Using National Survey Data to Analyze Children’s Health Insurance Coverage: An Assessment of Issues. 2. Implications for Adjusting the Number of Uninsured

05/21/1999

Each of the four forms of underreporting that we have discussed implies, potentially, a different type of bias in estimates of the number of uninsured children. Therefore, each form of underreporting implies a different strategy for using administrative estimates of Medicaid enrollment to correct for Medicaid underreporting and, by implication, adjust the number of uninsured.

First, when Medicaid is misreported as private coverage or other public coverage, the number of children who are uninsured is not increased. Consequently, if we wish to adjust for Medicaid underreporting we must first determine how much of the Medicaid shortfall is due to Medicaid being misreported as something else. Such children would be found among those who are simulated to be eligible but report coverage other than Medicaid. Beyond this, however, there are no easy guidelines for determining which children or even how many have misreported their Medicaid coverage. Second, when children are simply left out of a list of family members covered by Medicaid, they may indeed affect estimates of the uninsured. To determine the potential impact, we must first impute their Medicaid coverage based on the reports for other family members. In so doing, we will in effect “see” if these individual children were reported to have other coverage, and the change in the number of uninsured children after the imputation has been performed will indicate the net impact on estimates of the uninsured. Third, when Medicaid is simply not reported at all, it is still possible that other coverage was reported. Such children will be found among those simulated to be eligible for Medicaid, but they may or may not have reported other coverage. Again, there are no easy guidelines for identifying which children or how many fall into this category. Fourth, underreporting due to population undercoverage requires no adjustment of the survey data, but if administrative estimates of Medicaid enrollment are used to adjust for the other sources of error, then, in theory, estimates of Medicaid children who were excluded from the survey sample frame should be subtracted from the administrative counts. The bigger problem with population undercoverage, however, is the likely underestimation of the uninsured. If we are lowering the estimated number of uninsured to compensate for Medicaid underreporting, we need to keep in mind that some of the uninsured may not be included in the initial estimate. This problem has not been addressed at all in the literature, and we are not aware of any estimates of how many uninsured children may be missing from the survey estimates.

In using administrative estimates of Medicaid enrollment, it is important that the reference period of the data match the reference period of the survey estimates. HCFA(now known as CMS) reports Medicaid enrollment in terms of the number of people who were ever enrolled in a fiscal year. This number is considerably higher than the number who are enrolled at any one time. Therefore, the HCFA(now known as CMS) estimates of people ever enrolled in a year should not be used to correct survey estimates of Medicaid coverage at a point in time because this results in a substantial over-correction. From the published HCFA(now known as CMS) tabulations it is possible to derive an estimate of average monthly enrollment--but only for the entire population of enrollees, not children or any other subgroup. These average monthly estimates can be compared to survey estimates of enrollment at a point in time (subject to all of the caveats discussed in the preceding section).

The CPS presents a special problem. We have demonstrated that while the CPS estimate of uninsured children is commonly interpreted as a point in time estimate, the reported Medicaid coverage that this estimate reflects is clearly annual-ever enrollment. Adjusting the CPS estimate of the uninsured to compensate for the underreporting of annual-ever Medicaid enrollment produces a large reduction. We have to recognize that what this adjustment accomplishes is to move the CPS estimate of the uninsured closer to what it purports to be--namely, an estimate of the number of people who were uninsured for the entire year. Applying an adjustment based on annual-ever enrollment but continuing to interpret the CPS estimate of the uninsured as a point-in-time estimate is clearly wrong. Adjusting the Medicaid enrollment reported in the CPS to an average monthly estimate of Medicaid enrollment yields a much smaller adjustment and a correspondingly smaller impact on the uninsured, but it involves reinterpreting the reported enrollment figure as a point-in- time estimate--which we have seen that it is not. Invariably, efforts to “fix” the CPS estimates run into problems such as these because the CPS estimate of the uninsured is ultimately not what people interpret it to be but, instead, an estimate--with very large measurement error--of something else. We would do better to focus our attention on true point-in-time estimates, such as those provided by SIPP, NHIS, the CTS, and NSAF. However, until the turnaround in the release of SIPP and NHIS estimates can be improved substantially, policy analysts will continue to gravitate toward the CPS as their best source of information on what is happening to the population of uninsured children.