On April 28, 2021, 15 patients were admitted to intensive care in Sweden due to the coronavirus (COVID-19). The number of new patients in intensive care with confirmed coronavirus increased rapidly during March 2020, and then first peaked on April 22, 2020 when there were 49 new patients admitted. In the following period, the number started to drop and was down to just a few or no new patients per day. Since October 2020, however, an increasing number of patients were transferred to intensive care.

The number of people who were confirmed infected by the virus in the country had reached a total of 967,678 as of April 28, 2021. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In 2020, hospitals in Italy counted a total of approximately 5.2 thousand intensive care units (ICU). These resources proved to be insufficient in facing the coronavirus outbreak which hit the country in the first half of 2020. Among many measures and projects announced by the government in order to face the pandemic and to restart the economy, an increase in the resources granted to the national health service was also included. As of June 13, 2021, the number of ICUs in Italy was equal to 8,619. The plan to increase the number of ICUs was implemented in every region, and most of them reached the goal of 14 ICUs per 100 thousand inhabitants set by the government.

Coronavirus in Italy

After entering Italy at the beginning of February 2020, the coronavirus spread fast across the country, reaching worrying peaks in April. The strict lockdown implemented by the government, however, bore fruit. In fact, the number of new infections decreased significantly since the beginning of May. The north of the country was hit particularly hard by the outbreak, and the region with the highest number of cases was Lombardy. Moreover, the virus in the country has hit individuals of every age group in a uniform manner.

A high fatality rate

The coronavirus death rate appears to be higher in Italy than in most other countries. However, one or more comorbidities were detected in the vast majority of the patients deceased after contracting the virus. This indicates that the coronavirus likely contributed to worsening the condition of individuals who were already in a precarious state of health.

In 2022, Czechia had around 45.5 intensive care beds in hospitals per 100,000 population, that is the highest among the countries listed. This statistic shows intensive care bed density in hospitals in select countries worldwide in 2022.

Santé publique France's mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 epidemic, Public Health France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the different scenarios and implementing actions to prevent and limit the transmission of this virus on the national territory.

Daily hospital data relating to the COVID-19 epidemic by department and sex of the patient: number of hospitalized patients, number of people currently in intensive care or intensive care, cumulative number of people returned home, cumulative number of people who died.

For some patients, gender was not identified in the database. This can lead to a discrepancy between the H/F sum of an indicator and the total number of this indicator.

The region and iso 3166-1 codes of the zones have been added.

Warning: data under construction. May contain anomalies or missing data.

Abstract Objective: Analyze the association between demographic variables, morbidity and relative to the conditions of hospitalization with the expenses resulting from the admission of elderly people in intensive care units (ICU) of private hospitals in a capital of northeastern Brazil. Method: This is an epidemiological, analytical and sectional study, with a quantitative approach, in which data were collected regarding 312 hospitalizations of elderly people in the ICU of all private hospitals in Natal (RN), Brazil. The dependent variable was the cost of hospitalization and the independent variables related to the characterization of individuals in terms of socio-demographic profile, morbid condition and characteristics of hospitalization. Data were analyzed using descriptive statistics, Chi-square test, t test and multiple logistic regression with prevalence ratios (PR). Results: The average cost per hospitalization was R$ 4.266,05±3.322,50 for the low cost group and R$ 39.753,162±4.929,12 for the high cost group. It was found that hospitalization due to clinical (PR=1,81; 95%CI=1,06-3,09) and respiratory conditions (PR=2,48; 95CI%=1,48-5,24), the need for mechanical ventilation (PR=2,33; 95%CI=1,43-3,78) and complete or partial disorientation at the time of admission (PR=1,81; 95%CI=1,15-2,84) were associated with higher expenditure on hospitalizations in the multiple statistical model. Conclusion: The knowledge produced by the study may serve as a subsidy for the implementation of actions capable of promoting better health conditions for the elderly, reducing expenses related to their hospitalization in highly specialized sectors. In addition, the research raises evidence that the construction of protocols and lines of care guiding the work process in the intensive care sector, specifically created for the elderly, may be relevant in reducing the expenses resulting from hospitalization of the elderly.

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

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_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

A. SUMMARY Count of COVID+ patients admitted to the hospital. Patients who are hospitalized and test positive for COVID-19 may be admitted to an acute care bed (a regular hospital bed), or an intensive care unit (ICU) bed. This data shows the daily total count of COVID+ patients in these two bed types, and the data reflects totals from all San Francisco Hospitals.

B. HOW THE DATASET IS CREATED Hospital information is based on admission data reported to the San Francisco Department of Public Health.

C. UPDATE PROCESS Updated daily, dataset uploaded manually by staff

D. HOW TO USE THIS DATASET Each record represents how many people were hospitalized on the date recorded in either an ICU bed or acute care bed (shown as Med/Surg under DPHCategory field).

Data shown here include all San Francisco hospitals and will be updated daily with a two-day lag as information is collected and verified. Data may change as more current information becomes available.

The Covid-19 pandemic strongly impacted the state of health in France. Furthermore, people among the French population were not impacted the same way. The virus appeared more virulent depending on gender. As of April 5, 2023, 62 percent of people in intensive care units due to the COVID-19 virus in France were men.

