This dataset provides an estimate of the percent of Detroit residents who reported having health insurance at the time they completed the American Community Survey (ACS). The data is averaged over 5 years. This data can be also be accessed in Table S2701 on the American FactFinder website.Note that the data is provided by ZIP Code Tabulation Area (ZCTA), which may not exactly match USPS ZIP Code service areas. For more information: https://web.archive.org/web/20130617034846/http://www.census.gov/geo/reference/zctas.html

Content

The Affordable Care Act created the new Pre-Existing Condition Insurance Plan (PCIP) program to make health insurance available to Americans denied coverage by private insurance companies because of a pre-existing condition. Coverage for people living with such conditions as diabetes, asthma, cancer, and HIV/AIDS has often been priced out of the reach of most Americans who buy their own insurance, and this has resulted in a lack of coverage for millions. The temporary program covers a broad range of health benefits and is designed as a bridge for people with pre-existing conditions who cannot obtain health insurance coverage in today’s private insurance market. To learn more, visit PCIP.gov or HealthCare.gov.

Note: * Massachusetts and Vermont are guarantee issue states that have already implemented many of the broader market reforms included in the Affordable Care Act that take effect in 2014. Existing commercial plans offering guaranteed coverage at premiums comparable to PCIP are already available in both states.

Context

This is a dataset hosted by the Centers for Medicare & Medicaid Services (CMS). The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CMS's Data using Kaggle and all of the data sources available through the CMS organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Lily Banse on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The U.S. Census Bureau's Small Area Health Insurance Estimates program produces the only source of data for single-year estimates of health insurance coverage status for all counties in the U.S. by selected economic and demographic characteristics. This program is partially funded by the Centers for Disease Control and Prevention's (CDC) Division of Cancer Prevention and Control (DCPC). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the National Breast and Cervical Cancer Early Detection Program (NBCCEDP). For estimation, SAHIE uses statistical models that combine survey data from the American Community Survey (ACS) with administrative records data and Census 2020 data.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. 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. This layer is symbolized to show the count and percent uninsured. 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 (Not all lines of this ACS table are available in this feature 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.

The Affordable Care Act created the new Pre-Existing Condition Insurance Plan (PCIP) program to make health insurance available to Americans denied coverage by private insurance companies because of a pre-existing condition. People living with conditions like diabetes, asthma, cancer, and HIV-AIDS have often been priced out of affordable health insurance options, and this has left millions without insurance.

PCIP is a temporary program that covers a broad range of health benefits and is designed as a bridge for people with pre-existing conditions who cannot obtain health insurance coverage in todays private insurance market. As of May 31, 2011 24,712 Americans had insurance through PCIP and the coverage is making a difference. As of May 31, 2011 data from the federally run PCIP plan shows that of claims paid for the top 20 diagnoses, 30.2 percent were for diagnoses of heart disease and 25.8 percent were for diagnoses of cancer. A range of professional, inpatient and drug treatments were provided to these individuals.

Health insurance coverage rates, from the American Community Survey, 2014 5-year Average, by Zip. For the Detroit Tri-County region. Data Driven Detroit calculated the rates by dividing the total number of insured by the total number of people in each age group.

Most nonelderly Americans purchase health insurance through their employers, which sponsor a limited number of plans. Using a panel dataset representing over ten million insured lives, we estimate employees' preferences for different health plans and use the estimates to predict their choices if more plans were made available to them on the same terms, i.e., with equivalent subsidies and at large-group prices. Using conservative assumptions, we estimate a median welfare gain of 13 percent of premiums. A proper accounting of the costs and benefits of a transition from employer-sponsored to individually-purchased insurance should include this nontrivial gain. (JEL G22, I13, J32)

This feature service contains data from the American Community Survey: 5-year Estimates Subject Tables for all census tracts within Gallatin County. The attributes come from the Selected Characteristics of Health Insurance Coverage in the United States table (S2701). Processing Notes:Data was downloaded from the U.S. Census Bureau and imported into FME to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases. For example: Pct_Uninsured_EduB is the percent of the population that is without health insurance coverage, noninstitutionalized 26 years and over, with a Bachelor's degree or higherData DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyPct_Insured: Percent of the population with health insurance coveragePct_Uninsured: Percent of the population without health insurance coverageRace/Ethinicity:A: AsianAIAN: American Indian or Alaska NativeBAA: Black or African AmericanHL: Hispanic or LatinoNHPI: Native Hawaiian or other Pacific IslanderW: WhiteOther: Some other raceTwo: Two or more racesAnnual Income:IncUnder25k: Household income below $25,000Inc25kto50k:Household income from $25,000 to $49,999Inc50kto75k: Household income from $50,000 to $74,999Inc75kto100k: Household income from $75,000 to $99,999IncOver100k: Household income $100,000 and overEducational Attainment (Civilian noninstitutionalized population 26 years and over):EduB: Bachelor's degree or higherEduHS: High school graduate (includes equivalency)EduNHS: Less than high school graduateEduA: Some college or associate's degreeDownload Selected Characteristics of Health Insurance Coverage in the United States data for Gallatin County, MT. Additional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

Table from the American Community Survey (ACS) B27011 health insurance coverage status and type by employment status. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): B27011Data downloaded from: Census Bureau's Explore Census Data <div s

This survey was sponsored by Harvard school of Public Health & Robert Wood Johnson Foundation and was conducted from July 26-September 2, 2001 among a nationally representative sample of 1206 repsondents 18 years of age and older. Topics dealt with health and health care issues in America and primarily included questions dealing with health insurance.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31092256. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

For the original data source: https://data.census.gov/table/ACSST5Y2023.S2701. Layer published for the Equity Explorer, a web experience developed by the LA County CEO Anti-Racism, Diversity, and Inclusion (ARDI) initiative in collaboration with eGIS and ISD. Visit the Equity Explorer to explore health insurance status and other equity related datasets and indices, including the COVID Vulnerability and Recovery Index. Health insurance status for census tracts in LA County from the US Census American Communities Survey (ACS), 2023. Estimates are based on 2020 census tract boundaries, and tracts are joined to 2021 Supervisorial Districts, Service Planning Areas (SPA), and Countywide Statistical Areas (CSA). For more information about this dataset, please contact egis@isd.lacounty.gov.

Key Table Information.Table Title.Direct-Purchase Health Insurance by Sex by Age.Table ID.ACSDT1Y2024.B27005.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, and towns...

Local, state, tribal, and federal agencies use health insurance coverage data to plan government programs, determine eligibility criteria, and encourage eligible people to participate in health insurance programs. This map shows where those with no health insurance live. Map opens in Houston, TX. Use the bookmarks or search to see other cities. Zoom out to see map render data for counties and states.

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...

Overview

This is a subset of the NPHA dataset filtered down to develop and validate machine learning algorithms for predicting the number of doctors a survey respondent sees in a year. This dataset’s records represent seniors who responded to the NPHA survey.

Dataset Information

For what purpose was the dataset created? The National Poll on Healthy Aging dataset was created to gather insights on the health, healthcare, and health policy issues affecting Americans aged 50 and older. By focusing on the perspectives of older adults and their caregivers, the University of Michigan aimed to inform the public, healthcare providers, policymakers, and advocates about the various aspects of aging. This includes topics like health insurance, household composition, sleep issues, dental care, prescription medications, and caregiving, thereby providing a comprehensive understanding of the health-related needs and concerns of the older population.

Who funded the creation of the dataset? The dataset was funded by AARP and Michigan Medicine, the University of Michigan's academic medical centre.

What do the instances in this dataset represent? Each row represents a survey respondent.

Does the dataset contain data that might be considered sensitive in any way? Yes. There is information about race/ethnicity, gender, age.

Was there any data preprocessing performed? For this subset of the original NPHA dataset we chose 14 features related to health and sleep to use for the prediction task. We then removed all survey respondents with missing responses for any of the chosen features.

Has Missing Values? No

Introductory Paper National Poll on Healthy Aging (NPHA) By Malani, Preeti N., Kullgren, Jeffrey, and Solway, Erica. 2017 Published in Inter-university Consortium for Political and Social Research

Additional Variable Information

Class Labels

Number of Doctors Visited: The total count of different doctors the patient has seen = { 1: 0-1 doctors 2: 2-3 doctors 3: 4 or more doctors }

Age: The patient's age group = { 1: 50-64 2: 65-80 }

Physical Health: A self-assessment of the patient's physical well-being = { -1: Refused 1: Excellent 2: Very Good 3: Good 4: Fair 5: Poor }

Mental Health: A self-evaluation of the patient's mental or psychological health = { -1: Refused 1: Excellent 2: Very Good 3: Good 4: Fair 5: Poor }

Dental Health: A self-assessment of the patient's oral or dental health= { -1: Refused 1: Excellent 2: Very Good 3: Good 4: Fair 5: Poor }

Employment: The patient's employment status or work-related information = { -1: Refused 6 1: Working full-time 2: Working part-time 3: Retired 4: Not working at this time }

Stress Keeps Patient from Sleeping: Whether stress affects the patient's ability to sleep = { 0: No 1: Yes }

Medication Keeps Patient from Sleeping: Whether medication impacts the patient's sleep = { 0: No 1: Yes }

Pain Keeps Patient from Sleeping: Whether physical pain disturbs the patient's sleep = { 0: No 1: Yes }

Bathroom Needs Keeps Patient from Sleeping: Whether the need to use the bathroom affects the patient's sleep = { 0: No 1: Yes }

Unknown Keeps Patient from Sleeping: Unidentified factors affecting the patient's sleep = { 0: No 1: Yes }

Trouble Sleeping: General issues or difficulties the patient faces with sleeping = { 0: No 1: Yes }

