US Health Insurance

Exploring Rates, Benefits, and Providers

By Data Society [source]

About this dataset

This fascinating dataset from the Centers for Medicare & Medicaid Services provides an in-depth analysis of health insurance plans offered throughout the United States. Exploring this data, you can gain insights into how plan rates and benefits vary across states, explore how plan benefits relate to plan rates, and investigate how plans vary across insurance network providers.

The top-level directory includes six CSV files which contain information about: BenefitsCostSharing.csv; BusinessRules.csv; Network.csv; PlanAttributes.csv; Rate.csv; and ServiceArea.csv - as well as two additional CSV files which facilitate joining data across years: Crosswalk2015.csv (joining 2014 and 2015 data) and Crosswalk2016

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This Kaggle dataset contains comprehensive data on US health insurance Marketplace plans. The data was obtained from the Centers for Medicare & Medicaid Services and contains information such as plan rates and benefits, metal levels, dental coverage, and child/adult-only coverages.

In order to use this dataset effectively, it is important to understand the different columns/variables that make up the dataset. The columns are state, dental plan, multistate plan (2015 and 2016), metal level (2014-2016), child/adult-only coverage (2014-2016), FIPS code (Federal Information Processing Standard code for the particular state), zipcode, crosswalk level (level of crosswalk between 2014-2016 data sets), reason for crosswalk parameter.

Using this dataset can help you answer interesting questions about US health insurance Marketplace plans across different variables such as state or rate information. It may also be interesting to compare certain variables over time with respect to how they affect certain types of people or how they differ across states or regions. Additionally, an analysis of the different price points associated with various kinds of coverage could provide insights into which kinds of plans are most attractive in various marketplaces based on cost savings alone

Once you have a good understanding of your data by studying individual parameters in depth across multiple states or regions you can begin looking at correlations between different parameters You can identify patterns that emerge around common characteristics or trends within areas or across markets over time when you have gathered sufficient historical data:

• Does higher out of pocket limits tend to come with higher premiums?
• Are there more multi-state markets in some states than others?
• What type of metal levels does each region prefer?

Research Ideas

• Examining the impacts of age, metal levels and plan benefits on insurance rates in different states.
• Analyzing how dental plans vary across different states/regions and examining whether there are correlations between affordability and quality of care among plans with dental coverage options.
• Investigating how the Crosswalk level affects insurance rates by comparing insurance premiums from different metals level across states with varying Crosswalk Levels (e.g., how does a Bronze plan differ in cost for two states with differing Crosswalk Level 1 vs 2)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Crosswalk2016.csv | Column name | Description | |:------------------------------|:------------------------------------------------------------------------------------------------------------------------------| | State | The state in which...

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.

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?

Dataset Card for Medical Insurance Cost Prediction

The medical insurance dataset encompasses various factors influencing medical expenses, such as age, sex, BMI, smoking status, number of children, and region. This dataset serves as a foundation for training machine learning models capable of forecasting medical expenses for new policyholders. Its purpose is to shed light on the pivotal elements contributing to increased insurance costs, aiding the company in making more informed… See the full description on the dataset page: https://huggingface.co/datasets/rahulvyasm/medical_insurance_data.

The Health Insurance Questions and Answers dataset provides a comprehensive collection of common inquiries related to health insurance, along with informative responses. This resource offers individuals, healthcare professionals, and organizations valuable insights into the complex world of health insurance. It covers topics such as the fundamentals of health insurance, its significance, obtaining coverage, covered services, and explanations of key terms like premium, deductible, and copayment. The dataset also delves into various types of health insurance plans, including Health Maintenance Organizations (HMOs), Preferred Provider Organizations (PPOs), and Exclusive Provider Organizations (EPOs). Moreover, it addresses the impact of pre-existing conditions on coverage eligibility and discusses options for adding family members to insurance plans. Additionally, it explores the concept of open enrollment periods and the benefits of Health Savings Accounts (HSAs) and Flexible Spending Accounts (FSAs) for managing healthcare expenses. This dataset is a valuable resource for anyone seeking to understand, compare, and make informed decisions about health insurance.

