The RAND Health Insurance Experiment, Three Decades Later (original) (raw)
Related papers
Attrition in the RAND Health Insurance Experiment: A Response to Nyman
Journal of Health Politics, Policy and Law, 2008
In a prior article in this Journal John Nyman argues that the effect on health care use and spending found in the RAND Health Insurance Experiment is an artifact of greater voluntary attrition in the cost sharing plans relative to the free care plan. Specifically, he speculates that those in the cost sharing plans, when faced with a hospitalization, withdrew. His argument is implausible because: 1) Families facing a hospitalization would be worse off financially by withdrawing; 2) A large number of observational studies find a similar effect of cost sharing on use; 3) Those who left did not differ in their utilization prior to leaving; 4) If there had been no attrition and cost sharing did not reduce hospitalization rates, each adult in each family that withdrew would have had to have been hospitalized once each year for the duration of time they would otherwise have been in the Experiment, an implausibly high rate; 5) There are benign explanations for the higher attrition in the cost sharing plans. Finally, we obtained followup health status data on the great majority of those who left prematurely. We found the health status findings were insensitive to the inclusion of the attrition cases.
The Oregon health insurance experiment: evidence from the first year
2011
In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides a unique opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. In the year after random assignment, the treatment group selected by the lottery was about 25 percentage points more likely to have insurance than the control group that was not selected. We find that in this first year, the treatment group had substantively and statistically significantly higher health care utilization (including primary and preventive care as well as hospitalizations), lower out-of-pocket medical expenditures and medical debt (including fewer bills sent to collection), and better self-reported physical and mental health than the control group.
Refusal and Attrition in the Rand Health Insurance Experiment: A Response to Nyman
hcp.med.harvard.edu
John Nyman argues that the usual interpretation of the RAND Health Insurance Experiment -modest cost sharing reduces use of services with negligible effects on health for the average person − is an artifact that results from greater attrition by those in plans with cost sharing. In particular, he speculates that if those facing hospitalization on cost sharing plans differentially dropped out of the Experiment, the observed medical expenditure would be lower and health status would be better among those remaining in the cost sharing plans.
s response to reviews Is Expanding Medicare Coverage Cost-Effective ?
2004
We have responded to reviewer comments to our submission to BMC Medicine. The reviewer comments were very insightful, but we were not able to implement some of them for the technical reasons discussed below. We hope that you will nonetheless find the paper to be important, compelling, and worthy of publication in BMC Health Services Research. Policymakers critically need data on the incremental cost-effectiveness of supplemental insurance for Medicare recipients. Due to the high costs and ethical concerns of repeating the Rand Health Insurance Experiment (HIE), it is not likely that a randomized controlled trial will ever become available that outlines the costs and effectiveness of supplemental medical insurance. Nonetheless, given the plethora of medical advances made in the 20 years since the HIE, it is also likely that the extra care provided by private supplemental insurance is life saving. It is therefore necessary to rely upon large national health surveys linked to mortality data to estimate these effects. The paper should stimulate a good deal of interest and discussion within the health services research community, and should provide useful and critically needed data for policymakers.
Milbank Quarterly, 2002
Many policy proposals address the lack of insurance coverage, with the most commonly discussed being tax credits to individuals, expansions of existing public programs, subsidies for employers to offer coverage to their workers, and mandates for employers and individuals. Although some policy options may be favored (or disfavored) on theoretical or ideological grounds, many debates about policy center on empirical questions: How much will this option cost? How many people will obtain insurance coverage?Estimates of costs and consequences influence policy in three ways. First, the Office of Management and Budget, the Congressional Budget Office, the Centers for Medicare and Medicaid Services, the Treasury Department, and other government agencies incorporate estimates of the costs of proposals in their budget calculations. Particularly in times of fiscal restraint, the cost of a proposal is central to its legislative prospects. Second, recognizing the importance of final budget numbers, policy advocates include estimates in their advocacy.The fate of a proposal to expand health insurance is influenced by predictions of the proposal's effects on the number of newly insured and the cost of new coverage. Estimates vary widely, for reasons that are often hard to discern and evaluate. This article describes and compares the frameworks and parameters used for insurance modeling. It examines conventions and controversies surrounding a series of modeling parameters: how individuals respond to a change in the price of coverage, the extent of participation in a new plan by those already privately insured, firms' behavior, and the value of public versus private coverage. The article also suggests ways of making models more transparent and proposes "reference case" guidelines for modelers so that consumers can compare modeling results.