Retirement Age Men in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Retirement Age Women in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Women - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset presents the annual changes since 2013 in the cyclical retirement age, as well as various end-of-career indicators (average durations spent in and out of employment between the age of 50 and retirement), by socio-occupational category. The data update the results of the publication “People with disabilities leave the labour market younger but liquidate their retirement later” (Studies and results n° 1143) In the case of a sample survey, some indicators can be noisy and it is better to look at them on average over several years. Source: INSEE, Employment/Salary Survey: DREESThis dataset presents the annual changes since 2013 in the cyclical retirement age, as well as various end-of-career indicators (average durations spent in and out of employment between the age of 50 and retirement), by socio-occupational category. The data update the results of the publication “People with disabilities leave the labour market younger but liquidate their retirement later” (Studies and results n° 1143) In the case of a sample survey, some indicators can be noisy and it is better to look at them on average over several years. Source: INSEE, Employment/Salary Survey: DREES

Israel, Iceland, and Norway had the highest current retirement ages worldwide of the 45 countries included at 67 years. Meanwhile, Indonesia had the highest effective retirement age at 69.

Abstract copyright UK Data Service and data collection copyright owner. The 1994 Retirement Survey is the second wave of a panel survey designed to examine issues surrounding the transition to retirement. The panel nature of the Retirement Survey data provides a unique insight into the behaviour of this group of people at and around retirement age. The issues examined include those associated with retirement behaviour, health, incomes, pensions, assets and housing. The first wave of the survey was carried out in 1988-1989 (the data are held at the Archive under SN:2946). Two-thirds of the respondents from the first wave were re-interviewed in 1994. Main Topics: The dataset contains detailed information on incomes, assets, retirement, pension entitlements, housing, disability, caring responsibility and labour market participation. Attitudinal information was collected - in particular on views about retirement. Retrospective data were collected for each individual about lifetime, family, employment and pension histories in the first wave in 1988-1989 (SN:2946) and updated in this second wave. Standard Measures The scales developed by OPCS (now ONS) for the 1985 OPCS Disability Survey were used to measure the nature and severity of any disability among respondents in this wave. These scales are described in Martin, J., Meltzer, H. and Elliot, D. The prevalence of disability among adults (London: HMSO, 1988). For details of sampling please see technical report. Face-to-face interview Self-completion 1988 1994 ACCIDENTS AGE AGGRESSIVENESS ANXIETY ARITHMETIC ARMED FORCES ASSETS ATTITUDES BANK ACCOUNTS BEHAVIOURAL PROBLEMS BONDS BOREDOM BUILDING MAINTENANCE CARE OF DEPENDANTS CARE OF THE ELDERLY CHILD BENEFITS CHILD CARE CHILDREN CHRONIC ILLNESS CLOTHING COGNITION DISORDERS COGNITIVE PROCESSES COMMUNICATIONS COOKING COSTS COUNCIL TAX CULTURAL GOODS DEBILITATIVE ILLNESS DEBTS DEMENTIA DEPRESSION DIGESTIVE SYSTEM DI... DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL FEES ELDERLY EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY ENDOWMENT ASSURANCE Elderly FAMILIES FINANCIAL COMMITMENTS FINANCIAL EXPECTATIONS FINANCIAL RESOURCES FINANCIAL SUPPORT FOOD FULL TIME EMPLOYMENT GIFTS Great Britain HEADS OF HOUSEHOLD HEALTH HEALTH CONSULTATIONS HEARING HOLIDAYS HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSEWORK HOUSING HOUSING TENURE INCOME INCOME TAX INDUSTRIES INFLATION INFORMATION SOURCES INHERITANCE INSURANCE INTERPERSONAL CONFLICT INTERPERSONAL RELAT... INVESTMENT INVESTMENT RETURN JOB HUNTING JOB SATISFACTION LANDLORDS MANAGERS MENTAL DISORDERS MENTALLY DISABLED P... MORTGAGES MOTOR PROCESSES OCCUPATIONAL PENSIONS OLD AGE PARENTS PART TIME EMPLOYMENT PAYMENTS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL HYGIENE PHYSICAL ACTIVITIES PHYSICAL DISABILITIES PRIVATE PERSONAL PE... PSYCHIATRISTS QUALIFICATIONS RATES READING SKILLS REBATES REDUNDANCY RENTED ACCOMMODATION RENTS RESIDENTIAL CARE OF... RESIDENTIAL MOBILITY RESPITE CARE RETIREMENT Retirement SAVINGS SECOND HOMES SEIZURES SELF EMPLOYED SHARES SHOPPING SIBLINGS SICK PAY SOCIAL ACTIVITIES L... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIAL SECURITY CON... SOCIAL SUPPORT SPOUSES STANDARD OF LIVING STATE RETIREMENT PE... STRESS PSYCHOLOGICAL SUBSIDIARY EMPLOYMENT SUPERVISORS TAXATION TERMINATION OF SERVICE TIED HOUSING UNEARNED INCOME UNEMPLOYED UNEMPLOYMENT BENEFITS URINARY INCONTINENCE VISION IMPAIRMENTS VISITS PERSONAL WAGES WALKING WAR VETERANS WORK ATTITUDE WRITING SKILLS

