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 |

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...

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.

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

Exploration Ideas

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

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

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

Data Description

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

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

1. Original versions of the data

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

2. Combined CSV files that contain

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

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

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

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

3. SQLite database

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

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

Context

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

Content

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

Acknowledgements

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

Inspiration

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

This dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS;estimates at the LGA and PHN level were derived from the PHA estimates. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

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

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.

Global Private Health Insurance Coverage by Country, 2023 Discover more data with ReportLinker!

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

Abstract (en): In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. The Oregon Health Insurance Experiment follows and compares those selected in the lottery (treatment group) with those not selected (control group). The data collected and provided here include data from in-person interviews, three mail surveys, emergency department records, and administrative records on Medicaid enrollment, the initial lottery sign-up list, welfare benefits, and mortality. This data collection has seven data files: Dataset 1 contains administrative data on the lottery from the state of Oregon. These data include demographic characteristics that were recorded when individuals signed up for the lottery, date of lottery draw, and information on who was selected for the lottery, applied for the lotteried Medicaid plan if selected, and whose application for the lotteried plan was approved. Also included are Oregon mortality data for 2008 and 2009. Dataset 2 contains information from the state of Oregon on the individuals' participation in Medicaid, Supplemental Nutrition Assistance Program (SNAP), and Temporary Assistance to Needy Families (TANF). Datasets 3-5 contain the data from the initial, six month, and 12 month mail surveys, respectively. Topics covered by the surveys include demographic characteristics; health insurance, access to health care and health care utilization; health care needs, experiences, and costs; overall health status and changes in health; and depression and medical conditions and use of medications to treat them. Dataset 6 contains an analysis subset of the variables from the in-person interviews. Topics covered by the survey questionnaire include overall health, health insurance coverage, health care access, health care utilization, conditions and treatments, health behaviors, medical and dental costs, and demographic characteristics. The interviewers also obtained blood pressure and anthropometric measurements and collected dried blood spots to measure levels of cholesterol, glycated hemoglobin and C-reactive protein. Dataset 7 contains an analysis subset of the variables the study obtained for all emergency department (ED) visits to twelve hospitals in the Portland area during 2007-2009. These variables capture total hospital costs, ED costs, and the number of ED visits categorized by time of the visit (daytime weekday or nighttime and weekends), necessity of the visit (emergent, ED care needed, non-preventable; emergent, ED care needed, preventable; emergent, primary care treatable), ambulatory case sensitive status, whether or not the patient was hospitalized, and the reason for the visit (e.g., injury, abdominal pain, chest pain, headache, and mental disorders). The collection also includes a ZIP archive (Dataset 8) with Stata programs that replicate analyses reported in three articles by the principal investigators and others: Finkelstein, Amy et al "The Oregon Health Insurance Experiment: Evidence from the First Year". The Quarterly Journal of Economics. August 2012. Vol 127(3). Baicker, Katherine et al "The Oregon Experiment - Effects of Medicaid on Clinical Outcomes". New England Journal of Medicine. 2 May 2013. Vol 368(18). Taubman, Sarah et al "Medicaid Increases Emergency Department Use: Evidence from Oregon's Health Insurance Experiment". Science. 2 Jan 2014. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Presence of Common Scales: Patient Health Questionnaire-9 (PHQ-9) Total Severity Score SF-8 Health Survey Physical Component Score SF-8 Health Survey Mental Component Score Framingham Risk Score Response Rates: For the mail surveys, the response rates were 45 percent for the initial survey, 49 percent for the six month survey, and 41 percent for the 12 month survey. For the in-person survey the response rate was 59 percent. The individu...

