This comprehensive report chronicles the history of women in the military and as Veterans, profiles the characteristics of women Veterans in 2009, illustrates how women Veterans in 2009 utilized some of the major benefits and services offered by the Department of Veterans Affairs (VA), and discusses the future of women Veterans in relation to VA. The goal of this report is to gain an understanding of who our women Veterans are, how their military service affects their post-military lives, and how they can be better served based on these insights.

This data set consists of one row per federal fiscal year (FY) from FY 2005 - FY 2019, and reports the number and percent of users each of seven VA programs for Veterans who were in service at any time between August 2, 1990, and September 10, 2001, the dates of the Pre-9/11 Gulf War era. The denominator of percent is the number of living Veterans in the FY. The number and percent of users is cumulative since FY 2005. Thus, for example FY 2006 data includes all Veterans who served in the era, were alive at some time during FY 2006 and participated in the program at any time during FY 2005 and FY 2006.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES VETERAN STATUS - DP02 Universe - Civilian population 18 Year and over Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Veteran status is used to identify people with active duty military service and service in the military Reserves and the National Guard. Veterans are men and women who have served (even for a short time), but are not currently serving, on active duty in the U.S. Army, Navy, Air Force, Marine Corps, or the Coast Guard, or who served in the U.S. Merchant Marine during World War II. People who served in the National Guard or Reserves are classified as veterans only if they were ever called or ordered to active duty, not counting the 4-6 months for initial training or yearly summer camps.

This table provides state-level estimates of the percentage of Veterans who were VA pension recipients at the end of the fiscal years 2019, 2020, 2021, and 2023. Percents are rounded to the nearest tenth. Percents for fiscal year (FY) 2022 are not available by state. Prepared by the National Center for Veterans Analysis and Statistics. Sources: Department of Veterans Affairs, Office of Enterprise Integration, Veteran Population Projection Model (VetPop) 2020, Veteran Object FY 2023 data, United States Veterans Eligibility Trends & Statistics (USVETS) 2019, 2020, and 2021 data; Veterans Benefits Administration, VETSNET FY 2019, FY 2020, FY 2021, and FY 2023 pension data.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

Data from America's War factsheet with only those who served and living

The Office of Data Governance and Analysis (DGA) creates statistical data for various Veteran related projects. This table displays the count and percent, by county, of Veterans who are farmers and/or dairymen comparative for the entire state's population of Veteran farmers or dairymen in California for 2015. The data was created from our administrative database U.S. Veterans Eligibility Trends and Statistics (USVETS), for the recent event Apps for Ag Hackathon. The U.S. Veterans Eligibility Trends and Statistics (USVETS) is the single integrated dataset of Veteran demographic and socioeconomic data. It provides the most comprehensive picture of the Veteran population possible to support statistical, trend and longitudinal analysis. USVETS has both a static dataset, represents a single authoritative record of all living and deceased Veterans, and fiscal year datasets, represents a snapshot of a Veteran for each fiscal year. USVETS consists mainly of data sources from the Veterans Benefit Administration, the Veterans Health Administration, the Department of Defense’s Defense Manpower Data Center, and other data sources including commercial data sources. This dataset contains information about individual Veterans including demographics, details of military service, VA benefit usage, and more. The dataset contains one record per Veteran. It includes all living and deceased Veterans. USVETS data includes Veterans residing in states, US territories and foreign countries. VA uses this database to conduct statistical analytics, predictive modeling, and other data reporting. USVETS includes the software, hardware, and the associated processes that produce various VA work products and related files for Veteran analytics.

This report provides information regarding suicide mortality for the years 2001–2014. It incorporates the most recent mortality data from the VA/Department of Defense (DoD) Joint Suicide Data Repository and includes information for deaths from suicide among all known Veterans of U.S. military service. Data for the Joint VA/DoD Suicide Data Repository were obtained from the National Center for Health Statistics’ National Death Index through collaboration with the DoD, the CDC, and the VA/DoD Joint Suicide Data Repository initiative. Data available from the National Death Index include reports of mortality submitted from vital statistics systems in all 50 U.S. states, New York City, Washington D.C., Puerto Rico, and the U.S. Virgin Islands.

These deidentified datasets have been approved for public release by the VA Boston Healthcare System's Institutional Review Board and may be used without restriction. Please cite one or more of the source articles when using these data:

Feyman, Y, Auty, SG, Tenso, K, Strombotne, KL, Legler, A, & Griffith, KN. (2022). “County-Level Impact of the COVID-19 Pandemic on Excess Mortality Among U.S. Veterans: A Population-Based Study.” The Lancet Regional Health – Americas 5: 100093.

