This data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by managed care participation (comprehensive managed care, primary care case management, MLTSS, including PACE, behavioral health organizations, nonmedical prepaid health plans, medical-only prepaid health plans, and other). There are three metrics presented: (1) the number of beneficiaries ever enrolled in each managed care plan type over the year (duplicated count); (2) the number of beneficiaries enrolled in each managed care plan type as of an individual’s last month of enrollment (duplicated count); and (3) average monthly enrollment in each managed care plan type. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some cells have a value of “DS”. Some states have serious data quality issues, making the data unusable for calculating these measures. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state and year are considered unusable or of high concern based on DQ Atlas thresholds for the topics Enrollment in CMC, Enrollment in PCCM Programs, and Enrollment in BHO Plans. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Context

This public dataset was created by the Centers for Medicare & Medicaid Services. The data summarize counts of enrollees who are dually-eligible for both Medicare and Medicaid program, including those in Medicare Savings Programs. “Duals” represent 20 percent of all Medicare beneficiaries, yet they account for 34 percent of all spending by the program, according to the Commonwealth Fund . As a representation of this high-needs, high-cost population, these data offer a view of regions ripe for more intensive care coordination that can address complex social and clinical needs. In addition to the high cost savings opportunity to deliver upstream clinical interventions, this population represents the county-by-county volume of patients who are eligible for both state level (Medicaid) and federal level (Medicare) reimbursements and potential funding streams to address unmet social needs across various programs, waivers, and other projects. The dataset includes eligibility type and enrollment by quarter, at both the state and county level. These data represent monthly snapshots submitted by states to the CMS, which are inherently lower than ever-enrolled counts (which include persons enrolled at any time during a calendar year.) For more information on dually eligible beneficiaries

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_cms_dual_eligible_enrollment.

Sample Query

In what counties in Michigan has the number of dual-eligible individuals increased the most from 2015 to 2018? Find the counties in Michigan which have experienced the largest increase of dual enrollment households

duals_Jan_2015 AS ( SELECT Public_Total AS duals_2015, County_Name, FIPS FROM bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program WHERE State_Abbr = "MI" AND Date = '2015-12-01' ),

duals_increase AS ( SELECT d18.FIPS, d18.County_Name, d15.duals_2015, d18.duals_2018, (d18.duals_2018 - d15.duals_2015) AS total_duals_diff FROM duals_Jan_2018 d18 JOIN duals_Jan_2015 d15 ON d18.FIPS = d15.FIPS )

SELECT * FROM duals_increase WHERE total_duals_diff IS NOT NULL ORDER BY total_duals_diff DESC

All states (including the District of Columbia) provide data to the Centers for Medicare & Medicaid Services (CMS) on a range of Medicaid and Children’s Health Insurance Program (CHIP) eligibility and enrollment metrics. These data reflect state-reported information on Medicaid and CHIP eligibility renewals initiated and scheduled for completion during the reporting period. In addition to reporting the outcomes of renewals at the end of each reporting period, states also provide an update on renewals that were reported pending as of the end of a reporting period. For more information on these data, see Sections II and III of the Eligibility Processing Data Report specifications. Notes: Georgia reported data for individuals who continue to be eligible following a change in circumstances and were granted a new 12-month eligibility period during the reporting period, along with data on individuals due for renewal in the month. North Carolina reports renewal outcomes for only initiated renewals scheduled for completion in the report month, and as such, the data do not reflect renewals that should have been completed in the reporting period that the state was unable to initiate by the end of the report month.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by race and ethnicity overall and by three subpopulation topics: scope of Medicaid and CHIP benefits, age group, and eligibility category. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, and Puerto Rico who were enrolled for at least one day in the calendar year. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and the U.S. Virgin Islands are not included. Results shown for the age group and eligibility category subpopulation topics only include enrollees with comprehensive Medicaid and CHIP benefits in the year. Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on information shown in the brief: "Race and ethnicity of the national Medicaid and CHIP population in 2020." Enrollees are assigned to six race and ethnicity categories using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG). Enrollees are assigned to a child (ages 0-18) or adult (ages 19 and older) subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

This data set includes annual counts and percentages of Medicaid enrollees who are eligible for benefits based on disability, overall; by reason for qualification of disability benefits; and by four subpopulation topics: age group, dual eligibility status, race and ethnicity, and managed care participation. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, and the Northern Mariana Islands are not included. The Children’s Health Insurance Program (CHIP) does not confer eligibility based on disability, so Medicaid expansion CHIP (M-CHIP) and separate CHIP (S-CHIP) enrollees are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the dual eligibility, race and ethnicity, and managed care participation subpopulation topics are restricted to working-age adults (ages 19 to 64) with comprehensive Medicaid benefits. Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Medicaid enrollees who qualify for benefits based on disability in 2020." Enrollees are assigned to a disability category based on their latest reported eligibility group code and age in the calendar year. Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to a dual eligibility status subpopulation based on the dual eligibility code that applies to the majority of their enrolled-months during the year (Dual Eligibility Code). Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to a managed care participation subpopulation based on the managed care plan type code that applies to the majority of their enrolled-months during the year (Enrollment in CMC Plans). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

This dataset aggregates and displays the number of New York State Medicaid enrollees by eligibility year and month within each NYS Economic Region; health insurance plan information; and enrollee demographics. For more information, check out http://www.health.ny.gov/health_care/medicaid/, or go to the "About" tab.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using State-based Marketplaces (SBMs) that use their own eligibility and enrollment platforms Source: State-based Marketplace (SBM) operational data submitted to CMS. Each monthly reporting period occurs during the first through last day of the reported month. SBMs report relevant Marketplace activity from April 2023 (when unwinding-related renewals were initiated in most SBMs) through the end of a state’s Medicaid unwinding renewal period and processing timeline, which will vary by SBM. Some SBMs did not receive unwinding-related applications during reporting period months in April or May 2023 due to renewal processing timelines. SBMs that are no longer reporting Marketplace activity due to the completion of a state’s Medicaid unwinding renewal period are marked as NA. Some SBMs may revise data from a prior month and thus this data may not align with that previously reported. For April, Idaho’s reporting period was from February 1, 2023 to April 30, 2023. Notes: This table represents consumers whose Medicaid/CHIP coverage was denied or terminated following renewal and 1) whose applications were processed by an SBM through an integrated Medicaid, CHIP, and Marketplace eligibility system or 2) whose applications/information was sent by a state Medicaid or CHIP agency to an SBM through an account transfer process. Consumers who submitted applications to an SBM that can be matched to a Medicaid/CHIP record are also included. See the "Data Sources and Metrics Definition Overview" at http://www.medicaid.gov for a full description of the differences between the SBM operating systems and resulting data metrics, measure definitions, and general data limitations. As of the September 2023 report, this table was updated to differentiate between SBMs with an integrated Medicaid, CHIP, and Marketplace eligibility system and those with an account transfer process to better represent the percentage of QHP selections in relation to applicable consumers received and processed by the relevant SBM. State-specific variations are: - Maine’s data and Nevada’s April and May 2023 data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process. - Connecticut, Massachusetts, and Washington also report applications with consumers determined ineligible for Medicaid/CHIP due to procedural reasons. - Minnesota and New York report on eligibility and enrollment for their Basic Health Programs (BHP). Effective April 1, 2024, New York transitioned its BHP to a program operated under a section 1332 waiver, which expands eligibility to individuals with incomes up to 250% of FPL. As of the March 2024 data, New York reports on consumers with expanded eligibility and enrollment under the section 1332 waiver program in the BHP data. - Idaho’s April data on consumers eligible for a QHP with financial assistance do not depict a direct correlation to consumers with a QHP selection. - Virginia transitioned from using the HealthCare.gov platform in Plan Year 2023 to an SBM using its own eligibility and enrollment platform in Plan Year 2024. Virginia's data are reported in the HealthCare.gov and HeathCare.gov Transitions Marketplace Medicaid Unwinding Reports through the end of 2024 and is available in SBM reports as of the April 2024 report. Virginia's SBM data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process, and as a result are not directly comparable to their data in the HealthCare.gov data reports. - Only SBMs with an automatic plan assignment process have and report automatic QHP selections. These SBMs make automatic plan assignments into a QHP for a subset of individuals and provide a notification of options regarding active selection of an alternative plan and/or, if appli

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

This data set includes monthly counts and rates (per 1,000 beneficiaries) of acute care services, including emergency department (ED) visits, inpatient stays, intensive care unit (ICU) stays, and ICU stays that include ventilator use, provided to Medicaid and CHIP beneficiaries, by state. Users can filter to acute care services for any reason, or acute care services for COVID-19.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating acute care services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

The New York State Department of Health (NYS DOH) shares de-identified and aggregated metrics on the NYS Medicaid program through the Health Data NY catalog and as summary statistics on DOH website. Datasets vary by subject/scope, unit of analysis, years of data collection, and update frequency. Publicly-available datasets in the Health Data NY catalog address topics including:

• Medicaid Program Enrollment Rates and Demographics
• Health Home Quality and Utilization
• Inpatient Admissions and Emergency Room Visits
• Incentive Payments
• Inpatient Potentially Preventable Readmission (PPR) Rates
• Newborn Low Birth Weight Rates
• Pediatric and Adult Discharge Quality Indicators
• Potentially Preventable Emergency Visit (PPV) Rates
• Potentially Avoidable Antibiotic Prescribing Rates

For a fee, researchers at NYU Langone Health may acquire NYS Medicaid claims data by submitting a study proposal to the Health Evaluation and Analytics Lab (HEAL). For more information, click on the link to the NYS Medicaid Claims File under the Related Datasets section or search for the NYS Medicaid Claims File in the NYU Data Catalog.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by urban or rural residence. Results are shown overall; by state; and by four subpopulation topics: scope of Medicaid and CHIP benefits, race and ethnicity, disability-related eligibility category, and managed care participation. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands who were enrolled for at least one day in the calendar year, except where otherwise noted. Enrollees in Guam, American Samoa, and the Northern Mariana Islands are not included. Results shown overall (where subpopulation topic is "Total enrollees") and for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the race and ethnicity, disability category, and managed care participation subpopulation topics only include Medicaid and CHIP enrollees with comprehensive benefits. Results shown for the disability category subpopulation topic only include working-age adults (ages 19 to 64). Results for states with TAF data quality issues in the year have a value of "Unusable data." Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Rural Medicaid and CHIP enrollees in 2020." Enrollees are assigned to an urban or rural category based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF. Enrollees are assigned to the comprehensive benefits or limited benefits subpopulation according to the criteria in the "Identifying Beneficiaries with Full-Scope, Comprehensive, and Limited Benefits in the TAF" DQ Atlas brief. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to a disability category subpopulation using their latest reported eligibility group code and age in the year (Medicaid enrollees who qualify for benefits based on disability in 2020). Enrollees are assigned to a managed care participation subpopulation based on the managed care plan type code that applies to the majority of their enrolled-months during the year (Enrollment in CMC Plans). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

This dataset includes total enrollment in separate CHIP (S-CHIP) programs by month and state from April 2023 forward.

Sources: T-MSIS Analytic Files (TAF) and state-submitted enrollment totals. The data notes indicate when a state’s monthly total was a state-submitted value, rather than from T-MSIS.
Methods: Enrollment includes individuals enrolled in S-CHIP at any point during the coverage month, excluding those enrolled in dental-only coverage. The S-CHIP enrollment in this report also excludes enrollees covered by Medicaid expansion CHIP, a program in which a state receives federal funding to expand Medicaid eligibility to optional targeted low-income children that meets the requirements of section 2103 of the Social Security Act. If an individual is enrolled in both Medicaid or Medicaid-expansion CHIP and S-CHIP in a given month, TAF picks the program in which they were last enrolled.
Unless S-CHIP enrollment counts are replaced with a state-submitted value, each state's monthly S-CHIP enrollment is equal to the number of unique people in TAF with a CHIP_CODE = 3 (S-CHIP) and ELGBLTY_GRP_CD not equal to ‘66’ (Children Eligible for Dental Only Supplemental Coverage). More information about TAF is available at https://www.medicaid.gov/medicaid/data-systems/macbis/medicaid-chip-research-files/transformed-medicaid-statistical-information-system-t-msis-analytic-files-taf/index.html.

Note: A historic dataset with S-CHIP enrollment by month and state from April 2023 to June 2024 is also available at: https://data.medicaid.gov/dataset/d30cfc7c-4b32-4df1-b2bf-e0a850befd77. This historic dataset was created to fulfill reporting requirements under section 1902(tt)(1) of the Social Security Act, which was added by section 5131(b) of subtitle D of title V of division FF of the Consolidated Appropriations Act, 2023 (P.L. 117-328) (CAA, 2023). Please note that the methods used to count S-CHIP enrollees differ slightly between the two datasets; as a result, data users should exercise caution if comparing S-CHIP enrollment across the two datasets.
State notes: Alaska, District of Columbia, Hawaii, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, South Carolina, Vermont, and Wyoming do not have S-CHIP programs.
Maryland has an S-CHIP program for the from conception to end of pregnancy group that began in July 2023; April 2023 - June 2023 data for Maryland represents retroactive coverage.
Oregon moved all its S-CHIP enrollees, other than those in the from conception to the end of pregnancy group, to a Medicaid-expansion CHIP program effective January 1, 2024.
CHIP: Children's Health Insurance Program

ContextThe Affordable Care Act resulted in unprecedented reductions in the uninsured population through subsidized private insurance and an expansion of Medicaid. Early estimates from the beginning of 2014 showed that the Medicaid expansion decreased uninsured discharges and increased Medicaid discharges with no change in total discharges.ObjectiveTo provide new estimates of the effect of the ACA on discharges for specific conditions.Design, setting, and participantsWe compared outcomes between states that did and did not expand Medicaid using state-level all-capture discharge data from 2009–2014 for 42 states from the Healthcare Costs and Utilization Project’s FastStats database; for a subset of states we used data through 2015. We stratified the analysis by baseline uninsured rates and used difference-in-differences and synthetic control methods to select comparison states with similar baseline characteristics that did not expand Medicaid.Main outcomeOur main outcomes were total and condition-specific hospital discharges per 1,000 population and the share of total discharges by payer. Conditions reported separately in FastStats included maternal, surgical, mental health, injury, and diabetes.ResultsThe share of uninsured discharges fell in Medicaid expansion states with below (-4.39 percentage points (p.p.), -6.04 –-2.73) or above (-7.66 p.p., -9.07 –-6.24) median baseline uninsured rates. The share of Medicaid discharges increased in both small (6.42 p.p. 4.22–6.62) and large (10.5 p.p., 8.48–12.5) expansion states. Total and most condition-specific discharges per 1,000 residents did not change in Medicaid expansion states with high or low baseline uninsured rates relative to non-expansion states (0.418, p = 0.225), with one exception: diabetes. Discharges for that condition per 1,000 fell in states with high baseline uninsured rates relative to non-expansion states (-0.038 95% p = 0.027).ConclusionsEarly changes in payer mix identified in the first two quarters of 2014 continued through the Medicaid expansion’s first year and are distributed across all condition types studied. We found no change in total discharges between Medicaid expansion and non-expansion states, however residents of states that should have been most affected by the Medicaid expansion were less likely to be hospitalized for diabetes.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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...

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 data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by benefit package (full-scope, comprehensive, limited, or unknown). There are three metrics presented: (1) the number of beneficiaries ever enrolled with each benefit package over the year (duplicated count); (2) the number of beneficiaries enrolled with each benefit package as of an individual’s last month of enrollment (unduplicated count); and (3) average monthly enrollment with each benefit package.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating these measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable or of high concern based on DQ Atlas thresholds for the topic Restricted Benefits Code. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly.

As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a program in that calendar year and month. A recipient may have received benefits from multiple programs in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. NOTE: On 11/30/2018 the counts were revised because of a change in the way active recipients were counted in one source system.

This data set presents annual enrollment counts of Medicaid and CHIP beneficiaries by dual eligibility status for Medicaid and Medicare (full dual eligibility, partial dual eligibility, or not dually eligible). There are three metrics presented: (1) the number of beneficiaries ever dually eligible for Medicaid and Medicare over the year (duplicated count); (2) the number of beneficiaries dually eligible for Medicaid and Medicare as of an individual’s last month of enrollment (unduplicated count); and (3) average monthly eligibility for Medicaid and Medicare.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating these measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable or of high concern based on DQ Atlas thresholds for the topic Dually Enrolled in Medicare. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

This data set includes monthly enrollment counts of Medicaid and CHIP beneficiaries by managed care participation (comprehensive managed care, primary care case management, MLTSS, including PACE, behavioral health organizations, nonmedical prepaid health plans, medical-only prepaid health plans, and other). These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating these measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable or of high concern based on DQ Atlas thresholds for the topics Enrollment in CMC, Enrollment in PCCM Programs, and Enrollment in BHO Plans. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.