Assessing the Value of the NHIS for Studying Changes in State Health Coverage Policies: The Case of New York Sharon K. Long John A. Graves Stephen Zuckerman AcademyHealth Annual Research Meeting June 2007 Funded by the Robert Wood Johnson Foundation under the Urban Institute’s Assessing the New Federalism Project THE URBAN INSTITUTE

2 Motivation for Study Lack of health insurance is a persistent problem in the US Little consensus as to how to increase coverage States continue to be the primary source of innovation in coverage strategies Key challenge to evaluating state initiatives is lack of data

THE URBAN INSTITUTE 3 Focus of the Study Explore feasibility of National Health Interview Survey (NHIS) for state evaluations –Compare estimates to Current Population Survey (CPS) Use New York as test case: Estimate effects of New York’s expansion efforts on insurance coverage for parents and childless adults

THE URBAN INSTITUTE 4 Current Population Survey Nation’s primary source of information on labor force participation, employment and income Survey fielded every year with a sample of approximately 50,000 U.S. households Health insurance coverage asked in March supplement Asks about health insurance in prior calendar year

THE URBAN INSTITUTE 5 National Health Interview Survey Nation’s primary source of general health information for the civilian non-institutional population Survey fielded continuously every year with a sample of approximately 43,000 U.S. households Asks about health insurance coverage in the month prior to survey Also includes data on health indicators, access and use, and health-related behaviors

THE URBAN INSTITUTE 6 State-Level Estimates in NHIS and CPS Both are stratified multistage sample designs, with sample drawn from every state Neither is designed to provide reliable state-level estimates for all states However they do allow state-level estimates for the largest states Sample sizes are larger in CPS than NHIS

THE URBAN INSTITUTE 7 New York’s Reform Initiative Health Care Reform Act of 2000 –Family Health Plus (FHP)—public coverage –Healthy New York (HNY)—subsidized private coverage Disaster Relief Medicaid (DRM) –Temporary Medicaid expansion in New York City following September 11 th Timetable: –HNY started Jan 2001 –DRM started Sept 2001 –FHP implemented in upstate NY Oct 2001 and NYC Nov 2002

THE URBAN INSTITUTE 8 Data NHIS CPS (data for 1999 to 2004) Samples of parents and childless adults ages 19 to 64 NY samples sizes: –N= 19,036 in CPS –N= 10,189 in NHIS

THE URBAN INSTITUTE 9 Time Period Population Group Target Population Comparison Group Pre-reform Periodß 0 + ß 1 ß0ß0 Post-reform Periodß 0 + ß 1 + ß 2 + ß 3 ß 0 + ß 2 Difference in Outcome Between Time Periods ß 2 + ß 3 ß2ß2 Difference-in-Differences Estimateß3ß3 Difference-in-differences (DD) models: Y it = ß 0 + ß 1 TargetPop it + ß 2 Post it + ß 3 TargetPop it *Post it + ß 4 X it + ε it Evaluation Framework

THE URBAN INSTITUTE 10 Regression Model Estimate linear probability models controlling for age, race, gender, marital status, education, health and disability status All analyses weighted and adjust standard errors to account for complex survey designs Insurance categories: public and non-group (to capture HNY), private coverage, uninsured

THE URBAN INSTITUTE 11 DD Estimates of Impact of New York’s Expansion Efforts in the Post-DRM Period *(**) (***) Statistically significant from zero at the 10% (5%) (1%) level

THE URBAN INSTITUTE 12 Conclusions I: The Impacts of New York’s Expansion Efforts New York has been quite successful at reducing uninsurance for parents. Among childless adults, for whom the New York’s expansion efforts were more limited, we find no evidence of a significant decline in uninsurance.

THE URBAN INSTITUTE 13 Conclusions II: Using the NHIS for State Evaluations NHIS is viable data source for analyses of state- level initiatives –Current insurance status –More probes for state programs –Access and use measures Limitations –Smaller sample sizes –Access to state identifiers requires analyses be done at the National Center for Health Statistics’ Research Data Center