The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Notes: "Total Number of Veterans" represents FY 2021 projected Veteran counts from VA's Veteran Population Projection Model 2020 (VetPop20). These projections represent living Veterans as of 9/30/2021 and are made with the assumption that Veterans are not missing information (e.g., sex, age, etc.). "Veteran VA Users" represents historical Veteran VA user counts from VA's United States Veterans Eligibility Trends and Statistics 2021 (USVETS 2021). These counts represent Veterans who used any VA benefit or service during FY 2021 (includes both living and deceased Veterans as of end of FY 2021). "Veteran VA Healthcare Users" represents historical Veteran VA healthcare user counts from VA's United States Veterans Eligibility Trends and Statistics 2021 (USVETS 2021). These counts represent Veterans who used VA healthcare during FY 2021 (includes both living and deceased Veterans as of end of FY 2021). "Veteran VA Users" includes Veteran users of VA healthcare or any other VA benefit or service. There are 1,458 Veteran VA Users not shown in the table below whose sex is missing. Of these, 1,360 are missing age. There are 1,387 Veteran VA Healthcare Users not shown in the table below whose sex is missing. Of these, 1,360 are missing age. Sources: USVETS 2021 and VetPop20 Effective Date: 9/30/2021

Note: "Total Number of Veterans" represents FY 2020 projected Veteran counts from VA's Veteran Population Projection Model 2018 (VetPop18). These projections are made with the assumption that Veterans are not missing information (e.g. age, sex, etc.). Note: "Veteran VA Users" and "Veteran VA Healthcare Users" represent historical Veteran counts from VA's United States Veterans Eligibility Trends and Statistics 2020 (USVETS 2020). Note: "Veteran VA Users" includes Veteran users of VA healthcare or any other VA benefit or service. Note: There are 4,214 Veteran VA Users not shown in the table below whose sex is missing. Of these, 4,126 are missing age. There are 4,158 Veteran VA Healthcare Users not shown in the table below whose sex is missing. Of these, 4,125 are missing age. Sources: USVETS 2020 and VetPop18

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.

Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

Note: "Total Number of Veterans" represents FY 2020 projected Veteran counts from VA's Veteran Population Projection Model 2018 (VetPop18). These projections are made with the assumption that Veterans are not missing information (e.g. ethnicity, etc.). Note: "Veteran VA Users" and "Veteran VA Healthcare Users" represent historical Veteran counts from VA's United States Veterans Eligibility Trends and Statistics 2020 (USVETS 2020). Note: "Veteran VA Users" includes Veteran users of VA healthcare or any other VA benefit or service. Sources: USVETS 2020 and VetPop18

Notes: "Total Number of Veterans" represents FY 2021 projected Veteran counts from VA's Veteran Population Projection Model 2020 (VetPop20). These projections represent living Veterans as of 9/30/2021 and are made with the assumption that Veterans are not missing information (e.g., race, etc.). "Veteran VA Users" represents historical Veteran VA user counts from VA's United States Veterans Eligibility Trends and Statistics 2021 (USVETS 2021). These counts represent Veterans who used any VA benefit or service during FY 2021 (includes both living and deceased Veterans as of end of FY 2021). "Veteran VA Healthcare Users" represents historical Veteran VA healthcare user counts from VA's United States Veterans Eligibility Trends and Statistics 2021 (USVETS 2021). These counts represent Veterans who used VA healthcare during FY 2021 (includes both living and deceased Veterans as of end of FY 2021). "Veteran VA Users" includes Veteran users of VA healthcare or any other VA benefit or service. Sources: USVETS 2021 and VetPop20 Effective Date: 9/30/2021

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues. The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year. The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level. The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health. Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence. A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs. Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level

Some college or associate's degree Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in United States by age, education, race, gender, work experience and more.

In labor force Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in United States by age, education, race, gender, work experience and more.

