This dataset contains aggregate Medicaid payments, and counts for eligible recipients and recipients served by month and county in Iowa, starting with month ending 1/31/2011. Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.

This dataset represents the number of Medicaid eligible individuals receiving the various Medicaid services over time.

This map shows the percentage of Medicaid enrollees with at least one dental visitin within the last year by county. Counties are shaded based on quartile distribution. The lighter shaded counties have a higher percentage of Medicaid enrolees with at least one dental visit within the last year. The darker shaded counties have a lower percentage of Medicaid enrolees with at least one dental visit within the last year.This dataset contains the latest Community Health Indicator Report (CHRIS) data. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset..

Dataset

This dataset was created by NamitaSharma3

Contents

Managed Care Enrollment by Program and Population (Duals)

  Description

The Medicaid Managed Care Enrollment Report profiles enrollment statistics on Medicaid managed care programs on a plan-specific level. The managed care enrollment statistics include enrollees receiving comprehensive benefits and limited benefits and are point-in-time counts.

Because Medicaid beneficiaries may be enrolled concurrently in more than one type of managed care program (e.g., a Comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/managed-care-enrollment-by-program-and-population.

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 set accompanies the Profile of the California Medicare Population chartbook, published by the Office of Medicare Innovation and Integration in February 2022, and available at (https://www.dhcs.ca.gov/services/Documents/OMII-Medicare-Databook-February-18-2022.pdf). The three data files in this data set were analyzed from federal administrative data (the Medicare Master Beneficiary Summary File) for beneficiary characteristics as of March 2021. These datasets include: Medicare enrollment, Medicare Advantage enrollment (and its converse fee-for-service Medicare enrollment), dual Medi-Cal eligibility and enrollment (and its converse Medicare-only enrollment), by county. Medicare Savings Program enrollees were considered Medicare-only and not dually enrolled in Medi-Cal. All Medicare Part C beneficiaries, including PACE, Cal MediConnect and Special Needs Plans, were considered to have Medicare Advantage.

DHCS partnered with The SCAN Foundation and ATI Advisory in 2021 and 2022 to develop a series of chartbooks that provide information about Medicare beneficiaries in California. This work is supported by a grant from The SCAN Foundation to advance a coordinated and easily navigated system of high-quality services for older adults that preserve dignity and independence. For more information, visit www.TheSCANFoundation.org.

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, please refer to the following website: (

This indicator provides information about medically underserved areas and/or populations (MUA/Ps), as determined by the federal Health Resources and Services Administration (HRSA). Each designated area includes multiple census tracts.State Primary Care Offices submit applications to HRSA to designate specific areas within counties as MUA/Ps. The MUA/P designation is made using the Index of Medical Underservice (IMU) score, which includes four components: provider per 1,000 population, percent of population under poverty, percent of population ages 65 years and older, and infant mortality rate. The IMU scores ranges from 0-100. Lower scores indicate higher needs. An IMU score of 62 or below qualifies for designation as an MUA/P. Note: if an area is not designated as an MUA/P, it does not mean it is not underserved, only that an application has not been filed for the area and that official designation has not been given.The MUAs within Los Angeles County consist of groups of urban census tracts (namely service areas). MUPs have a shortage of primary care health services for a specific population within a geographic area. These populations may face economic, cultural, or language barriers to health care, such as: people experiencing homelessness, people who are low-income, people who are eligible for Medicaid, Native Americans, or migrant farm workers. All the MUPs that have been designated within Los Angeles County are among low-income populations of selected census tract groups. Due to the nature of the designation process, a census tract may be designated as both an MUA and an MUP and as multiple MUAs. MUA/P designations help establish health maintenance organizations or community health centers in high-need areas.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

New York County Mental Health Utilization

Recipient Counts, Rates, and Expenditures 2006-2016

By State of New York [source]

About this dataset

This dataset offers insightful summary information regarding mental health services funded by Medicaid from Local Fiscal years 2006 to 2016. These reports provide insight into mental health service utilization, such as Comprehensive Outpatient Program Services and Community Support Program payments where applicable. With data refreshed on a monthly basis, these reports offer the opportunity to gain invaluable access to influential information about an important and often overlooked or undervalued aspect of the population’s collective wellbeing. Whether you are a public serviced provider looking for ways to better serve individuals or just someone wanting insight into population trends in mental health services, this dataset is sure to provide value. Carve out valuable time in your day as you explore its contents. Because it may just be that scholarly look at a how people access quality care that gives you pause to think more deeply about our society and your part within it!

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How to use the dataset

This dataset provides detailed summary information about mental health services utilization funded through Medicaid for various local fiscal years from 2006-2016. In order to use this dataset effectively, it is important to understand the different components of the data and what they represent.

The columns included in this dataset include Row Created Date Time, Service Year, OMH Region Code, OMH Region Label, County Label, Age Group,” “Rate Code Group,” “Recipient Count By County” “Count of Recipients By Rate Code Group And County,” and “Units Total. These columns offer valuable insight into various aspects of Medicaid-funded mental health service utilization by local fiscal year as well as specifics regarding recipient demographics such as county label and age group.

Once you have familiarized yourself with what each represent, you can use this data to conduct your analysis on how Medicaid-funded utilized has changed over time or how certain age groups or counties tend to utilize more/less services than others. You can also look at trends within the rate code group column and see which services are most commonly used by these populations.

In short, this dataset provides a wealth of useful information about organizations of mental health service utilization among New York's counties from 2006 - 2016 that can be further broken down into demographic units for further analysis if desired

Research Ideas

• Analyzing trends in service utilization for each county and how it changes over time to identify areas of greatest need and reinvestment.
• Correlating mental health service utilization with other economic, health, or education data points to provide insights into the overall well-being of a region.
• Leveraging geographical analysis tools such as GIS to map out mental health services across different districts and counties on an interactive platform that allows people to quickly find resources in their area

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: county-mental-health-profiles-2006-2016-1.csv | Column name | Description | |:-------------------------------------------------------|:--------------------------------------------------------------------| | Row Created Date Time | Date and time the row was created. (DateTime) | | Service Year | Year of service. (Integer) | | OMH Region Code | Code for the OMH region. (Integer) | | OMH Region Label | Label for the OMH region. (String) | | County Label | Label for the county. (String) | | Age Group | Age group of the recipient. (String) | | Rate Code Group | Group of rate codes. (String) | | **Recipient Count By Co...

Behavioral Health Services Provided to the Medicaid and CHIP Population

  Description

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… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/behavioral-health-servicesprovided-to-the-medicaid.

Primary language spoken by the Medicaid and CHIP population

  Description

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees by primary language spoken (English, Spanish, and all other languages). Results are shown overall; by state; and by five subpopulation topics: race and ethnicity, age group, scope of Medicaid and CHIP benefits, urban or rural residence, and eligibility category. These results were… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/primary-language-spoken-by-the-medicaid-and-chip-p.

Key Table Information.Table Title.Medicaid/Means-Tested Public Coverage by Sex by Age.Table ID.ACSDT1Y2024.C27007.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and ...

The Medicare Monthly Enrollment data provides current monthly information on the number of Medicare beneficiaries with hospital/medical coverage and prescription drug coverage, available for several geographic areas including national, state/territory, and county. The hospital/medical coverage data can be broken down further by health care delivery (Original Medicare versus Medicare Advantage and Other Health Plans) and the prescription drug coverage data can be examined by those enrolled in stand-alone Prescription Drug Plans and those enrolled in Medicare Advantage Prescription Drug plans. The dataset provides monthly and yearly enrollee trends.

The datasets contain number of Medicaid PDI hospitalizations (numerator), county or zip Medicaid population (denominator), observed rate, expected number of hospitalizations and rate, and risk-adjusted rate for Agency for Healthcare Research and Quality Pediatric Quality Indicators – Pediatric (AHRQ PDI) for Medicaid enrollees beginning in 2011. The Agency for Healthcare Research and Quality (AHRQ) Pediatric Quality Indicators (PDIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. Both the Urinary Tract Infection and Gastroenteritis PDIs include admissions for patients aged 3 months through 17 years. The asthma PDI includes admissions for patients aged 2 through 17 years. Eligible admissions for the Diabetes Short-term Complications PDI includes admissions for patients aged 6 through 17 years.

Key Table Information.Table Title.Allocation of Medicaid/Means-Tested Public Coverage.Table ID.ACSDT1Y2024.B992707.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and...

This dataset contains the total number of Medi-Cal Managed Care enrollees based on the reported month, plan type, county, and health plan.

The datasets contain number of Medicaid PQI hospitalizations (numerator), county Medicaid population (denominator), observed rate, expected number of hospitalizations and rate, and risk-adjusted rate for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) for Medicaid enrollees beginning in 2011.

This chart shows the potentially preventable emergency visit (PPV) risk adjusted rates per 100 for Medicaid beneficiaries by patient county and year. The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for Medicaid beneficiaries by patient county and patient zip code beginning in 2011.

The Potentially Preventable Visits (PPV) obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.

The rates were calculated using Medicaid inpatient and outpatient data for the numerator and Medicaid enrollment in the county or zip code for the denominator.

The observed, expected and risk adjusted rates for PPV are presented by either resident county (including a statewide total) or resident zip code (including a statewide total). For more information, check out: http://www.health.ny.gov/health_care/medicaid/. The "About" tab contains additional details concerning this dataset.

A polygon feature class based on zip code boundaries showing the average Medicaid enrollment in Miami-Dade County in 2010.Updated: Not Planned The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere