Blog & News
Changes to Child and Adult Core Sets to Advance Equity (SHVS Cross-Post)
June 10, 2024:The following Expert Perspective (EP) is cross-posted from State Health & Value Strategies. Authors: Elizabeth Lukanen and Lacey Hartman, SHADAC
Original posting date June 7, 2024. Find the original post here on the SHVS website.
The Child and Adult Core Sets were established to measure the quality of care for Medicaid and Children’s Health Insurance Program (CHIP) enrollees, nationally and at the state level, based on a uniform set of measures. The goal of the Core Sets is to monitor performance and improve the quality of healthcare. Starting in fiscal year (FY) 2025, states will be required to report a subset of Child and Adult Core Set measures (see Table 1) by race and ethnicity, sex, and geography.
By requiring data disaggregation for key populations of interest, policymakers, advocates and researchers will have a new tool to measure, monitor and inform policies and practices that focus on health equity.
Table 1: Core Set Measures Subject to Stratification (10 Measures in Total)
Child Core Set Measures (7 of 27 measures) |
|||
Measure Name |
National Quality Forum (NQF) # |
Measure Steward |
Data Collection Method |
Well-Child Visits in the First 30 Months of Life (W30-CH) |
1392 |
National Committee for Quality Assurance (NCQA) |
Administrative |
Child and Adolescent Well-Care Visits (WCV–CH) |
1516 |
NCQA |
Administrative |
Oral Evaluation, Dental Services (OEV-CH) |
2517 |
Dental Quality Alliance (DQA) (American Dental Association) |
Administrative |
Follow-Up After Hospitalization for Mental Illness: Ages 6 to 17 (FUH-CH) |
576 |
NCQA |
Administrative |
Prenatal and Postpartum Care Up to Age 20 (PPC2-CH) |
1517* |
NCQA |
Administrative or hybrid |
Live Births Weighing Less Than 2,500 Grams (LBW–CH) – CMS calculates on behalf of states |
1382 |
Centers for Disease Control and Prevention (CDC)/National Center for Health Statistics (NCHS) |
State vital records |
Low-Risk Cesarean Delivery (LRCD-CH) – |
Not applicable |
CDC/NCHS |
State vital records |
Adult Core Set Behavioral Health Measures (3 of 11 measures) |
|||
Measure Name |
NQF # |
Measure Steward |
Data Collection Method |
Initiation and Engagement of Substance Use Disorder Treatment (IET-AD) |
0004 |
NCQA |
Administrative or electronic health record |
Follow-Up After Emergency Department Visit for Substance Use: Age 18 and Older (FUA-AD) |
3488 |
NCQA |
Administrative |
Follow-Up After Hospitalization for Mental Illness: Ages 18 and older (FUH-AD) |
0576 |
NCQA |
Administrative |
*No longer endorsed by NQF.
States will be required to stratify these mandatory measures using the following categories:
- Race and ethnicity: Using the newly released 2024 Revisions to the Office of Management and Budget’s Statistical Policy Directive No. 15: Standards for Maintaining, Collecting, and Presenting Federal Data on Race and Ethnicity. Minimum categories include:
- American Indian or Alaska Native
- Asian
- Black or African American
- Hispanic or Latino
- Middle Eastern or North African
- Native Hawaiian or Pacific Islander
- White
- Sex: Defined as biologic sex, using the 2011 HHS Implementation Guidance on Data Collection Standards for Race, Ethnicity, Sex, Primary Language, and Disability Status. Categories include:
- Male
- Female
- Geography: Using the core-based statistical area (CBSA), which are county-based statistical areas defined by large population areas, as a minimum standard. Those categories include:
- Metropolitan statistical area (population core of 50,000 or more)
- Micropolitan statistical area (population core of 10,000 to 49,999)
- Outside Core Based statistical area
Background and Context
Every year, the Secretary of the U.S. Department of Health and Human Services is required to review and update the Child and Adult Core Sets. This review is designed to detect measurement gaps and to identify and recommend revisions to improve and strengthen the Core Sets. This review is led by a workgroup that includes input from a variety of stakeholders including states, managed care plans, healthcare providers, and quality experts. In response to the annual review process, state reporting of these measures has evolved and starting in FY 2024, reporting of the Child Core Set and the Core Set of behavioral health measures for adults enrolled in Medicaid became mandatory.
Over time, the Core Sets have been specifically recognized as a critical tool to monitor health disparities. Increasing stratification of the measures is a priority area for the Centers for Medicare & Medicaid Services (CMS) and in guidance released in 2022, CMS explicitly encouraged states to “use Core Set data to identify disparities in care and to develop targeted quality improvement efforts to advance health equity.”
During the review of the 2025 Core Set Measures, the Core Set review workgroup discussed using stratified Core Sets data to advance health equity. Workgroup members who represent state and enrollee perspectives both highlighted the importance of disaggregation for assessing member experience and monitoring equity while also acknowledging the challenges inherent to data collection and reporting. In response to this discussion, the following challenges and considerations were highlighted in the final FY 2025 recommendations report:
- Data on enrollee demographics is of variable quality, with missing and unknown data (enrollees who don’t provide data and related hesitancy).
- Administrative burden to collect this information.
- Political considerations for how the data are collected and reported (balancing state legislative agendas compared to CMS requirements).
- Technical challenges, such as having multiple conflicting sources of data.
- Misalignment of reporting stratification categories with other federal and state program requirements.
- Need to engage enrollees in the collection and use of these data.
- Need for technical assistance to states to meet the new reporting requirements.
Despite these challenges, there was consensus among the workgroup about the importance of stratification, which aligned with the new requirement that 10 measures be reported by race and ethnicity, sex, and geography. Specifically, for FY 2025 reporting, states will be required to report stratified data for the seven Child Core Set measures listed above in Table 1 (25% of the 27 measures) and three of the Adult Core Set behavioral health measures (25% of 11 behavioral health measures).
Required stratification of additional measures will increase over time and the intent is that all eligible mandatory Core Set measures will be disaggregated by FY 2028. States have the option of reporting stratified data for all measures starting in FY 2025.
People’s experience of health inequities based on race and ethnicity, sexual orientation, gender identity, geography, immigration status, and other factors, is a longstanding and pervasive problem that is deeply rooted in discrimination and structural racism. The Medicaid program, through its policy, financial, and programmatic levers, is uniquely situated to address the health inequities experienced by the program’s diverse population of enrollees. Key to these efforts is the availability of comparable state programmatic and performance data to identify and track progress.
For this reason, the move toward disaggregated Core Sets data by race and ethnicity, sex, and geography is an important step to improve the monitoring of health care access and quality for Medicaid enrollees, to further identify where disparities exist and to develop and evaluate quality improvement efforts. However, this is a small step that needs to expand to identify other groups that have been economically and socially marginalized such as those with a disability, individuals who identify as lesbian, gay, bisexual, transgender, queer, intersex, or outside the gender binary (LGBTQI+), and those with limited English proficiency.
In addition to expanding data disaggregation to include a broader range of groups, it will also be important to provide states with the necessary resources and technical assistance to analyze and report these data in an accurate and comparable way. As was also noted in comments to the workgroup, enrollee engagement in the collection and use of these data is also key for advancing health equity. Community engagement is a way to establish and build trust and to develop interventions informed by the lived experience of Medicaid enrollees.
Finally, it will be critical to provide states, providers, and community partners with the resources and tools necessary to ensure that timely action is taken to actually address, and not just report on, systemic inequities.
Want to stay informed on the latest data collection practices, survey updates, and more? Sign up for our monthly newsletter here, and find health equity resources here on the SHVS site.