The Veterans Health Administration Medical Facilities dataset includes Veteran Affairs hospitals, Veteran Affairs Residential Rehabilitation Treatment Programs (RRTP), Veteran Affairs Nursing Home Care Units (NHCU), Veteran Affairs Outpatient Clinics (VAOC), Vet Centers, and Veteran Affairs Medical Centers (VAMC). It should not include planned and suspended (non-operational) sites and mobile clinics. These definitions were set by the Veterans Health Administration (VHA) Policy Board in December 1998 and are the basis for defining the category and the additional service types for each VHA service site. These definitions cover sites generally owned by the Department of Veterans Affairs (VA) with the exception of leased and contracted community-based outpatient clinics (CBOCs). 1. VA HOSPITAL: an institution (health care site) that is owned, staffed and operated by VA and whose primary function is to provide inpatient services. NOTE: Each geographically unique inpatient division of an integrated facility is counted as a separate hospital. 2. VA RESIDENTIAL REHABILITATION TREATMENT PROGRAM (RRTP): provides comprehensive health and social services in a VA facility for eligible veterans who are ambulatory and do not require the level of care provided in nursing homes. 3. VA NURSING HOME CARE UNITS (NHCU): provides care to individuals who are not in need of hospital care, but who require nursing care and related medical or psychosocial services in an institutional setting. VA NHCUs are facilities designed to care for patients who require a comprehensive care management system coordinated by an interdisciplinary team. Services provided include nursing, medical, rehabilitative, recreational, dietetic, psychosocial, pharmaceutical, radiological, laboratory, dental and spiritual. 4. VA OUTPATIENT CLINICS: a. Community-Based Outpatient Clinic (CBOC): a VA-operated, VA-funded, or VA-reimbursed health care facility or site geographically distinct or separate from a parent medical facility. This term encompasses all types of VA outpatient clinics, except hospital-based, independent and mobile clinics. Satellite, community-based, and outreach clinics have been redefined as CBOCs. Technically, CBOCs fall into four Categories, which are: > (i) VA-owned. A CBOC that is owned and staffed by VA. > (ii) Leased. A CBOC where the space is leased (contracted), but is staffed by VA. NOTE: This includes donated space staffed by VA. > (iii) Contracted. A CBOC where the space and the staff are not VA. This is typically a Healthcare Management Organization (HMO)-type provided where multiple sites can be associated with a single station identifier. > (iv) Not Operational. A CBOC which has been approved by Congress, but has not yet begun operating. b. Hospital-Based Outpatient Clinic: outpatient clinic functions located at a hospital. c. Independent Outpatient Clinic: a full-time, self-contained, freestanding, ambulatory care clinic that has no management, program, or fiscal relationship to a VA medical facility. Primary and specialty health care services are provided in an outpatient setting. 5. VET CENTER: Provides professional readjustment counseling, community education, outreach to special populations, brokering of services with community agencies, and access to links between the veteran and VA. 6. VA MEDICAL CENTER (VAMC): a medical center is a unique VA site of care providing two or more types of services that reside at a single physical site location. The services provided are the primary service as tracked in the VHA Site Tracking (VAST) (i.e., VA Hospital, Nursing Home, Domiciliary, independent outpatient clinic (IOC), hospital-based outpatient clinic (HBOC), and CBOC). The definition of VA medical center does not include the Vet Centers as an identifying service. This dataset is based upon GFI data received from the National Geospatial-Intelligence Agency (NGA). At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 09/21/2007 and the newest record dates from 10/15/2007.

