A partially corrected estimate of medicaid enrollment and
uninsurance: Results from an imputational model developed off linked survey and
administrative data
Affiliations: [a] Vice President, Director of Public Health Research,
NORC at the University of Chicago. 55 E Monroe, Suite 3000, Chicago IL,
USA | [b] Abt Associates, 55 Wheeler St., Cambridge MA,
UK | [c] Assistant Professor, Division of Health Care Policy
and Research, Mayo Clinic, 200 First Street SW, Pavilion 5, Rochester MN,
USA | [d] Urban Institute, 2100 M Street, N.W., Washington, DC,
USA | [e] Health Policy Center, Urban Institute, 2100 M Street,
Washington, DC, USA
Correspondence:
[*]
Corresponding author: Michael Davern, Ph.D., Vice
President, Director of Public Health Research, NORC at the University of
Chicago. 55 E Monroe, Suite 3000, Chicago IL 60603, USA. E-mail:
[email protected]
Abstract: To improve the utility of estimates of Medicaid enrollment and uninsurance from
the Current Population Survey (CPS) we use linked data from the CPS and the
Medicaid Statistical Information System (MSIS) to build a probabilistic
imputation model that partially corrects the public use data files for
systematic under-reporting of Medicaid. We estimate the probability that a CPS
survey case was enrolled in Medicaid, conditional on whether or not in the CPS
the individual responded that they had Medicaid. We use the imputed data to
develop adjusted estimates of Medicaid enrollment and uninsurance by demographic
characteristics. The net Medicaid enrollment total using our imputation model
for CY 2006 and 2007 is 41.0, compared to 34.0 million using the standard CPS
variables. The resulting net adjusted uninsurance estimate is 4.5% below the
unadjusted estimate.