This layer was retired on November 1, 2020. Data include only community infections, not infections that happen in congregate settings. Congregate settings include jails, assisted living, and shelters. We restrict to community infections because infections that occur in congregate settings can be controlled through infection control efforts within the institution/facility. Users of the data should use the symptom onset date or estimated start of the infectious period (i.e., when a person can transmit the disease to another person), rather than the report date to give a better understanding of how the virus is spread across the District. June 22 was the calendar date where the policy effect took place. Data through June 22 are reported on June 24, and represent a symptom onset date of June 15. This data is used to calculate the Reopening DC metric with number of days where cases in the community by date of symptom onset (for symptomatic individuals) or estimated start of infectious period (for asymptomatic individuals) have decreased. A day of decrease is defined as a day where the number of new cases is less than 2 standard deviations of the 5 day rolling average from the previous low OR there has not been 3 days of consecutive increase. The count resets to the day with the closest most recent value when a peak is detected. The days in between are no longer counted. The goal of this metric is to reach 14 days of sustained decrease, with a final value below 131 cases per day (2 standard deviations below the initial peak). Data are subject to change on a daily basis and reported at a 9-day lag for proper analysis.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Citywide/6859-spec. COVID-19 vaccinations administered to Chicago residents based on the reported race-ethnicity and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ··People with an original booster dose: Number of people who have a completed vaccine series and have received at least one additional monovalent dose. This includes people who received a monovalent booster dose and immunocompromised people who received an additional primary dose of COVID-19 vaccine. Monovalent doses were created from the original strain of the virus that causes COVID-19. People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group. Note that each age group has a row where race-ethnicity is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2019 1-year estimates. For some of the age groups by which COVID-19 vaccine has been authorized in the United States, race-ethnicity distributions were specifically reported in the ACS estimates. For others, race-ethnicity distributions were estimated by the Chicago Department of Public Health (CDPH) by weighting the available race-ethnicity distributions, using proportions of constituent age groups. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity) who have each vaccination status as of the date, divided by the estimated number of Chicago residents in each subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Data reported in I-CARE only include doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that c

Retirement age by class of worker and gender, annual.

We find and estimate probability mass functions for labor force related random variables. Complete life expectancy (by age, gender, and two years of labor force history) is decomposed into expected years of future labor force activity and inactivity as well as into expected years until final separation from the labor force and expected years in retirement. We also calculate expected age at retirement and expected years in retirement for people who actually retire. Two consecutive years of inactivity, especially in middle age, is a key indicator for both men and women when accounting for future labor force participation and retirement. For example, women (men) who are out of the labor force at age 49 and again out of the labor force at age 50, can expect to be in the labor force seven (eight) fewer years in the future than their counterparts who were in the labor force at ages 49 and 50. In addition, they have expected retirement ages 4.5–5.5 years younger than their active counterparts.

