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available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells.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.

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 Primary Health Network (PHN) …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 Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. 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)

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 the fiscal year of 2024, public sector health insurers across India recorded insurance premiums worth about 410 billion Indian rupees. That same year, private sector health insurers saw premiums aggregating to over 345 billion rupees. In total, the value of health insurance premiums reached about one trillion Indian rupees for the first time.

The number of people with private health insurance which is additional health cover to that provided under Medicare, to reimburse all or part of the cost of hospital or other medical services …Show full descriptionThe number of people with private health insurance which is additional health cover to that provided under Medicare, to reimburse all or part of the cost of hospital or other medical services incurred by an individual, 2014-15 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/ 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. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2017): ; 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, 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 Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

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 MarketScan health claims database is a compilation of nearly 110 million patient records with information from more than 100 private insurance carriers and large self-insuring companies. Public forms of insurance (i.e., Medicare and Medicaid) are not included, nor are small (< 100 employees) or medium (1000 employees). We excluded the relatively few (n=6735) individuals over 65 years of age because Medicare is the primary insurance of U.S. adults over 65. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

This table shows the number of uninsured people against health costs on a reference date, broken down by origin, gender and age. With effect from 2006, the number of uninsured people has been defined as the number of people who are registered in the GBA (Municipal Personal Records Database) and who are obliged to take out insurance under the Health Insurance Act, but who have not taken out health insurance as referred to in that law. The limitation to the number of uninsured persons in the GBA means that uninsured persons among illegal immigrants, cross-border workers who live abroad and work in the Netherlands and Dutch nationals who live abroad (for example the so-called pensioners) are not taken into account. Data available: 2006-2010 Status of the figures: The figures in the table for 2006 up to and including 2009 are final figures. The figures for 2010 are provisional figures. Changes as of June 6, 2012: This table has been discontinued. When will new numbers come out? This table has been discontinued. For further information see Statistics Netherlands starts new series Uninsured against health insurance.

Sri Lanka LK: Domestic Private Health Expenditure: % of Current Health Expenditure data was reported at 45.151 % in 2015. This records an increase from the previous number of 41.799 % for 2014. Sri Lanka LK: Domestic Private Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 39.731 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 45.151 % in 2015 and a record low of 31.971 % in 2003. Sri Lanka LK: Domestic Private Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Health Statistics. Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

This explorer provides sample premium information for individual ACA-compliant health insurance plans available to Iowans for 2025 based on age, rating area and metal level. These are premiums for individuals, not families. Please note that not every plan ID is available in every county. On or after November 1, 2024, please go to www.healthcare.gov to determine if your plan is available in the county you reside in.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. As of August 2024, CMS is no longer releasing the “HealthCare.gov” metrics. Historical data between July 2023-July 2024 will remain available. The “HealthCare.gov Transitions” metrics, which are the CAA, 2023 required metrics, will continue to be released.
Sources: HealthCare.gov application and policy data through May 5, 2024, and T-MSIS Analytic Files (TAF) through March 2024 (TAF version 7.1 with T-MSIS enrollment through the end of March 2024). Data include consumers in HealthCare.gov states where the first unwinding renewal cohort is due on or after the end of reporting month (state identification based on HealthCare.gov policy and application data). State data start being reported in the month when the state's first unwinding renewal cohort is due. April data include Arizona, Arkansas, Florida, Indiana, Iowa, Kansas, Nebraska, New Hampshire, Ohio, Oklahoma, South Dakota, Utah, West Virginia, and Wyoming. May data include the previous states and the following new states: Alaska, Delaware, Georgia, Hawaii, Montana, North Dakota, South Carolina, Texas, and Virginia. June data include the previous states and the following new states: Alabama, Illinois, Louisiana, Michigan, Missouri, Mississippi, North Carolina, Tennessee, and Wisconsin. July data include the previous states and Oregon. All HealthCare.gov states are included in this version of the report.
Notes:

1. This table includes Marketplace consumers who: 1) submitted a HealthCare.gov application on or after the start of each state’s first reporting month; and 2) who can be linked to an enrollment record in TAF that shows Medicaid or CHIP enrollment between March 2023 and the latest reporting month.
2. Cumulative counts show the number of unique consumers from the included population who had a Marketplace application submitted or a HealthCare.gov Marketplace policy on or after the start of each state’s first reporting month through the latest reporting month. Net counts show the difference between the cumulative counts through a given reporting month and previous reporting months.
3. The data used to produce the metrics are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. For example, the April 2023 reporting period extends from Monday, April 3 through Sunday, April 30.
4. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified.
5. Data do not represent Marketplace consumers who had a confirmed Medicaid/CHIP loss. Future reporting will look at coverage transitions for people who lost Medicaid/CHIP.
6. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations.

Data notes:

1. Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data on 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in December 2023, data from December 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through November 2023.
2. The report may include negative 'net counts,' which reflect that there were cumulatively fewer counts from one month to the next.
3. Wyoming has negative ‘net counts’ for most of its metrics in March 2024, including 'Marketplace Consumers with Previous Medicaid or CHIP Enrollment.' Negative counts reflect that there were cumulatively fewer individual counts from one month to the next. The net percentages have been replaced with an asterisk for March 2024 due to the uninterpretable values.

Percentages shown are of total Marketplace consumers with previous Medicaid or CHIP enrollment.
Some cells have a value of "NR" or not reported. State data starts being reported in the month when the state's first unwinding renewal cohort is due.

APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; NR: Not reported; QHP: Qualified Health Plan

This dataset contains counts for Medicaid recipients served by month in Iowa, starting with month ending 1/31/2011.

Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.

The objective of this project is to study the willingness to pay (WTP) for health insurance (HI) of individuals working in the informal sector in Sierra Leone, using a purposely-designed survey of a representative sample of this sector. We elicit the WTP using the Double-Bounded Dichotomous Choice with Follow Up method. We also examine the factors that are positively and negatively associated with the likelihood of the respondents to answer affirmatively to joining a HI scheme and to paying three different possible premiums, to join the HI scheme. We additionally analyze the individual and household characteristics associated with the maximum amount the household is willing to pay to join the HI scheme. The results indicate that the average WTP for the HI is 20,237.16 SLL (3.6 USD) per adult but it ranges from about 14,000 SLL (2.5 USD) to about 35,000 SLL (6.2 USD) depending on region, occupation, household and respondent characteristics. The analysis of the maximum WTP indicates that living outside the Western region and working in farming instead of petty trade are associated with a decrease in the maximum premium respondents are WTP for the HI scheme. Instead, the maximum WTP is positively associated to being a driver or a biker; having secondary or tertiary education (as opposed to not having any); the number of pregnant women in the household; having a TV; and, having paid for the last medical requirement. In summary, the various analyses show that a premium for the HI package could be set at approximately 20,000 SLL (3.54 USD) but also that establishing a single premium for all individuals in the informal sector could be risky. The efficient functioning of a HI scheme relies on covering as much of the population as possible, in order to spread risks and make the scheme viable. The impact of the various population characteristics raises the issue of how to rate premiums. In other words, setting a premium that may be too high for a big proportion of the population could mean losing many potential enrollees and might have viability consequences for the operation of the scheme. The data were obtained by running a Discrete Choice Experiment (DCE) conducted in the Northern and Western regions in 2013. The DCE was used to learn households' preferences regarding a HIS by allowing them to choose to participate or not in a set of HISs described by their distinct attributes and their levels, including cost. Statistics Sierra Leone (SSL) designed the sample and selected locations for this study based on recent pre-census data containing settlement names, population, and household sizes. A two-stage stratified random sampling method was used to identify the households. The first stage involved stratifying the population by region/district, and the second by rural and urban location in each district. The purpose was to have a representative sample of informal sector households in both villages (rural areas) and major towns (urban areas). Since the decision to purchase health care is more often a household decision rather than an individual one, the household was chosen as the unit of reference. SSL calculated the sample size of 1670, considering an informal sector population of 1,380,110 individuals, with the objective to achieve a 95% confidence interval around participation in the HIS and a margin of error of 2.4%. Due to failure to pass the dominance test (understanding of the questions) and incomplete data during the questionnaire administration, 1,458 households' data were used for the final analysis, which provided 39,366 observations for the analysis of nine choice sets with three alternatives.

Explore World Bank Health, Nutrition and Population Statistics dataset featuring a wide range of indicators such as School enrollment, UHC service coverage index, Fertility rate, and more from countries like Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

School enrollment, tertiary, UHC service coverage index, Wanted fertility rate, People with basic handwashing facilities, urban population, Rural population, AIDS estimated deaths, Domestic private health expenditure, Fertility rate, Domestic general government health expenditure, Age dependency ratio, Postnatal care coverage, People using safely managed drinking water services, Unemployment, Lifetime risk of maternal death, External health expenditure, Population growth, Completeness of birth registration, Urban poverty headcount ratio, Prevalence of undernourishment, People using at least basic sanitation services, Prevalence of current tobacco use, Urban poverty headcount ratio, Tuberculosis treatment success rate, Low-birthweight babies, Female headed households, Completeness of birth registration, Urban population growth, Antiretroviral therapy coverage, Labor force, and more.

