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1.  The Impact of Gaps in Health Insurance Coverage on Immunization Status for Young Children 
Health Services Research  2008;43(5 Pt 1):1619-1636.
To examine the impact of full-year versus intermittent public and private health insurance coverage on the immunization status of children aged 19–35 months.
Data Source
2001 State and Local Area Integrated Telephone Survey's National Survey of Children with Special Health Care Needs (NS-CSHCN) and the 2000–2002 National Immunization Survey (NIS).
Study Design
Linked health insurance data from 2001 NS-CSHCN with verified immunization status from the 2000–2002 NIS for a nationally representative sample of 8,861 nonspecial health care needs children. Estimated adjusted rates of up-to-date (UTD) immunization status using multivariate logistic regressions for seven recommended immunizations and three series.
Principal Findings
Children with public full-year coverage were significantly more likely to be UTD for two series of recommended vaccines, (4:3:1:3) and (4:3:1:3:3), compared with children with private full-year coverage. For three out of 10 immunizations and series tested, children with private part-year coverage were significantly less likely to be UTD than children with private full-year coverage.
Our findings raise concerns about access to needed immunizations for children with gaps in private health insurance coverage and challenge the prevailing belief that private health insurance represents the gold standard with regard to UTD status for young children.
PMCID: PMC2653891  PMID: 18522671
Immunization; vaccine; health care access
2.  Are the Current Population Survey Uninsurance Estimates Too High? An Examination of the Imputation Process 
Health Services Research  2007;42(5):2038-2055.
Research Objective
To determine whether the imputation procedure used to replace missing data by the U.S. Census Bureau produces bias in the estimates of health insurance coverage in the Current Population Survey's (CPS) Annual Social and Economic Supplement (ASEC).
Data Source
2004 CPS-ASEC.
Study Design
Eleven percent of the respondents to the monthly CPS do not take the ASEC supplement and the entire supplement for these respondents is imputed by the Census Bureau. We compare the health insurance coverage of these “full-supplement imputations” with those respondents answering the ASEC supplement. We then compare demographic characteristics of the two groups and model the likelihood of having insurance coverage given the data are imputed controlling for demographic characteristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we remove the full-supplement imputations and reweight the data, and we also use the multivariate regression model to simulate what the uninsurance rate would be under the counter-factual simulation that no cases had the full-supplement imputation.
Population Studied
The noninstitutionalized U.S. population under 65 years of age in 2004.
Data Extraction Methods
The CPS-ASEC survey was extracted from the U.S. Census Bureau's FTP web page in September of 2004 (
Principal Findings
In the 2004 CPS-ASEC, 59.3 percent of the full-supplement imputations under age 65 years had private health insurance coverage as compared with 69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement imputations have a 26.4 percent uninsurance rate while all others have an uninsurance rate of 16.6 percent. Having imputed data remains a significant predictor of health insurance coverage in multivariate models with demographic controls. Both our reweighting strategy and our counterfactual modeling show that the uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured due to this imputation bias).
The imputed ASEC data are coding too many people to be uninsured. The situation is complicated by the current survey items in the ASEC instrument allowing all members of a household to be assigned coverage with the single press of a button. The Census Bureau should consider altering its imputation specifications and, more importantly, altering how it collects survey data from those who respond to the supplement.
Implications for Policy Delivery or Practice
The bias affects many different policy simulations, policy evaluations and federal funding allocations that rely on the CPS-ASEC data.
Primary Funding Source
The Robert Wood Johnson Foundation.
PMCID: PMC2254560  PMID: 17850532
Health insurance coverage; current population survey; annual social and economic supplement; hotdeck imputation; item nonresponse; missing data
3.  The Effect of Income Question Design in Health Surveys on Family Income, Poverty and Eligibility Estimates 
Health Services Research  2005;40(5 Pt 1):1534-1552.
To compare systematic differences between an “omnibus” income measure that asks for total family income amounts with a central survey item and an aggregated income measure that sums specific amounts of income obtained from multiple income sources from everyone within a family.
Data Source
The 2001 Current Population Survey-Demographic Supplement (CPS-DS). Data were collected from 78,000 households from February through April 2001.
Study Design
First, we compare the omnibus family income to the aggregated family income amounts for each family. Second, we use the various aggregated family income sources to predict whether there is a mismatch between the omnibus and aggregated family income amounts. Finally, we assign a new aggregated amount of income that is restricted to be within the range of the omnibus amount to observe differences in poverty rates.
Data Collection
Data were extracted from University of Michigan's ICPSR website.
Principal Findings
There is a great deal of variation between the omnibus family income measure and the aggregated family income measure, with the omnibus amount generally being lower than the aggregated. As a result, the percent of people estimated to be in poverty is higher using the omnibus income item.
Health surveys generally rely on an omnibus income measure and analysts should be aware that the income estimates derived from it are limited with respect to poverty determination, and the related concept of eligibility estimation. Analysts of health surveys should also consider matching respondents or multiple imputation to improve the usability of the data.
PMCID: PMC1361202  PMID: 16174146
Income measurement; survey design; poverty; eligibility; imputation

Results 1-3 (3)