Blog & News
2023 National Health Interview Survey (NHIS) Early Release: Estimates from Quarter 4 (October to December) Hold Steady
June 05, 2024:The National Center for Health Statistics (NCHS) has released quarterly estimates of health insurance coverage beginning in October 2022 through December 2023 from the National Health Interview Survey (NHIS) as a part of the NHIS Early Release Program. Each quarter covers a three-month span, and this blog specifically looks at survey data from the most recent quarter (Q4 – October to December 2023) and notes any differences compared to the same time period in 2022.
Between Q4 of 2022 and Q4 of 2023, rates of uninsurance, public coverage, and private coverage for adults (age 18-64) remained mostly unchanged. Across all age groups, there were slight decreases in the rates of uninsurance and private coverage, and a slight increase in the rate of public coverage, but none of these changes were statistically significant.
Figure 1: Health Insurance Coverage Rates by Type (Adults Aged 18-64), Q4 2022 vs. Q4 2023*
*No changes were statistically significant at 95% confidence.
- Brief: Comparing Federal Government Surveys That Count the Uninsured: 2023
- Blog: 2023 NHIS Early Release Estimates from Quarter 3 Hold Steady
- Blog: Insurance Coverage Trend Analysis – How Does the Household Pulse Survey Compare?
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Blog & News
2023 NHIS Early Release: Estimates from Quarter 3 (July to September) Hold Steady
April 01, 2024:The National Center for Health Statistics (NCHS) has released quarterly estimates of health insurance coverage beginning in July 2022 through September 2023 from the National Health Interview Survey (NHIS) as part of the NHIS Early Release Program. Each quarter covers a three-month span, and this blog specifically looks at survey data from the most recent quarter (Q3 - July to September 2023) and notes any differences compared to the same time period in 2022.
Between Q3 of 2022 and Q3 of 2023, rates of uninsurance, public coverage, and private coverage for adults (age 18-64) remained mostly unchanged. There was a small increase in the rate of public coverage for all ages and a small decrease in the rate of uninsurance overall, but these changes were not statistically significant.
Figure 1: Health Insurance Coverage Rates by Type (Adults Age 18-64), Q3 2022 vs. Q3 2023
[1] Centers for Medicare & Medicaid Services (CMS). (2023, December 18). Medicaid and CHIP Enrollment: Child and Youth Data Snapshot. https://www.medicaid.gov/sites/default/files/2023-12/medicaid-unwinding-child-data-snapshot.pdf
Blog & News
2020 Public Use Microdata Area (PUMA) Updates in the 2022 American Community Survey
March 26, 2024:A Public Use Microdata Area (PUMA) is a type of geographic unit created for statistical purposes. PUMAs represent geographic areas with a population size of 100,000–200,000 within a state (PUMAs cannot cross state lines). PUMAs are the smallest level of geography available in American Community Survey (ACS) microdata. They are designed to protect respondent confidentiality while simultaneously allowing analysts to produce estimates for small geographic areas.
Every ten years, the decennial census results are used to redefine ACS PUMA boundaries to account for shifts in population and continue to maintain respondent confidentiality. This process is intended to yield geographic definitions that are meaningful to many stakeholders.
Most recently, new PUMAs were created based on the 2020 Census; these 2020 PUMAs were implemented in the ACS starting in the 2022 data year. Although Public Use Microdata Area components remain consistent to the extent possible, they are updated based on census results and revised criteria. Therefore, they are not directly comparable with PUMAs from any previous ACS data years. For example, the 2020 PUMAs used in the 2022 data year are distinct from the 2010 PUMAs, which were used in the 2012–2021 ACS data years.
The 2020 PUMAs were created based on definitions that include two substantive changes relative to the 2010 PUMAs:
1) An increase in the minimum population threshold for the minimum size of partial counties from 2,400 to 10,000. Increasing the population minimum for a PUMA-county part aims to further protect the confidentiality of respondents. However, exceptions are allowed on a case-by-case basis in order to maintain the stability of PUMA definitions (that were based on the previous minimum of 2,400) and due to unique geography.
2) Allowing noncontiguous geographic areas. Allowing PUMAs to include noncontiguous geographic areas aims to avoid unnecessarily splitting up demographic groups in order to provide more meaningful data. This change is not intended to create highly fragmented PUMAs.
Other than the two changes listed above, PUMA criteria remained consistent, such as treating 100,000 as a strict minimum population size for PUMAs. The maximum population size for PUMAs can exceed a population of 200,000 in certain instances due to expected population declines or geographic constraints.
Generally, counties and census tracts are the building block geographies for PUMAs. This maximizes the stability of PUMA boundaries and therefore reduces disclosure risks. Counties may be combined or split as needed, depending on population size. Additionally, PUMAs are designed to avoid unnecessarily splitting metropolitan areas, minor civil divisions (MCDs) with a functional government, and areas of American Indian/Alaska Native (AIAN) and Native Hawaiian people. Carlton County in Minnesota, for example, illustrates that splitting a county was necessary to avoid splitting Fond du Lac reservation into multiple PUMAs (U.S. Census Bureau, 2022).
Figure 1: Minnesota PUMA 00400 Includes Fond du Lac Reservation in Its Entirety
Note: Minnesota PUMA 00400 featuring Fond Du Lac Reservation in its entirety. Reprinted from Final Criteria for Public Use Microdata Areas for the 2020 Census and the American Community Survey from https://www2.census.gov/geo/pdfs/reference/puma2020/2020PUMA_FinalCriteria.pdf.
Due to periodic changes in Public Use Microdata Area boundaries, states as a whole are the most granular geographic variable in the ACS microdata that can be compared consistently across all years. However, IPUMS USA offers harmonized geographic variables that greatly expand the options for analyzing ACS data across both time and geography. These variables are less granular than PUMAs, but more granular than states. For example, CONSPUMA and CPUMA0010 combine ACS PUMAs to create consistent geographies that can be compared across multiple decades of PUMA definitions; these have not yet been updated to include the new 2020 PUMAs. Currently, CITY, COUNTYFIP, and MET2013 allows users to compare cities, counties, and metropolitan areas, respectively, over years 2005-2022.
IPUMS USA also provides in-depth geographic resources for ACS data users, including interactive maps of the 2010 and 2020 PUMAs. For example, below is a map of the Twin Cities metropolitan area, Minnesota, showing 2020 PUMAs in blue and 2010 PUMAs in orange (IPUMS USA, 2020). This map illustrates that the updated PUMA criteria resulted in changes to PUMA boundaries in urban areas and some aggregation of PUMAs in more rural areas.
Figure 2: IPUMS USA Map of Twin Cities Metropolitan Area, Minnesota 2010 and 2020 PUMAs
Note: IPUMS USA Map of Twin Cities Metropolitan Area, Minnesota 2010 and 2020 PUMAs. Reprinted from 2020 PUMA DEFINITIONS: WEB MAP OF 2010 AND 2020 PUMAS from IPUMS USA, https://usa.ipums.org/usa/volii/pumas20.shtml.
Looking for other data and information from the American Community Survey? Learn more with the following SHADAC products:
2022 ACS Tables: State and County Uninsured Rates
American Indian/Alaska Native (AIAN) and Native Hawaiian and Other Pacific Islander (NHPI) data added to State Health Compare ACS estimates
Explore the Data on State Health Compare
Sources:
U.S. Census Bureau. (2022, July 25). Final Criteria for Public Use Microdata Areas for the 2020 Census and the American Community Survey. https://www2.census.gov/geo/pdfs/reference/puma2020/2020PUMA_FinalCriteria.pdf
IPUMS USA (2020). 2020 PUMA Definitions: Web Map of 2010 and 2020 PUMAs. https://usa.ipums.org/usa/volii/pumas20.shtml.
Publication
ACS 5-year Estimates: State and County Uninsured
Following the release of 2022 single-year estimates of health insurance coverage, household income, and poverty levels, the U.S. Census Bureau has now made available 5-year American Community Survey data files. These ACS 5 Year estimates are generated by pooling together five years of American Community Survey (ACS) data to produce estimates for areas and subgroups with smaller populations.
