In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

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.

As of September 26, the hospitalization rate in the United States due to COVID-19 was highest for those aged 85 years and older. This statistic shows the cumulative rate of laboratory-confirmed COVID-19-associated hospitalizations in the U.S. as of September 26, 2020, by age group.

Graph and download economic data for Rate of Preventable Hospital Admissions (5-year estimate) in Sussex County, DE (DISCONTINUED) (DMPCRATE010005) from 2008 to 2015 about Sussex County, DE; preventable; admissions; hospitals; DE; 5-year; rate; and USA.

The first Social Drivers of Health (SDoH) dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken, expected payer, percent of employment, percent of home ownership, percent of park access and percent of access to basic kitchen facilities by the stated year. Preventable hospitalizations rates were created by dividing the number of patients who are 18 years and older and were admitted to a hospital for at least one of the preventable hospitalization diagnoses (see list below) by the total number of hospitalizations. List of preventable hospitalization diagnoses: diabetes with short-term complications, diabetes with long-term complications, uncontrolled diabetes without complications, diabetes with lower-extremity amputation, chronic obstructive pulmonary disease, asthma, hypertension, heart failure, angina without a cardiac procedure, dehydration, bacterial pneumonia, or urinary tract infection were counted as a preventable hospitalization. These conditions correspond with the conditions used in the Agency for Healthcare Research and Quality’s (AHRQ), Prevention Quality Indicator - Overall Composite Measure (PQI #90). The SDoH "overtime" dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken and expected payer overtime in the stated year range.

The dataset contains hospitalization counts and rates, statewide and by county, for 10 ambulatory care sensitive conditions plus 4 composite measures. Hospitalizations due to these medical conditions are potentially preventable through access to high-quality outpatient care. The conditions include: diabetes short-term complications; diabetes long-term complications; chronic obstructive pulmonary disease (COPD) or asthma in older adults (age 40 and over); hypertension; heart failure; community-acquired pneumonia; urinary tract infection; uncontrolled diabetes; asthma in younger adults (age 18-39); and lower-extremity amputation among patients with diabetes. The composite measures include overall, acute conditions, chronic conditions, and diabetes (new, 2016). The data provides a good starting point for assessing quality of health services in the community. The data does not measure hospital quality. Note: In 2015, HCAI (formerly OSHPD) only released the first three quarters of data due to a change in the reporting of diagnoses from ICD-9-CM to ICD-10-CM codes, effective October 1, 2015. Due to the significant differences resulting from the code change, the ICD-9-CM data is distinguished from the ICD-10-CM data in the data file beginning in 2016.

In the fiscal year no to no, the hospitalization rate in Canada stood at ***** hospitalizations per 100,000 population. Since no, the rate at which people were hospitalized in Canada has gradually decreased. Hospitalization rates saw a sharp drop in the beginning of the COVID pandemic and stabilized somewhat.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 3 procedures performed (Carotid Endarterectomy, Pancreatic Resection, and Percutaneous Coronary Intervention) in California hospitals. The 2023 IMIs were generated using AHRQ Version 2024, while previous years' IMIs were generated with older versions of AHRQ software (2022 IMIs by Version 2023, 2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Hospitalization Discharge Rates in Lake County, Illinois. Explanation of field attributes: Anxiety Disorder - Anxiety disorders are characterized by excessive fear or stress that is difficult to control and negatively and substantially impacts daily functioning. This is a rate per 100,000. Mood Disorder – Mood disorders are characterized by the elevation or lowering of a person's mood, such as depression or bipolar disorder. This is a rate per 100,000. Alcohol Rehabilitation – Alcohol rehabilitation is a term for the medical and/or psychotherapeutic treatment for dependency on alcohol. This is a rate per 100,000. Diabetes – Diabetes is a chronic disease in which blood sugar (glucose) levels are above normal. This is a rate per 100,000. Hypertension – Hypertension is a chronic disease in which blood pressure (the force of the blood flowing blood vessels) is consistently high. This is a rate per 100,000. Asthma - Asthma is a condition in which airways narrow, swell, and produce extra mucus leading to difficulty in breathing. This is a rate per 100,000. Senior Falls Emergency Room Visit – Senior falls refers to individuals who are 65 years or older who have a fall and injure themselves. This is a rate per 100,000. Hospital Discharges – Hospital discharge is defined as the release of a patient who has stayed at least one night in hospital. This is a rate per 100,000. Mental Health Emergency Room Visit – Mental health conditions/ or mental illnesses refer to disorders generally characterized by dysregulation of mood, thought, and/or behavior. This is a rate per 100,000. Total Mental Health – Mental health conditions/ or mental illnesses refer to disorders generally characterized by dysregulation of mood, thought, and/or behavior. This is a rate per 100,000. Total Ambulatory Care Sensitive Conditions – Ambulatory Care Sensitive Conditions (ACSCs) are defined as conditions where effective community care and case management can help prevent the need for hospital admission. This is a rate per 100,000.