BackgroundGlobally, road traffic accidents (RTAs) cause over 1.35 million deaths each year, with an additional 50 million people suffering disabilities. Ethiopia has the highest number of road traffic accidents, with over 14,000 people killed and over 45,000 injured annually. This study aimed to assess survival status and predictors of mortality among road traffic accident adult patients admitted to intensive care units of Referral Hospitals in Tigray, 2024.MethodsAn institution-based retrospective follow-up study design was conducted from January 8, 2019, to December 11, 2023, on 333 patient charts. A bivariable Cox-regression analysis was performed to estimate crude hazard ratios (CHR). Subsequently, a multivariable Cox regression analysis was performed to estimate the Adjusted Hazard Ratios (AHR). Finally, AHR with p-value less than 0.05 was used to measure the association between dependent and independent variables.ResultThe incidence of mortality for road traffic accident victims, was 21 per 1000 person-days observation with (95% CI: 16, 27.6) and the median survival time was 14 days. The predictors of mortality in this study were the value of oxygen saturation on admission ≤ 89% (AHR = 4.9; 95%CI: 1.4–17.2), Intracranial hemorrhage (AHR = 3.3; 95% CI: 1.02–11), chest injury (AHR = 3.2; 95%CI: 1.38–7.59), victims with age catgories of 31–45 years (AHR = 0.3; 95% CI: 0.1–0.88) and 46–60 years (AHR = 0.22; 95% CI: 0.06–0.89).ConclusionA concerningly high mortality rate from car accidents were found in Referral Hospitals of Tigray. To improve the survival rates, healthcare providers should focus on victims with very low oxygen levels, head injuries, chest injuries, and older victims.

Latvia LV: Hospital Beds: per 1000 People data was reported at 5.900 Number in 2011. This records an increase from the previous number of 5.320 Number for 2010. Latvia LV: Hospital Beds: per 1000 People data is updated yearly, averaging 10.440 Number from Dec 1980 (Median) to 2011, with 25 observations. The data reached an all-time high of 14.309 Number in 1985 and a record low of 5.320 Number in 2010. Latvia LV: Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

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 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.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. 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. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

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

Ventilatory strategies and outcomes for patients acutely admitted to hospital

Dataset 14.0 Version 1.0 15.2.2021

Background. Acute respiratory failure is commonly encountered in the emergency department (ED). Early treatment can have positive effects on long-term outcome. Non-invasive ventilation is commonly used for patients with respiratory failure during acute exacerbations of chronic obstructive lung disease and congestive heart failure. For other patients, including neuromuscular dysfunction, mechanical ventilation may be needed. For refractory hypoxemia, new rescue therapies have emerged to help improve the oxygenation, and in some cases mortality. This dataset summarises the demography, admitting complaint, serial physiology, treatments and ventilatory strategies in patients admitted with hypoxaemia. Management options and rescue therapies including extracorporeal membrane oxygenation are included.

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.

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. ITU capacity increased to 250 beds during the COVID pandemic. 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”. The electronic record captures ventilatory parameters.

Scope: All hospitalised patients with hypoxaemia requiring ventilatory support from 2000 onwards. 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), severity, ventilatory requirements, 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: Synthetic data. Post discharge care contacts.

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.

The coronavirus (COVID-19) outbreak caused massive troubles in Italy. The resilience of the Italian healthcare system and the limited capacity of hospitals were among the most challenging issues facing authorities. As the graph shows, the spread of the virus put hospitals and medical staff under a lot of pressure. At its peak, in April 2020, the number of COVID-19 patients treated in intensive care units (ICU) across the country exceeded four thousand. As of January 8, 2025, 44 COVID-19 patients were being treated in the ICU. The limited capacity of Intensive Care Units has been a dramatic issue in Italy since the start of the pandemic. In the last months, however, the country saw the end of this terrible situation: as of November 2023, roughly 85 percent of the total Italian population was fully vaccinated. For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

ICU Beds in Norway decreased to 3.10 per 1000 people in 2019 from 3.13 per 1000 people in 2018. This dataset includes a chart with historical data for Norway ICU Beds.

Ces Données proviennent du Ministère de la Santé sur la crise COVID19 au Luxembourg de la période de février à juillet 2020, ce jeu de données contient : le nombre de personnes hospitalisées en soins normaux et intensifs, le nombre de décès, le nombre de personnes ayant quitté l’hôpital, nombre de personnes testées pour COVID et nombre de personnes testées COVID+This Dashboard allows to visualize the data on the Covid 19 crisis in Luxembourg relative to the number of patients admitted in normal or intensive care.The Data comes from the Ministry of Health on the COVID19 crisis in Luxembourg from February to July, this dataset contains: the number of people hospitalized in normal and intensive care, the number of deaths, the number of people who left hospital, the number of people tested for COVID and the number of people tested for COVID+.Source : G-D Luxembourg geoportal

ICU Beds in Denmark decreased to 2.47 per 1000 people in 2020 from 2.49 per 1000 people in 2019. This dataset includes a chart with historical data for Denmark ICU Beds.

Laos LA: Hospital Beds: per 1000 People data was reported at 1.500 Number in 2012. This records an increase from the previous number of 0.700 Number for 2010. Laos LA: Hospital Beds: per 1000 People data is updated yearly, averaging 0.927 Number from Dec 1960 (Median) to 2012, with 7 observations. The data reached an all-time high of 2.570 Number in 1990 and a record low of 0.472 Number in 1960. Laos LA: Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.World Bank: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

This dataset contains counts of in-hospital births by mother’s age groups (i.e., teen mothers, typical aged mothers and older mothers) based on the mother’s county of residence and year. This dataset does not include all births in California; only those births that occurred in a hospital. Modified on October 11, 2018