This dataset identifies health care spending at medical services such as hospitals, physicians, clinics, and nursing homes etc. as well as for medical products such as medicine, prescription glasses and hearing aids. This dataset pertains to personal health care spending in general. Other datasets in this series include Medicaid personal health care spending and Medicare personal health care spending.

United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data was reported at 0.781 % in 2013. This records a decrease from the previous number of 0.856 % for 2012. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data is updated yearly, averaging 0.880 % from Dec 1995 (Median) to 2013, with 18 observations. The data reached an all-time high of 1.078 % in 2000 and a record low of 0.724 % in 2008. United States US: Proportion of Population Spending More Than 25% of Household Consumption or Income on Out-of-Pocket Health Care Expenditure: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Poverty. Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure, expressed as a percentage of a total population of a country; ; Wagstaff et al. Progress on catastrophic health spending: results for 133 countries. A retrospective observational study, Lancet Global Health 2017.; Weighted Average;

This dataset is a list of healthcare expenditure categorized by state of residence in 2009 . All health spending is displayed in millions of dollars. Total health spending includes all privately and publicly funded hospital care, physician services, nursing home care, and prescription drugs etc. by state of residence. This spending includes hospital spending and is the total net revenue that is calculated as gross charges less contractual adjustments, bad debts, and charity care.

Context

This indicator calculates the average expenditure on health per person. It contributes to understand the health expenditure relative to the population size facilitating international comparison. The Organization for Economic Co-operation and Development (OECD) defines current health spending as:

Health spending measures the final consumption of health care goods and services (i.e. current health expenditure) including personal health care (curative care, rehabilitative care, long-term care, ancillary services and medical goods) and collective services (prevention and public health services as well as health administration), but excluding spending on investments. Health care is financed through a mix of financing arrangements including government spending and compulsory health insurance (“Government/compulsory”) as well as voluntary health insurance and private funds such as households’ out-of-pocket payments, NGOs and private corporations (“Voluntary”). This indicator is presented as a total and by type of financing (“Government/compulsory”, “Voluntary”, “Out-of-pocket”) and is measured as a share of GDP, as a share of total health spending and in USD per capita (using economy-wide PPPs).

OECD (2020), Health spending (indicator). doi: 10.1787/8643de7e-en (Accessed on 19 September 2020)

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

This dataset categorizes healthcare expenditure by service and by state of residence. The columns are different services and the rows are the states. The categories of services include hospital care, physician and clinical services, other professional services, prescription drugs and other medical nondurables, nursing home care, dental services, home healthcare, medical durables and other health, residential and personal care.

United States US: Health Expenditure: Public: % of GDP data was reported at 8.279 % in 2014. This records an increase from the previous number of 8.045 % for 2013. United States US: Health Expenditure: Public: % of GDP data is updated yearly, averaging 6.710 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 8.279 % in 2014 and a record low of 5.614 % in 1999. United States US: Health Expenditure: Public: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

United States US: Health Expenditure: Public: % of Government Expenditure data was reported at 21.293 % in 2014. This records an increase from the previous number of 20.780 % for 2013. United States US: Health Expenditure: Public: % of Government Expenditure data is updated yearly, averaging 18.457 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 21.293 % in 2014 and a record low of 15.921 % in 1995. United States US: Health Expenditure: Public: % of Government Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Public health expenditure consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and nongovernmental organizations), and social (or compulsory) health insurance funds.; ; World Health Organization Global Health Expenditure database (see http://apps.who.int/nha/database for the most recent updates).; Weighted average;

The current healthcare spending in Southeast Asia was forecast to continuously increase between 2024 and 2029 by in total 98.6 billion U.S. dollars (+52.88 percent). After the fifteenth consecutive increasing year, the spending is estimated to reach 285 billion U.S. dollars and therefore a new peak in 2029. Notably, the current healthcare spending of was continuously increasing over the past years.According to Worldbank health spending includes expenditures with regards to healthcare services and goods. The spending refers to current spending of both governments and consumers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending in countries like Central Asia and Southern Asia.

This dataset tracks the updates made on the dataset "DEV DQS Personal healthcare spending: United States" as a repository for previous versions of the data and metadata.

The CarePrecise U.S. HCP/HCO Collection Dataset includes deep data on all 6.7 million U.S. HIPAA-covered healthcare practitioners and organizations. Monthly full updates. Includes linkages between the individual practitioners and their practice groups, hospitals, and hospital systems. Licensing plans are available for basic (internal use), derivative products, and redistribution. Data updates are delivered quarterly or monthly to suit customer need; FTP push is available, standard delivery is via CDN. Single download for evaluation is available. CarePrecise is a leader in the fields of HCP/HCO data, supplying provider data to the industry since 2008. Note regarding pricing: The Collection price shown in Pricing is separate from email addresses. Email addresses are priced as low as $0.075 per, based on volume. Pricing shown is without derivative product (DP) licensing for use in web applications; DP license ranges in price from $1,900/year to $9,000/year on top of data purchase, based on application and overall exposure estimate. DP license is sold in two-year term and requires a license agreement.

The Health Care Satellite Account measures U.S. health care spending to treat diseases, like cancer or diabetes, rather than by place of service, like a hospital or doctor's office, offering a new way of analyzing the health care sector.

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.

Context

This dataset simply combines publicly available data to characterise a country based on healthcare factors, economy, government and demographics.

Content

All data are given per 100.000 inhabitants where this is appropriate scores are given as absolute values and so are spending and demographics. Each row represents one country. Data that is included covers the following topics:

Healthcare: - Staff including: Nurses and Physicians per 100.000 inhabitants - Infrastructure including: Beds, Chnage of beds between 2018 and 2019 and the change of bed numbers since 2013, Intensive Care Unit (ICU) beds, ventilators and Extra Corporal Membrane Oxygenation (ECMO), machines per 100.000 inhabitants - Total spending on healthcare in US dollars per capita.

Demographics: - The median age for entire population and each gender - The percentage of the population within age brackets - Total population - Population per km2 - Population change between 2018 and 2019

Government The used scores are from the Economist intelligence unit and describe how democratic a country is and how the government works. These can be used to compare countries based on their government type.

Acknowledgements

All data is publicly available and just has been brought together in one place. The sources are:

• Population densities

• Populations

• ECMO machines

• Hospital beds

• Number of physicians

• Health expenditure per capita

• Democracy indexes

• Median age

• OECD nurses per capita

Inspiration

These data are meant as metadata to decide which countries are comparable. I am working on healthcare data so the inspiration is to compare health statistics between countries and make an informed decision about how comparable they are. Could be used for any non healthcare related task as well.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

This statistic shows a ranking of the estimated per capita consumer spending on healthcare in 2020 in Latin America and the Caribbean, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The global current health expenditure as a share of the GDP in was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the seventh consecutive increasing year, the share is estimated to reach **** percent and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. It is depicted here in relation to the total gross domestic product (GDP) of the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current health expenditure as a share of the GDP in countries like North America and the Americas.

This layer shows the average amount spent on health insurance per household in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The pop-up is configured to include the following information for each geography level: Average annual health insurance spending per householdBreakdown of average annual health insurance spendingBreakdown of Blue Cross/Blue Shield insurance spendingThis map shows Esri's 2016 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2016 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in categories including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabulary