In 2023, nearly ** percent of people in the United States had public health insurance, the share of people with private health insurance has gradually increased in the provided time interval. This statistic contains data on the number of U.S. Americans with government health insurance coverage from 1997 to 2023.

In 2023, 10.9 percent of people aged 18 to 64 in the United States didn't have health insurance, the lowest in the provided time interval. This statistic contains data on the percentage of U.S. Americans without health insurance coverage from 1997 to 2023, by age.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

This dataset contains detailed demographic and health-related information for individuals alongside their corresponding medical insurance charges. It includes features such as age, sex, BMI, number of children, smoking status, region, and total insurance cost. This dataset is covered from the USA.

The dataset is ideal for building and evaluating machine learning models that predict healthcare costs based on personal and lifestyle factors.

Dataset Features

1. age: Age of the individual in years.

2. sex: Biological sex of the individual (male or female).

3. BMI: Body Mass Index — the numeric measure of body fat based on height and weight.

4. children: Number of dependent children covered by the insurance plan.

5. smoker: Smoking status of the individual (yes or no).

6. region: Geographic region of the individual within the United States (northeast, northwest, southeast, or southwest).

7. charges: Individual medical insurance cost billed by the insurer.

Distribution

• Format: CSV (Comma-Separated Values)

• Data Volume: Rows: 1,338 records

• 7 Columns: age, sex, BMI, children, smoker, region, charges

• File Size: Approximately 56 KB

Usage

This dataset is ideal for a variety of applications:

Medical Cost Prediction: Train regression models to estimate insurance charges based on demographic and lifestyle factors

Health Economics Research: Analyze how factors like smoking, BMI, and age impact healthcare costs.

Geographic Coverage:

• United States: the dataset includes individuals from four regions: northeast, northwest, southeast, and southwest.

• Time Range: The exact dates of data collection are not specified, but the data reflects typical insurance and demographic patterns observed in recent years.

• Demographics: Includes a diverse range of individuals: Age Range: From 18 to 64 years old Gender: Male and female Lifestyle Factors: Smoking status and BMI Dependents: Number of children covered by the insurance

License

CC0

Who Can Use It

• Data Scientists: For training machine learning models.
• Researchers: For academic or scientific studies.
• Businesses: For analysis, insights, or AI development.

Analysis of ‘Health Insurance Coverage’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hhs/health-insurance on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Affordable Care Act (ACA) is the name for the comprehensive health care reform law and its amendments which addresses health insurance coverage, health care costs, and preventive care. The law was enacted in two parts: The Patient Protection and Affordable Care Act was signed into law on March 23, 2010 by President Barack Obama and was amended by the Health Care and Education Reconciliation Act on March 30, 2010.

Content

This dataset provides health insurance coverage data for each state and the nation as a whole, including variables such as the uninsured rates before and after Obamacare, estimates of individuals covered by employer and marketplace healthcare plans, and enrollment in Medicare and Medicaid programs.

Acknowledgements

The health insurance coverage data was compiled from the US Department of Health and Human Services and US Census Bureau.

Inspiration

How has the Affordable Care Act changed the rate of citizens with health insurance coverage? Which states observed the greatest decline in their uninsured rate? Did those states expand Medicaid program coverage and/or implement a health insurance marketplace? What do you predict will happen to the nationwide uninsured rate in the next five years?

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Health Insurance Coverage: Total Number of People in New York (DISCONTINUED) (NYHICTOTAL) from 1999 to 2012 about health, insurance, NY, persons, and USA.

This map service shows the locations of healthcare facilities (hospitals, medical centers, federally qualified health centers, home health services, and nursing homes) in the United States. The data was provided by the U.S. Department of Health Human Services and is current as of 2012.The data is symbolized by facility type:Hospital: an institution providing medical and surgical treatment and nursing care for sick or injured people.Medical Center: a health care facility staffed and equipped to care for many patients and for a large number of various kinds of diseases and dysfunctions, using sophisticated technology.Federally Qualified Health Center: a community-based organization that provides comprehensive primary care and preventative care, including health, oral, and mental health/substance abuse services to persons of all ages, regardless of their ability to pay or health insurance status.Home Health Service: health care or supportive care provided in the patient's home by health care professionals (often referred to as home health care or formal care).Nursing Home: provides a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living.Other data sources include: Data.gov_Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

In 2023, around ** percent of people in the United States had private health insurance. This represents a steady decrease since 2015. This statistic contains data on the number of U.S. Americans with private health insurance coverage from 1997 to 2023.

