In 2019, almost seven percent of people aged 18 to 44 years in the United States were hospitalized at least once. Unsurprisingly, hospitalization among people aged 65 years and older was higher at almost 17 percent.

Hospital Stays Hospitalization in the U.S. has decreased since 1997. In 2019, a total of 7.3 percent of people in the U.S. were hospitalized at least once. Hospitalization rates for females have been higher than males for the past two decades. In 2019, almost eight percent of females were hospitalized, compared to only five percent of males. The average length of stay in a hospital is currently 6.2 days. However, this varies greatly by state. In Wyoming, for example, the average length of stay is 9.2 days, which is more than twice the average length of stay New York.

Hospital Costs Community hospital expenses per inpatient stay in the United States have been constantly increasing. The average expenses for a community hospital per inpatient stay in 2019 was around 14,101 U.S. dollars. Expectedly, hospital care expenditure has also been increasing in the past two decades. In 2020, around 1.27 trillion U.S. dollars were spent on hospital care.

In 2023, there were 786,390 hospital admissions in Denmark, 555,973 of which were an emergency. Throughout the documented period, there was a higher number of emergency cases each year.

Provisional Monthly Hospital Episode Statistics for Admitted Patient Care, Outpatient and Accident and Emergency data - April 2019 - February 2020

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.

Published: 16 June 2020 - Hospital Episodes Statistics (HES) is a data warehouse containing records of all patients admitted to NHS hospitals in England. It contains details of inpatient care, outpatient appointments and A&E attendance records. Hospital episode statistics (HES) statistics are produced and published on a monthly basis. This data is provisional and should therefore be treated as an estimate until the final National Statistics annual publications.

This statistic depicts the number of hospital admissions for a primary diagnosis of obesity in England, in 2019/20, by gender and region of England. The number of inpatient admissions for obesity are significantly higher for women than for men. In London, there were almost 645 admissions for men, compared to approximately 1.8 thousand admissions for women.

In 2019, 7.3 percent of all persons aged one year and over had one or more hospital stays in the United States. This statistic shows the percentage of U.S. persons with one or more hospital stays in the past year from 1997 to 2019.

This is a publication on Accident and Emergency (A&E) activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2020. It contains final data and replaces the provisional data that are published each month. This is a joint publication between NHS Digital and NHS England. This collaboration enables data to be brought together from two different sources enabling inclusion of a wider set of breakdowns and measures and a more complete picture to be presented. The data sources for this publication are Hospital Episode Statistics (HES) and A&E Attendances and Emergency Admissions Monthly Situation Reports (MSitAE). This publication releases some high level analyses of both HES and MSitAE data relating to A&E attendances in NHS hospitals, minor injury units and walk-in centres. It includes analysis by patient demographics, time spent in A&E, distributions by time of arrival and day of week, arriving by ambulance, performance times, waits for admission and re-attendances to A&E within 7 days. The following additional analyses are also included in this report: • Comparison of 4 hour and 12 hour waits between the four home nations, England, Scotland, Northern Ireland and Wales • A&E attendances by Index of Multiple Deprivation (IMD) • A&E attendances by ethnicity Additional exploratory analyses have also been included as part of this release that seek to review reported data quality to inform future uses of the data.

This dataset contains estimates of mortality and number of hospital admissions that can be attributed to temperature, from observations and climate projections, and includes some of the underlying climate data. The data are divided into the subdirectories ‘epi_model’, ‘HadUKgrid’, ‘London’, ‘regimes’, and ‘UKCP18’ as follows:

epi_model: - Model fits of exposure-response relationships

HadUKgrid: - Temperature-attributable mortality/hospital admission time series for the observed record (1981/1991-2018) - List of the 10 highest mortality days from 1991 to 2018 based on UK-total temperature-related mortality

London: - Average daily temperature by London boroughs simulated with an urban model, October 2015 to 2019 - Attributable hospital admission by London boroughs based on the above temperature time series

regimes: - Weather regime and pattern classification for the observed record (1850/1979-2019)

UKCP18: - Attributable mortality time series for UKCP18 climate projections (1900-2099)

Further details including file contents and methods can be found in the README.txt files for each dataset. This dataset was produced for the UK Climate Resilience Programme - Addressing the resilience needs of the UK health sector: climate service pilots.

