DQS Medicaid coverage among persons under age 65, by selected characteristics: United States

  Description

Data on Medicaid coverage among people under age 65, in the United States, by selected population characteristics. Data from Health, United States. SOURCE: National Center for Health Statistics, National Health Interview Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from:… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/dqs-medicaid-coverage-among-persons-under-age-65-b.

This map shows where people have Medicaid or means-tested healthcare coverage in the US (ages under 65). This is shown by State, County, and Census Tract, and uses the most current ACS 5-year estimates.

Minimum Data Set Frequency

  Description

The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/minimum-data-set-frequency.

Data on Medicaid coverage among people under age 65, in the United States, by selected population characteristics. Data from Health, United States. SOURCE: National Center for Health Statistics, National Health Interview Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

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.

Centers for Medicare & Medicaid Services - Nursing HomesThis feature layer, utilizing data from the Centers for Medicare & Medicaid Services (CMS), displays the locations of nursing homes in the U.S. Nursing homes provide a type of residential care. They are a place of residence for people who require constant nursing care and have significant deficiencies with activities of daily living. Per CMS, "Nursing homes, which include Skilled Nursing Facilities (SNFs) and Nursing Facilities (NFs), are required to be in compliance with Federal requirements to receive payment under the Medicare or Medicaid programs. The Secretary of the United States Department of Health & Human Services has delegated to the CMS and the State Medicaid Agency the authority to impose enforcement remedies against a nursing home that does not meet Federal requirements." This layer includes currently active nursing homes, including number of certified beds, address, and other information.Bridgepoint Sub-Acute and Rehab Capitol HillData downloaded: August 1, 2024Data source: Provider InformationData modification: This dataset includes only those facilities with addresses that were appropriately geocoded.For more information: Nursing homes including rehab servicesFor feedback, please contact: ArcGIScomNationalMaps@esri.comCenters for Medicare & Medicaid ServicesPer USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events. The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits. Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system. Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data. Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department. Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

Nursing Home Compare has detailed information about every Medicare and Medicaid nursing home in the country. A nursing home is a place for people who can’t be cared for at home and need 24-hour nursing care. These are the official datasets used on the Medicare.gov Nursing Home Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at every Medicare and Medicaid-certified nursing home in the country, including over 15,000 nationwide.

The Long-Term Care Minimum Data Set (MDS) is a standardized, primary screening and assessment tool of health status that forms the foundation of the comprehensive assessment for all residents in a Medicare and-or Medicaid-certified long-term care facility. The MDS contains items that measure physical, psychological and psychosocial functioning. The items in the MDS give a multidimensional view of the patients functional capacities and helps staff to identify health problems.

analyze the basic stand alone medicare claims public use files (bsapufs) with r and monetdb the centers for medicare and medicaid services (cms) took the plunge. the famous medicare 5% sample has been released to the public, free of charge. jfyi - medicare is the u.s. government program that provides health insurance to 50 million elderly and disabled americans. the basic stand alone medicare claims public use files (bsapufs) contain either person- or event-level data on inpatient stays, durable medical equipment purchases, prescription drug fills, hospice users, doctor visits, home health provision , outpatient hospital procedures, skilled nursing facility short-term residents, as well as aggregated statistics for medicare beneficiaries with chronic conditions and medicare beneficiaries living in nursing homes. oh sorry, there's one catch: they only provide sas scripts to analyze everything. cue the villian music. that bored old game of monopoly ends today. the initial release of the 2008 bsapufs was accompanied by some major fanfare in the world of health policy , a big win for government transparency. unfortunately, the final files that cleared the confidentiality hurdles are heavily de-identified and obfuscated. prime examples: none of the files can be linked to any other file. not across years, not across expenditure categories costs are rounded to the nearest fifth or tenth dollar at lower values, nearest thousandth at higher values ages are categorized into five year bands so these files are baldly inferior to the unsquelched, linkable data only available through an expensive formal application process. any researcher with a budget flush enough to afford a sas license (the only statistical software mentioned in the cms official documentation) can probably also cough up the money to buy the identifiable data through resdac (resdac, btw, rocks). soapbox: cms released free public data sets that could only be analyzed with a software package costing thousands of dollars. so even though the actual data sets were free, researchers still needed deep pock ets to buy sas. meanwhile, the unsquelched and therefore superior data sets are also available for many thousands of dollars. researchers with funding would (reasonably) just buy the better data. researchers without any financial resources - the target audience of free, public data - were left out in the cold. no wonder these bsapufs haven't been used much. that ends now. using r, monetdb, and the personal computer you already own (mine cost $700 in 2009), researchers can, for the first time, seriously analyze these medicare public use files without spending another dime. woah. plus hey guess what all you researcher fat-cats with your federal grant streams and your proprietary software licenses: r + monetdb runs one heckuva lot faster than sas. woah^2. dump your sas license water wings and learn how to swim. the scripts below require monetdb . click here for step-by-step instructions of how to install it on windows and click here for speed tests. vroom. since the bsapufs comprise 5% of the medicare population, ya generally need to multiply any counts or sums by twenty. although the individuals represented in these claims are randomly sampled, this data should not be treated like a complex survey sample, meaning that the creation of a survey object is unnecessary. most bsapufs generalize to either the total or fee-for-service medicare population, but each file is different so give the documentation a hard stare before that eureka moment. this new github repository contains three scripts: 2008 - download all csv files.R loop through and download every zip file hosted by cms unzip the contents of each zipped file to the working directory 2008 - import all csv files into monetdb.R create the batch (.bat) file needed to initiate the monet database in the f uture loop through each csv file in the current working directory and import them into the monet database create a well-documented block of code to re-initiate the monetdb server in the future 2008 - replicate cms publications.R initiate the same monetdb server instance, unsing the same well-documented block of code as above replicate nine sets of statistics found in data tables provided by cms < a href="https://github.com/ajdamico/usgsd/tree/master/Basic%20Stand%20Alone%20Medicare%20Claims%20Public%20Use%20Files">click here to view these three scripts for more detail about the basic stand alone medicare claims public use files (bsapufs), visit: the centers for medicare and medicaid's bsapuf homepage a joint academyhealth webinar given by the organizations that partnered to create these files - cms, impaq, norc notes: the replication script has oodles of easily-modified syntax and should be viewed for analysis examples. if you know the name of the data table you want to examine, you can quickly modify these general monetdb analysis examples too. just run sql queries - sas users, that's "proc...

BackgroundReducing health inequities in marginalized populations, including people with Medicaid insurance, requires care transformation to address medical and social needs that is supported and incentivized by tailored payment methods. Collaboration across health care stakeholders is essential to overcome health system fragmentation and implement sustainable reform in the United States (U.S.). This paper explores how multi-stakeholder teams operationalized the Roadmap to Advance Health Equity model during early stages of their journey to (a) build cultures of equity and (b) integrate health equity into care transformation and payment reform initiatives.MethodsAdvancing Health Equity: Leading Care, Payment, and Systems Transformation is a national program in the U.S. funded by the Robert Wood Johnson Foundation that brings together multi-stakeholder teams to design and implement initiatives to advance health equity. Each team consisted of representatives from state Medicaid agencies, Medicaid managed care organizations, and health care delivery organizations in seven U.S. states. Between June and September 2021, semi-structured interviews were conducted with representatives (n = 23) from all seven teams about experiences implementing the Roadmap to Advance Health Equity model with technical assistance from Advancing Health Equity.ResultsFacilitators of building cultures of equity included (1) build upon preexisting intra-organizational cultures of equity, (2) recruit and promote diverse staff and build an inclusive culture, and (3) train staff on health equity and anti-racism. Teams faced challenges building inter-organizational cultures of equity. Facilitators of identifying a health equity focus area and its root causes included (1) use data to identify a health equity focus and (2) overcome stakeholder assumptions about inequities. Facilitators of implementing care transformation and payment reform included (1) partner with Medicaid members and individual providers and (2) support and incentivize equitable care and outcomes with payment. Facilitators of sustainability planning included (1) identify evidence of improved health equity focus and (2) maintain relationships among stakeholders. Teams faced challenges determining the role of the state Medicaid agency.ConclusionsMulti-stakeholder teams shared practical strategies for implementing the Roadmap to Advance Health Equity that can inform future efforts to build intra- and inter-organizational cultures of equity and integrate health equity into care delivery and payment systems.

The Resident Assessment Instrument/Minimum Data Set (RAI/MDS) is a comprehensive assessment and care planning process used by the nursing home industry since 1990 as a requirement for nursing home participation in the Medicare and Medicaid programs. The RAI/MDS provides data for monitoring changes in resident status that are consistent and reliable over time. The VA commitment to quality propelled the implementation of the RAI/MDS in its nursing homes now known as VA Community Living Centers (CLC). In addition to providing consistent clinical information, the RAI/MDS can be used as a measure of both quality and resource utilization, thereby serving as a benchmark for quality and cost data within the VA as well as with community based nursing facilities. Workload based on RAI/MDS can be calculated electronically by the interactions of the elements of the MDS data and grouped into 53 categories referred to as Resource Utilization Groups (RUG-IV). Residents are assessed quarterly. The data is grouped for analysis at the Austin Information Technology Center (AITC). Conversion to electronic data entry and transmission to the AITC was completed system-wide by year-end 2000. In 2010, the Centeres for Medicare and Medicaide Services released a significantly upgraded version, MDS 3.0, to begin to be implemented on October 1, 2011 in VHA CLCs. Training is underway currently. The MDS 3.0 will generate a new set of Quality Indicators and Quality Monitors as well the RUGs will increase to 64 RUGs from the current 53 RUG groups.

The Minimum Data Set (MDS) is part of the federally mandated process for clinical assessment of all residents in Medicare or Medicaid certified nursing homes. This process provides a comprehensive assessment of each residents functional capabilities and helps nursing home staff identify health problems. These public use reports are meant to begin the process of sharing information from the national MDS database.

Hospitals Registered with MedicareThis feature layer, utilizing data from the Centers of Medicare and Medicaid Services (CMS), depicts all hospitals that are currently registered with Medicare in the U.S. Per NIH, "Since the passage of Medicare legislation in 1965, Section 1861 of the Social Security Act has stated that hospitals participating in Medicare must meet certain requirements specified in the act and that the Secretary of the Department of Health, Education and Welfare (HEW) [now the Department of Health and Human Services (DHHS)] may impose additional requirements found necessary to ensure the health and safety of Medicare beneficiaries receiving services in hospitals. On this basis, the Conditions of Participation, a set of regulations setting minimum health and safety standards for hospitals participating in Medicare, were promulgated in 1966 and substantially revised in 1986."Ascension Columbia St Mary's HospitalData currency: 11/26/2024Data modification: This data was created using the geocoding process on the CSV file.Data downloaded from: Hospital General InformationFor more information: HospitalsSupport documentation: Data dictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.comCenters of Medicare and Medicaid ServicesPer USA.gov, "The Centers for Medicare and Medicaid Services (CMS) provides health coverage to more than 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program, and the Health Insurance Marketplace. The CMS seeks to strengthen and modernize the Nation’s health care system, to provide access to high quality care and improved health at lower costs."

IntroductionThe Sickle Cell Data Collection Program (SCDC) is a multi-state initiative utilizing multiple data sources to estimate population prevalence of Sickle Cell Disease (SCD) with the goal of improving quality of life and health outcomes among those affected. SCDC in Tennessee operates as a multi-site, interdisciplinary team using multiple sources of data to learn more about SCD in Tennessee.MethodsThis analysis characterizes the number, demographics, and proximity to specialty care of individuals living with SCD in Tennessee who have been covered by Medicaid or identified by newborn screening. We compared demographic patterns of individuals with SCD living in rural areas with those living in urban areas, as well as those living in counties contributing more than 50 individuals to the cohort, respectively, to demographic patterns of individuals with SCD in the rest of the state, using Chi-Square or Fisher’s exact tests.ResultsFindings show that overall, 66.1% of all SCD patients identified through newborn screening were residents of Davidson and Shelby counties at the time of birth, and 81.8% of those identified through Medicaid claims lived in Davidson, Hamilton, Knox, Madison, Montgomery, Rutherford, or Shelby County. In total, 8.6% of the cohort lived in rural settings and 91.4% in urban settings. Of the 95 counties in Tennessee, 75 (78.9%) had at least 1 to 40 residents with SCD, yet of these 75 counties, less than half had a hematology/oncology trained provider practicing within them.DiscussionThis analysis brings us closer to understanding how many people with SCD live in rural areas of Tennessee and the challenges they face in seeking the care needed to adequately manage their disease. Acute healthcare utilization remains highest in the young adulthood years. This analysis provides insight into how healthcare utilization patterns among individuals with SCD vary by age group and over time.

This measure answers the question of what number and percentage of residents are living below the federal poverty level, which means they meet certain threshold set by a set of parameters and computation performed by the Census Bureau. Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). Data collected from the U.S. Census Bureau, American Communities Survey (1yr), Poverty Status in the Past 12 Months (Table S1701). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

To: State, territorial, tribal, and local administrators of agencies and programs focused on child, youth, and family health and well-being

Dear Colleague,

Maternal and infant health is an urgent priority, and a coordinated effort across health and human services is crucial to foster positive maternal health outcomes. The Administration for Children and Families (ACF) and other divisions of the U.S. Department of Health and Human Services (HHS) are responsible for many programs that support maternal and infant health, including home visiting, Head Start, child care, Medicaid, TANF, child support and others. One under-recognized risk to pregnant women and babies is the increasing rates of syphilis and congenital syphilis, now at their highest levels since 1950. While syphilis can be cured with proper testing and treatment, if left untreated it can lead to severe health complications and can be transmitted as congenital syphilis when an infected mother passes the disease to her baby during pregnancy or childbirth. This can result in outcomes that include miscarriages, stillbirths, low birth weight, and long-term health complications. Congenital syphilis is preventable with early detection and treatment.

New CDC data

paint a concerning picture, revealing that more than 3,700 babies were born with congenital syphilis in 2022—a dramatic increase compared to just 350 cases in 2012. This tenfold rise over the past decade follows rising syphilis cases among women of reproductive age combined with social and economic factors

that create barriers to high-quality prenatal care, declines in the prevention infrastructure, and a lack of access to resources. Of particular concern is the increase in

cases

among American Indian and Alaskan Native populations.

HHS established the National Syphilis and Congenital Syphilis Syndemic Federal Task Force

, led by the Office of the Assistant Secretary for Health (OASH), in September 2023 to work to reduce syphilis and congenital syphilis through a variety of efforts. The Task Force members, from a variety of health and human services agencies across the federal government, have been working closely with many external partners to improve testing, treatment, and public awareness.

While some of those most at risk may not be seeking or receiving health care or medical attention, they are likely receiving services and benefits from ACF-funded programs, as well as Medicaid, SNAP, and WIC, which are administered by human services agencies across states, counties, tribes, and territories. Human services providers can play an important role in addressing the syphilis epidemic by raising awareness and helping to facilitate access to early testing and treatment. There are simple tests and effective antibiotic treatments, but many people are not aware of their risks nor where to obtain tests. Staff at human services agencies have a unique opportunity to intervene and help protect the health of pregnant women and babies by educating clients on the risks and encouraging early and regular prenatal care, including testing, and treatment when necessary.

Here are some ways you and your staff can get involved:

Thank you for your support and partnership. Together we can make a meaningful difference in curbing this epidemic and saving lives.

/s/Meg Sullivan, MD, MPHPrincipal Deputy Assistant Secretary Administration for Children and Families

/s/David M. Johnson, MPHDeputy Assistant Secretary for Health Director, OASH Regional OfficesOffice of the Assistant Secretary for Health

Metadata-only record linking to the original dataset. Open original dataset below.

Medical providers terminated from Tennessee's Medicaid program.

Individuals and businesses excluded from Alaska Medical Assistance Program