Efforts to expand health coverage generally express the potential impact in national terms, ignoring that states differ greatly in demographic and economic circumstances and in health insurance markets and thus will experience different impacts from any expansion strategy.
To demonstrate that uniform national strategies that target the uninsured do not generate uniform national outcomes, researchers at Columbia University’s Mailman School of Public Health reported in a June 7 Health Affairs web exclusive on their comparison of the effects of a standard tax credit and Medicaid expansion proposals.
Sherry Glied, department chairman for health policy and management, tells State Health Watch she and her colleagues examined the state-by-state impacts of five types of insurance expansion policies — refundable tax credits for the nongroup market, tax credits for small-firm workers and their dependents, expansion of Medicaid eligibility to include all low-income adults, an expansion to low-income uninsured children not now eligible for SCHIP, and an extension of Medicaid eligibility to all parents of SCHIP-eligible children.
“There is no such thing as an ‘average’ state,” she says. “Thus, the national effects of policies can be misleading. A policy that serves one state well may be relatively ineffective in a neighboring state. Some states would do relatively well — and others would do relatively poorly — under all of the federal reform proposals considered in the study.”
According to Ms. Glied, the characteristics of each state that are most important in assessing the varying impact of different expansion proposals include:
1. Percentage of the uninsured living in poverty
Proposals targeting the uninsured and determining eligibility based on income establish eligibility cutoffs nationally, but national income cutoffs don’t account for state level differences in cost of health care or cost of living generally.
“Although absolute incomes are much higher in Alaska than in Oklahoma, for example, the much higher cost of living in Alaska leaves the two states with similarly high uninsurance rates but very different eligibility for income-based expansion proposals,” Ms. Glied writes. “The percentage of a state’s uninsured population with incomes below 100% of poverty is an important indicator of the extent to which income-based coverage policies would expand eligibility in that state. There is a nearly twofold difference among states in the percentage of the uninsured earning less than 100% of poverty. Identical income-targeted policies will expand eligibility much more in Hawaii, where 43% of the uninsured earn less than 100% of poverty, than in New Hampshire, where only 24% do.
2. Percentage of uninsured people tied to small firms
The firm-size distribution of employment is a function of local industrial patterns and population density. Small firms are more common in rural than in urban areas. Policy proposals targeting the small group market will be more effective in states with a small-firm oriented distribution of employment than in states with more large firms. Thus, in Vermont, Idaho, and Montana, about 60% of uninsured people have a connection to a small firm, while in states such as Virginia and Louisiana, as well as Washington, DC, fewer than 40% of the uninsured are linked to small firms.
3. Cost of nongroup health insurance
Controlling for income, according to Ms. Glied, states with higher health care costs tend to have higher uninsurance rates.
In public program expansions, public payers absorb cross-state variation in costs. In Medicare, thus, costs per beneficiary are more than twice as high in Miami as in Minnesota. The higher costs in Miami are spread among taxpayers nationally. And in Medicaid, the federal match means that additional expenses associated with higher health care costs are divided between state and federal taxpayers.
In the tax credit proposal, by contrast, the amount of the individual subsidy is fixed nationally and individuals, rather than the state or federal governments, must bear the burden of higher-than-average health care costs. A tax credit will cover a larger share of the cost of insurance, and thus presumably lead more people to take up coverage, in areas where existing nongroup premiums are relatively low. Ms. Glied and her colleagues estimated that nongroup premiums average less than $2,000 per person in Utah and Kansas, but more than $4,000 per person in Maine, New Hampshire, New Jersey, and New York, meaning an equal size tax credit would have much smaller effects in the latter states.
Ms. Glied says the problem of high health care costs is compounded in the states with high health insurance costs that also have high costs of living and a distribution of income that is above the national average.
4. Eligibility levels of public insurance programs
States differ greatly in the degree to which they already have expanded their Medicaid and SCHIP programs.
The researchers estimate that 11% of the currently uninsured population (4.6 million people) would gain health coverage under the tax credit proposal, reducing the overall uninsurance rate by 17%.
