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.

In 2023, the Health Insurance Market reached a value of USD 2,476 billion, and it is projected to surge to USD 3,974 billion by 2030.

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

Health Insurance Coverage reports the prevalance of Health Insurance coverage disaggregated by age group.

The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (2017, 2018 and 2019) concerning non-life health insurance portfolio. This dataset comprises 228,711 rows and 42 columns. Each row signifies a insured (individual) policy, while each column represents a distinct variable.

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

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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...

As of 2024, nearly *** million people in the United States had some kind of health insurance, a significant increase from around *** million insured people in 2010. However, as of 2024, there were still approximately ** million people in the United States without any kind of health insurance. Insurance coverage The United States does not have universal health insurance, and so health care cost is mostly covered through different private and public insurance programs. In 2021, almost ** percent of the insured population of the United States were insured through employers, while **** percent of people were insured through Medicaid, and **** percent of people through Medicare. As of 2022, about *** percent of people were uninsured in the U.S., compared to ** percent in 2010. The Affordable Care Act The Affordable Care Act (ACA) significantly reduced the number of uninsured people in the United States, from **** million uninsured people in 2013 to **** million people in 2015. However, since the repeal of the individual mandate the number of people without health insurance has risen. Healthcare reform in the United States remains an ongoing political issue with public opinion on a Medicare-for-all plan consistently divided.

Insurance Dataset for Predicting Health Insurance Premiums in the US" is a collection of data on various factors that can influence medical costs and premiums for health insurance in the United States. The dataset includes information on 10 variables, including age, gender, body mass index (BMI), number of children, smoking status, region, income, education, occupation, and type of insurance plan. The dataset was created using a script that generated a million records of randomly sampled data points, ensuring that the data represented the population of insured individuals in the US. The dataset can be used to build and test machine learning models for predicting insurance premiums and exploring the relationship between different factors and medical costs.

In 2020, around **** percent of the U.S. population had private health insurance coverage. This share slightly decreased to **** percent in 2024. Medicare and Medicaid together provided healthcare coverage to approximately ** percent of the population in the United States. U.S. population with and without health insurance In 2022, over half of the U.S. population had health insurance coverage through their place of employment, around 54.5 percent. Approximately 35 percent had coverage through some form of government plan in the same year. While still low, the U.S. population without health insurance has decreased slightly from the previous year. A large portion of those without health insurance are between 19 and 25 years of age. Approximately ** percent of adults in this age group did not have health insurance in 2021. Health expenditure The United States spent approximately ****** U.S. dollars per capita on health in 2022 while in comparison, the Canadian government expended some ***** U.S. dollars per capita in the same year. However, higher health spending did not equate to a better health system or outcomes and when ranked with other comparable high-income countries, the U.S. came in last on nearly all health performance categories from access of care to health outcomes.

Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

The percentage of people in the United States with health insurance has increased over the past decade with a noticeably sharp increase in 2014 when the Affordable Care Act (ACA) was enacted. As of 2024, around ** percent of people in the United States had some form of health insurance, compared to around ** percent in 2010. Despite the increases in the percentage of insured people in the U.S., there were still over ** million people in the United States without health insurance as of 2024. Insurance coverage Health insurance in the United States consists of different private and public insurance programs such as those provided by private employers or those provided publicly through Medicare and Medicaid. Almost half of the insured population in the United States were insured privately through an employer as of 2021, while **** percent of people were insured through Medicaid, and **** percent through Medicare . The Affordable Care Act The Affordable Care Act (ACA), enacted in 2014, has significantly reduced the number of uninsured people in the United States. In 2014, the percentage of U.S. individuals with health insurance increased to almost ** percent. Furthermore, the percentage of people without health insurance reached an all time low in 2022. Public opinion on healthcare reform in the United States remains an ongoing political issue with public opinion consistently divided.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

United States Health Insurance: Premium Per Member Per Month data was reported at 364.000 USD in Sep 2024. This stayed constant from the previous number of 364.000 USD for Jun 2024. United States Health Insurance: Premium Per Member Per Month data is updated quarterly, averaging 262.000 USD from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 364.000 USD in Sep 2024 and a record low of 178.000 USD in Sep 2013. United States Health Insurance: Premium Per Member Per Month data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

The Data set is National Health Insurance Scheme (NHIS) data with excess zero count. The data were obtained from health Ogun State health facility, Ota, Ogun State. Claims made by 116 users of National Health Insurance Scheme (NHIS) user's from September 2016 to July 2017. Response variable is Number of Encounter, while predictors are Sex, Age of patients, number of drugs prescribed (DrugAdm), and Number of drugs out of stock (DrugOS). The data is over-dispersed with dispersion parameter of 1.4980 since the dispersion parameter is greater than 1. Model such as Bayesian and frequentist techniques can be used to model the data. Such work can be found in the study by Adesina et. al (2017), Adesina et. al (2018), Adesina et al (2019), Dare et al (2019), Adesina et. al (2021).

In 2024, ***** percent of the total population of the United States were uninsured. However, **** of all individuals in the United States had employer-sponsored health coverage. This statistic depicts the distribution of health insurance status of the total population in the United States for 2024

In 2024, nearly ** percent of people in the United States had public health insurance, the share of people with private health insurance has gradually increased in the provided time interval. This statistic contains data on the number of U.S. Americans with government health insurance coverage from 1997 to 2024.

This dataset contains information on the relationship between personal attributes (age, gender, BMI, family size, smoking habits), geographic factors, and their impact on medical insurance charges. It can be used to study how these features influence insurance costs and develop predictive models for estimating healthcare expenses. Age: The insured person's age.

Sex: Gender (male or female) of the insured.

BMI (Body Mass Index): A measure of body fat based on height and weight.

Children: The number of dependents covered.

Smoker: Whether the insured is a smoker (yes or no).

Region: The geographic area of coverage.

Charges: The medical insurance costs incurred by the insured person.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

A dataset-style roundup of U.S. large group health insurance statistics for 2025–2026, covering premiums, employer/employee contributions, offer and take-up rates, self-funded vs fully insured adoption, stop-loss usage, compliance benchmarks, prescription drug spend, telehealth adoption, cost-sharing, and 2026 outlook.

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