Uncovering the Missing Medicaid Cases and Assessing Their Bias for Estimates of the Uninsured
Call, K. T., G. Davidson, A. S. Sommers, R. Feldman, P. Farseth, and T. Rockwood. 2001. “Uncovering the Missing Medicaid Cases and Assessing Their Bias for Estimates of the Uninsured.” Inquiry 38 (4): 396-408.
General population surveys of health insurance coverage are thought to undercount Medicaid enrollment, which may bias estimates of the uninsured. This article describes the results of an experiment undertaken in conjunction with a general population survey in Minnesota. Responses to health insurance questions by a known sample of public program enrollees are analyzed to determine possible reasons for the undercount and the amount of bias introduced in estimates of uninsured people. While public program enrollees often misreport the type of coverage they have, the impact on estimates of those without insurance is negligible. Restrictions to generalizing the finding beyond this study are discussed.
Publication
State Variation in SCHIP Allocations: How Much Is There, What Are Its Sources, and Can It Be Reduced?
Davern, M., L. A. Blewett, B. Bershadsky, K. T. Call, and T. Rockwood. 2003. “State Variation in SCHIP Allocations: How Much Is There, What Are Its Sources, and Can It Be Reduced?” Inquiry 40 (2): 184-187.
Allocations for the State Children's Health Insurance Program (SCHIP) varied 22% per state between 1999 and 2002. The funding fluctuations present significant problems for states as they develop budget priorities under difficult fiscal conditions. We examine sources of the variation in state allocations during the first four years of SCHIP, focusing on the Current Population Survey's "child component" of the allocation formula. We consider the trade-offs in using alternative estimates from the American Community Survey and model-based estimation. Obtaining reliable estimates of need for SCHIP allocations is critical for states dependent on federal support for insurance programs.
Publication
Hospital Provision of Uncompensated Care and Public Program Enrollment
Blewett, L. A., G. Davidson, M. Brown, and R. Maude-Griffin. 2003. “Hospital Provision of Uncompensated Care and Public Program Enrollment.” Medical Care Research and Review 60 (4): 509-527.
Hospital provision of uncompensated care is partly a function of insurance coverage of state populations. As states expand insurance coverage options and reduce the number of uninsured, hospital provision of uncompensated care should also decrease. Controlling for hospital characteristics and market factors, the authors estimate that increases in MinnesotaCare (a state-subsidized health insurance program for the working poor) enrollment resulted in a 5-year cumulative savings of $58.6 million in hospital uncompensated care costs. Efforts to evaluate access expansions should take into account the costs of the program and the savings associated with reductions in hospital uncompensated care.
Publication
Evaluating Behavioral Health Services in Minnesota's Medicaid Population Using the Experience of Care and Health Outcomes
Beebe, T.J., P. A. Harrison, J. A. McRae, and S. E. Asche. 2003. “Evaluating Behavioral Health Services in Minnesota's Medicaid Population Using the Experience of Care and Health Outcomes (ECHO tm) Survey.” Journal of Health Care for the Poor and Underserved 14 (4): 608-621.
Publication
Management Tools in Medicaid and State Children’s Health Insurance Program SCHIP
Welch, W. P., B. Rudolph, L. A. Blewett, S. Parente, C. Brach, D. Love, and R. Harmon. 2006. “Management Tools in Medicaid and State Children’s Health Insurance Program SCHIP.” Journal of Ambulatory Care Management 29(4): 272-282.
Medicaid and the State Children's Health Insurance Program need analytic tools to manage their programs. Drawing upon extensive discussions with experts in states, this article describes the state of the art in tool use, making several observations: (1) Several states have linked Medicaid/State Children's Health Insurance Program administrative data to other data (eg, birth and death records) to measure access to care. (2) Several states use managed care encounter data to set payment rates. (3) The analysis of pharmacy claims data appears widespread. The article also describes "lessons learned" regarding building capacity and improving data to support the implementation of management tools.