The Impact of COVID-19 on Veterans in America

County-Level Data on Veteran Cases and Proportion of Population

About this dataset

Welcome to the Kaggle dataset on The Impact of COVID-19 on Veterans in the United States! This dataset contains data on confirmed cases of COVID-19 in counties across the United States, as well as information on the percentage of each county's population that are veterans. With this dataset, you can investigate how the pandemic has impacted veterans specifically, and compare veteran case rates to the general population. How do veteran cases differ across age groups? Are there any geographical patterns? What can we learn about risk factors for COVID-19 among veterans? Download the dataset and explore for yourself today!

How to use the dataset

This dataset includes information on the number of confirmed cases of COVID-19 by county, as well as the percentage of the population in each county that are veterans. This data can be used to examine the relationship between veteran cases and the proportion of population who are veterans.

To do this, simply look at the 'CASES' and 'VET_CASES' columns for each county. The 'CASES' column represents the total number of confirmed cases of COVID-19 in that county, while the 'VET_CASES' column represents the number of confirmed cases among veterans. To compare these two values, simply divide 'VET_CASES' by 'CASES'. This will give you a ratio of veteran cases to total cases for each county.

You can then use this ratio to compare counties and see which ones have a higher proportion of veteran cases. This data can be used to help understand where more outreach may be needed to support veterans during this pandemic

Research Ideas

• Find the correlation between the number of veterans in a county and the number of confirmed cases of COVID-19.
• Find the counties with the highest percentage of veterans and the lowest number of confirmed cases of COVID-19.
• Predict how many veterans in a county will contract COVID-19 based on the percentage of veterans in the population

Columns

File: CountyVACOVID.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------------------------------------------------| | FIPS | Federal Information Processing Standards code that uniquely identifies counties within the USA. (String) | | COUNTY | County name. (String) | | STATE | State name. (String) | | POP | County population. (Integer) | | VETS | Number of veterans in the county. (Integer) | | VET_PERCENT | Percentage of the population that are veterans. (Float) | | CASES | Number of confirmed cases of COVID-19 in the county. (Integer) | | YESTER_CASES | Number of confirmed cases of COVID-19 in the county from the previous day. (Integer) | | VET_CASES | Number of confirmed cases of COVID-19 in veterans in the county. (Integer) | | VET_YESTER | Number of confirmed cases of COVID-19 in veterans in the county from the previous day. (Integer) | | LOWER_Hospitalizations | Lower bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | UPPER_Hospitalizations | Upper bound of the 95% confidence interval for the number of hospitalizations due to COVID-19 in the county. (Integer) | | DATE | Date of data. (Date) |

File: VAChart.csv | Column name | Description | |:------------------------|:----------------------------------------------------------------------------------| | DATE | Date of data. (Date) | | US Cases | The number of confirmed cases of COVID-19 in the United States. (Integer) | | **New US ...

These spreadsheets contain the percent of Veteran farmers and dairymen by county for the state of California.

To show count of Post 9/11 Veterans (Living only) by County for the creation of a heat map to align with Wounded Warrior Projects’ programming.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This data contains the number of veterans per county who have an Illinois address recorded in the IDVA Veteran Database(not all Illinois veterans).

The Compensation and Pension by County dataset is a count of the number of veterans receiving disability compensation or pension payments from the Department of Veterans Affairs. The data is reported at the county level, by age group and by % disability rating for each state plus recipients in Guam, Philippines and Puerto Rico.