Tenso, K, Strombotne, KL, Feyman, Y, Auty, SG, Legler, A, & Griffith KN. (in press). “Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic.” Medical Care.

Avila, CJ, Feyman, Y, Auty, SG, Mulugeta, M, Strombotne, KL, Legler, A, & Griffith, KN. (in progress). “Racial and ethnic disparities in excess mortality due to COVID-19 among U.S. veterans.” Health Services Research.

This dataset includes over 200 US military cemeteries, compiled using information from the National Cemetery Administration, National Park Service, American Battlefield Commission, US Army, state veterans departments, and others. For the majority of cemeteries, within the description field, you will find a link to the cemetery's web page along with the physical address. This data was found online at http://bbs.keyhole.com/ubb/showflat.php/Cat/0/Number/132750/an/0/page/0#132750.

Key Table Information.Table Title.Period of Military Service for Civilian Veterans 18 Years and Over.Table ID.ACSDT1Y2024.B21002.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, countie...

VBA EDUCATION PROGRAM to provide educational assistance to persons entering the Armed Forces after December 31, 1976, and before July 1, 1985; to assist persons in obtaining an education they might otherwise not be able to afford; and to promote and assist the all volunteer military program of the United States by attracting qualified persons to serve in the Armed Forces. The participant must have entered on active duty on or after January 1, 1977, and before July 1, 1985, and either served on active duty for more than 180 continuous days receiving an other than dishonorable discharge, or have been discharged after January, 1, 1977 because of a service-connected disability. Also eligible are participants who serve for more than 180 days and who continue on active duty and have completed their first period of obligated service (or 6 years of active duty, whichever comes first). Participants must also have satisfactorily contributed to the program. (Satisfactory contribution consists of monthly deduction of $25 to $100 from military pay, up to a maximum of $2,700, for deposit in a special training fund.) Participants may make lump-sum contributions. No individuals on active duty in the Armed Forces may initially begin contributing to this program after March 31, 1987.

Connecticut State Archives Archival Record Group (RG) #069:050, Noble (William H. and Henrietta) Pension Applications General William H. Noble and his daughter Henrietta M. Noble, Pension Agents in Bridgeport, assisted veterans and their descendants to secure pensions from the United States Government. The collection includes correspondence and official papers that document their work with veterans of the Civil War and Spanish American War. The files are arranged alphabetically by veteran’s name. The database contains the following information: veteran’s name, rank, pension file application number, date enlisted, date discharged, and military unit. People may request a copy of a file by contacting the staff of the History & Genealogy Unit by telephone (860) 757-6580 or email. When requesting a copy of a record, please include at least the name of the individual, date, and residence. Abbreviations of Connecticut Military Branch of Service: · CLB – Connecticut Light Battery · CVA – Connecticut Volunteer Artillery · CVC – Connecticut Volunteer Cavalry · CVHA – Connecticut Volunteer Heavy Artillery · CVI – Connecticut Volunteer Infantry · CVLB – Connecticut Volunteer Light Battery

This report uses data from the 2014 American Community Survey and shows the demographic and socioeconomic characteristics of Veterans who belong to the Vietnam Veteran cohort. The spreadsheet includes variables like: raw numbers, gender, education, median personal income, age groups, and other variables.

VBA BENEFIT PROGRAM to compensate veterans for disabilities incurred or aggravated during military service according to the average impairment in earning capacity such disability would cause in civilian occupations. Persons who have suffered disabilities resulting from service in the Armed Forces of the United States. The disability must have been incurred or aggravated by service in the line of duty. Separation from service must have been under other than dishonorable conditions for the period in which the disability was incurred or aggravated.

VBA EDUCATION BENEFITS PROGRAM to help servicepersons adjust to civilian life after separation from military service, assist in the recruitment and retention of highly qualified personnel in the active and reserve components in the Armed Forces by providing education benefits, and to provide educational opportunities to the dependents of certain service members and veterans. Individuals who entered active duty after September 10, 2001 may be eligible for the Post-9/11 GI Bill. Individuals can use the Post-9/11 GI Bill after serving 90 days on active duty (excluding entry level and skill training). Only periods of active duty under title 10 will be used to establish eligibility for the Post 9/11 GI Bill. A high school diploma or equivalency certificate is always required for eligibility. Individuals who are eligible for the Montgomery GI Bill – Active Duty (chapter 30), the Montgomery GI Bill – Selected Reserve (chapter 1606), or the Reserve Educational Assistance Program (REAP) will have to make an irrevocable election to relinquish eligibility under one of those benefit programs to establish eligibility under the Post-9/11 GI Bill. The dependent children of a person who died in the line of duty while serving as a member of the Armed Forces may be eligible to use benefits under the Fry Scholarship provision of the Post-9/11 GI Bill. The spouse and/or child(ren) of a veteran or service member may be eligible for the Post 9/11 GI Bill if the veteran or service member transfers entitlement to those dependents. Eligibility to transfer entitlement to dependents is determined by the Department of Defense. This is not a complete list of eligibility requirements. For more information on the latest changes to the Post-9/11 GI Bill go to the VA web-site.