Changelog:

Update September 20, 2021: Data and overview updated to reflect data used in the September 15 story Over Half of States Have Rolled Back Public Health Powers in Pandemic. It includes 303 state or local public health leaders who resigned, retired or were fired between April 1, 2020 and Sept. 12, 2021. Previous versions of this dataset reflected data used in the Dec. 2020 and April 2021 stories.

Overview

Across the U.S., state and local public health officials have found themselves at the center of a political storm as they combat the worst pandemic in a century. Amid a fractured federal response, the usually invisible army of workers charged with preventing the spread of infectious disease has become a public punching bag.

In the midst of the coronavirus pandemic, at least 303 state or local public health leaders in 41 states have resigned, retired or been fired since April 1, 2020, according to an ongoing investigation by The Associated Press and KHN.

According to experts, that is the largest exodus of public health leaders in American history.

Many left due to political blowback or pandemic pressure, as they became the target of groups that have coalesced around a common goal — fighting and even threatening officials over mask orders and well-established public health activities like quarantines and contact tracing. Some left to take higher profile positions, or due to health concerns. Others were fired for poor performance. Dozens retired. An untold number of lower level staffers have also left.

The result is a further erosion of the nation’s already fragile public health infrastructure, which KHN and the AP documented beginning in 2020 in the Underfunded and Under Threat project.

Findings

The AP and KHN found that:

• One in five Americans live in a community that has lost its local public health department leader during the pandemic
• Top public health officials in 28 states have left state-level departments ## Using this data To filter for data specific to your state, use this query

To get total numbers of exits by state, broken down by state and local departments, use this query

Methodology

KHN and AP counted how many state and local public health leaders have left their jobs between April 1, 2020 and Sept. 12, 2021.

The government tasks public health workers with improving the health of the general population, through their work to encourage healthy living and prevent infectious disease. To that end, public health officials do everything from inspecting water and food safety to testing the nation’s babies for metabolic diseases and contact tracing cases of syphilis.

Many parts of the country have a health officer and a health director/administrator by statute. The analysis counted both of those positions if they existed. For state-level departments, the count tracks people in the top and second-highest-ranking job.

The analysis includes exits of top department officials regardless of reason, because no matter the reason, each left a vacancy at the top of a health agency during the pandemic. Reasons for departures include political pressure, health concerns and poor performance. Others left to take higher profile positions or to retire. Some departments had multiple top officials exit over the course of the pandemic; each is included in the analysis.

Reporters compiled the exit list by reaching out to public health associations and experts in every state and interviewing hundreds of public health employees. They also received information from the National Association of City and County Health Officials, and combed news reports and records.

Public health departments can be found at multiple levels of government. Each state has a department that handles these tasks, but most states also have local departments that either operate under local or state control. The population served by each local health department is calculated using the U.S. Census Bureau 2019 Population Estimates based on each department’s jurisdiction.

KHN and the AP have worked since the spring on a series of stories documenting the funding, staffing and problems around public health. A previous data distribution detailed a decade's worth of cuts to state and local spending and staffing on public health. That data can be found here.

Attribution

Findings and the data should be cited as: "According to a KHN and Associated Press report."

Is Data Missing?

If you know of a public health official in your state or area who has left that position between April 1, 2020 and Sept. 12, 2021 and isn't currently in our dataset, please contact authors Anna Maria Barry-Jester annab@kff.org, Hannah Recht hrecht@kff.org, Michelle Smith mrsmith@ap.org and Lauren Weber laurenw@kff.org.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included).

The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. These revisions are due to improvements in the imputation methodology.

Note from the WAS team - November 2023:
"The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates."

Survey Periodicity - "Waves" to "Rounds"
Due to the survey periodicity moving from "Waves" (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019.

Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.

