In 2020, nursing home residents in the United States were mostly *****, ************, ****** and over the age of ** years. The gender distribution was roughly six women to four men. Despite a ***** of residents being over 85 years, some ** percent were under the age of 65 years.

In 2020, the density rate for nurses in Qatar was **** per 10,000 people, the highest in that year among the Gulf Cooperation Council (GCC) countries. The total number of nurses in the GCC region in that year exceeded *** thousand.

Note: For information on data collection, confidentiality protection, nonsampling error, subject definitions, and guidance on using the data, visit the 2020 Census Demographic and Housing Characteristics File (DHC) Technical Documentation webpage..To protect respondent confidentiality, data have undergone disclosure avoidance methods which add "statistical noise" - small, random additions or subtractions - to the data so that no one can reliably link the published data to a specific person or household. The Census Bureau encourages data users to aggregate small populations and geographies to improve accuracy and diminish implausible results..For 2020 Group Quarters Definitions and Code List, see Appendix B in the 2020 Census Demographic and Housing Characteristics File (DHC) Technical Documentation..Source: U.S. Census Bureau, 2020 Census Demographic and Housing Characteristics File (DHC)

This statistic shows the distribution of the nurse workforce in registered nursing in Canada, sorted by province, in 2020. In that year, around ** percent of all Canadian registered nurses were in Ontario.

Introduction

Nursing Burnout Statistics: Nursing burnout has emerged as a significant global concern, characterized by emotional exhaustion, depersonalization, and a diminished sense of personal accomplishment. A 2023 meta-analysis encompassing 94 studies reported a global prevalence of nursing burnout at 30%, with variations across regions and specialties.

In the United States, a 2020 survey indicated that nearly 62% of nurses experienced burnout, with the rate rising to 69% among those under 25 years old. Similarly, a 2023 study found that 91.1% of nurses reported high levels of burnout, compared to 79.9% among other healthcare workers.

Contributing factors to this phenomenon include understaffing, extended work hours, and high patient-to-nurse ratios. The American Nurses Foundation reported in 2023 that 56% of nurses experienced burnout, with 64% feeling significant job-related stress. Moreover, 40% of nurses felt they had poor control over their workload, describing their daily work as hectic or intense.

Addressing nursing burnout necessitates systemic changes, including improved staffing, supportive work environments, and accessible mental health resources. Implementing such measures is crucial to safeguard both healthcare providers and patients.

In 2025, **** out of ten surveyed hospitals in the U.S. reported a registered nurse vacancy rate higher than *** percent. RN vacancy has improved compared to the previous year. While the RN vacancy rate has dropped somewhat, the time it takes to hire new staff has not, meaning hospitals will continue to face RN shortages.

Density of nursing and midwifery personnel of Russian Federation plummeted by 29.76% from 8.9 number per thousand population in 2019 to 6.2 number per thousand population in 2020. Since the 1.46% rise in 2014, density of nursing and midwifery personnel sank by 32.90% in 2020.

Density of nursing and midwifery personnel of Finland increased by 2.38% from 18.9 number per thousand population in 2019 to 19.3 number per thousand population in 2020. Since the 0.53% fall in 2016, density of nursing and midwifery personnel rose by 3.09% in 2020.

According to a survey conducted in December 2020, the coronavirus pandemic exacerbated unhealthy experiences among nurses in the United States. At that time, over two-thirds of respondents stated to have had sleeping problems. The second most negative experience which has increased during the pandemic among nurses was overeating.

Density of nursing and midwifery personnel of Poland increased by 2.14% from 6.2 number per thousand population in 2020 to 6.3 number per thousand population in 2021. Since the 0.25% fall in 2018, density of nursing and midwifery personnel surged by 13.17% in 2021.

Density of nursing and midwifery personnel of Lao People’s Democratic Republic increased by 0.17% from 1.2 number per thousand population in 2018 to 1.2 number per thousand population in 2020. Since the 4.14% fall in 2017, density of nursing and midwifery personnel fell by 1.66% in 2020.

The Nursing Home Cost Report (RHCF) is a uniform report completed by New York nursing homes to report income, expenses, assets, liabilities, and statistics to the Department of Health (DOH). Under DOH regulations (Part 86-2.2), nursing homes are required to file financial and statistical data with DOH annually. The data filed is part of the cost report and is received electronically through a secured network. This data is used to develop Medicaid rates, assist in the formulation of reimbursement methodologies, and analyze trends.

Historical Dataset of Hphs Nursing And Health Sciences Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2011-2023),Total Classroom Teachers Trends Over Years (2011-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2011-2023),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2011-2023),White Student Percentage Comparison Over Years (2011-2023),Two or More Races Student Percentage Comparison Over Years (2011-2015),Diversity Score Comparison Over Years (2011-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2019-2023),Reading and Language Arts Proficiency Comparison Over Years (2010-2019),Math Proficiency Comparison Over Years (2010-2019),Overall School Rank Trends Over Years (2010-2019),Graduation Rate Comparison Over Years (2011-2019)

This layer contains a Vermont-only subset of block group level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.*VCGI exported a Vermont-only subset of the nation-wide layer to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator County_Name: County Name State_Name: State Name P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division COUNTY: County (FIPS) COUNTYNS: County (NS) TRACT: Census Tract BLKGRP: Block Group AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLAT: Internal Point (Latitude) INTPTLON: Internal Point (Longitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized.Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

Graph and download economic data for All Employees, Skilled Nursing Care Facilities (CES6562310001) from Jan 1990 to Jul 2025 about nursing homes, nursing, health, education, establishment survey, services, employment, and USA.

This layer contains various census levels 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, BLOCK, BLKGRP, and TBLKGRP.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual tract level, since this data has been protected using differential privacy.**To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual tracts will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the tract itself.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

In 2024, the average cost for U.S. hospitals to make up for one registered nurse who left (i.e. hire and train a new nurse) amounted to ****** U.S. dollars. This has increased by roughly *** percent compared to the previous year, and has been increasing year over year. To reduce such unnecessary cost, hospitals must strive for better retention.

This report shows monthly numbers of NHS Hospital and Community Health Service (HCHS) staff working in NHS Trusts and CCGs in England (excluding primary care staff). Data is available as headcount and full-time equivalents and are available every month for 30 September 2009 onwards. This data is an accurate summary of the validated data extracted from the NHS HR and Payroll system. Additional statistics on staff in NHS Trusts and CCGs and information for NHS Support Organisations and Central Bodies are published each: September (showing June statistics) December (showing September statistics) March (showing December statistics) June (showing March statistics) Quarterly NHS Staff Earnings and monthly NHS Staff Sickness Absence reports and data relating to the General Practice workforce and the Independent Healthcare Provider workforce are also available via the Related Links below. In last month's publication we described some upcoming changes intended to be implemented in data published from January 2021 onwards. Two of these changes are related to improvements in processing and data quality routines that will feed through into the data presented. We also described a change to an additional resource we will be making available ahead of current timescales, featuring data relating to the main headline staff groups and the Nurses staff group. After seeking feedback on these proposals, we can confirm all the above will be implemented in data published on 28 January 2021. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678.

Density of nursing and midwifery personnel of Czech Republic increased by 1.66% from 9.2 number per thousand population in 2020 to 9.4 number per thousand population in 2021. Since the 1.21% fall in 2016, density of nursing and midwifery personnel surged by 12.30% in 2021.