VBA EDUCATION PROGRAMS to provide, through purchase and/or fabrication, prosthetic and related appliances, equipment and services to eligible veterans so that they may live and work as productive citizens. Veterans eligible for prosthetic services are service-connected veterans seeking care for a service-connected disability; veterans with compensable service-connected disabilities generally rated 10 percent or more; former prisoners of war, veterans discharged or released from active military service for a disability that was incurred or aggravated in the line of duty, and veterans who are in receipt of Section 1151 benefits; veterans who are in receipt of increased pension based on a need of regular aid and attendance or by reason of being permanently housebound; veterans who have annual income and net worth below the "means test" threshold; all other veterans who are not required to pay a copayment for their care, i.e., veterans of the Mexican border period and World War I, compensated zero (0) percent service-connected veterans who are receiving statutory awards, veterans exposed to a toxic substance, radiation or environmental hazard (limited to certain disabilities); and veterans who must pay a copayment for their care. Ineligible veterans are nonservice-connected veterans residing or sojourning in foreign lands.

National Center for Health Statistics (NCHS) population health survey data have been linked to VA administrative data containing information on military service history and VA benefit program utilization. The linked data can provide information on the health status and access to health care for VA program beneficiaries. In addition, researchers can compare the health of Veterans within and outside the VA health care system and compare Veterans to non-Veterans in the civilian non-institutionalized U.S. population. Due to confidentiality requirements, the Restricted-use NCHS-VA Linked Data Files are accessible only through the NCHS Research Data Center (RDC) Network. All interested researchers must submit a research proposal to the RDC. Please see the NCHS RDC website (https://www.cdc.gov/rdc/index.htm) for instructions on submitting a proposal.

VBA EDUCATION PROGRAMS to provide, through purchase and/or fabrication, prosthetic and related appliances, equipment and services to eligible veterans so that they may live and work as productive citizens. Veterans eligible for prosthetic services are service-connected veterans seeking care for a service-connected disability; veterans with compensable service-connected disabilities generally rated 10 percent or more; former prisoners of war, veterans discharged or released from active military service for a disability that was incurred or aggravated in the line of duty, and veterans who are in receipt of Section 1151 benefits; veterans who are in receipt of increased pension based on a need of regular aid and attendance or by reason of being permanently housebound; veterans who have annual income and net worth below the "means test" threshold; all other veterans who are not required to pay a copayment for their care, i.e., veterans of the Mexican border period and World War I, compensated zero (0) percent service-connected veterans who are receiving statutory awards, veterans exposed to a toxic substance, radiation or environmental hazard (limited to certain disabilities); and veterans who must pay a copayment for their care. Ineligible veterans are nonservice-connected veterans residing or sojourning in foreign lands.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Significant effort has been put forth to increase understanding regarding the role of the human microbiome in health- and disease-related processes. In turn, the United States (US) Veteran Microbiome Project (US-VMP) was conceptualized as a means by which to serially collect microbiome and health-related data from those seeking care within the Veterans Health Administration (VHA). In this manuscript, exposures related to military experiences, as well as conditions and health-related factors among patients seen in VHA clinical settings are discussed in relation to common psychological and physical outcomes. Upon enrollment in the study, Veterans complete psychometrically sound (i.e., reliable and valid) measures regarding their past and current medical history. Participants also provide skin, oral, and gut microbiome samples, and permission to track their health status via the VHA electronic medical record. To date, data collection efforts have been cross-diagnostic. Within this manuscript, we describe current data collection practices and procedures, as well as highlight demographic, military, and psychiatric characteristics of the first 188 Veterans enrolled in the study. Based on these findings, we assert that this cohort is unique as compared to those enrolled in recent large-scale studies of the microbiome. To increase understanding regarding disease and health among diverse cohorts, efforts such as the US-VMP are vital. Ongoing barriers and facilitators to data collection are discussed, as well as future research directions, with an emphasis on the importance of shifting current thinking regarding the microbiome from a focus on normalcy and dysbiosis to health promotion and disease prevention.