Paul Shafer is an assistant professor of health law, policy, and management at the Boston University School of Public Health. He tweets at @shaferpr.
We spend a lot of time debating who should be eligible for public assistance and how generous it should be. But we spend a lot less time on how we get those who are eligible into the programs they qualify for. You can be dropped from Medicaid for missing a single letter, an example of the precarious nature of our ‘safety net’.
The introduction of the Marketplace gave states a reason to update their enrollment infrastructure, all states now accept online applications for Medicaid and nearly all able to determine eligibility in real-time (within 24 hours). However, that doesn’t solve the problem of getting people to apply or keeping them from dropping out when they are still eligible, which has historically been a big problem.
An underused and poorly studied provision of the 2009 law reauthorizing the Children’s Health Insurance Program (CHIP) is called Express Lane Eligibility (ELE). It gives states the authority to use information from other public programs, like Supplemental Nutrition Assistance Program (SNAP), Temporary Assistance for Needy Families, and others, for determining eligibility for Medicaid and CHIP coverage for kids. The authority was extended through fiscal year 2027 in the Bipartisan Budget Act of 2018.
This sounds like an obvious thing to do, but states previously did not have the authority to use data from other agencies to make enrollment determinations for CHIP and Medicaid. For example, if states could use income tax data, then there would be no need to prove income or lack thereof for eligibility purposes. This makes intuitive sense, a way to make it easier for applicants and more efficient for the state.
California passed a version of ELE back in 2001, with local philanthropies funding a pilot to link the National School Lunch Program and Medi-Cal in seven school districts from 2003 to 2006. The state struggled with data matching and when combined with already high Medi-Cal enrollment rates, about half of ELE applications generated were for children already enrolled. There were also concerns that worries about immigration issues led to families not completing the simplified application. Though implementation was problematic, the authors of the evaluation concluded that a ‘”no wrong door” approach to health care’ still held promise.
Congress gave states a lot of latitude in how to implement ELE with lots of policy options available to enroll the one in five children who were eligible for CHIP or Medicaid but unenrolled and to keep them enrolled. So what happened? Only 14 states are currently using it, and they are all doing so in different combinations of programs. Some for CHIP, some for Medicaid, only four for both and a lot of variation in which programs or agencies they ‘talk’ to for ELE.
Unfortunately, we don’t know very much about the consequences of states’ ELE implementation. I only found only a handful of commissioned reports and four papers in peer-reviewed journals that rigorously evaluate ELE.
A report commissioned by the Department of Health and Human Services (HHS) found that across the adopting states, ELE increased enrollment by about 6% and saved millions of dollars in administrative costs. By analyzing each state’s implementation as a separate case study, the report found that ELE increased retention of those eligible and reduced administrative burdens on state social service agencies.
South Carolina had ranked 45th in health insurance coverage for low-income children before its ELE program was implemented in 2011. Afterwards, it helped enroll another 92,000 children and keep 276,000 on CHIP and Medicaid. The Louisiana experience shows why automation is so important. It was the first state to implement the automatic enrollment option for ELE, sending out more than 20,000 Medicaid cards in 2010 alone. Then, it switched to an ‘opt-in’ approach via a checkbox on the SNAP application, which resulted in a 62% drop in monthly Medicaid enrollment through ELE.
A study published in 2014, whose authors were part of the HHS-funded evaluation team, echoed the enrollment findings of the commissioned reports. They found that ELE was associated with a 5.6% increase in Medicaid and a 4.2% increase in combined CHIP and Medicaid enrollment. Their results also suggest that this effect may get stronger in the long-run. Another study from 2014, from authors also part of the HHS evaluation team, noted children enrolled via ELE used care less and less intensively, and were therefore less expensive to cover.
Massachusetts incorporated ELE for Medicaid renewals for parents and the expansion population, finding that “ELE participation was the strongest predictor of continuous coverage during the 90-day period following MassHealth annual review”. The rate of coverage loss within 90 days of review was more than ten times higher in the non-ELE household group (34%) than in the ELE group (2-4%). Though ELE was initially conceived as a way to reduce the number of uninsured children, using the same process and data infrastructure for the adult population not surprisingly also increases retention in Medicaid.
Under the Trump administration, we have seen the number of uninsured children jump by 400,000 and proportion of adults who are uninsured or avoided health care due to cost inch upwards. SNAP is also increasingly hard to get, with hundreds of thousands of food insecure families kicked off the program. A nationwide embrace of ELE coupled with a streamlining of the eligibility criteria for certain programs could go a long way towards helping low income populations stay healthy, housed, and fed.