Up to 14M people could lose Medicaid coverage when health emergency ends: study

As many as 14.2 million people could lose Medicaid coverage when the public health emergency for COVID-19 ends, a new analysis finds.

The analysis from the Kaiser Family Foundation (KFF) projects that between 5.3 million and 14.2 million people could lose Medicaid when the public health emergency ends. Under a coronavirus relief bill passed in 2020, states received extra Medicaid funding in exchange for not removing anyone from the Medicaid rolls for the duration of the public health emergency.

Once the emergency ends, states will resume removing people who are no longer eligible for Medicaid, leading to a significant amount of upheaval, where even some people who remain eligible could fall through bureaucratic cracks in the system. Others who are no longer eligible for Medicaid might not know that they are eligible for other types of coverage, like subsidized health insurance on the Affordable Care Act (ACA) marketplaces.  

The KFF analysis does not estimate how many of the people losing Medicaid would become uninsured and how many would find other coverage, like on the ACA marketplaces.  

The potential coverage losses in Medicaid add a complication for the Biden administration as it decides when to end the public health emergency, which it has been renewing every 90 days. It is currently set to expire on July 15. Officials have said they will give states 60 days notice for when it will end, so that notice would have to come by mid-May if it is going to end this summer.  

There has been significant growth in Medicaid recipients due to the requirement not to remove people. There will be 22.2 million more people in the program in 2022 compared to 2019, a roughly 25 percent increase, the analysis found.  

The analysis reached its estimates of coverage losses using surveys of state Medicaid directors, who estimated how much enrollment would decline in their state when the emergency ended. But it acknowledges that there is significant uncertainty around the estimates.