Content by Keyword: Uninsured and Under Insured

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Multivariate linear regression models were used to predict health plan expenditures by plan type. The regression results to predict expenditures are presented in Tables A3 to A8.

A multi-nominal logistic regression was used to create the plan choice estimates.  These were all new estimates that included claims based measures of chronic illness.  The plan choice equation applied to MEPS, along with the intercept terms for the turned down population and the uninsured, is presented in Table A1.  The regression coefficients

The following example illustrates how the SOTU subsidies were simulated.  Suppose Joe is eligible for a $500 low-income premium tax credit. 18 After the credit, his net premium is $1,500 - $500 = $1,000.  Assuming his income tax rate is 20 %, he receives an income tax deduction on the net premium of $1,000 * .20 = $200.  He also receives an em

The variables used in the simulation are: "SCLUB" = contribution to HSA based on age and income, "SCUB" = gap between contribution and HDHP deductible (which can be negative if SCLUB exceeds the deductible), and "PREMIUM" = total premium.  The total premium is equal to the insurance premium plus the tax-adjusted SCLUB.  According to current la

Three data sources were used to complete this analysis.  These data sources and the steps taken to prepare the database are described in Figure 1.  The data sources include:

Original and Current Simulation Methods in Detail Below, we describe the methods used to complete the simulation.  Much of the original approach was used again.  However, we also made substantial revisions in the approach besides just modeling the 2006 Blue Book as opposed the 2004 Blue Book proposals.  Throughout this appendix, major

Our revised simulation model is more complete and adaptable than our previous version.  It is better able to model the 2006 State of the Union policy proposals by taking into account health status as well as allowing for age and income-related contributions to HSAs.  Without this last change, we would have been unable to model the 2006 proposals

There are two significant caveats to the new model.  The first is an adjustment we made to the HDHP benefit design.  For this analysis we chose to have the HDHP take-up match our previous analysis for comparison to previous proposed policies.  The adjustment we made was to increase the coinsurance rate of the HDHP from 0% to 5%.  This is also

While we have developed an improved model for this analysis, several of the old caveats remain from the original study.  The first is that we do not observe the uninsured in our plan choice estimation.  Thus we have to add intercept terms in our prediction equation to calibrate the level of uninsured to match that reported in the market for both

In Tables 5 through 7 we consider the independent effects of each of the proposed policy changes listed above.  Table 5 shows the results of providing favorable tax treatment of HDHP premiums.  The HDHP market grows to 7.2 million, a 4.0 million person increase.  The total subsidy cost, after netting out ESI subsidy savings, is $5.14 billion.

Table 4 shows our estimate of HSA take-up from the 2006 SOTU compared with the 2003 MMA baseline.  We predict that an additional 12,420,002 people would take up individual HSAs, of whom 9,502,893 (approximately 77 percent) were previously uninsured.  Those who were already insured, but who switched in individual HAS, would be drawn from other

We simulated the impact of complete implementation of the SOTU proposals on health insurance take-up and taxes, as well as the impact of each part of the proposal (i.e. tax preferences for premiums, tax preferences for HSA contributions, and the low-income tax credit) if it were separately implemented.  This allows a comparison of the total impac

We simulated the proposals outlined in the President's 2006 State of the Union (SOTU) address and explained in the 2006 Blue Book .  As we understand that proposal, it has three related parts:

The results of the iterative simulations are displayed in Table 3.  The iteration results at baseline focus on the group market.

To use a specific hypothetical example, suppose the eHealthinsurance.com premium for a HDHP is $100 per month, but people who choose such plans are exceptionally healthy with actual medical expenses of only $50 per month.  Even after adding $15 (for example) per month for administrative costs, the premium for people who actually choose the HDHP i

Our prior simulations used insurance premiums from MEPS, eHealthinsurance.com, or from the employers in our study, as described above.  However, that approach is incomplete because it does not account for health risk and other factors that would influence premiums for the individuals who actually choose each option.

Table 2 shows the results of the baseline simulation prior to iterations.  We predict that approximately 3.2 million people who are not eligible for employer-sponsored health insurance (ESI) will choose an individual HDHP.  This estimate was calibrated to be the same as our previous analysis in order to compare the proposed policy changes in 200

In the new baseline simulations, we made an important improvement to the health plan choice model by including the effect of health status as a variable that affects plan choice.  This is important for two reasons.  First, health status may be an important factor in predicting plan choice, so the addition of this variable should improve the fit