As mentioned previously, the CPS has historically generated an “ever covered” count of Medicaid persons well below that reported by CMS administrative data for the civilian noninstitutionalized population. For CY 1995, the CPS showed just over 30 million persons covered by Medicaid. CMS data, however, suggests that approximately 39 million non-institutionalized persons were covered under Medicaid some time in that year.10 By CY 2002, this discrepancy has doubled, with the CPS finding fewer than 29 million ever covered by Medicaid, while CMS data (2003 CMS Statistics, and 2004 Program Information on Medicaid and SCHIP, as well as projections from the CMS Office of the Actuary) suggests an ever enrolled in Medicaid count on the order of 46 million in the non institutionalized population.11 The historical data suggests the discrepancy is growing.12 This may be due to the growth in Medicaid among non cash and part year persons, both of which are groups that the CPS has historically had difficulty identifying.
Looking at subpopulations, for CY 2001 CMS found between 22.7 and 23.9 million children with Medicaid at some point during the year,13 plus some disabled children included in the 8.0 million “disabled” category. For comparison, MEPS found 20.8 million children, the SIPP found 20.7 million children, and the March 2002 CPS found 14.3 million children.
The challenge for our analysis was to determine a reasonable methodology to adjust the CPS to match CMS data for Medicaid enrollees. While on the surface it may seem reasonable to assign Medicaid to those who report being uninsured on the CPS, such a decision rule would be overly simplistic given that people can report more than one coverage type on the CPS. That is, just as individuals can have more that one coverage type over the course of a year, either sequentially or simultaneously, the CPS gives respondents an opportunity to report more than one type of coverage.
This question of how to assign Medicaid to enough individuals to match CMS totals has important implications for the number of uninsured to be found on an adjusted CPS. If most of the individuals to whom Medicaid is assigned have reported other coverage, then the new estimate of the uninsured that results will be similar to the current estimate. But if most of the individuals to whom Medicaid is assigned have reported being uninsured, then the resulting adjusted uninsured estimate will be significantly lower.
In the end, we chose to not to directly consider respondents’ reported coverage status as a factor in determining whether to assign Medicaid coverage. Rather, the method that we use to control to CMS enrollment totals is to assign Medicaid coverage to those who fit the CPS demographic and income profile of persons who already report Medicaid on the CPS. This “looking at the CPS Medicaid persons” is done on a cell by cell basis, where the cells are based on age (<21, 21- 64, 65+), type of Medicaid, and duration of Medicaid (full vs. part year), and is described in detail in the appendix to this paper.
Our methodology results in a middle ground with respect to whether individuals assigned coverage come from the uninsured or not, with half of the newly covered Medicaid enrollees coming from the previously uninsured and half from those with other coverage. That is, assigning Medicaid coverage to persons who look demographically like the CPS persons with Medicaid results in taking about half of the shortfall from other insured groups (but did not remove the insurance they did respond to having).
For CY 2003, correcting for a Medicaid undercount of 17.1 million (10.4 million children, 5.5 million adults, and 1.2 million aged) lowers the full-year uninsured estimate by just over 9 million persons. For CY 2003, with this adjustment, the almost 36 million uninsured (as compared to an unadjusted 45.0 million) is more consistent with the full-year uninsured count reported by MEPS of 31.7 million, though still higher than other surveys’ full year uninsured estimates.14
How much of the Medicaid undercount is comprised of otherwise insured persons cannot be known with precision. As discussed in CBO’s May 2003 report,15 some of the undercount is certainly mislabeling of other coverage, such as non employer private insurance. Moreover, research done on the point-in-time Medicaid population in Minnesota found that persons known to have Medicaid who responded inaccurately to a survey were unlikely to erroneously report themselves to be uninsured. Instead, the Minnesota survey's undercounted Medicaid population was primarily drawn from other insured categories.16
Applying this experience to the CPS, however, is obviously difficult. The CPS appears to overstate the uninsured substantially compared to other surveys. The Minnesota survey was only conducted on persons known to have Medicaid. It is unknown how well that survey’s methods would have worked if they were applied to the full population or how their survey’s participants would have responded if faced with the full CPS questionnaire (not just the insurance questions). Also, the survey was a conducted in a state with a low uninsured rate and with substantial nonMedicaid public coverage when compared to national levels. The CBO report concludes that “It is not known how those findings may be generalized to other states or other surveys.”
A RAND working paper17 published in June of this past year takes a look at the Medicaid undercount in the context of California specific data. Using multiple years of data from the CPS and from Medi-Cal, the California Medicaid program, the authors were able to look at the insurance profile of those persons who were in both data sets, and assess the impact of adjusting for the undercount on the uninsured. For the March 2000 survey, the authors found there to be 9.7% under reporting of Medicaid for children in California, and adjusting for this had the uninsured rate drop from 17.8% to 11.5%. By comparison, we found underreporting of 9.2%, and our adjustment produced a drop in the uninsured rate from 16.9% to 11.0%. Thus these two methods seem to arrive at a similar point of adjustment for Medicaid undercount, when looking at California specific data.
While some studies have addressed the issue of misclassified insurance status, these draw on the fact that they are checking insurance for a specific point in time. Given the CPS asks about “insurance in the last year,” it makes sense that the recall errors in CPS would be different than those being asked about “insurance now.” It is possible that persons who had insurance at some point during the prior year (and perhaps for only part of the year) and no longer have it at time of survey would not indicate that they had that insurance, thinking instead of their current status. Our adjustments are based on trying to get back to the “ever insured in prior year” concept, as asked by the Current Population Survey.
10 The derivation of the Medicaid control totals and its use in adjusting the file is described in detail in the Technical Appendix to this document.
11 While MSIS data has approximately 47 million ever covered in FY 01, data from CMS (2003 CMS Statistics, found at http://www.cms.hhs.gov/researchers/pubs/03cmsstats.pdf, Table 11) comes in with 44.3 million eligibles (persons receiving benefits + non users) in FY 01, 51.0 million in FY 02, and 53.0 million in FY 03. data compendium comes in slightly lower with 46 million ever enrolled in FY 01, and projections of 49 and 51 million for FY 02 and FY 03, respectively. In addition, it has point in time totals at 39.9 million for FY 02 and 41.4 million for FY 03.
12 See “Table 14: Uninsured Impact in Millions of Persons,” section 3.3 of the Technical Appendix.
13 See footnote #10 for different estimates of Medicaid enrollment from CMS.
14 See Table 2 – unpublished AHRQ tabulations of MEPS.
15 “How Many People Lack Health Insurance and For How Long?,” Congressional Budget Office, May 2003 (http://www.cbo.gov/showdoc.cfm?index=4210&sequence=0)
16 “Uncovering the Missing Medicaid Cases and Assessing their Bias for Estimates of the Uninsured,” Kathleen Call, et al., Inquiry 38: 396-408 (Winter 2001/02)
17 “Under-Reporting of Medicaid and Welfare in the Current Population Survey,” Jacob Alex Klerman, Jeanne S.
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