The interactive map provided below offers users the opportunity to explore health insurance coverage estimates, specifically the percentage of uninsured individuals for each state and all counties for the pooled years 2018-2022.* These data can be accessed via the Census Bureau’s data.census.gov tool. Click on a county in the map below to view state/county data tables. Counties are easily searchable through bookmarks in each state file.
2018-2022 American Community Survey (ACS) 5-Year Estimates: Percent Uninsured, Total Civilian Noninstitutionalized Population by County
Click here to view estimates for Puerto Rico by municipio.
Click here to view a 50-state table of estimates.
About the American Community Survey and ACS Data
The ACS is a household survey that began in 2005 and produces annually updated data on a variety of population characteristics, including health insurance coverage. In total, the ACS surveys approximately three million US households each year. An important feature of the ACS is that it includes a large enough sample for state‐level and sub‐state estimates. The Census Bureau provides ACS 1 year estimates and ACS 5 year estimates.
The Census Bureau publishes 1-year estimates for areas with populations of 65,000 or more and 5-year estimates (covering 60 months) for all statistical, legal, and administrative entities.
The ACS began asking survey respondents about health insurance coverage during the 2008 calendar year. Specifically, the survey asks respondents about current coverage for each person in the respondent’s household. A person is categorized as “insured” if he or she has coverage at the point in time at which the survey is administered.
*The U.S. Census Bureau has extensively cautioned against the use of single-year 2020 ACS estimates due to disruptions caused by the COVID-19 pandemic (e.g., limited means of data collection, such as shutdowns of mail operations, switches to telephone-first methodologies, etc., leading to low response rates and nonresponse bias). However, the Census Bureau believes that course corrections to address nonresponse bias, and the larger sample resulting from pooled data mean that the “data are fit for public release, government and business uses, and understanding the social and economic characteristics of the U.S. population and economy.”[1]
How Are These Estimates Different from the Estimates that SHADAC Publishes Using Census Bureau Micro-Data Files?
Two definitions used by the Census Bureau to generate the tabulations above differ from those that SHADAC uses to generate tabulations for State Health Compare. The definitional differences are as follows:
Family
The Census Bureau defines a family as “all related people in a household.”
SHADAC defines a family using a measure called the “Health Insurance Unit” (HIU), which includes all individuals who would likely be considered a family unit in determining eligibility for either private or public coverage.
To learn more about the 2020 update of SHADAC's Health Insurance Unit (HIU) see our HIU resource page, which houses two issue briefs: The first describes the SHADAC HIU, its purpose, the most recent update, and improvements to HIU data inputs; and the second outlines the impacts of using the SHADAC HIU in analysis so that researchers can assess whether the SHADAC HIU is suitable for their research and what the potential impacts of its use might be. The page also provides a link to STATA and SAS codes to aid in the use of the HIU variable.
Family Income
The Census Bureau determines family income as a percentage of the Federal Poverty Level (FPL), which is a definition of poverty used primarily for statistical purposes. For example, FPL is used to estimate the number of Americans living in poverty each year.
SHADAC determines family income as a percentage of the U.S. Department of Health and Human Services’ Federal Poverty Guideline (FPG), which is a measure used for administrative purposes. For example, FPG is used to determine eligibility for federal programs such as Medicaid and CHIP, as well as the Supplemental Nutrition Assistance Program (SNAP).
Check out our blog post from April 2023 to learn more about the difference between FPL and FPG.
Related ACS Materials:
Blog: 2022 ACS Tables: State and County Uninsured Rates, with Comparison Year 2021
[1] U.S. Census Bureau. (2022, February 7). Census Bureau Update on 2016–2020 American Community Survey (ACS) 5-Year Estimates [Press Release CB22-RTQ.01]. https://www.census.gov/newsroom/press-releases/2022/acs-5-year-estimates-update.html