The National Medical Expenditure Survey (NMES) series provides information on health expenditures by or on behalf of families and individuals, the financing of these expenditures, and each person's use of services. Public Use Tape 16 is the second public use data release from the NMES Health Insurance Plans Survey (HIPS). The purpose of the HIPS was to verify information reported by respondents to two components of the NMES, the Household Survey and the Survey of American Indians and Alaska Natives (SAIAN), about their health insurance coverage. Additional details were also obtained from the employers, unions, and insurance companies through which coverage was provided. Parts 1 and 2 of Public Use Tape 16 are files that can be used to link data to Household Survey policyholders in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: POLICYHOLDERS OF PRIVATE INSURANCE: PREMIUMS, PAYMENT SOURCES, AND TYPES AND SOURCE OF COVERAGE PUBLIC USE TAPE 15. These link files permit identification of the records in the Private Health Insurance Benefit Database (Parts 3-17 of this collection) that describe the specific benefits held by the policyholders. These files also permit linkage to the personal and socioeconomic characteristics for these policyholders found in NATIONAL MEDICAL EXPENDITURE SURVEY, 1987: HOUSEHOLD SURVEY, POPULATION CHARACTERISTICS AND PERSON-LEVEL UTILIZATION, ROUNDS 1-4 PUBLIC USE TAPE 13. Future link files will permit linkage of the Benefit Database to persons in the SAIAN and to dependents of policyholders in the Household Survey. The section files of the Benefit Database, Parts 4-13, contain information on Health Maintenance Organizations (HMOs), copayments, basic coverage, hospital and medical services, cost-containment provisions, major medical coverage, dental care, prescription drugs, vision and hearing care, and Medicare benefits. The schedule files, Parts 14-17, contain specific deductible amounts, dollar benefits, coinsurance provisions, maximum benefits, and benefit periods. Wherever possible, copies of policies or booklets describing the coverage and benefits were obtained in order to abstract this information.

This dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS;estimates at the LGA and PHN level were derived from the PHA estimates. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset provides a realistic look at how important lifestyle and human characteristics are used to calculate health insurance rates. It presents a wide range of people, each characterized by their age, gender, BMI, number of dependents, smoking status, and geographic location, as well as the associated insurance bills they received.

We can find important patterns by examining this dataset, like: Why smokers pay noticeably higher premiums How age and BMI affect medical expenses Whether insurance costs are higher in some areas The connection between charges and family size

The total number of Insurance Affordability Programs (IAPs) applications received through County Human Services Agency offices by submission channel (whether submitted online, in-person, phone, e-mail, mail/fax, other, or unknown) during a reporting period. The “outreach” submission channel may be included in the “other” submission channel commencing with 2016 Quarter 3. IAPs include Medi-Cal or subsidized Qualified Health Plans (QHPs) offered through Covered California. This dataset is part of the public reporting requirements set forth in the California Welfare and Institutions Code 14102.5(1)(A).

This dataset includes the number of eligible individuals selected and enrolled in a Covered California qualified health plans (QHPs) by rating region and by reporting period. California is comprised of 19 rating regions, and each region has different pricing and health insurance options. Covered California reported data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes eligible individuals who selected and enrolled in a QHP, and paid their first premium. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

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.

This dataset includes the total number of newly eligible individuals by Insurance Affordability Program (IAP), by reporting period. IAPs include Medi-Cal, Covered California subsidized and unsubsidized Qualified Health Plans (QHP), and the Medi-Cal Access Program (MCAP). Covered California subsidized and unsubsidized QHP newly eligible data includes those who selected and enrolled in a QHP, and paid their first premium. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

This dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Population Health Area (PHA) …Show full descriptionThis dataset, released April 2017, contains the estimated number of people, aged 18 years and over, with private health insurance hospital cover, 2014-15. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SR" - Indirectly age-standardised ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

This dataset includes the number of eligible individuals selected and enrolled in a Covered California qualified health plans (QHPs) by metal tier and by reporting period. Covered California offers four primary levels of health insurance coverage options: Bronze, Silver, Gold and Platinum, and a minimum coverage plan for those who qualify in addition to metal tiers. Covered California reported data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes eligible individuals who selected and enrolled in a QHP, and paid their first premium. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.