In 2023, there were over **** million hospital admissions in the United States. The number of hospitals in the U.S. has decreased in recent years, although the country faces an increasing elder population. Predictably, the elderly account for the largest share of hospital admissions in the U.S. Hospital stays Stays in hospitals are more common among females than males, with around *** percent of females reporting one or more hospital stays in the past year, compared to *** percent of males. Furthermore, **** percent of those aged 65 years and older had a hospitalization in the past year, compared to just *** percent of those aged 18 to 44 years. The average length of a stay in a U.S. hospital is *** days. Hospital beds In 2022, there were ******* hospital beds in the U.S. In the past few years, there has been a decrease in the number of hospital beds available. This is unsurprising given the decrease in the number of overall hospitals. In 2021, the occupancy rate of hospitals in the U.S. was ** percent.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents daily COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laborat

In 2019, almost ***** percent of people aged 18 to 44 years in the United States were hospitalized at least once. Unsurprisingly, hospitalization among people aged 65 years and older was higher, at almost ** percent. Hospital Stays Hospitalization in the U.S. has decreased since 1997. In 2019, a total of *** percent of people in the U.S. were hospitalized at least once. Hospitalization rates for females have been higher than males for the past two decades. In 2019, almost ***** percent of females were hospitalized, compared to only **** percent of males. The average length of stay in a hospital is currently *** days. However, this varies greatly by state. In Wyoming, for example, the average length of stay is *** days, which is more than twice the average length of stay in New York. Hospital Costs Community hospital expenses per inpatient stay in the United States have been constantly increasing. The average expenses for a community hospital per inpatient stay in 2019 was around ****** U.S. dollars. Expectedly, hospital care expenditure has also been increasing in the past two decades. In 2020, around **** trillion U.S. dollars were spent on hospital care.

The previous highest peak in the reported time interval of COVID-19 hospitalizations was the week ending January 9, 2021. A year later in the week ending January 8, 2022, a new peak was recorded. However, this time hospitalizations were more spread out in the age groups, with those under 65 years making up roughly 60 percent of total hospitalizations, compared to 50 percent back in January 2021. This statistic illustrates the weekly number of COVID-19 associated hospitalizations in the United States from the week ending March 7, 2020 to February 5, 2022, by age group.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset contains the annual number of hospital discharges, crude hospitalization rates with corresponding 95% confidence intervals, and age-adjusted hospitalization rates (per 10,000 children and adults aged 5 to 64 years) with corresponding 95% confidence intervals, for the years 2000 – 2011, by Chicago U.S. Postal Service ZIP code or ZIP code aggregate. See the full dataset description for more information at http://bit.ly/PKI8p0.

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.

According to the data, it is estimated that in 2023-2024 there were around ******* hospitalizations due to influenza in the United States. This statistic depicts the estimated number of hospitalizations for influenza in the United States from 2010 to 2024.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset contains the annual number of hospital discharges, crude hospitalization rates with corresponding 95% confidence intervals, and age-adjusted hospitalization rates with corresponding 95% confidence intervals, for the years 2000 – 2011, by Chicago U.S. Postal Service ZIP code or ZIP code aggregate. See the full description at http://bit.ly/Os5wnn.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 tests, cases, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported daily, with

This dataset is not being updated as hospitals are no longer mandated to report COVID Hospitalizations to CDPH.

Data is from the California COVID-19 State Dashboard at https://covid19.ca.gov/state-dashboard/

Note: Hospitalization counts include all patients diagnosed with COVID-19 during their stay. This does not necessarily mean they were hospitalized because of COVID-19 complications or that they experienced COVID-19 symptoms.

Note: Cumulative totals are not available due to the fact that hospitals report the total number of patients each day (as opposed to new patients).

In the United States, the highest rate of hospitalizations due to influenza are among those aged 65 years and older. During the 2023-2024 flu season, the rate of hospitalizations due to influenza among this age group was about 401 per 100,000 population, compared to a rate of around 47 per 100,000 for those aged 5 to 17 years. Influenza is a common viral infection that usually does not require medical treatment. However, for the very young, the old, and those with certain pre-existing conditions, influenza can be serious and even deadly. The burden of influenza in the United States The impact of influenza in the United States varies from year to year depending on the strain that is most prevalent during that season and the immunity in the population. Preliminary estimates show that around 28,000 people died from influenza during the 2023-2024 flu season. However, during the 2017-2018 flu season, an estimated 52,000 people lost their lives to influenza. The importance of flu vaccines The best way to avoid catching the flu and to reduce the virus’s overall burden on society is by receiving an annual flu vaccination. The CDC currently recommends that everyone over 6 months of age should get a flu vaccination every year, preferably by the end of October. The flu vaccine is safe, efficient, and reduces the number of illnesses, hospitalizations, and deaths caused by the virus. For example, during the 2022-2023 flu season, it was estimated that vaccinations averted almost 65 thousand influenza-related hospitalizations. However, despite the proven benefits and wide availability of flu vaccinations, a large percentage of people in the United States fail to receive a vaccination every year. During the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years were vaccinated against influenza, compared to 70 percent of those aged 65 years and older.

ABSTRACT Background: The cholelithiasis is disease of surgical resolution with about 60,000 hospitalizations per year in the Sistema Único de Saúde (SUS - Brazilian National Health System) of the Rio Grande do Sul state. Aim: To describe the profile of hospitalizations for cholecystitis and cholelithiasis performed by the SUS of Rio Grande do Sul state, 2011-2013. Methods: Hospital Information System data from the National Health System through morbidity list for cholelithiasis and cholecystitis (ICD-10 K80-K81). Variables studied were sex, age, number of hospitalizations and approved Hospitalization Authorizations (AIH), total amount and value of hospital services generated, days and average length of stay, mortality, mortality and case fatality ratio, from health regions of the Rio Grande do Sul. Results: During 2011-2013 there were 60,517 hospitalizations for cholecystitis and cholelithiasis, representing 18.86 hospitalizations per 10,000 inhabitants/year, most often in the age group from 60 to 69 years (41.34 admissions per 10,000 inhabitants/year) and female (27.72 hospitalizations per 10,000 inhabitants/year). The fatality rate presented an inverse characteristic: 13.52 deaths per 1,000 admissions/year for males, compared with 7.12 deaths per 1,000 admissions/year in females. The state had an average total amount spent and value of hospital services of R$ 16,244,050.60 and R$ 10,890,461.31, respectively. The health region "Capital/Gravataí Valley" exhibit the highest total expenditure and hospital services, and the largest number of deaths, and average length of stay. Conclusion: The hospitalization and lethality coefficients, the deaths, the length of stay and spending related to admissions increased from 50 years old. Females had a higher frequency and higher values spent on hospitalization, while the male higher coefficient of mortality and mean hospital stay.

Summary The daily occupancy number of COVID-19 designated hospital beds in Maryland. Description The MD COVID-19 - Total Currently Hospitalized - Acute and ICU data layer is a collection of the statewide cumulative total of individuals who tested positive for COVID-19 that have been reported each day via CRISP as currently occupying a COVID-19 bed in a Maryland hospital facility. MD COVID-19 - Total Currently Hospitalized comprises four subsets: Adult Acute Care Beds, Adult ICU Beds Pediatrics Acute Care Beds and Pediatrics ICU Care Beds.

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.

Note: This dataset has been limited to show metrics for Ramsey County, Minnesota.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information: As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS). While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations. Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files. Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Calculation of county-level hospital metrics: County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level. Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

Metric details: Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Thursdays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections. New hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week). New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data] New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week. New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data] COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction. COVID-19 Inpatient Bed Occupancy Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 Inpatient Bed Occupancy percent change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction. COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction. COVID-19 ICU Bed Occupancy Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 ICU Bed Occupancy percent change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past week, compared with the prior week, in the in the entire jurisdiction. For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.

Note: This dataset was archived on 10/6/23. Statewide hospitalization data is available in the New York State Statewide COVID-19 Hospitalizations and Beds dataset. This dataset includes the number of patients hospitalized, and number of patients in the intensive care unit (ICU) among patients with lab-confirmed COVID-19 disease by hospital region and reporting date. The primary goal of publishing this dataset is to provide users with timely information about hospitalizations among patients with lab-confirmed COVID-19 disease. The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals are required to complete this survey daily and data reflects the number of patients hospitalized and number of patients in the ICU reported by hospitals through the survey each day. These data include NYS resident and non-NYS resident hospitalizations. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in mid-March 2020. To calculate regional totals, the number of patients hospitalized and number of patients in the ICU are each summed by hospital region and reporting date. The information in this dataset is updated daily on NY Forward; New York State’s resource for COVID-19 testing, early warning monitoring, and regional daily hospitalization dashboards. More information can be found at forward.ny.gov.

NOTE: This layer is deprecated (last updated 3/14/2022). This was formerly a daily update. Summary The cumulative number of COVID-19 positive Maryland residents who have been hospitalized. Description The MD COVID-19 - Total Hospitalizations data layer is a collection of the statewide cumulative total of individuals who tested positive for COVID-19 that have been reported each day by each local health department as having been hospitalized. As published to coronavirus.maryland.gov, this is the "Ever Hospitalized" number. "Ever Hospitalized" refers to the cumulative number of individuals who were admitted to the hospital at some point during their COVID-19 illness. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This data set includes total charges (in dollars) billed for hospitalizations in Virginia for 12 chronic conditions by year. Data set includes hospitalizations data from 2016 to the most current year for Virginia residents.

The 12 chronic conditions in the dataset include: Alzheimer's Disease, Arthritis, Asthma, Cardiovascular Disease, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Diabetes, High Blood Cholesterol, hypertension, Ischemic Heart Disease, Non-Alzheimer's Dementia, and Stroke. The International Classification of Diseases, Tenth Revision (ICD-10) codes are used to identify chronic disease indicators. Definitions are based on the Chronic Disease Data Warehouse developed under the Centers for Medicare & Medicaid Services (CMS). Patients hospitalized with a chronic condition for any reason found in a diagnosis list of up to 18 available diagnoses are included. More information can be found here: https://www2.ccwdata.org/web/guest/condition-categories-chronic

As of 9/12/2024, we will begin reporting on hospitalization data again using a new San Francisco specific dataset. Updated data can be accessed here. On 5/1/2024, hospitalization data reporting will change from mandatory to optional for all hospitals nationwide. We will be pausing the refresh of the underlying data beginning 5/2/2024. 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 National Healthcare Safety Network (NHSN) and provided by the California Department of Public Health (CDPH). C. UPDATE PROCESS Updates automatically every week. 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). The dataset shown here includes all San Francisco hospitals and updates weekly with data for the past Sunday-Saturday as information is collected and verified. Data may change as more current information becomes available. E. CHANGE LOG9/12/2024 -Hospitalization data are now being tracked through a new source and are available here. 5/1/2024 - hospitalization data reporting to the National Healthcare Safety Network (NHSN) changed from mandatory to optional for all hospitals nationwide. We will be pausing the refresh of the underlying data beginning 5/2/2024. 12/14/2023 – added column “hospitalreportingpct” to indicate the percentage of hospitals who submitted data on each report date. 8/7/2023 - In response to the end of the federal public health emergency on 5/11/2023 the California Hospital Association (CHA) stopped the collection and dissemination of COVID-19 hospitalization data. In alignment with the California Department of Public Health (CDPH), hospitalization data from 5/11/2023 onward are being pulled from the National Healthcare Safety Network (NHSN). The NHSN data is updated weekly and does not include information on COVID suspected (PUI) patients. 4/9/2021 - dataset updated daily with a four-day data lag.

ABSTRACT Objective To analyze the profile of psychiatric hospitalizations of women and adolescents in the years 2018 and 2019 in a general hospital. Methods A quantitative study with a cross-sectional design was carried out, whose setting was the psychiatric inpatient unit of a general hospital, in Porto Alegre, in Rio Grande do Sul. Data collection occurred through the electronic medical records of hospitalized women for the years 2018 and 2019 The variables age, mental health diagnosis, number of days of hospitalization, origin and pregnancy were analyzed using descriptive statistics using the software Statistical Package for Social Sciences. Results A total of 418 hospitalizations were obtained, 132 corresponding to adolescents and 79 to pregnant women. The average age was 28.7 years. The average hospital stay was 28.5 days. The main places of origin are the psychiatric emergency services. The main diagnosis was depression (46.4%) and bipolar mood disorder (23.9%). Conclusion Knowing the epidemiological profile of female psychiatric hospitalizations provides an opportunity for a differential treatment to be established, since the mental health care of women is multifaceted, complex and intersectional and requires scientific knowledge, as well as an integrated approach to health services. Mental disorders also affect adolescents and pregnant women, and it is essential to know the needs of these populations in order to develop specialized care.