SHADAC's 2020 Health Insurance Unit and Updated Defining Families Issue Brief

July 2023:

In 2012, SHADAC created and released the SHADAC “Health Insurance Unit,” or “HIU,” to aid researchers in reconciling the differences between the way that a “family” is defined in federal surveys (such as the U.S. Census Bureau’s American Community Survey [ACS] or Current Population Survey [CPS]) with the way a “family” is defined by most private and public insurance programs, specifically for the purposes of analyzing health insurance coverage. The SHADAC HIU has been updated over time and has now been updated again in response to changes to policy and program eligibility rules.

To support the use of the SHADAC HIU we have developed three products:

2020 Update of SHADAC’s Health Insurance Unit (July 2020): An issue brief that describes the SHADAC HIU, its purpose, the most recent update, and improvements to HIU data inputs made available by IPUMS.

*RECENTLY UPDATED* Defining Family for Studies of Health Insurance Coverage (July 2023): A brief that outlines the impacts of using the SHADAC HIU in analysis—specifically, analysis showing how the population distribution of family income changes using three different definitions of family: all members in the same household (Census definition), the definition used by the IPUMS (described below), and the SHADAC HIU (described in detail in a companion brief). Researchers can use this brief to assess whether the SHADAC HIU is suitable for their analysis and what the potential impacts of its use might be.

Stata Code (January 2021): Technical documentation of statistical code in STATA and SAS to help researchers to employ the SHADAC HIU in their own analysis using ACS microdata downloaded from IPUMS USA.

 

Archived documentation from previous versions of the SHADAC HIU include:
"Defining Family for Studies of Health Insurance Coverage" August 2021
"Using SHADAC Health Insurance Unit (HIU) and Federal Poverty Guideline (FPG) Microdata Variables" November 2013
"Defining “Family” for Studies of Health Insurance Coverage" March 2012 

For questions about the SHADAC HIU or to receive technical assistance regarding its use, please feel free to contact SHADAC analysts at shadac@umn.edu

Lukanen, Elizabeth
Zylla, Emily

Best Practices for Publicly Reporting State Unwinding Data (Cross-Post)

The following content is cross-posted from State Health & Value Strategies. 
Authors: Elizabeth Lukanen, Emily Zylla, SHADAC

Original publication date: June 30, 2023.

The unwinding of the Medicaid continuous coverage requirement represents the largest nationwide coverage transition since the Affordable Care Act. With the restarting of eligibility redeterminations, millions of Medicaid enrollees are now at risk of losing their coverage and there is intense interest in monitoring the impact on coverage. To promote transparency, the Centers for Medicare & Medicaid Services (CMS) is requiring that states report the results of eligibility redeterminations and many states are making that data public. SHVS continues to monitor the states that are publicly reporting their unwinding data either in the form of data dashboards or static pdfs and the states posting copies of their required CMS Monthly Unwinding Data reports.

As advocates and researchers start to analyze unwinding data, it has become clear that it is difficult to compare different states for a variety of reasons. For example, states are initiating redeterminations and therefore disenrollments in different months and some states are staging redeterminations in a way that focuses on certain groups first, such as starting with people least likely to remain eligible. States are using a variety of different strategies to mitigate procedural terminations. States also use different terminology, definitions, population denominators, and timeframes on their reporting, which also makes it difficult to compare one state’s data to another.

In spite of data challenges, there are some best practices states can follow when reporting unwinding data.

• Release CMS Monthly Reports. While we typically don’t recommend releasing competing sources of data on the same topic, in this case, we recommend releasing CMS Monthly Reports alongside any state-specific data reporting to help satisfy calls from advocates and the media for comparable data across states. However, we recommend that states provide context about why the information may differ on state dashboards from CMS monthly reports. Kansas, for example, explains that data on their monthly unwinding reports is different from data on their dashboard because members move between reported categories.
   
• Timely release. Given the magnitude and scrutiny of unwinding coverage transitions, we recommend that states produce a data report that can be updated easily and frequently – at least monthly. We recommend prioritizing accuracy and timeliness over depth. And, if states are refreshing state unwinding data monthly, they should consider releasing it in tandem with the CMS Monthly Unwinding data reports.
   
• Prioritize key measures. Because timeliness of this data is so important, the report or dashboard needs to have enough data points to convey key information, but be limited enough to update quickly. In this case, we recommend focusing on measures that are already being reported (CMS Performance Indicators and indicators from the CMS Monthly Unwinding Data report). Key measures include:
  • Renewals initiated
  • Successful renewals
  • Ex-parte/passive renewals
  • Coverage terminations
  • Procedural terminations
  • Medicaid cases sent to the Marketplace
  • Call center volume
  • Number of terminations who reenroll in a specific period of time (e.g. 90 days)
     
• Publish disaggregated data. While required CMS data reporting includes very limited data disaggregation, there is a great interest in understanding who is being impacted by the unwinding. At a minimum, we recommend displaying data breakdowns by:
  • Program type
  • Age (children versus adults)
  • Race
  • Ethnicity
  • Language
  • Income
  • Geography (ZIP code is best, but by county or any other level lower than statewide is helpful)
     
• Provide context and transparency. As noted above, there are many reasons that the data reported by different states might be difficult to compare. We strongly encourage states to use their reporting vehicle to contextualize and explain various measures. This includes:
  • Document data revisions. We anticipate that states may be asked by CMS to make various adjustments retroactively to their data. States do not need to wait until data is final to post it publicly. They can address this by including revision dates when new data are posted. And wherever possible, maintain an archive of older data for comparability.
  • Include both proportions and counts. Proportions are especially useful to help make comparisons across states on measures like procedural denials, but counts are critical for understanding the denominators being used and to get a sense for the magnitude of impacts within states.
  • Include data labels, definitions, and context. Provide clear data labels, establish defined date ranges for data being displayed, add data refresh dates (as needed), describe population numerators and denominators if relevant (including exclusions such as pending cases). If the format allows, link to information that can help contextualize the information. This might include the state’s unwinding timeline, information about how redeterminations are being staged or any analysis or reaction to the results to date. For example, California posts their plan for prioritizing and distributing renewals on the same page as their unwinding data. Kentucky also has a webpage dedicated to unwinding that includes the state’s unwinding dashboard (the Kentucky Medicaid Renewals Data Snapshot), their required CMS unwinding reports and materials from monthly stakeholder sessions that provide a summary of the federal requirements, state goals and activities and their progress to-date.

Related SHVS Expert Perspectives:

• Using Surveys to Monitor Coverage Transitions During the Unwinding of the Medicaid Continuous Coverage Requirement

• State Dashboards to Monitor the Unwinding of the Medicaid Continuous Coverage Requirement 

• State-Based Marketplace Transition Data During the Unwinding

• States Reporting of Medicaid Unwinding Reinstatement Data

Hest, Emily
Hest, Robert

Community and Uninsured Profile In Action: Local Case Studies

With the recent end of the public health emergency and subsequent “unwinding” of the Medicaid continuous coverage requirement, many Americans are now having to navigate changes in health insurance coverage. And while coverage transitions aren’t unique to this moment, the unwinding has spurred many states to think critically about how to mitigate the impacts of this event on potentially vulnerable populations.

Existing data on local communities is invaluable for states considering their approach for helping their residents secure coverage both now and beyond this historic moment. The Minnesota Community and Uninsured Profile is one example of the type of data that can make a meaningful difference in efforts to reach and cover communities. The Minnesota Community and Uninsured Profile features population details and characteristics that can be applied for a variety of different purposes; below are two case studies highlighting how the profile can support the enrollment efforts of local organizations and departments.

Community and Uninsured Profile in Action

Open Door Health Center Case Study

Open Door Health Center (ODHC) is a community health center that serves southern Minnesota. Given that ODHC serves a broad geographic area with limited staffing and resources, the team has to be strategic about how they do community outreach and enrollment. The profile helps ODHC identify specific areas in southern Minnesota with concentrations of uninsured individuals who are potentially disconnected from health care resources. Their navigators can then conduct enrollment and educational events within those areas, equipped with the important context on the local population found in the profile.

Staff at ODHC used the “ZIP Code Uninsured Rates” tab in the profile to hone in on the 24 community ZIP Codes in their region (South Central, MN) with available data. Among those communities, Mankato (56001) has the largest number of uninsured at 1,719 people which could make it a logical site for enrollment and outreach efforts.

By entering “56001” in the Uninsured Profile tab, they could get more pertinent information about Mankato to help effectively target outreach. For example, they would see that among the uninsured in this community:
 

• More than a quarter are likely income eligible for Minnesota Medical Assistance (<138% of the federal poverty level [FPL]) and more than half are likely income eligible for subsidized coverage through MNsure (138-400% FPL)
   
• Nearly 70% are male;
  
  
  
  
   
• More than 80% are currently employed;

• And nearly 60% are between the ages of 26 and 44.
  
  
  
  
  
  
   

Based on these characteristics, staff at ODHC could set up enrollment events in partnership with employers of low-wage workers, or target outreach with messages and venues that speak to male residents or adults in early to middle age. ODHC could be confident that many of the uninsured community members they reach would be income-eligible for some type of free or subsidized coverage.

Further, looking at the profile, ODHC staff could observe that though there are a relatively small number of uninsured Hispanic/Latino residents of this community (211 persons), that group has a high uninsured rate (10.1%), especially compared to Mankato’s comparatively low community uninsured rate of 3.5%. This could indicate that further work needs to be done specifically to cover the Hispanic and Latino residents of Mankato.

Nicollet County Case Study

Nicollet County's local public health department serves a population of about 33,000. Like ODHC, they use the profile to review the demographics of those who are uninsured in their community to inform outreach efforts. The Community and Uninsured Profile also helps them target influenza and COVID-19 vaccinations for underinsured and uninsured community members. The department also uses the profile to better understand the characteristics of individuals who are uninsured in their communities through demographic filters such as race and ethnicity.

Looking in the ZIP Code Uninsured Rates tab, there are two ZIP-Code-defined communities (with available data) in Nicollet County: St. Peter (56082) and North Mankato (56003). Both have relatively low uninsured rates at 3.6% and 3.1%, respectively, and both have around 500 uninsured residents.

However, there are important differences between these communities that are evident when looking in the Uninsured Profile tab. For example, though less than 5% of the uninsured in North Mankato appear to be income-eligible for Medical Assistance (<138% FPL), more than 36% of the uninsured in St. Peter appear to be income-eligible for that program.

Further, whereas none of the uninsured in North Mankato are Hispanic or Latino, Hispanic or Latino residents make up more than 60% of the uninsured in St. Peter. And there is a similar story by citizenship status and nativity; whereas all of North Mankato’s uninsured are U.S. born, more than 50% of St. Peter’s uninsured are foreign born, and more than 90% of those are not citizens, which creates policy and financial barriers to accessing insurance and health care.

The different characteristics of the uninsured in these two communities call for different strategies for outreach and enrollment efforts, such as providing culturally appropriate materials, selecting different sites or partners for enrollment events, and being prepared to enroll uninsured community members into different types of health coverage programs.

What are your Community and Uninsured Profile stories?

Are you a part of an organization that uses the Minnesota Community and Uninsured Profile? We would love to hear stories of how you have used the profile in your work. If you have a story to share, please send it to shadac@umn.edu. 

What the Growing Medicaid Undercount Means for Data Users and Policymakers

SHADAC hosted a one-hour webinar on Wednesday, April 5th to discuss the rising Medicaid undercount. With many states and stakeholders eager to understand the topic further, this webinar provided an overview of issues related to the Medicaid undercount along with relevant policy implications. View a recording of this webinar below!

 

Download the slidedeck from this webinar here.

Attendees learned:

• The extent to which, how, and why 2021 coverage data underestimated Medicaid coverage and/or overestimated uninsurance

• The data and methodology used to calculate the undercount

• How policy and advocacy may be impacted by the growing Medicaid undercount

The panel also answered questions from the audience following their presentation. Due to time constraints, the panelists were unable to answer all audience-submitted questions live. Please click here to view those additional Q&A. 

Speakers

Lynn Blewett

Director @SHADAC

Dr. Lynn Blewett is the Founding Director of SHADAC, as well as a Professor in the Division of Health Policy and Management at the University of Minnesota, School of Public Health, where she teaches graduate courses on the U.S. health care system and international health systems. Her health policy experience includes legislative work for the U.S. Senate and state policy work as Director of the Health Economics Program for the Minnesota Department of Health.

Benjamin Sommers

Senior Counselor @Assistant Secretary for Planning and Evaluation (ASPE)

Dr. Benjamin Sommers is a health economist, primary care physician, and professor. Dr. Sommers currently serves as a Senior Counselor to the Assistant Secretary for Planning and Evaluation (ASPE) at the U.S. Department of Health and Human Services, where he was previously the Deputy Assistant Secretary for Health Policy. He is also the Huntley Quelch Professor of Health Care Economics at the Harvard School of Public Health. His research interests include health insurance markets, Medicaid, health disparities, and the health care safety net.

Robert Hest

Senior Research Fellow @SHADAC

Robert Hest is a Senior Research Fellow at SHADAC. Mr. Hest provides expertise in survey data, quantitative data analysis, data visualization, and health coverage policy. Mr. Hest also manages SHADAC’s State Health Compare website, coordinating data processing, quality assurance, dissemination, and documentation of data. In addition, Mr. Hest provides technical assistance to states and other organizations on topics related to health coverage, cost, and access.

Genevieve Kenney

Co-Director and Senior Fellow @Urban Institute

Genevieve M. Kenney is co-Director and a Senior Fellow in the Health Policy Center at the Urban Institute. Dr. Kenney is an expert on Medicaid, CHIP, and health outcomes for low-income adults, children, and families. She has also played a lead role in a number of Medicaid and CHIP evaluations, including multiple congressionally mandated evaluations of CHIP, and has conducted state-level evaluations of the implementation of managed care and other service delivery reform initiatives and policy changes in Medicaid and CHIP.

Joan Alker

Executive Director @Georgetown University Center for Children and Families

Joan Alker is the Executive Director and Co-Founder of the Georgetown University Center for Children and Families and a Research Professor at the McCourt School of Public Policy at Georgetown University. She is a nationally recognized expert on Medicaid and the Children’s Health Insurance Program (CHIP) and is the lead author of CCF’s annual report on children’s health coverage trends.