This dataset shows the number of hospital admissions for influenza-like illness, pneumonia, or include ICD-10-CM code (U07.1) for 2019 novel coronavirus. Influenza-like illness is defined as a mention of either: fever and cough, fever and sore throat, fever and shortness of breath or difficulty breathing, or influenza. Patients whose ICD-10-CM code was subsequently assigned with only an ICD-10-CM code for influenza are excluded. Pneumonia is defined as mention or diagnosis of pneumonia. Baseline data represents the average number of people with COVID-19-like illness who are admitted to the hospital during this time of year based on historical counts. The average is based on the daily avg from the rolling same week (same day +/- 3 days) from the prior 3 years. Percent change data represents the change in count of people admitted compared to the previous day. Data sources include all hospital admissions from emergency department visits in NYC. Data are collected electronically and transmitted to the NYC Health Department hourly. This dataset is updated daily. All identifying health information is excluded from the dataset.

The Connected Bradford ICNARC : Flexible Data Model (FDM) Contains routinely collected data for approximately 13,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.

The Bradford Royal Infirmary FDM last build date was 2023-05-15 and contains data up to : 2019-10-22. The observation period for this data is: 2002-07-21 to 2019-10-22

The FDM is made up of 2source table using routinely collected data for 13,936 individuals.

The tables are as supplied with minimal reformatting.

The source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables)

It includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications, theatre, spells, pharmacy etc as per the ICNARC standard.

Contains data on the number of hospital admissions (inpatient settings only) related to drug misuse. Data is available in Excel or CSV format. Latest data is for 2018/19.

Percentage change in the hospital admission rates from 1998–2019 in Australia.

This file contains data on hospitalisations in Dutch hospitals of persons registered in the Basic Registration Persons (BRP). The data comes from the National Basic Registration of Hospital Care (LBZ) of Dutch Hospital Data (DHD).

More information on how to access the data:

https://www.cbs.nl/nl-nl/onze-diensten/maatwerk-en-microdata/microdata-zelf-onderzoek-doen

