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

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

Directly age and sex standardised admission rate for emergency admissions for acute conditions that should not usually require hospital admission per 100,000 registered patients, 95% confidence intervals (CI). March 2022 - The coronavirus (COVID-19) pandemic began to have an impact on Hospital Episode Statistics (HES) data late in the 2019-20 financial year, which continued into the 2020-21 financial year. This means we are seeing different patterns in the submitted data, for example, fewer patients being admitted to hospital, and therefore statistics which contain data from this period should be interpreted with care. Further information is available in the annual HES publication: https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity/2020-21/covid-19-impact As of the October 2020 release, the CCG OIS is now published on an annual basis, as a result provisional data periods will no longer be published. The annual update will be based on finalised data for the April to March reporting period each year. As of the March 2020 release, the data included in the December 2019 publication for the 2018/19, July 2018 to June 2019 (Provisional) and October 2018 to September 2019 (Provisional) data periods has been revised. This is due to a revision of a large proportion of records for East Sussex Healthcare NHS Trust (RXC) which had missing information for the condition the patient was in hospital for and other conditions the patients suffer from. The revised data for these reporting periods also differs from that originally published in December 2019 in that the HES database is routinely updated (overwritten) on a monthly basis for the year in progress. Data for the two provisional periods remain provisional, but is now more complete than it was when the December 2019 publication was released. This effect cannot be readily separated from the effect of the East Sussex Healthcare NHS Trust (RXC) resubmission which also took place after processing for the December 2019 publication. Legacy unique identifier: P01844

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

Context

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

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

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

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

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

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

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

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

Inspiration

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

On 11/14/2025, we launched updated hospitalization reporting using data from the National Healthcare Safety Network (NHSN). The new dataset includes hospital admissions for respiratory viruses including COVID-19, flu, and RSV. You can access the new dataset here. A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week. B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center. San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS). C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available. D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week. The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate. E. CHANGE LOG 11/14/2025 COVID-19 hosipital admissions is tracked in a new dataset 7/18/2025 - Dataset update is paused to assess data quality and completeness. 9/12/2024 - We updated the data source for our COVID-19 hospitalization data to a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

Counts and rates of deaths and hospital admissions associated with temperature for England and Wales from 2001 to 2020.

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

In 2022/23, there were approximately 35 thousand detentions under the Mental Health Act on admission to hospital in England, the second-highest number reported since 2015/16. This statistic displays the number of detentions on admission to hospital under the Mental Health Act 1983 in England from 2009/10 to 2022/23.

All data relating to “Coronavirus (COVID-19) hospital admissions by vaccination and pregnancy status, England”.

This is a report on admitted patient care activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2022. It contains final data and replaces the provisional data that are released each month. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients at NHS hospitals in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with a number of breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Hospital Adult Critical Care (ACC) data are now included within this report, following the discontinuation of the 'Hospital Adult Critical Care Activity' publication. The ACC data tables are not a designated National Statistic and they remain separate from the APC data tables. The ACC data used in this publication draws on records submitted by providers as an attachment to the admitted patient care record. These data show the number of adult critical care records during the period, with a number of breakdowns including admission details, discharge details, patient demographics and clinical information. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Supplementary analysis has been produced, by NHS Digital, containing experimental statistics using the Paediatric Critical Care Minimum Data Set (PCCMDS) data, collected by NHS Digital, against activity published in NHS Reference Costs. This analysis seeks to assist users of the data in understanding the data quality of reported paediatric critical care data. Also included within this release, is supplementary analysis that has been produced in addition to the Retrospective Review of Surgery for Urogynaecological Prolapse and Stress Urinary Incontinence using Tape or Mesh: Hospital Episode Statistics (HES), Experimental Statistics, April 2008 - March 2017. It contains a count of Finished Consultant Episodes (FCEs) where a procedure for urogynaecological prolapse or stress urinary incontinence using tape or mesh has been recorded during the April 2021 to March 2022 period. Please Note: A summary of information relating to procedures for the treatment of Stress Urinary Incontinence is published here for transparency and scrutiny. Follow up is taking place with individual Trusts to confirm that specific treatment is as described for activity occurring since April 2021. This will lead to more accurate information on these procedures that occurred since April 2021 being being available in the future. In collating this information, it has already become clear that some Trusts mis-coded these procedures in Commissioning Data Set return used to produce these statistics. Alongside this the clinical coding guidance has been refined to enable more accurate identification of specific treatments. The data published here has been published for transparency purposes. However, for these reasons small numbers reported on treatments for this condition should be used as a starting point for further investigation rather than a definitive view.