Abstract (en): The Study of Women's Health Across the Nation (SWAN), is a multi-site longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological, and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers, and women learn how mid-life experiences affect health and quality of life during aging. The data include questions about doctor visits, medical conditions, medications, treatments, medical procedures, relationships, smoking, and menopause related information. The study is co-sponsored by the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), the National Institutes of Health (NIH), and the NIH Office of Research on Women's Health. The study began in 1994. Between 1999 and 2001, 2,710 of the 3,302 women that joined SWAN were seen for their third follow-up visit. The research centers are located in the following communities: Detroit, Michigan; Boston, Massachusetts; Chicago, Illinois; Oakland and Los Angeles, California; Newark, New Jersey; and Pittsburgh, Pennsylvania. SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Presence of Common Scales: Raw data can be used to create CES-D and SF-36 scores. Response Rates: 16,065 completed the screening interview. 3,302 were enrolled in the longitudinal study. 2,881 completed the first follow-up visit. 2,748 completed the second follow-up visit. 2,710 completed the third follow-up visit. Datasets:DS1: Study of Womens Health Across the Nation (SWAN): Visit 03 Dataset, [United States], 1999-2001 Women age 40 through 55, living in designated geographic areas, with the ability to speak English or other designated languages (Japanese, Cantonese, or Spanish), who had the cognitive ability to provide verbal informed consent, and had membership in a specific site's targeted ethnic group. Smallest Geographic Unit: None Site-specific sampling frames were used and encompassed a range of types, including lists of households, telephone numbers, and individual names of women. 2019-05-29 This data collection has been enhanced in the following ways. The title of the study was updated to match current ICPSR standards. Variable labels have been revised to spell out abbreviations and acronyms, and to correct prior misspellings. The variables in the dataset have also been reordered to match the documentation provided by the Principal Investigator. A fuller version of the question text pertaining to individual variables was completed, and now available in the ICPSR codebook. An additional document was included in this release that lists all the publications based off of the SWAN data series. Lastly, the study is now available for online analysis.2018-08-22 The data were updated to adjust missing values.2014-02-12 This data collection is now publicly available. Funding institution(s): United States Department of Health and Human Services. National Institutes of Health (NR004061). United States Department of Health and Human Services. National Institutes of Health. National Institute on Aging (AG012495, AG012505, AG012539, AG012546, AG012553, AG012554). United States Department of Health and Human Services. National Institutes of Health. National Institute of Nursing Research (AG012535). United States Department of Health and Human Services. National Institutes of Health. Office of Research on Women's Health (AG012531). face-to-face interview self-enumerated questionnaire

Food allergies impose a large clinical and financial burden on patients and the health care system. However, little is known about the factors associated with health care resource use and costs. The aim of this study was to investigate health care resource use and costs in individuals with food allergies utilizing health care in the United States. We conducted a retrospective analysis of insurance claims data from the Merative MarketScan Research Databases (indexed from 1 January 2015 to 30 June 2022). All-cause and food allergy-related health care resource use, direct medical, and out-of-pocket costs for medical services were estimated for 12 months post-index using International Classification of Diseases [ICD] codes. Of 355,520 individuals with food allergies continuously enrolled in a health insurance plan for ≥12 months pre- and post-index, 17% had a food allergy-related emergency department visit and 0.9% were hospitalized. The top patient characteristic associated with all-cause and food allergy-related hospitalizations, all-cause costs, and food allergy-related outpatient visit costs was a Charlson Comorbidity Index score of ≥2. Food allergy-related direct medical and out-of-pocket costs were high among patients with a food allergy-related visit. Out-of-pocket cost per patient per year for outpatient visits, emergency department visits, and hospitalizations had an estimated mean of $1631 for patients with food allergy-related visits, which is ∼11% of the total costs for these services ($14,395 per patient per year). Study limitations are primarily related to the nature of claims databases, including generalizability and reliance on ICD codes. Nevertheless, MarketScan databases provide robust patient-level insights into health care resource use and costs from a large, commercially insured patient population. The health care resource use of patients with food allergies imposes a burden on both the health care system and on patients and their families, especially if patients had comorbidities. Some people with food allergies might need extra visits to the doctor or hospital to manage allergic reactions to food, and these visits add to the cost of medical services for both families and for health care providers. Using records of health insurance claims, we looked into the factors affecting medical visits and costs in people with food allergies in the United States. For people with food allergies, having additional medical conditions (measured using the Charleson Comorbidity Index) were linked with extra medical visits and costs. Out-of-pocket costs were high for people who visited a doctor or hospital for their food allergies (costing each person more than $1,600 per year). The total medical cost of food allergy-related care was $14,395 per person per year, paid for by families and health care providers. Our findings might help to better manage and treat people with food allergies and reduce medical costs.