Declines in the uninsurance rate by state would vary by a factor of nearly 5, from 4.4% in New Hampshire to 20.5% in Utah. States such as Utah, Kansas, and Oregon that would see the greatest percentage declines in their uninsured populations share two features — a low average nongroup premium and a large proportion of the uninsured earning less than 100% of poverty and thus eligible for the full tax credit.
Limited impact for small firms
The small-firm tax credit proposal would have a much smaller aggregate impact than the individual tax credit, according to the study. It is estimated to increase the number of newly insured Americans by 14 million people or 3.3% of the uninsured.
The effect across states varies by a factor of 2.4, from 2% of the uninsured in Washington, DC, to 4.7% in Montana. States that would experience a greater than average increase in their insured population have relatively large proportions of their nonelderly, uninsured populations employed on, or dependents of employees in, small firms.
Expanding public coverage to adults with incomes below 133% of poverty would decrease the U.S. uninsurance rate by 1.9 percentage points and increase the number of insured Americans by 4.7 million or 11.5% of the uninsured population.
But the range of effects across states is much wider because several states have already enacted expansions for this population.
The decrease in the uninsured population ranges from no effect in states with existing adult Medicaid eligibility limits above 133% of poverty, such as Vermont, Utah, and Massachusetts, to a high of 18.3% in Alabama and West Virginia.
Expanding SCHIP could help
In aggregate, expanding SCHIP to children under 300% of poverty would make 1 million of the 9.3 million uninsured children eligible for SCHIP. Some 420,000 of the newly eligible children would be expected to take up public insurance; however, because historically SCHIP take-up rates have been inversely proportional to income, so that expansions to higher income children tend to have smaller effects.
The range of effects on the entire uninsured population is expected to vary from zero in states where eligibility already exceeds this level, such as New Jersey, Missouri, and Maryland, to 4.7% in states with low existing SCHIP eligibility, such as South Carolina.
Although the immediate aim of a proposed Medicaid expansion policy to parents of SCHIP-eligible children is to provide uninsured adults with health coverage, it also would affect uninsured children because children are more likely to be enrolled in coverage if their parents also are eligible. This proposal is estimated to increase the number of insured Americans by some 2 million or 5.2% of the U.S. uninsured population. The effects by state range from 0.7% of the uninsured in Tennessee to 10.3% in Arkansas. The effects are greatest in states with the largest population of low-income families.
According to Ms. Glied, states with low nongroup premiums, low average incomes, and few prior expansion efforts would tend to do well under all the proposals. But others, such as Maryland, Massachusetts, New York, and Wisconsin, which already have undertaken expansions and have high nongroup premiums, would do relatively worse than average under all proposals.
States with relatively low healthcare costs and moderate incomes, such as California, Oregon, and Washington, would do better with tax credits than with public expansions, while lower-income states with moderately high health costs, such as Alabama, Kentucky, and West Virginia, would do relatively better under public expansion.
Ms. Glied notes that some analysts argue that federal health policy-makers have greater health care expertise, better revenue collection abilities, and the ability to avoid the race to the bottom inherent in intrastate competition, while others say that state policy-makers can use their understanding of local conditions to craft policies that best reflect states’ value and priorities.
But, she says, the study results suggest that in the context of expansion to low-income uninsured populations, theoretical dichotomy between the federal and state governments doesn’t apply.
“The factors that generate variation in uninsurance rates among states will themselves affect the application of federal policy,” Ms. Glied writes.
“Uniform federal income eligibility limits will leave more people uninsured in high-income states where the cost of living is high. Uniform expansion in eligibility will have less effect on uninsured people in states that generously subsidize safety net providers and have expanded public program eligibility. Uniform tax credits will provide less benefit to people in states where the cost of medical care is high,” she points out.
“In each of these situations, layering uniform national policies over the existing differences among states will not necessarily narrow these differences. Instead, a policy goal of interstate uniformity in outcomes may require interstate variability in policies,” Ms. Glied adds,
[Contact Ms. Glied at (212) 305-0299 or by e-mail: Sagl@columbia.edu.]