CHIPRA Mandated Evaluation of Express Lane Eligibility: First Year Findings. c. Limitations


Data for this analysis may be subject to significant recall bias, as they are based on individuals’ recollection of complex events and activities, most of which occurred more than a year prior to our data collection. This is particularly true in states where ELE was adopted as part of a broader initiative. For example, in Oregon, requirements for income documentation were simplified, 12-month continuous eligibility was introduced, and the mailing of redetermination notices was automated around the same time that ELE was introduced. With the exception of programming costs, which many states document on a “per-job” basis, staff members were unlikely to have documented their time spent on ELE implementation. In short, although we have made estimates using the best available information, we acknowledge that in every state, some information is likely to be missing or inaccurate, which may in turn lead our estimates to overstate or understate the true costs of ELE.

For two ELE programs, Maryland and Iowa CHIP, concerns about missing data have led us to exclude certain estimates from our findings. In Maryland, the Centers for Medicare & Medicaid Services (CMS)-mandated markers for identifying ELE children were not available, preventing a count of ELE enrollees and hence an analysis of costs or savings per enrollee. Additionally, the Office of the Comptroller—Maryland Medicaid’s ELE partner agency—has played a large role in ELE, but staff were not available to answer some key questions. Accordingly, Maryland’s initial programming costs are not presented here since they were all borne by the Office of the Comptroller. For Iowa CHIP, the process now called ELE has been in place since 2004, and has completely replaced the most relevant counterfactual process—a manual referral procedure—against which ELE costs and savings would ideally be measured. Consequently, information about savings and costs per application for Iowa CHIP could not be calculated and are not presented.

Lastly, in a number of states, traditional enrollment processes have changed as a result of ELE, complicating the comparison and possibly understating the savings associated with ELE. For example, in Alabama, eligibility staff now look at the Supplemental Nutrition Assistance Program (SNAP) and Temporary Assistance for Needy Families (TANF) databases for all children at initial application to establish income-eligibility for Medicaid based on net income as found by TANF or SNAP; this was not the case before ELE was implemented. Differences between ELE and non-ELE enrollment processes are still apparent, but it is important to recognize that, in some cases, ELE and the traditional enrollment pathways are now more similar than ELE and the pathways in place before ELE. We expect that site visits in the second year will provide more in-depth information about the ELE policies pursued in each state. As with other aspects of our future research, the additional information obtained through site visits could require us to revisit some of the interim conclusions articulated in this report.

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