Snapshot img

Health Insurance Market Size 2025-2029

The health insurance market size is forecast to increase by USD 1,341 billion at a CAGR of 7.3% between 2024 and 2029.

The market experiences robust growth, fueled by the increasing demand for comprehensive coverage due to heightened healthcare awareness and a growing emphasis on preventive health. This trend is further driven by the escalating costs of healthcare services and medical treatments, which underscores the importance of insurance as a financial safeguard. However, market expansion encounters significant challenges. Regulatory hurdles impact adoption, as governments and regulatory bodies implement stringent regulations to ensure affordability and accessibility for consumers. Supply chain inconsistencies, such as disparities in provider networks and reimbursement rates, temper growth potential. This is particularly evident in the rising prevalence of chronic conditions such as cancer, stroke, and kidney failure, which necessitate ongoing medication and hospitalization. Additionally, another trend is the shift towards online sales and digital platforms for purchasing insurance policies and accessing healthcare services.
To capitalize on opportunities and navigate challenges effectively, companies must stay informed of regulatory changes and collaborate with healthcare providers to streamline operations and maintain competitive pricing. By focusing on innovation, transparency, and customer-centric solutions, insurers can differentiate themselves in a competitive landscape and meet the evolving needs of health-conscious consumers.

What will be the Size of the Health Insurance Market during the forecast period?

Request Free Sample

In the dynamic market, chronic disease management and mental health coverage have emerged as significant areas of focus. Health insurance networks strive to offer comprehensive solutions, integrating geriatric care, preventive care, and end-of-life care into their offerings. Innovation drives the industry, with wellness programs, home health care, and telemedicine becoming increasingly popular. Compliance with regulations, including those related to maternity care, newborn care, and substance abuse treatment, is crucial.
Specialty care and provider networks continue to shape the landscape, while ethics and claims processing remain critical components of health insurance services. Incorporating mental health coverage into plans and addressing the needs of the aging population are key trends shaping the market.

How is this Health Insurance Industry segmented?

The health insurance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Public
  Private


Type

  Life insurance
  Term insurance


Age Group

  Adults
  Senior citizens
  Minors


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Service Insights

The public segment is estimated to witness significant growth during the forecast period.

In the dynamic market, various entities play crucial roles in shaping its landscape. Public organizations, such as the National Health Service (NHS) in the UK and Medicare in Australia, are leading providers due to increased government involvement in ensuring universal healthcare access. These programs offer comprehensive coverage, affordable premiums, and a focus on preventive care. Collaborations with commercial insurers, legislative frameworks, and investments in healthcare infrastructure further expand their reach. Quality is a top priority, with health insurance advisors and brokers facilitating the selection of plans that best fit businesses and individuals. Prescription drug coverage is a significant consideration, and self-funded health insurance and health reimbursement arrangements offer flexibility for employers.

Group health insurance and individual health insurance provide different solutions for various needs, with portability ensuring continuity. Health insurance cybersecurity and technology are essential, with health insurance portals, virtual care, and telemedicine transforming the industry. Health savings accounts, flexible spending accounts, and out-of-pocket maximums help manage costs. Managed care and employer-sponsored health insurance are common, with health insurance plans catering to diverse needs. Regulations and compliance are critical, with long-term care insurance addressing specific healthcare requirements. Disability insurance and life insurance provide additional coverage, while the marketing and transparency ensure consumer understanding. Point-of-service (POS) plans and dental/vision insurance of

This layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black)This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This report tracks the number of people covered by private health insurance in the United States. The data includes coverage either provided by employers or purchased directly from an insurer or a health maintenance organization. The data does not include government-provided health insurance such as Medicaid, Medicare and military health care. Data is sourced from the US Census Bureau.

This dataset contains demographic and personal health information for individuals, along with the corresponding medical insurance charges billed to them. It is commonly used to build predictive models for insurance costs and to explore relationships between factors such as age, BMI, smoking status, and region on medical expenses.

Features: - age: Age of the primary beneficiary (integer) - sex: Gender of the individual (male, female) - bmi: Body mass index, providing a measure of body fat based on height and weight (float) - children: Number of children/dependents covered by the insurance (integer) - smoker: Smoking status of the individual (yes, no) - region: Residential area in the US (northeast, northwest, southeast, southwest) - charges: Individual medical costs billed by health insurance (float, in USD)

Applications: This dataset is frequently used in regression modeling, cost prediction, and data visualization tasks. It is ideal for learning how lifestyle and demographic factors impact healthcare expenses and serves as a foundational dataset for applied machine learning in health economics.

Imputed employer-sponsored health insurance coverage data which when linked to the March Annual Social and Economic Supplement to the Current Population Survey (March CPS), generates estimates of the number of individuals with different types of insurance coverage.

This layer shows the percentage of the civilian noninstitutionalized population who do not have insurance. This is shown by census tract centroids. The data values are from the 2012-2016 American Community Survey 5-year estimate in the B27001 Table for health insurance coverage status broken down by by age and sex characteristics.This map helps to answer a few questions:How many people in the United States don't have health insurance?Where are the concentrations of uninsured population?This map helps to tell a local pattern about insurance in the United States. The data can be stratified by different age and sex characteristics in order to create additional maps. By default, the pop-up provides a breakdown of total male and female uninsured population. This data was downloaded from the United States Census Bureau American Fact Finder on March 1, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas. The data contains additional attributes that can be used for mapping and analysis. Nationally, the breakdown of insurance for the civilian noninstitutionalized population in the US is:

Total: 313,576,137 +/-10,365

Male: 153,162,940 +/-12,077

Under 6 years: 12,227,441 +/-11,224

With health insurance coverage 11,643,526 +/-12,783

No health insurance coverage 583,915 +/-6,438

6 to 17 years: 25,282,489 +/-12,396

With health insurance coverage 23,659,835 +/-16,339

No health insurance coverage 1,622,654 +/-14,500

18 to 24 years: 15,350,990 +/-8,369

With health insurance coverage 12,112,729 +/-19,586

No health insurance coverage 3,238,261 +/-24,081

25 to 34 years: 20,901,264 +/-8,155

With health insurance coverage 15,669,472 +/-36,401

No health insurance coverage 5,231,792 +/-38,887

35 to 44 years: 19,499,072 +/-6,321

With health insurance coverage 15,722,620 +/-41,969

No health insurance coverage 3,776,452 +/-41,916

45 to 54 years: 20,965,500 +/-5,283

With health insurance coverage 17,819,431 +/-33,014

No health insurance coverage 3,146,069 +/-31,181

55 to 64 years: 19,068,251 +/-3,959

With health insurance coverage 17,076,497 +/-20,830

No health insurance coverage 1,991,754 +/-19,813

65 to 74 years: 12,168,198 +/-3,453

With health insurance coverage 12,041,594 +/-4,736

No health insurance coverage 126,604 +/-3,207

75 years and over: 7,699,735 +/-3,458

With health insurance coverage 7,657,815 +/-3,794

No health insurance coverage 41,920 +/-1,719

Female: 160,413,197 +/-8,724

Under 6 years: 11,684,980 +/-10,395

With health insurance coverage 11,115,775 +/-13,062

No health insurance coverage 569,205 +/-7,132

6 to 17 years: 24,280,468 +/-11,445

With health insurance coverage 22,723,174 +/-14,642

No health insurance coverage 1,557,294 +/-13,468

18 to 24 years: 15,151,707 +/-5,432

With health insurance coverage 12,591,379 +/-16,744

No health insurance coverage 2,560,328 +/-18,826

25 to 34 years: 21,367,510 +/-4,829

With health insurance coverage 17,505,087 +/-32,122

No health insurance coverage 3,862,423 +/-31,651

35 to 44 years: 20,279,901 +/-4,751

With health insurance coverage 17,146,763 +/-32,076

No health insurance coverage 3,133,138 +/-31,659

45 to 54 years: 21,975,842 +/-5,087

With health insurance coverage 19,083,932 +/-27,415

No health insurance coverage 2,891,910 +/-25,022

55 to 64 years: 20,665,987 +/-3,867

With health insurance coverage 18,537,874 +/-18,484

No health insurance coverage 2,128,113 +/-16,614

65 to 74 years: 13,896,484 +/-3,882

With health insurance coverage 13,730,727 +/-6,177

No health insurance coverage 165,757 +/-3,857

75 years and over: 11,110,318 +/-3,977

With health insurance coverage 11,037,661 +/-4,391

No health insurance coverage 72,657 +/-2,120 Data note from the US Census Bureau:[ACS] 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

As of 2023, nearly *** million people in the United States had some kind of health insurance, a significant increase from around *** million insured people in 2010. However, as of 2023, there were still approximately ** million people in the United States without any kind of health insurance. Insurance coverage The United States does not have universal health insurance, and so health care cost is mostly covered through different private and public insurance programs. In 2021, almost ** percent of the insured population of the United States were insured through employers, while **** percent of people were insured through Medicaid, and **** percent of people through Medicare. As of 2022, about *** percent of people were uninsured in the U.S., compared to ** percent in 2010. The Affordable Care Act The Affordable Care Act (ACA) significantly reduced the number of uninsured people in the United States, from **** million uninsured people in 2013 to **** million people in 2015. However, since the repeal of the individual mandate the number of people without health insurance has risen. Healthcare reform in the United States remains an ongoing political issue with public opinion on a Medicare-for-all plan consistently divided.

This data set includes socioeconomic factors within the Town of Dumfries such as people in the labor force, people without health insurance, etc. This information comes from the most recent U.S. Census provided by the United States Census Bureau. Data will be updated accordingly with the schedule of the U.S Census. https://data.census.gov/cedsci/profile?g=1600000US5123760

With a disability 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 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014.

This layer shows the predominant level of insurance coverage for non-citizens in the USA. This is shown by county centroids. The data values are from the 2012-2016 American Community Survey 5-year estimate in the B27020 Table for health insurance coverage status and type by citizenship status. This map helps to answer a few questions:Do non-citizens have health insurance?Where are the non-citizens in the US?The color of the symbols represent the most common form of insurance held by foreign born non-citizens in the USA. This predominance map style compares the count of people who are insured or not insured, and returns the value with the highest count.Foreign born non-citizen without insuranceForeign born non-citizen with insuranceThe size of the symbol represents the count of all non-citizens in the area, which shows in the legend as "sum of categories". The strength of the color represents HOW predominant the form of insurance is for non-citizens. The stronger the symbol, the larger proportion of the non-citizens.This map is designed for a dark basemap such as the Human Geography Basemap or the Dark Gray Canvas Basemap. It helps show a regional pattern about the uninsured and insured non-citizen population. This data was downloaded from the United States Census Bureau American Fact Finder on March 1, 2018. It was then joined with 2016 vintage centroid points and hosted to ArcGIS Online and into the Living Atlas. The data contains additional attributes that can be used for mapping and analysis. Nationally, the breakdown of insurance for the civilian noninstitutionalized population in the US is:Total:313,576,137+/-10,365Native Born:271,739,505+/-102,340With health insurance coverage246,142,724+/-281,131With private health insurance186,765,058+/-576,448With public coverage92,452,853+/-209,370No health insurance coverage25,596,781+/-190,502Foreign Born:41,836,632+/-109,590Naturalized:19,819,629+/-35,976With health insurance coverage17,489,342+/-42,261With private health insurance12,927,060+/-50,505With public coverage6,687,375+/-16,733No health insurance coverage2,330,287+/-20,148Noncitizen:22,017,003+/-118,842With health insurance coverage13,243,825+/-44,108With private health insurance9,320,483+/-26,031With public coverage4,459,972+/-34,270No health insurance coverage8,773,178+/-86,951Data note from the US Census Bureau:[ACS] 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.