Prescription Sleep Medication: Information about any sleep medication prescribed to the patient = { -1: Refused 1: Use regularly 2: Use occasionally 3: Do not use }

Race: The patient's racial or ethnic background = { -2: Not asked -1: REFUSED 1: White, Non-Hispanic 2: Black, Non-Hispanic 3: Other, Non-Hispanic 4: Hispanic 5: 2+ Races, Non-Hispanic } Gender: The gender identity of the patient = { -2: Not asked -1: REFUSED 1: Male 2: Female }

Table from the American Community Survey (ACS) 5-year series on disabilities and health insurance related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes C21007 Age by Veteran Status by Poverty Status in the Past 12 Months by Disability Status, B27010 Types of Health Insurance Coverage by Age, B22010 Receipt of Food Stamps/SNAP by Disability Status for Households. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): C21007, B27010, B22010Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within Arc

Key Table Information.Table Title.Health Insurance Coverage Status by Sex by Age.Table ID.ACSDT1Y2024.B27001.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, and towns...

About Dataset

The dataset contains 2 .csv files This file contains various demographic and health-related data for different regions. Here's a brief description of each column:

File 1st

• avganncount: Average number of cancer cases diagnosed annually.

• avgdeathsperyear: Average number of deaths due to cancer per year.

• target_deathrate: Target death rate due to cancer.

• incidencerate: Incidence rate of cancer.

• medincome: Median income in the region.

• popest2015: Estimated population in 2015.

• povertypercent: Percentage of population below the poverty line.

• studypercap: Per capita number of cancer-related clinical trials conducted.

• binnedinc: Binned median income.

• medianage: Median age in the region.

• pctprivatecoveragealone: Percentage of population covered by private health insurance alone.

• pctempprivcoverage: Percentage of population covered by employee-provided private health insurance.

• pctpubliccoverage: Percentage of population covered by public health insurance.

• pctpubliccoveragealone: Percentage of population covered by public health insurance only.

• pctwhite: Percentage of White population.

• pctblack: Percentage of Black population.

• pctasian: Percentage of Asian population.

• pctotherrace: Percentage of population belonging to other races.

• pctmarriedhouseholds: Percentage of married households. birthrate: Birth rate in the region.

File 2nd

This file contains demographic information about different regions, including details about household size and geographical location. Here's a description of each column:

• statefips: The FIPS code representing the state.

• countyfips: The FIPS code representing the county or census area within the state.

• avghouseholdsize: The average household size in the region.

• geography: The geographical location, typically represented as the county or census area name followed by the state name.

Each row in the file represents a specific region, providing details about household size and geographical location. This information can be used for various demographic analyses and studies.

Medicaid and the Children's Health Insurance Program (CHIP) provide health insurance coverage to approximately 85 million Americans as of late 2023. There is substantial variation in eligibility criteria, application procedures, premiums, and other programmatic characteristics across states and over time. Analyzing changes in Medicaid policies is important for state and federal agencies and other stakeholders, but such analysis requires data on historical programmatic characteristics that are often not available in a form ready for quantitative analysis. Our objective is to fill this gap by synthesizing existing qualitative policy data to create a new data resource that facilitates Medicaid policy research. Our source data were the 50-state surveys of Medicaid and CHIP eligibility, enrollment, and cost-sharing policies conducted near annually by KFF since 2000, which we originally coded through 2020. These reports are a rich source of point-in-time information but not operationalized for quantitative analysis. Through a review of the measures captured in the KFF surveys, we developed five Medicaid policy domains with 122 measures in total, with each coded by state-quarter—1) eligibility (28 measures), 2) enrollment and renewal processes (39), 3) premiums (16), 4) cost-sharing (26), and 5) managed care (13). 1 (June 28, 2023) – original version 2 (March 14, 2024) – re-reviewed, corrected (where necessary), and extended five income eligibility measures (inc_inf, inc_child_1_5, inc_child_6_18, inc_parents, and inc_preg) through January 2023

Context

In the United States, Medicare is a single-payer, national social insurance program administered by the U.S. federal government since 1966. It provides health insurance for Americans aged 65 and older who have worked and paid into the system through the payroll tax. Source: https://en.wikipedia.org/wiki/Medicare_(United_States)

Content

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarizes the utilization and payments for procedures, services, and prescription drugs provided to Medicare beneficiaries by specific inpatient and outpatient hospitals, physicians, and other suppliers. The dataset includes the following data.

Common inpatient and outpatient services All physician and other supplier procedures and services All Part D prescriptions. Providers determine what they will charge for items, services, and procedures provided to patients and these charges are the amount that providers bill for an item, service, or procedure.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:medicare

https://cloud.google.com/bigquery/public-data/medicare

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What is the total number of medications prescribed in each state?

What is the most prescribed medication in each state?

What is the average cost for inpatient and outpatient treatment in each city and state?

Which are the most common inpatient diagnostic conditions in the United States?

Which cities have the most number of cases for each diagnostic condition?

What are the average payments for these conditions in these cities and how do they compare to the national average?