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

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

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

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

Background: The COVID-19 pandemic disrupted hospital care, as hospitals had to deal with a highly infectious virus, while at the same time continuing to fulfill the ongoing health service needs of their communities. This study examines the direct effects of COVID-19 on the delivery of inpatient care in Croatia.Materials and Methods: The research is a retrospective, comparative analysis of the hospital admission rate across all Diagnosis Related Group (DRG) classes before and during the pandemic. It is based on DRG data from all non-specialized acute hospitals in Croatia, which account for 96% of national inpatient activity. The study also used COVID-19 data from the Croatian Institute of Public Health (CIPH).Results: The results show a 21% decrease in the total number of admissions [incident rate ratio (IRR) 0.8, p < 0.0001] across the hospital network during the pandemic in 2020, with the greatest drop occurring in April, when admissions plunged by 51%. The decrease in activity occurred in non-elective DRG classes such as cancers, stroke, major chest procedures, heart failure, and renal failure. Coinciding with this reduction however, there was a 37% increase (IRR 1.39, p < 0.0001) in case activity across six COVID-19 related DRG classes.Conclusions: The reduction in hospital inpatient activity during 2020, can be attributed to a number of factors such as lock-downs and quarantining, reorganization of hospital operations, the rationing of the medical workforce, and the reluctance of people to seek hospital care. Further research is needed to examine the consequences of disruption to hospital care in Croatia. Our recommendation is to invest multidisciplinary effort in reviewing response procedures to emergencies such as COVID-19 with the aim of minimizing their impact on other, and equally important community health care needs.

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 labo

Crude rate of cost of admissions for alcohol-related conditions (Broad definition) per head of population.

Rationale Alcohol misuse across the UK is a significant public health problem with major health, social, and economic consequences. This indicator aims to highlight the impact of alcohol-related conditions on inpatient hospital services in England. High costs of alcohol-related admissions are indicative of poor population health and high alcohol consumption. This indicator highlights the resource implications of alcohol-related conditions and supports the arguments for local health promotion initiatives. Publication of this indicator will allow national and local cost estimates to be updated and consistently monitored going forward. This measure accounts for just one aspect of the cost of alcohol to society, but there are others such as primary care, crime, ambulatory services, and specialist treatment services as well as broader costs such as unemployment and loss of productivity.

The Government has said that everyone has a role to play in reducing the harmful use of alcohol. This indicator is one of the key contributions by the Government (and the Department of Health and Social Care) to promote measurable, evidence-based prevention activities at a local level, and supports the national ambitions to reduce harm set out in the Government's Alcohol Strategy. This ambition is part of the monitoring arrangements for the Responsibility Deal Alcohol Network. Alcohol-related admissions can be reduced through local interventions to reduce alcohol misuse and harm.

References: (1) PHE (2020) The Burden of Disease in England compared with 22 peer countries https://www.gov.uk/government/publications/global-burden-of-disease-for-england-international-comparisons/the-burden-of-disease-in-england-compared-with-22-peer-countries-executive-summary

Definition of numerator The total cost (£s) of alcohol-related admissions (Broad). Admissions to hospital where the primary diagnosis is an alcohol-related condition, or a secondary diagnosis is an alcohol-related external cause.

More specifically, hospital admissions records are identified where the admission is a finished episode [epistat = 3]; the admission is an ordinary admission, day case or maternity [classpat = 1, 2 or 5]; it is an admission episode [epiorder = 1]; the sex of the patient is valid [sex = 1 or 2]; there is a valid age at start of episode [startage between 0 and 150 or between 7001 and 7007]; the region of residence is one of the English regions, no fixed abode or unknown [resgor <= K or U or Y]; the episode end date [epiend] falls within the financial year, and an alcohol-attributable ICD10 code appears in the primary diagnosis field [diag_01] or an alcohol-related external cause code appears in any diagnosis field [diag_nn].

For each episode identified, an alcohol-attributable fraction is applied to the primary diagnosis field or an alcohol-attributable external cause code appears in one of the secondary codes based on the diagnostic codes, age group, and sex of the patient. Where there is more than one alcohol-related ICD10 code among the 20 possible diagnostic codes, the code with the largest alcohol-attributable fraction is selected; in the event of there being two or more codes with the same alcohol-attributable fraction within the same episode, the one from the lowest diagnostic position is selected. For a detailed list of all alcohol-attributable diseases, including ICD 10 codes and relative risks, see ‘Alcohol-attributable fractions for England: an update’ (2). Alcohol-related hospital admission episodes were extracted from HES according to the Broad definition and admissions flagged as either elective or non-elective based on the admission method field.

