An Overblown Fear About S- Chip Editorial NY TIMES October 16, 2007
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An Overblown Fear About S- Chip Editorial NY TIMES October 16, 2007 http://www.nytimes.com/2007/10/16/opinion/16tue2.html
Drop Coverage? To hear the Bush administration tell it, expanding the State Children’s Health Insurance Program would entice hordes of families to drop their private coverage and put their children on the public dole. As the Health and Human Services secretary, Michael Leavitt, argued in a recent television appearance, states that cover middle- income children as well as the poor are essentially telling people to “cancel your private insurance and we’ll have the government pay for it.”
Right or Wrong? There are several things wrong with that claim. First, nobody who enrolls in S-chip would be living on government handouts. The families would all be paying appropriate premiums and co- payments. It is also highly unlikely that a lot of people would drop private coverage to enroll in S-chip. States already monitor such substitution and take a number of steps to deter it. New York estimates that only about 3% of the children enrolled in the program came from families that dropped employer coverage to obtain S- chip. Mathematica Policy Research, in a report prepared for the federal government, looked at states across the country and pegged the typical substitution rate at less than 10 percent. Using a broader methodology and peering into the future, the nonpartisan Congressional Budget Office estimates that the bill vetoed by President Bush would increase enrollment in S-chip and Medicaid by 5.8 million in 2012. Of that total, 3.8 million children would otherwise be uninsured and 2 million would be children who could have gotten private insurance in the absence of S-chip. Even if that 1-in-3 substitution rate should turn out to be accurate, it is still far better than denying insurance to millions of American children.
What are impacts? Some critics of S-chip like to cite substitution estimates that are much higher. Mathematica found that so-called “population-based studies” estimated the substitution rate at 10% to 56%, depending on the approach and assumptions used. These studies capture not only families that dropped private coverage to go into the S-chip program but also families that had an opportunity later to take out private insurance yet stayed on the public program. The problem with these studies is that they assume that all parents that dropped or decided not to go with private coverage did so because of the availability of S-chip. They ignore other very possible circumstances, such as when families lose their private coverage because a parent dies or loses a job. These studies also take no account of whether a private policy, though theoretically available, was too costly to be affordable for a low- income worker. President Bush’s preferred policy is to provide families with tax deductions to help pay for private insurance. Responsible economists estimate that such an approach would do far less to enroll uninsured children than would the proposed expansion of S-chip. The president’s own budget proposal for maintaining the current S-chip program is so stingy that it would not even cover the number of children currently enrolled — and would probably increase the number of children forced to go without health coverage by hundreds of thousands.
“Take-up” and “Crowd-out” What are the net impacts of social insurance program implementation? Are people now insured, who were previously uninsured (take-up), or are the new programs simply crowding out other forms of insurance?
Improvement Target Population Increased Eligibility Impacts of improving coverage
Improvement Target Population Increased Eligibility Previously Uninsured Previously Insured Take-up Crowd- out Crowd- out Additional Coverage Impacts of improving coverage
Additional Utilization Additional Coverage Access Impacts of improving coverage
Additional Utilization Better Health Outcomes Additional Coverage Access Cost- Effectiveness CE = Cost/ Utilization Incremental Program Costs Impacts of improving coverage