(See Note below regarding 2015 data). The dataset contains hospitalization counts and rates (age 18+), statewide and by county, for 7 potentially-preventable adverse events that occur during a hospital stay. They provide a perspective on complications and iatrogenic events and help assess total incidence within a region. The measures, based upon the Agency for Healthcare Research and Quality’s (AHRQ’s) Patient Safety Indicators (PSIs), include: retained surgical item or unretrieved device fragment, iatrogenic pneumothorax, central venous catheter-related blood stream infection, postoperative wound dehiscence, accidental puncture or laceration, transfusion reaction, and perioperative hemorrhage or hematoma. Note: HCAI is only releasing the first 3 quarters of 2015 data due to a change in the reporting of diagnoses/procedures from ICD-9-CM to ICD-10-CM/PCS effective October 1, 2015, and the inability of the AHRQ software to handle both code sets concurrently.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated 8/14/2024. Hospitalization Rate Related To Alzheimer's Or Other Dementias - This indicator shows the rate of hospitalizations related to Alzheimer's or other dementias (per 100,000 population). In the US, an estimated 5.4 million people are living with Alzheimer’s disease. Reducing the proportion of hospitalizations related to Alzheimer's and other dementias can decrease burdens on individuals, families, and the health care system in 2014.

This dataset contains counts and rates (per 10,000 residents) of asthma hospitalizations among Californians statewide and by county. The data are stratified by age group (all ages, 0-17, 18+, 0-4, 5-17, 18-64, 65+) and race/ethnicity (white, black, Hispanic, Asian/Pacific Islander, American Indian/Alaskan Native). The data are derived from the Department of Health Care Access and Information Patient Discharge Data. These data include hospitalizations from all licensed hospitals in California. These data are based only on primary discharge diagnosis codes. On October 1, 2015, diagnostic coding for asthma transitioned from ICD-9-CM (493) to ICD-10-CM (J45). Because of this change, CDPH and CDC do not recommend comparing data from 2015 (or earlier) to 2016 (or later). NOTE: Rates are calculated from the total number of asthma hospitalizations (not the unique number of individuals).

This study quantifies the impact of COVID-19 vaccination on hospitalization for COVID-19 infection in a South African private health insurance population. This retrospective cohort study is based on the analysis of demographic and claims records for 550,332 individuals belonging to two health insurance funds between 1 March 2020 and 31 December 2022. A Cox Proportional Hazards model was used to estimate the impact of vaccination (non-vaccinated, partly vaccinated, fully vaccinated) on COVID-19 hospitalization risk; and zero-inflated negative binomial models were used to estimate the impact of vaccination on hospital utilization and hospital expenditure for COVID-19 infection, with adjustments for age, sex, comorbidities and province of residence. In comparison to the non-vaccinated, the hospitalization rate for COVID-19 was 94.51% (aHR 0.06, 95%CI 0.06, 0.07) and 93.49% (aHR 0.07, 95%CI 0.06, 0.07) lower for the partly and fully vaccinated respectively; hospital utilization was 17.70% (95% CI 24.78%, 9.95%) and 20.04% (95% CI 28.26%, 10.88%) lower; the relative risk of zero hospital days was 4.34 (95% CI 4.02, 4.68) and 18.55 (95% CI 17.12, 20.11) higher; hospital expenditure was 32.83% (95% CI 41.06%, 23.44%) and 55.29% (95% CI 61.13%, 48.57%) lower; and the relative risk of zero hospital expenditure was 4.38 (95% CI 4.06, 4.73) and 18.61 (95% CI 17.18, 20.16) higher for the partly and fully vaccinated respectively. Taken together, findings indicate that all measures of hospitalization for COVID-19 infection were significantly lower in the partly or fully vaccinated in comparison to the non-vaccinated. The use of real-world data and an aggregated level of analysis resulted in the study having several limitations. While the overall results may not be generalizable to other populations, the findings add to the evidence based on the impact of COVID-19 vaccination during the period of the pandemic.

This statistic shows the improvement in mortality rates 2007-2009 amongst all hospitals in the United States, sorted by mortality rates for inhospital care as well as ** and *** days following hospitalization. In addition to presenting information on improvement in the United States overall, this graph includes further data on hospitals of differing quality ratings. In the United States overall, mortality rates improved by *** percent, but in five-star hospitals, mortality rates improved by **** percent.

In 2022, the average hospitalization rate in Italy was equal to ***** per 1,000 inhabitants. However, this figure varied considerably from region to region. According to the graph, the region with the highest hospitalization rate in the country was Aosta Valley, with roughly *** hospitalized people per 1,000 inhabitants. By contrast, Apulia had the lowest hospitalization rate of ***** per 1,000 inhabitants. This statistic shows the hospitalization rate in Italy in 2022, by region.

The Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) a network that conducts active, population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among children and adults. COVID-NET, along with the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and the Influenza Hospitalization Surveillance Network (FluSurv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. COVID-NET is CDC’s source for important data on rates of hospitalizations associated with COVID-19. Hospitalization rates show how many people in the surveillance area are hospitalized with COVID-19, compared to the total number of people residing in that area. Data are preliminary and subject to change as more data become available. Data will be updated weekly.

(See Note below regarding 2015 data). The dataset contains hospitalization counts and rates, statewide and by county, for 4 medical procedures for which there could be possible over- or under-use and for which utilization varies across hospitals or geographic areas. High or low rates, by themselves, do not represent poor quality of care. Instead, the information is intended to inform consumers about local practice patterns or identify potential problem areas that might need further study. The procedures, based upon the Agency for Healthcare Research and Quality’s (AHRQ’s) Inpatient Quality Indicators (IQIs), include: coronary artery bypass graft (CABG) (age 40+), percutaneous coronary intervention (PCI) (age 40+), hysterectomy (age 18+), and laminectomy or spinal fusion (age 18+). Note: HCAI is only releasing the first 3 quarters of 2015 data due to a change in the reporting of diagnoses/procedures from ICD-9-CM to ICD-10-CM/PCS effective October 1, 2015, and the inability of the AHRQ software to handle both code sets concurrently.

Context

These data files contain information about hospitalisation and Intensive Care Unit (ICU) admission rates and current occupancy for COVID-19 by date and country. The data are updated weekly.

Source

• https://data.europa.eu/euodp/en/data/dataset/hospital-and-icu-admission-rates-and-occupancy-for-covid-19

As of June 10, 2023, the cumulative hospitalization rate in the United States due to COVID-19 was lowest for Non-Hispanic Asian or Pacific Islanders and highest among Non-Hispanic American Indian or Alaska Natives. This statistic shows the cumulative rate of laboratory-confirmed COVID-19-associated hospitalizations in the U.S. as of June 10, 2023, by race and ethnicity.

This map shows the 2001–2010 average rate of hospitalizations classified as “heat-related” by medical professionals in 23 states that participate in CDC’s hospitalization tracking program. Rates are based on hospital discharge records for May 1 to September 30 of every year. Rates have been age-adjusted to account for differences in the population distribution over time and between states—for example, if one state has a higher proportion of older adults than another. For more information: www.epa.gov/climatechange/science/indicators