This is a simple proportion analysis to determine the number of veterans who may be impacted by food scarcity in the United states by county. The population of veterans in each county (9L_VetPop2016_County) was used with the total population in each county (DataDownload3.18) to determine the proportion of veterans in each county. We assumed that veterans were just as likely as anyone else to be in food scarcity and multiplied the proportion of veterans in each county by the number of low access people in the county to determine the number of food insecure veterans by county. We also used statewide very low food secure percentage as a conservative estimate of the number of veterans affected by food scarcity.This dataset was not created to be a perfect representation of the exact number of food insecure veterans. In fact, it is a very rough calculation. However, this back of the envelope calculation shows that the number of food insecure veterans is likely very high. Using county level food access we find that up to 3 million veterans could be affected by low food access, as a conservative estimate, we use the state level "very low food security percentage" and find that a minimum of 200 thousand veterans are likely food insecure. For calculations see sheet "Calculations" in DataDownload3.18.xlsVeteran Population in counties of the United States.(9L_VetPOP2016_Count.csv)https://va.gov/vetdata/Veteran_Population.aspFood Insecurity By County (DataDownload3.18.xls)https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-and-documentation-downloads/

The Geographic Distribution of VA Expenditures (GDX) is an annual report that shows estimated VA expenditures for major programmatic areas by geographic area (state, county, and congressional district). The major programmatic areas are: Compensation and Pension; Readjustment (Education) and Vocational Rehabilitation; Insurance; Construction; and, Medical and Administrative.

The Geographic Distribution of VA Expenditures (GDX) is an annual report that shows estimated VA expenditures for major programmatic areas by geographic area (state, county, and congressional district). The major programmatic areas are: Compensation and Pension; Readjustment (Education) and Vocational Rehabilitation; Insurance; Construction; and, Medical and Administrative.

In 2020, surveys conducted among people experiencing homelessness in King County, Washington found that 55 percent of those who were veterans suffered from post-traumatic stress disorder (PTSD), compared to 39 percent of those who were not veterans. This statistic shows the percentage of veteran and non-veteran homeless persons in King County, Washington who stated they had select health conditions as of 2020.

Veterans by largest Race and Ethnicity categories, by Health and Human Services Service Area.

This indicator provides the provides the percentage of civilian veterans by race/ethnicity group.

Veterans are persons 18 years and over who ever served on active duty. A civilian veteran refers to persons 18 years or older who served on active duty in any military branch or served in the National Guard or military reserves (only those ever called or ordered to active duty were classified as veterans). It does not include persons currently in active duty.

Source: U.S. Census Bureau; 2013-2017 American Community Survey 5-Year Estimates, Table S2101.

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.

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.

Graph and download economic data for Resident Population in Richmond County, VA (VARICH9POP) from 1970 to 2024 about Richmond County, VA; VA; residents; population; and USA.

The Department of Veterans Affairs (VA) provides healthcare services to its veterans across the USA including territories and possessions. Healthcare services are delivered through 18 geographically divided administrative areas called Veterans Integrated Services Networks (VISN). Each VISN is divided into healthcare areas called Markets and Submarkets. Each Submarket is divided into Sectors and each Sector comprises one or more counties. In 1995 a process was created to coordinate and review the realignment of the Heath Care Networks. The Capital Asset Realignment for Enhanced Services (CARES) process established VISN 'subsets' called Markets, Submarkets and Sectors which, being smaller than VISNs, allowed for more precise analyses for greater access measurement to health care.

The County layer is the base geographic unit of the VISN-Market-Submarket-Sector-County hierarchy. The key attribute in this data set is the FIPS which is defined as a string of 5 characters with unique alphanumeric combinations for each site. The first 2 are the State FIPS code and the next 3 designate the County FIPS code. Example: '01031' is the FIPS for Coffee County, Alabama.

A Sector is a cluster of geographically adjacent counties within a VA Submarket. The process of aggregating counties into sectors uses a combination of automated algorithms and manual inspection of maps. The key attribute in this data set is the SECTOR which is defined as a string of eight characters broken down into four parts in the order of VISN (2-char), Market (1-char), Submarket (1-char), and Sector(1-char) connected by a hyphen. For example, Sector 12-a-3-A indicates VISN 12, Market a, Submarket 3 and Sector A.

Sub-markets reflect a clustering of the enrollee population within a market and are an aggregation of Sectors. The key attribute in this data set is the SUBMARKET which is defined as a string of six characters broken down in three parts in the order of VISN (2-char), Market (1-char), and Submarket (1-char) connected by a hyphen. For example, Submarket 12-a-3 indicates VISN 12, Market a, and Submarket 3.

CARES defines Markets as "an aggregated geographic area having a sufficient population and geographic size to both benefit from the coordination and planning of health care services and to support a full healthcare delivery system (i.e. primary care, mental health care, inpatient care, tertiary care, and long term care)". Each Market is built from Submarkets. The key attribute in this data set is the MARKET which is defined as a string of four characters broken down in two parts in the order of HCN (2-char) and Market (1-char) connected by a hyphen. For example, Market 12-a indicates VISN 12 and Market a.

The key attribute in the VISN data set is defined as a string of two characters from 01-23, excluding 3, 11, 13, 14 and 18; a VISN also has an officially recognized VA title. For example, VISN 06 is the Mid-Atlantic Health Care Network. VISNs can span across neighboring countries to include areas that are not contiguous. For example, VISN 08 includes Florida and Puerto Rico in addition to most of Florida and southern Georgia, and VISN 20 includes Alaska and parts of the northwest conterminous United States. Each VISN is built from Markets, Submarkets, Sectors and Counties derived from Census (2010) County data.

Because VISNs are composed of VHA markets, VISN boundaries align with the outer edges of their constituent markets’ boundaries. Markets cross state borders wherever it is necessary to keep outpatient clinics (e.g. Community-Based Outpatient Clinics(CBOCs)) and their catchment areas in the same market as their parent medical centers. Thus, VISN boundaries also cross state borders. In 2016 senior leadership considered the challenge of conforming VISN boundaries to MyVA Districts, which coincide with state boundaries. It was agreed that VHA would not separate outpatient clinics from their parent medical centers due to added complexity. Many outpatient providers hold clinics at their mother facilities and clinics are on the same health record as their parent facilities. VISN and market maps created by VHA Policy and Planning conform to these principals and are the official maps for VHA VISNs and markets.

While the Planning Systems Support Group (PSSG) develops the feature classes depicting the various VHA geographies, the PSSG does not have the authority to modify or reorganize the boundaries. The boundaries are developed at higher levels of the VHA and passed to the PSSG to be translated into spatial features.

Veterans: Period of Service by census tract and Health and Human Services Service Area. Veterans: Civilians 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. Source: U.S. Census Bureau; 2013-2017 American Community Survey 5-Year Estimates, Table B21002.

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.

Veterans - Median Income and Poverty, by City. Veterans: Civilians 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. Source: U.S. Census Bureau; 2012-2016 American Community Survey 5-Year Estimates, Table S2101

This report provides county-level estimates of the number of Veterans who were receiving VA Disability Compensation benefits as of the end of fiscal year 2023. It includes the Veterans’ total service-connected disability (SCD) rating, age group, and sex. Blank values represent small cell counts that have been suppressed to protect the identity of Veterans as well as some cell counts that have been suppressed in order to prevent the determination of the values of the aforementioned small cell counts. Some categories may not sum to the total due to missing information (e.g., age, sex, etc.). The availability of sex and age data is limited as some records have no sex or birthdate available. In the table, there are 404 Veterans whose sex is not available and 113 Veterans whose age is not available. The number of Veterans who were disability compensation recipients during FY 2023 but were no longer disability compensation recipients at the end of FY 2023 is 138,646. These Veterans are not included in the table. Source: Department of Veterans Affairs, Office of Enterprise Integration, Veteran Object FY23 data and Veterans Benefits Administration VETSNET FY 2023 compensation data. Prepared by National Center for Veterans Analysis & Statistics, www.va.gov/vetdata.

Financial overview and grant giving statistics of Blair County War Veterans Council Inc

Graph and download economic data for Resident Population in Alleghany County, VA (VAALLE5POP) from 1970 to 2024 about Alleghany County, VA; VA; residents; population; and USA.

Financial overview and grant giving statistics of Veterans Of Madison County Inc