Blog & News
Tracking Health Insurance Coverage During the Unwinding: Monthly Data from the Household Pulse SurveyUpdated on September 20, 2023:
This update uses data from the September release of the Household Pulse Survey, collected from August 23 – September 4, 2023.
The unwinding of the Medicaid continuous coverage requirement represents the largest nationwide coverage transition since the Affordable Care Act. Since February 2020, enrollment in Medicaid and the Children’s Health Insurance Program (CHIP) has increased by 23 million enrollees and analysis indicates that as many as 15 million individuals will exit Medicaid to other coverage or become uninsured. This blog uses data from the U.S. Census Bureau’s Household Pulse Survey (HPS) to track trends in adult health insurance coverage rates as states “unwind” the Medicaid continuous coverage requirement and restart standard redetermination procedures.
Given the intense interest from policymakers and the media in monitoring coverage transitions during the unwinding, many states have released Medicaid administrative data showing their progress, with some State-based Marketplaces also reporting transition data. Though administrative data can show the number of successful Medicaid renewals and coverage terminations along with transitions to Marketplace coverage, they cannot provide information on transitions to other sources of coverage, such as employer-sponsored insurance or provide an estimate of changing rates of uninsured individuals.
As states continue the process of redetermining beneficiaries’ Medicaid eligibility, this resource will help track transitions in coverage. Specifically, it will present rates of primary source of health insurance coverage by type (Employer/Military, Direct Purchase, Medicaid/CHIP) and rates of no insurance as they are observed in the HPS. Estimates will be presented at the state and national level by selected individual and geographic characteristics. The survey does not include children, so the analysis is limited to adults 18 and older.
This blog will be updated on a monthly basis as new HPS data are released and compare the latest monthly coverage estimates (reference above in the subtitle) to estimates from March 2023, the last month before the unwinding began.
Limited to statistically significant changes at the 95% confidence level.
- There were no observed changes in coverage among all adults at the national level
- Though unchanged nationally, a small number of states saw changes in the share of adults who had employer-sponsored insurance as their primary source of coverage
- The share of adults with Direct Purchase coverage saw small but significant changes, such as declines in three states (Delaware, Illinois, and Kansas) and among those with previous year household incomes at or above $100,000 as well as increases in two states (Maryland and Wyoming)
- The share of adults with no coverage saw modest changes among subpopulations (non-Hispanic White individuals, those age 65+) and in a few states (Alaska, D.C., and Kansas)
- There were few significant state-level changes in the share of adults with Any Medicaid/CHIP, or Medicaid/CHIP as primary source of coverage
Select a coverage type from the orange box on the right in the dashboard below to filter the visualizations.
Methods and Data
This analysis uses public use microdata from the Household Pulse Survey (HPS), a monthly, nationally representative, quick-turnaround survey that collects data on topics including household demographics, education, employment, food sufficiency, financial spending, housing security, and physical and mental health, in addition to current health insurance coverage.
The survey has a typical monthly sample size of 60,000 to 80,000 U.S. adults and is designed to produce state-level (and a select number of metropolitan-level) estimates of the civilian noninstitutionalized adult population. The survey does not include children (those age 17 or younger).
Data is collected for approximately two weeks each month from adults (age 18 or older) via a short, online survey and is released on a monthly basis. Readers should keep in mind that the HPS emphasis on producing near-real-time data comes with the tradeoff of lower levels of data quality compared with “gold standard” surveys such as the American Community Survey (ACS). These data quality issues include very low response rates (e.g., 6.7% response rate in the March 2023 survey), underrepresentation of harder-to-reach groups (e.g., adults with lower levels of education, young adults), a lack of editing and imputation for most variables, and likely some degree of nonresponse bias. For these reasons, HPS estimates should be treated with a greater degree of caution than estimates from other federal surveys.
Further, like other surveys, the HPS relies on respondents’ self-reporting their coverage, which is often associated with known biases such as the Medicaid Undercount and reflects respondents’ (sometimes imperfect) knowledge of their own coverage rather than the reality reflected in administrative data sources.