This dataset contains medical insurance cost information for 1338 individuals. It includes demographic and health-related variables such as age, sex, BMI, number of children, smoking status, and residential region in the US. The target variable is charges, which represents the medical insurance cost billed to the individual.

The dataset is commonly used for:

Regression modeling

Health economics research

Insurance pricing analysis

Machine learning education and tutorials

Columns

age: Age of primary beneficiary (int)

sex: Gender of beneficiary (male, female)

bmi: Body Mass Index, a measure of body fat based on height and weight (float)

children: Number of children covered by health insurance (int)

smoker: Smoking status of the beneficiary (yes, no)

region: Residential region in the US (northeast, northwest, southeast, southwest)

charges: Medical insurance cost billed to the beneficiary (float)

Potential Uses

Build predictive models for medical costs Explore how smoking and BMI impact charges Teach students about regression and feature engineering Analyze healthcare affordability trends

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

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.

Content

This dataset has no description from FRED.

Context

This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1999-01-01

• Observation End : 2012-01-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

This map shows where people have Medicaid or means-tested healthcare coverage in the US (ages under 65). This is shown by State, County, and Census Tract, and uses the most current ACS 5-year estimates.

NOTE: This dataset has been retired and marked as historical-only. The recommended dataset to use in its place is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccination-Coverage-Region-HCEZ-/5sc6-ey97. COVID-19 vaccinations administered to Chicago residents by Healthy Chicago Equity Zones (HCEZ) based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f Vaccination Status Definitions: ·People with at least one vaccine dose: Number of people who have received at least one dose of any COVID-19 vaccine, including the single-dose Johnson & Johnson COVID-19 vaccine. ·People with a completed vaccine series: Number of people who have completed a primary COVID-19 vaccine series. Requirements vary depending on age and type of primary vaccine series received. ·People with a bivalent dose: Number of people who received a bivalent (updated) dose of vaccine. Updated, bivalent doses became available in Fall 2022 and were created with the original strain of COVID-19 and newer Omicron variant strains. Weekly cumulative totals by vaccination status are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" so care should be taken when summing rows. Vaccinations are counted based on the date on which they were administered. Weekly cumulative totals are reported from the week ending Saturday, December 19, 2020 onward (after December 15, when vaccines were first administered in Chicago) through the Saturday prior to the dataset being updated. Population counts are from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-year estimates. Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who have each vaccination status as of the date, divided by the estimated number of people in that subgroup. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. CDPH uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact its estimates. Data reported in I-CARE only includes doses administered in Illinois and some doses administered outside of Illinois reported historically by Illinois providers. Doses administered by the federal Bureau of Prisons and Department of Defense are also not currently reported in I-CARE. The Veterans Health Administration began reporting doses in I-CARE beginning September 2022. Due to people receiving vaccinations that are not recorded in I-CARE that can be linked to their record, such as someone receiving a vaccine dose in another state, the number of people with a completed series or a booster dose is underesti

Key Table Information.Table Title.Health Insurance Coverage Status by Age (White Alone).Table ID.ACSDT1Y2024.B27001A.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, a...