The cost of each admission episode was calculated using the National Cost Collection (published by NHS England) main schedule dataset for the corresponding financial year applied to elective and non-elective admission episodes. The healthcare resource group (HRG) was identified using the HES field SUSHRG [SUS Generated HRG], which is the SUS PbR derived HRG code at episode level. Healthcare Resource Groups (HRGs) are standard groupings of clinically similar treatments which use common levels of healthcare resource. The elective admissions were assigned an average of the elective and day-case costs. The non-electives were assigned an average of the non-elective long stay and non-elective short stay costs. Where the HRG was not available or did not match the National Reference Costs look-up table, an average elective or non-elective cost was imputed. This may result in the cost of these admissions being underestimated. For each record, the AAF was multiplied by the reference cost and the resulting values were aggregated by the required output geographies to provide numerators for the cost per capita indicator.

References: (2) PHE (2020) Alcohol-attributable fractions for England: an update https://www.gov.uk/government/publications/alcohol-attributable-fractions-for-england-an-update

Definition of denominator Mid-year population estimates.

Caveats Not all alcohol-related conditions require inpatient services, so this indicator is only one measure of the alcohol-related health problems in each local area. However, inpatient admissions are easily monitored, and this indicator provides local authorities with a routine method of monitoring the health impacts of alcohol in their local populations.

The Healthcare Resource Group cost assigned to each hospital admission is for the initial admission episode only and doesn’t include costs related to alcohol in any subsequent episodes in the hospital spell. Where the HRG was not available or did not match the National Reference Costs look-up table, an average elective or non-elective cost was imputed. This may result in the cost of these admissions being underestimated. It must be noted that the numerator is based on the financial year and the denominator on calendar mid-year population estimates, e.g., 2019/20 admission rates are constructed from admission counts for the 2019/20 financial year and mid-year population estimates for the 2020 calendar year. Data for England includes records with geography 'No fixed abode'. Alcohol-attributable fractions were not available for children. Conditions where low levels of alcohol consumption are protective (have a negative alcohol-attributable fraction) are not included in the calculation of the indicator. This does not include attendance at Accident and Emergency departments. Hospital Episode Statistics overall is well completed. However, year-on-year variations exist due to poor completion from a proportion of trusts.

Analysis has revealed significant differences across the country in the coding of cancer patients in the Hospital Episode Statistics. In particular, in some areas, regular attenders at hospital for treatments like chemotherapy and radiotherapy are being incorrectly recorded as ordinary or day-case admissions. Since cancer admissions form part of the overarching alcohol-related admission national indicators, the inconsistent recording across the country for cancer patients has some implication for these headline measures.

Cancer admissions make up approximately a quarter of the total number of alcohol-related admissions. Analysis suggests that, although most Local Authorities would remain within the same RAG group compared with the England average if cancer admissions were removed, the ranking of Local Authorities within RAG groups would be altered. We are continuing to monitor the impact of this issue and to consider ways of improving the consistency between areas. The COVID-19 pandemic had a large impact on hospital activity with a reduction in admissions in 2020 to 2021. Because of this, NHS Digital has been unable to analyse coverage (measured as the difference between expected and actual records submitted by NHS Trusts) in the normal way. There may have been issues around coverage in some areas which were not identified as a result.

Number and proportion of infant deaths that did not link to ONS mortality record and number and proportion of infant deaths that were indicated using HES data only.

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 hosp

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. The COVID-19 pandemic has wider impacts on individuals' health, and their use of healthcare services, than those that occur as the direct result of infection. Reasons for this may include: * Individuals being reluctant to use health services because they do not want to burden the NHS or are anxious about the risk of infection. * The health service delaying preventative and non-urgent care such as some screening services and planned surgery. * Other indirect effects of interventions to control COVID-19, such as mental or physical consequences of distancing measures. This dataset provides information on trend data regarding the wider impact of the pandemic on hospital admissions. Data are shown by age group, sex, broad deprivation category and specialty groups. Information is also available at different levels of geographical breakdown such as Health Boards, Health and Social Care partnerships, and Scotland totals. This data is also available on the COVID-19 Wider Impact Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications.

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_

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

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 as of the initial date of reporting for each weekly metric. 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, hosp