The HPS’ health insurance coverage measure is similar to that used in the ACS and asks respondents: “Are you currently covered by any of the following types of health insurance or health coverage plans?” Respondents are allowed to select “Yes” or “No” from among the following coverage types:
1. “Insurance through a current or former employer or union (through yourself or another family member)”;
2. “Insurance purchased directly from an insurance company, including marketplace coverage (through yourself or another family member)”;
3. “Medicare, for people 65 and older, or people with certain disabilities”;
4. “Medicaid, Medical Assistance, or any kind of government-assistance plan for those with low incomes or a disability”;
5. “TRICARE or other military health care”;
6. “VA (including those who have ever used or enrolled for VA health care)”;
7. “Indian Health Service”; or
The response options for employer coverage , TRICARE , and VA  were combined into one Employer/Military coverage category, and respondents were considered uninsured if they didn’t affirmatively report any coverage under options 1-6.
SHADAC’s primary source of coverage hierarchy was applied to determine which payer was likely primary when a respondent reported multiple sources of coverage (see SHADAC brief for more information).
For example, the hierarchy would classify a respondent reporting both Medicaid/CHIP coverage and Employer/Military coverage as having Employer/Military as a primary source of coverage, as Employer coverage typically acts as the primary payer for individuals with Employer and Medicaid coverage.
Estimates with a relative standard error (standard error divided by the percentage estimate) of 30% or greater, based on an unweighted denominator count of less than 50, based on an unweighted numerator count of less than five, or with a weighted estimate of exactly 0% or 100% were considered statistically unreliable and were suppressed.
Two-sided t-tests were used to assess statistically significant differences between the most recent data month and the baseline month (i.e., March). A lack of statistically significant difference does not affirmatively establish that there was no significant difference but rather that the data presented here are not sufficient to show a significant difference.
Blog & News
State Dashboards to Monitor the Unwinding of the Medicaid Continuous Coverage Requirement (Cross-Post)September 9, 2023:
The following content is cross-posted from State Health & Value Strategies.
Authors: Elizabeth Lukanen, Emily Zylla, and Lindsey Theis, SHADAC
This expert perspective (EP) will be updated by SHADAC experts as additional dashboards/reports go live. Please visit the State Health & Values Strategies webpage for the most recent version of this EP.
Original publication date: March 16, 2023. Updated September 9, 2023.
The unwinding of the Medicaid continuous coverage requirement represents the largest nationwide coverage transition since the Affordable Care Act, with significant health equity implications. As states restart eligibility redeterminations, millions of Medicaid enrollees will be at risk of losing their coverage with some portion exiting because they are no longer eligible, some losing coverage due to administrative challenges despite continued eligibility, and some transitioning to another source of coverage. As part of this process, the Centers for Medicare & Medicaid Services (CMS) requires states to closely track and monitor the impacts of the resumption of eligibility redeterminations and disenrollments, and on July 28, 2023, CMS released the first batch of these data. CMS’ commitment to transparency is mirrored by calls from advocates and researchers eager to see how progress is being made as people enrolled in Medicaid have their eligibility redetermined.
Given the intense focus on coverage transitions during the unwinding and the delays and caveats of the CMS data, many states have published their own state data dashboards to monitor progress. Data dashboards are useful for publishing dynamic data that is in high demand. They allow states to make proactive decisions about what data to release and on what schedule and then organize that data in an easy-to-digest visual format that facilitates the interpretation of key trends and patterns at a glance. Given the intense interest in unwinding data to monitor the impact of coverage transitions, releasing data in this format also allows states to follow some specific best practices, including providing additional detail about definitions, timeframes, and state context that will be important for communicating the unique and specific circumstances that states are experiencing during unwinding. An SHVS expert perspective highlights best practices for states to follow when publicly reporting unwinding data.
States Publicly Posting Unwinding Data
To date, 42 states including the District of Columbia (D.C.) have publicly published unwinding data in some format (this does not include states with pre-existing enrollment dashboards that don’t specifically identify unwinding cohorts). In most cases, state Medicaid departments are releasing those data, although some State-Based Marketplaces (SBMs) are also publishing unwinding data (see the SHVS expert perspective on SBM Marketplace Transition Data During the Unwinding). Of the 42 states currently reporting data:
- 21 are releasing state unwinding data online in either an interactive dashboard or static pdf format.
- Two states have only released copies of their required CMS Monthly Unwinding Data.
- 19 states are releasing both state data and their CMS Monthly Unwinding Data reports.
In some cases states are publishing unwinding information in an ad-hoc way—such as in a report to a legislature. Because these data are not being systematically reported, they are not represented in the map above.
SHADAC will continue to update this expert perspective as more states publish their unwinding data.
Variation in States’ Reporting of Indicators
There is a wide variation in the indicators that states are reporting on their state data dashboards and reports. Of the 42 states reporting publicly, most are now reporting renewal and termination data. Some states are also reporting other interesting indicators such as:
- Reasons for procedural denials
- Number of cases terminated that are re-enrolled or reinstated in Medicaid
- Number of Medicaid cases sent to the Marketplace
- Enrollment in CHIP
It’s important to note that states use different terminology, definitions, population denominators, and timeframes on their dashboards making it difficult to compare one state’s data to another. In some instances, the data displayed on state dashboards also varies from what states include in their monthly reports to CMS (see Georgetown’s State Unwinding Renewal Data Tracker for a summary of states’ monthly CMS reports). Another cause of variation in the types of indicators reported across states is that states began disenrolling people from Medicaid in different months; for example, five states began disenrollments in April and another 14 states began disenrollments in May. Also, CMS has allowed states to delay terminations for one month and conduct additional outreach.
Unwinding Indicators & Disaggregated Data Reported by States
Few states are reporting disaggregated data on their dashboards. The most common breakdowns that states are providing are by program and geography (typically by county). Although CMS only asks states to report data by modified adjusted gross income (MAGI) and non-disability applications, versus disability applications, additional data breakdowns by age, race, ethnicity, and program type can elucidate important trends about the disproportionate impact of unwinding on groups that have been economically or socially marginalized.
Visit the SHVS Expert perspective for a selection of state dashboard examples including: Arizona, Colorado, Massachusetts, Minnesota, New Hampshire, Ohio, Oregon, Utah, and Washington.
Blog & News
Minnesota’s COVID-19 vaccine campaign left vulnerable groups with lagging ratesAugust 2, 2023:
Health inequities are nothing new in Minnesota, but the pandemic placed them in a new light. Numerous studies have reported disparities in how COVID-19 affects many vulnerable groups, often placing them at higher risk of infection, hospitalization, and death. And the inequitable effects of the disease itself are not the only cause for alarm. Once COVID-19 vaccines received authorization, disparities in vaccination rates also became a concern, especially as surveys indicated widespread hesitancy and lagging uptake.
Partnering with the Minnesota Electronic Health Record Consortium, SHADAC delved deep into an analysis of COVID-19 vaccination rates in Minnesota. Unlike many other studies, we were able to examine not only how COVID-19 vaccination rates differed across different demographic groups, but also how those disparities developed over time. This type of analysis was possible because of the Consortium’s unique dataset, which matches data from large health care providers’ electronic health records—including detailed demographics with immunization records from the Minnesota Department of Health, ultimately covering almost all people who received a COVID-19 vaccine in the state.
Understanding the dynamics of vaccination disparities over time was a crucial element of our study. While we present data on COVID-19 vaccination rates for different demographic subpopulations at the end of 2022, we also examined the time it took to reach 50 percent of people in different demographic groups with COVID-19 vaccinations. That measure of time to vaccination is important because, in a public health crisis during which hundreds or even thousands of people were dying each day at its peak in the U.S. alone, the speed with which people were vaccinated was critical to saving lives.
Our approach also allowed us to glean unique insights on COVID-19 vaccination disparities that are hidden below the headline numbers. For instance, although Minnesota’s Black, Latino, and White populations each had similar COVID-19 vaccination rates (61 percent, 61 percent, and 65 percent, respectively) at the end of 2022, they took distinctly different paths to reach that point. Within six months of the first COVID-19 vaccine receiving emergency use authorization, Minnesota vaccinated half of the state’s White population, but it took twice as long (12 months) for the state to vaccinate half of its Black and Latino populations.
Another crucial but under-recognized factor in COVID-19 vaccination disparities is the way public health policies placed some groups as a disadvantage in accessing vaccines in the early days of limited supply. Because experience showed that elderly people were at higher risk of severe disease and death from COVID-19, they were prioritized to be among the first people eligible for vaccines. But nationwide and in Minnesota, the White population skews older, effectively baking racial and ethnic disparities into the vaccine prioritization criteria. For that reason, we also stratified racial and ethnic groups’ COVID vaccination data by age—an approach that uncovered complex dynamics.
We found that disparities in COVID-19 vaccination rates were relatively small among Minnesota’s elderly population. For instance, Minnesota succeeded in vaccinating half of the elderly population across all racial and ethnic groups within three or four months—an example of how health inequities can be minimized. But disparities were stark among young adults, age 19-24. While Minnesota vaccinated half of Asian and Native Hawaiian and Pacific Islander young adults within five months, it took roughly twice as long to vaccinate half of Latino young adults (nine months), White young adults (ten months), and Black young adults (eleven months). And stunningly, Minnesota had failed to reach half of American Indian and Alaska Native young adults by the end of 2022—approximately 24 months after vaccines were first authorized.
We also examined COVID-19 vaccination rates by other demographic categories, finding higher vaccination rates and quicker vaccination progress among older population groups, urban and suburban communities, and females. Additionally, our study found disappointing rates of COVID-19 vaccination among children, particularly younger children. At the end of 2022, less than one-half of children age 5-11 had been vaccinated, despite having been eligible for vaccines for over a year; and less than one-tenth of children age six months to four years had been vaccinated, despite having been eligible for roughly six months. Despite a common, incorrect notion that COVID-19 is harmless for children, other researchers have found that COVID-19 was a top 10 cause of death for children during the pandemic, so those low vaccination rates are needlessly leaving Minnesota kids at risk.
Together, the findings from this new study highlight two main points: First, Minnesota’s COVID-19 vaccination efforts resulted in clear disparities. When looking at high-level data, it is easy to miss those disparities because some of them narrowed over time. But looking at detailed data illustrates the ways that certain groups were left vulnerable to COVID-19 for much longer than others. Second, our findings on disparities in the time Minnesota took to vaccinate half of different subpopulations demonstrate the importance of monitoring such health equity measures over time. Time is critical in an emergency such as the pandemic, and eventually closing gaps in health disparities simply isn’t good enough. Health equity requires urgency.
Disparities in Minnesota’s COVID-19 Vaccination Rates
A new SHADAC issue brief found stark disparities not only in the share of subpopulations that have been immunized against COVID-19 in Minnesota, but also in the length of time it took vaccines to reach most people in these different groups.
For instance, by the end of 2022, 80 percent of Asian and Native Hawaiian and Pacific Islander people in Minnesota were vaccinated against COVID-19, but only 53 percent of American Indian and Alaska Native people in the state were vaccinated.
Similarly, while Minnesota successfully vaccinated half of the state’s Asian and Native Hawaiian and Pacific Islander population and White population within six months of the first COVID-19 vaccine authorization, the state took fifteen months to reach vaccination for half of the American Indian and Alaska Native population.
The study also found wider and more obvious disparities among younger segments of Minnesota’s population. For instance, half of the elderly population (age 65+) in all racial and ethnic groups were vaccinated within similar time frames of just three or four months. However, among young adults age 19-24, Minnesota was able to vaccinate half of Asian and Native Hawaiian and Pacific Islander young adults within five months but took roughly twice as long to reach half of Latino young adults (nine months), White young adults (ten months), and Black young adults (eleven months). And, shockingly, Minnesota had failed to reach half of American Indian and Alaska Native young adults by the end of 2022—almost two years after vaccines were first authorized.
The analysis also looked at vaccination data by categories of geography and sex, and for children separately, as vaccines for their age group were developed and authorized later than for adults. All data used in SHADAC’s analysis were provided through a collaboration with the Minnesota Electronic Health Record Consortium, a partnership of state health systems and public health agencies aimed at providing comprehensive and timely data to better inform policy.
Click on the image above to read the full brief.