Users should note that issues with linking have been reported and the WAS team are currently investigating.

Secure Access WAS data
The Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:

• Wards
• Parliamentary Constituency Areas for Wave 1 only
• Census Output Areas
• Lower Layer Super Output Areas
• Local Authorities
  
• Local Education Authorities

Prospective users of the Secure Access version of the WAS will need to fulfil additional requirements, including completion of face-to-face training, and agreement to the Secure Access User Agreement and Licence Compliance Policy, in order to obtain permission to use that version (see 'Access' section below). Users are therefore strongly encouraged to download the EUL version (SN 7215) to see if it contains sufficient detail for their needs, before considering making an application for the Secure Access version.

Latest Edition Information
For the ninth edition (October 2022), the Round 7 person and household data have been updated. The Round 7 Wave 1 Variable Catalogue Excel file has also been updated.

Retirement Age Men in Finland increased to 64.75 Years in 2025 from 64.50 Years in 2024. This dataset provides - Finland Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• nvestigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact the ELSA Data team at NatCen on elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Special Licence Data:

Special Licence Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence (see 'Access' section below). Users are advised to obtain the latest edition of SN 5050 (the End User Licence version) before making an application for Special Licence data, to see whether that is suitable for their needs. A separate application must be made for each Special Licence study.

Special Licence Access versions of ELSA include:

• Primary data from Wave 8 onwards (SN 8346) includes all the variables in the EUL primary dataset (SN 5050) as well as year and month of birth, consolidated ethnicity and country of birth, marital status, and more detailed medical history variables.
• Wave 8 Pension Age Data (SN 8375) includes all the variables in the EUL pension age data (SN 5050) as well as year and age reached state pension age variables.
• Wave 8 Sexual Self-Completion Data (SN 8376) includes sensitive variables from the sexual self-completion questionnaire.
• Wave 3 (2007) Harmonized Life History (SN 8831) includes retrospective information on previous histories, specifically, detailed data on previous partnership, children, residential, health, and work histories.
• Detailed geographical identifier files for Waves 1-10 which are grouped by identifier held under SN 8429 (Local Authority District Pre-2009 Boundaries), SN 8439 (Local Authority District Post-2009 Boundaries), SN 8430 (Local Authority Type Pre-2009 Boundaries), SN 8441 (Local Authority Type Post-2009 Boundaries), SN 8431 (Quintile Index of Multiple Deprivation Score), SN 8432 (Quintile Population Density for Postcode Sectors), SN 8433 (Census 2001 Rural-Urban Indicators), SN 8437 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8429 (Local Authority District Pre-2009 Boundaries) or SN 8439 (Local Authority District Post-2009 Boundaries)
• either SN 8430 (Local Authority Type Pre-2009 Boundaries) or SN 8441(Local Authority Type Post-2009 Boundaries)
• either SN 8433 (Census 2001 Rural-Urban Indicators) or SN 8437 (Census 2011 Rural-Urban Indicators)

ELSA Wave 6 and Wave 8 Self-Completion Questionnaires included an open-ended question where respondents could add any other comments they may wish to note down. These responses have been transcribed and anonymised. Researchers can request access to these transcribed responses for research purposes by contacting the...

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.

Context

RxNorm is a name of a US-specific terminology in medicine that contains all medications available on US market. Source: https://en.wikipedia.org/wiki/RxNorm

RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software, including those of First Databank, Micromedex, Gold Standard Drug Database, and Multum. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary. Source: https://www.nlm.nih.gov/research/umls/rxnorm/

Content

RxNorm was created by the U.S. National Library of Medicine (NLM) to provide a normalized naming system for clinical drugs, defined as the combination of {ingredient + strength + dose form}. In addition to the naming system, the RxNorm dataset also provides structured information such as brand names, ingredients, drug classes, and so on, for each clinical drug. Typical uses of RxNorm include navigating between names and codes among different drug vocabularies and using information in RxNorm to assist with health information exchange/medication reconciliation, e-prescribing, drug analytics, formulary development, and other functions.

This public dataset includes multiple data files originally released in RxNorm Rich Release Format (RXNRRF) that are loaded into Bigquery tables. The data is updated and archived on a monthly basis.

The following tables are included in the RxNorm dataset:

• RXNCONSO contains concept and source information

• RXNREL contains information regarding relationships between entities

• RXNSAT contains attribute information

• RXNSTY contains semantic information

• RXNSAB contains source info

• RXNCUI contains retired rxcui codes

• RXNATOMARCHIVE contains archived data

• RXNCUICHANGES contains concept changes

Update Frequency: Monthly

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.nlm.nih.gov/research/umls/rxnorm/

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nlm_rxnorm

https://cloud.google.com/bigquery/public-data/rxnorm

Dataset Source: Unified Medical Language System RxNorm. The dataset is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. This dataset uses publicly available data from the U.S. National Library of Medicine (NLM), National Institutes of Health, Department of Health and Human Services; NLM is not responsible for the dataset, does not endorse or recommend this or any other dataset.

Banner Photo by @freestocks from Unsplash.

Inspiration

What are the RXCUI codes for the ingredients of a list of drugs?

Which ingredients have the most variety of dose forms?

In what dose forms is the drug phenylephrine found?

What are the ingredients of the drug labeled with the generic code number 072718?

This report presents statistics on women’s smoking status at time of delivery, at Sub Integrated Care Board (Sub-ICB), Integrated Care Board (ICB), regional and national levels. This release includes provisional data for quarter 2 of 2024-25 using data from the Smoking at Time of Delivery data collection which is submitted by commissioners (presented as SATOD v1). Alongside this and for the second time, comparative data using the Maternity Services Dataset (MSDS) is also presented using data submitted by Trusts (presented as SATOD v2) as a time series from quarter 1 of 2022-23 to quarter 2 of 2024-25. This is available for the same geographical breakdowns and includes an additional breakdown for Local Authorities. This will be repeated for subsequent quarters in 2024-25 to see how the estimates from both data sources align with a view to retiring the Smoking at Time of Delivery data collection at the end of this financial year. Until then, SATOD v1 remains the primary data source for this publication Earlier this year, a proposal for the data source for this publication to be changed to the Maternity Services Dataset was included in a wider consultation: Health and social care statistical outputs published by DHSC (including OHID), NHSBSA, UKHSA, ONS and NHS England. A link to this is in the Related Links below. If you would still like to feedback your views on the SATOD data collection retirement and replacement with MSDS, then please contact us on: england.maternityanalysis@nhs.net In March 2025, a minor correction has been made to the 2024-25 SATOD v1 national totals in the Quarterly Comparison tab of the SATOD data tables so they now reflect a year to date total.

Spotzi's Income dataset for the United States offers valuable insights into the intricacies of yearly income at various levels. This dataset is meticulously curated, presenting a detailed analysis of total income, types of household earnings, and the critical aspect of whether households are above the poverty level. This dataset is available at Census Block level, and allows for a holistic understanding of the economic landscape at both regional and national scales.

What is included?

Each data variable is presented as a percentage of the total population within each selected area. Please see below for a complete list of available data variables:

Income

• Individual Yearly Income: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Individual Yearly Income - Female: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Individual Yearly Income - Male: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Household Income Earning Status: Earns Income, Does Not Earn Income
• Households Below Poverty Level

Household Earnings By Type

• Salary
• Interest Dividends
• Retirement Income
• Self-Employment

• • Individual Yearly Income: Marketers can leverage individual income data to tailor their strategies based on the financial capacities of their target audience. For example, luxury brands may target individuals with higher income brackets, while budget-conscious brands may focus on those with lower income levels.
  • Household Earning by Type: Marketers can use this data to understand the sources of household income, allowing for targeted campaigns. For example, financial services may tailor promotions based on the types of income earned, offering retirement planning services to those with significant retirement income.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing