In 2022, about 1.4 million veterans were living in Texas - the most out of any state. Florida, California, Pennsylvania, and Virginia rounded out the top five states with the highest veteran population in that year.

In 2023, 42.14 percent of U.S. men aged 75 years and over were veterans - the highest share of any age group or gender. In comparison, less than one percent of women aged 75 and over were veterans at that time.

United States - Population Level - Women, Total Veterans, 18 Years and over was 2077.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Women, Total Veterans, 18 Years and over reached a record high of 2259.00000 in October of 2014 and a record low of 1375.00000 in June of 2002. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Women, Total Veterans, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

About this dataset

Context

The dataset tabulates the Veteran town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Veteran town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Veteran town was 3,235, a 0.61% decrease year-by-year from 2022. Previously, in 2022, Veteran town population was 3,255, a decline of 1.30% compared to a population of 3,298 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Veteran town decreased by 41. In this period, the peak population was 3,352 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Veteran town is shown in this column.
• Year on Year Change: This column displays the change in Veteran town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Veteran town Population by Year. You can refer the same here

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

United States - Population Level - Veterans, Gulf War Era II, 18 Years and over was 5628.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Veterans, Gulf War Era II, 18 Years and over reached a record high of 5669.00000 in May of 2025 and a record low of 1144.00000 in January of 2006. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Veterans, Gulf War Era II, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

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/

This data collection provides information on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons 14 years old and over. Also supplied are personal characteristics such as age, sex, race, marital status, veteran status, household relationships, educational background, and Spanish origin. The September 1989 Current Population Survey contains two supplements. The Veterans Supplement provides estimates on the demographic and labor force characteristics of veterans who had service- connected disabilities as compared with all other veterans. The data also identify Vietnam theater veterans--those who actually served in Vietnam, Laos, or Cambodia. Estimates of the number of veterans with service-connected disabilities along with their labor force participation rate, occupation, unemployment rate, and use of certain educational and job training programs are provided. The Cardiovascular Disease Risk Factor Supplement provides estimates on the three major risk factors for heart disease: cigarette smoking, high blood pressure, and high blood cholesterol. Data relating to cigarette smoking identify current smokers, former smokers, and nonsmokers. Items on high blood pressure and on high blood cholesterol measure the number of people who have been diagnosed by health professionals as being in these two risk categories and the number who follow the advice of a health professional in treating or controlling these conditions.

Veteran Health IT Market Outlook

According to our latest research, the global veteran health IT market size in 2024 stands at USD 5.3 billion, reflecting the sector’s robust expansion driven by increasing digitalization in healthcare for veterans. The market is set to grow at a CAGR of 8.7% from 2025 to 2033, reaching an estimated USD 11.1 billion by 2033. This growth is propelled by the urgent need for integrated healthcare solutions, rising investments in health IT infrastructure, and the ongoing demand for improved patient outcomes among veterans. As per the latest research, the adoption of advanced technologies such as electronic health records (EHR), telehealth, and data analytics is fundamentally transforming the veteran healthcare landscape, setting the stage for sustained market expansion over the forecast period.

One of the primary growth factors driving the veteran health IT market is the increasing prevalence of chronic illnesses and mental health issues among veterans, which necessitates a comprehensive and integrated approach to healthcare delivery. Veterans, due to their unique service-related health challenges, require a coordinated system that can efficiently manage their medical records, ensure continuity of care, and facilitate timely interventions. The deployment of sophisticated health IT solutions, including EHR and telehealth platforms, enables healthcare providers to deliver personalized care, streamline administrative processes, and enhance patient engagement. Furthermore, the growing emphasis on value-based care models and outcome-driven reimbursement policies is encouraging healthcare organizations to invest in robust IT infrastructures, further accelerating market growth.

Another significant driver for the veteran health IT market is the rapid adoption of cloud-based and interoperable solutions, which are revolutionizing the way healthcare data is stored, accessed, and shared across multiple care settings. Cloud-based platforms offer scalability, cost-efficiency, and enhanced data security, addressing many of the operational challenges faced by veteran healthcare facilities. These solutions support real-time data exchange, facilitate remote monitoring, and improve care coordination among multidisciplinary teams. The integration of advanced analytics and artificial intelligence further empowers healthcare providers to gain actionable insights from vast datasets, optimize resource allocation, and predict patient risks, ultimately leading to better health outcomes for veterans.

Government initiatives and funding play a pivotal role in shaping the veteran health IT market. In countries like the United States, the Department of Veterans Affairs (VA) has been at the forefront of implementing large-scale IT modernization projects aimed at enhancing the quality and accessibility of veteran healthcare services. Substantial investments in upgrading legacy systems, improving cybersecurity, and expanding telehealth capabilities have created a fertile environment for innovation and collaboration among technology vendors, healthcare providers, and policymakers. Moreover, the increasing focus on regulatory compliance, data privacy, and interoperability standards is compelling market participants to develop solutions that meet stringent government requirements, further driving the adoption of health IT among veteran care providers.

From a regional perspective, North America dominates the veteran health IT market, accounting for the largest share due to its advanced healthcare infrastructure, high concentration of veteran populations, and proactive government support. However, the Asia Pacific region is emerging as a lucrative market, fueled by rising healthcare expenditures, expanding digital health initiatives, and growing awareness about the benefits of health IT among military and veteran care facilities. Europe is also witnessing steady growth, driven by the increasing adoption of EHR and telemedicine solutions in public and private healthcare sectors. Latin America and the Middle East & Africa, while still in the nascent stages of health IT adoption, present significant opportunities for market expansion, particularly as governments prioritize healthcare modernization and digital transformation.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.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..The categories under period of service are not necessarily mutually exclusive. Veterans may have served in more than one period..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.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..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..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..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.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..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..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, there were around 1.11 million active duty U.S. Armed Forces personnel stationed within the United States. In that year, there were 156,418 U.S. Armed Forces personnel stationed in California, the most of any state.

United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over was 3126.00000 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over reached a record high of 5567.00000 in October of 2007 and a record low of 3126.00000 in June of 2025. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Level - Men, Veterans, Other Service Periods, 18 Years and over - last updated from the United States Federal Reserve on July of 2025.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The 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 2018-2022). 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: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Graph and download economic data for Unemployment Rate - Total Veterans, 18 Years and over (LNU04049526) from Jan 2000 to Jul 2025 about 18 years +, veterans, household survey, unemployment, rate, and USA.

In 2022, the leading causes of death among veterans in the United States were heart disease, cancer, and unintentional injuries. Heart disease and cancer account for the majority of deaths among U.S. veterans, which is also true for people in the United States in general. The leading causes of death among veterans vs the U.S. as a whole Although the leading causes of death for U.S. veterans are similar to the overall leading causes of death in the United States, there are some differences. For example, even though heart disease and cancer are the leading causes of death for the entire United States and veterans specifically, death rates for both heart disease and cancer are much higher among veterans. In 2022, the death rate for heart disease among veterans was around 239 per 100,000 population, compared to an overall rate of 167 per 100,000 population for the United States. Another clear difference is that while suicide was the seventh leading cause of death among veterans in 2022, it was not among the leading ten causes of death for the United States as a whole. Suicide among veterans Given the stressful and often dangerous work of U.S. military personnel, the mental health of U.S. veterans remains a prevalent issue. In 2022, it was estimated that around 7.6 percent of U.S. veterans aged 18 to 49 years had serious thoughts of suicide in the past year, while 1.4 percent made suicide plans, and .4 percent attempted suicide. That year there were around 6,407 suicide deaths among veterans in the United States. Veterans suffering from substance use disorders or mental health conditions are much more likely to die from suicide than veterans who do not have such disorders.

There are 2 datasets of high-risk patient populations; one from calendar year 2014 (N1 = 937,407), for which we used International Classification of Disease Version 9 (ICD9) codes to identify comorbid conditions, and a second, more recent population selected from June 2017 to June 2018 (N2 = 979,607) for use with the newer International Classification of Disease Version 10 (ICD10) codes. DOI: 10.1109/JBHI.2019.2948734

Primary sclerosing cholangitis (PSC) is associated with hepatobiliary and colorectal cancers, but it remains uncertain if PSC increases the risk for pancreatic cancer. While some European studies have suggested an increased risk of pancreatic cancer in PSC patients, other studies have not. And these studies did not well account for presence or absence of concomitant inflammatory bowel disease (IBD). The purpose of this study is to investigate the prevalence of pancreatic cancer in United States veterans with PSC both with and without IBD.MethodsThis retrospective study used International Classification of Diseases, Tenth Revision (ICD-10) codes to identify patients with PSC, IBD, and pancreatic cancer from the Veterans Affairs (VA) Corporate Data Warehouse. The prevalence of pancreatic cancer in patients with PSC only, IBD only, PSC with IBD, and neither PSC nor IBD were compared. Logistic regression was used to control for age, gender, chronic pancreatitis, diabetes mellitus, and tobacco and alcohol use.ResultsA total of 946 patients with PSC were identified from a population of over 9 million veterans. 486 (51.4%) of these had concurrent IBD. Additionally 112,653 patients with IBD without PSC were identified. When adjusted for confounding factors, patients with PSC had a significantly higher prevalence of pancreatic cancer compared to the general population and those with IBD without PSC (2.4% vs. 0.2% and 0.5%, respectively).ConclusionsVeterans with PSC, particularly those without concomitant IBD, have a high prevalence of pancreatic cancer compared to the general veteran population. Our findings support the need for multicenter prospective studies investigating the benefits of screening for pancreatic cancer in patients with PSC.

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, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..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..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.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..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..The categories under period of service are not necessarily mutually exclusive. Veterans may have served in more than one period..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..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.