The 2010 National Survey of Residential Care Facilities (NSRCF) is a first-ever national probability sample survey that collects data on U.S. residential care providers, their staffs and services, and the people they serve. It is designed to provide national estimates of the number of residential care facilities operating in the United States, the number of residents receiving care, and the characteristics of both the facilities and their residents. NSRCF was conducted between March and November 2010. All residential care facilities that participated in the survey were places that were licensed, registered, listed, certified, or otherwise regulated by the state and that had 4 or more licensed, certified, or registered beds, provided room and board with at least two meals a day, around-the-clock on-site supervision, and help with personal care such as bathing and dressing or health related services such as medication management. These facilities served a predominantly adult population and had at least one current resident. Facilities licensed to serve the mentally ill or the developmentally disabled populations exclusively were excluded from the survey.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

The Nationwide Inpatient Sample (NIS) is a database focused on hospital stay information. Users are able to use the NIS to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. Background The Nationwide Inpatient Sample (NIS) is maintained by the Healthcare Cost and Utilization Project. The NIS is the largest all-payer inpatient care database in the United States. It contains data from approximately 8 million hospital stays each year. The 2009 NIS contains all discharge data from 1,050 hospitals located in 44 States, approximating a 20-percent stratified sample of U.S. community hospitals. The sampling frame for the 2009 NIS is a sample of hospitals that comprises approximately 95 percent of all hospital discharges in the United States. The NIS is the only national hospital database containing charge information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. User functionality Users must pay to access the database. NIS releases for data years 1988-2009 are available from the HCUP Central Distributor. The 2009 NIS may be purchased for $50 for students and $350 for all others on a single DVD-ROM with accompanying documentation. . Data Notes NIS data are available from 1988 to 2009. The number of states in the NIS has grown from 8 in the first year to 44 at present. Beginning with the 2002 NIS, severity adjustment data elements including APR-DRGs, APS-DRGs, Disease Staging, and AHRQ Comorbidity Indicators, are available. Begi nning with the 2005 NIS, Diagnosis and Procedure Groups Files containing data elements from AHRQ software tools designed to facilitate the use of the ICD-9-CM diagnostic and procedure information are available. Beginning with the 2007 NIS, data elements describing hospital structural characteristics and provision of outpatient services are available in the Hospital Weights file. NIS Release 1 includes data from 8-11 States and spans the years 1988 to 1992. NIS Releases 2 and 3 contain data from 17 States for 1993 and 1994, respectively. NIS Releases 4 and 5 contain data from 19 States for 1995 and 1996. NIS Release 6 contains data from 22 States for 1997. NIS 1998 contains data from 22 States. NIS 1999 contains data from 24 States. NIS 2000 contains data from 28 States. NIS 2001 contains data from 33 States. NIS 2002 contains data from 35 States. NIS 2003 contains data from 37 States. NIS 2004 contains data from 37 States. NIS 2005 contains data from 37 States. NIS 2006 contains data from 38 States. NIS 2007 contains data from 40 States. NIS 2008 contains data from 42 States.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Read about this volunteer-driven effort, access data and apps, and contribute your own testing and vaccination site data: https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataItem details page: https://giscorps.maps.arcgis.com/home/item.html?id=c50a1a352e944a66aed98e61952051efThis feature layer view contains information about locations where government agencies and healthcare providers are directing members of the public to access COVID-19 vaccination. It only includes locations that have been publicly shared by agencies or providers. Some providers may have been authorized as vaccine points of distribution but have not yet received vaccine doses. In other cases, some providers may have received doses but do not have current vaccine availability. For sites that offer both COVID-19 testing and vaccination, the start date likely refers to the date they started offering COVID-19 testing. Creation dates do not reflect the date a provider began vaccinating; they only reflect the date we added them to our data. Please submit new vaccination sites or updated vaccination site information via this form: https://arcg.is/10S1ib. GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy of this template and emailing it to admin@giscorps.org. This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers and should not be considered complete or authoritative. Please contact providers or your local health department directly for official information and vaccination eligibility requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to vaccination site locations. GISCorps does not share any vaccination site location information not previously made public or provided to us by one of those entities.Data dictionary document: https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharing

The number of smokers in the United Arab Emirates was forecast to continuously increase between 2024 and 2029 by in total 0.1 million individuals (+4.13 percent). After the fourteenth consecutive increasing year, the number of smokers is estimated to reach 2.53 million individuals and therefore a new peak in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smokers in countries like Kuwait and Lebanon.

The number of physicians in the United Arab Emirates was forecast to continuously increase between 2024 and 2029 by in total 8.2 thousand physicians (+23.71 percent). After the fifteenth consecutive increasing year, the number of physicians is estimated to reach 42.78 thousand physicians and therefore a new peak in 2029. Notably, the number of physicians of was continuously increasing over the past years.Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of physicians in countries like Bahrain and Israel.