Blewett, L. A., and T. J. Beebe. 2004. “State Efforts to Measure the Health Care Safety Net.” Public Health Reports 119 (2): 125-135.

This article describes the role states could play in a national effort to measure and monitor the public health safety net. The authors developed a data collection framework using information from five states on two components of the safety net: structure and demand. Because states are the primary vehicle for access expansions and programs to care for the poor, the authors suggest that they be the primary coordinating mechanism for data collection on the safety net. Because the necessary mechanisms for more uniform standards or criteria to evaluate state data collection activities and capacity remain undeveloped, they recommend using existing data to begin building state capacity to measure and monitor the safety net.

Davern, M., H. Rodin, T. Beebe, and K. T. Call. 2005. “The Effect of Income Question Design in Health Surveys on Family Income, Poverty and Eligibility Estimates.” Health Services Research 40(5):1534-1552.

OBJECTIVE: 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. CONCLUSIONS: 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.

Kincheloe, J., E. R. Brown, J. Frates, K. T. Call, W. Yen, and J. Watkins. 2006. “Can We Trust Population Surveys to Count Medicaid Enrollees and the Uninsured?” Health Affairs 25(4): 1163-1167.

Health foundations, such as the Robert Wood Johnson Foundation (RWJF), make multimillion-dollar investments in programs to expand insurance coverage. These efforts are driven largely by estimates of the number of uninsured people derived from population surveys, which might overestimate the number of uninsured people if they under-count people enrolled in Medicaid. This paper reports the results of the RWJF-funded California Medicaid Undercount Experiment (CMUE) to estimate the extent of underreporting of Medicaid in the California Health Interview Survey (CHIS) and its effect on estimates of uninsurance. Although some over- and underreporting occurs, overall CHIS Medicaid estimates match administrative counts for adults.

Blewett, L. A. and M. Davern. 2006. “Meeting the Need for State Level Estimates of Health Insurance Coverage: What Has Been Done and How it Can Be Improved” Health Services Research 41(3): 946-75.

OBJECTIVE: Critically review estimates of health insurance coverage available from different sources, including the federal government, state survey initiatives, and foundation-sponsored surveys for use in state policy research.

STUDY SETTING AND DESIGN: We review the surveys in an attempt to flesh out the current weaknesses of survey data for state policy uses. The main data sources assessed in this analysis are federal government surveys (such as the Current Population Survey's Annual Social and Economic Supplement, and the National Health Interview Survey), foundation-supported surveys (National Survey of America's Families, and the Community Tracking Survey), and state-sponsored surveys.

PRINCIPAL FINDINGS: Despite information on estimates of health insurance coverage from six federal surveys, states find the data lacking for state policy purposes. We document the need for state representative data on the uninsured and the recent history of state data collection efforts spurred in part by the Health Resources Services Administration State Planning Grant program. We assess the state estimates of uninsurance from the Current Population Survey and make recommendations for a new consolidated federal survey with better state representative data.

CONCLUSIONS: We think there are several options to consider for coordinating a federal and state data collection strategy to inform state and national policy on coverage and access.

Davern, M., H. Rodin, K. T. Call, and L. A. Blewett. 2007. “Are the CPS Uninsurance Estimates Too High? An Examination of Imputation.” Health Services Research 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 (http://www.bls.census.gov/ferretftp.htm). 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). CONCLUSIONS: 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.