicators.The first and generic is to achieve the production, with the highest possible degree of quality, of a battery of basic or context indicators, which serve or can serve as a reference.The second objective, would be to achieve methodological homogeneity and precision in the calculation in relation to other internal systems of indicators, and especially those defined by Eurostat, to rework and elaborate series that add the temporal perspective and design and implement dynamic file formats that allow the organisation and access to all information. Finally, the specific objective of the operation would focus on the coordination, management, verification and archiving of the indicators system.

它是一个大型、免费的数据集，由9万多名病人的健康数据信息构成。这些病人来自大型医疗中心的ICU和住院部，时间为2001年至2019年。日后或许会更新更多数据。It is a large, free dataset consisting of health data from more than 90,000 patients. The patients came from the ICU and inpatient unit of large medical centers from 2001 to 2019.More data may be updated in the future.

该数据集主要包括了人口统计数据（demographics）、在病床进行的生命体征测量（每小时约1个数据点）、实验室检查结果、治疗（procedures）、药物、护理人员工作记录、影像报告、死亡信息（包括院内和院外）等信息。The dataset mainly includes demographics, vital sign measurements performed in the hospital bed (about 1 data point per hour), laboratory test results, treatment procedures, medications, nursing staff work records, image reports, death information (both in-hospital and out-of-hospital), and other information.

该数据集可用于支持广泛的研究工作，包括流行病学、临床决策规则优化、医疗电子工具开发。它具有三个显著的优点：可供全世界研究者广泛使用；涵盖多元化且数据庞大的ICU患者群体；包括大量的时序数据，包括实验室检查结果、电子文档、临床监测数据。The dataset can be used to support a wide range of research efforts, including epidemiology, optimization of clinical decision rules, and development of medical electronic tools. It has three significant advantages: it can be widely used by researchers around the world; Covering a diverse and data-rich ICU patient population; Includes a large amount of time-series data, including laboratory test results, electronic documents, and clinical monitoring data.

Hosp 模块中的其他信息包括实验室测量(Labevents，d _ labitem) ，微生物培养(microbiologyevents，d _ micro) ，提供者订单(poe，poe _ Details) ，药物管理(emar，emar _ Details) ，药物处方(prescriptions, pharmacy) ，医院账单信息(diagnoses_icd, d_icd_diagnoses, procedures_icd, d_icd_procedures, hcpcsevents, d_hcpcs,drgcodes) ，在线医疗记录数据(omr)和服务相关信息(服务)。

Provider information在提供程序表中可用。Provider _ id 列是一个去身份化的字符串，它唯一地表示一个护理提供者。由于 Provider _ id 在整个模块的不同上下文中使用，前缀通常出现在数据表中，用于上下文化护理提供者与事件的关系（比如是负责抽血，还是监护等等不同的，相当于关联到了不同的护士）。例如，接纳病人到医院的提供者在接纳表中记录为 access _ Provider _ id。所有后缀为 Provider _ id 的列都可以链接到 Provider table。

Context

This dataset is a collection of the COVID-19 data maintained by Our World in Data.

Content

The CSV and XLSX files follow a format of 1 row per location and date. The JSON version is split by country ISO code, with static variables and an array of daily records.

The variables represent all of our main data related to confirmed cases, deaths, hospitalizations, and testing, as well as other variables of potential interest.

Confirmed cases

Confirmed deaths

Hospital & ICU

Policy responses

Reproduction rate

Tests & positivity

| Variable | Description | |:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------...

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

179 views (4 recent) Non-expenditure health care data provide information on institutions providing health care in countries, on resources used and on output produced in the framework of health care provision. Data on health care form a major element of public health information as they describe the capacities available for different types of health care provision as well as potential 'bottlenecks' observed. The quantity and quality of health care services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. The output-related data ('activities') refer to contacts between patients and the health care system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients and medical procedures performed in hospitals. Annual national and regional data are provided in absolute numbers and in population-standardised rates (per 100 000 inhabitants). Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Health care data on activities are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising health care and may not always be completely comparable.

Total hospital beds which are regularly maintained, staffed and are immediately available for the care of admitted patients.

Information on Medical Facilities in Various Countries

1. Hospital beds (per 10 000 population) 2. Hospital beds (per 10 000 population) 3. Country has national policy or strategy on the use of social media by government organizations 4. Total density per 100 000 population: District/rural hospitals 5. Total density per 100 000 population: Provincial hospitals 6. Total density per 100 000 population, Specialized hospitals 7. Community health workers density (per 10 000 population)

Let me know if there are any other factors we can use, and I will update the column accordingly.

Let's keep the fight up against COVID-19

Data Sources: Global Health Observatory API: https://www.who.int/data/gho

Tasks to do: Append Proper list of Countries as per SpatialDim

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated.Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis.Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5–15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated.Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

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.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

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

• 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.

• Time period: data for the previous MMWR week (Sunday-Saturday) 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 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”.

Notes: June 1, 2023: Due to incomplete or missing hospital data received for the May 21, 2023, through May 27, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for the Commonwealth of the Northern Mariana Islands (CNMI) and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 1, 2023.

June 8, 2023: Due to incomplete or missing hospital data received for the May 28, 2023, through June 3, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and American Samoa (AS) and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 8, 2023.

June 15, 2023: Due to incomplete or missing hospital data received for the June 4, 2023, through June 10, 2023, reporting period,

This data is taken from LG Inform (http://lginform.local.gov.uk Data Ref ID 27). It shows the delayed transfers of care, average weekly rate in Plymouth Delayed transfers of care, average weekly rate - Final available dataset: fin_2008_09 This indicator measures the impact of hospital services (acute and non-acute) and community-based care in facilitating timely and appropriate discharge from all hospitals for all adults. This measures the ability of the whole system to ensure appropriate discharge from hospital for the entire adult population, and is an indicator of the effectiveness of the interface between health and social care services. This indicator shows the average weekly rate of delayed transfers of care from all NHS hospitals, acute and non-acute, per 100,000 population aged 18+. A delayed transfer of care occurs when a patient is ready for transfer from a hospital bed, but is still occupying such a bed. This was previously reported as NI 131. Source name: Department of Health Collection name: Unify2 Data Collection - MSitDT Polarity: Low is good Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) 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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

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.