Abstract

Taiwan launched a single-payer National Health Insurance program on March 1, 1995.

Documentation

Taiwan launched a single-payer National Health Insurance program on March 1, 1995. As of 2014, 99.9% of Taiwan\342\200\231s population were enrolled. Foreigners in Taiwan are also eligible for this program. The database of this program contains registration files and original claim data for reimbursement. Large computerized databases derived from this system by the National Health Insurance Administration (the former Bureau of National Health Insurance, BNHI), Ministry of Health and Welfare (the former Department of Health, DOH), Taiwan and maintained by the National Health Research Institutes, Taiwan, are provided to scientists in Taiwan for research purposes.

An article describing these data in greater detail can be found here: Lessons From the Taiwan National Health Insurance Research Database

Patient characteristics Individuals enrolled in the Taiwanese national healthcare system

Data overview Data categories Inpatient Outpatient Pharmacy data Over-the-counter drugs Chinese medicine Clinician information Hospital information

Linkages include Household Birth certificate Death certificate Cancer Immunization record Reportable infectious disease

Notes If you are interested in a collaboration working with these data, please contact the Dr Ann Hsing at .

The Analysis of Longitudinal Claims Databases (R1 Part B): Effect of Variation in Health Coverage, Employment, and Community Resources on Adverse Events and Healthcare Costs and Utilization, United States is the second of a three-part project that examined claims data from Medicare, Medicaid, and/or Optum databases to explore aging trajectories, use of preventative services, and healthcare outcomes for individuals with several types of physical disabilities. This study made use of existing national databases to examine various health outcomes among individuals with disability. Using 2007-2016 Medicaid and Medicare Data, the researchers conducted three separate types of analyses: At the state level, examine the effect of variation in health coverage and related health policies on adverse health events and health outcomes among youth and adults with disability. At the county level, examine the variation in employment and community participatory living on adverse health and health outcomes among youth and adult with disability. At the state level, examine the effect of variation in Medicaid long-term care and community centers on health outcomes among youth and adult with disability.

Abstract

The MarketScan Medicare Supplemental Database provides detailed cost, use and outcomes data for healthcare services performed in both inpatient and outpatient settings.

It Include Medicare Supplemental records for all years, and Medicare Advantage records starting in 2020. This page also contains the MarketScan Medicare Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 250 million patients (medical, drug and dental)

%3C!-- --%3E

• Laboratory results

%3C!-- --%3E

• Hospital discharges

%3C!-- --%3E

The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers and Medicare.

Usage

This page contains the MarketScan Medicare Database.

We also have the following on other pages:

• The MarketScan Commercial Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

%3C!-- --%3E

**Starting in 2026, there will be a data access fee for using the full dataset **

(though the 1% sample will remain free to use). The pricing structure and other

**relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

The Flood Insurance Rate Map (FIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The FIRM Database is derived from Flood Insurance Studies (FISs), previously published FIRMs, flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). This file is georeferenced to the Earth's surface using the Geographic Coordinate System (GCS) and North American Datum of 1983.

The McGRAW Life Insurance Data and Lead database is a comprehensive and invaluable resource for accessing individuals actively searching for life insurance. This data is perfect for businesses looking to enhance their direct mail and email campaigns with high-quality, targeted leads.

Why Choose McGRAW Life Insurance Data?

In the competitive life insurance industry, the quality of your leads can make all the difference. McGRAW provides the most accurate and meticulously curated life insurance leads available. Our database includes information on 16 million individuals, both aged and in real-time, ensuring you can connect with prospects at various stages of their life insurance journey.

Features and Benefits:

1. Timely Inquiries:

   • 30, 60, 90-day aged leads: Reach out to individuals who have recently inquired about life insurance.
   • Up to 12 months: Access data on prospects who have shown interest in the past year.

2. High-Quality Leads:

   • Our leads are more than just names and contact numbers; they represent individuals actively seeking life insurance solutions. This means you can eliminate the guesswork and focus your resources on converting qualified leads into loyal customers.

3. Opt-ins specifically for final expense

4. High-intent consumers ready to speak in real time

5. Long-form, form-filled (10+ fields)

6. CPAs lower than industry average

7. National geographic coverage

8. API posting preferred, with same-week setup

9. Multi-level compliance

10 Proven campaign tactics for telemarketing, texting, and emailing

Our database is built on inquiries and quotes gathered over the past 30, 60, 90 days, and up to 12 months. This ensures that you are targeting individuals who are actively searching for life insurance, providing a valuable resource for your marketing strategies.

By partnering with McGRAW, you can strategically target your marketing efforts and achieve unparalleled results. Our life insurance leads enable you to connect with prospects who are already interested in life insurance products, making your campaigns more effective and efficient.

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 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.

Abstract

The Colorado All Payer Claims Database (CO APCD) is a state-legislated, secure health care claims database compliant with all federal privacy laws. It contains nearly 920 million claims for approximately 65 percent of insured lives in Colorado, with information from 42 commercial health insurance plans. Health insurance payers submit data monthly and the entire database is refreshed every other month, so the CO ACPD is continually evolving and being enhanced.

Usage

The dataset was extracted by the Center for Improving Value in Health Care (CIVHC) to support Stanford University COVID Long Haul Analysis. It includes medical, pharmacy, and dental claims files with coverage dates from 01/01/2012 to 08/31/2021.

For more information of CO APCD please refer to https://www.civhc.org/get-data/whats-in-the-co-apcd/

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Metadata access is required to view this section.

These Unemployment Insurance (UI) Data are produced from state-reported data contained in the Unemployment Insurance Data Base (UIDB) as well as UI-related data from outside sources (e.g., Bureau of Labor Statistics data on employment and unemployment and U.S. Department of Treasury data on state UI trust fund activities). These represent one way to research and track the employment status of those employed in the arts.

Insurance Statistics: No of Registered Insurers: Mandatory Insurance data was reported at 2.000 Unit in 2023. This records a decrease from the previous number of 3.000 Unit for 2022. Insurance Statistics: No of Registered Insurers: Mandatory Insurance data is updated yearly, averaging 3.000 Unit from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 3.000 Unit in 2022 and a record low of 2.000 Unit in 2023. Insurance Statistics: No of Registered Insurers: Mandatory Insurance data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA001: Insurance Statistics: Key Indicators.

These data were collected to evaluate the Partnership for Long-Term Care (PLTC), a project in which the Robert Wood Johnson Foundation awarded grants to four states -- California, Connecticut, Indiana, and New York -- to work with private insurers to create long-term care insurance policies that were more affordable and provided better protection against impoverishment than those generally available. PLTC policies combine private long-term care insurance with special Medicaid eligibility standards that protect assets of the insured once private insurance benefits are exhausted. This collection was extracted from a database compiled from data submitted by three of the PLTC states: California, Connecticut, and Indiana (New York refused participation). It comprises seven parts, which can be linked together using common identifying variables. Part 1, Insured, describes the characteristics of each issued policy and includes variables covering the effective policy date, policy type, elimination periods, maximum benefits, inflation protection mode, and annualized premium, as well as the year of birth, sex, marital status, and state of residence of the insured. Each insured person is represented by one or more records: one record for the initial PLTC policy, plus a separate record for each change to the policy, if any. Part 2, Changes, consists of policy change records used to update the policies in Part 1. Assessments for benefits are recorded in Part 3. This file includes variables on the assessment date, whether the insured met policy criteria at the time of the assessment, disability date, deficiencies in activities of daily living, and MSQ and Folstein test scores. Parts 4-6 describe service payments and utilization: reporting period (quarter), type of service received by the insured, service amount billed, days of service rendered, and amount of remaining benefits (dollars and days). Part 7 contains information on persons denied application to PLTC policies, including date of denial, type and amount of coverage sought, reason for denial, and the sex, year of birth, and marital status of the applicant.

United States Property & Casualty Insurance: Net Underwriting Gain (Loss ) data was reported at 24.989 USD bn in Dec 2024. This records an increase from the previous number of 6.184 USD bn for Sep 2024. United States Property & Casualty Insurance: Net Underwriting Gain (Loss ) data is updated quarterly, averaging 4.567 USD bn from Mar 2012 (Median) to Dec 2024, with 52 observations. The data reached an all-time high of 24.989 USD bn in Dec 2024 and a record low of -30.037 USD bn in Sep 2023. United States Property & Casualty Insurance: Net Underwriting Gain (Loss ) 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.RG012: Property & Casualty Insurance: Industry Financial Snapshots.

Outcome incidence rates for patients initiating authorized generics (AGs) versus generics, and patients switching from brand-name products to AGs versus generics, after 1:1 propensity score matching in each database.

Basemap datasets comprise five of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the framework themes of orthoimagery and elevation are packaged in separate NFIP Metadata Profiles): cadastral, geodetic control, governmental unit, transportation, and hydrography (water areas and lines). These data include an encoding of the geographic extent of the features and a minimal number of attributes needed to identify and describe the features. (Source: Circular A16)

Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data was reported at 57.000 Unit in 2023. This records a decrease from the previous number of 59.000 Unit for 2022. Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data is updated yearly, averaging 55.000 Unit from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 61.000 Unit in 2017 and a record low of 45.000 Unit in 2011. Indonesia Insurance Statistics: Number of Registered Insurers: Life Insurers data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA001: Insurance Statistics: Key Indicators.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).The file is georeferenced to earth's surface using the Lambert Conformal Conic projection and the Arkansas State Plane NAD83 South Zone coordinate system. The specifications for the horizontal control of Base Map data files are consistent with those required for mapping at a scale of 1:24,000

The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

Switzerland Non Life Insurance: Claims Paid: Liability and Motor data was reported at 4,676.000 CHF mn in 2016. This records a decrease from the previous number of 4,802.000 CHF mn for 2015. Switzerland Non Life Insurance: Claims Paid: Liability and Motor data is updated yearly, averaging 4,628.000 CHF mn from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 4,918.000 CHF mn in 2009 and a record low of 3,844.000 CHF mn in 2000. Switzerland Non Life Insurance: Claims Paid: Liability and Motor data remains active status in CEIC and is reported by Swiss Financial Market Supervisory Authority. The data is categorized under Global Database’s Switzerland – Table CH.RG011: Non Life Insurance: Claims Paid.

This data package contains the Physician Quality Reporting System (PQRS), Performance Rates for Individual Eligible Professionals (EP) PQRS, Consumer Assessment of Healthcare Providers and Systems (CAHPS) and Group Practice.

description: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000. This database was developed for the Polk County DFIRM project in 2007 by CF3R/Baker for FEMA Region 6.; abstract: The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000. This database was developed for the Polk County DFIRM project in 2007 by CF3R/Baker for FEMA Region 6.