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

This table shows the average costs per person for care that falls under the Health Insurance Act (Zvw), per type of care provided, and the share of people for whom costs have been declared. The data is broken down by age, gender and origin. This only concerns care from the basic package (basic insurance). This table does not include the following costs: - healthcare costs that fall outside the Zvw and are reimbursed through another insurance policy, for example through a supplementary insurance policy; - healthcare costs that do not fall under the Zvw but under another legal framework, for example AWBZ/Wlz, Wmo or Youth Act; - healthcare costs that fall outside the Zvw and are paid through personal payments. The healthcare costs include the costs that are ultimately paid by the insured themselves due to the mandatory or voluntary deductible. Only if the insured person has received an invoice and has not submitted it to the insurer, for example because the deductible has not been reached, the costs are not included in the figures. Other personal payments, such as for insured care provided by a provider not contracted by the insurer, or statutory personal payments, such as a maximum number of treatments or additional payments per treatment, are not included in the figures. This table relates to the population of persons who meet each of the following conditions: - they have been registered in the Municipal Personal Records Database (BRP) for at least 1 day during the year in question; - they have been insured for basic insurance under the Zvw. Data available from: 2020 Status of the figures: The figures for 2020 are final. Figures for reporting year 2021 are provisional. Changes as of September 8, 2023: The 2020 figures have been corrected. Due to a processing error, the healthcare costs of two percent of insured people were mistakenly set to zero. The average costs and the percentage of people with healthcare costs are both approximately two percent higher after correction. Changes as of September 1, 2023: Provisional figures for 2021 have been added, the provisional figures for 2020 have been made final. When will new figures be available? The provisional figures for 2022 will be published in the fourth quarter of 2024 and the figures for 2021 will be finalized.

The USRDS is the largest and most comprehensive national ESRD surveillance system in the US (Collins et al., 2015). The USRDS contains data on all ESRD cases in the US through the Medical Evidence Report CMS-2728 which is mandated for all new patients diagnosed with ESRD (Foley and Collins, 2013). Detailed information about the USRDS can be found on their website (http://www.usrds.org). The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data stored as csv files. This dataset is associated with the following publication: Kosnik, M., D. Reif, D. Lobdell, T. Astell-Burt, X. Feng, J. Hader, and J. Hoppin. Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 14(3): e0214094, (2019).

Users can download public-access datasets regarding topics such as: health insurance coverage, access to care, child well-being , utilization of services, and health status. BackgroundThe State and Local Area Integrated Telephone Survey (SLAITS) was developed by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) and is sponsored by both public and private organizations. SLAITS provides health care data at state and local levels for the development and implementation of health programs and policies. Survey research topics include health insurance covera ge, access to care, perceived health status, utilization of services, and measurement of child well-being. Surveys moderated by SLAITS include: Health, Child Well-Being and Welfare, National Survey of Early Childhood Health, National Survey of Children with Special Health Care Needs, National Survey of Children’s Health, National Asthma Survey, National Survey of Adoptive Parents, Survey of Adult Transition and Health, National Survey of Adoptive Parents of Children with Special Health Care Needs. This data can help users to track changes arising from health and welfare services. User FunctionalityUsers can download public-access datasets to compare responses across states and to the United States. Data NotesThe SLAITS random-digit dial (RDD) and sampling frame are the same as the ongoing National Immunization Survey (NIS) operated by the CDC. The survey is composed of standardized questions to facilitate comparison across states. SLAITS also includes customized questions for each state to address state-specific data needs. SLAITS targets population subgroups including people with specific health conditions and low-income families. There is a rapid turnaround between data collection and availability, which enables users to track changes resulting from health and welfare services. Data collection dates vary and are indicated with each survey. Depending on the survey, data are available on national, state, and regional levels.

Introduction

The dataset comprises over 12,000 chat conversations, each focusing on specific Healthcare related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Healthcare topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Healthcare use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in English Healthcare interactions. This diversity ensures the dataset accurately represents the language used by English speakers in Healthcare contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to English Healthcare interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Healthcare customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

This structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.

Data Format and Structure

The dataset is available in JSON, CSV, and TXT formats, with each conversation containing attributes like participant identifiers and chat

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Many factors influence health plan choices. Classical individual-level determinants include socioeconomic and health-related characteristics, and risk attitudes. However, little is known to what extent personality traits can determine insurance choices. Using representative survey data from Switzerland, we investigate the associations between choices of health plans and traditional individual factors as well as personality traits. We employ dominance analysis to explore the relative importance of the different predictors. We find that personality traits play an at least equally important role in predicting health plan choices as common factors like age, health status, and income. Our results have implications regarding recent efforts to empower people in making better health plan choices and support theoretical models that integrate insights from behavioral sciences.