Methodology

Hospitalisations are linked to the Basic Registration Persons (BRP) based on Citizen Service Number (BSN) pseudonyms and for a small part based on the combination of date of birth, gender and postcode. The return (after verification and correction) of the BRP coupling of LBZ shots is 97.3 % in 2013, 99.1 % in 2014, 99.5 % in 2015/2016, 99.6 % in 2017, 99.7 % in 2018/2019, 99.8 % in 2020 and 99.9 % in 2021. This file displays only the LBZ records associated with the BRP. As a person ID, the variable RINPERSOON is added, a meaningless and dimensionless number, which can be used to link to other CBS SSB files. A height weight has been included with which the (linked to the BRP) shots can be increased to all shots. The variables involved in the weighting are gender, care type of hospital admission, admission specialism and the patient’s municipal code. The LBZ files are there since 2013. For the 1995-2012 registration years, there are files of hospitalisations based on the LMR, the predecessor of the LBZ. In the linked LMR files (LMRBASISJJJJJ) the images missing in the LMR are not included. As of 2013, microrecords for the previously missing recordings are delivered to the LBZ by the hospitals. These incomplete records contain some data about the admission and the patient, but for example no diagnoses. In 2013 and 2014, some hospitals had failed to deliver these incomplete records to the LBZ. For these recordings, CBS requested some recording data from the hospitals, so that they could largely be linked to the BRP and could be added to LBZBASISJJJJ as incomplete records. Thus, in LBZBASISJJJY, almost all recordings are present, of which only some of the records lack the main diagnosis and main operation and some other variables in a small part of the records. This 'incomplete records ' in LBZBASISYYY, 23.3 % of hospital admissions linked to the BRP in 2013, 12.5 % in 2014, 6.3 % in 2015, 7.7 % in 2016, 10.7 % in 2017, 10.5 % in 2018, 13.8 % in 2019, 12.7 % in 2020 and 15.4 % in 2021. In clinical shots, 14.9 % was incomplete in 2013, 3.0 % in 2014, 0.3 % in 2015 and 0 % in 2016 and later years. For daily admissions, 30.2 % was incomplete in 2013, 22.6 % in 2014, 13.7 % in 2015, 17.3 % in 2016, 23.4 % in 2017, 22.9 % in 2018, 29.1 % in 2019, 27.1 % in 2020 and 32.1 % in 2021. At the 'long-term observations without overnight stay ' 0.4 % is incomplete in 2015 and 0 % in 2016 and later years. CBS has imputated the missing information in these incomplete records by taking this information from a fully recorded record that resembles the incomplete record as much as possible. In the imputation procedure, a complete record is sought with equal recording specialism, care type and gender, with a & distance function ' a most similar record is chosen, using the variables age, death in hospital, migration background from BRP, type of hospital, duration of admission and urgency of admission. The imputed values are displayed in the file in the variables with the suffix &imp ' in the name and are thus distinguishable from the actual recorded variables. Please note that the imputated variables are not suitable for use in (longitudinal) examination of recordings of individuals; these are not real data. For cross-sectional research at sufficiently aggregated level, these imputed variables can be used. It should be noted that the quality of the diagnosis at the complete daily admissions is not always high, e.g. because some hospitals have derived the main diagnoses from the typical diagnosis recorded for the entire treatment (diagnosis combination, DBC care product).

Population

Hospitalisations that have taken place in all general and academic Dutch hospitals. In addition, hospitalisations within three categorical hospitals (two kankerk clinic and an eye hospital) have been admitted. These data on hospital admissions are from the National Basic Registration of Hospital Care (LBZ). In the file are both day shots, clinical shots and from 2015 also & long-term observations without overnight stay ' registered. Outpatient contacts are not included in this file. Data on admission and dismissal are included in LBZBASISJYY, the main diagnosis and (if applicable) the main operation carried out. All diagnoses recorded during the recording (including side diagnoses) are included in the file LBZDIAGNOSENJJJ. Only hospitalisations linked to the BRP are included in LBZBASISJYYY (see 'Used methodology ' for more details).

Panama Hospital Admissions: No. of Patients data was reported at 18,936.000 Person in Dec 2024. This records a decrease from the previous number of 21,031.000 Person for Nov 2024. Panama Hospital Admissions: No. of Patients data is updated monthly, averaging 25,113.000 Person from Jan 2019 (Median) to Dec 2024, with 72 observations. The data reached an all-time high of 30,456.000 Person in Oct 2019 and a record low of 18,090.000 Person in Jul 2020. Panama Hospital Admissions: No. of Patients data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G010: Hospital Admissions. [COVID-19-IMPACT]

Panama Hospital Admissions: No. of Patients: Public data was reported at 16,095.000 Person in Dec 2024. This records a decrease from the previous number of 17,957.000 Person for Nov 2024. Panama Hospital Admissions: No. of Patients: Public data is updated monthly, averaging 21,700.500 Person from Jan 2019 (Median) to Dec 2024, with 72 observations. The data reached an all-time high of 26,871.000 Person in Oct 2019 and a record low of 15,290.000 Person in Jul 2020. Panama Hospital Admissions: No. of Patients: Public data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G010: Hospital Admissions. [COVID-19-IMPACT]

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 statistic displays the number of admissions to an NHS (National Health Service) hospital with a primary diagnosis of drug related mental health and behavioral disorders in England, in 2019/20, by gender and region. In this period, 965 men and 350 women in the North West of England were admitted to hospital for drug related mental health and behavioral disorders.