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.

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

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)

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.

View data on member enrollment, application activity, Customer Service Center statistics, and more.

description: 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.; abstract: 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.

As of 2020, 26 states had premium or enrollment fees and 21 states had cost sharing requirements for CHIP. States can establish premiums and cost sharing for Medicaid and CHIP under certain restrictions, such as when enrollees' household income is above a certain percentage of the federal poverty level. This statistic shows the number of U.S. states with Medicaid/CHIP premiums, enrollment fees, and cost sharing requirements for children as of January 1, 2020.

The dataset contains the number of Medicaid Low Birth Weight newborns (numerator), the number of county Medicaid newborns (denominator), and observed rate for Agency for Healthcare Research and Quality Prevention Quality Indicator 9 (PQI 9) – Newborn Low Birth Weight Rate for Medicaid enrollees beginning in 2012. The Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators (PQIs) 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. The observed rate for Low Birth Weight is presented by resident county (including a statewide total). The observed rate for low birth weight by resident zip code (including a statewide total) can be found here: https://health.data.ny.gov/Health/Medicaid-Inpatient-Prevention-Quality-Indicators-P/vk5f-rgqm/. For more information, check out: http://www.health.ny.gov/health_care/medicaid/. The "About" tab contains additional details concerning this dataset.

Those without insurance and those who are under insured, particularly adults on Medicaid, experience many access-barriers to receiving dental healthcare due to the low volume of private clinics accepting patients with Medicaid as their payer and to the high volume of patients with Medicaid as their payer the University of Iowa Dental Clinics currently have, which also makes the average wait-time to see a dental practitioner much longer. Adults utilizing Medicaid to access dental healthcare are at the highest risk for experiencing barriers to care or not receiving care at all. Primary data collected for the Community Status Assessment shows that Black or African American Johnson County residents have the highest percentage of Medicaid users at 55.17%, followed by Hispanic/Latino/a/x Johnson County residents at 26.92%. Barriers occur when the initial point of contact does not happen, or the organization/clinic cannot/will not accept patient due to organizational capacity or financial frustrations with Medicaid reimbursements and denials. Equity is also gaping between private providers and state-owned clinics with the recent “Methods and Standards for Establishing Payment Rates for Other Types of Care” adapted by Iowa’s Legislation.

Characteristics associated with COVID-19 case rates among Florida county populations.

This dataset contains Potentially Avoidable Antibiotic Prescribing observed and risk-adjusted rates for adult Medicaid enrollees by provider county beginning in 2010.

Potentially Avoidable Antibiotic Prescriptions are antibiotic prescriptions filled for the treatment of acute respiratory infections for which antibiotics are not indicated, contributing to bacterial drug resistance. Index visits for acute respiratory infections and corresponding prescription fills were identified through the use of previously published methods.

The rates were calculated using Medicaid outpatient claims and encounters, and prescription drug data.

The observed and risk adjusted rates are presented by provider county (including a statewide total).

IntroductionTelehealth can potentially improve the quality of healthcare through increased access to primary care. While telehealth use increased during the COVID-19 pandemic, racial/ethnic disparities in the use of telemedicine persisted during this period. Little is known about the relationship between health coverage and patient race/ethnicity after the onset of the COVID-19 pandemic.ObjectiveThis study examines how differences in patient race/ethnicity and health coverage are associated with the number of in-person vs. telehealth visits among patients with chronic conditions before and after California's stay-at-home order (SAHO) was issued on 19 March 2020.MethodsWe used weekly patient visit data (in-person (N = 63, 491) and telehealth visits (N = 55, 472)) from seven primary care sites of an integrated, multi-specialty medical group in Los Angeles County that served a diverse patient population between January 2020 and December 2020 to examine differences in telehealth visits reported for Latino and non-Latino Asian, Black, and white patients with chronic conditions (type 2 diabetes, pre-diabetes, and hypertension). After adjusting for age and sex, we estimate differences by race/ethnicity and the type of insurance using an interrupted time series with a multivariate logistic regression model to study telehealth use by race/ethnicity and type of health coverage before and after the SAHO. A limitation of our research is the analysis of aggregated patient data, which limited the number of individual-level confounders in the regression analyses.ResultsOur descriptive analysis shows that telehealth visits increased immediately after the SAHO for all race/ethnicity groups. Our adjusted analysis shows that the likelihood of having a telehealth visit was lower among uninsured patients and those with Medicaid or Medicare coverage compared to patients with private insurance. Latino and Asian patients had a lower probability of telehealth use compared with white patients.DiscussionTo address access to chronic care management through telehealth, we suggest targeting efforts on uninsured adults and those with Medicare or Medicaid coverage, who may benefit from increased telehealth use to manage their chronic care.

This map image layer represents the U.S. Department of Health and Human Services (HHS) emPOWER Program, a partnership between ASPR and the Centers for Medicare and Medicaid Services, provides dynamic data and mapping tools to help communities protect the health of more than 4.1 million Medicare beneficiaries who live independently and rely on electricity-dependent medical equipment and health care servicesASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices. Data includes the following DME and devices: cardiac devices (left, right, and bi-ventricular assistive devices (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators (invasive, non-invasive and oscillating vests), bi-level positive airway pressure device (BiPAP), oxygen concentrator, enteral feeding tube, intravenous (IV) infusion pump, suction pump, end-stage renal disease (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric bed. Purpose: Over 2.5 million Medicare beneficiaries rely on electricity-dependent medical equipment, such as ventilators, to live independently in their homes. Severe weather and other emergencies, especially those with long power outages, can be life-threatening for these individuals. The HHS emPOWER Map and public REST Service give every public health official, emergency manager, hospital, first responder, electric company, and community member the power to discover the electricity-dependent Medicare population in their state/territory, county, and ZIP Code. Data Source: The REST Service’s data is developed from Medicare Fee-For-Service (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and children]) and Medicare Advantage (Part C) (>21M 65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and children]) beneficiary administrative claims data. This data does not include individuals that are only enrolled in a State Medicaid Program. Note that Medicare DME are subject to insurance claim reimbursement caps (e.g. rental caps) that differ by type, so the DME may have different “look-back” periods (e.g. ventilators are 13 months and oxygen concentrators are 36 months). ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g., P.O. Box ZIP Codes). To capture the complete population data, the HHS emPOWER Program identified the larger boundary ZIP Code (Parent) within which the non-boundary ZIP Code (Child) resides. The totals are added together and displayed under the parent ZIP Code. Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners and is intended to be used to help inform and support emergency preparedness, response, recovery, and mitigation activities in all communities. Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential priority for the HHS emPOWER Program. Therefore, all personally identifiable information are removed from the data and numerous de-identification methods are applied to significantly minimize, if not completely mitigate, any potential for deduction of small cells or re-identification risk. For example, any cell size found between the range of 1 and 10 is masked and shown as 11.HHS emPOWER Program Executive SummaryHHS emPOWER Program Informational Power Point.

CLICK ON THE ABOVE IMAGE TO LAUNCH THE MAP - Healthcare access issues vary greatly between urban and rural areas of New Mexico. Launch the map to explore alternate ways to classify geographies as urban or rural. These classifications are often used for food access as well as healthcare access.BIBLIOGRAPHY WITH LINKS:Rural Definitions for Health Policy, Harvey Licht, a presentation for the University of New Mexico Center for Health Policy: : http://nmcdc.maps.arcgis.com/home/item.html?id=7076f283b8de4bb69bf3153bc42e0402New Mexico Rural-Urban Counties Comparison Tables - October 2017, Harvey Licht, A preliminary compilation for the National Conference of State Legislators Rural Health Plan Taskforce : https://nmcdc.maps.arcgis.com/home/item.html?id=d3ca56e99f8b45c58522b2f9e061999eFrontier and Remote Areas Map - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=56b4005256244499a58f863c17bbac8aFURTHER READING:What is Rural? Rural Health Information Hub: https://www.ruralhealthinfo.org/topics/what-is-ruralDefining Rural. Research and Training Center on Disability in Rural Communities: http://rtc.ruralinstitute.umt.edu/resources/defining-rural/What is Rural? USDA: https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/National Center for Health Statistics Urban–Rural Classification Scheme: https://www.cdc.gov/nchs/data_access/urban_rural.htm.Health-Related Behaviors by Urban-Rural County Classification — United States, 2013, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6605a1.htm?s_cid=ss6605a1_wExtending Work on Rural Health Disparities, The Journal of Rural Health: http://onlinelibrary.wiley.com/doi/10.1111/jrh.12241/fullMinority Populations Driving Community Growth in the Rural West, Headwaters Economics: https://headwaterseconomics.org/economic-development/trends-performance/minority-populations-driving-county-growth/ Methodology - https://headwaterseconomics.org/wp-content/uploads/Minorities_Methods.pdfThe Role of Medicaid in Rural America, Kaiser Family Foundation: http://www.kff.org/medicaid/issue-brief/the-role-of-medicaid-in-rural-america/The Future of the Frontier: Water, Energy & Climate Change in America’s Most Remote Communities: http://frontierus.org/wp-content/uploads/2017/09/FUTURE-OF-THE-FRONTIER_Final-Version_Spring-2017.pdfRural and Urban Differences in Passenger-Vehicle–Occupant Deaths and Seat Belt Use Among Adults — United States, 2014, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6617a1.htm

California Birth Report totals by Birth Characteristics to inform the public, stakeholders, and researchers.

The DHCS Medi-Cal Birth Statistics tables present the descriptive statistics for California resident births that occurred in a hospital setting, including data on maternal characteristics, delivery methods, and select birth outcomes such as low birthweight and preterm delivery. Tables also include key comorbidities and health behaviors known to influence birth outcomes, such as hypertension, diabetes, substance use, pre-pregnancy weight, and smoking during pregnancy.

DHCS additionally presents birth statistics for women participating in the Medi-Cal Fee-For-Service (FFS) and managed care delivery systems, as well as births financed by private insurance, births financed by other public funding sources, and births among uninsured mothers. Medi-Cal data reflect mothers that were deemed as Medi-Cal certified eligible.

Note: Data for maternal comorbidities including hypertension, diabetes, and substance use have been provisionally omitted among calendar years 2020-2022 for the time being.

Data Overview: ASPR, in partnership with the Centers for Medicare and Medicaid Services (CMS), provide de-identified and aggregated Medicare beneficiary claims data at the state/territory, county, and ZIP code levels in the HHS emPOWER Map and this public HHS emPOWER REST Service. The REST Service includes aggregated data from the Medicare Fee-For-Service (Parts A&B) and Medicare Advantage (Part C) Programs for beneficiaries who rely on electricity-dependent durable medical equipment (DME) and cardiac implantable devices.

Data includes the following DME and devices: Cardiac devices (left, right, and bi-ventricular assistive devices
  (LVAD, RVAD, BIVAD) and total artificial hearts (TAH)), ventilators
  (invasive, non-invasive and oscillating vests), bi-level positive airway
  pressure device (BiPAP), oxygen concentrator, enteral feeding tube,
  intravenous (IV) infusion pump, suction pump, end-stage renal disease
  (ESRD) at-home dialysis, motorized wheelchair or scooter, and electric
  bed.



Purpose: Over 3 million Medicare beneficiaries rely on electricity-dependent
 medical equipment, such as ventilators, to live independently in their
 homes. Severe weather and other emergencies, especially those with long
 power outages, can be life-threatening for these individuals. The HHS
 emPOWER Map and public REST Service give every public health official,
 emergency manager, hospital, first responder, electric company, and
 community member the power to discover the electricity-dependent Medicare
 population in their state/territory, county, and ZIP Code.



Data Source: The REST Service’s data is developed from Medicare Fee-For-Service
  (Part A & B) (>33M 65+, blind, ESRD [dialysis], dual-eligible,
  disabled [adults and children]) and Medicare Advantage (Part C) (>21M
  65+, blind, ESRD [dialysis], dual-eligible, disabled [adults and
  children]) beneficiary administrative claims data. This data does not
  include individuals that are only enrolled in a State Medicaid Program.
  Note that Medicare DME are subject to insurance claim reimbursement caps
  (e.g. rental caps) that differ by type, so the DME may have different
  “look-back” periods (e.g. ventilators are 13 months and oxygen
  concentrators are 36 months).



ZIP Code Aggregation: Some ZIP Codes do not have specific geospatial boundary data (e.g.,
  P.O. Box ZIP Codes). To capture the complete population data, the HHS
  emPOWER Program identified the larger boundary ZIP Code (Parent) within
  which the non-boundary ZIP Code (Child) resides. The totals are added
  together and displayed under the parent ZIP Code.




Approved Data Uses: The public HHS emPOWER REST Service is approved for use by all partners
  and is intended to be used to help inform and support emergency
  preparedness, response, recovery, and mitigation activities in all
  communities.






Privacy Protections: Protecting the privacy of Medicare beneficiaries is an essential
  priority for the HHS emPOWER Program. Therefore, all personally
  identifiable information are removed from the data and numerous
  de-identification methods are applied to significantly minimize, if not
  completely mitigate, any potential for deduction of small cells or
  re-identification risk. For example, any cell size found between the
  range of 1 and 10 is masked and shown as 11.