NOTE: This dataset replaces two previous ones. Please see below. Chicago residents who are up to date with COVID-19 vaccines, based on the reported address, race-ethnicity, sex, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals who meet the CDC’s updated COVID-19 vaccination criteria based on their age and prior vaccination history. For surveillance purposes, up to date is defined based on the following criteria: People ages 5 years and older: · Are up to date when they receive 1+ doses of a COVID-19 vaccine during the current season. Children ages 6 months to 4 years: · Children who have received at least two prior COVID-19 vaccine doses are up to date when they receive one additional dose of COVID-19 vaccine during the current season, regardless of vaccine product. · Children who have received only one prior COVID-19 vaccine dose are up to date when they receive one additional dose of the current season's Moderna COVID-19 vaccine or two additional doses of the current season's Pfizer-BioNTech COVID-19 vaccine. · Children who have never received a COVID-19 vaccination are up to date when they receive either two doses of the current season's Moderna vaccine or three doses of the current season's Pfizer-BioNTech vaccine. This dataset takes the place of two previous datasets, which cover doses administered from December 15, 2020 through September 13, 2023 and are marked has historical: - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Vaccinations-Chicago-Residents/2vhs-cf6b - https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccinations-by-Age-and-Race-Ethnicity/37ac-bbe3. Data Notes: Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity, sex, and age group. Note that race-ethnicity, age, and sex all have an option for “All” so care should be taken when summing rows. Coverage percentages are calculated based on the cumulative number of people in each race-ethnicity/age/sex population subgroup who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller demographic groupings with smaller populations. Additionally, the medical provider may report incorrect demographic information for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage. All coverage percentages are capped at 99%. Weekly cumulative counts and coverage percentages are reported from the week ending Saturday, September 16, 2023 onward through the Saturday prior to the dataset being updated. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. The Chicago Department of Public Health uses the most complete data available to estimate COVID-19 vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Individuals may receive vaccinations that are not recorded in the Illinois immunization registry, I-CARE, such as those administered in another state, causing underestimation of the number individuals who are up to date. Inconsistencies in records of separate doses administered to the same person, such as slight variations in dates of birth, can result in duplicate records for a person and underestimate the number of people who are up to date.

Medicare is a federal health insurance program for those aged 65 and older, certain people under 65 with disabilities, and people of any age with end-stage renal disease in the United States (US). Medicare covers about 96% of all US citizens aged 65 and older. These data have been used to describe patterns of morbidity and mortality and burden of disease, compare the effectiveness of pharmacologic therapies, examine the cost of care, evaluate the effects of provider practices on the delivery of care, and explore the effects of important policy changes on physician practices and patient outcomes. In 2014, 16% of Medicare beneficiaries were under the age of 65 years, 46% were between 65 and 74 years, 25% between 75 and 84 years, and 12% over the age of 85 years. Fifty-five percent of beneficiaries were female, 76% were white, 10% black, 9% Hispanic, and 5% Asian or other/unknown race.

This historic dataset with total enrollment in separate CHIP programs by month and state was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). For each month from April 1, 2023, through June 30, 2024, states were required to submit to CMS (on a timely basis), and CMS was required to make public, certain monthly data, including the total number of beneficiaries who were enrolled in a separate CHIP program. Accordingly, this historic dataset contains separate CHIP enrollment by month and state between April 2023 and June 2024.

CMS will continue to publicly report separate CHIP enrollment by month and state (beyond the historic CAA/Unwinding period) in a new dataset, which is available at [link]. Please note that the methods used to count separate CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing separate CHIP enrollment across the two datasets.

Sources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state’s monthly total was a state-submitted value, rather than from T-MSIS.

TAF data were pulled as follows:
April 2023 enrollment - TAF as of August 2023
May 2023 enrollment - TAF as of August 2023
June 2023 enrollment - TAF as of September 2023
July 2023 enrollment - TAF as of October 2023
August 2023 enrollment - TAF as of November 2023
September 2023 enrollment - TAF as of December 2023
October 2023 enrollment - TAF as of January 2024
November 2023 enrollment - TAF as of February 2024
December 2023 enrollment - TAF as of March 2024
January 2024 enrollment - TAF as of April 2024
February 2024 enrollment - TAF as of May 2024
March 2024 enrollment - TAF as of June 2024
April 2024 enrollment – TAF as of July 2024
May 2024 enrollment – TAF as of August 2024
June 2024 enrollment – TAF as of September 2024

TAF are produced one month after the T-MSIS submission month. For example, TAF as of August 2023 is based on July T-MSIS submissions.

Notes: The separate CHIP enrollment in this report is not inclusive of enrollees covered by Medicaid expansion CHIP. Enrollment includes individuals enrolled in separate CHIP at any point during the month but excludes those enrolled in both Medicaid and separate CHIP during the month. See the Data Sources and Metrics Definitions Overview document for a full description of the data sources, metric definitions, and general data limitations.

Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have separate CHIP Programs. Maryland has a separate CHIP program that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage.

This document includes separate CHIP data submitted to CMS by states via T-MSIS or a separate collection form. These data include reporting metrics consistent with section 1902(tt)(1) of the Social Security Act.

CHIP: Children's Health Insurance Program

Data notes: (a) State-submitted value; data not from T-MSIS
(b1) May 2023 enrollment pulled from TAF as of September 2023
(b2) Data was restated using TAF as of October 2023
(b3) Data was restated using TAF as of April 2024
(b4) Data was restated using TAF as of July 2024
(b5) Data was restated using TAF as of August 2024
(c) Enrollment counts include postpartum women with coverage funded via a Health Services Initiative

Key Table Information.Table Title.Selected Characteristics of Health Insurance Coverage in the United States.Table ID.ACSST1Y2024.S2701.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Subject Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, c...

The Medical Expenditure Panel Survey (MEPS) is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers. Data is publicly-available for two of the four MEPS components: the Household Component and the Insurance Component. Access to Medical Provider Component and Nursing Home Component data requires an application to the Agency for Health Care Research and Quality (AHRQ).

Hospitals Registered with MedicareThis feature layer, utilizing data from the Centers of Medicare and Medicaid Services (CMS), depicts all hospitals that are currently registered with Medicare in the U.S. Per NIH, "Since the passage of Medicare legislation in 1965, Section 1861 of the Social Security Act has stated that hospitals participating in Medicare must meet certain requirements specified in the act and that the Secretary of the Department of Health, Education and Welfare (HEW) [now the Department of Health and Human Services (DHHS)] may impose additional requirements found necessary to ensure the health and safety of Medicare beneficiaries receiving services in hospitals. On this basis, the Conditions of Participation, a set of regulations setting minimum health and safety standards for hospitals participating in Medicare, were promulgated in 1966 and substantially revised in 1986."Ascension Columbia St Mary's HospitalData currency: 9/22/2021Data modification: this data was created using the geocoding process on the CSV file.Data downloaded from: Hospital General InformationFor more information: HospitalsFor feedback, please contact: ArcGIScomNationalMaps@esri.comThumbnail image courtesy of Tim EvansonCenters of Medicare and Medicaid ServicesPer USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

DQS Medicaid coverage among persons under age 65, by selected characteristics: United States

  Description

Data on Medicaid coverage among people under age 65, in the United States, by selected population characteristics. Data from Health, United States. SOURCE: National Center for Health Statistics, National Health Interview Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from:… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/dqs-medicaid-coverage-among-persons-under-age-65-b.

This study aimed to systematically identify comorbidity patterns associated with schizophrenia using large-scale real-world health insurance data. The cohort comprised 86 million individuals enrolled in Aetna employer-sponsored health insurance plans across the United States of America, with data collected retrospectively from 2008 to 2019. The cohort included individuals aged 15 years or older who were diagnosed with schizophrenia, as well as matched controls. Follow-up occurred continuously during each participant’s insurance coverage, with an average observation period of around six years.

Abstract

The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.

Methodology

The Merative™ MarketScan® Commercial (CCAE) Database and Medicare (MDCR) Database contain data from individuals who have employer-sponsored insurance (ESI) as either primary coverage or Medicare supplemental coverage. To project this population to the national population of individuals with ESI, the MarketScan Commercial Insurance Weights were constructed using the Public Use Microdata Sample (PUMS) of the American Community Survey (ACS) conducted by the U.S. Census Bureau.1 2 These estimates are designed to weight individuals in the MarketScan databases to reflect the national ESI population by demographic stratum; health care use varies appreciably across demographic strata.

The MarketScan Commercial Insurance Weights currently project to the total U.S. ESI population. This group represents a large market share of health care services, drugs, and medical devices.